diff --git a/Notebooks/02_data_wrangling.ipynb b/Notebooks/02_data_wrangling.ipynb
index a52eb6c24..a458b79ab 100644
--- a/Notebooks/02_data_wrangling.ipynb
+++ b/Notebooks/02_data_wrangling.ipynb
@@ -2749,7 +2749,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2763,7 +2763,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.12.7"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/Notebooks/02_data_wrangling_DM.ipynb b/Notebooks/02_data_wrangling_DM.ipynb
new file mode 100644
index 000000000..0dfe60ee4
--- /dev/null
+++ b/Notebooks/02_data_wrangling_DM.ipynb
@@ -0,0 +1,4864 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2 Data wrangling<a id='2_Data_wrangling'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.1 Contents<a id='2.1_Contents'></a>\n",
+    "* [2 Data wrangling](#2_Data_wrangling)\n",
+    "  * [2.1 Contents](#2.1_Contents)\n",
+    "  * [2.2 Introduction](#2.2_Introduction)\n",
+    "    * [2.2.1 Recap Of Data Science Problem](#2.2.1_Recap_Of_Data_Science_Problem)\n",
+    "    * [2.2.2 Introduction To Notebook](#2.2.2_Introduction_To_Notebook)\n",
+    "  * [2.3 Imports](#2.3_Imports)\n",
+    "  * [2.4 Objectives](#2.4_Objectives)\n",
+    "  * [2.5 Load The Ski Resort Data](#2.5_Load_The_Ski_Resort_Data)\n",
+    "  * [2.6 Explore The Data](#2.6_Explore_The_Data)\n",
+    "    * [2.6.1 Find Your Resort Of Interest](#2.6.1_Find_Your_Resort_Of_Interest)\n",
+    "    * [2.6.2 Number Of Missing Values By Column](#2.6.2_Number_Of_Missing_Values_By_Column)\n",
+    "    * [2.6.3 Categorical Features](#2.6.3_Categorical_Features)\n",
+    "      * [2.6.3.1 Unique Resort Names](#2.6.3.1_Unique_Resort_Names)\n",
+    "      * [2.6.3.2 Region And State](#2.6.3.2_Region_And_State)\n",
+    "      * [2.6.3.3 Number of distinct regions and states](#2.6.3.3_Number_of_distinct_regions_and_states)\n",
+    "      * [2.6.3.4 Distribution Of Resorts By Region And State](#2.6.3.4_Distribution_Of_Resorts_By_Region_And_State)\n",
+    "      * [2.6.3.5 Distribution Of Ticket Price By State](#2.6.3.5_Distribution_Of_Ticket_Price_By_State)\n",
+    "        * [2.6.3.5.1 Average weekend and weekday price by state](#2.6.3.5.1_Average_weekend_and_weekday_price_by_state)\n",
+    "        * [2.6.3.5.2 Distribution of weekday and weekend price by state](#2.6.3.5.2_Distribution_of_weekday_and_weekend_price_by_state)\n",
+    "    * [2.6.4 Numeric Features](#2.6.4_Numeric_Features)\n",
+    "      * [2.6.4.1 Numeric data summary](#2.6.4.1_Numeric_data_summary)\n",
+    "      * [2.6.4.2 Distributions Of Feature Values](#2.6.4.2_Distributions_Of_Feature_Values)\n",
+    "        * [2.6.4.2.1 SkiableTerrain_ac](#2.6.4.2.1_SkiableTerrain_ac)\n",
+    "        * [2.6.4.2.2 Snow Making_ac](#2.6.4.2.2_Snow_Making_ac)\n",
+    "        * [2.6.4.2.3 fastEight](#2.6.4.2.3_fastEight)\n",
+    "        * [2.6.4.2.4 fastSixes and Trams](#2.6.4.2.4_fastSixes_and_Trams)\n",
+    "  * [2.7 Derive State-wide Summary Statistics For Our Market Segment](#2.7_Derive_State-wide_Summary_Statistics_For_Our_Market_Segment)\n",
+    "  * [2.8 Drop Rows With No Price Data](#2.8_Drop_Rows_With_No_Price_Data)\n",
+    "  * [2.9 Review distributions](#2.9_Review_distributions)\n",
+    "  * [2.10 Population data](#2.10_Population_data)\n",
+    "  * [2.11 Target Feature](#2.11_Target_Feature)\n",
+    "    * [2.11.1 Number Of Missing Values By Row - Resort](#2.11.1_Number_Of_Missing_Values_By_Row_-_Resort)\n",
+    "  * [2.12 Save data](#2.12_Save_data)\n",
+    "  * [2.13 Summary](#2.13_Summary)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.2 Introduction<a id='2.2_Introduction'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This step focuses on collecting your data, organizing it, and making sure it's well defined. Paying attention to these tasks will pay off greatly later on. Some data cleaning can be done at this stage, but it's important not to be overzealous in your cleaning before you've explored the data to better understand it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2.1 Recap Of Data Science Problem<a id='2.2.1_Recap_Of_Data_Science_Problem'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The purpose of this data science project is to come up with a pricing model for ski resort tickets in our market segment. Big Mountain suspects it may not be maximizing its returns, relative to its position in the market. It also does not have a strong sense of what facilities matter most to visitors, particularly which ones they're most likely to pay more for. This project aims to build a predictive model for ticket price based on a number of facilities, or properties, boasted by resorts (*at the resorts).* \n",
+    "This model will be used to provide guidance for Big Mountain's pricing and future facility investment plans."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2.2 Introduction To Notebook<a id='2.2.2_Introduction_To_Notebook'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notebooks grow organically as we explore our data. If you used paper notebooks, you could discover a mistake and cross out or revise some earlier work. Later work may give you a reason to revisit earlier work and explore it further. The great thing about Jupyter notebooks is that you can edit, add, and move cells around without needing to cross out figures or scrawl in the margin. However, this means you can lose track of your changes easily. If you worked in a regulated environment, the company may have a a policy of always dating entries and clearly crossing out any mistakes, with your initials and the date.\n",
+    "\n",
+    "**Best practice here is to commit your changes using a version control system such as Git.** Try to get into the habit of adding and committing your files to the Git repository you're working in after you save them. You're are working in a Git repository, right? If you make a significant change, save the notebook and commit it to Git. In fact, if you're about to make a significant change, it's a good idea to commit before as well. Then if the change is a mess, you've got the previous version to go back to.\n",
+    "\n",
+    "**Another best practice with notebooks is to try to keep them organized with helpful headings and comments.** Not only can a good structure, but associated headings help you keep track of what you've done and your current focus. Anyone reading your notebook will have a much easier time following the flow of work. Remember, that 'anyone' will most likely be you. Be kind to future you!\n",
+    "\n",
+    "In this notebook, note how we try to use well structured, helpful headings that frequently are self-explanatory, and we make a brief note after any results to highlight key takeaways. This is an immense help to anyone reading your notebook and it will greatly help you when you come to summarise your findings. **Top tip: jot down key findings in a final summary at the end of the notebook as they arise. You can tidy this up later.** This is a great way to ensure important results don't get lost in the middle of your notebooks."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this, and subsequent notebooks, there are coding tasks marked with `#Code task n#` with code to complete. The `___` will guide you to where you need to insert code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.3 Imports<a id='2.3_Imports'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Placing your imports all together at the start of your notebook means you only need to consult one place to check your notebook's dependencies. By all means import something 'in situ' later on when you're experimenting, but if the imported dependency ends up being kept, you should subsequently move the import statement here with the rest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 1#\n",
+    "#Import pandas, matplotlib.pyplot, and seaborn in the correct lines below\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import os\n",
+    "\n",
+    "from library.sb_utils import save_file\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.4 Objectives<a id='2.4_Objectives'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are some fundamental questions to resolve in this notebook before you move on.\n",
+    "\n",
+    "* Do you think you may have the data you need to tackle the desired question?\n",
+    "    * Have you identified the required target value?\n",
+    "    * Do you have potentially useful features?\n",
+    "* Do you have any fundamental issues with the data?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.5 Load The Ski Resort Data<a id='2.5_Load_The_Ski_Resort_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# the supplied CSV data file is the raw_data directory\n",
+    "ski_data = pd.read_csv('../raw_data/ski_resort_data.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Good first steps in auditing the data are the info method and displaying the first few records with head."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 330 entries, 0 to 329\n",
+      "Data columns (total 27 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   Name               330 non-null    object \n",
+      " 1   Region             330 non-null    object \n",
+      " 2   state              330 non-null    object \n",
+      " 3   summit_elev        330 non-null    int64  \n",
+      " 4   vertical_drop      330 non-null    int64  \n",
+      " 5   base_elev          330 non-null    int64  \n",
+      " 6   trams              330 non-null    int64  \n",
+      " 7   fastEight          164 non-null    float64\n",
+      " 8   fastSixes          330 non-null    int64  \n",
+      " 9   fastQuads          330 non-null    int64  \n",
+      " 10  quad               330 non-null    int64  \n",
+      " 11  triple             330 non-null    int64  \n",
+      " 12  double             330 non-null    int64  \n",
+      " 13  surface            330 non-null    int64  \n",
+      " 14  total_chairs       330 non-null    int64  \n",
+      " 15  Runs               326 non-null    float64\n",
+      " 16  TerrainParks       279 non-null    float64\n",
+      " 17  LongestRun_mi      325 non-null    float64\n",
+      " 18  SkiableTerrain_ac  327 non-null    float64\n",
+      " 19  Snow Making_ac     284 non-null    float64\n",
+      " 20  daysOpenLastYear   279 non-null    float64\n",
+      " 21  yearsOpen          329 non-null    float64\n",
+      " 22  averageSnowfall    316 non-null    float64\n",
+      " 23  AdultWeekday       276 non-null    float64\n",
+      " 24  AdultWeekend       279 non-null    float64\n",
+      " 25  projectedDaysOpen  283 non-null    float64\n",
+      " 26  NightSkiing_ac     187 non-null    float64\n",
+      "dtypes: float64(13), int64(11), object(3)\n",
+      "memory usage: 69.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Code task 2#\n",
+    "#Call the info method on ski_data to see a summary of the data\n",
+    "ski_data.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`AdultWeekday` is the price of an adult weekday ticket. `AdultWeekend` is the price of an adult weekend ticket. The other columns are potential features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This immediately raises the question of what quantity will you want to model? You know you want to model the ticket price, but you realise there are two kinds of ticket price!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>state</th>\n",
+       "      <th>summit_elev</th>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <th>base_elev</th>\n",
+       "      <th>trams</th>\n",
+       "      <th>fastEight</th>\n",
+       "      <th>fastSixes</th>\n",
+       "      <th>fastQuads</th>\n",
+       "      <th>...</th>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alyeska Resort</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>3939</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1610.0</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>669.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>550.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Eaglecrest Ski Area</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>2600</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hilltop Ski Area</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>2090</td>\n",
+       "      <td>294</td>\n",
+       "      <td>1796</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arizona Snowbowl</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>11500</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>777.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sunrise Park Resort</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>11100</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>800.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>115.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>250.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Name   Region    state  summit_elev  vertical_drop  \\\n",
+       "0       Alyeska Resort   Alaska   Alaska         3939           2500   \n",
+       "1  Eaglecrest Ski Area   Alaska   Alaska         2600           1540   \n",
+       "2     Hilltop Ski Area   Alaska   Alaska         2090            294   \n",
+       "3     Arizona Snowbowl  Arizona  Arizona        11500           2300   \n",
+       "4  Sunrise Park Resort  Arizona  Arizona        11100           1800   \n",
+       "\n",
+       "   base_elev  trams  fastEight  fastSixes  fastQuads  ...  LongestRun_mi  \\\n",
+       "0        250      1        0.0          0          2  ...            1.0   \n",
+       "1       1200      0        0.0          0          0  ...            2.0   \n",
+       "2       1796      0        0.0          0          0  ...            1.0   \n",
+       "3       9200      0        0.0          1          0  ...            2.0   \n",
+       "4       9200      0        NaN          0          1  ...            1.2   \n",
+       "\n",
+       "   SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  yearsOpen  \\\n",
+       "0             1610.0           113.0             150.0       60.0   \n",
+       "1              640.0            60.0              45.0       44.0   \n",
+       "2               30.0            30.0             150.0       36.0   \n",
+       "3              777.0           104.0             122.0       81.0   \n",
+       "4              800.0            80.0             115.0       49.0   \n",
+       "\n",
+       "   averageSnowfall  AdultWeekday  AdultWeekend  projectedDaysOpen  \\\n",
+       "0            669.0          65.0          85.0              150.0   \n",
+       "1            350.0          47.0          53.0               90.0   \n",
+       "2             69.0          30.0          34.0              152.0   \n",
+       "3            260.0          89.0          89.0              122.0   \n",
+       "4            250.0          74.0          78.0              104.0   \n",
+       "\n",
+       "   NightSkiing_ac  \n",
+       "0           550.0  \n",
+       "1             NaN  \n",
+       "2            30.0  \n",
+       "3             NaN  \n",
+       "4            80.0  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 3#\n",
+    "#Call the head method on ski_data to print the first several rows of the data\n",
+    "ski_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output above suggests you've made a good start getting the ski resort data organized. You have plausible column headings. You can already see you have a missing value in the `fastEight` column"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.6 Explore The Data<a id='2.6_Explore_The_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.6.1 Find Your Resort Of Interest<a id='2.6.1_Find_Your_Resort_Of_Interest'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Your resort of interest is called Big Mountain Resort. Check it's in the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>151</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Big Mountain Resort</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Montana</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Montana</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>6817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>2353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>4464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>105.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>3.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>3000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>600.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>72.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>333.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <td>81.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>81.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>600.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   151\n",
+       "Name               Big Mountain Resort\n",
+       "Region                         Montana\n",
+       "state                          Montana\n",
+       "summit_elev                       6817\n",
+       "vertical_drop                     2353\n",
+       "base_elev                         4464\n",
+       "trams                                0\n",
+       "fastEight                          0.0\n",
+       "fastSixes                            0\n",
+       "fastQuads                            3\n",
+       "quad                                 2\n",
+       "triple                               6\n",
+       "double                               0\n",
+       "surface                              3\n",
+       "total_chairs                        14\n",
+       "Runs                             105.0\n",
+       "TerrainParks                       4.0\n",
+       "LongestRun_mi                      3.3\n",
+       "SkiableTerrain_ac               3000.0\n",
+       "Snow Making_ac                   600.0\n",
+       "daysOpenLastYear                 123.0\n",
+       "yearsOpen                         72.0\n",
+       "averageSnowfall                  333.0\n",
+       "AdultWeekday                      81.0\n",
+       "AdultWeekend                      81.0\n",
+       "projectedDaysOpen                123.0\n",
+       "NightSkiing_ac                   600.0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 4#\n",
+    "#Filter the ski_data dataframe to display just the row for our resort with the name 'Big Mountain Resort'\n",
+    "#Hint: you will find that the transpose of the row will give a nicer output. DataFrame's do have a\n",
+    "#transpose method, but you can access this conveniently with the `T` property.\n",
+    "ski_data[ski_data.Name == 'Big Mountain Resort'].T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's good that your resort doesn't appear to have any missing values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.6.2 Number Of Missing Values By Column<a id='2.6.2_Number_Of_Missing_Values_By_Column'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Count the number of missing values in each column and sort them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>166</td>\n",
+       "      <td>50.303030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>143</td>\n",
+       "      <td>43.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <td>54</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>51</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>51</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>51</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>47</td>\n",
+       "      <td>14.242424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>46</td>\n",
+       "      <td>13.939394</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>14</td>\n",
+       "      <td>4.242424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1.515152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.212121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.909091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.303030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   count          %\n",
+       "fastEight            166  50.303030\n",
+       "NightSkiing_ac       143  43.333333\n",
+       "AdultWeekday          54  16.363636\n",
+       "AdultWeekend          51  15.454545\n",
+       "daysOpenLastYear      51  15.454545\n",
+       "TerrainParks          51  15.454545\n",
+       "projectedDaysOpen     47  14.242424\n",
+       "Snow Making_ac        46  13.939394\n",
+       "averageSnowfall       14   4.242424\n",
+       "LongestRun_mi          5   1.515152\n",
+       "Runs                   4   1.212121\n",
+       "SkiableTerrain_ac      3   0.909091\n",
+       "yearsOpen              1   0.303030\n",
+       "total_chairs           0   0.000000\n",
+       "Name                   0   0.000000\n",
+       "Region                 0   0.000000\n",
+       "double                 0   0.000000\n",
+       "triple                 0   0.000000\n",
+       "quad                   0   0.000000\n",
+       "fastQuads              0   0.000000\n",
+       "fastSixes              0   0.000000\n",
+       "trams                  0   0.000000\n",
+       "base_elev              0   0.000000\n",
+       "vertical_drop          0   0.000000\n",
+       "summit_elev            0   0.000000\n",
+       "state                  0   0.000000\n",
+       "surface                0   0.000000"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 5#\n",
+    "#Count (using `.sum()`) the number of missing values (`.isnull()`) in each column of \n",
+    "#ski_data as well as the percentages (using `.mean()` instead of `.sum()`).\n",
+    "#Order them (increasing or decreasing) using sort_values\n",
+    "#Call `pd.concat` to present these in a single table (DataFrame) with the helpful column names 'count' and '%'\n",
+    "missing = pd.concat([ski_data.isnull().sum(), 100 * ski_data.isnull().mean()], axis=1)\n",
+    "missing.columns=['count', '%']\n",
+    "missing.sort_values(by= 'count', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`fastEight` has the most missing values, at just over 50%. Unfortunately, you see you're also missing quite a few of your desired target quantity, the ticket price, which is missing 15-16% of values. `AdultWeekday` is missing in a few more records than `AdultWeekend`. What overlap is there in these missing values? This is a question you'll want to investigate. You should also point out that `isnull()` is not the only indicator of missing data. Sometimes 'missingness' can be encoded, perhaps by a -1 or 999. Such values are typically chosen because they are \"obviously\" not genuine values. If you were capturing data on people's heights and weights but missing someone's height, you could certainly encode that as a 0 because no one has a height of zero (in any units). Yet such entries would not be revealed by `isnull()`. Here, you need a data dictionary and/or to spot such values as part of looking for outliers. Someone with a height of zero should definitely show up as an outlier!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.6.3 Categorical Features<a id='2.6.3_Categorical_Features'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So far you've examined only the numeric features. Now you inspect categorical ones such as resort name and state. These are discrete entities. 'Alaska' is a name. Although names can be sorted alphabetically, it makes no sense to take the average of 'Alaska' and 'Arizona'. Similarly, 'Alaska' is before 'Arizona' only lexicographically; it is neither 'less than' nor 'greater than' 'Arizona'. As such, they tend to require different handling than strictly numeric quantities. Note, a feature _can_ be numeric but also categorical. For example, instead of giving the number of `fastEight` lifts, a feature might be `has_fastEights` and have the value 0 or 1 to denote absence or presence of such a lift. In such a case it would not make sense to take an average of this or perform other mathematical calculations on it. Although you digress a little to make a point, month numbers are also, strictly speaking, categorical features. Yes, when a month is represented by its number (1 for January, 2 for Februrary etc.) it provides a convenient way to graph trends over a year. And, arguably, there is some logical interpretation of the average of 1 and 3 (January and March) being 2 (February). However, clearly December of one years precedes January of the next and yet 12 as a number is not less than 1. The numeric quantities in the section above are truly numeric; they are the number of feet in the drop, or acres or years open or the amount of snowfall etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>state</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alyeska Resort</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Eaglecrest Ski Area</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hilltop Ski Area</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arizona Snowbowl</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sunrise Park Resort</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>Meadowlark Ski Lodge</td>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Wyoming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>Sleeping Giant Ski Resort</td>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Wyoming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>Snow King Resort</td>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Wyoming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>Snowy Range Ski &amp; Recreation Area</td>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Wyoming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>White Pine Ski Area</td>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Wyoming</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>330 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  Name   Region    state\n",
+       "0                       Alyeska Resort   Alaska   Alaska\n",
+       "1                  Eaglecrest Ski Area   Alaska   Alaska\n",
+       "2                     Hilltop Ski Area   Alaska   Alaska\n",
+       "3                     Arizona Snowbowl  Arizona  Arizona\n",
+       "4                  Sunrise Park Resort  Arizona  Arizona\n",
+       "..                                 ...      ...      ...\n",
+       "325               Meadowlark Ski Lodge  Wyoming  Wyoming\n",
+       "326          Sleeping Giant Ski Resort  Wyoming  Wyoming\n",
+       "327                   Snow King Resort  Wyoming  Wyoming\n",
+       "328  Snowy Range Ski & Recreation Area  Wyoming  Wyoming\n",
+       "329                White Pine Ski Area  Wyoming  Wyoming\n",
+       "\n",
+       "[330 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 6#\n",
+    "#Use ski_data's `select_dtypes` method to select columns of dtype 'object'\n",
+    "ski_data.select_dtypes('object')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You saw earlier on that these three columns had no missing values. But are there any other issues with these columns? Sensible questions to ask here include:\n",
+    "\n",
+    "* Is `Name` (or at least a combination of Name/Region/State) unique?\n",
+    "* Is `Region` always the same as `state`?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.3.1 Unique Resort Names<a id='2.6.3.1_Unique_Resort_Names'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Name\n",
+       "Crystal Mountain    2\n",
+       "Alyeska Resort      1\n",
+       "Brandywine          1\n",
+       "Boston Mills        1\n",
+       "Alpine Valley       1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 7#\n",
+    "#Use pandas' Series method `value_counts` to find any duplicated resort names\n",
+    "ski_data['Name'].value_counts().head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You have a duplicated resort name: Crystal Mountain."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q: 1** Is this resort duplicated if you take into account Region and/or state as well?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Alyeska Resort, Alaska    1\n",
+       "Snow Trails, Ohio         1\n",
+       "Brandywine, Ohio          1\n",
+       "Boston Mills, Ohio        1\n",
+       "Alpine Valley, Ohio       1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 8#\n",
+    "#Concatenate the string columns 'Name' and 'Region' and count the values again (as above)\n",
+    "(ski_data['Name'] + ', ' + ski_data['Region']).value_counts().head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Alyeska Resort, Alaska    1\n",
+       "Snow Trails, Ohio         1\n",
+       "Brandywine, Ohio          1\n",
+       "Boston Mills, Ohio        1\n",
+       "Alpine Valley, Ohio       1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 9#\n",
+    "#Concatenate 'Name' and 'state' and count the values again (as above)\n",
+    "(ski_data['Name'] + ', ' + ski_data['state']).value_counts().head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "**NB** because you know `value_counts()` sorts descending, you can use the `head()` method and know the rest of the counts must be 1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**A: 1** Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "taking those other factors into account deduplicates the Crystal Mountain entries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>state</th>\n",
+       "      <th>summit_elev</th>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <th>base_elev</th>\n",
+       "      <th>trams</th>\n",
+       "      <th>fastEight</th>\n",
+       "      <th>fastSixes</th>\n",
+       "      <th>fastQuads</th>\n",
+       "      <th>...</th>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>Crystal Mountain</td>\n",
+       "      <td>Michigan</td>\n",
+       "      <td>Michigan</td>\n",
+       "      <td>1132</td>\n",
+       "      <td>375</td>\n",
+       "      <td>757</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>56.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>Crystal Mountain</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>7012</td>\n",
+       "      <td>3100</td>\n",
+       "      <td>4400</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>2600.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>486.0</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 Name      Region       state  summit_elev  vertical_drop  \\\n",
+       "104  Crystal Mountain    Michigan    Michigan         1132            375   \n",
+       "295  Crystal Mountain  Washington  Washington         7012           3100   \n",
+       "\n",
+       "     base_elev  trams  fastEight  fastSixes  fastQuads  ...  LongestRun_mi  \\\n",
+       "104        757      0        0.0          0          1  ...            0.3   \n",
+       "295       4400      1        NaN          2          2  ...            2.5   \n",
+       "\n",
+       "     SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  yearsOpen  \\\n",
+       "104              102.0            96.0             120.0       63.0   \n",
+       "295             2600.0            10.0               NaN       57.0   \n",
+       "\n",
+       "     averageSnowfall  AdultWeekday  AdultWeekend  projectedDaysOpen  \\\n",
+       "104            132.0          54.0          64.0              135.0   \n",
+       "295            486.0          99.0          99.0                NaN   \n",
+       "\n",
+       "     NightSkiing_ac  \n",
+       "104            56.0  \n",
+       "295             NaN  \n",
+       "\n",
+       "[2 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data[ski_data['Name'] == 'Crystal Mountain']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So there are two Crystal Mountain resorts, but they are clearly two different resorts in two different states. This is a powerful signal that you have unique records on each row."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.3.2 Region And State<a id='2.6.3.2_Region_And_State'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What's the relationship between region and state?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You know they are the same in many cases (e.g. both the Region and the state are given as 'Michigan'). In how many cases do they differ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 10#\n",
+    "#Calculate the number of times Region does not equal state\n",
+    "(ski_data.Region != ski_data.state).sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You know what a state is. What is a region? You can tabulate the distinct values along with their respective frequencies using `value_counts()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Region\n",
+       "New York               33\n",
+       "Michigan               29\n",
+       "Sierra Nevada          22\n",
+       "Colorado               22\n",
+       "Pennsylvania           19\n",
+       "Wisconsin              16\n",
+       "New Hampshire          16\n",
+       "Vermont                15\n",
+       "Minnesota              14\n",
+       "Idaho                  12\n",
+       "Montana                12\n",
+       "Massachusetts          11\n",
+       "Washington             10\n",
+       "New Mexico              9\n",
+       "Maine                   9\n",
+       "Wyoming                 8\n",
+       "Utah                    7\n",
+       "Salt Lake City          6\n",
+       "North Carolina          6\n",
+       "Oregon                  6\n",
+       "Connecticut             5\n",
+       "Ohio                    5\n",
+       "Virginia                4\n",
+       "West Virginia           4\n",
+       "Illinois                4\n",
+       "Mt. Hood                4\n",
+       "Alaska                  3\n",
+       "Iowa                    3\n",
+       "South Dakota            2\n",
+       "Arizona                 2\n",
+       "Nevada                  2\n",
+       "Missouri                2\n",
+       "Indiana                 2\n",
+       "New Jersey              2\n",
+       "Rhode Island            1\n",
+       "Tennessee               1\n",
+       "Maryland                1\n",
+       "Northern California     1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data['Region'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A casual inspection by eye reveals some non-state names such as Sierra Nevada, Salt Lake City, and Northern California. Tabulate the differences between Region and state. On a note regarding scaling to larger data sets, you might wonder how you could spot such cases when presented with millions of rows. This is an interesting point. Imagine you have access to a database with a Region and state column in a table and there are millions of rows. You wouldn't eyeball all the rows looking for differences! Bear in mind that our first interest lies in establishing the answer to the question \"Are they always the same?\" One approach might be to ask the database to return records where they differ, but limit the output to 10 rows. If there were differences, you'd only get up to 10 results, and so you wouldn't know whether you'd located all differences, but you'd know that there were 'a nonzero number' of differences. If you got an empty result set back, then you would know that the two columns always had the same value. At the risk of digressing, some values in one column only might be NULL (missing) and different databases treat NULL differently, so be aware that on many an occasion a seamingly 'simple' question gets very interesting to answer very quickly!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state       Region             \n",
+       "California  Sierra Nevada          20\n",
+       "            Northern California     1\n",
+       "Nevada      Sierra Nevada           2\n",
+       "Oregon      Mt. Hood                4\n",
+       "Utah        Salt Lake City          6\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 11#\n",
+    "#Filter the ski_data dataframe for rows where 'Region' and 'state' are different,\n",
+    "#group that by 'state' and perform `value_counts` on the 'Region'\n",
+    "(ski_data[ski_data.Region != ski_data.state]\n",
+    " .groupby('state')['Region']\n",
+    " .value_counts())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The vast majority of the differences are in California, with most Regions being called Sierra Nevada and just one referred to as Northern California."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.3.3 Number of distinct regions and states<a id='2.6.3.3_Number_of_distinct_regions_and_states'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Region    38\n",
+       "state     35\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 12#\n",
+    "#Select the 'Region' and 'state' columns from ski_data and use the `nunique` method to calculate\n",
+    "#the number of unique values in each\n",
+    "ski_data[['Region', 'state']].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because a few states are split across multiple named regions, there are slightly more unique regions than states."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.3.4 Distribution Of Resorts By Region And State<a id='2.6.3.4_Distribution_Of_Resorts_By_Region_And_State'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If this is your first time using [matplotlib](https://matplotlib.org/3.2.2/index.html)'s [subplots](https://matplotlib.org/3.2.2/api/_as_gen/matplotlib.pyplot.subplots.html), you may find the online documentation useful."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 13#\n",
+    "#Create two subplots on 1 row and 2 columns with a figsize of (12, 8)\n",
+    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 8))\n",
+    "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n",
+    "ski_data.Region.value_counts().plot(kind='barh', ax=ax[0])\n",
+    "#Give the plot a helpful title of 'Region'\n",
+    "ax[0].set_title('Region')\n",
+    "#Label the xaxis 'Count'\n",
+    "ax[0].set_xlabel('Count')\n",
+    "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n",
+    "ski_data.state.value_counts().plot(kind='barh', ax=ax[1])\n",
+    "#Give the plot a helpful title of 'state'\n",
+    "ax[1].set_title('state')\n",
+    "#Label the xaxis 'Count'\n",
+    "ax[1].set_xlabel('Count')\n",
+    "#Give the subplots a little \"breathing room\" with a wspace of 0.5\n",
+    "plt.subplots_adjust(wspace=0.5);\n",
+    "#You're encouraged to explore a few different figure sizes, orientations, and spacing here\n",
+    "# as the importance of easy-to-read and informative figures is frequently understated\n",
+    "# and you will find the ability to tweak figures invaluable later on"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How's your geography? Looking at the distribution of States, you see New York accounting for the majority of resorts. Our target resort is in Montana, which comes in at 13th place. You should think carefully about how, or whether, you use this information. Does New York command a premium because of its proximity to population? Even if a resort's State were a useful predictor of ticket price, your main interest lies in Montana. Would you want a model that is skewed for accuracy by New York? Should you just filter for Montana and create a Montana-specific model? This would slash your available data volume. Your problem task includes the contextual insight that the data are for resorts all belonging to the same market share. This suggests one might expect prices to be similar amongst them. You can look into this. A boxplot grouped by State is an ideal way to quickly compare prices. Another side note worth bringing up here is that, in reality, the best approach here definitely would include consulting with the client or other domain expert. They might know of good reasons for treating states equivalently or differently. The data scientist is rarely the final arbiter of such a decision. But here, you'll see if we can find any supporting evidence for treating states the same or differently."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.3.5 Distribution Of Ticket Price By State<a id='2.6.3.5_Distribution_Of_Ticket_Price_By_State'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our primary focus is our Big Mountain resort, in Montana. Does the state give you any clues to help decide what your primary target response feature should be (weekend or weekday ticket prices)?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.6.3.5.1 Average weekend and weekday price by state<a id='2.6.3.5.1_Average_weekend_and_weekday_price_by_state'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Alaska</th>\n",
+       "      <td>47.333333</td>\n",
+       "      <td>57.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>81.500000</td>\n",
+       "      <td>83.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>78.214286</td>\n",
+       "      <td>81.416667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>90.714286</td>\n",
+       "      <td>90.714286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>47.800000</td>\n",
+       "      <td>56.800000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             AdultWeekday  AdultWeekend\n",
+       "state                                  \n",
+       "Alaska          47.333333     57.333333\n",
+       "Arizona         81.500000     83.500000\n",
+       "California      78.214286     81.416667\n",
+       "Colorado        90.714286     90.714286\n",
+       "Connecticut     47.800000     56.800000"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 14#\n",
+    "# Calculate average weekday and weekend price by state and sort by the average of the two\n",
+    "# Hint: use the pattern dataframe.groupby(<grouping variable>)[<list of columns>].mean()\n",
+    "state_price_means = ski_data.groupby('state')[['AdultWeekday', 'AdultWeekend']].mean()\n",
+    "state_price_means.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The next bit simply reorders the index by increasing average of weekday and weekend prices\n",
+    "# Compare the index order you get from\n",
+    "# state_price_means.index\n",
+    "# with\n",
+    "# state_price_means.mean(axis=1).sort_values(ascending=False).index\n",
+    "# See how this expression simply sits within the reindex()\n",
+    "(state_price_means.reindex(index=state_price_means.mean(axis=1)\n",
+    "    .sort_values(ascending=False)\n",
+    "    .index)\n",
+    "    .plot(kind='barh', figsize=(10, 10), title='Average ticket price by State'))\n",
+    "plt.xlabel('Price ($)');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "The figure above represents a dataframe with two columns, one for the average prices of each kind of ticket. This tells you how the average ticket price varies from state to state. But can you get more insight into the difference in the distributions between states?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.6.3.5.2 Distribution of weekday and weekend price by state<a id='2.6.3.5.2_Distribution_of_weekday_and_weekend_price_by_state'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, you can transform the data into a single column for price with a new categorical column that represents the ticket type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 15#\n",
+    "#Use the pd.melt function, pass in the ski_data columns 'state', 'AdultWeekday', and 'Adultweekend' only,\n",
+    "#specify 'state' for `id_vars`\n",
+    "#gather the ticket prices from the 'Adultweekday' and 'AdultWeekend' columns using the `value_vars` argument,\n",
+    "#call the resultant price column 'Price' via the `value_name` argument,\n",
+    "#name the weekday/weekend indicator column 'Ticket' via the `var_name` argument\n",
+    "ticket_prices = pd.melt(ski_data[['state', 'AdultWeekday', 'AdultWeekend']], \n",
+    "                        id_vars='state', \n",
+    "                        var_name='Ticket', \n",
+    "                        value_vars=['AdultWeekday', 'AdultWeekend'], \n",
+    "                        value_name='Price')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>65.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>89.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>74.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     state        Ticket  Price\n",
+       "0   Alaska  AdultWeekday   65.0\n",
+       "1   Alaska  AdultWeekday   47.0\n",
+       "2   Alaska  AdultWeekday   30.0\n",
+       "3  Arizona  AdultWeekday   89.0\n",
+       "4  Arizona  AdultWeekday   74.0"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ticket_prices.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is now in a format we can pass to [seaborn](https://seaborn.pydata.org/)'s [boxplot](https://seaborn.pydata.org/generated/seaborn.boxplot.html) function to create boxplots of the ticket price distributions for each ticket type for each state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 16#\n",
+    "#Create a seaborn boxplot of the ticket price dataframe we created above,\n",
+    "#with 'state' on the x-axis, 'Price' as the y-value, and a hue that indicates 'Ticket'\n",
+    "#This will use boxplot's x, y, hue, and data arguments.\n",
+    "plt.subplots(figsize=(12, 8))\n",
+    "sns.boxplot(x='state', y='Price', hue='Ticket', data=ticket_prices)\n",
+    "plt.xticks(rotation='vertical')\n",
+    "plt.ylabel('Price ($)')\n",
+    "plt.xlabel('State');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Aside from some relatively expensive ticket prices in California, Colorado, and Utah, most prices appear to lie in a broad band from around 25 to over 100 dollars. Some States show more variability than others. Montana and South Dakota, for example, both show fairly small variability as well as matching weekend and weekday ticket prices. Nevada and Utah, on the other hand, show the most range in prices. Some States, notably North Carolina and Virginia, have weekend prices far higher than weekday prices. You could be inspired from this exploration to consider a few potential groupings of resorts, those with low spread, those with lower averages, and those that charge a premium for weekend tickets. However, you're told that you are taking all resorts to be part of the same market share, you  could argue against further segment the resorts. Nevertheless, ways to consider using the State information in your modelling include:\n",
+    "\n",
+    "* disregard State completely\n",
+    "* retain all State information\n",
+    "* retain State in the form of Montana vs not Montana, as our target resort is in Montana\n",
+    "\n",
+    "You've also noted another effect above: some States show a marked difference between weekday and weekend ticket prices. It may make sense to allow a model to take into account not just State but also weekend vs weekday."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Thus we currently have two main questions you want to resolve:\n",
+    "\n",
+    "* What do you do about the two types of ticket price?\n",
+    "* What do you do about the state information?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.6.4 Numeric Features<a id='2.6.4_Numeric_Features'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Having decided to reserve judgement on how exactly you utilize the State, turn your attention to cleaning the numeric features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.4.1 Numeric data summary<a id='2.6.4.1_Numeric_data_summary'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>4591.818182</td>\n",
+       "      <td>3735.535934</td>\n",
+       "      <td>315.0</td>\n",
+       "      <td>1403.75</td>\n",
+       "      <td>3127.5</td>\n",
+       "      <td>7806.00</td>\n",
+       "      <td>13487.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>1215.427273</td>\n",
+       "      <td>947.864557</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>461.25</td>\n",
+       "      <td>964.5</td>\n",
+       "      <td>1800.00</td>\n",
+       "      <td>4425.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>3374.000000</td>\n",
+       "      <td>3117.121621</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>869.00</td>\n",
+       "      <td>1561.5</td>\n",
+       "      <td>6325.25</td>\n",
+       "      <td>10800.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>0.172727</td>\n",
+       "      <td>0.559946</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>164.0</td>\n",
+       "      <td>0.006098</td>\n",
+       "      <td>0.078087</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>0.184848</td>\n",
+       "      <td>0.651685</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>1.018182</td>\n",
+       "      <td>2.198294</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>0.933333</td>\n",
+       "      <td>1.312245</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>1.619130</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>1.833333</td>\n",
+       "      <td>1.815028</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>2.621212</td>\n",
+       "      <td>2.059636</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>8.266667</td>\n",
+       "      <td>5.798683</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>10.00</td>\n",
+       "      <td>41.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>326.0</td>\n",
+       "      <td>48.214724</td>\n",
+       "      <td>46.364077</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>19.00</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>60.00</td>\n",
+       "      <td>341.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>279.0</td>\n",
+       "      <td>2.820789</td>\n",
+       "      <td>2.008113</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>325.0</td>\n",
+       "      <td>1.433231</td>\n",
+       "      <td>1.156171</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>327.0</td>\n",
+       "      <td>739.801223</td>\n",
+       "      <td>1816.167441</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>85.00</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>690.00</td>\n",
+       "      <td>26819.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>284.0</td>\n",
+       "      <td>174.873239</td>\n",
+       "      <td>261.336125</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>50.00</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>200.50</td>\n",
+       "      <td>3379.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>279.0</td>\n",
+       "      <td>115.103943</td>\n",
+       "      <td>35.063251</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>97.00</td>\n",
+       "      <td>114.0</td>\n",
+       "      <td>135.00</td>\n",
+       "      <td>305.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>329.0</td>\n",
+       "      <td>63.656535</td>\n",
+       "      <td>109.429928</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>50.00</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>69.00</td>\n",
+       "      <td>2019.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>316.0</td>\n",
+       "      <td>185.316456</td>\n",
+       "      <td>136.356842</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>69.00</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>300.00</td>\n",
+       "      <td>669.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <td>276.0</td>\n",
+       "      <td>57.916957</td>\n",
+       "      <td>26.140126</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>40.00</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>71.00</td>\n",
+       "      <td>179.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>279.0</td>\n",
+       "      <td>64.166810</td>\n",
+       "      <td>24.554584</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>47.00</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>77.50</td>\n",
+       "      <td>179.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>283.0</td>\n",
+       "      <td>120.053004</td>\n",
+       "      <td>31.045963</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>100.00</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>139.50</td>\n",
+       "      <td>305.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>187.0</td>\n",
+       "      <td>100.395722</td>\n",
+       "      <td>105.169620</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>40.00</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>114.00</td>\n",
+       "      <td>650.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   count         mean          std    min      25%     50%  \\\n",
+       "summit_elev        330.0  4591.818182  3735.535934  315.0  1403.75  3127.5   \n",
+       "vertical_drop      330.0  1215.427273   947.864557   60.0   461.25   964.5   \n",
+       "base_elev          330.0  3374.000000  3117.121621   70.0   869.00  1561.5   \n",
+       "trams              330.0     0.172727     0.559946    0.0     0.00     0.0   \n",
+       "fastEight          164.0     0.006098     0.078087    0.0     0.00     0.0   \n",
+       "fastSixes          330.0     0.184848     0.651685    0.0     0.00     0.0   \n",
+       "fastQuads          330.0     1.018182     2.198294    0.0     0.00     0.0   \n",
+       "quad               330.0     0.933333     1.312245    0.0     0.00     0.0   \n",
+       "triple             330.0     1.500000     1.619130    0.0     0.00     1.0   \n",
+       "double             330.0     1.833333     1.815028    0.0     1.00     1.0   \n",
+       "surface            330.0     2.621212     2.059636    0.0     1.00     2.0   \n",
+       "total_chairs       330.0     8.266667     5.798683    0.0     5.00     7.0   \n",
+       "Runs               326.0    48.214724    46.364077    3.0    19.00    33.0   \n",
+       "TerrainParks       279.0     2.820789     2.008113    1.0     1.00     2.0   \n",
+       "LongestRun_mi      325.0     1.433231     1.156171    0.0     0.50     1.0   \n",
+       "SkiableTerrain_ac  327.0   739.801223  1816.167441    8.0    85.00   200.0   \n",
+       "Snow Making_ac     284.0   174.873239   261.336125    2.0    50.00   100.0   \n",
+       "daysOpenLastYear   279.0   115.103943    35.063251    3.0    97.00   114.0   \n",
+       "yearsOpen          329.0    63.656535   109.429928    6.0    50.00    58.0   \n",
+       "averageSnowfall    316.0   185.316456   136.356842   18.0    69.00   150.0   \n",
+       "AdultWeekday       276.0    57.916957    26.140126   15.0    40.00    50.0   \n",
+       "AdultWeekend       279.0    64.166810    24.554584   17.0    47.00    60.0   \n",
+       "projectedDaysOpen  283.0   120.053004    31.045963   30.0   100.00   120.0   \n",
+       "NightSkiing_ac     187.0   100.395722   105.169620    2.0    40.00    72.0   \n",
+       "\n",
+       "                       75%      max  \n",
+       "summit_elev        7806.00  13487.0  \n",
+       "vertical_drop      1800.00   4425.0  \n",
+       "base_elev          6325.25  10800.0  \n",
+       "trams                 0.00      4.0  \n",
+       "fastEight             0.00      1.0  \n",
+       "fastSixes             0.00      6.0  \n",
+       "fastQuads             1.00     15.0  \n",
+       "quad                  1.00      8.0  \n",
+       "triple                2.00      8.0  \n",
+       "double                3.00     14.0  \n",
+       "surface               3.00     15.0  \n",
+       "total_chairs         10.00     41.0  \n",
+       "Runs                 60.00    341.0  \n",
+       "TerrainParks          4.00     14.0  \n",
+       "LongestRun_mi         2.00      6.0  \n",
+       "SkiableTerrain_ac   690.00  26819.0  \n",
+       "Snow Making_ac      200.50   3379.0  \n",
+       "daysOpenLastYear    135.00    305.0  \n",
+       "yearsOpen            69.00   2019.0  \n",
+       "averageSnowfall     300.00    669.0  \n",
+       "AdultWeekday         71.00    179.0  \n",
+       "AdultWeekend         77.50    179.0  \n",
+       "projectedDaysOpen   139.50    305.0  \n",
+       "NightSkiing_ac      114.00    650.0  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 17#\n",
+    "#Call ski_data's `describe` method for a statistical summary of the numerical columns\n",
+    "#Hint: there are fewer summary stat columns than features, so displaying the transpose\n",
+    "#will be useful again\n",
+    "ski_data.describe().T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Recall you're missing the ticket prices for some 16% of resorts. This is a fundamental problem that means you simply lack the required data for those resorts and will have to drop those records. But you may have a weekend price and not a weekday price, or vice versa. You want to keep any price you have."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    82.424242\n",
+       "2    14.242424\n",
+       "1     3.333333\n",
+       "Name: count, dtype: float64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n",
+    "missing_price.value_counts()/len(missing_price) * 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just over 82% of resorts have no missing ticket price, 3% are missing one value, and 14% are missing both. You will definitely want to drop the records for which you have no price information, however you will not do so just yet. There may still be useful information about the distributions of other features in that 14% of the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.4.2 Distributions Of Feature Values<a id='2.6.4.2_Distributions_Of_Feature_Values'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that, although we are still in the 'data wrangling and cleaning' phase rather than exploratory data analysis, looking at distributions of features is immensely useful in getting a feel for whether the values look sensible and whether there are any obvious outliers to investigate. Some exploratory data analysis belongs here, and data wrangling will inevitably occur later on. It's more a matter of emphasis. Here, we're interesting in focusing on whether distributions look plausible or wrong. Later on, we're more interested in relationships and patterns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 25 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 18#\n",
+    "#Call ski_data's `hist` method to plot histograms of each of the numeric features\n",
+    "#Try passing it an argument figsize=(15,10)\n",
+    "#Try calling plt.subplots_adjust() with an argument hspace=0.5 to adjust the spacing\n",
+    "#It's important you create legible and easy-to-read plots\n",
+    "ski_data.hist(figsize=(15,10))\n",
+    "plt.subplots_adjust(hspace=0.5);\n",
+    "#Hint: notice how the terminating ';' \"swallows\" some messy output and leads to a tidier notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What features do we have possible cause for concern about and why?\n",
+    "\n",
+    "* SkiableTerrain_ac because values are clustered down the low end,\n",
+    "* Snow Making_ac for the same reason,\n",
+    "* fastEight because all but one value is 0 so it has very little variance, and half the values are missing,\n",
+    "* fastSixes raises an amber flag; it has more variability, but still mostly 0,\n",
+    "* trams also may get an amber flag for the same reason,\n",
+    "* yearsOpen because most values are low but it has a maximum of 2019, which strongly suggests someone recorded calendar year rather than number of years."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.6.4.2.1 SkiableTerrain_ac<a id='2.6.4.2.1_SkiableTerrain_ac'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "39    26819.0\n",
+       "Name: SkiableTerrain_ac, dtype: float64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 19#\n",
+    "#Filter the 'SkiableTerrain_ac' column to print the values greater than 10000\n",
+    "ski_data.SkiableTerrain_ac[ski_data.SkiableTerrain_ac > 10000]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q: 2** One resort has an incredibly large skiable terrain area! Which is it?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>39</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Silverton Mountain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Colorado</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Colorado</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>13487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>3087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>10400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>26819.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>175.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>17.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>400.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <td>79.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>79.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>181.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   39\n",
+       "Name               Silverton Mountain\n",
+       "Region                       Colorado\n",
+       "state                        Colorado\n",
+       "summit_elev                     13487\n",
+       "vertical_drop                    3087\n",
+       "base_elev                       10400\n",
+       "trams                               0\n",
+       "fastEight                         0.0\n",
+       "fastSixes                           0\n",
+       "fastQuads                           0\n",
+       "quad                                0\n",
+       "triple                              0\n",
+       "double                              1\n",
+       "surface                             0\n",
+       "total_chairs                        1\n",
+       "Runs                              NaN\n",
+       "TerrainParks                      NaN\n",
+       "LongestRun_mi                     1.5\n",
+       "SkiableTerrain_ac             26819.0\n",
+       "Snow Making_ac                    NaN\n",
+       "daysOpenLastYear                175.0\n",
+       "yearsOpen                        17.0\n",
+       "averageSnowfall                 400.0\n",
+       "AdultWeekday                     79.0\n",
+       "AdultWeekend                     79.0\n",
+       "projectedDaysOpen               181.0\n",
+       "NightSkiing_ac                    NaN"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 20#\n",
+    "#Now you know there's only one, print the whole row to investigate all values, including seeing the resort name\n",
+    "#Hint: don't forget the transpose will be helpful here\n",
+    "ski_data[ski_data.SkiableTerrain_ac > 10000].T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**A: 2** Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\tSilverton Mountain"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But what can you do when you have one record that seems highly suspicious?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can see if your data are correct. Search for \"silverton mountain skiable area\". If you do this, you get some [useful information](https://www.google.com/search?q=silverton+mountain+skiable+area)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Silverton Mountain information](images/silverton_mountain_info.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can spot check data. You see your top and base elevation values agree, but the skiable area is very different. Your suspect value is 26819, but the value you've just looked up is 1819. The last three digits agree. This sort of error could have occured in transmission or some editing or transcription stage. You could plausibly replace the suspect value with the one you've just obtained. Another cautionary note to make here is that although you're doing this in order to progress with your analysis, this is most definitely an issue that should have been raised and fed back to the client or data originator as a query. You should view this \"data correction\" step as a means to continue (documenting it carefully as you do in this notebook) rather than an ultimate decision as to what is correct."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26819.0"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 21#\n",
+    "#Use the .loc accessor to print the 'SkiableTerrain_ac' value only for this resort\n",
+    "ski_data.loc[39, 'SkiableTerrain_ac']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 22#\n",
+    "#Use the .loc accessor again to modify this value with the correct value of 1819\n",
+    "ski_data.loc[39, 'SkiableTerrain_ac'] = 1819"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1819.0"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 23#\n",
+    "#Use the .loc accessor a final time to verify that the value has been modified\n",
+    "ski_data.loc[39, 'SkiableTerrain_ac']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NB whilst you may become suspicious about your data quality, and you know you have missing values, you will not here dive down the rabbit hole of checking all values or web scraping to replace missing values.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What does the distribution of skiable area look like now?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ski_data.SkiableTerrain_ac.hist(bins=30)\n",
+    "plt.xlabel('SkiableTerrain_ac')\n",
+    "plt.ylabel('Count')\n",
+    "plt.title('Distribution of skiable area (acres) after replacing erroneous value');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You now see a rather long tailed distribution. You may wonder about the now most extreme value that is above 8000, but similarly you may also wonder about the value around 7000. If you wanted to spend more time manually checking values you could, but leave this for now. The above distribution is plausible."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.6.4.2.2 Snow Making_ac<a id='2.6.4.2.2_Snow_Making_ac'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11    3379.0\n",
+       "18    1500.0\n",
+       "Name: Snow Making_ac, dtype: float64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data['Snow Making_ac'][ski_data['Snow Making_ac'] > 1000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>11</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Heavenly Mountain Resort</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Sierra Nevada</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>California</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>10067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>3500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>7170</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>97.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>5.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>4800.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>3379.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>155.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>64.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>360.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>157.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                         11\n",
+       "Name               Heavenly Mountain Resort\n",
+       "Region                        Sierra Nevada\n",
+       "state                            California\n",
+       "summit_elev                           10067\n",
+       "vertical_drop                          3500\n",
+       "base_elev                              7170\n",
+       "trams                                     2\n",
+       "fastEight                               0.0\n",
+       "fastSixes                                 2\n",
+       "fastQuads                                 7\n",
+       "quad                                      1\n",
+       "triple                                    5\n",
+       "double                                    3\n",
+       "surface                                   8\n",
+       "total_chairs                             28\n",
+       "Runs                                   97.0\n",
+       "TerrainParks                            3.0\n",
+       "LongestRun_mi                           5.5\n",
+       "SkiableTerrain_ac                    4800.0\n",
+       "Snow Making_ac                       3379.0\n",
+       "daysOpenLastYear                      155.0\n",
+       "yearsOpen                              64.0\n",
+       "averageSnowfall                       360.0\n",
+       "AdultWeekday                            NaN\n",
+       "AdultWeekend                            NaN\n",
+       "projectedDaysOpen                     157.0\n",
+       "NightSkiing_ac                          NaN"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data[ski_data['Snow Making_ac'] > 3000].T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can adopt a similar approach as for the suspect skiable area value and do some spot checking. To save time, here is a link to the website for [Heavenly Mountain Resort](https://www.skiheavenly.com/the-mountain/about-the-mountain/mountain-info.aspx). From this you can glean that you have values for skiable terrain that agree. Furthermore, you can read that snowmaking covers 60% of the trails."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What, then, is your rough guess for the area covered by snowmaking?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2880.0"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    ".6 * 4800"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is less than the value of 3379 in your data so you may have a judgement call to make. However, notice something else. You have no ticket pricing information at all for this resort. Any further effort spent worrying about values for this resort will be wasted. You'll simply be dropping the entire row!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.6.4.2.3 fastEight<a id='2.6.4.2.3_fastEight'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Look at the different fastEight values more closely:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "fastEight\n",
+       "0.0    163\n",
+       "1.0      1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.fastEight.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Drop the fastEight column in its entirety; half the values are missing and all but the others are the value zero. There is essentially no information in this column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 24#\n",
+    "#Drop the 'fastEight' column from ski_data. Use inplace=True\n",
+    "ski_data.drop(columns='fastEight', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What about yearsOpen? How many resorts have purportedly been open for more than 100 years?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "34      104.0\n",
+       "115    2019.0\n",
+       "Name: yearsOpen, dtype: float64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 25#\n",
+    "#Filter the 'yearsOpen' column for values greater than 100\n",
+    "ski_data.yearsOpen[ski_data.yearsOpen > 100]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Okay, one seems to have been open for 104 years. But beyond that, one is down as having been open for 2019 years. This is wrong! What shall you do about this?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What does the distribution of yearsOpen look like if you exclude just the obviously wrong one?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 26#\n",
+    "#Call the hist method on 'yearsOpen' after filtering for values under 1000\n",
+    "#Pass the argument bins=30 to hist(), but feel free to explore other values\n",
+    "ski_data.yearsOpen[ski_data.yearsOpen < 1000].hist(bins=30)\n",
+    "plt.xlabel('Years open')\n",
+    "plt.ylabel('Count')\n",
+    "plt.title('Distribution of years open excluding 2019');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above distribution of years seems entirely plausible, including the 104 year value. You can certainly state that no resort will have been open for 2019 years! It likely means the resort opened in 2019. It could also mean the resort is due to open in 2019. You don't know when these data were gathered!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's review the summary statistics for the years under 1000."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    328.000000\n",
+       "mean      57.695122\n",
+       "std       16.841182\n",
+       "min        6.000000\n",
+       "25%       50.000000\n",
+       "50%       58.000000\n",
+       "75%       68.250000\n",
+       "max      104.000000\n",
+       "Name: yearsOpen, dtype: float64"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.yearsOpen[ski_data.yearsOpen < 1000].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The smallest number of years open otherwise is 6. You can't be sure whether this resort in question has been open zero years or one year and even whether the numbers are projections or actual. In any case, you would be adding a new youngest resort so it feels best to simply drop this row."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ski_data = ski_data[ski_data.yearsOpen < 1000]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.6.4.2.4 fastSixes and Trams<a id='2.6.4.2.4_fastSixes_and_Trams'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The other features you had mild concern over, you will not investigate further. Perhaps take some care when using these features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.7 Derive State-wide Summary Statistics For Our Market Segment<a id='2.7_Derive_State-wide_Summary_Statistics_For_Our_Market_Segment'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You have, by this point removed one row, but it was for a resort that may not have opened yet, or perhaps in its first season. Using your business knowledge, you know that state-wide supply and demand of certain skiing resources may well factor into pricing strategies. Does a resort dominate the available night skiing in a state? Or does it account for a large proportion of the total skiable terrain or days open?\n",
+    "\n",
+    "If you want to add any features to your data that captures the state-wide market size, you should do this now, before dropping any more rows. In the next section, you'll drop rows with missing price information. Although you don't know what those resorts charge for their tickets, you do know the resorts exists and have been open for at least six years. Thus, you'll now calculate some state-wide summary statistics for later use."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Many features in your data pertain to chairlifts, that is for getting people around each resort. These aren't relevant, nor are the features relating to altitudes. Features that you may be interested in are:\n",
+    "\n",
+    "* TerrainParks\n",
+    "* SkiableTerrain_ac\n",
+    "* daysOpenLastYear\n",
+    "* NightSkiing_ac\n",
+    "\n",
+    "When you think about it, these are features it makes sense to sum: the total number of terrain parks, the total skiable area, the total number of days open, and the total area available for night skiing. You might consider the total number of ski runs, but understand that the skiable area is more informative than just a number of runs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A fairly new groupby behaviour is [named aggregation](https://pandas-docs.github.io/pandas-docs-travis/whatsnew/v0.25.0.html). This allows us to clearly perform the aggregations you want whilst also creating informative output column names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>580.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>California</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25948.0</td>\n",
+       "      <td>2738.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>587.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>22</td>\n",
+       "      <td>43682.0</td>\n",
+       "      <td>3258.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>428.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>5</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>256.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         state  resorts_per_state  state_total_skiable_area_ac  \\\n",
+       "0       Alaska                  3                       2280.0   \n",
+       "1      Arizona                  2                       1577.0   \n",
+       "2   California                 21                      25948.0   \n",
+       "3     Colorado                 22                      43682.0   \n",
+       "4  Connecticut                  5                        358.0   \n",
+       "\n",
+       "   state_total_days_open  state_total_terrain_parks  \\\n",
+       "0                  345.0                        4.0   \n",
+       "1                  237.0                        6.0   \n",
+       "2                 2738.0                       81.0   \n",
+       "3                 3258.0                       74.0   \n",
+       "4                  353.0                       10.0   \n",
+       "\n",
+       "   state_total_nightskiing_ac  \n",
+       "0                       580.0  \n",
+       "1                        80.0  \n",
+       "2                       587.0  \n",
+       "3                       428.0  \n",
+       "4                       256.0  "
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 27#\n",
+    "#Add named aggregations for the sum of 'daysOpenLastYear', 'TerrainParks', and 'NightSkiing_ac'\n",
+    "#call them 'state_total_days_open', 'state_total_terrain_parks', and 'state_total_nightskiing_ac',\n",
+    "#respectively\n",
+    "#Finally, add a call to the reset_index() method (we recommend you experiment with and without this to see\n",
+    "#what it does)\n",
+    "state_summary = ski_data.groupby('state').agg(\n",
+    "    resorts_per_state=pd.NamedAgg(column='Name', aggfunc='size'), #could pick any column here\n",
+    "    state_total_skiable_area_ac=pd.NamedAgg(column='SkiableTerrain_ac', aggfunc='sum'),\n",
+    "    state_total_days_open=pd.NamedAgg(column='daysOpenLastYear', aggfunc='sum'),\n",
+    "    state_total_terrain_parks=pd.NamedAgg(column='TerrainParks', aggfunc='sum'),\n",
+    "    state_total_nightskiing_ac=pd.NamedAgg(column='NightSkiing_ac', aggfunc='sum')\n",
+    ").reset_index()\n",
+    "state_summary.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.8 Drop Rows With No Price Data<a id='2.8_Drop_Rows_With_No_Price_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You know there are two columns that refer to price: 'AdultWeekend' and 'AdultWeekday'. You can calculate the number of price values missing per row. This will obviously have to be either 0, 1, or 2, where 0 denotes no price values are missing and 2 denotes that both are missing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    82.317073\n",
+       "2    14.329268\n",
+       "1     3.353659\n",
+       "Name: count, dtype: float64"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n",
+    "missing_price.value_counts()/len(missing_price) * 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "About 14% of the rows have no price data. As the price is your target, these rows are of no use. Time to lose them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 28#\n",
+    "#Use `missing_price` to remove rows from ski_data where both price values are missing\n",
+    "ski_data = ski_data[missing_price != 2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.9 Review distributions<a id='2.9_Review_distributions'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 25 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ski_data.hist(figsize=(15, 10))\n",
+    "plt.subplots_adjust(hspace=0.5);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These distributions are much better. There are clearly some skewed distributions, so keep an eye on `fastQuads`, `fastSixes`, and perhaps `trams`. These lack much variance away from 0 and may have a small number of relatively extreme values.  Models failing to rate a feature as important when domain knowledge tells you it should be is an issue to look out for, as is a model being overly influenced by some extreme values. If you build a good machine learning pipeline, hopefully it will be robust to such issues, but you may also wish to consider nonlinear transformations of features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.10 Population data<a id='2.10_Population_data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Population and area data for the US states can be obtained from [wikipedia](https://simple.wikipedia.org/wiki/List_of_U.S._states). Listen, you should have a healthy concern about using data you \"found on the Internet\". Make sure it comes from a reputable source. This table of data is useful because it allows you to easily pull and incorporate an external data set. It also allows you to proceed with an analysis that includes state sizes and populations for your 'first cut' model. Be explicit about your source (we documented it here in this workflow) and ensure it is open to inspection. All steps are subject to review, and it may be that a client has a specific source of data they trust that you should use to rerun the analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 29#\n",
+    "#Use pandas' `read_html` method to read the table from the URL below\n",
+    "states_url = 'https://simple.wikipedia.org/w/index.php?title=List_of_U.S._states&oldid=7168473'\n",
+    "usa_states = pd.read_html(states_url)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(usa_states)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(usa_states)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Name &amp; postal abbs. [1]</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Cities</th>\n",
+       "      <th>Established[A]</th>\n",
+       "      <th>Population [B][3]</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Total area[4]</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Land area[4]</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Water area[4]</th>\n",
+       "      <th>Number of Reps.</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>Name &amp; postal abbs. [1]</th>\n",
+       "      <th>Name &amp; postal abbs. [1].1</th>\n",
+       "      <th>Capital</th>\n",
+       "      <th>Largest[5]</th>\n",
+       "      <th>Established[A]</th>\n",
+       "      <th>Population [B][3]</th>\n",
+       "      <th>mi2</th>\n",
+       "      <th>km2</th>\n",
+       "      <th>mi2</th>\n",
+       "      <th>km2</th>\n",
+       "      <th>mi2</th>\n",
+       "      <th>km2</th>\n",
+       "      <th>Number of Reps.</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>AL</td>\n",
+       "      <td>Montgomery</td>\n",
+       "      <td>Birmingham</td>\n",
+       "      <td>Dec 14, 1819</td>\n",
+       "      <td>4903185</td>\n",
+       "      <td>52420</td>\n",
+       "      <td>135767</td>\n",
+       "      <td>50645</td>\n",
+       "      <td>131171</td>\n",
+       "      <td>1775</td>\n",
+       "      <td>4597</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>AK</td>\n",
+       "      <td>Juneau</td>\n",
+       "      <td>Anchorage</td>\n",
+       "      <td>Jan 3, 1959</td>\n",
+       "      <td>731545</td>\n",
+       "      <td>665384</td>\n",
+       "      <td>1723337</td>\n",
+       "      <td>570641</td>\n",
+       "      <td>1477953</td>\n",
+       "      <td>94743</td>\n",
+       "      <td>245384</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>AZ</td>\n",
+       "      <td>Phoenix</td>\n",
+       "      <td>Phoenix</td>\n",
+       "      <td>Feb 14, 1912</td>\n",
+       "      <td>7278717</td>\n",
+       "      <td>113990</td>\n",
+       "      <td>295234</td>\n",
+       "      <td>113594</td>\n",
+       "      <td>294207</td>\n",
+       "      <td>396</td>\n",
+       "      <td>1026</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arkansas</td>\n",
+       "      <td>AR</td>\n",
+       "      <td>Little Rock</td>\n",
+       "      <td>Little Rock</td>\n",
+       "      <td>Jun 15, 1836</td>\n",
+       "      <td>3017804</td>\n",
+       "      <td>53179</td>\n",
+       "      <td>137732</td>\n",
+       "      <td>52035</td>\n",
+       "      <td>134771</td>\n",
+       "      <td>1143</td>\n",
+       "      <td>2961</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>California</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>Sacramento</td>\n",
+       "      <td>Los Angeles</td>\n",
+       "      <td>Sep 9, 1850</td>\n",
+       "      <td>39512223</td>\n",
+       "      <td>163695</td>\n",
+       "      <td>423967</td>\n",
+       "      <td>155779</td>\n",
+       "      <td>403466</td>\n",
+       "      <td>7916</td>\n",
+       "      <td>20501</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Name & postal abbs. [1]                                 Cities               \\\n",
+       "  Name & postal abbs. [1] Name & postal abbs. [1].1      Capital   Largest[5]   \n",
+       "0                 Alabama                        AL   Montgomery   Birmingham   \n",
+       "1                  Alaska                        AK       Juneau    Anchorage   \n",
+       "2                 Arizona                        AZ      Phoenix      Phoenix   \n",
+       "3                Arkansas                        AR  Little Rock  Little Rock   \n",
+       "4              California                        CA   Sacramento  Los Angeles   \n",
+       "\n",
+       "  Established[A] Population [B][3] Total area[4]          Land area[4]  \\\n",
+       "  Established[A] Population [B][3]           mi2      km2          mi2   \n",
+       "0   Dec 14, 1819           4903185         52420   135767        50645   \n",
+       "1    Jan 3, 1959            731545        665384  1723337       570641   \n",
+       "2   Feb 14, 1912           7278717        113990   295234       113594   \n",
+       "3   Jun 15, 1836           3017804         53179   137732        52035   \n",
+       "4    Sep 9, 1850          39512223        163695   423967       155779   \n",
+       "\n",
+       "           Water area[4]         Number of Reps.  \n",
+       "       km2           mi2     km2 Number of Reps.  \n",
+       "0   131171          1775    4597               7  \n",
+       "1  1477953         94743  245384               1  \n",
+       "2   294207           396    1026               9  \n",
+       "3   134771          1143    2961               4  \n",
+       "4   403466          7916   20501              53  "
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "usa_states = usa_states[0]\n",
+    "usa_states.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note, in even the last year, the capability of `pd.read_html()` has improved. The merged cells you see in the web table are now handled much more conveniently, with 'Phoenix' now being duplicated so the subsequent columns remain aligned. But check this anyway. If you extract the established date column, you should just get dates. Recall previously you used the `.loc` accessor, because you were using labels. Now you want to refer to a column by its index position and so use `.iloc`. For a discussion on the difference use cases of `.loc` and `.iloc` refer to the [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 30#\n",
+    "#Use the iloc accessor to get the pandas Series for column number 4 from `usa_states`\n",
+    "#It should be a column of dates\n",
+    "established = usa_states.iloc[:, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     Dec 14, 1819\n",
+       "1      Jan 3, 1959\n",
+       "2     Feb 14, 1912\n",
+       "3     Jun 15, 1836\n",
+       "4      Sep 9, 1850\n",
+       "5      Aug 1, 1876\n",
+       "6      Jan 9, 1788\n",
+       "7      Dec 7, 1787\n",
+       "8      Mar 3, 1845\n",
+       "9      Jan 2, 1788\n",
+       "10    Aug 21, 1959\n",
+       "11     Jul 3, 1890\n",
+       "12     Dec 3, 1818\n",
+       "13    Dec 11, 1816\n",
+       "14    Dec 28, 1846\n",
+       "15    Jan 29, 1861\n",
+       "16     Jun 1, 1792\n",
+       "17    Apr 30, 1812\n",
+       "18    Mar 15, 1820\n",
+       "19    Apr 28, 1788\n",
+       "20     Feb 6, 1788\n",
+       "21    Jan 26, 1837\n",
+       "22    May 11, 1858\n",
+       "23    Dec 10, 1817\n",
+       "24    Aug 10, 1821\n",
+       "25     Nov 8, 1889\n",
+       "26     Mar 1, 1867\n",
+       "27    Oct 31, 1864\n",
+       "28    Jun 21, 1788\n",
+       "29    Dec 18, 1787\n",
+       "30     Jan 6, 1912\n",
+       "31    Jul 26, 1788\n",
+       "32    Nov 21, 1789\n",
+       "33     Nov 2, 1889\n",
+       "34     Mar 1, 1803\n",
+       "35    Nov 16, 1907\n",
+       "36    Feb 14, 1859\n",
+       "37    Dec 12, 1787\n",
+       "38    May 29, 1790\n",
+       "39    May 23, 1788\n",
+       "40     Nov 2, 1889\n",
+       "41     Jun 1, 1796\n",
+       "42    Dec 29, 1845\n",
+       "43     Jan 4, 1896\n",
+       "44     Mar 4, 1791\n",
+       "45    Jun 25, 1788\n",
+       "46    Nov 11, 1889\n",
+       "47    Jun 20, 1863\n",
+       "48    May 29, 1848\n",
+       "49    Jul 10, 1890\n",
+       "Name: (Established[A], Established[A]), dtype: object"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "established"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extract the state name, population, and total area (square miles) columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>state_population</th>\n",
+       "      <th>state_area_sq_miles</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>4903185</td>\n",
+       "      <td>52420</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>731545</td>\n",
+       "      <td>665384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>7278717</td>\n",
+       "      <td>113990</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arkansas</td>\n",
+       "      <td>3017804</td>\n",
+       "      <td>53179</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>California</td>\n",
+       "      <td>39512223</td>\n",
+       "      <td>163695</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        state  state_population  state_area_sq_miles\n",
+       "0     Alabama           4903185                52420\n",
+       "1      Alaska            731545               665384\n",
+       "2     Arizona           7278717               113990\n",
+       "3    Arkansas           3017804                53179\n",
+       "4  California          39512223               163695"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 31#\n",
+    "#Now use the iloc accessor again to extract columns 0, 5, and 6 and the dataframe's `copy()` method\n",
+    "#Set the names of these extracted columns to 'state', 'state_population', and 'state_area_sq_miles',\n",
+    "#respectively.\n",
+    "usa_states_sub = usa_states.iloc[:, [0, 5, 6]].copy()\n",
+    "usa_states_sub.columns = ['state', 'state_population', 'state_area_sq_miles']\n",
+    "usa_states_sub.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Do you have all the ski data states accounted for?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Massachusetts', 'Pennsylvania', 'Rhode Island', 'Virginia'}"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 32#\n",
+    "#Find the states in `state_summary` that are not in `usa_states_sub`\n",
+    "#Hint: set(list1) - set(list2) is an easy way to get items in list1 that are not in list2\n",
+    "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n",
+    "missing_states"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No?? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you look at the table on the web, you can perhaps start to guess what the problem is. You can confirm your suspicion by pulling out state names that _contain_ 'Massachusetts', 'Pennsylvania', or 'Virginia' from usa_states_sub:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20    Massachusetts[C]\n",
+       "37     Pennsylvania[C]\n",
+       "38     Rhode Island[D]\n",
+       "45         Virginia[C]\n",
+       "47       West Virginia\n",
+       "Name: state, dtype: object"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Delete square brackets and their contents and try again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20    Massachusetts\n",
+       "37     Pennsylvania\n",
+       "38     Rhode Island\n",
+       "45         Virginia\n",
+       "47    West Virginia\n",
+       "Name: state, dtype: object"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 33#\n",
+    "#Use pandas' Series' `replace()` method to replace anything within square brackets (including the brackets)\n",
+    "#with the empty string. Do this inplace, so you need to specify the arguments:\n",
+    "#to_replace='\\[.*\\]' #literal square bracket followed by anything or nothing followed by literal closing bracket\n",
+    "#value='' #empty string as replacement\n",
+    "#regex=True #we used a regex in our `to_replace` argument\n",
+    "#inplace=True #Do this \"in place\"\n",
+    "usa_states_sub.state.replace(to_replace=r'\\[.*?\\]', value='', regex=True, inplace=True)\n",
+    "usa_states_sub.state[usa_states_sub.state.str.contains('Massachusetts|Pennsylvania|Rhode Island|Virginia')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 34#\n",
+    "#And now verify none of our states are missing by checking that there are no states in\n",
+    "#state_summary that are not in usa_states_sub (as earlier using `set()`)\n",
+    "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n",
+    "missing_states"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Better! You have an empty set for missing states now. You can confidently add the population and state area columns to the ski resort data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <th>state_population</th>\n",
+       "      <th>state_area_sq_miles</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>731545</td>\n",
+       "      <td>665384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>7278717</td>\n",
+       "      <td>113990</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>California</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25948.0</td>\n",
+       "      <td>2738.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>587.0</td>\n",
+       "      <td>39512223</td>\n",
+       "      <td>163695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>22</td>\n",
+       "      <td>43682.0</td>\n",
+       "      <td>3258.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>428.0</td>\n",
+       "      <td>5758736</td>\n",
+       "      <td>104094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>5</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>256.0</td>\n",
+       "      <td>3565278</td>\n",
+       "      <td>5543</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         state  resorts_per_state  state_total_skiable_area_ac  \\\n",
+       "0       Alaska                  3                       2280.0   \n",
+       "1      Arizona                  2                       1577.0   \n",
+       "2   California                 21                      25948.0   \n",
+       "3     Colorado                 22                      43682.0   \n",
+       "4  Connecticut                  5                        358.0   \n",
+       "\n",
+       "   state_total_days_open  state_total_terrain_parks  \\\n",
+       "0                  345.0                        4.0   \n",
+       "1                  237.0                        6.0   \n",
+       "2                 2738.0                       81.0   \n",
+       "3                 3258.0                       74.0   \n",
+       "4                  353.0                       10.0   \n",
+       "\n",
+       "   state_total_nightskiing_ac  state_population  state_area_sq_miles  \n",
+       "0                       580.0            731545               665384  \n",
+       "1                        80.0           7278717               113990  \n",
+       "2                       587.0          39512223               163695  \n",
+       "3                       428.0           5758736               104094  \n",
+       "4                       256.0           3565278                 5543  "
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 35#\n",
+    "#Use 'state_summary's `merge()` method to combine our new data in 'usa_states_sub'\n",
+    "#specify the arguments how='left' and on='state'\n",
+    "state_summary = state_summary.merge(usa_states_sub, how='left', on='state')\n",
+    "state_summary.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Having created this data frame of summary statistics for various states, it would seem obvious to join this with the ski resort data to augment it with this additional data. You will do this, but not now. In the next notebook you will be exploring the data, including the relationships between the states. For that you want a separate row for each state, as you have here, and joining the data this soon means you'd need to separate and eliminate redundances in the state data when you wanted it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.11 Target Feature<a id='2.11_Target_Feature'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, what will your target be when modelling ticket price? What relationship is there between weekday and weekend prices?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 36#\n",
+    "#Use ski_data's `plot()` method to create a scatterplot (kind='scatter') with 'AdultWeekday' on the x-axis and\n",
+    "#'AdultWeekend' on the y-axis\n",
+    "ski_data.plot(x='AdultWeekday', y='AdultWeekend', kind='scatter');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A couple of observations can be made. Firstly, there is a clear line where weekend and weekday prices are equal. Weekend prices being higher than weekday prices seem restricted to sub $100 resorts. Recall from the boxplot earlier that the distribution for weekday and weekend prices in Montana seemed equal. Is this confirmed in the actual data for each resort? Big Mountain resort is in Montana, so the relationship between these quantities in this state are particularly relevant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <th>AdultWeekday</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>42.0</td>\n",
+       "      <td>42.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>63.0</td>\n",
+       "      <td>63.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>49.0</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>48.0</td>\n",
+       "      <td>48.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>46.0</td>\n",
+       "      <td>46.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>67.0</td>\n",
+       "      <td>67.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>151</th>\n",
+       "      <td>81.0</td>\n",
+       "      <td>81.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     AdultWeekend  AdultWeekday\n",
+       "141          42.0          42.0\n",
+       "142          63.0          63.0\n",
+       "143          49.0          49.0\n",
+       "144          48.0          48.0\n",
+       "145          46.0          46.0\n",
+       "146          39.0          39.0\n",
+       "147          50.0          50.0\n",
+       "148          67.0          67.0\n",
+       "149          47.0          47.0\n",
+       "150          39.0          39.0\n",
+       "151          81.0          81.0"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 37#\n",
+    "#Use the loc accessor on ski_data to print the 'AdultWeekend' and 'AdultWeekday' columns for Montana only\n",
+    "ski_data.loc[ski_data.state == 'Montana', ['AdultWeekend', 'AdultWeekday']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Is there any reason to prefer weekend or weekday prices? Which is missing the least?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AdultWeekend    4\n",
+       "AdultWeekday    7\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Weekend prices have the least missing values of the two, so drop the weekday prices and then keep just the rows that have weekend price."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ski_data.drop(columns='AdultWeekday', inplace=True)\n",
+    "ski_data.dropna(subset=['AdultWeekend'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(277, 25)"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Perform a final quick check on the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.11.1 Number Of Missing Values By Row - Resort<a id='2.11.1_Number_Of_Missing_Values_By_Row_-_Resort'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Having dropped rows missing the desired target ticket price, what degree of missingness do you have for the remaining rows?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>5</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>4</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>4</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>198</th>\n",
+       "      <td>4</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>4</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     count     %\n",
+       "329      5  20.0\n",
+       "62       5  20.0\n",
+       "141      5  20.0\n",
+       "86       5  20.0\n",
+       "74       5  20.0\n",
+       "146      5  20.0\n",
+       "184      4  16.0\n",
+       "108      4  16.0\n",
+       "198      4  16.0\n",
+       "39       4  16.0"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing = pd.concat([ski_data.isnull().sum(axis=1), 100 * ski_data.isnull().mean(axis=1)], axis=1)\n",
+    "missing.columns=['count', '%']\n",
+    "missing.sort_values(by='count', ascending=False).head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These seem possibly curiously quantized..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.,  4.,  8., 12., 16., 20.])"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing['%'].unique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Yes, the percentage of missing values per row appear in multiples of 4."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "%\n",
+       "0.0     107\n",
+       "4.0      94\n",
+       "8.0      45\n",
+       "12.0     15\n",
+       "16.0     10\n",
+       "20.0      6\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "missing['%'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is almost as if values have been removed artificially... Nevertheless, what you don't know is how useful the missing features are in predicting ticket price. You shouldn't just drop rows that are missing several useless features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 277 entries, 0 to 329\n",
+      "Data columns (total 25 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   Name               277 non-null    object \n",
+      " 1   Region             277 non-null    object \n",
+      " 2   state              277 non-null    object \n",
+      " 3   summit_elev        277 non-null    int64  \n",
+      " 4   vertical_drop      277 non-null    int64  \n",
+      " 5   base_elev          277 non-null    int64  \n",
+      " 6   trams              277 non-null    int64  \n",
+      " 7   fastSixes          277 non-null    int64  \n",
+      " 8   fastQuads          277 non-null    int64  \n",
+      " 9   quad               277 non-null    int64  \n",
+      " 10  triple             277 non-null    int64  \n",
+      " 11  double             277 non-null    int64  \n",
+      " 12  surface            277 non-null    int64  \n",
+      " 13  total_chairs       277 non-null    int64  \n",
+      " 14  Runs               274 non-null    float64\n",
+      " 15  TerrainParks       233 non-null    float64\n",
+      " 16  LongestRun_mi      272 non-null    float64\n",
+      " 17  SkiableTerrain_ac  275 non-null    float64\n",
+      " 18  Snow Making_ac     240 non-null    float64\n",
+      " 19  daysOpenLastYear   233 non-null    float64\n",
+      " 20  yearsOpen          277 non-null    float64\n",
+      " 21  averageSnowfall    268 non-null    float64\n",
+      " 22  AdultWeekend       277 non-null    float64\n",
+      " 23  projectedDaysOpen  236 non-null    float64\n",
+      " 24  NightSkiing_ac     163 non-null    float64\n",
+      "dtypes: float64(11), int64(11), object(3)\n",
+      "memory usage: 56.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "ski_data.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are still some missing values, and it's good to be aware of this, but leave them as is for now."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.12 Save data<a id='2.12_Save_data'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(277, 25)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save this to your data directory, separately. Note that you were provided with the data in `raw_data` and you should saving derived data in a separate location. This guards against overwriting our original data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Directory ../data was created.\n",
+      "Writing file.  \"../data\\ski_data_cleaned.csv\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "# save the data to a new csv file\n",
+    "datapath = '../data'\n",
+    "save_file(ski_data, 'ski_data_cleaned.csv', datapath)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing file.  \"../data\\state_summary.csv\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "# save the state_summary separately.\n",
+    "datapath = '../data'\n",
+    "save_file(state_summary, 'state_summary.csv', datapath)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.13 Summary<a id='2.13_Summary'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q: 3** Write a summary statement that highlights the key processes and findings from this notebook. This should include information such as the original number of rows in the data, whether our own resort was actually present etc. What columns, if any, have been removed? Any rows? Summarise the reasons why. Were any other issues found? What remedial actions did you take? State where you are in the project. Can you confirm what the target feature is for your desire to predict ticket price? How many rows were left in the data? Hint: this is a great opportunity to reread your notebook, check all cells have been executed in order and from a \"blank slate\" (restarting the kernel will do this), and that your workflow makes sense and follows a logical pattern. As you do this you can pull out salient information for inclusion in this summary. Thus, this section will provide an important overview of \"what\" and \"why\" without having to dive into the \"how\" or any unproductive or inconclusive steps along the way."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**A: 3** Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our raw data consists of 330 rows and 27 columns. The dataframe  organizes information on ski resorts around the country and has the benefit of  including our employer’s resort. \n",
+    "\n",
+    "Ticket price is our variable of interest and most of the resorts in the set have both weekday and weekend entries, but 14% are missing both values.\n",
+    "\n",
+    "Potential issues: \n",
+    "SkiableTerrain_ac because values are clustered down the low end,\n",
+    "Snow Making_ac for the same reason,\n",
+    "fastEight because all but one value is 0 so it has very little variance, and half the values are missing,\n",
+    "fastSixes raises an amber flag; it has more variability, but still mostly 0,\n",
+    "trams also may get an amber flag for the same reason,\n",
+    "yearsOpen because most values are low but it has a maximum of 2019, which strongly suggests someone recorded calendar year rather than number of years.\n",
+    "\n",
+    "Corrected one seemingly obvious errant value in Skiable_Terrain_ac after looking at the information available online, identified a questionable value in SnowMaking_ac (associated resort lacks any price info and will be discarded later), we are dropping our fastEight column because we only have non-zero or non-missing values for one resort, after reviewing yearsOpen we eliminated another resort because of ambiguity about its age, \n",
+    "\n",
+    "In order to leverage state level information, we are compiling state-wide summaries for four features:\n",
+    "TerrainParks\n",
+    "SkiableTerrain_ac\n",
+    "daysOpenLastYear\n",
+    "NightSkiing_ac\n",
+    "At this point, following up on the issue of missing values in the two ticket price, we are eliminating all resorts that lack both weekday and weekend pricing, accounting for about 14% of the original data set,\n",
+    "After reviewing the distributions of the columns following the elimination of the price-less resorts, we note skews in fastQuads, fastSixes, and trams and while we will leave these unchanged for now, we may elect to use some transformations to facilitate their inclusion in a meaningful way.\n",
+    "Using information from Wikipedia, we sought and integrated additional state-wide information, notably 'state_population', and 'state_area_sq_miles', to add some additional dimension to our analyses\n",
+    "Further investigation of the Montana specific price data reveals that there seems to be little variation between weekday and weekend pricing at most resorts in the state, and because we have more information for weekend prices, we are electing to drop weekday prices for all of the resorts in the dataset.\n",
+    "\n",
+    "After removing the weekday prices, we investigated the data again to assess missingness. Doing so, we see a possible quantization(almost as if values have been removed artificially.) We will not investigate this further at this stage, however, beyond noting that 9 of the 25 columns are missing some number of values indicating that 16 of those have no null values.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Notebooks/03_exploratory_data_analysis_DM.ipynb b/Notebooks/03_exploratory_data_analysis_DM.ipynb
new file mode 100644
index 000000000..d2dfae949
--- /dev/null
+++ b/Notebooks/03_exploratory_data_analysis_DM.ipynb
@@ -0,0 +1,4562 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3 Exploratory Data Analysis<a id='3_Exploratory_Data_Analysis'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.1 Contents<a id='3.1_Contents'></a>\n",
+    "* [3 Exploratory Data Analysis](#3_Exploratory_Data_Analysis)\n",
+    "  * [3.1 Contents](#3.1_Contents)\n",
+    "  * [3.2 Introduction](#3.2_Introduction)\n",
+    "  * [3.3 Imports](#3.3_Imports)\n",
+    "  * [3.4 Load The Data](#3.4_Load_The_Data)\n",
+    "    * [3.4.1 Ski data](#3.4.1_Ski_data)\n",
+    "    * [3.4.2 State-wide summary data](#3.4.2_State-wide_summary_data)\n",
+    "  * [3.5 Explore The Data](#3.5_Explore_The_Data)\n",
+    "    * [3.5.1 Top States By Order Of Each Of The Summary Statistics](#3.5.1_Top_States_By_Order_Of_Each_Of_The_Summary_Statistics)\n",
+    "      * [3.5.1.1 Total state area](#3.5.1.1_Total_state_area)\n",
+    "      * [3.5.1.2 Total state population](#3.5.1.2_Total_state_population)\n",
+    "      * [3.5.1.3 Resorts per state](#3.5.1.3_Resorts_per_state)\n",
+    "      * [3.5.1.4 Total skiable area](#3.5.1.4_Total_skiable_area)\n",
+    "      * [3.5.1.5 Total night skiing area](#3.5.1.5_Total_night_skiing_area)\n",
+    "      * [3.5.1.6 Total days open](#3.5.1.6_Total_days_open)\n",
+    "    * [3.5.2 Resort density](#3.5.2_Resort_density)\n",
+    "      * [3.5.2.1 Top states by resort density](#3.5.2.1_Top_states_by_resort_density)\n",
+    "    * [3.5.3 Visualizing High Dimensional Data](#3.5.3_Visualizing_High_Dimensional_Data)\n",
+    "      * [3.5.3.1 Scale the data](#3.5.3.1_Scale_the_data)\n",
+    "        * [3.5.3.1.1 Verifying the scaling](#3.5.3.1.1_Verifying_the_scaling)\n",
+    "      * [3.5.3.2 Calculate the PCA transformation](#3.5.3.2_Calculate_the_PCA_transformation)\n",
+    "      * [3.5.3.3 Average ticket price by state](#3.5.3.3_Average_ticket_price_by_state)\n",
+    "      * [3.5.3.4 Adding average ticket price to scatter plot](#3.5.3.4_Adding_average_ticket_price_to_scatter_plot)\n",
+    "    * [3.5.4 Conclusion On How To Handle State Label](#3.5.4_Conclusion_On_How_To_Handle_State_Label)\n",
+    "    * [3.5.5 Ski Resort Numeric Data](#3.5.5_Ski_Resort_Numeric_Data)\n",
+    "      * [3.5.5.1 Feature engineering](#3.5.5.1_Feature_engineering)\n",
+    "      * [3.5.5.2 Feature correlation heatmap](#3.5.5.2_Feature_correlation_heatmap)\n",
+    "      * [3.5.5.3 Scatterplots of numeric features against ticket price](#3.5.5.3_Scatterplots_of_numeric_features_against_ticket_price)\n",
+    "  * [3.6 Summary](#3.6_Summary)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.2 Introduction<a id='3.2_Introduction'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At this point, you should have a firm idea of what your data science problem is and have the data you believe could help solve it. The business problem was a general one of modeling resort revenue. The data you started with contained some ticket price values, but with a number of missing values that led to several rows being dropped completely. You also had two kinds of ticket price. There were also some obvious issues with some of the other features in the data that, for example, led to one column being completely dropped, a data error corrected, and some other rows dropped. You also obtained some additional US state population and size data with which to augment the dataset, which also required some cleaning.\n",
+    "\n",
+    "The data science problem you subsequently identified is to predict the adult weekend ticket price for ski resorts."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.3 Imports<a id='3.3_Imports'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.preprocessing import scale\n",
+    "\n",
+    "from library.sb_utils import save_file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.4 Load The Data<a id='3.4_Load_The_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.4.1 Ski data<a id='3.4.1_Ski_data'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ski_data = pd.read_csv('../data/ski_data_cleaned.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 277 entries, 0 to 276\n",
+      "Data columns (total 25 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   Name               277 non-null    object \n",
+      " 1   Region             277 non-null    object \n",
+      " 2   state              277 non-null    object \n",
+      " 3   summit_elev        277 non-null    int64  \n",
+      " 4   vertical_drop      277 non-null    int64  \n",
+      " 5   base_elev          277 non-null    int64  \n",
+      " 6   trams              277 non-null    int64  \n",
+      " 7   fastSixes          277 non-null    int64  \n",
+      " 8   fastQuads          277 non-null    int64  \n",
+      " 9   quad               277 non-null    int64  \n",
+      " 10  triple             277 non-null    int64  \n",
+      " 11  double             277 non-null    int64  \n",
+      " 12  surface            277 non-null    int64  \n",
+      " 13  total_chairs       277 non-null    int64  \n",
+      " 14  Runs               274 non-null    float64\n",
+      " 15  TerrainParks       233 non-null    float64\n",
+      " 16  LongestRun_mi      272 non-null    float64\n",
+      " 17  SkiableTerrain_ac  275 non-null    float64\n",
+      " 18  Snow Making_ac     240 non-null    float64\n",
+      " 19  daysOpenLastYear   233 non-null    float64\n",
+      " 20  yearsOpen          277 non-null    float64\n",
+      " 21  averageSnowfall    268 non-null    float64\n",
+      " 22  AdultWeekend       277 non-null    float64\n",
+      " 23  projectedDaysOpen  236 non-null    float64\n",
+      " 24  NightSkiing_ac     163 non-null    float64\n",
+      "dtypes: float64(11), int64(11), object(3)\n",
+      "memory usage: 54.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "ski_data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
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+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>state</th>\n",
+       "      <th>summit_elev</th>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <th>base_elev</th>\n",
+       "      <th>trams</th>\n",
+       "      <th>fastSixes</th>\n",
+       "      <th>fastQuads</th>\n",
+       "      <th>quad</th>\n",
+       "      <th>...</th>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alyeska Resort</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>3939</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1610.0</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>669.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>550.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Eaglecrest Ski Area</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>2600</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hilltop Ski Area</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>2090</td>\n",
+       "      <td>294</td>\n",
+       "      <td>1796</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arizona Snowbowl</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>11500</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>777.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sunrise Park Resort</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>11100</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>800.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>115.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>250.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Name   Region    state  summit_elev  vertical_drop  \\\n",
+       "0       Alyeska Resort   Alaska   Alaska         3939           2500   \n",
+       "1  Eaglecrest Ski Area   Alaska   Alaska         2600           1540   \n",
+       "2     Hilltop Ski Area   Alaska   Alaska         2090            294   \n",
+       "3     Arizona Snowbowl  Arizona  Arizona        11500           2300   \n",
+       "4  Sunrise Park Resort  Arizona  Arizona        11100           1800   \n",
+       "\n",
+       "   base_elev  trams  fastSixes  fastQuads  quad  ...  TerrainParks  \\\n",
+       "0        250      1          0          2     2  ...           2.0   \n",
+       "1       1200      0          0          0     0  ...           1.0   \n",
+       "2       1796      0          0          0     0  ...           1.0   \n",
+       "3       9200      0          1          0     2  ...           4.0   \n",
+       "4       9200      0          0          1     2  ...           2.0   \n",
+       "\n",
+       "   LongestRun_mi  SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  \\\n",
+       "0            1.0             1610.0           113.0             150.0   \n",
+       "1            2.0              640.0            60.0              45.0   \n",
+       "2            1.0               30.0            30.0             150.0   \n",
+       "3            2.0              777.0           104.0             122.0   \n",
+       "4            1.2              800.0            80.0             115.0   \n",
+       "\n",
+       "   yearsOpen  averageSnowfall  AdultWeekend  projectedDaysOpen  NightSkiing_ac  \n",
+       "0       60.0            669.0          85.0              150.0           550.0  \n",
+       "1       44.0            350.0          53.0               90.0             NaN  \n",
+       "2       36.0             69.0          34.0              152.0            30.0  \n",
+       "3       81.0            260.0          89.0              122.0             NaN  \n",
+       "4       49.0            250.0          78.0              104.0            80.0  \n",
+       "\n",
+       "[5 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.4.2 State-wide summary data<a id='3.4.2_State-wide_summary_data'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state_summary = pd.read_csv('../data/state_summary.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 35 entries, 0 to 34\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column                       Non-Null Count  Dtype  \n",
+      "---  ------                       --------------  -----  \n",
+      " 0   state                        35 non-null     object \n",
+      " 1   resorts_per_state            35 non-null     int64  \n",
+      " 2   state_total_skiable_area_ac  35 non-null     float64\n",
+      " 3   state_total_days_open        35 non-null     float64\n",
+      " 4   state_total_terrain_parks    35 non-null     float64\n",
+      " 5   state_total_nightskiing_ac   35 non-null     float64\n",
+      " 6   state_population             35 non-null     int64  \n",
+      " 7   state_area_sq_miles          35 non-null     int64  \n",
+      "dtypes: float64(4), int64(3), object(1)\n",
+      "memory usage: 2.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "state_summary.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <th>state_population</th>\n",
+       "      <th>state_area_sq_miles</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>731545</td>\n",
+       "      <td>665384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>7278717</td>\n",
+       "      <td>113990</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>California</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25948.0</td>\n",
+       "      <td>2738.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>587.0</td>\n",
+       "      <td>39512223</td>\n",
+       "      <td>163695</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>22</td>\n",
+       "      <td>43682.0</td>\n",
+       "      <td>3258.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>428.0</td>\n",
+       "      <td>5758736</td>\n",
+       "      <td>104094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>5</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>256.0</td>\n",
+       "      <td>3565278</td>\n",
+       "      <td>5543</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         state  resorts_per_state  state_total_skiable_area_ac  \\\n",
+       "0       Alaska                  3                       2280.0   \n",
+       "1      Arizona                  2                       1577.0   \n",
+       "2   California                 21                      25948.0   \n",
+       "3     Colorado                 22                      43682.0   \n",
+       "4  Connecticut                  5                        358.0   \n",
+       "\n",
+       "   state_total_days_open  state_total_terrain_parks  \\\n",
+       "0                  345.0                        4.0   \n",
+       "1                  237.0                        6.0   \n",
+       "2                 2738.0                       81.0   \n",
+       "3                 3258.0                       74.0   \n",
+       "4                  353.0                       10.0   \n",
+       "\n",
+       "   state_total_nightskiing_ac  state_population  state_area_sq_miles  \n",
+       "0                       580.0            731545               665384  \n",
+       "1                        80.0           7278717               113990  \n",
+       "2                       587.0          39512223               163695  \n",
+       "3                       428.0           5758736               104094  \n",
+       "4                       256.0           3565278                 5543  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.5 Explore The Data<a id='3.5_Explore_The_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.5.1 Top States By Order Of Each Of The Summary Statistics<a id='3.5.1_Top_States_By_Order_Of_Each_Of_The_Summary_Statistics'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What does the state-wide picture for your market look like?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state_summary_newind = state_summary.set_index('state')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.1.1 Total state area<a id='3.5.1.1_Total_state_area'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "Alaska        665384\n",
+       "California    163695\n",
+       "Montana       147040\n",
+       "New Mexico    121590\n",
+       "Arizona       113990\n",
+       "Name: state_area_sq_miles, dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary_newind.state_area_sq_miles.sort_values(ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Your home state, Montana, comes in at third largest."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.1.2 Total state population<a id='3.5.1.2_Total_state_population'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "California      39512223\n",
+       "New York        19453561\n",
+       "Pennsylvania    12801989\n",
+       "Illinois        12671821\n",
+       "Ohio            11689100\n",
+       "Name: state_population, dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary_newind.state_population.sort_values(ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "California dominates the state population figures despite coming in second behind Alaska in size (by a long way). The resort's state of Montana was in the top five for size, but doesn't figure in the most populous states. Thus your state is less densely populated."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.1.3 Resorts per state<a id='3.5.1.3_Resorts_per_state'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "New York        33\n",
+       "Michigan        28\n",
+       "Colorado        22\n",
+       "California      21\n",
+       "Pennsylvania    19\n",
+       "Name: resorts_per_state, dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary_newind.resorts_per_state.sort_values(ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "New York comes top in the number of resorts in our market. Is this because of its proximity to wealthy New Yorkers wanting a convenient skiing trip? Or is it simply that its northerly location means there are plenty of good locations for resorts in that state?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.1.4 Total skiable area<a id='3.5.1.4_Total_skiable_area'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "Colorado      43682.0\n",
+       "Utah          30508.0\n",
+       "California    25948.0\n",
+       "Montana       21410.0\n",
+       "Idaho         16396.0\n",
+       "Name: state_total_skiable_area_ac, dtype: float64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary_newind.state_total_skiable_area_ac.sort_values(ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "New York state may have the most resorts, but they don't account for the most skiing area. In fact, New York doesn't even make it into the top five of skiable area. Good old Montana makes it into the top five, though. You may start to think that New York has more, smaller resorts, whereas Montana has fewer, larger resorts. Colorado seems to have a name for skiing; it's in the top five for resorts and in top place for total skiable area."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.1.5 Total night skiing area<a id='3.5.1.5_Total_night_skiing_area'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "New York        2836.0\n",
+       "Washington      1997.0\n",
+       "Michigan        1946.0\n",
+       "Pennsylvania    1528.0\n",
+       "Oregon          1127.0\n",
+       "Name: state_total_nightskiing_ac, dtype: float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary_newind.state_total_nightskiing_ac.sort_values(ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "New York dominates the area of skiing available at night. Looking at the top five in general, they are all the more northerly states. Is night skiing in and of itself an appeal to customers, or is a consequence of simply trying to extend the skiing day where days are shorter? Is New York's domination here because it's trying to maximize its appeal to visitors who'd travel a shorter distance for a shorter visit? You'll find the data generates more (good) questions rather than answering them. This is a positive sign! You might ask your executive sponsor or data provider for some additional data about typical length of stays at these resorts, although you might end up with data that is very granular and most likely proprietary to each resort. A useful level of granularity might be \"number of day tickets\" and \"number of weekly passes\" sold."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.1.6 Total days open<a id='3.5.1.6_Total_days_open'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "Colorado         3258.0\n",
+       "California       2738.0\n",
+       "Michigan         2389.0\n",
+       "New York         2384.0\n",
+       "New Hampshire    1847.0\n",
+       "Name: state_total_days_open, dtype: float64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary_newind.state_total_days_open.sort_values(ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The total days open seem to bear some resemblance to the number of resorts. This is plausible. The season will only be so long, and so the more resorts open through the skiing season, the more total days open we'll see. New Hampshire makes a good effort at making it into the top five, for a small state that didn't make it into the top five of resorts per state. Does its location mean resorts there have a longer season and so stay open longer, despite there being fewer of them?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.5.2 Resort density<a id='3.5.2_Resort_density'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are big states which are not necessarily the most populous. There are states that host many resorts, but other states host a larger total skiing area. The states with the most total days skiing per season are not necessarily those with the most resorts. And New York State boasts an especially large night skiing area. New York had the most resorts but wasn't in the top five largest states, so the reason for it having the most resorts can't be simply having lots of space for them. New York has the second largest population behind California. Perhaps many resorts have sprung up in New York because of the population size? Does this mean there is a high competition between resorts in New York State, fighting for customers and thus keeping prices down? You're not concerned, per se, with the absolute size or population of a state, but you could be interested in the ratio of resorts serving a given population or a given area.\n",
+    "\n",
+    "So, calculate those ratios! Think of them as measures of resort density, and drop the absolute population and state size columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.450867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>0.027477</td>\n",
+       "      <td>1.754540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>California</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25948.0</td>\n",
+       "      <td>2738.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>587.0</td>\n",
+       "      <td>0.053148</td>\n",
+       "      <td>12.828736</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>22</td>\n",
+       "      <td>43682.0</td>\n",
+       "      <td>3258.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>428.0</td>\n",
+       "      <td>0.382028</td>\n",
+       "      <td>21.134744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>5</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>256.0</td>\n",
+       "      <td>0.140242</td>\n",
+       "      <td>90.203861</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         state  resorts_per_state  state_total_skiable_area_ac  \\\n",
+       "0       Alaska                  3                       2280.0   \n",
+       "1      Arizona                  2                       1577.0   \n",
+       "2   California                 21                      25948.0   \n",
+       "3     Colorado                 22                      43682.0   \n",
+       "4  Connecticut                  5                        358.0   \n",
+       "\n",
+       "   state_total_days_open  state_total_terrain_parks  \\\n",
+       "0                  345.0                        4.0   \n",
+       "1                  237.0                        6.0   \n",
+       "2                 2738.0                       81.0   \n",
+       "3                 3258.0                       74.0   \n",
+       "4                  353.0                       10.0   \n",
+       "\n",
+       "   state_total_nightskiing_ac  resorts_per_100kcapita  resorts_per_100ksq_mile  \n",
+       "0                       580.0                0.410091                 0.450867  \n",
+       "1                        80.0                0.027477                 1.754540  \n",
+       "2                       587.0                0.053148                12.828736  \n",
+       "3                       428.0                0.382028                21.134744  \n",
+       "4                       256.0                0.140242                90.203861  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# The 100_000 scaling is simply based on eyeballing the magnitudes of the data\n",
+    "state_summary['resorts_per_100kcapita'] = 100_000 * state_summary.resorts_per_state / state_summary.state_population\n",
+    "state_summary['resorts_per_100ksq_mile'] = 100_000 * state_summary.resorts_per_state / state_summary.state_area_sq_miles\n",
+    "state_summary.drop(columns=['state_population', 'state_area_sq_miles'], inplace=True)\n",
+    "state_summary.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With the removal of the two columns that only spoke to state-specific data, you now have a Dataframe that speaks to the skiing competitive landscape of each state. It has the number of resorts per state, total skiable area, and days of skiing. You've translated the plain state data into something more useful that gives you an idea of the density of resorts relative to the state population and size."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How do the distributions of these two new features look?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "state_summary.resorts_per_100kcapita.hist(bins=30)\n",
+    "plt.xlabel('Number of resorts per 100k population')\n",
+    "plt.ylabel('count');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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H9MzKtGnTtGLFCi1durRUyw8ePFgDBgwI3s7OzlZaWprS09NLtbOl5ff7lZGRoSHLKii3wBO29Z4K3w3vGLZ1FfahQ4cO8nq9YVvv6YY+BNCHAPoQQB8C6ENAWftQ+M5IaUUsrOzYsUP9+vXTnDlzFBMTU6rH+Hy+Yv9KrtfrPSmTJLfAo9z80yusnIw+nKz+nm7oQwB9CKAPAfQhgD4ElLYPZe1VxMLK8uXLtXv3bjVr1iw4lp+fr88//1yvvPKKcnNzFRUVFanyAACAIyIWVq699lp9++23IWP33Xef6tevr0GDBhFUAACApAiGlfj4eDVq1ChkrFKlSqpWrVqRcQAAcPZy4u+sAAAAHEvE/87KkebPnx/pEgAAgGM4swIAAJxGWAEAAE4jrAAAAKcRVgAAgNMIKwAAwGmEFQAA4DTCCgAAcBphBQAAOI2wAgAAnEZYAQAATiOsAAAApxFWAACA0wgrAADAaYQVAADgNMIKAABwGmEFAAA4jbACAACcRlgBAABOI6wAAACnEVYAAIDTCCsAAMBphBUAAOA0wgoAAHAaYQUAADiNsAIAAJxGWAEAAE4jrAAAAKcRVgAAgNMIKwAAwGmEFQAA4DTCCgAAcFrFSBeA8Krz1KxyP3brqBvDWAkAAOHBmRUAAOA0wgoAAHAaYQUAADiNsAIAAJxGWAEAAE4jrAAAAKcRVgAAgNMIKwAAwGmEFQAA4DTCCgAAcBphBQAAOI2wAgAAnEZYAQAATiOsAAAApxFWAACA0wgrAADAaYQVAADgNMIKAABwGmEFAAA4jbACAACcRlgBAABOI6wAAACnEVYAAIDTCCsAAMBphBUAAOA0wgoAAHAaYQUAADiNsAIAAJxGWAEAAE4jrAAAAKcRVgAAgNMIKwAAwGmEFQAA4DTCCgAAcBphBQAAOC2iYeWFF17QFVdcofj4eCUnJ+uWW27Rhg0bIlkSAABwTETDyoIFC9SnTx8tWbJEGRkZysvLU3p6un799ddIlgUAABxSMZIbnz17dsjtyZMnKzk5WcuXL1ebNm0iVBUAAHBJRMPK0bKysiRJVatWLfb+3Nxc5ebmBm9nZ2dLkvx+v/x+f9jqKFyXr4KFbZ2ng6N7WHg7nL09HdGHAPoQQB8C6EMAfQgoax/K2i+PmTnximxm6tKli/bt26eFCxcWu8zw4cM1YsSIIuNTpkxRXFzcyS4RAACEQU5Ojrp166asrCwlJCSUuLwzYaVPnz6aNWuWFi1apFq1ahW7THFnVtLS0rR3795S7Wxp+f1+ZWRkaMiyCsot8IRtva77bnjHkNuFfejQoYO8Xm+Eqoo8+hBAHwLoQwB9CKAPAWXtQ3Z2tpKSkkodVpx4G6hv37768MMP9fnnnx8zqEiSz+eTz+crMu71ek/KJMkt8Cg3/+wJK8fq4cnq7+mGPgTQhwD6EEAfAuhDQGn7UNZeRTSsmJn69u2rmTNnav78+apbt24kywEAAA6KaFjp06ePpkyZon/961+Kj4/Xrl27JEmJiYmKjY2NZGkAAMAREf07KxMmTFBWVpbatWunlJSU4M/06dMjWRYAAHBIxN8GAgAAOB6+GwgAADiNsAIAAJxGWAEAAE4jrAAAAKcRVgAAgNMIKwAAwGmEFQAA4DTCCgAAcBphBQAAOI2wAgAAnEZYAQAATiOsAAAApxFWAACA0wgrAADAaYQVAADgNMIKAABwGmEFAAA4jbACAACcRlgBAABOI6wAAACnEVYAAIDTCCsAAMBphBUAAOA0wgoAAHAaYQUAADiNsAIAAJxGWAEAAE4jrAAAAKcRVgAAgNMIKwAAwGmEFQAA4DTCCgAAcFrFSBcAd9R5albIbV+U6cUWUqPh/6PcfM9J2+7WUTeW+7FH13yqtnsiTqTmExGp/T0RZ9vxPR2PEcomUvPjdJ+XnFkBAABOI6wAAACnEVYAAIDTCCsAAMBphBUAAOA0wgoAAHAaYQUAADiNsAIAAJxGWAEAAE4jrAAAAKcRVgAAgNMIKwAAwGmEFQAA4DTCCgAAcBphBQAAOI2wAgAAnEZYAQAATiOsAAAApxFWAACA0wgrAADAaYQVAADgNMIKAABwGmEFAAA4jbACAACcRlgBAABOI6wAAACnEVYAAIDTCCsAAMBphBUAAOA0wgoAAHAaYQUAADiNsAIAAJxGWAEAAE4jrAAAAKdFPKy89tprqlu3rmJiYtSsWTMtXLgw0iUBAACHRDSsTJ8+Xf3799czzzyjlStX6pprrlGnTp20ffv2SJYFAAAcEtGwMmbMGPXq1UsPPPCAGjRooHHjxiktLU0TJkyIZFkAAMAhFSO14cOHD2v58uV66qmnQsbT09O1ePHiYh+Tm5ur3Nzc4O2srCxJ0i+//CK/3x+22vx+v3JyclTRX0H5BZ6wrfd0U7HAlJNTcNL78PPPP5f7sRXzfj3p2y2cDz///LO8Xm+5t1foRGo+ESfSZyn8fSiNU3F8y6qkPrhY88kQifngorL2IVLz42Rvt6x9OHDggCTJzEpXhEXIjz/+aJLsiy++CBkfOXKk1atXr9jHDBs2zCTxww8//PDDDz9nwM+OHTtKlRkidmalkMcT+j92MysyVmjw4MEaMGBA8HZBQYF++eUXVatW7ZiPKY/s7GylpaVpx44dSkhICNt6Tzf0IYA+BNCHAPoQQB8C6ENAWftgZjpw4IBSU1NLtf6IhZWkpCRFRUVp165dIeO7d+/WueeeW+xjfD6ffD5fyNg555xzskpUQkLCWT35CtGHAPoQQB8C6EMAfQigDwFl6UNiYmKp1xuxC2yjo6PVrFkzZWRkhIxnZGSodevWEaoKAAC4JqJvAw0YMED33HOPmjdvrlatWumNN97Q9u3b9eCDD0ayLAAA4JCIhpU777xTP//8s/785z8rMzNTjRo10scff6zatWtHsiz5fD4NGzasyFtOZxv6EEAfAuhDAH0IoA8B9CHgZPfBY1bazw0BAACcehH/c/sAAADHQ1gBAABOI6wAAACnEVYAAIDTCCtHee2111S3bl3FxMSoWbNmWrhwYaRLOqleeOEFXXHFFYqPj1dycrJuueUWbdiwIWSZnj17yuPxhPy0bNkyQhWfHMOHDy+yjzVq1Ajeb2YaPny4UlNTFRsbq3bt2mnNmjURrPjkqFOnTpE+eDwe9enTR9KZOxc+//xz3XTTTUpNTZXH49EHH3wQcn9pjn9ubq769u2rpKQkVapUSTfffLP+/e9/n8K9OHHH64Pf79egQYPUuHFjVapUSampqbr33nu1c+fOkHW0a9euyBy56667TvGenJiS5kNpngdn+nyQVOzvCo/Ho5deeim4TLjmA2HlCNOnT1f//v31zDPPaOXKlbrmmmvUqVMnbd++PdKlnTQLFixQnz59tGTJEmVkZCgvL0/p6en69dfQL726/vrrlZmZGfz5+OOPI1TxydOwYcOQffz222+D97344osaM2aMXnnlFS1dulQ1atRQhw4dgl/GdaZYunRpSA8K/2hj165dg8uciXPh119/VZMmTfTKK68Ue39pjn///v01c+ZMTZs2TYsWLdLBgwfVuXNn5efnn6rdOGHH60NOTo5WrFihIUOGaMWKFXr//fe1ceNG3XzzzUWW7d27d8gcef31109F+WFT0nyQSn4enOnzQVLI/mdmZuqtt96Sx+PR7bffHrJcWObDCXwX4RmnRYsW9uCDD4aM1a9f35566qkIVXTq7d692yTZggULgmM9evSwLl26RK6oU2DYsGHWpEmTYu8rKCiwGjVq2KhRo4Jjhw4dssTERJs4ceIpqjAy+vXrZxdccIEVFBSY2dkxFyTZzJkzg7dLc/z3799vXq/Xpk2bFlzmxx9/tAoVKtjs2bNPWe3hdHQfivP111+bJNu2bVtwrG3bttavX7+TW9wpVFwfSnoenK3zoUuXLta+ffuQsXDNB86s/J/Dhw9r+fLlSk9PDxlPT0/X4sWLI1TVqZeVlSVJqlq1asj4/PnzlZycrHr16ql3797avXt3JMo7qb7//nulpqaqbt26uuuuu7R582ZJ0pYtW7Rr166QueHz+dS2bdszem4cPnxY7777ru6///6QLwo9G+bCkUpz/JcvXy6/3x+yTGpqqho1anRGz5GsrCx5PJ4i39H23nvvKSkpSQ0bNtQTTzxxxp2BlI7/PDgb58NPP/2kWbNmqVevXkXuC8d8iPi3Lrti7969ys/PL/Iliueee26RL1s8U5mZBgwYoKuvvlqNGjUKjnfq1Eldu3ZV7dq1tWXLFg0ZMkTt27fX8uXLz5i/2njllVfqb3/7m+rVq6effvpJzz33nFq3bq01a9YEj39xc2Pbtm2RKPeU+OCDD7R//3717NkzOHY2zIWjleb479q1S9HR0apSpUqRZc7U3x+HDh3SU089pW7duoV8cV337t1Vt25d1ahRQ999950GDx6sb775psj3wJ3OSnoenI3z4Z133lF8fLxuu+22kPFwzQfCylGO/B+kFHgBP3rsTPXII49o9erVWrRoUcj4nXfeGfx3o0aN1Lx5c9WuXVuzZs0qMjFPV506dQr+u3HjxmrVqpUuuOACvfPOO8EL5862uTFp0iR16tQp5Cvcz4a5cCzlOf5n6hzx+/266667VFBQoNdeey3kvt69ewf/3ahRI1100UVq3ry5VqxYocsvv/xUl3pSlPd5cKbOB0l666231L17d8XExISMh2s+8DbQ/0lKSlJUVFSR1Lt79+4i/6M6E/Xt21cffvih5s2bp1q1ah132ZSUFNWuXVvff//9Karu1KtUqZIaN26s77//PvipoLNpbmzbtk2ffvqpHnjggeMudzbMhdIc/xo1aujw4cPat2/fMZc5U/j9ft1xxx3asmWLMjIyQs6qFOfyyy+X1+s9o+fI0c+Ds2k+SNLChQu1YcOGEn9fSOWfD4SV/xMdHa1mzZoVOTWVkZGh1q1bR6iqk8/M9Mgjj+j999/XZ599prp165b4mJ9//lk7duxQSkrKKagwMnJzc7Vu3TqlpKQET2EeOTcOHz6sBQsWnLFzY/LkyUpOTtaNN9543OXOhrlQmuPfrFkzeb3ekGUyMzP13XffnVFzpDCofP/99/r0009VrVq1Eh+zZs0a+f3+M3qOHP08OFvmQ6FJkyapWbNmatKkSYnLlns+nPAlumeQadOmmdfrtUmTJtnatWutf//+VqlSJdu6dWukSztpHnroIUtMTLT58+dbZmZm8CcnJ8fMzA4cOGCPP/64LV682LZs2WLz5s2zVq1aWc2aNS07OzvC1YfP448/bvPnz7fNmzfbkiVLrHPnzhYfHx889qNGjbLExER7//337dtvv7W7777bUlJSzqgeFMrPz7fzzjvPBg0aFDJ+Js+FAwcO2MqVK23lypUmycaMGWMrV64MfsqlNMf/wQcftFq1atmnn35qK1assPbt21uTJk0sLy8vUrtVZsfrg9/vt5tvvtlq1aplq1atCvl9kZuba2ZmP/zwg40YMcKWLl1qW7ZssVmzZln9+vXtsssuO2P6UNrnwZk+HwplZWVZXFycTZgwocjjwzkfCCtHefXVV6127doWHR1tl19+echHeM9Ekor9mTx5spmZ5eTkWHp6ulWvXt28Xq+dd9551qNHD9u+fXtkCw+zO++801JSUszr9VpqaqrddttttmbNmuD9BQUFNmzYMKtRo4b5fD5r06aNffvttxGs+OT5n//5H5NkGzZsCBk/k+fCvHnzin0e9OjRw8xKd/x/++03e+SRR6xq1aoWGxtrnTt3Pu16c7w+bNmy5Zi/L+bNm2dmZtu3b7c2bdpY1apVLTo62i644AJ79NFH7eeff47sjpXR8fpQ2ufBmT4fCr3++usWGxtr+/fvL/L4cM4Hj5lZ2c7FAAAAnDpcswIAAJxGWAEAAE4jrAAAAKcRVgAAgNMIKwAAwGmEFQAA4DTCCgAAcBphBQAAOI2wgrPC1q1b5fF4tGrVqkiXErR+/Xq1bNlSMTExatq0aaTLwQmYP3++PB6P9u/fH+lSTivDhw8Pmfs9e/bULbfcErF64C7CCk6Jnj17yuPxaNSoUSHjH3zwwRn7leklGTZsmCpVqqQNGzZo7ty5kS6nRG+//bbOOeecSJdRJu+//746duyopKSkY4bV3Nxc9e3bV0lJSapUqZJuvvlm/fvf/w5ZZt++fbrnnnuUmJioxMRE3XPPPQSTMHjiiSdOi7mPyCOs4JSJiYnR6NGji3xt+uns8OHD5X7spk2bdPXVV6t27dql+vbaE93eifD7/RHZbmkdq75ff/1VV111VZGQfKT+/ftr5syZmjZtmhYtWqSDBw+qc+fOys/PDy7TrVs3rVq1SrNnz9bs2bO1atUq3XPPPWHfj0jLz89XQUHBKdte5cqVSz33cZYr/1ccAaXXo0cP69y5s9WvX98GDhwYHJ85c6YdOQ2HDRtmTZo0CXns2LFjrXbt2iHr6tKli40cOdKSk5MtMTHRhg8fbn6/35544gmrUqWK1axZ0yZNmhR8TOGXsE2dOtVatWplPp/PLrnkkuAXsBVas2aNderUySpVqmTJycn2hz/8wfbs2RO8v23bttanTx977LHHrFq1atamTZti9zc/P99GjBhhNWvWtOjoaGvSpIl98sknwft11BeDDRs2rNj1HGt7JdX5j3/8wxo1amQxMTFWtWpVu/baa+3gwYOlqq2wV9OnT7e2bduaz+ezt95665g1v/rqq3bhhReaz+ez5ORku/3224vdFzOzyZMnW2Jios2cOdMuuugi8/l8dt111xX5grcPP/zQLr/8cvP5fFa3bt3g8T2yfxMmTLCbb77Z4uLibOjQocfc5pH7tHLlypDx/fv3m9frtWnTpgXHfvzxR6tQoYLNnj3bzMzWrl1rkmzJkiXBZb788kuTZOvXrzez///Ct3379plZ4Esfb7jhBrvyyiuP+aVtxztGeXl59thjj1liYqJVrVrVBg4caPfee6916dIl+PjatWvb2LFjQ9bZpEmTkLn08ssvW6NGjSwuLs5q1aplDz30kB04cCB4f+Hx+Oijj6xBgwYWFRVlmzdvttzcXBs4cKClpqZaXFyctWjRoshz5WiSbOLEiXbjjTdabGys1a9f3xYvXmzff/+9tW3b1uLi4qxly5b2ww8/BB9z9PO98LldqKCgwEaPHm1169a1mJgYu/TSS+0f//hH8P5ffvnFunXrZklJSRYTE2MXXnihvfXWW8etE6cnwgpOicJfQu+//77FxMTYjh07zKz8YSU+Pt769Olj69evt0mTJpkk69ixo40cOdI2btxozz77rHm93uCLYOGLVa1ateyf//ynrV271h544AGLj4+3vXv3mpnZzp07LSkpyQYPHmzr1q2zFStWWIcOHex3v/tdcNtt27a1ypUr28CBA239+vW2bt26Yvd3zJgxlpCQYFOnTrX169fbk08+aV6v1zZu3GhmZpmZmdawYUN7/PHHLTMzM+QF5EjFba+kOnfu3GkVK1a0MWPG2JYtW2z16tX26quvBrdRUm2FvapTp47NmDHDNm/ebNu2bbNx48ZZQkKCZWZmBmteunSpRUVF2ZQpU2zr1q22YsUK++tf/3rMeTB58mTzer3WvHlzW7x4sS1btsxatGhhrVu3Di4ze/ZsS0hIsLfffts2bdpkc+bMsTp16tjw4cODy0iy5ORkmzRpkm3atMm2bt16zG0euU9Hh5W5c+eaJPvll19Cxi+99NJgAJo0aZIlJiYWWWdiYmLwhfHIsLJ//367+uqr7brrrguGj6OVdIxGjx5tiYmJwbnaq1cvi4+PL3NYGTt2rH322We2efNmmzt3rl188cX20EMPBe8vPB6tW7e2L774wtavX28HDx60bt26WevWre3zzz+3H374wV566SXz+XzBOVIcSVazZk2bPn26bdiwwW655RarU6eOtW/f3mbPnm1r1661li1b2vXXXx98TElh5emnn7b69evb7NmzbdOmTTZ58mTz+Xw2f/58MzPr06ePNW3a1JYuXWpbtmyxjIwM+/DDD49ZI05fhBWcEkf+EmrZsqXdf//9Zlb+sFK7dm3Lz88Pjl188cV2zTXXBG/n5eVZpUqVbOrUqWb2/y9Wo0aNCi7j9/utVq1aNnr0aDMzGzJkiKWnp4dse8eOHSbJNmzYYGaB8NC0adMS9zc1NdVGjhwZMnbFFVfYww8/HLx99AtLcYrbXkl1Ll++3CQd8wW8pNoKezVu3LiQZQr/F36kGTNmWEJCgmVnZx93P45cx9FnKdatW2eS7KuvvjIzs2uuucaef/75kMf913/9l6WkpARvS7L+/fuXaptH7tPRYeW9996z6OjoIst36NDB/vjHP5qZ2ciRI+2iiy4qssxFF10UrLMwrKxfv96aNGlit912m+Xm5h6znpKOUUpKSrFztaxh5Wh///vfrVq1asHbhcdj1apVwbEffvjBPB6P/fjjjyGPvfbaa23w4MHHXLck+9Of/hS8XXj26cgznFOnTrWYmJjg7eOFlYMHD1pMTIwtXrw4ZDu9evWyu+++28zMbrrpJrvvvvuOWRPOHBVP2vtLwDGMHj1a7du31+OPP17udTRs2FAVKvz/JVfnnnuuGjVqFLwdFRWlatWqaffu3SGPa9WqVfDfFStWVPPmzbVu3TpJ0vLlyzVv3jxVrly5yPY2bdqkevXqSZKaN29+3Nqys7O1c+dOXXXVVSHjV111lb755ptS7uH/O3p7JdWZnp6ua6+9Vo0bN1bHjh2Vnp6u3//+96pSpUqZaitpPyWpQ4cOql27ts4//3xdf/31uv7663XrrbcqLi7umI8p7Huh+vXr65xzztG6devUokULLV++XEuXLtXIkSODy+Tn5+vQoUPKyckJrrs09ZWXmYVc+F3cReBHLyNJ1113na644gr9/e9/V1RU1DHX36RJk2Meo6ysLGVmZhY7V82sTPsxb948Pf/881q7dq2ys7OVl5enQ4cO6ddff1WlSpUkSdHR0br00kuDj1mxYoXMLDjfC+Xm5pZ4fcmR6zn33HMlSY0bNw4ZO3TokLKzs5WQkHDcda1du1aHDh1Shw4dQsYPHz6syy67TJL00EMP6fbbb9eKFSuUnp6uW265Ra1btz7uenF6IqzglGvTpo06duyop59+Wj179gy5r0KFCkV+IRd38aTX6w257fF4ih0rzcWChS84BQUFuummmzR69Ogiy6SkpAT/XfhLvrTrLVTci1tpHL29kuqMiopSRkaGFi9erDlz5mj8+PF65pln9NVXXwVfbEpTW2n2Mz4+XitWrND8+fM1Z84cDR06VMOHD9fSpUuP+8mh4vpw5HEYMWKEbrvttiLLxMTElKm+ktSoUUOHDx/Wvn37VKVKleD47t27gy96NWrU0E8//VTksXv27Am+IBe68cYbNWPGDK1duzbkRfpoxztGVatWLVXtJT1Xtm3bphtuuEEPPvignn32WVWtWlWLFi1Sr169QpaLjY0NOR4FBQWKiorS8uXLiwSu4gLykY58Dhaus7ix0jwvC5eZNWuWatasGXKfz+eTJHXq1Enbtm3TrFmz9Omnn+raa69Vnz599Je//KXE9eP0wqeBEBGjRo3SRx99pMWLF4eMV69eXbt27Qr5JRzOv42yZMmS4L/z8vK0fPly1a9fX5J0+eWXa82aNapTp44uvPDCkJ+yvDAmJCQoNTVVixYtChlfvHixGjRocML7UJo6PR6PrrrqKo0YMUIrV65UdHS0Zs6ceUK1RUdHh3xCplDFihV13XXX6cUXX9Tq1au1detWffbZZ8dcT15enpYtWxa8vWHDBu3fvz/kOGzYsKHIvl144YUhZ9PCoVmzZvJ6vcrIyAiOZWZm6rvvvguGlVatWikrK0tff/11cJmvvvpKWVlZRf4XP2rUKPXo0UPXXnut1q5de9xtH+sYJSYmKiUlpdi5eqTq1asrMzMzeDs7O1tbtmwJ3l62bJny8vL08ssvq2XLlqpXr5527txZYk8uu+wy5efna/fu3UX6X6NGjRIfHy6XXHKJfD6ftm/fXqSOtLS04HLVq1dXz5499e6772rcuHF64403TlmNOHU4s4KIaNy4sbp3767x48eHjLdr10579uzRiy++qN///veaPXu2PvnkkxJPGZfWq6++qosuukgNGjTQ2LFjtW/fPt1///2SpD59+ujNN9/U3XffrYEDByopKUk//PCDpk2bpjfffPO4p/WPNnDgQA0bNkwXXHCBmjZtqsmTJ2vVqlV67733TngfSqpz2bJlmjt3rtLT05WcnKyvvvpKe/bsCYaR8tZWp04dHTx4UHPnzlWTJk0UFxenzz77TJs3b1abNm1UpUoVffzxxyooKNDFF198zPV4vV717dtX//mf/ymv16tHHnlELVu2VIsWLSRJQ4cOVefOnZWWlqauXbuqQoUKWr16tb799ls999xzZerVL7/8ou3btwdfpDds2CApcLakRo0aSkxMVK9evfT444+rWrVqqlq1qp544gk1btxY1113nSSpQYMGuv7669W7d2+9/vrrkqQ//vGP6ty5c7H7+Ze//EX5+flq37695s+fHwxhR/rqq6+Oe4z69eunUaNGBefqmDFjivxdl/bt2+vtt9/WTTfdpCpVqmjIkCEhc/SCCy5QXl6exo8fr5tuuklffPGFJk6cWGLP6tWrp+7du+vee+/Vyy+/rMsuu0x79+7VZ599psaNG+uGG24oRedPXHx8vJ544gk99thjKigo0NVXX63s7GwtXrxYlStXVo8ePTR06FA1a9ZMDRs2VG5urv77v/87LP8hgIMidrUMzipHX+VvZrZ161bz+Xx29DScMGGCpaWlWaVKlezee++1kSNHFvvR5SO1bdvW+vXrFzJ25AWIhRdYTpkyxa688kqLjo62Bg0a2Ny5c0Mes3HjRrv11lvtnHPOCX78sn///lZQUHDM7RTnyI8He73eIh8PNiv9BbbFbe94da5du9Y6duxo1atXN5/PZ/Xq1bPx48eXurZjXYxqZvbggw9atWrVgh9dXrhwobVt29aqVKlisbGxdumll9r06dOPuT+FF+nOmDHDzj//fIuOjrb27dsXudB09uzZ1rp1a4uNjbWEhARr0aKFvfHGG8H7JdnMmTOP27vC7emoj1wX1l7ot99+s0ceecSqVq1qsbGx1rlz5yIfpf7555+te/fuFh8fb/Hx8da9e/fgx5TNin502cysb9++lpKSErw4+0glHSO/32/9+vWzhIQEO+ecc2zAgAFFPrqclZVld9xxhyUkJFhaWpq9/fbbRebUmDFjLCUlxWJjY61jx472t7/9LaTO4i6aNjM7fPiwDR061OrUqWNer9dq1Khht956q61evfqYvT76mBQ3j47uU2k+uvzXv/7VLr74YvN6vVa9enXr2LGjLViwwMzMnn32WWvQoIHFxsZa1apVrUuXLrZ58+Zj1ojTl8esjFdsAUA5vf322+rfvz9//bUcevbsqf379+uDDz6IdCnAKcc1KwAAwGmEFQAA4DTeBgIAAE7jzAoAAHAaYQUAADiNsAIAAJxGWAEAAE4jrAAAAKcRVgAAgNMIKwAAwGmEFQAA4LT/BRSpKcyCUDEwAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "state_summary.resorts_per_100ksq_mile.hist(bins=30)\n",
+    "plt.xlabel('Number of resorts per 100k square miles')\n",
+    "plt.ylabel('count');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So they have quite some long tails on them, but there's definitely some structure there."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.2.1 Top states by resort density<a id='3.5.2.1_Top_states_by_resort_density'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "Vermont          2.403889\n",
+       "Wyoming          1.382268\n",
+       "New Hampshire    1.176721\n",
+       "Montana          1.122778\n",
+       "Idaho            0.671492\n",
+       "Name: resorts_per_100kcapita, dtype: float64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary.set_index('state').resorts_per_100kcapita.sort_values(ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "New Hampshire    171.141299\n",
+       "Vermont          155.990017\n",
+       "Massachusetts    104.225886\n",
+       "Connecticut       90.203861\n",
+       "Rhode Island      64.724919\n",
+       "Name: resorts_per_100ksq_mile, dtype: float64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary.set_index('state').resorts_per_100ksq_mile.sort_values(ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vermont seems particularly high in terms of resorts per capita, and both New Hampshire and Vermont top the chart for resorts per area. New York doesn't appear in either!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.5.3 Visualizing High Dimensional Data<a id='3.5.3_Visualizing_High_Dimensional_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You may be starting to feel there's a bit of a problem here, or at least a challenge. You've constructed some potentially useful and business relevant features, derived from summary statistics, for each of the states you're concerned with. You've explored many of these features in turn and found various trends. Some states are higher in some but not in others. Some features will also be more correlated with one another than others.\n",
+    "\n",
+    "One way to disentangle this interconnected web of relationships is via [principle components analysis](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA) (PCA). This technique will find linear combinations of the original features that are uncorrelated with one another and order them by the amount of variance they explain. You can use these derived features to visualize the data in a lower dimension (e.g. 2 down from 7) and know how much variance the representation explains. You can also explore how the original features contribute to these derived features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The basic steps in this process are:\n",
+    "\n",
+    "1. scale the data (important here because our features are heterogenous)\n",
+    "2. fit the PCA transformation (learn the transformation from the data)\n",
+    "3. apply the transformation to the data to create the derived features\n",
+    "4. (optionally) use the derived features to look for patterns in the data and explore the coefficients"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.3.1 Scale the data<a id='3.5.3.1_Scale_the_data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You only want numeric data here, although you don't want to lose track of the state labels, so it's convenient to set the state as the index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Alaska</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.450867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>0.027477</td>\n",
+       "      <td>1.754540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>21</td>\n",
+       "      <td>25948.0</td>\n",
+       "      <td>2738.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>587.0</td>\n",
+       "      <td>0.053148</td>\n",
+       "      <td>12.828736</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>22</td>\n",
+       "      <td>43682.0</td>\n",
+       "      <td>3258.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>428.0</td>\n",
+       "      <td>0.382028</td>\n",
+       "      <td>21.134744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>5</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>256.0</td>\n",
+       "      <td>0.140242</td>\n",
+       "      <td>90.203861</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             resorts_per_state  state_total_skiable_area_ac  \\\n",
+       "state                                                         \n",
+       "Alaska                       3                       2280.0   \n",
+       "Arizona                      2                       1577.0   \n",
+       "California                  21                      25948.0   \n",
+       "Colorado                    22                      43682.0   \n",
+       "Connecticut                  5                        358.0   \n",
+       "\n",
+       "             state_total_days_open  state_total_terrain_parks  \\\n",
+       "state                                                           \n",
+       "Alaska                       345.0                        4.0   \n",
+       "Arizona                      237.0                        6.0   \n",
+       "California                  2738.0                       81.0   \n",
+       "Colorado                    3258.0                       74.0   \n",
+       "Connecticut                  353.0                       10.0   \n",
+       "\n",
+       "             state_total_nightskiing_ac  resorts_per_100kcapita  \\\n",
+       "state                                                             \n",
+       "Alaska                            580.0                0.410091   \n",
+       "Arizona                            80.0                0.027477   \n",
+       "California                        587.0                0.053148   \n",
+       "Colorado                          428.0                0.382028   \n",
+       "Connecticut                       256.0                0.140242   \n",
+       "\n",
+       "             resorts_per_100ksq_mile  \n",
+       "state                                 \n",
+       "Alaska                      0.450867  \n",
+       "Arizona                     1.754540  \n",
+       "California                 12.828736  \n",
+       "Colorado                   21.134744  \n",
+       "Connecticut                90.203861  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 1#\n",
+    "#Create a new dataframe, `state_summary_scale` from `state_summary` whilst setting the index to 'state'\n",
+    "state_summary_scale = state_summary.set_index('state')\n",
+    "#Save the state labels (using the index attribute of `state_summary_scale`) into the variable 'state_summary_index'\n",
+    "state_summary_index = state_summary_scale.index\n",
+    "#Save the column names (using the `columns` attribute) of `state_summary_scale` into the variable 'state_summary_columns'\n",
+    "state_summary_columns = state_summary_scale.columns\n",
+    "state_summary_scale.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above shows what we expect: the columns we want are all numeric and the state has been moved to the index. Although, it's not necessary to step through the sequence so laboriously, it is often good practice even for experienced professionals. It's easy to make a mistake or forget a step, or the data may have been holding out a surprise! Stepping through like this helps  validate both your work and the data!\n",
+    "\n",
+    "Now use `scale()` to scale the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state_summary_scale = scale(state_summary_scale)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note, `scale()` returns an ndarray, so you lose the column names. Because you want to visualise scaled data, you already copied the column names. Now you can construct a dataframe from the ndarray here and reintroduce the column names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.806912</td>\n",
+       "      <td>-0.392012</td>\n",
+       "      <td>-0.689059</td>\n",
+       "      <td>-0.816118</td>\n",
+       "      <td>0.069410</td>\n",
+       "      <td>0.139593</td>\n",
+       "      <td>-0.689999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.933558</td>\n",
+       "      <td>-0.462424</td>\n",
+       "      <td>-0.819038</td>\n",
+       "      <td>-0.726994</td>\n",
+       "      <td>-0.701326</td>\n",
+       "      <td>-0.644706</td>\n",
+       "      <td>-0.658125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.472706</td>\n",
+       "      <td>1.978574</td>\n",
+       "      <td>2.190933</td>\n",
+       "      <td>2.615141</td>\n",
+       "      <td>0.080201</td>\n",
+       "      <td>-0.592085</td>\n",
+       "      <td>-0.387368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.599351</td>\n",
+       "      <td>3.754811</td>\n",
+       "      <td>2.816757</td>\n",
+       "      <td>2.303209</td>\n",
+       "      <td>-0.164893</td>\n",
+       "      <td>0.082069</td>\n",
+       "      <td>-0.184291</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.553622</td>\n",
+       "      <td>-0.584519</td>\n",
+       "      <td>-0.679431</td>\n",
+       "      <td>-0.548747</td>\n",
+       "      <td>-0.430027</td>\n",
+       "      <td>-0.413557</td>\n",
+       "      <td>1.504408</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   resorts_per_state  state_total_skiable_area_ac  state_total_days_open  \\\n",
+       "0          -0.806912                    -0.392012              -0.689059   \n",
+       "1          -0.933558                    -0.462424              -0.819038   \n",
+       "2           1.472706                     1.978574               2.190933   \n",
+       "3           1.599351                     3.754811               2.816757   \n",
+       "4          -0.553622                    -0.584519              -0.679431   \n",
+       "\n",
+       "   state_total_terrain_parks  state_total_nightskiing_ac  \\\n",
+       "0                  -0.816118                    0.069410   \n",
+       "1                  -0.726994                   -0.701326   \n",
+       "2                   2.615141                    0.080201   \n",
+       "3                   2.303209                   -0.164893   \n",
+       "4                  -0.548747                   -0.430027   \n",
+       "\n",
+       "   resorts_per_100kcapita  resorts_per_100ksq_mile  \n",
+       "0                0.139593                -0.689999  \n",
+       "1               -0.644706                -0.658125  \n",
+       "2               -0.592085                -0.387368  \n",
+       "3                0.082069                -0.184291  \n",
+       "4               -0.413557                 1.504408  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 2#\n",
+    "#Create a new dataframe from `state_summary_scale` using the column names we saved in `state_summary_columns`\n",
+    "state_summary_scaled_df = pd.DataFrame(state_summary_scale, columns=state_summary_columns)\n",
+    "state_summary_scaled_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 3.5.3.1.1 Verifying the scaling<a id='3.5.3.1.1_Verifying_the_scaling'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is definitely going the extra mile for validating your steps, but provides a worthwhile lesson."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First of all, check the mean of the scaled features using panda's `mean()` DataFrame method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "resorts_per_state             -7.295751e-17\n",
+       "state_total_skiable_area_ac   -4.163336e-17\n",
+       "state_total_days_open          7.692260e-17\n",
+       "state_total_terrain_parks      4.599495e-17\n",
+       "state_total_nightskiing_ac     7.612958e-17\n",
+       "resorts_per_100kcapita         5.075305e-17\n",
+       "resorts_per_100ksq_mile        5.075305e-17\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 3#\n",
+    "#Call `state_summary_scaled_df`'s `mean()` method\n",
+    "state_summary_scaled_df.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is pretty much zero!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Perform a similar check for the standard deviation using pandas's `std()` DataFrame method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "resorts_per_state              1.014599\n",
+       "state_total_skiable_area_ac    1.014599\n",
+       "state_total_days_open          1.014599\n",
+       "state_total_terrain_parks      1.014599\n",
+       "state_total_nightskiing_ac     1.014599\n",
+       "resorts_per_100kcapita         1.014599\n",
+       "resorts_per_100ksq_mile        1.014599\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 4#\n",
+    "#Call `state_summary_scaled_df`'s `std()` method\n",
+    "state_summary_scaled_df.std()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Well, this is a little embarrassing. The numbers should be closer to 1 than this! Check the documentation for [scale](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html) to see if you used it right. What about [std](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.std.html), did you mess up there? Is one of them not working right?\n",
+    "\n",
+    "The keen observer, who already has some familiarity with statistical inference and biased estimators, may have noticed what's happened here. `scale()` uses the biased estimator for standard deviation (ddof=0). This doesn't mean it's bad! It simply means it calculates the standard deviation of the sample it was given. The `std()` method, on the other hand, defaults to using ddof=1, that is it's normalized by N-1. In other words, the `std()` method default is to assume you want your best estimate of the population parameter based on the given sample. You can tell it to return the biased estimate instead:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "resorts_per_state              1.0\n",
+       "state_total_skiable_area_ac    1.0\n",
+       "state_total_days_open          1.0\n",
+       "state_total_terrain_parks      1.0\n",
+       "state_total_nightskiing_ac     1.0\n",
+       "resorts_per_100kcapita         1.0\n",
+       "resorts_per_100ksq_mile        1.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 5#\n",
+    "#Repeat the previous call to `std()` but pass in ddof=0 \n",
+    "state_summary_scaled_df.std(ddof=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There! Now it agrees with `scale()` and our expectation. This just goes to show different routines to do ostensibly the same thing can have different behaviours. Good practice is to keep validating your work and checking the documentation!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.3.2 Calculate the PCA transformation<a id='3.5.3.2_Calculate_the_PCA_transformation'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fit the PCA transformation using the scaled data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state_pca = PCA().fit(state_summary_scale)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the cumulative variance ratio with number of components."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 6#\n",
+    "#Call the `cumsum()` method on the 'explained_variance_ratio_' attribute of `state_pca` and\n",
+    "#create a line plot to visualize the cumulative explained variance ratio with number of components\n",
+    "#Set the xlabel to 'Component #', the ylabel to 'Cumulative ratio variance', and the\n",
+    "#title to 'Cumulative variance ratio explained by PCA components for state/resort summary statistics'\n",
+    "#Hint: remember the handy ';' at the end of the last plot call to suppress that untidy output\n",
+    "plt.subplots(figsize=(10, 6))\n",
+    "plt.plot(state_pca.explained_variance_ratio_.cumsum())\n",
+    "plt.xlabel('Component #')\n",
+    "plt.ylabel('Cumulative ratio variance')\n",
+    "plt.title('Cumulative variance ratio explained by PCA components for state/resort summary statistics');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first two components seem to account for over 75% of the variance, and the first four for over 95%."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:** It is important to move quickly when performing exploratory data analysis. You should not spend hours trying to create publication-ready figures. However, it is crucially important that you can easily review and summarise the findings from EDA. Descriptive axis labels and titles are _extremely_ useful here. When you come to reread your notebook to summarise your findings, you will be thankful that you created descriptive plots and even made key observations in adjacent markdown cells."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Apply the transformation to the data to obtain the derived features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 7#\n",
+    "#Call `state_pca`'s `transform()` method, passing in `state_summary_scale` as its argument\n",
+    "state_pca_x = state_pca.transform(state_summary_scale)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(35, 7)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_pca_x.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plot the first two derived features (the first two principle components) and label each point with the name of the state."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Take a moment to familiarize yourself with the code below. It will extract the first and second columns from the transformed data (`state_pca_x`) as x and y coordinates for plotting. Recall the state labels you saved (for this purpose) for subsequent calls to `plt.annotate`. Grab the second (index 1) value of the cumulative variance ratio to include in your descriptive title; this helpfully highlights the percentage variance explained\n",
+    "by the two PCA components you're visualizing. Then create an appropriately sized and well-labelled scatterplot\n",
+    "to convey all of this information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = state_pca_x[:, 0]\n",
+    "y = state_pca_x[:, 1]\n",
+    "state = state_summary_index\n",
+    "pc_var = 100 * state_pca.explained_variance_ratio_.cumsum()[1]\n",
+    "plt.subplots(figsize=(10,8))\n",
+    "plt.scatter(x=x, y=y)\n",
+    "plt.xlabel('First component')\n",
+    "plt.ylabel('Second component')\n",
+    "plt.title(f'Ski states summary PCA, {pc_var:.1f}% variance explained')\n",
+    "for s, x, y in zip(state, x, y):\n",
+    "    plt.annotate(s, (x, y))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.3.3 Average ticket price by state<a id='3.5.3.3_Average_ticket_price_by_state'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, all point markers for the states are the same size and colour. You've visualized relationships between the states based on features such as the total skiable terrain area, but your ultimate interest lies in ticket prices. You know ticket prices for resorts in each state, so it might be interesting to see if there's any pattern there."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "Alaska         57.333333\n",
+       "Arizona        83.500000\n",
+       "California     81.416667\n",
+       "Colorado       90.714286\n",
+       "Connecticut    56.800000\n",
+       "Name: AdultWeekend, dtype: float64"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 8#\n",
+    "#Calculate the average 'AdultWeekend' ticket price by state\n",
+    "state_avg_price = ski_data.groupby('state')['AdultWeekend'].mean()\n",
+    "state_avg_price.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "state_avg_price.hist(bins=30)\n",
+    "plt.title('Distribution of state averaged prices')\n",
+    "plt.xlabel('Mean state adult weekend ticket price')\n",
+    "plt.ylabel('count');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.3.4 Adding average ticket price to scatter plot<a id='3.5.3.4_Adding_average_ticket_price_to_scatter_plot'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At this point you have several objects floating around. You have just calculated average ticket price by state from our ski resort data, but you've been looking at principle components generated from other state summary data. We extracted indexes and column names from a dataframe and the first two principle components from an array. It's becoming a bit hard to keep track of them all. You'll create a new DataFrame to do this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PC1</th>\n",
+       "      <th>PC2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Alaska</th>\n",
+       "      <td>-1.336533</td>\n",
+       "      <td>-0.182208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>-1.839049</td>\n",
+       "      <td>-0.387959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>3.537857</td>\n",
+       "      <td>-1.282509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>4.402210</td>\n",
+       "      <td>-0.898855</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>-0.988027</td>\n",
+       "      <td>1.020218</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  PC1       PC2\n",
+       "state                          \n",
+       "Alaska      -1.336533 -0.182208\n",
+       "Arizona     -1.839049 -0.387959\n",
+       "California   3.537857 -1.282509\n",
+       "Colorado     4.402210 -0.898855\n",
+       "Connecticut -0.988027  1.020218"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 9#\n",
+    "#Create a dataframe containing the values of the first two PCA components\n",
+    "#Remember the first component was given by state_pca_x[:, 0],\n",
+    "#and the second by state_pca_x[:, 1]\n",
+    "#Call these 'PC1' and 'PC2', respectively and set the dataframe index to `state_summary_index`\n",
+    "pca_df = pd.DataFrame({'PC1': state_pca_x[:, 0], 'PC2': state_pca_x[:, 1]}, index=state_summary_index)\n",
+    "pca_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That worked, and you have state as an index."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "Alaska         57.333333\n",
+       "Arizona        83.500000\n",
+       "California     81.416667\n",
+       "Colorado       90.714286\n",
+       "Connecticut    56.800000\n",
+       "Name: AdultWeekend, dtype: float64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# our average state prices also have state as an index\n",
+    "state_avg_price.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Alaska</th>\n",
+       "      <td>57.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>83.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>81.416667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>90.714286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>56.800000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             AdultWeekend\n",
+       "state                    \n",
+       "Alaska          57.333333\n",
+       "Arizona         83.500000\n",
+       "California      81.416667\n",
+       "Colorado        90.714286\n",
+       "Connecticut     56.800000"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# we can also cast it to a dataframe using Series' to_frame() method:\n",
+    "state_avg_price.to_frame().head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now you can concatenate both parts on axis 1 and using the indexes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PC1</th>\n",
+       "      <th>PC2</th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Alaska</th>\n",
+       "      <td>-1.336533</td>\n",
+       "      <td>-0.182208</td>\n",
+       "      <td>57.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>-1.839049</td>\n",
+       "      <td>-0.387959</td>\n",
+       "      <td>83.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>3.537857</td>\n",
+       "      <td>-1.282509</td>\n",
+       "      <td>81.416667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>4.402210</td>\n",
+       "      <td>-0.898855</td>\n",
+       "      <td>90.714286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>-0.988027</td>\n",
+       "      <td>1.020218</td>\n",
+       "      <td>56.800000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  PC1       PC2  AdultWeekend\n",
+       "state                                        \n",
+       "Alaska      -1.336533 -0.182208     57.333333\n",
+       "Arizona     -1.839049 -0.387959     83.500000\n",
+       "California   3.537857 -1.282509     81.416667\n",
+       "Colorado     4.402210 -0.898855     90.714286\n",
+       "Connecticut -0.988027  1.020218     56.800000"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 10#\n",
+    "#Use pd.concat to concatenate `pca_df` and `state_avg_price` along axis 1\n",
+    "# remember, pd.concat will align on index\n",
+    "pca_df = pd.concat([pca_df, state_avg_price], axis=1)\n",
+    "pca_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You saw some range in average ticket price histogram above, but it may be hard to pick out differences if you're thinking of using the value for point size. You'll add another column where you seperate these prices into quartiles; that might show something."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PC1</th>\n",
+       "      <th>PC2</th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <th>Quartile</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Alaska</th>\n",
+       "      <td>-1.336533</td>\n",
+       "      <td>-0.182208</td>\n",
+       "      <td>57.333333</td>\n",
+       "      <td>(53.1, 60.4]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>-1.839049</td>\n",
+       "      <td>-0.387959</td>\n",
+       "      <td>83.500000</td>\n",
+       "      <td>(78.4, 93.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>3.537857</td>\n",
+       "      <td>-1.282509</td>\n",
+       "      <td>81.416667</td>\n",
+       "      <td>(78.4, 93.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>4.402210</td>\n",
+       "      <td>-0.898855</td>\n",
+       "      <td>90.714286</td>\n",
+       "      <td>(78.4, 93.0]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>-0.988027</td>\n",
+       "      <td>1.020218</td>\n",
+       "      <td>56.800000</td>\n",
+       "      <td>(53.1, 60.4]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  PC1       PC2  AdultWeekend      Quartile\n",
+       "state                                                      \n",
+       "Alaska      -1.336533 -0.182208     57.333333  (53.1, 60.4]\n",
+       "Arizona     -1.839049 -0.387959     83.500000  (78.4, 93.0]\n",
+       "California   3.537857 -1.282509     81.416667  (78.4, 93.0]\n",
+       "Colorado     4.402210 -0.898855     90.714286  (78.4, 93.0]\n",
+       "Connecticut -0.988027  1.020218     56.800000  (53.1, 60.4]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca_df['Quartile'] = pd.qcut(pca_df.AdultWeekend, q=4, precision=1)\n",
+    "pca_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PC1              float64\n",
+       "PC2              float64\n",
+       "AdultWeekend     float64\n",
+       "Quartile        category\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Note that Quartile is a new data type: category\n",
+    "# This will affect how we handle it later on\n",
+    "pca_df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This looks great. But, let's have a healthy paranoia about it. You've just created a whole new DataFrame by combining information. Do we have any missing values? It's a narrow DataFrame, only four columns, so you'll just print out any rows that have any null values, expecting an empty DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PC1</th>\n",
+       "      <th>PC2</th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <th>Quartile</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Rhode Island</th>\n",
+       "      <td>-1.843646</td>\n",
+       "      <td>0.761339</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   PC1       PC2  AdultWeekend Quartile\n",
+       "state                                                  \n",
+       "Rhode Island -1.843646  0.761339           NaN      NaN"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca_df[pca_df.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ah, Rhode Island. How has this happened? Recall you created the original ski resort state summary dataset in the previous step before removing resorts with missing prices. This made sense because you wanted to capture all the other available information. However, Rhode Island only had one resort and its price was missing. You have two choices here. If you're interested in looking for any pattern with price, drop this row. But you are also generally interested in any clusters or trends, then you'd like to see Rhode Island even if the ticket price is unknown. So, replace these missing values to make it easier to handle/display them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because `Quartile` is a category type, there's an extra step here. Add the category (the string 'NA') that you're going to use as a replacement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\demoo\\AppData\\Local\\Temp\\ipykernel_17928\\2495745839.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
+      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
+      "\n",
+      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
+      "\n",
+      "\n",
+      "  pca_df['AdultWeekend'].fillna(pca_df.AdultWeekend.mean(), inplace=True)\n",
+      "C:\\Users\\demoo\\AppData\\Local\\Temp\\ipykernel_17928\\2495745839.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
+      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
+      "\n",
+      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
+      "\n",
+      "\n",
+      "  pca_df['Quartile'].fillna('NA', inplace=True)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "PC1             -1.843646\n",
+       "PC2              0.761339\n",
+       "AdultWeekend    64.124388\n",
+       "Quartile               NA\n",
+       "Name: Rhode Island, dtype: object"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca_df['AdultWeekend'].fillna(pca_df.AdultWeekend.mean(), inplace=True)\n",
+    "pca_df['Quartile'] = pca_df['Quartile'].cat.add_categories('NA')\n",
+    "pca_df['Quartile'].fillna('NA', inplace=True)\n",
+    "pca_df.loc['Rhode Island']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note, in the above Quartile has the string value 'NA' that you inserted. This is different to `numpy`'s NaN type.\n",
+    "\n",
+    "You now have enough information to recreate the scatterplot, now adding marker size for ticket price and colour for the discrete quartile."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice in the code below how you're iterating over each quartile and plotting the points in the same quartile group as one. This gives a list of quartiles for an informative legend with points coloured by quartile and sized by ticket price (higher prices are represented by larger point markers)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = pca_df.PC1\n",
+    "y = pca_df.PC2\n",
+    "price = pca_df.AdultWeekend\n",
+    "quartiles = pca_df.Quartile\n",
+    "state = pca_df.index\n",
+    "pc_var = 100 * state_pca.explained_variance_ratio_.cumsum()[1]\n",
+    "fig, ax = plt.subplots(figsize=(10,8))\n",
+    "for q in quartiles.cat.categories:\n",
+    "    im = quartiles == q\n",
+    "    ax.scatter(x=x[im], y=y[im], s=price[im], label=q)\n",
+    "ax.set_xlabel('First component')\n",
+    "ax.set_ylabel('Second component')\n",
+    "plt.legend()\n",
+    "ax.set_title(f'Ski states summary PCA, {pc_var:.1f}% variance explained')\n",
+    "for s, x, y in zip(state, x, y):\n",
+    "    plt.annotate(s, (x, y))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, you see the same distribution of states as before, but with additional information about the average price. There isn't an obvious pattern. The red points representing the upper quartile of price can be seen to the left, the right, and up top. There's also a spread of the other quartiles as well. In this representation of the ski summaries for each state, which accounts for some 77% of the variance, you simply do not seeing a pattern with price."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above scatterplot was created using matplotlib. This is powerful, but took quite a bit of effort to set up. You have to iterate over the categories, plotting each separately, to get a colour legend. You can also tell that the points in the legend have different sizes as well as colours. As it happens, the size and the colour will be a 1:1 mapping here, so it happily works for us here. If we were using size and colour to display fundamentally different aesthetics, you'd have a lot more work to do. So matplotlib is powerful, but not ideally suited to when we want to visually explore multiple features as here (and intelligent use of colour, point size, and even shape can be incredibly useful for EDA).\n",
+    "\n",
+    "Fortunately, there's another option: seaborn. You saw seaborn in action in the previous notebook, when you wanted to distinguish between weekend and weekday ticket prices in the boxplot. After melting the dataframe to have ticket price as a single column with the ticket type represented in a new column, you asked seaborn to create separate boxes for each type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 11#\n",
+    "#Create a seaborn scatterplot by calling `sns.scatterplot`\n",
+    "#Specify the dataframe pca_df as the source of the data,\n",
+    "#specify 'PC1' for x and 'PC2' for y,\n",
+    "#specify 'AdultWeekend' for the pointsize (scatterplot's `size` argument),\n",
+    "#specify 'Quartile' for `hue`\n",
+    "#specify pca_df.Quartile.cat.categories for `hue_order` - what happens with/without this?\n",
+    "x = pca_df.PC1\n",
+    "y = pca_df.PC2\n",
+    "state = pca_df.index\n",
+    "plt.subplots(figsize=(12, 10))\n",
+    "# Note the argument below to make sure we get the colours in the ascending\n",
+    "# order we intuitively expect!\n",
+    "sns.scatterplot(x='PC1', y='PC2', size='AdultWeekend', hue='Quartile', \n",
+    "                hue_order=pca_df.Quartile.cat.categories, data=pca_df)\n",
+    "#and we can still annotate with the state labels\n",
+    "for s, x, y in zip(state, x, y):\n",
+    "    plt.annotate(s, (x, y))   \n",
+    "plt.title(f'Ski states summary PCA, {pc_var:.1f}% variance explained');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Seaborn does more! You should always care about your output. What if you want the ordering of the colours in the legend to align intuitively with the ordering of the quartiles? Add a `hue_order` argument! Seaborn has thrown in a few nice other things:\n",
+    "\n",
+    "* the aesthetics are separated in the legend\n",
+    "* it defaults to marker sizes that provide more contrast (smaller to larger)\n",
+    "* when starting with a DataFrame, you have less work to do to visualize patterns in the data\n",
+    "\n",
+    "The last point is important. Less work means less chance of mixing up objects and jumping to erroneous conclusions. This also emphasizes the importance of getting data into a suitable DataFrame. In the previous notebook, you `melt`ed the data to make it longer, but with fewer columns, in order to get a single column of price with a new column representing a categorical feature you'd want to use. A **key skill** is being able to wrangle data into a form most suited to the particular use case."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Having gained a good visualization of the state summary data, you can discuss and follow up on your findings."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the first two components, there is a spread of states across the first component. It looks like Vermont and New Hampshire might be off on their own a little in the second dimension, although they're really no more extreme than New York and Colorado are in the first dimension. But if you were curious, could you get an idea what it is that pushes Vermont and New Hampshire up?\n",
+    "\n",
+    "The `components_` attribute of the fitted PCA object tell us how important (and in what direction) each feature contributes to each score (or coordinate on the plot). **NB we were sensible and scaled our original features (to zero mean and unit variance)**. You may not always be interested in interpreting the coefficients of the PCA transformation in this way, although it's more likely you will when using PCA for EDA as opposed to a preprocessing step as part of a machine learning pipeline. The attribute is actually a numpy ndarray, and so has been stripped of helpful index and column names. Fortunately, you thought ahead and saved these. This is how we were able to annotate the scatter plots above. It also means you can construct a DataFrame of `components_` with the feature names for context:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.486079</td>\n",
+       "      <td>0.318224</td>\n",
+       "      <td>0.489997</td>\n",
+       "      <td>0.488420</td>\n",
+       "      <td>0.334398</td>\n",
+       "      <td>0.187154</td>\n",
+       "      <td>0.192250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.085092</td>\n",
+       "      <td>-0.142204</td>\n",
+       "      <td>-0.045071</td>\n",
+       "      <td>-0.041939</td>\n",
+       "      <td>-0.351064</td>\n",
+       "      <td>0.662458</td>\n",
+       "      <td>0.637691</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.177937</td>\n",
+       "      <td>0.714835</td>\n",
+       "      <td>0.115200</td>\n",
+       "      <td>0.005509</td>\n",
+       "      <td>-0.511255</td>\n",
+       "      <td>0.220359</td>\n",
+       "      <td>-0.366207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.056163</td>\n",
+       "      <td>-0.118347</td>\n",
+       "      <td>-0.162625</td>\n",
+       "      <td>-0.177072</td>\n",
+       "      <td>0.438912</td>\n",
+       "      <td>0.685417</td>\n",
+       "      <td>-0.512443</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.209186</td>\n",
+       "      <td>0.573462</td>\n",
+       "      <td>-0.250521</td>\n",
+       "      <td>-0.388608</td>\n",
+       "      <td>0.499801</td>\n",
+       "      <td>-0.065077</td>\n",
+       "      <td>0.399461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.818390</td>\n",
+       "      <td>0.092319</td>\n",
+       "      <td>-0.238198</td>\n",
+       "      <td>-0.448118</td>\n",
+       "      <td>-0.246196</td>\n",
+       "      <td>-0.058911</td>\n",
+       "      <td>0.009146</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-0.090273</td>\n",
+       "      <td>-0.127021</td>\n",
+       "      <td>0.773728</td>\n",
+       "      <td>-0.613576</td>\n",
+       "      <td>0.022185</td>\n",
+       "      <td>-0.007887</td>\n",
+       "      <td>-0.005631</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   resorts_per_state  state_total_skiable_area_ac  state_total_days_open  \\\n",
+       "0           0.486079                     0.318224               0.489997   \n",
+       "1          -0.085092                    -0.142204              -0.045071   \n",
+       "2          -0.177937                     0.714835               0.115200   \n",
+       "3           0.056163                    -0.118347              -0.162625   \n",
+       "4          -0.209186                     0.573462              -0.250521   \n",
+       "5           0.818390                     0.092319              -0.238198   \n",
+       "6          -0.090273                    -0.127021               0.773728   \n",
+       "\n",
+       "   state_total_terrain_parks  state_total_nightskiing_ac  \\\n",
+       "0                   0.488420                    0.334398   \n",
+       "1                  -0.041939                   -0.351064   \n",
+       "2                   0.005509                   -0.511255   \n",
+       "3                  -0.177072                    0.438912   \n",
+       "4                  -0.388608                    0.499801   \n",
+       "5                  -0.448118                   -0.246196   \n",
+       "6                  -0.613576                    0.022185   \n",
+       "\n",
+       "   resorts_per_100kcapita  resorts_per_100ksq_mile  \n",
+       "0                0.187154                 0.192250  \n",
+       "1                0.662458                 0.637691  \n",
+       "2                0.220359                -0.366207  \n",
+       "3                0.685417                -0.512443  \n",
+       "4               -0.065077                 0.399461  \n",
+       "5               -0.058911                 0.009146  \n",
+       "6               -0.007887                -0.005631  "
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(state_pca.components_, columns=state_summary_columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the row associated with the second component, are there any large values?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It looks like `resorts_per_100kcapita` and `resorts_per_100ksq_mile` might count for quite a lot, in a positive sense. Be aware that sign matters; a large negative coefficient multiplying a large negative feature will actually produce a large positive PCA score."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>17</th>\n",
+       "      <th>29</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>Vermont</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <td>16</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <td>3427.0</td>\n",
+       "      <td>7239.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <td>1847.0</td>\n",
+       "      <td>1777.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <td>43.0</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <td>376.0</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <td>1.176721</td>\n",
+       "      <td>2.403889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "      <td>171.141299</td>\n",
+       "      <td>155.990017</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        17          29\n",
+       "state                        New Hampshire     Vermont\n",
+       "resorts_per_state                       16          15\n",
+       "state_total_skiable_area_ac         3427.0      7239.0\n",
+       "state_total_days_open               1847.0      1777.0\n",
+       "state_total_terrain_parks             43.0        50.0\n",
+       "state_total_nightskiing_ac           376.0        50.0\n",
+       "resorts_per_100kcapita            1.176721    2.403889\n",
+       "resorts_per_100ksq_mile         171.141299  155.990017"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary[state_summary.state.isin(['New Hampshire', 'Vermont'])].T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>17</th>\n",
+       "      <th>29</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <td>0.839478</td>\n",
+       "      <td>0.712833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <td>-0.277128</td>\n",
+       "      <td>0.104681</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <td>1.118608</td>\n",
+       "      <td>1.034363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <td>0.921793</td>\n",
+       "      <td>1.233725</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <td>-0.245050</td>\n",
+       "      <td>-0.747570</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <td>1.711066</td>\n",
+       "      <td>4.226572</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "      <td>3.483281</td>\n",
+       "      <td>3.112841</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   17        29\n",
+       "resorts_per_state            0.839478  0.712833\n",
+       "state_total_skiable_area_ac -0.277128  0.104681\n",
+       "state_total_days_open        1.118608  1.034363\n",
+       "state_total_terrain_parks    0.921793  1.233725\n",
+       "state_total_nightskiing_ac  -0.245050 -0.747570\n",
+       "resorts_per_100kcapita       1.711066  4.226572\n",
+       "resorts_per_100ksq_mile      3.483281  3.112841"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary_scaled_df[state_summary.state.isin(['New Hampshire', 'Vermont'])].T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So, yes, both states have particularly large values of `resorts_per_100ksq_mile` in absolute terms, and these put them more than 3 standard deviations from the mean. Vermont also has a notably large value for `resorts_per_100kcapita`. New York, then, does not seem to be a stand-out for density of ski resorts either in terms of state size or population count."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.5.4 Conclusion On How To Handle State Label<a id='3.5.4_Conclusion_On_How_To_Handle_State_Label'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can offer some justification for treating all states equally, and work towards building a pricing model that considers all states together, without treating any one particularly specially. You haven't seen any clear grouping yet, but you have captured potentially relevant state data in features most likely to be relevant to your business use case. This answers a big question!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.5.5 Ski Resort Numeric Data<a id='3.5.5_Ski_Resort_Numeric_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After what may feel a detour, return to examining the ski resort data. It's worth noting, the previous EDA was valuable because it's given us some potentially useful features, as well as validating an approach for how to subsequently handle the state labels in your modeling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Alyeska Resort</td>\n",
+       "      <td>Eaglecrest Ski Area</td>\n",
+       "      <td>Hilltop Ski Area</td>\n",
+       "      <td>Arizona Snowbowl</td>\n",
+       "      <td>Sunrise Park Resort</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>3939</td>\n",
+       "      <td>2600</td>\n",
+       "      <td>2090</td>\n",
+       "      <td>11500</td>\n",
+       "      <td>11100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>2500</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>294</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>1800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>250</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>1796</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>9200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>76.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>65.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>1610.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>777.0</td>\n",
+       "      <td>800.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>113.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>115.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>669.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>85.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>78.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>104.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>550.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                0                    1                 2  \\\n",
+       "Name               Alyeska Resort  Eaglecrest Ski Area  Hilltop Ski Area   \n",
+       "Region                     Alaska               Alaska            Alaska   \n",
+       "state                      Alaska               Alaska            Alaska   \n",
+       "summit_elev                  3939                 2600              2090   \n",
+       "vertical_drop                2500                 1540               294   \n",
+       "base_elev                     250                 1200              1796   \n",
+       "trams                           1                    0                 0   \n",
+       "fastSixes                       0                    0                 0   \n",
+       "fastQuads                       2                    0                 0   \n",
+       "quad                            2                    0                 0   \n",
+       "triple                          0                    0                 1   \n",
+       "double                          0                    4                 0   \n",
+       "surface                         2                    0                 2   \n",
+       "total_chairs                    7                    4                 3   \n",
+       "Runs                         76.0                 36.0              13.0   \n",
+       "TerrainParks                  2.0                  1.0               1.0   \n",
+       "LongestRun_mi                 1.0                  2.0               1.0   \n",
+       "SkiableTerrain_ac          1610.0                640.0              30.0   \n",
+       "Snow Making_ac              113.0                 60.0              30.0   \n",
+       "daysOpenLastYear            150.0                 45.0             150.0   \n",
+       "yearsOpen                    60.0                 44.0              36.0   \n",
+       "averageSnowfall             669.0                350.0              69.0   \n",
+       "AdultWeekend                 85.0                 53.0              34.0   \n",
+       "projectedDaysOpen           150.0                 90.0             152.0   \n",
+       "NightSkiing_ac              550.0                  NaN              30.0   \n",
+       "\n",
+       "                                  3                    4  \n",
+       "Name               Arizona Snowbowl  Sunrise Park Resort  \n",
+       "Region                      Arizona              Arizona  \n",
+       "state                       Arizona              Arizona  \n",
+       "summit_elev                   11500                11100  \n",
+       "vertical_drop                  2300                 1800  \n",
+       "base_elev                      9200                 9200  \n",
+       "trams                             0                    0  \n",
+       "fastSixes                         1                    0  \n",
+       "fastQuads                         0                    1  \n",
+       "quad                              2                    2  \n",
+       "triple                            2                    3  \n",
+       "double                            1                    1  \n",
+       "surface                           2                    0  \n",
+       "total_chairs                      8                    7  \n",
+       "Runs                           55.0                 65.0  \n",
+       "TerrainParks                    4.0                  2.0  \n",
+       "LongestRun_mi                   2.0                  1.2  \n",
+       "SkiableTerrain_ac             777.0                800.0  \n",
+       "Snow Making_ac                104.0                 80.0  \n",
+       "daysOpenLastYear              122.0                115.0  \n",
+       "yearsOpen                      81.0                 49.0  \n",
+       "averageSnowfall               260.0                250.0  \n",
+       "AdultWeekend                   89.0                 78.0  \n",
+       "projectedDaysOpen             122.0                104.0  \n",
+       "NightSkiing_ac                  NaN                 80.0  "
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.head().T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.5.1 Feature engineering<a id='3.5.5.1_Feature_engineering'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Having previously spent some time exploring the state summary data you derived, you now start to explore the resort-level data in more detail. This can help guide you on how (or whether) to use the state labels in the data. It's now time to merge the two datasets and engineer some intuitive features. For example, you can engineer a resort's share of the supply for a given state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.450867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>0.027477</td>\n",
+       "      <td>1.754540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>California</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25948.0</td>\n",
+       "      <td>2738.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>587.0</td>\n",
+       "      <td>0.053148</td>\n",
+       "      <td>12.828736</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>22</td>\n",
+       "      <td>43682.0</td>\n",
+       "      <td>3258.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>428.0</td>\n",
+       "      <td>0.382028</td>\n",
+       "      <td>21.134744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>5</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>256.0</td>\n",
+       "      <td>0.140242</td>\n",
+       "      <td>90.203861</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         state  resorts_per_state  state_total_skiable_area_ac  \\\n",
+       "0       Alaska                  3                       2280.0   \n",
+       "1      Arizona                  2                       1577.0   \n",
+       "2   California                 21                      25948.0   \n",
+       "3     Colorado                 22                      43682.0   \n",
+       "4  Connecticut                  5                        358.0   \n",
+       "\n",
+       "   state_total_days_open  state_total_terrain_parks  \\\n",
+       "0                  345.0                        4.0   \n",
+       "1                  237.0                        6.0   \n",
+       "2                 2738.0                       81.0   \n",
+       "3                 3258.0                       74.0   \n",
+       "4                  353.0                       10.0   \n",
+       "\n",
+       "   state_total_nightskiing_ac  resorts_per_100kcapita  resorts_per_100ksq_mile  \n",
+       "0                       580.0                0.410091                 0.450867  \n",
+       "1                        80.0                0.027477                 1.754540  \n",
+       "2                       587.0                0.053148                12.828736  \n",
+       "3                       428.0                0.382028                21.134744  \n",
+       "4                       256.0                0.140242                90.203861  "
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_summary.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Alyeska Resort</td>\n",
+       "      <td>Eaglecrest Ski Area</td>\n",
+       "      <td>Hilltop Ski Area</td>\n",
+       "      <td>Arizona Snowbowl</td>\n",
+       "      <td>Sunrise Park Resort</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>3939</td>\n",
+       "      <td>2600</td>\n",
+       "      <td>2090</td>\n",
+       "      <td>11500</td>\n",
+       "      <td>11100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>2500</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>294</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>1800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>250</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>1796</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>9200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>76.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>65.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>1610.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>777.0</td>\n",
+       "      <td>800.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>113.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>115.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>669.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>85.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>78.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>104.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>550.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>1577.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <td>345.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>237.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <td>580.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.027477</td>\n",
+       "      <td>0.027477</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "      <td>0.450867</td>\n",
+       "      <td>0.450867</td>\n",
+       "      <td>0.450867</td>\n",
+       "      <td>1.75454</td>\n",
+       "      <td>1.75454</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          0                    1  \\\n",
+       "Name                         Alyeska Resort  Eaglecrest Ski Area   \n",
+       "Region                               Alaska               Alaska   \n",
+       "state                                Alaska               Alaska   \n",
+       "summit_elev                            3939                 2600   \n",
+       "vertical_drop                          2500                 1540   \n",
+       "base_elev                               250                 1200   \n",
+       "trams                                     1                    0   \n",
+       "fastSixes                                 0                    0   \n",
+       "fastQuads                                 2                    0   \n",
+       "quad                                      2                    0   \n",
+       "triple                                    0                    0   \n",
+       "double                                    0                    4   \n",
+       "surface                                   2                    0   \n",
+       "total_chairs                              7                    4   \n",
+       "Runs                                   76.0                 36.0   \n",
+       "TerrainParks                            2.0                  1.0   \n",
+       "LongestRun_mi                           1.0                  2.0   \n",
+       "SkiableTerrain_ac                    1610.0                640.0   \n",
+       "Snow Making_ac                        113.0                 60.0   \n",
+       "daysOpenLastYear                      150.0                 45.0   \n",
+       "yearsOpen                              60.0                 44.0   \n",
+       "averageSnowfall                       669.0                350.0   \n",
+       "AdultWeekend                           85.0                 53.0   \n",
+       "projectedDaysOpen                     150.0                 90.0   \n",
+       "NightSkiing_ac                        550.0                  NaN   \n",
+       "resorts_per_state                         3                    3   \n",
+       "state_total_skiable_area_ac          2280.0               2280.0   \n",
+       "state_total_days_open                 345.0                345.0   \n",
+       "state_total_terrain_parks               4.0                  4.0   \n",
+       "state_total_nightskiing_ac            580.0                580.0   \n",
+       "resorts_per_100kcapita             0.410091             0.410091   \n",
+       "resorts_per_100ksq_mile            0.450867             0.450867   \n",
+       "\n",
+       "                                            2                 3  \\\n",
+       "Name                         Hilltop Ski Area  Arizona Snowbowl   \n",
+       "Region                                 Alaska           Arizona   \n",
+       "state                                  Alaska           Arizona   \n",
+       "summit_elev                              2090             11500   \n",
+       "vertical_drop                             294              2300   \n",
+       "base_elev                                1796              9200   \n",
+       "trams                                       0                 0   \n",
+       "fastSixes                                   0                 1   \n",
+       "fastQuads                                   0                 0   \n",
+       "quad                                        0                 2   \n",
+       "triple                                      1                 2   \n",
+       "double                                      0                 1   \n",
+       "surface                                     2                 2   \n",
+       "total_chairs                                3                 8   \n",
+       "Runs                                     13.0              55.0   \n",
+       "TerrainParks                              1.0               4.0   \n",
+       "LongestRun_mi                             1.0               2.0   \n",
+       "SkiableTerrain_ac                        30.0             777.0   \n",
+       "Snow Making_ac                           30.0             104.0   \n",
+       "daysOpenLastYear                        150.0             122.0   \n",
+       "yearsOpen                                36.0              81.0   \n",
+       "averageSnowfall                          69.0             260.0   \n",
+       "AdultWeekend                             34.0              89.0   \n",
+       "projectedDaysOpen                       152.0             122.0   \n",
+       "NightSkiing_ac                           30.0               NaN   \n",
+       "resorts_per_state                           3                 2   \n",
+       "state_total_skiable_area_ac            2280.0            1577.0   \n",
+       "state_total_days_open                   345.0             237.0   \n",
+       "state_total_terrain_parks                 4.0               6.0   \n",
+       "state_total_nightskiing_ac              580.0              80.0   \n",
+       "resorts_per_100kcapita               0.410091          0.027477   \n",
+       "resorts_per_100ksq_mile              0.450867           1.75454   \n",
+       "\n",
+       "                                               4  \n",
+       "Name                         Sunrise Park Resort  \n",
+       "Region                                   Arizona  \n",
+       "state                                    Arizona  \n",
+       "summit_elev                                11100  \n",
+       "vertical_drop                               1800  \n",
+       "base_elev                                   9200  \n",
+       "trams                                          0  \n",
+       "fastSixes                                      0  \n",
+       "fastQuads                                      1  \n",
+       "quad                                           2  \n",
+       "triple                                         3  \n",
+       "double                                         1  \n",
+       "surface                                        0  \n",
+       "total_chairs                                   7  \n",
+       "Runs                                        65.0  \n",
+       "TerrainParks                                 2.0  \n",
+       "LongestRun_mi                                1.2  \n",
+       "SkiableTerrain_ac                          800.0  \n",
+       "Snow Making_ac                              80.0  \n",
+       "daysOpenLastYear                           115.0  \n",
+       "yearsOpen                                   49.0  \n",
+       "averageSnowfall                            250.0  \n",
+       "AdultWeekend                                78.0  \n",
+       "projectedDaysOpen                          104.0  \n",
+       "NightSkiing_ac                              80.0  \n",
+       "resorts_per_state                              2  \n",
+       "state_total_skiable_area_ac               1577.0  \n",
+       "state_total_days_open                      237.0  \n",
+       "state_total_terrain_parks                    6.0  \n",
+       "state_total_nightskiing_ac                  80.0  \n",
+       "resorts_per_100kcapita                  0.027477  \n",
+       "resorts_per_100ksq_mile                  1.75454  "
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# DataFrame's merge method provides SQL-like joins\n",
+    "# here 'state' is a column (not an index)\n",
+    "ski_data = ski_data.merge(state_summary, how='left', on='state')\n",
+    "ski_data.head().T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Having merged your state summary features into the ski resort data, add \"state resort competition\" features:\n",
+    "\n",
+    "* ratio of resort skiable area to total state skiable area\n",
+    "* ratio of resort days open to total state days open\n",
+    "* ratio of resort terrain park count to total state terrain park count\n",
+    "* ratio of resort night skiing area to total state night skiing area\n",
+    "\n",
+    "Once you've derived these features to put each resort within the context of its state,drop those state columns. Their main purpose was to understand what share of states' skiing \"assets\" is accounted for by each resort."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ski_data['resort_skiable_area_ac_state_ratio'] = ski_data.SkiableTerrain_ac / ski_data.state_total_skiable_area_ac\n",
+    "ski_data['resort_days_open_state_ratio'] = ski_data.daysOpenLastYear / ski_data.state_total_days_open\n",
+    "ski_data['resort_terrain_park_state_ratio'] = ski_data.TerrainParks / ski_data.state_total_terrain_parks\n",
+    "ski_data['resort_night_skiing_state_ratio'] = ski_data.NightSkiing_ac / ski_data.state_total_nightskiing_ac\n",
+    "\n",
+    "ski_data.drop(columns=['state_total_skiable_area_ac', 'state_total_days_open', \n",
+    "                       'state_total_terrain_parks', 'state_total_nightskiing_ac'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.5.2 Feature correlation heatmap<a id='3.5.5.2_Feature_correlation_heatmap'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A great way to gain a high level view of relationships amongst the features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 12#\n",
+    "#Show a seaborn heatmap of correlations in ski_data\n",
+    "#Hint: call pandas' `corr()` method on `ski_data` and pass that into `sns.heatmap`\n",
+    "plt.subplots(figsize=(12,10))\n",
+    "sns.heatmap(ski_data.corr(numeric_only=True),);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is a lot to take away from this. First, summit and base elevation are quite highly correlated. This isn't a surprise. You can also see that you've introduced a lot of multicollinearity with your new ratio features; they are negatively correlated with the number of resorts in each state. This latter observation makes sense! If you increase the number of resorts in a state, the share of all the other state features will drop for each. An interesting observation in this region of the heatmap is that there is some positive correlation between the ratio of night skiing area with the number of resorts per capita. In other words, it seems that when resorts are more densely located with population, more night skiing is provided.\n",
+    "\n",
+    "Turning your attention to your target feature, `AdultWeekend` ticket price, you see quite a few reasonable correlations. `fastQuads` stands out, along with `Runs` and `Snow Making_ac`. The last one is interesting. Visitors would seem to value more guaranteed snow, which would cost in terms of snow making equipment, which would drive prices and costs up. Of the new features, `resort_night_skiing_state_ratio` seems the most correlated with ticket price. If this is true, then perhaps seizing a greater share of night skiing capacity is positive for the price a resort can charge.\n",
+    "\n",
+    "As well as `Runs`, `total_chairs` is quite well correlated with ticket price. This is plausible; the more runs you have, the more chairs you'd need to ferry people to them! Interestingly, they may count for more than the total skiable terrain area. For sure, the total skiable terrain area is not as useful as the area with snow making. People seem to put more value in guaranteed snow cover rather than more variable terrain area.\n",
+    "\n",
+    "The vertical drop seems to be a selling point that raises ticket prices as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.5.5.3 Scatterplots of numeric features against ticket price<a id='3.5.5.3_Scatterplots_of_numeric_features_against_ticket_price'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Correlations, particularly viewing them together as a heatmap, can be a great first pass at identifying patterns. But correlation can mask relationships between two variables. You'll now create a series of scatterplots to really dive into how ticket price varies with other numeric features."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define useful function to create scatterplots of ticket prices against desired columns\n",
+    "def scatterplots(columns, ncol=None, figsize=(15, 8)):\n",
+    "    if ncol is None:\n",
+    "        ncol = len(columns)\n",
+    "    nrow = int(np.ceil(len(columns) / ncol))\n",
+    "    fig, axes = plt.subplots(nrow, ncol, figsize=figsize, squeeze=False)\n",
+    "    fig.subplots_adjust(wspace=0.5, hspace=0.6)\n",
+    "    for i, col in enumerate(columns):\n",
+    "        ax = axes.flatten()[i]\n",
+    "        ax.scatter(x = col, y = 'AdultWeekend', data=ski_data, alpha=0.5)\n",
+    "        ax.set(xlabel=col, ylabel='Ticket price')\n",
+    "    nsubplots = nrow * ncol    \n",
+    "    for empty in range(i+1, nsubplots):\n",
+    "        axes.flatten()[empty].set_visible(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 13#\n",
+    "#Use a list comprehension to build a list of features from the columns of `ski_data` that\n",
+    "#are _not_ any of 'Name', 'Region', 'state', or 'AdultWeekend'\n",
+    "features = [feature for feature in ski_data.columns if feature not in ['Name', 'Region', 'state', 'AdultWeekend']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 32 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatterplots(features, ncol=4, figsize=(15, 15))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the scatterplots you see what some of the high correlations were clearly picking up on. There's a strong positive correlation with `vertical_drop`. `fastQuads` seems very useful. `Runs` and `total_chairs` appear quite similar and also useful. `resorts_per_100kcapita` shows something interesting that you don't see from just a headline correlation figure. When the value is low, there is quite a variability in ticket price, although it's capable of going quite high. Ticket price may drop a little before then climbing upwards as the number of resorts per capita increases. Ticket price could climb with the number of resorts serving a population because it indicates a popular area for skiing with plenty of demand. The lower ticket price when fewer resorts serve a population may similarly be because it's a less popular state for skiing. The high price for some resorts when resorts are rare (relative to the population size) may indicate areas where a small number of resorts can benefit from a monopoly effect. It's not a clear picture, although we have some interesting signs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, think of some further features that may be useful in that they relate to how easily a resort can transport people around. You have the numbers of various chairs, and the number of runs, but you don't have the ratio of chairs to runs. It seems logical that this ratio would inform you how easily, and so quickly, people could get to their next ski slope! Create these features now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ski_data['total_chairs_runs_ratio'] = ski_data.total_chairs / ski_data.Runs\n",
+    "ski_data['total_chairs_skiable_ratio'] = ski_data.total_chairs / ski_data.SkiableTerrain_ac\n",
+    "ski_data['fastQuads_runs_ratio'] = ski_data.fastQuads / ski_data.Runs\n",
+    "ski_data['fastQuads_skiable_ratio'] = ski_data.fastQuads / ski_data.SkiableTerrain_ac"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatterplots(['total_chairs_runs_ratio', 'total_chairs_skiable_ratio', \n",
+    "              'fastQuads_runs_ratio', 'fastQuads_skiable_ratio'], ncol=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At first these relationships are quite counterintuitive. It seems that the more chairs a resort has to move people around, relative to the number of runs, ticket price rapidly plummets and stays low. What we may be seeing here is an exclusive vs. mass market resort effect; if you don't have so many chairs, you can charge more for your tickets, although with fewer chairs you're inevitably going to be able to serve fewer visitors. Your price per visitor is high but your number of visitors may be low. Something very useful that's missing from the data is the number of visitors per year.\n",
+    "\n",
+    "It also appears that having no fast quads may limit the ticket price, but if your resort covers a wide area then getting a small number of fast quads may be beneficial to ticket price."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3.6 Summary<a id='3.6_Summary'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q: 1** Write a summary of the exploratory data analysis above. What numerical or categorical features were in the data? Was there any pattern suggested of a relationship between state and ticket price? What did this lead us to decide regarding which features to use in subsequent modeling? What aspects of the data (e.g. relationships between features) should you remain wary of when you come to perform feature selection for modeling? Two key points that must be addressed are the choice of target feature for your modelling and how, if at all, you're going to handle the states labels in the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**A: 1** Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data science problem identified is to predict the adult weekend ticket price for ski resorts.\n",
+    "\n",
+    "Outside of the names and state/region labels for our resorts, our features are entirely numerical. These include the following: summit elevation, vertical drop, base elevation, number of trams, number of fastSixes, number of fastQuads, number of quad, number of TerrainParks, Longest Run in miles, SkiableTerrain area, Snow Making area, days Open Last Year, years Open, average Snowfall, Adult Weekend Ticket Price, projected Days Open, and NightSkiing area\n",
+    "\n",
+    "We also created state-wide statistics including: resorts per state, state total skiable area, state total days open, state total terrain parks, state total nightskiing area, state population, and state area sq miles\n",
+    "\n",
+    "Our investigation into patterns related to states and ticket price suggest that we can treat states equally and include them in our modeling without any need for weighting or adjustments based on state.\n",
+    "\n",
+    "We also developed a number of state resort competition features:\n",
+    "ratio of resort skiable area to total state skiable area\n",
+    "ratio of resort days open to total state days open\n",
+    "ratio of resort terrain park count to total state terrain park count\n",
+    "ratio of resort night skiing area to total state night skiing area\n",
+    "\n",
+    "Using a heatmap to illustrate correlations between features, we identify a number of promising items for further investigation: the number of fast Quads, the number of runs, snow making area, ratio of resort night skiing area to total state night skiing area, total chairs, \n",
+    "\n",
+    "We also employed scatterplots of the numeric features to further illuminate important positive relationships between features and ticket price: vertical drop, fast Quads, runs, and total chairs,while the plot with resorts per 100k capita shows a pattern that isn’t exactly straightforward but does move in a positive direction at higher values\n",
+    "\n",
+    "Finally, we created a set of ratios to capture the impact of transport options given the number of runs and the area covered: 'total_chairs_runs_ratio', 'total_chairs_skiable_ratio', 'fastQuads_runs_ratio', 'fastQuads_skiable_ratio'\n",
+    "\n",
+    "The scatterplots for these metrics did not support a straightforward interpretation, It seems that the more chairs relative to the number of runs, ticket price drops. What we may be seeing here is an exclusive vs. mass market resort effect; if you don't have so many chairs, you can charge more for your tickets, although with fewer chairs you're inevitably going to be able to serve fewer visitors. Your price per visitor is high but your number of visitors may be low. Something very useful that's missing from the data is the number of visitors per year.\n",
+    "\n",
+    "So we need to incorporate the visitor information, to get more insight here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Alyeska Resort</td>\n",
+       "      <td>Eaglecrest Ski Area</td>\n",
+       "      <td>Hilltop Ski Area</td>\n",
+       "      <td>Arizona Snowbowl</td>\n",
+       "      <td>Sunrise Park Resort</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>3939</td>\n",
+       "      <td>2600</td>\n",
+       "      <td>2090</td>\n",
+       "      <td>11500</td>\n",
+       "      <td>11100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>2500</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>294</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>1800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>250</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>1796</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>9200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>76.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>65.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>1610.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>777.0</td>\n",
+       "      <td>800.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>113.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>115.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>669.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>85.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>78.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>104.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>550.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.027477</td>\n",
+       "      <td>0.027477</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "      <td>0.450867</td>\n",
+       "      <td>0.450867</td>\n",
+       "      <td>0.450867</td>\n",
+       "      <td>1.75454</td>\n",
+       "      <td>1.75454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_skiable_area_ac_state_ratio</th>\n",
+       "      <td>0.70614</td>\n",
+       "      <td>0.280702</td>\n",
+       "      <td>0.013158</td>\n",
+       "      <td>0.492708</td>\n",
+       "      <td>0.507292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_days_open_state_ratio</th>\n",
+       "      <td>0.434783</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.434783</td>\n",
+       "      <td>0.514768</td>\n",
+       "      <td>0.485232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_terrain_park_state_ratio</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_night_skiing_state_ratio</th>\n",
+       "      <td>0.948276</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.051724</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs_runs_ratio</th>\n",
+       "      <td>0.092105</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.230769</td>\n",
+       "      <td>0.145455</td>\n",
+       "      <td>0.107692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs_skiable_ratio</th>\n",
+       "      <td>0.004348</td>\n",
+       "      <td>0.00625</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.010296</td>\n",
+       "      <td>0.00875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads_runs_ratio</th>\n",
+       "      <td>0.026316</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.015385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads_skiable_ratio</th>\n",
+       "      <td>0.001242</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00125</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 0                    1  \\\n",
+       "Name                                Alyeska Resort  Eaglecrest Ski Area   \n",
+       "Region                                      Alaska               Alaska   \n",
+       "state                                       Alaska               Alaska   \n",
+       "summit_elev                                   3939                 2600   \n",
+       "vertical_drop                                 2500                 1540   \n",
+       "base_elev                                      250                 1200   \n",
+       "trams                                            1                    0   \n",
+       "fastSixes                                        0                    0   \n",
+       "fastQuads                                        2                    0   \n",
+       "quad                                             2                    0   \n",
+       "triple                                           0                    0   \n",
+       "double                                           0                    4   \n",
+       "surface                                          2                    0   \n",
+       "total_chairs                                     7                    4   \n",
+       "Runs                                          76.0                 36.0   \n",
+       "TerrainParks                                   2.0                  1.0   \n",
+       "LongestRun_mi                                  1.0                  2.0   \n",
+       "SkiableTerrain_ac                           1610.0                640.0   \n",
+       "Snow Making_ac                               113.0                 60.0   \n",
+       "daysOpenLastYear                             150.0                 45.0   \n",
+       "yearsOpen                                     60.0                 44.0   \n",
+       "averageSnowfall                              669.0                350.0   \n",
+       "AdultWeekend                                  85.0                 53.0   \n",
+       "projectedDaysOpen                            150.0                 90.0   \n",
+       "NightSkiing_ac                               550.0                  NaN   \n",
+       "resorts_per_state                                3                    3   \n",
+       "resorts_per_100kcapita                    0.410091             0.410091   \n",
+       "resorts_per_100ksq_mile                   0.450867             0.450867   \n",
+       "resort_skiable_area_ac_state_ratio         0.70614             0.280702   \n",
+       "resort_days_open_state_ratio              0.434783             0.130435   \n",
+       "resort_terrain_park_state_ratio                0.5                 0.25   \n",
+       "resort_night_skiing_state_ratio           0.948276                  NaN   \n",
+       "total_chairs_runs_ratio                   0.092105             0.111111   \n",
+       "total_chairs_skiable_ratio                0.004348              0.00625   \n",
+       "fastQuads_runs_ratio                      0.026316                  0.0   \n",
+       "fastQuads_skiable_ratio                   0.001242                  0.0   \n",
+       "\n",
+       "                                                   2                 3  \\\n",
+       "Name                                Hilltop Ski Area  Arizona Snowbowl   \n",
+       "Region                                        Alaska           Arizona   \n",
+       "state                                         Alaska           Arizona   \n",
+       "summit_elev                                     2090             11500   \n",
+       "vertical_drop                                    294              2300   \n",
+       "base_elev                                       1796              9200   \n",
+       "trams                                              0                 0   \n",
+       "fastSixes                                          0                 1   \n",
+       "fastQuads                                          0                 0   \n",
+       "quad                                               0                 2   \n",
+       "triple                                             1                 2   \n",
+       "double                                             0                 1   \n",
+       "surface                                            2                 2   \n",
+       "total_chairs                                       3                 8   \n",
+       "Runs                                            13.0              55.0   \n",
+       "TerrainParks                                     1.0               4.0   \n",
+       "LongestRun_mi                                    1.0               2.0   \n",
+       "SkiableTerrain_ac                               30.0             777.0   \n",
+       "Snow Making_ac                                  30.0             104.0   \n",
+       "daysOpenLastYear                               150.0             122.0   \n",
+       "yearsOpen                                       36.0              81.0   \n",
+       "averageSnowfall                                 69.0             260.0   \n",
+       "AdultWeekend                                    34.0              89.0   \n",
+       "projectedDaysOpen                              152.0             122.0   \n",
+       "NightSkiing_ac                                  30.0               NaN   \n",
+       "resorts_per_state                                  3                 2   \n",
+       "resorts_per_100kcapita                      0.410091          0.027477   \n",
+       "resorts_per_100ksq_mile                     0.450867           1.75454   \n",
+       "resort_skiable_area_ac_state_ratio          0.013158          0.492708   \n",
+       "resort_days_open_state_ratio                0.434783          0.514768   \n",
+       "resort_terrain_park_state_ratio                 0.25          0.666667   \n",
+       "resort_night_skiing_state_ratio             0.051724               NaN   \n",
+       "total_chairs_runs_ratio                     0.230769          0.145455   \n",
+       "total_chairs_skiable_ratio                       0.1          0.010296   \n",
+       "fastQuads_runs_ratio                             0.0               0.0   \n",
+       "fastQuads_skiable_ratio                          0.0               0.0   \n",
+       "\n",
+       "                                                      4  \n",
+       "Name                                Sunrise Park Resort  \n",
+       "Region                                          Arizona  \n",
+       "state                                           Arizona  \n",
+       "summit_elev                                       11100  \n",
+       "vertical_drop                                      1800  \n",
+       "base_elev                                          9200  \n",
+       "trams                                                 0  \n",
+       "fastSixes                                             0  \n",
+       "fastQuads                                             1  \n",
+       "quad                                                  2  \n",
+       "triple                                                3  \n",
+       "double                                                1  \n",
+       "surface                                               0  \n",
+       "total_chairs                                          7  \n",
+       "Runs                                               65.0  \n",
+       "TerrainParks                                        2.0  \n",
+       "LongestRun_mi                                       1.2  \n",
+       "SkiableTerrain_ac                                 800.0  \n",
+       "Snow Making_ac                                     80.0  \n",
+       "daysOpenLastYear                                  115.0  \n",
+       "yearsOpen                                          49.0  \n",
+       "averageSnowfall                                   250.0  \n",
+       "AdultWeekend                                       78.0  \n",
+       "projectedDaysOpen                                 104.0  \n",
+       "NightSkiing_ac                                     80.0  \n",
+       "resorts_per_state                                     2  \n",
+       "resorts_per_100kcapita                         0.027477  \n",
+       "resorts_per_100ksq_mile                         1.75454  \n",
+       "resort_skiable_area_ac_state_ratio             0.507292  \n",
+       "resort_days_open_state_ratio                   0.485232  \n",
+       "resort_terrain_park_state_ratio                0.333333  \n",
+       "resort_night_skiing_state_ratio                     1.0  \n",
+       "total_chairs_runs_ratio                        0.107692  \n",
+       "total_chairs_skiable_ratio                      0.00875  \n",
+       "fastQuads_runs_ratio                           0.015385  \n",
+       "fastQuads_skiable_ratio                         0.00125  "
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.head().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing file.  \"../data\\ski_data_step3_features.csv\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save the data \n",
+    "\n",
+    "datapath = '../data'\n",
+    "save_file(ski_data, 'ski_data_step3_features.csv', datapath)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Notebooks/04_preprocessing_and_training_DM.ipynb b/Notebooks/04_preprocessing_and_training_DM.ipynb
new file mode 100644
index 000000000..ef538d86e
--- /dev/null
+++ b/Notebooks/04_preprocessing_and_training_DM.ipynb
@@ -0,0 +1,6254 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4 Pre-Processing and Training Data<a id='4_Pre-Processing_and_Training_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.1 Contents<a id='4.1_Contents'></a>\n",
+    "* [4 Pre-Processing and Training Data](#4_Pre-Processing_and_Training_Data)\n",
+    "  * [4.1 Contents](#4.1_Contents)\n",
+    "  * [4.2 Introduction](#4.2_Introduction)\n",
+    "  * [4.3 Imports](#4.3_Imports)\n",
+    "  * [4.4 Load Data](#4.4_Load_Data)\n",
+    "  * [4.5 Extract Big Mountain Data](#4.5_Extract_Big_Mountain_Data)\n",
+    "  * [4.6 Train/Test Split](#4.6_Train/Test_Split)\n",
+    "  * [4.7 Initial Not-Even-A-Model](#4.7_Initial_Not-Even-A-Model)\n",
+    "    * [4.7.1 Metrics](#4.7.1_Metrics)\n",
+    "      * [4.7.1.1 R-squared, or coefficient of determination](#4.7.1.1_R-squared,_or_coefficient_of_determination)\n",
+    "      * [4.7.1.2 Mean Absolute Error](#4.7.1.2_Mean_Absolute_Error)\n",
+    "      * [4.7.1.3 Mean Squared Error](#4.7.1.3_Mean_Squared_Error)\n",
+    "    * [4.7.2 sklearn metrics](#4.7.2_sklearn_metrics)\n",
+    "        * [4.7.2.0.1 R-squared](#4.7.2.0.1_R-squared)\n",
+    "        * [4.7.2.0.2 Mean absolute error](#4.7.2.0.2_Mean_absolute_error)\n",
+    "        * [4.7.2.0.3 Mean squared error](#4.7.2.0.3_Mean_squared_error)\n",
+    "    * [4.7.3 Note On Calculating Metrics](#4.7.3_Note_On_Calculating_Metrics)\n",
+    "  * [4.8 Initial Models](#4.8_Initial_Models)\n",
+    "    * [4.8.1 Imputing missing feature (predictor) values](#4.8.1_Imputing_missing_feature_(predictor)_values)\n",
+    "      * [4.8.1.1 Impute missing values with median](#4.8.1.1_Impute_missing_values_with_median)\n",
+    "        * [4.8.1.1.1 Learn the values to impute from the train set](#4.8.1.1.1_Learn_the_values_to_impute_from_the_train_set)\n",
+    "        * [4.8.1.1.2 Apply the imputation to both train and test splits](#4.8.1.1.2_Apply_the_imputation_to_both_train_and_test_splits)\n",
+    "        * [4.8.1.1.3 Scale the data](#4.8.1.1.3_Scale_the_data)\n",
+    "        * [4.8.1.1.4 Train the model on the train split](#4.8.1.1.4_Train_the_model_on_the_train_split)\n",
+    "        * [4.8.1.1.5 Make predictions using the model on both train and test splits](#4.8.1.1.5_Make_predictions_using_the_model_on_both_train_and_test_splits)\n",
+    "        * [4.8.1.1.6 Assess model performance](#4.8.1.1.6_Assess_model_performance)\n",
+    "      * [4.8.1.2 Impute missing values with the mean](#4.8.1.2_Impute_missing_values_with_the_mean)\n",
+    "        * [4.8.1.2.1 Learn the values to impute from the train set](#4.8.1.2.1_Learn_the_values_to_impute_from_the_train_set)\n",
+    "        * [4.8.1.2.2 Apply the imputation to both train and test splits](#4.8.1.2.2_Apply_the_imputation_to_both_train_and_test_splits)\n",
+    "        * [4.8.1.2.3 Scale the data](#4.8.1.2.3_Scale_the_data)\n",
+    "        * [4.8.1.2.4 Train the model on the train split](#4.8.1.2.4_Train_the_model_on_the_train_split)\n",
+    "        * [4.8.1.2.5 Make predictions using the model on both train and test splits](#4.8.1.2.5_Make_predictions_using_the_model_on_both_train_and_test_splits)\n",
+    "        * [4.8.1.2.6 Assess model performance](#4.8.1.2.6_Assess_model_performance)\n",
+    "    * [4.8.2 Pipelines](#4.8.2_Pipelines)\n",
+    "      * [4.8.2.1 Define the pipeline](#4.8.2.1_Define_the_pipeline)\n",
+    "      * [4.8.2.2 Fit the pipeline](#4.8.2.2_Fit_the_pipeline)\n",
+    "      * [4.8.2.3 Make predictions on the train and test sets](#4.8.2.3_Make_predictions_on_the_train_and_test_sets)\n",
+    "      * [4.8.2.4 Assess performance](#4.8.2.4_Assess_performance)\n",
+    "  * [4.9 Refining The Linear Model](#4.9_Refining_The_Linear_Model)\n",
+    "    * [4.9.1 Define the pipeline](#4.9.1_Define_the_pipeline)\n",
+    "    * [4.9.2 Fit the pipeline](#4.9.2_Fit_the_pipeline)\n",
+    "    * [4.9.3 Assess performance on the train and test set](#4.9.3_Assess_performance_on_the_train_and_test_set)\n",
+    "    * [4.9.4 Define a new pipeline to select a different number of features](#4.9.4_Define_a_new_pipeline_to_select_a_different_number_of_features)\n",
+    "    * [4.9.5 Fit the pipeline](#4.9.5_Fit_the_pipeline)\n",
+    "    * [4.9.6 Assess performance on train and test data](#4.9.6_Assess_performance_on_train_and_test_data)\n",
+    "    * [4.9.7 Assessing performance using cross-validation](#4.9.7_Assessing_performance_using_cross-validation)\n",
+    "    * [4.9.8 Hyperparameter search using GridSearchCV](#4.9.8_Hyperparameter_search_using_GridSearchCV)\n",
+    "  * [4.10 Random Forest Model](#4.10_Random_Forest_Model)\n",
+    "    * [4.10.1 Define the pipeline](#4.10.1_Define_the_pipeline)\n",
+    "    * [4.10.2 Fit and assess performance using cross-validation](#4.10.2_Fit_and_assess_performance_using_cross-validation)\n",
+    "    * [4.10.3 Hyperparameter search using GridSearchCV](#4.10.3_Hyperparameter_search_using_GridSearchCV)\n",
+    "  * [4.11 Final Model Selection](#4.11_Final_Model_Selection)\n",
+    "    * [4.11.1 Linear regression model performance](#4.11.1_Linear_regression_model_performance)\n",
+    "    * [4.11.2 Random forest regression model performance](#4.11.2_Random_forest_regression_model_performance)\n",
+    "    * [4.11.3 Conclusion](#4.11.3_Conclusion)\n",
+    "  * [4.12 Data quantity assessment](#4.12_Data_quantity_assessment)\n",
+    "  * [4.13 Save best model object from pipeline](#4.13_Save_best_model_object_from_pipeline)\n",
+    "  * [4.14 Summary](#4.14_Summary)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.2 Introduction<a id='4.2_Introduction'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In preceding notebooks, performed preliminary assessments of data quality and refined the question to be answered. You found a small number of data values that gave clear choices about whether to replace values or drop a whole row. You determined that predicting the adult weekend ticket price was your primary aim. You threw away records with missing price data, but not before making the most of the other available data to look for any patterns between the states. You didn't see any and decided to treat all states equally; the state label didn't seem to be particularly useful.\n",
+    "\n",
+    "In this notebook you'll start to build machine learning models. Before even starting with learning a machine learning model, however, start by considering how useful the mean value is as a predictor. This is more than just a pedagogical device. You never want to go to stakeholders with a machine learning model only to have the CEO point out that it performs worse than just guessing the average! Your first model is a baseline performance comparitor for any subsequent model. You then build up the process of efficiently and robustly creating and assessing models against it. The development we lay out may be little slower than in the real world, but this step of the capstone is definitely more than just instructional. It is good practice to build up an understanding that the machine learning pipelines you build work as expected. You can validate steps with your own functions for checking expected equivalence between, say, pandas and sklearn implementations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.3 Imports<a id='4.3_Imports'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import pickle\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn import __version__ as sklearn_version\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.preprocessing import scale\n",
+    "from sklearn.model_selection import train_test_split, cross_validate, GridSearchCV, learning_curve\n",
+    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
+    "from sklearn.dummy import DummyRegressor\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.feature_selection import SelectKBest, f_regression\n",
+    "import datetime\n",
+    "\n",
+    "from library.sb_utils import save_file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.4 Load Data<a id='4.4_Load_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Alyeska Resort</td>\n",
+       "      <td>Eaglecrest Ski Area</td>\n",
+       "      <td>Hilltop Ski Area</td>\n",
+       "      <td>Arizona Snowbowl</td>\n",
+       "      <td>Sunrise Park Resort</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>3939</td>\n",
+       "      <td>2600</td>\n",
+       "      <td>2090</td>\n",
+       "      <td>11500</td>\n",
+       "      <td>11100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>2500</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>294</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>1800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>250</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>1796</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>9200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>76.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>65.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>1610.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>777.0</td>\n",
+       "      <td>800.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>113.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>115.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>60.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>669.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>250.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>85.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>78.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>104.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>550.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.027477</td>\n",
+       "      <td>0.027477</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "      <td>0.450867</td>\n",
+       "      <td>0.450867</td>\n",
+       "      <td>0.450867</td>\n",
+       "      <td>1.75454</td>\n",
+       "      <td>1.75454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_skiable_area_ac_state_ratio</th>\n",
+       "      <td>0.70614</td>\n",
+       "      <td>0.280702</td>\n",
+       "      <td>0.013158</td>\n",
+       "      <td>0.492708</td>\n",
+       "      <td>0.507292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_days_open_state_ratio</th>\n",
+       "      <td>0.434783</td>\n",
+       "      <td>0.130435</td>\n",
+       "      <td>0.434783</td>\n",
+       "      <td>0.514768</td>\n",
+       "      <td>0.485232</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_terrain_park_state_ratio</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.25</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_night_skiing_state_ratio</th>\n",
+       "      <td>0.948276</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.051724</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs_runs_ratio</th>\n",
+       "      <td>0.092105</td>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.230769</td>\n",
+       "      <td>0.145455</td>\n",
+       "      <td>0.107692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs_skiable_ratio</th>\n",
+       "      <td>0.004348</td>\n",
+       "      <td>0.00625</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.010296</td>\n",
+       "      <td>0.00875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads_runs_ratio</th>\n",
+       "      <td>0.026316</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.015385</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads_skiable_ratio</th>\n",
+       "      <td>0.001242</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00125</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 0                    1  \\\n",
+       "Name                                Alyeska Resort  Eaglecrest Ski Area   \n",
+       "Region                                      Alaska               Alaska   \n",
+       "state                                       Alaska               Alaska   \n",
+       "summit_elev                                   3939                 2600   \n",
+       "vertical_drop                                 2500                 1540   \n",
+       "base_elev                                      250                 1200   \n",
+       "trams                                            1                    0   \n",
+       "fastSixes                                        0                    0   \n",
+       "fastQuads                                        2                    0   \n",
+       "quad                                             2                    0   \n",
+       "triple                                           0                    0   \n",
+       "double                                           0                    4   \n",
+       "surface                                          2                    0   \n",
+       "total_chairs                                     7                    4   \n",
+       "Runs                                          76.0                 36.0   \n",
+       "TerrainParks                                   2.0                  1.0   \n",
+       "LongestRun_mi                                  1.0                  2.0   \n",
+       "SkiableTerrain_ac                           1610.0                640.0   \n",
+       "Snow Making_ac                               113.0                 60.0   \n",
+       "daysOpenLastYear                             150.0                 45.0   \n",
+       "yearsOpen                                     60.0                 44.0   \n",
+       "averageSnowfall                              669.0                350.0   \n",
+       "AdultWeekend                                  85.0                 53.0   \n",
+       "projectedDaysOpen                            150.0                 90.0   \n",
+       "NightSkiing_ac                               550.0                  NaN   \n",
+       "resorts_per_state                                3                    3   \n",
+       "resorts_per_100kcapita                    0.410091             0.410091   \n",
+       "resorts_per_100ksq_mile                   0.450867             0.450867   \n",
+       "resort_skiable_area_ac_state_ratio         0.70614             0.280702   \n",
+       "resort_days_open_state_ratio              0.434783             0.130435   \n",
+       "resort_terrain_park_state_ratio                0.5                 0.25   \n",
+       "resort_night_skiing_state_ratio           0.948276                  NaN   \n",
+       "total_chairs_runs_ratio                   0.092105             0.111111   \n",
+       "total_chairs_skiable_ratio                0.004348              0.00625   \n",
+       "fastQuads_runs_ratio                      0.026316                  0.0   \n",
+       "fastQuads_skiable_ratio                   0.001242                  0.0   \n",
+       "\n",
+       "                                                   2                 3  \\\n",
+       "Name                                Hilltop Ski Area  Arizona Snowbowl   \n",
+       "Region                                        Alaska           Arizona   \n",
+       "state                                         Alaska           Arizona   \n",
+       "summit_elev                                     2090             11500   \n",
+       "vertical_drop                                    294              2300   \n",
+       "base_elev                                       1796              9200   \n",
+       "trams                                              0                 0   \n",
+       "fastSixes                                          0                 1   \n",
+       "fastQuads                                          0                 0   \n",
+       "quad                                               0                 2   \n",
+       "triple                                             1                 2   \n",
+       "double                                             0                 1   \n",
+       "surface                                            2                 2   \n",
+       "total_chairs                                       3                 8   \n",
+       "Runs                                            13.0              55.0   \n",
+       "TerrainParks                                     1.0               4.0   \n",
+       "LongestRun_mi                                    1.0               2.0   \n",
+       "SkiableTerrain_ac                               30.0             777.0   \n",
+       "Snow Making_ac                                  30.0             104.0   \n",
+       "daysOpenLastYear                               150.0             122.0   \n",
+       "yearsOpen                                       36.0              81.0   \n",
+       "averageSnowfall                                 69.0             260.0   \n",
+       "AdultWeekend                                    34.0              89.0   \n",
+       "projectedDaysOpen                              152.0             122.0   \n",
+       "NightSkiing_ac                                  30.0               NaN   \n",
+       "resorts_per_state                                  3                 2   \n",
+       "resorts_per_100kcapita                      0.410091          0.027477   \n",
+       "resorts_per_100ksq_mile                     0.450867           1.75454   \n",
+       "resort_skiable_area_ac_state_ratio          0.013158          0.492708   \n",
+       "resort_days_open_state_ratio                0.434783          0.514768   \n",
+       "resort_terrain_park_state_ratio                 0.25          0.666667   \n",
+       "resort_night_skiing_state_ratio             0.051724               NaN   \n",
+       "total_chairs_runs_ratio                     0.230769          0.145455   \n",
+       "total_chairs_skiable_ratio                       0.1          0.010296   \n",
+       "fastQuads_runs_ratio                             0.0               0.0   \n",
+       "fastQuads_skiable_ratio                          0.0               0.0   \n",
+       "\n",
+       "                                                      4  \n",
+       "Name                                Sunrise Park Resort  \n",
+       "Region                                          Arizona  \n",
+       "state                                           Arizona  \n",
+       "summit_elev                                       11100  \n",
+       "vertical_drop                                      1800  \n",
+       "base_elev                                          9200  \n",
+       "trams                                                 0  \n",
+       "fastSixes                                             0  \n",
+       "fastQuads                                             1  \n",
+       "quad                                                  2  \n",
+       "triple                                                3  \n",
+       "double                                                1  \n",
+       "surface                                               0  \n",
+       "total_chairs                                          7  \n",
+       "Runs                                               65.0  \n",
+       "TerrainParks                                        2.0  \n",
+       "LongestRun_mi                                       1.2  \n",
+       "SkiableTerrain_ac                                 800.0  \n",
+       "Snow Making_ac                                     80.0  \n",
+       "daysOpenLastYear                                  115.0  \n",
+       "yearsOpen                                          49.0  \n",
+       "averageSnowfall                                   250.0  \n",
+       "AdultWeekend                                       78.0  \n",
+       "projectedDaysOpen                                 104.0  \n",
+       "NightSkiing_ac                                     80.0  \n",
+       "resorts_per_state                                     2  \n",
+       "resorts_per_100kcapita                         0.027477  \n",
+       "resorts_per_100ksq_mile                         1.75454  \n",
+       "resort_skiable_area_ac_state_ratio             0.507292  \n",
+       "resort_days_open_state_ratio                   0.485232  \n",
+       "resort_terrain_park_state_ratio                0.333333  \n",
+       "resort_night_skiing_state_ratio                     1.0  \n",
+       "total_chairs_runs_ratio                        0.107692  \n",
+       "total_chairs_skiable_ratio                      0.00875  \n",
+       "fastQuads_runs_ratio                           0.015385  \n",
+       "fastQuads_skiable_ratio                         0.00125  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data = pd.read_csv('../data/ski_data_step3_features.csv')\n",
+    "ski_data.head().T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.5 Extract Big Mountain Data<a id='4.5_Extract_Big_Mountain_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Big Mountain is your resort. Separate it from the rest of the data to use later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "big_mountain = ski_data[ski_data.Name == 'Big Mountain Resort']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>124</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Big Mountain Resort</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Montana</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Montana</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>6817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>2353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>4464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>105.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>3.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>3000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>600.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>72.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>333.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>81.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>600.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <td>1.122778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "      <td>8.161045</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_skiable_area_ac_state_ratio</th>\n",
+       "      <td>0.140121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_days_open_state_ratio</th>\n",
+       "      <td>0.129338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_terrain_park_state_ratio</th>\n",
+       "      <td>0.148148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_night_skiing_state_ratio</th>\n",
+       "      <td>0.84507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs_runs_ratio</th>\n",
+       "      <td>0.133333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs_skiable_ratio</th>\n",
+       "      <td>0.004667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads_runs_ratio</th>\n",
+       "      <td>0.028571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads_skiable_ratio</th>\n",
+       "      <td>0.001</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    124\n",
+       "Name                                Big Mountain Resort\n",
+       "Region                                          Montana\n",
+       "state                                           Montana\n",
+       "summit_elev                                        6817\n",
+       "vertical_drop                                      2353\n",
+       "base_elev                                          4464\n",
+       "trams                                                 0\n",
+       "fastSixes                                             0\n",
+       "fastQuads                                             3\n",
+       "quad                                                  2\n",
+       "triple                                                6\n",
+       "double                                                0\n",
+       "surface                                               3\n",
+       "total_chairs                                         14\n",
+       "Runs                                              105.0\n",
+       "TerrainParks                                        4.0\n",
+       "LongestRun_mi                                       3.3\n",
+       "SkiableTerrain_ac                                3000.0\n",
+       "Snow Making_ac                                    600.0\n",
+       "daysOpenLastYear                                  123.0\n",
+       "yearsOpen                                          72.0\n",
+       "averageSnowfall                                   333.0\n",
+       "AdultWeekend                                       81.0\n",
+       "projectedDaysOpen                                 123.0\n",
+       "NightSkiing_ac                                    600.0\n",
+       "resorts_per_state                                    12\n",
+       "resorts_per_100kcapita                         1.122778\n",
+       "resorts_per_100ksq_mile                        8.161045\n",
+       "resort_skiable_area_ac_state_ratio             0.140121\n",
+       "resort_days_open_state_ratio                   0.129338\n",
+       "resort_terrain_park_state_ratio                0.148148\n",
+       "resort_night_skiing_state_ratio                 0.84507\n",
+       "total_chairs_runs_ratio                        0.133333\n",
+       "total_chairs_skiable_ratio                     0.004667\n",
+       "fastQuads_runs_ratio                           0.028571\n",
+       "fastQuads_skiable_ratio                           0.001"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "big_mountain.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(277, 36)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ski_data = ski_data[ski_data.Name != 'Big Mountain Resort']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(276, 36)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.6 Train/Test Split<a id='4.6_Train/Test_Split'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So far, you've treated ski resort data as a single entity. In machine learning, when you train your model on all of your data, you end up with no data set aside to evaluate model performance. You could keep making more and more complex models that fit the data better and better and not realise you were overfitting to that one set of samples. By partitioning the data into training and testing splits, without letting a model (or missing-value imputation) learn anything about the test split, you have a somewhat independent assessment of how your model might perform in the future. An often overlooked subtlety here is that people all too frequently use the test set to assess model performance _and then compare multiple models to pick the best_. This means their overall model selection process is  fitting to one specific data set, now the test split. You could keep going, trying to get better and better performance on that one data set, but that's  where cross-validation becomes especially useful. While training models, a test split is very useful as a final check on expected future performance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What partition sizes would you have with a 70/30 train/test split?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(193.2, 82.8)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(ski_data) * .7, len(ski_data) * .3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(ski_data.drop(columns='AdultWeekend'), \n",
+    "                                                    ski_data.AdultWeekend, test_size=0.3, \n",
+    "                                                    random_state=47)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((193, 35), (83, 35))"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape, X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((193,), (83,))"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((193, 32), (83, 32))"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 1#\n",
+    "#Save the 'Name', 'state', and 'Region' columns from the train/test data into names_train and names_test\n",
+    "#Then drop those columns from `X_train` and `X_test`. Use 'inplace=True'\n",
+    "names_list = ['Name', 'state', 'Region']\n",
+    "names_train = X_train[names_list]\n",
+    "names_test = X_test[names_list]\n",
+    "X_train.drop(columns=names_list, inplace=True)\n",
+    "X_test.drop(columns=names_list, inplace=True)\n",
+    "X_train.shape, X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "summit_elev                             int64\n",
+       "vertical_drop                           int64\n",
+       "base_elev                               int64\n",
+       "trams                                   int64\n",
+       "fastSixes                               int64\n",
+       "fastQuads                               int64\n",
+       "quad                                    int64\n",
+       "triple                                  int64\n",
+       "double                                  int64\n",
+       "surface                                 int64\n",
+       "total_chairs                            int64\n",
+       "Runs                                  float64\n",
+       "TerrainParks                          float64\n",
+       "LongestRun_mi                         float64\n",
+       "SkiableTerrain_ac                     float64\n",
+       "Snow Making_ac                        float64\n",
+       "daysOpenLastYear                      float64\n",
+       "yearsOpen                             float64\n",
+       "averageSnowfall                       float64\n",
+       "projectedDaysOpen                     float64\n",
+       "NightSkiing_ac                        float64\n",
+       "resorts_per_state                       int64\n",
+       "resorts_per_100kcapita                float64\n",
+       "resorts_per_100ksq_mile               float64\n",
+       "resort_skiable_area_ac_state_ratio    float64\n",
+       "resort_days_open_state_ratio          float64\n",
+       "resort_terrain_park_state_ratio       float64\n",
+       "resort_night_skiing_state_ratio       float64\n",
+       "total_chairs_runs_ratio               float64\n",
+       "total_chairs_skiable_ratio            float64\n",
+       "fastQuads_runs_ratio                  float64\n",
+       "fastQuads_skiable_ratio               float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 2#\n",
+    "#Check the `dtypes` attribute of `X_train` to verify all features are numeric\n",
+    "X_train.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "summit_elev                             int64\n",
+       "vertical_drop                           int64\n",
+       "base_elev                               int64\n",
+       "trams                                   int64\n",
+       "fastSixes                               int64\n",
+       "fastQuads                               int64\n",
+       "quad                                    int64\n",
+       "triple                                  int64\n",
+       "double                                  int64\n",
+       "surface                                 int64\n",
+       "total_chairs                            int64\n",
+       "Runs                                  float64\n",
+       "TerrainParks                          float64\n",
+       "LongestRun_mi                         float64\n",
+       "SkiableTerrain_ac                     float64\n",
+       "Snow Making_ac                        float64\n",
+       "daysOpenLastYear                      float64\n",
+       "yearsOpen                             float64\n",
+       "averageSnowfall                       float64\n",
+       "projectedDaysOpen                     float64\n",
+       "NightSkiing_ac                        float64\n",
+       "resorts_per_state                       int64\n",
+       "resorts_per_100kcapita                float64\n",
+       "resorts_per_100ksq_mile               float64\n",
+       "resort_skiable_area_ac_state_ratio    float64\n",
+       "resort_days_open_state_ratio          float64\n",
+       "resort_terrain_park_state_ratio       float64\n",
+       "resort_night_skiing_state_ratio       float64\n",
+       "total_chairs_runs_ratio               float64\n",
+       "total_chairs_skiable_ratio            float64\n",
+       "fastQuads_runs_ratio                  float64\n",
+       "fastQuads_skiable_ratio               float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 3#\n",
+    "#Repeat this check for the test split in `X_test`\n",
+    "X_test.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You have only numeric features in your X now!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.7 Initial Not-Even-A-Model<a id='4.7_Initial_Not-Even-A-Model'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A good place to start is to see how good the mean is as a predictor. In other words, what if you simply say your best guess is the average price?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "63.811088082901556"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 4#\n",
+    "#Calculate the mean of `y_train`\n",
+    "train_mean = y_train.mean()\n",
+    "train_mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`sklearn`'s `DummyRegressor` easily does this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[63.81108808]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 5#\n",
+    "#Fit the dummy regressor on the training data\n",
+    "#Hint, call its `.fit()` method with `X_train` and `y_train` as arguments\n",
+    "#Then print the object's `constant_` attribute and verify it's the same as the mean above\n",
+    "dumb_reg = DummyRegressor(strategy='mean')\n",
+    "dumb_reg.fit(X_train, y_train)\n",
+    "dumb_reg.constant_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How good is this? How closely does this match, or explain, the actual values? There are many ways of assessing how good one set of values agrees with another, which brings us to the subject of metrics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.7.1 Metrics<a id='4.7.1_Metrics'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.7.1.1 R-squared, or coefficient of determination<a id='4.7.1.1_R-squared,_or_coefficient_of_determination'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One measure is $R^2$, the [coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination). This is a measure of the proportion of variance in the dependent variable (our ticket price) that is predicted by our \"model\". The linked Wikipedia articles gives a nice explanation of how negative values can arise. This is frequently a cause of confusion for newcomers who, reasonably, ask how can a squared value be negative?\n",
+    "\n",
+    "Recall the mean can be denoted by $\\bar{y}$, where\n",
+    "\n",
+    "$$\\bar{y} = \\frac{1}{n}\\sum_{i=1}^ny_i$$\n",
+    "\n",
+    "and where $y_i$ are the individual values of the dependent variable.\n",
+    "\n",
+    "The total sum of squares (error), can be expressed as\n",
+    "\n",
+    "$$SS_{tot} = \\sum_i(y_i-\\bar{y})^2$$\n",
+    "\n",
+    "The above formula should be familiar as it's simply the variance without the denominator to scale (divide) by the sample size.\n",
+    "\n",
+    "The residual sum of squares is similarly defined to be\n",
+    "\n",
+    "$$SS_{res} = \\sum_i(y_i-\\hat{y})^2$$\n",
+    "\n",
+    "where $\\hat{y}$ are our predicted values for the depended variable.\n",
+    "\n",
+    "The coefficient of determination, $R^2$, here is given by\n",
+    "\n",
+    "$$R^2 = 1 - \\frac{SS_{res}}{SS_{tot}}$$\n",
+    "\n",
+    "Putting it into words, it's one minus the ratio of the residual variance to the original variance. Thus, the baseline model here, which always predicts $\\bar{y}$, should give $R^2=0$. A model that perfectly predicts the observed values would have no residual error and so give $R^2=1$. Models that do worse than predicting the mean will have increased the sum of squares of residuals and so produce a negative $R^2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 6#\n",
+    "#Calculate the R^2 as defined above\n",
+    "def r_squared(y, ypred):\n",
+    "    \"\"\"R-squared score.\n",
+    "    \n",
+    "    Calculate the R-squared, or coefficient of determination, of the input.\n",
+    "    \n",
+    "    Arguments:\n",
+    "    y -- the observed values\n",
+    "    ypred -- the predicted values\n",
+    "    \"\"\"\n",
+    "    ybar = np.sum(y) / len(y) #yes, we could use np.mean(y)\n",
+    "    sum_sq_tot = np.sum((y - ybar)**2) #total sum of squares error\n",
+    "    sum_sq_res = np.sum((y - ypred)**2) #residual sum of squares error\n",
+    "    R2 = 1.0 - sum_sq_res / sum_sq_tot\n",
+    "    return R2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make your predictions by creating an array of length the size of the training set with the single value of the mean."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([63.81108808, 63.81108808, 63.81108808, 63.81108808, 63.81108808])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_tr_pred_ = train_mean * np.ones(len(y_train))\n",
+    "y_tr_pred_[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remember the `sklearn` dummy regressor? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([63.81108808, 63.81108808, 63.81108808, 63.81108808, 63.81108808])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_tr_pred = dumb_reg.predict(X_train)\n",
+    "y_tr_pred[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can see that `DummyRegressor` produces exactly the same results and saves you having to mess about broadcasting the mean (or whichever other statistic we used - check out the [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyRegressor.html) to see what's available) to an array of the appropriate length. It also gives you an object with `fit()` and `predict()` methods as well so you can use them as conveniently as any other `sklearn` estimator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r_squared(y_train, y_tr_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Exactly as expected, if you use the average value as your prediction, you get an $R^2$ of zero _on our training set_. What if you use this \"model\" to predict unseen values from the test set? Remember, of course, that your \"model\" is trained on the training set; you still use the training set mean as your prediction."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make your predictions by creating an array of length the size of the test set with the single value of the (training) mean."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.0031235200417913944"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_te_pred = train_mean * np.ones(len(y_test))\n",
+    "r_squared(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generally, you can expect performance on a test set to be slightly worse than on the training set. As you are getting an $R^2$ of zero on the training set, there's nowhere to go but negative!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$R^2$ is a common metric, and interpretable in terms of the amount of variance explained, it's less appealing if you want an idea of how \"close\" your predictions are to the true values. Metrics that summarise the difference between predicted and actual values are _mean absolute error_ and _mean squared error_."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.7.1.2 Mean Absolute Error<a id='4.7.1.2_Mean_Absolute_Error'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is very simply the average of the absolute errors:\n",
+    "\n",
+    "$$MAE = \\frac{1}{n}\\sum_i^n|y_i - \\hat{y}|$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 7#\n",
+    "#Calculate the MAE as defined above\n",
+    "def mae(y, ypred):\n",
+    "    \"\"\"Mean absolute error.\n",
+    "    \n",
+    "    Calculate the mean absolute error of the arguments\n",
+    "\n",
+    "    Arguments:\n",
+    "    y -- the observed values\n",
+    "    ypred -- the predicted values\n",
+    "    \"\"\"\n",
+    "    abs_error = np.abs(y - ypred)\n",
+    "    mae = np.mean(abs_error)\n",
+    "    return mae"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "17.92346371714677"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mae(y_train, y_tr_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19.136142081278486"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mae(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mean absolute error is arguably the most intuitive of all the metrics, this essentially tells you that, on average, you might expect to be off by around \\\\$19 if you guessed ticket price based on an average of known values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.7.1.3 Mean Squared Error<a id='4.7.1.3_Mean_Squared_Error'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another common metric (and an important one internally for optimizing machine learning models) is the mean squared error. This is simply the average of the square of the errors:\n",
+    "\n",
+    "$$MSE = \\frac{1}{n}\\sum_i^n(y_i - \\hat{y})^2$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#Code task 8#\n",
+    "#Calculate the MSE as defined above\n",
+    "def mse(y, ypred):\n",
+    "    \"\"\"Mean square error.\n",
+    "    \n",
+    "    Calculate the mean square error of the arguments\n",
+    "\n",
+    "    Arguments:\n",
+    "    y -- the observed values\n",
+    "    ypred -- the predicted values\n",
+    "    \"\"\"\n",
+    "    sq_error = (y - ypred)**2\n",
+    "    mse = np.mean(sq_error)\n",
+    "    return mse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "614.1334096969046"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mse(y_train, y_tr_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "581.4365441953483"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mse(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So here, you get a slightly better MSE on the test set than you did on the train set. And what does a squared error mean anyway? To convert this back to our measurement space, we often take the square root, to form the _root mean square error_ thus:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([24.78171523, 24.11299534])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sqrt([mse(y_train, y_tr_pred), mse(y_test, y_te_pred)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.7.2 sklearn metrics<a id='4.7.2_sklearn_metrics'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Functions are good, but you don't want to have to define functions every time we want to assess performance. `sklearn.metrics` provides many commonly used metrics, included the ones above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.7.2.0.1 R-squared<a id='4.7.2.0.1_R-squared'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, -0.0031235200417913944)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(y_train, y_tr_pred), r2_score(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.7.2.0.2 Mean absolute error<a id='4.7.2.0.2_Mean_absolute_error'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(17.92346371714677, 19.136142081278486)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.7.2.0.3 Mean squared error<a id='4.7.2.0.3_Mean_squared_error'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(614.1334096969046, 581.4365441953483)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_squared_error(y_train, y_tr_pred), mean_squared_error(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.7.3 Note On Calculating Metrics<a id='4.7.3_Note_On_Calculating_Metrics'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When calling functions to calculate metrics, it is important to take care in the order of the arguments. Two of the metrics above actually don't care if the arguments are reversed; one does. Which one cares?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In a Jupyter code cell, running `r2_score?` will bring up the docstring for the function, and `r2_score??` will bring up the actual code of the function! Try them and compare the source for `sklearn`'s function with yours. Feel free to explore what happens when you reverse the order of the arguments and compare behaviour of `sklearn`'s function and yours."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[1;31mSignature:\u001b[0m\n",
+       "\u001b[0mr2_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[1;33m*\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0mmultioutput\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'uniform_average'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0mforce_finite\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+       "\u001b[1;31mSource:\u001b[0m   \n",
+       "\u001b[1;33m@\u001b[0m\u001b[0mvalidate_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[1;33m{\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[1;34m\"y_true\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"array-like\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[1;34m\"y_pred\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"array-like\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[1;34m\"sample_weight\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"array-like\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[1;34m\"multioutput\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m            \u001b[0mStrOptions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m\"raw_values\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"uniform_average\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"variance_weighted\"\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m            \u001b[1;34m\"array-like\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m            \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[1;34m\"force_finite\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"boolean\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0mprefer_skip_nested_validation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m\u001b[1;32mdef\u001b[0m \u001b[0mr2_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[1;33m*\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0mmultioutput\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"uniform_average\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0mforce_finite\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[1;34m\"\"\":math:`R^2` (coefficient of determination) regression score function.\n",
+       "\n",
+       "    Best possible score is 1.0 and it can be negative (because the\n",
+       "    model can be arbitrarily worse). In the general case when the true y is\n",
+       "    non-constant, a constant model that always predicts the average y\n",
+       "    disregarding the input features would get a :math:`R^2` score of 0.0.\n",
+       "\n",
+       "    In the particular case when ``y_true`` is constant, the :math:`R^2` score\n",
+       "    is not finite: it is either ``NaN`` (perfect predictions) or ``-Inf``\n",
+       "    (imperfect predictions). To prevent such non-finite numbers to pollute\n",
+       "    higher-level experiments such as a grid search cross-validation, by default\n",
+       "    these cases are replaced with 1.0 (perfect predictions) or 0.0 (imperfect\n",
+       "    predictions) respectively. You can set ``force_finite`` to ``False`` to\n",
+       "    prevent this fix from happening.\n",
+       "\n",
+       "    Note: when the prediction residuals have zero mean, the :math:`R^2` score\n",
+       "    is identical to the\n",
+       "    :func:`Explained Variance score <explained_variance_score>`.\n",
+       "\n",
+       "    Read more in the :ref:`User Guide <r2_score>`.\n",
+       "\n",
+       "    Parameters\n",
+       "    ----------\n",
+       "    y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
+       "        Ground truth (correct) target values.\n",
+       "\n",
+       "    y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
+       "        Estimated target values.\n",
+       "\n",
+       "    sample_weight : array-like of shape (n_samples,), default=None\n",
+       "        Sample weights.\n",
+       "\n",
+       "    multioutput : {'raw_values', 'uniform_average', 'variance_weighted'}, \\\n",
+       "            array-like of shape (n_outputs,) or None, default='uniform_average'\n",
+       "\n",
+       "        Defines aggregating of multiple output scores.\n",
+       "        Array-like value defines weights used to average scores.\n",
+       "        Default is \"uniform_average\".\n",
+       "\n",
+       "        'raw_values' :\n",
+       "            Returns a full set of scores in case of multioutput input.\n",
+       "\n",
+       "        'uniform_average' :\n",
+       "            Scores of all outputs are averaged with uniform weight.\n",
+       "\n",
+       "        'variance_weighted' :\n",
+       "            Scores of all outputs are averaged, weighted by the variances\n",
+       "            of each individual output.\n",
+       "\n",
+       "        .. versionchanged:: 0.19\n",
+       "            Default value of multioutput is 'uniform_average'.\n",
+       "\n",
+       "    force_finite : bool, default=True\n",
+       "        Flag indicating if ``NaN`` and ``-Inf`` scores resulting from constant\n",
+       "        data should be replaced with real numbers (``1.0`` if prediction is\n",
+       "        perfect, ``0.0`` otherwise). Default is ``True``, a convenient setting\n",
+       "        for hyperparameters' search procedures (e.g. grid search\n",
+       "        cross-validation).\n",
+       "\n",
+       "        .. versionadded:: 1.1\n",
+       "\n",
+       "    Returns\n",
+       "    -------\n",
+       "    z : float or ndarray of floats\n",
+       "        The :math:`R^2` score or ndarray of scores if 'multioutput' is\n",
+       "        'raw_values'.\n",
+       "\n",
+       "    Notes\n",
+       "    -----\n",
+       "    This is not a symmetric function.\n",
+       "\n",
+       "    Unlike most other scores, :math:`R^2` score may be negative (it need not\n",
+       "    actually be the square of a quantity R).\n",
+       "\n",
+       "    This metric is not well-defined for single samples and will return a NaN\n",
+       "    value if n_samples is less than two.\n",
+       "\n",
+       "    References\n",
+       "    ----------\n",
+       "    .. [1] `Wikipedia entry on the Coefficient of determination\n",
+       "            <https://en.wikipedia.org/wiki/Coefficient_of_determination>`_\n",
+       "\n",
+       "    Examples\n",
+       "    --------\n",
+       "    >>> from sklearn.metrics import r2_score\n",
+       "    >>> y_true = [3, -0.5, 2, 7]\n",
+       "    >>> y_pred = [2.5, 0.0, 2, 8]\n",
+       "    >>> r2_score(y_true, y_pred)\n",
+       "    0.948...\n",
+       "    >>> y_true = [[0.5, 1], [-1, 1], [7, -6]]\n",
+       "    >>> y_pred = [[0, 2], [-1, 2], [8, -5]]\n",
+       "    >>> r2_score(y_true, y_pred,\n",
+       "    ...          multioutput='variance_weighted')\n",
+       "    0.938...\n",
+       "    >>> y_true = [1, 2, 3]\n",
+       "    >>> y_pred = [1, 2, 3]\n",
+       "    >>> r2_score(y_true, y_pred)\n",
+       "    1.0\n",
+       "    >>> y_true = [1, 2, 3]\n",
+       "    >>> y_pred = [2, 2, 2]\n",
+       "    >>> r2_score(y_true, y_pred)\n",
+       "    0.0\n",
+       "    >>> y_true = [1, 2, 3]\n",
+       "    >>> y_pred = [3, 2, 1]\n",
+       "    >>> r2_score(y_true, y_pred)\n",
+       "    -3.0\n",
+       "    >>> y_true = [-2, -2, -2]\n",
+       "    >>> y_pred = [-2, -2, -2]\n",
+       "    >>> r2_score(y_true, y_pred)\n",
+       "    1.0\n",
+       "    >>> r2_score(y_true, y_pred, force_finite=False)\n",
+       "    nan\n",
+       "    >>> y_true = [-2, -2, -2]\n",
+       "    >>> y_pred = [-2, -2, -2 + 1e-8]\n",
+       "    >>> r2_score(y_true, y_pred)\n",
+       "    0.0\n",
+       "    >>> r2_score(y_true, y_pred, force_finite=False)\n",
+       "    -inf\n",
+       "    \"\"\"\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0mxp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdevice_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_namespace_and_device\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmultioutput\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[1;33m)\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0mdtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_find_matching_floating_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmultioutput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_reg_targets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmultioutput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[1;33m)\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0mcheck_consistent_length\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[1;32mif\u001b[0m \u001b[0m_num_samples\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[0mmsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"R^2 score is not well-defined with less than two samples.\"\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mUndefinedMetricWarning\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[1;32mreturn\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"nan\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[0msample_weight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[0mweight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[0mweight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0mnumerator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mweight\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m**\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[0mdenominator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[0mweight\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0m_average\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m**\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[1;33m)\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[1;32mreturn\u001b[0m \u001b[0m_assemble_r2_explained_variance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[0mnumerator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnumerator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[0mdenominator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdenominator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[0mn_outputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[0mmultioutput\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmultioutput\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[0mforce_finite\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mforce_finite\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[0mxp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m        \u001b[0mdevice\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdevice_\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
+       "\u001b[0m    \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+       "\u001b[1;31mFile:\u001b[0m      c:\\users\\demoo\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_regression.py\n",
+       "\u001b[1;31mType:\u001b[0m      function"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "r2_score??"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, -3.041041349306602e+30)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# train set - sklearn\n",
+    "# correct order, incorrect order\n",
+    "r2_score(y_train, y_tr_pred), r2_score(y_tr_pred, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-0.0031235200417913944, 0.0)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# test set - sklearn\n",
+    "# correct order, incorrect order\n",
+    "r2_score(y_test, y_te_pred), r2_score(y_te_pred, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.0, -3.041041349306602e+30)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# train set - using our homebrew function\n",
+    "# correct order, incorrect order\n",
+    "r_squared(y_train, y_tr_pred), r_squared(y_tr_pred, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\demoo\\AppData\\Local\\Temp\\ipykernel_17464\\1803819837.py:15: RuntimeWarning: divide by zero encountered in scalar divide\n",
+      "  R2 = 1.0 - sum_sq_res / sum_sq_tot\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(-0.0031235200417913944, -inf)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# test set - using our homebrew function\n",
+    "# correct order, incorrect order\n",
+    "r_squared(y_test, y_te_pred), r_squared(y_te_pred, y_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can get very different results swapping the argument order. It's worth highlighting this because data scientists do this too much in the real world! Don't be one of them! Frequently the argument order doesn't matter, but it will bite you when you do it with a function that does care. It's sloppy, bad practice and if you don't make a habit of putting arguments in the right order, you will forget!\n",
+    "\n",
+    "Remember:\n",
+    "* argument order matters,\n",
+    "* check function syntax with `func?` in a code cell"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.8 Initial Models<a id='4.8_Initial_Models'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.8.1 Imputing missing feature (predictor) values<a id='4.8.1_Imputing_missing_feature_(predictor)_values'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Recall when performing EDA, you imputed (filled in) some missing values in pandas. You did this judiciously for exploratory/visualization purposes. You left many missing values in the data. You can impute missing values using scikit-learn, but note that you should learn values to impute from a train split and apply that to the test split to then assess how well your imputation worked."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.8.1.1 Impute missing values with median<a id='4.8.1.1_Impute_missing_values_with_median'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There's missing values. Recall from your data exploration that many distributions were skewed. Your first thought might be to impute missing values using the median."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.1.1 Learn the values to impute from the train set<a id='4.8.1.1.1_Learn_the_values_to_impute_from_the_train_set'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "summit_elev                           2215.000000\n",
+       "vertical_drop                          750.000000\n",
+       "base_elev                             1300.000000\n",
+       "trams                                    0.000000\n",
+       "fastSixes                                0.000000\n",
+       "fastQuads                                0.000000\n",
+       "quad                                     1.000000\n",
+       "triple                                   1.000000\n",
+       "double                                   1.000000\n",
+       "surface                                  2.000000\n",
+       "total_chairs                             7.000000\n",
+       "Runs                                    28.000000\n",
+       "TerrainParks                             2.000000\n",
+       "LongestRun_mi                            1.000000\n",
+       "SkiableTerrain_ac                      170.000000\n",
+       "Snow Making_ac                          96.500000\n",
+       "daysOpenLastYear                       109.000000\n",
+       "yearsOpen                               57.000000\n",
+       "averageSnowfall                        120.000000\n",
+       "projectedDaysOpen                      115.000000\n",
+       "NightSkiing_ac                          70.000000\n",
+       "resorts_per_state                       15.000000\n",
+       "resorts_per_100kcapita                   0.248243\n",
+       "resorts_per_100ksq_mile                 22.902162\n",
+       "resort_skiable_area_ac_state_ratio       0.051458\n",
+       "resort_days_open_state_ratio             0.071225\n",
+       "resort_terrain_park_state_ratio          0.069444\n",
+       "resort_night_skiing_state_ratio          0.077081\n",
+       "total_chairs_runs_ratio                  0.200000\n",
+       "total_chairs_skiable_ratio               0.040323\n",
+       "fastQuads_runs_ratio                     0.000000\n",
+       "fastQuads_skiable_ratio                  0.000000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# These are the values we'll use to fill in any missing values\n",
+    "X_defaults_median = X_train.median()\n",
+    "X_defaults_median"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.1.2 Apply the imputation to both train and test splits<a id='4.8.1.1.2_Apply_the_imputation_to_both_train_and_test_splits'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 9#\n",
+    "#Call `X_train` and `X_test`'s `fillna()` method, passing `X_defaults_median` as the values to use\n",
+    "#Assign the results to `X_tr` and `X_te`, respectively\n",
+    "X_tr = X_train.fillna(X_defaults_median)\n",
+    "X_te = X_test.fillna(X_defaults_median)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.1.3 Scale the data<a id='4.8.1.1.3_Scale_the_data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As you have features measured in many different units, with numbers that vary by orders of magnitude, start off by scaling them to put them all on a consistent scale. The [StandardScaler](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) scales each feature to zero mean and unit variance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 10#\n",
+    "#Call the StandardScaler`s fit method on `X_tr` to fit the scaler\n",
+    "#then use it's `transform()` method to apply the scaling to both the train and test split\n",
+    "#data (`X_tr` and `X_te`), naming the results `X_tr_scaled` and `X_te_scaled`, respectively\n",
+    "scaler = StandardScaler()\n",
+    "scaler.fit(X_tr)\n",
+    "X_tr_scaled = scaler.transform(X_tr)\n",
+    "X_te_scaled = scaler.transform(X_te)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.1.4 Train the model on the train split<a id='4.8.1.1.4_Train_the_model_on_the_train_split'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm = LinearRegression().fit(X_tr_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.1.5 Make predictions using the model on both train and test splits<a id='4.8.1.1.5_Make_predictions_using_the_model_on_both_train_and_test_splits'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 11#\n",
+    "#Call the `predict()` method of the model (`lm`) on both the (scaled) train and test data\n",
+    "#Assign the predictions to `y_tr_pred` and `y_te_pred`, respectively\n",
+    "y_tr_pred = lm.predict(X_tr_scaled)\n",
+    "y_te_pred = lm.predict(X_te_scaled)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.1.6 Assess model performance<a id='4.8.1.1.6_Assess_model_performance'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8177988515690604, 0.7209725843435146)"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# r^2 - train, test\n",
+    "median_r2 = r2_score(y_train, y_tr_pred), r2_score(y_test, y_te_pred)\n",
+    "median_r2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Recall that you estimated ticket price by simply using a known average. As expected, this produced an $R^2$ of zero for both the training and test set, because $R^2$ tells us how much of the variance you're explaining beyond that of using just the mean, and you were using just the mean. Here we see that our simple linear regression model explains over 80% of the variance on the train set and over 70% on the test set. Clearly you are onto something, although the much lower value for the test set suggests you're overfitting somewhat. This isn't a surprise as you've made no effort to select a parsimonious set of features or deal with multicollinearity in our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8.547850301825429, 9.407020118581318)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 12#\n",
+    "#Now calculate the mean absolute error scores using `sklearn`'s `mean_absolute_error` function\n",
+    "# as we did above for R^2\n",
+    "# MAE - train, test\n",
+    "median_mae = mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)\n",
+    "median_mae"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using this model, then, on average you'd expect to estimate a ticket price within \\\\$9 or so of the real price. This is much, much better than the \\\\$19 from just guessing using the average. There may be something to this machine learning lark after all!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(111.89581253658478, 161.73156451192264)"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 13#\n",
+    "#And also do the same using `sklearn`'s `mean_squared_error`\n",
+    "# MSE - train, test\n",
+    "median_mse = mean_squared_error(y_train, y_tr_pred), mean_squared_error(y_test, y_te_pred)\n",
+    "median_mse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.8.1.2 Impute missing values with the mean<a id='4.8.1.2_Impute_missing_values_with_the_mean'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You chose to use the median for filling missing values because of the skew of many of our predictor feature distributions. What if you wanted to try something else, such as the mean?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.2.1 Learn the values to impute from the train set<a id='4.8.1.2.1_Learn_the_values_to_impute_from_the_train_set'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "summit_elev                           4074.554404\n",
+       "vertical_drop                         1043.196891\n",
+       "base_elev                             3020.512953\n",
+       "trams                                    0.103627\n",
+       "fastSixes                                0.072539\n",
+       "fastQuads                                0.673575\n",
+       "quad                                     1.010363\n",
+       "triple                                   1.440415\n",
+       "double                                   1.813472\n",
+       "surface                                  2.497409\n",
+       "total_chairs                             7.611399\n",
+       "Runs                                    41.188482\n",
+       "TerrainParks                             2.434783\n",
+       "LongestRun_mi                            1.293122\n",
+       "SkiableTerrain_ac                      448.785340\n",
+       "Snow Making_ac                         129.601190\n",
+       "daysOpenLastYear                       110.100629\n",
+       "yearsOpen                               56.559585\n",
+       "averageSnowfall                        162.310160\n",
+       "projectedDaysOpen                      115.920245\n",
+       "NightSkiing_ac                          86.384615\n",
+       "resorts_per_state                       16.264249\n",
+       "resorts_per_100kcapita                   0.424802\n",
+       "resorts_per_100ksq_mile                 40.957785\n",
+       "resort_skiable_area_ac_state_ratio       0.097205\n",
+       "resort_days_open_state_ratio             0.126014\n",
+       "resort_terrain_park_state_ratio          0.116022\n",
+       "resort_night_skiing_state_ratio          0.155024\n",
+       "total_chairs_runs_ratio                  0.271441\n",
+       "total_chairs_skiable_ratio               0.070483\n",
+       "fastQuads_runs_ratio                     0.010401\n",
+       "fastQuads_skiable_ratio                  0.001633\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 14#\n",
+    "#As we did for the median above, calculate mean values for imputing missing values\n",
+    "# These are the values we'll use to fill in any missing values\n",
+    "X_defaults_mean = X_train.mean()\n",
+    "X_defaults_mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By eye, you can immediately tell that your replacement values are much higher than those from using the median."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.2.2 Apply the imputation to both train and test splits<a id='4.8.1.2.2_Apply_the_imputation_to_both_train_and_test_splits'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_tr = X_train.fillna(X_defaults_mean)\n",
+    "X_te = X_test.fillna(X_defaults_mean)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.2.3 Scale the data<a id='4.8.1.2.3_Scale_the_data'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "scaler.fit(X_tr)\n",
+    "X_tr_scaled = scaler.transform(X_tr)\n",
+    "X_te_scaled = scaler.transform(X_te)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.2.4 Train the model on the train split<a id='4.8.1.2.4_Train_the_model_on_the_train_split'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm = LinearRegression().fit(X_tr_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.2.5 Make predictions using the model on both train and test splits<a id='4.8.1.2.5_Make_predictions_using_the_model_on_both_train_and_test_splits'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_tr_pred = lm.predict(X_tr_scaled)\n",
+    "y_te_pred = lm.predict(X_te_scaled)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4.8.1.2.6 Assess model performance<a id='4.8.1.2.6_Assess_model_performance'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8170154093990024, 0.7163814716959958)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(y_train, y_tr_pred), r2_score(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8.536884040670977, 9.416375625789279)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(112.37695054778278, 164.39269309524377)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_squared_error(y_train, y_tr_pred), mean_squared_error(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These results don't seem very different to when you used the median for imputing missing values. Perhaps it doesn't make much difference here. Maybe your overtraining dominates. Maybe other feature transformations, such as taking the log, would help. You could try with just a subset of features rather than using all of them as inputs.\n",
+    "\n",
+    "To perform the median/mean comparison, you copied and pasted a lot of code just to change the function for imputing missing values. It would make more sense to write a function that performed the sequence of steps:\n",
+    "1. impute missing values\n",
+    "2. scale the features\n",
+    "3. train a model\n",
+    "4. calculate model performance\n",
+    "\n",
+    "But these are common steps and `sklearn` provides something much better than writing custom functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.8.2 Pipelines<a id='4.8.2_Pipelines'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "One of the most important and useful components of `sklearn` is the [pipeline](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html). In place of `panda`'s `fillna` DataFrame method, there is `sklearn`'s `SimpleImputer`. Remember the first linear model above performed the steps:\n",
+    "\n",
+    "1. replace missing values with the median for each feature\n",
+    "2. scale the data to zero mean and unit variance\n",
+    "3. train a linear regression model\n",
+    "\n",
+    "and all these steps were trained on the train split and then applied to the test split for assessment.\n",
+    "\n",
+    "The pipeline below defines exactly those same steps. Crucially, the resultant `Pipeline` object has a `fit()` method and a `predict()` method, just like the `LinearRegression()` object itself. Just as you might create a linear regression model and train it with `.fit()` and predict with `.predict()`, you can wrap the entire process of imputing and feature scaling and regression in a single object you can train with `.fit()` and predict with `.predict()`. And that's basically a pipeline: a model on steroids."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.8.2.1 Define the pipeline<a id='4.8.2.1_Define_the_pipeline'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipe = make_pipeline(\n",
+    "    SimpleImputer(strategy='median'), \n",
+    "    StandardScaler(), \n",
+    "    LinearRegression()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sklearn.pipeline.Pipeline"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(pipe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(True, True)"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hasattr(pipe, 'fit'), hasattr(pipe, 'predict')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.8.2.2 Fit the pipeline<a id='4.8.2.2_Fit_the_pipeline'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, a single call to the pipeline's `fit()` method combines the steps of learning the imputation (determining what values to use to fill the missing ones), the scaling (determining the mean to subtract and the variance to divide by), and then training the model. It does this all in the one call with the training data as arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
+       "                ('standardscaler', StandardScaler()),\n",
+       "                ('linearregression', LinearRegression())])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 15#\n",
+    "#Call the pipe's `fit()` method with `X_train` and `y_train` as arguments\n",
+    "pipe.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.8.2.3 Make predictions on the train and test sets<a id='4.8.2.3_Make_predictions_on_the_train_and_test_sets'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_tr_pred = pipe.predict(X_train)\n",
+    "y_te_pred = pipe.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.8.2.4 Assess performance<a id='4.8.2.4_Assess_performance'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8177988515690604, 0.7209725843435146)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(y_train, y_tr_pred), r2_score(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And compare with your earlier (non-pipeline) result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.8177988515690604, 0.7209725843435146)"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "median_r2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8.547850301825429, 9.407020118581318)"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Compare with your earlier result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8.547850301825429, 9.407020118581318)"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "median_mae"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(111.89581253658478, 161.73156451192264)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_squared_error(y_train, y_tr_pred), mean_squared_error(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare with your earlier result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(111.89581253658478, 161.73156451192264)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "median_mse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These results confirm the pipeline is doing exactly what's expected, and results are identical to your earlier steps. This allows you to move faster but with confidence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.9 Refining The Linear Model<a id='4.9_Refining_The_Linear_Model'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You suspected the model was overfitting. This is no real surprise given the number of features you blindly used. It's likely a judicious subset of features would generalize better. `sklearn` has a number of feature selection functions available. The one you'll use here is `SelectKBest` which, as you might guess, selects the k best features. You can read about SelectKBest \n",
+    "[here](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html#sklearn.feature_selection.SelectKBest). `f_regression` is just the [score function](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html#sklearn.feature_selection.f_regression) you're using because you're performing regression. It's important to choose an appropriate one for your machine learning task."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.9.1 Define the pipeline<a id='4.9.1_Define_the_pipeline'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Redefine your pipeline to include this feature selection step:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 16#\n",
+    "#Add `SelectKBest` as a step in the pipeline between `StandardScaler()` and `LinearRegression()`\n",
+    "#Don't forget to tell it to use `f_regression` as its score function\n",
+    "pipe = make_pipeline(\n",
+    "    SimpleImputer(strategy='median'), \n",
+    "    StandardScaler(),\n",
+    "    SelectKBest(f_regression),\n",
+    "    LinearRegression()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.9.2 Fit the pipeline<a id='4.9.2_Fit_the_pipeline'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;selectkbest&#x27;,\n",
+       "                 SelectKBest(score_func=&lt;function f_regression at 0x000001EFF0E01760&gt;)),\n",
+       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;selectkbest&#x27;,\n",
+       "                 SelectKBest(score_func=&lt;function f_regression at 0x000001EFF0E01760&gt;)),\n",
+       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SelectKBest<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.feature_selection.SelectKBest.html\">?<span>Documentation for SelectKBest</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SelectKBest(score_func=&lt;function f_regression at 0x000001EFF0E01760&gt;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
+       "                ('standardscaler', StandardScaler()),\n",
+       "                ('selectkbest',\n",
+       "                 SelectKBest(score_func=<function f_regression at 0x000001EFF0E01760>)),\n",
+       "                ('linearregression', LinearRegression())])"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipe.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.9.3 Assess performance on the train and test set<a id='4.9.3_Assess_performance_on_the_train_and_test_set'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_tr_pred = pipe.predict(X_train)\n",
+    "y_te_pred = pipe.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7674914326052744, 0.6259877354190837)"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(y_train, y_tr_pred), r2_score(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9.501495079727485, 11.20183019033205)"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This has made things worse! Clearly selecting a subset of features has an impact on performance. `SelectKBest` defaults to k=10. You've just seen that 10 is worse than using all features. What is the best k? You could create a new pipeline with a different value of k:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.9.4 Define a new pipeline to select a different number of features<a id='4.9.4_Define_a_new_pipeline_to_select_a_different_number_of_features'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 17#\n",
+    "#Modify the `SelectKBest` step to use a value of 15 for k\n",
+    "pipe15 = make_pipeline(\n",
+    "    SimpleImputer(strategy='median'), \n",
+    "    StandardScaler(),\n",
+    "    SelectKBest(f_regression, k=15),\n",
+    "    LinearRegression()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.9.5 Fit the pipeline<a id='4.9.5_Fit_the_pipeline'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-3 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;selectkbest&#x27;,\n",
+       "                 SelectKBest(k=15,\n",
+       "                             score_func=&lt;function f_regression at 0x000001EFF0E01760&gt;)),\n",
+       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;selectkbest&#x27;,\n",
+       "                 SelectKBest(k=15,\n",
+       "                             score_func=&lt;function f_regression at 0x000001EFF0E01760&gt;)),\n",
+       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SelectKBest<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.feature_selection.SelectKBest.html\">?<span>Documentation for SelectKBest</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SelectKBest(k=15, score_func=&lt;function f_regression at 0x000001EFF0E01760&gt;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
+       "                ('standardscaler', StandardScaler()),\n",
+       "                ('selectkbest',\n",
+       "                 SelectKBest(k=15,\n",
+       "                             score_func=<function f_regression at 0x000001EFF0E01760>)),\n",
+       "                ('linearregression', LinearRegression())])"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipe15.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.9.6 Assess performance on train and test data<a id='4.9.6_Assess_performance_on_train_and_test_data'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_tr_pred = pipe15.predict(X_train)\n",
+    "y_te_pred = pipe15.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7924096060483825, 0.6376199973170795)"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r2_score(y_train, y_tr_pred), r2_score(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9.211767769307114, 10.488246867294356)"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You could keep going, trying different values of k, training a model, measuring performance on the test set, and then picking the model with the best test set performance. There's a fundamental problem with this approach: _you're tuning the model to the arbitrary test set_! If you continue this way you'll end up with a model works well on the particular quirks of our test set _but fails to generalize to new data_. The whole point of keeping a test set is for it to be a set of that new data, to check how well our model might perform on data it hasn't seen.\n",
+    "\n",
+    "The way around this is a technique called _cross-validation_. You partition the training set into k folds, train our model on k-1 of those folds, and calculate performance on the fold not used in training. This procedure then cycles through k times with a different fold held back each time. Thus you end up building k models on k sets of data with k estimates of how the model performs on unseen data but without having to touch the test set."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.9.7 Assessing performance using cross-validation<a id='4.9.7_Assessing_performance_using_cross-validation'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cv_results = cross_validate(pipe15, X_train, y_train, cv=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.63760862, 0.72831381, 0.74443537, 0.5487915 , 0.50441472])"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_scores = cv_results['test_score']\n",
+    "cv_scores"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Without using the same random state for initializing the CV folds, your actual numbers will be different."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.6327128053007864, 0.09502487849877721)"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(cv_scores), np.std(cv_scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These results highlight that assessing model performance in inherently open to variability. You'll get different results depending on the quirks of which points are in which fold. An advantage of this is that you can also obtain an estimate of the variability, or uncertainty, in your performance estimate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.44, 0.82])"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.round((np.mean(cv_scores) - 2 * np.std(cv_scores), np.mean(cv_scores) + 2 * np.std(cv_scores)), 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.9.8 Hyperparameter search using GridSearchCV<a id='4.9.8_Hyperparameter_search_using_GridSearchCV'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pulling the above together, we have:\n",
+    "* a pipeline that\n",
+    "    * imputes missing values\n",
+    "    * scales the data\n",
+    "    * selects the k best features\n",
+    "    * trains a linear regression model\n",
+    "* a technique (cross-validation) for estimating model performance\n",
+    "\n",
+    "Now you want to use cross-validation for multiple values of k and use cross-validation to pick the value of k that gives the best performance. `make_pipeline` automatically names each step as the lowercase name of the step and the parameters of the step are then accessed by appending a double underscore followed by the parameter name. You know the name of the step will be 'selectkbest' and you know the parameter is 'k'.\n",
+    "\n",
+    "You can also list the names of all the parameters in a pipeline like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['memory', 'steps', 'verbose', 'simpleimputer', 'standardscaler', 'selectkbest', 'linearregression', 'simpleimputer__add_indicator', 'simpleimputer__copy', 'simpleimputer__fill_value', 'simpleimputer__keep_empty_features', 'simpleimputer__missing_values', 'simpleimputer__strategy', 'standardscaler__copy', 'standardscaler__with_mean', 'standardscaler__with_std', 'selectkbest__k', 'selectkbest__score_func', 'linearregression__copy_X', 'linearregression__fit_intercept', 'linearregression__n_jobs', 'linearregression__positive'])"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 18#\n",
+    "#Call `pipe`'s `get_params()` method to get a dict of available parameters and print their names\n",
+    "#using dict's `keys()` method\n",
+    "pipe.get_params().keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above can be particularly useful as your pipelines becomes more complex (you can even nest pipelines within pipelines)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "k = [k+1 for k in range(len(X_train.columns))]\n",
+    "grid_params = {'selectkbest__k': k}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now you have a range of `k` to investigate. Is 1 feature best? 2? 3? 4? All of them? You could write a for loop and iterate over each possible value, doing all the housekeeping oyurselves to track the best value of k. But this is  a common task so there's a built in function in `sklearn`. This is [`GridSearchCV`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html).\n",
+    "This takes the pipeline object, in fact it takes anything with a `.fit()` and `.predict()` method. In simple cases with no feature selection or imputation or feature scaling etc. you may see the classifier or regressor object itself directly passed into `GridSearchCV`. The other key input is the parameters and values to search over. Optional parameters include the cross-validation strategy and number of CPUs to use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr_grid_cv = GridSearchCV(pipe, param_grid=grid_params, cv=5, n_jobs=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                        SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                       (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                                       (&#x27;selectkbest&#x27;,\n",
+       "                                        SelectKBest(score_func=&lt;function f_regression at 0x000001EFF0E01760&gt;)),\n",
+       "                                       (&#x27;linearregression&#x27;,\n",
+       "                                        LinearRegression())]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={&#x27;selectkbest__k&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
+       "                                            12, 13, 14, 15, 16, 17, 18, 19, 20,\n",
+       "                                            21, 22, 23, 24, 25, 26, 27, 28, 29,\n",
+       "                                            30, ...]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                        SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                       (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                                       (&#x27;selectkbest&#x27;,\n",
+       "                                        SelectKBest(score_func=&lt;function f_regression at 0x000001EFF0E01760&gt;)),\n",
+       "                                       (&#x27;linearregression&#x27;,\n",
+       "                                        LinearRegression())]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={&#x27;selectkbest__k&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
+       "                                            12, 13, 14, 15, 16, 17, 18, 19, 20,\n",
+       "                                            21, 22, 23, 24, 25, 26, 27, 28, 29,\n",
+       "                                            30, ...]})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;selectkbest&#x27;,\n",
+       "                 SelectKBest(k=8,\n",
+       "                             score_func=&lt;function f_regression at 0x000001EFF0E01760&gt;)),\n",
+       "                (&#x27;linearregression&#x27;, LinearRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SelectKBest<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.feature_selection.SelectKBest.html\">?<span>Documentation for SelectKBest</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SelectKBest(k=8, score_func=&lt;function f_regression at 0x000001EFF0E01760&gt;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[('simpleimputer',\n",
+       "                                        SimpleImputer(strategy='median')),\n",
+       "                                       ('standardscaler', StandardScaler()),\n",
+       "                                       ('selectkbest',\n",
+       "                                        SelectKBest(score_func=<function f_regression at 0x000001EFF0E01760>)),\n",
+       "                                       ('linearregression',\n",
+       "                                        LinearRegression())]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={'selectkbest__k': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
+       "                                            12, 13, 14, 15, 16, 17, 18, 19, 20,\n",
+       "                                            21, 22, 23, 24, 25, 26, 27, 28, 29,\n",
+       "                                            30, ...]})"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr_grid_cv.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_mean = lr_grid_cv.cv_results_['mean_test_score']\n",
+    "score_std = lr_grid_cv.cv_results_['std_test_score']\n",
+    "cv_k = [k for k in lr_grid_cv.cv_results_['param_selectkbest__k']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'selectkbest__k': 8}"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 19#\n",
+    "#Print the `best_params_` attribute of `lr_grid_cv`\n",
+    "lr_grid_cv.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 20#\n",
+    "#Assign the value of k from the above dict of `best_params_` and assign it to `best_k`\n",
+    "best_k = lr_grid_cv.best_params_['selectkbest__k']\n",
+    "plt.subplots(figsize=(10, 5))\n",
+    "plt.errorbar(cv_k, score_mean, yerr=score_std)\n",
+    "plt.axvline(x=best_k, c='r', ls='--', alpha=.5)\n",
+    "plt.xlabel('k')\n",
+    "plt.ylabel('CV score (r-squared)')\n",
+    "plt.title('Pipeline mean CV score (error bars +/- 1sd)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above suggests a good value for k is 8. There was an initial rapid increase with k, followed by a slow decline. Also noticeable is the variance of the results greatly increase above k=8. As you increasingly overfit, expect greater swings in performance as different points move in and out of the train/test folds."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Which features were most useful? Step into your best model, shown below. Starting with the fitted grid search object, you get the best estimator, then the named step 'selectkbest', for which you can its `get_support()` method for a logical mask of the features selected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selected = lr_grid_cv.best_estimator_.named_steps.selectkbest.get_support()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarly, instead of using the 'selectkbest' named step, you can access the named step for the linear regression model and, from that, grab the model coefficients via its `coef_` attribute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "vertical_drop        10.767857\n",
+       "Snow Making_ac        6.290074\n",
+       "total_chairs          5.794156\n",
+       "fastQuads             5.745626\n",
+       "Runs                  5.370555\n",
+       "LongestRun_mi         0.181814\n",
+       "trams                -4.142024\n",
+       "SkiableTerrain_ac    -5.249780\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 21#\n",
+    "#Get the linear model coefficients from the `coef_` attribute and store in `coefs`,\n",
+    "#get the matching feature names from the column names of the dataframe,\n",
+    "#and display the results as a pandas Series with `coefs` as the values and `features` as the index,\n",
+    "#sorting the values in descending order\n",
+    "coefs = lr_grid_cv.best_estimator_.named_steps.linearregression.coef_\n",
+    "features = X_train.columns[selected]\n",
+    "pd.Series(coefs, index=features).sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These results suggest that vertical drop is your biggest positive feature. This makes intuitive sense and is consistent with what you saw during the EDA work. Also, you see the area covered by snow making equipment is a strong positive as well. People like guaranteed skiing! The skiable terrain area is negatively associated with ticket price! This seems odd. People will pay less for larger resorts? There could be all manner of reasons for this. It could be  an effect whereby larger resorts can host more visitors at any one time and so can charge less per ticket. As has been mentioned previously, the data are missing information about visitor numbers. Bear in mind,  the coefficient for skiable terrain is negative _for this model_. For example, if you kept the total number of chairs and fastQuads constant, but increased the skiable terrain extent, you might imagine the resort is worse off because the chairlift capacity is stretched thinner."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.10 Random Forest Model<a id='4.10_Random_Forest_Model'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A model that can work very well in a lot of cases is the random forest. For regression, this is provided by `sklearn`'s `RandomForestRegressor` class.\n",
+    "\n",
+    "Time to stop the bad practice of repeatedly checking performance on the test split. Instead, go straight from defining the pipeline to assessing performance using cross-validation. `cross_validate` will perform the fitting as part of the process. This uses the default settings for the random forest so you'll then proceed to investigate some different hyperparameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.10.1 Define the pipeline<a id='4.10.1_Define_the_pipeline'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 22#\n",
+    "#Define a pipeline comprising the steps:\n",
+    "#SimpleImputer() with a strategy of 'median'\n",
+    "#StandardScaler(),\n",
+    "#and then RandomForestRegressor() with a random state of 47\n",
+    "RF_pipe = make_pipeline(\n",
+    "    SimpleImputer(strategy='median'),\n",
+    "    StandardScaler(),\n",
+    "    RandomForestRegressor(random_state=47)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.10.2 Fit and assess performance using cross-validation<a id='4.10.2_Fit_and_assess_performance_using_cross-validation'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 23#\n",
+    "#Call `cross_validate` to estimate the pipeline's performance.\n",
+    "#Pass it the random forest pipe object, `X_train` and `y_train`,\n",
+    "#and get it to use 5-fold cross-validation\n",
+    "rf_default_cv_results = cross_validate( RF_pipe, X_train, y_train, cv=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.69249204, 0.78061953, 0.77546915, 0.62190924, 0.61742339])"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf_cv_scores = rf_default_cv_results['test_score']\n",
+    "rf_cv_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.6975826707112506, 0.07090742940774528)"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(rf_cv_scores), np.std(rf_cv_scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.10.3 Hyperparameter search using GridSearchCV<a id='4.10.3_Hyperparameter_search_using_GridSearchCV'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Random forest has a number of hyperparameters that can be explored, however here you'll limit yourselves to exploring some different values for the number of trees. You'll try it with and without feature scaling, and try both the mean and median as strategies for imputing missing values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'randomforestregressor__n_estimators': [10,\n",
+       "  12,\n",
+       "  16,\n",
+       "  20,\n",
+       "  26,\n",
+       "  33,\n",
+       "  42,\n",
+       "  54,\n",
+       "  69,\n",
+       "  88,\n",
+       "  112,\n",
+       "  143,\n",
+       "  183,\n",
+       "  233,\n",
+       "  297,\n",
+       "  379,\n",
+       "  483,\n",
+       "  615,\n",
+       "  784,\n",
+       "  1000],\n",
+       " 'standardscaler': [StandardScaler(), None],\n",
+       " 'simpleimputer__strategy': ['mean', 'median']}"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n_est = [int(n) for n in np.logspace(start=1, stop=3, num=20)]\n",
+    "grid_params = {\n",
+    "        'randomforestregressor__n_estimators': n_est,\n",
+    "        'standardscaler': [StandardScaler(), None],\n",
+    "        'simpleimputer__strategy': ['mean', 'median']\n",
+    "}\n",
+    "grid_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 24#\n",
+    "#Call `GridSearchCV` with the random forest pipeline, passing in the above `grid_params`\n",
+    "#dict for parameters to evaluate, 5-fold cross-validation, and all available CPU cores (if desired)\n",
+    "rf_grid_cv = GridSearchCV(RF_pipe, param_grid=grid_params, cv=5, n_jobs=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-5 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                        SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                       (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                                       (&#x27;randomforestregressor&#x27;,\n",
+       "                                        RandomForestRegressor(random_state=47))]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={&#x27;randomforestregressor__n_estimators&#x27;: [10, 12, 16, 20,\n",
+       "                                                                 26, 33, 42, 54,\n",
+       "                                                                 69, 88, 112,\n",
+       "                                                                 143, 183, 233,\n",
+       "                                                                 297, 379, 483,\n",
+       "                                                                 615, 784,\n",
+       "                                                                 1000],\n",
+       "                         &#x27;simpleimputer__strategy&#x27;: [&#x27;mean&#x27;, &#x27;median&#x27;],\n",
+       "                         &#x27;standardscaler&#x27;: [StandardScaler(), None]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[(&#x27;simpleimputer&#x27;,\n",
+       "                                        SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                                       (&#x27;standardscaler&#x27;, StandardScaler()),\n",
+       "                                       (&#x27;randomforestregressor&#x27;,\n",
+       "                                        RandomForestRegressor(random_state=47))]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={&#x27;randomforestregressor__n_estimators&#x27;: [10, 12, 16, 20,\n",
+       "                                                                 26, 33, 42, 54,\n",
+       "                                                                 69, 88, 112,\n",
+       "                                                                 143, 183, 233,\n",
+       "                                                                 297, 379, 483,\n",
+       "                                                                 615, 784,\n",
+       "                                                                 1000],\n",
+       "                         &#x27;simpleimputer__strategy&#x27;: [&#x27;mean&#x27;, &#x27;median&#x27;],\n",
+       "                         &#x27;standardscaler&#x27;: [StandardScaler(), None]})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),\n",
+       "                (&#x27;standardscaler&#x27;, None),\n",
+       "                (&#x27;randomforestregressor&#x27;,\n",
+       "                 RandomForestRegressor(n_estimators=69, random_state=47))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">None</label><div class=\"sk-toggleable__content fitted\"><pre>None</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(n_estimators=69, random_state=47)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=5,\n",
+       "             estimator=Pipeline(steps=[('simpleimputer',\n",
+       "                                        SimpleImputer(strategy='median')),\n",
+       "                                       ('standardscaler', StandardScaler()),\n",
+       "                                       ('randomforestregressor',\n",
+       "                                        RandomForestRegressor(random_state=47))]),\n",
+       "             n_jobs=-1,\n",
+       "             param_grid={'randomforestregressor__n_estimators': [10, 12, 16, 20,\n",
+       "                                                                 26, 33, 42, 54,\n",
+       "                                                                 69, 88, 112,\n",
+       "                                                                 143, 183, 233,\n",
+       "                                                                 297, 379, 483,\n",
+       "                                                                 615, 784,\n",
+       "                                                                 1000],\n",
+       "                         'simpleimputer__strategy': ['mean', 'median'],\n",
+       "                         'standardscaler': [StandardScaler(), None]})"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 25#\n",
+    "#Now call the `GridSearchCV`'s `fit()` method with `X_train` and `y_train` as arguments\n",
+    "#to actually start the grid search. This may take a minute or two.\n",
+    "rf_grid_cv.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'randomforestregressor__n_estimators': 69,\n",
+       " 'simpleimputer__strategy': 'median',\n",
+       " 'standardscaler': None}"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 26#\n",
+    "#Print the best params (`best_params_` attribute) from the grid search\n",
+    "rf_grid_cv.best_params_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It looks like imputing with the median helps, but scaling the features doesn't."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.6951357 , 0.79430697, 0.77170917, 0.62254707, 0.66499334])"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf_best_cv_results = cross_validate(rf_grid_cv.best_estimator_, X_train, y_train, cv=5)\n",
+    "rf_best_scores = rf_best_cv_results['test_score']\n",
+    "rf_best_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.7097384501425082, 0.06451341966873386)"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(rf_best_scores), np.std(rf_best_scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You've marginally improved upon the default CV results. Random forest has many more hyperparameters you could tune, but we won't dive into that here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 27#\n",
+    "#Plot a barplot of the random forest's feature importances,\n",
+    "#assigning the `feature_importances_` attribute of \n",
+    "#`rf_grid_cv.best_estimator_.named_steps.randomforestregressor` to the name `imps` to then\n",
+    "#create a pandas Series object of the feature importances, with the index given by the\n",
+    "#training data column names, sorting the values in descending order\n",
+    "plt.subplots(figsize=(10, 5))\n",
+    "imps = rf_grid_cv.best_estimator_.named_steps.randomforestregressor.feature_importances_\n",
+    "rf_feat_imps = pd.Series(imps, index=X_train.columns).sort_values(ascending=False)\n",
+    "rf_feat_imps.plot(kind='bar')\n",
+    "plt.xlabel('features')\n",
+    "plt.ylabel('importance')\n",
+    "plt.title('Best random forest regressor feature importances');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Encouragingly, the dominant top four features are in common with your linear model:\n",
+    "* fastQuads\n",
+    "* Runs\n",
+    "* Snow Making_ac\n",
+    "* vertical_drop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.11 Final Model Selection<a id='4.11_Final_Model_Selection'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Time to select your final model to use for further business modeling! It would be good to revisit the above model selection; there is undoubtedly more that could be done to explore possible hyperparameters.\n",
+    "It would also be worthwhile to investigate removing the least useful features. Gathering or calculating, and storing, features adds business cost and dependencies, so if features genuinely are not needed they should be removed.\n",
+    "Building a simpler model with fewer features can also have the advantage of being easier to sell (and/or explain) to stakeholders.\n",
+    "Certainly there seem to be four strong features here and so a model using only those would probably work well.\n",
+    "However, you want to explore some different scenarios where other features vary so keep the fuller \n",
+    "model for now. \n",
+    "The business is waiting for this model and you have something that you have confidence in to be much better than guessing with the average price.\n",
+    "\n",
+    "Or, rather, you have two \"somethings\". You built a best linear model and a best random forest model. You need to finally choose between them. You can calculate the mean absolute error using cross-validation. Although `cross-validate` defaults to the $R^2$ [metric for scoring](https://scikit-learn.org/stable/modules/model_evaluation.html#scoring) regression, you can specify the mean absolute error as an alternative via\n",
+    "the `scoring` parameter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.11.1 Linear regression model performance<a id='4.11.1_Linear_regression_model_performance'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 'neg_mean_absolute_error' uses the (negative of) the mean absolute error\n",
+    "lr_neg_mae = cross_validate(lr_grid_cv.best_estimator_, X_train, y_train, \n",
+    "                            scoring='neg_mean_absolute_error', cv=5, n_jobs=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10.499032338015294, 1.6220608976799664)"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr_mae_mean = np.mean(-1 * lr_neg_mae['test_score'])\n",
+    "lr_mae_std = np.std(-1 * lr_neg_mae['test_score'])\n",
+    "lr_mae_mean, lr_mae_std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11.793465668669326"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_test, lr_grid_cv.best_estimator_.predict(X_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.11.2 Random forest regression model performance<a id='4.11.2_Random_forest_regression_model_performance'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rf_neg_mae = cross_validate(rf_grid_cv.best_estimator_, X_train, y_train, \n",
+    "                            scoring='neg_mean_absolute_error', cv=5, n_jobs=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9.644639167595688, 1.3528565172191818)"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf_mae_mean = np.mean(-1 * rf_neg_mae['test_score'])\n",
+    "rf_mae_std = np.std(-1 * rf_neg_mae['test_score'])\n",
+    "rf_mae_mean, rf_mae_std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.537730050637332"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_absolute_error(y_test, rf_grid_cv.best_estimator_.predict(X_test))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.11.3 Conclusion<a id='4.11.3_Conclusion'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The random forest model has a lower cross-validation mean absolute error by almost \\\\$1. It also exhibits less variability. Verifying performance on the test set produces performance consistent with the cross-validation results."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.12 Data quantity assessment<a id='4.12_Data_quantity_assessment'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, you need to advise the business whether it needs to undertake further data collection. Would more data be useful? We're often led to believe more data is always good, but gathering data invariably has a cost associated with it. Assess this trade off by seeing how performance varies with differing data set sizes. The `learning_curve` function does this conveniently."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fractions = [.2, .25, .3, .35, .4, .45, .5, .6, .75, .8, 1.0]\n",
+    "train_size, train_scores, test_scores = learning_curve(pipe, X_train, y_train, train_sizes=fractions)\n",
+    "train_scores_mean = np.mean(train_scores, axis=1)\n",
+    "train_scores_std = np.std(train_scores, axis=1)\n",
+    "test_scores_mean = np.mean(test_scores, axis=1)\n",
+    "test_scores_std = np.std(test_scores, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/f75Ar8XJkyeVmZmpiIiIHOsuXbZ//361bdtWBw8e1BtvvKGVK1dqzZo1jutnCvph0c7V194uJCTE6bH92sLLHf9y/XL06FFJ+Q8ps9eSV72ZmZk6efKk0/JL2/71118yxig8PDzH+2D16tWX/RA3YsQIvfDCC7rtttv01Vdf6ZdfftGaNWt03XXXOb0GrrxXr4Sfn5/TUFspa5hgly5d9OWXX+qZZ57R999/r19//dURRFx5b1zar1JW37r6vrrc++L48ePy8PBQcHCwUztXh+pd7vU8fvy40tPT9dZbb+Xo11tuuUWSrvgD+ptvvqlnn31WCxYsUMeOHRUcHKzbbrtNu3btuuy2p0+f1i233KK0tDR9++23CgoKcqy72vdiXlzpy6NHj7o8WUluP0OSNHLkyBx1DxkyRNLF1/q1117TY489phYtWuiLL77Q6tWrtWbNGsXGxjrV07dvX3344Yfat2+f7rjjDoWFhalFixZaunSp03G/+uqrHMe0Dw21H9OVfQFlHdeYAaWMu7u7OnXqpG+//VZ//vmny7/kczsbEhISosOHD+dYfujQIUlZIVCSPDw8NGLECI0YMUKnTp3Sd999p3/84x/q2rWrDhw4ID8/P4WGhmratGmaNm2a9u/fr//9738aNWqUjhw5okWLFqlnz55as2ZNrrXde++9GjFihGbPnq0JEybok08+0W233aYKFSo42syZM0cxMTGaN2+e03NJSUlx6flnV6FCBdlsNkc4ye7SZQsWLFBycrK+/PJLx5kQSVf9XUyuvvZX63L9Yp9d7s8//8y3Vkl51uvm5ubUV1LO91toaKhsNptWrlyZ64Q1l5ttbs6cOXrwwQc1ceJEp+XHjh1T+fLlHY9dea9eidx+frZu3apNmzZp9uzZ6tevn2N5fHz8FR2jKISEhCg9PV0nTpxwCme5vfdzc7nXs0KFCnJ3d1ffvn31+OOP57qPmJiYK6rd399f48aN07hx4/TXX385zp717NnT8Uec3KSlpemOO+7Q7t27tXLlyhz/R17te/FqVKxYMd+ftexy+xmSssJy7969c93Gfq3lnDlz1KFDB02fPt1p/enTp3Ns89BDD+mhhx5ScnKyfvzxR40ZM0Y9evTQ77//rqpVqyo0NFSNGzfWhAkTcj2m/Y9JruwLKOs4YwaUQqNHj5YxRoMGDVJqamqO9Wlpafrqq68uu59OnTrphx9+cIQBu48//lh+fn65TtFdvnx53XnnnXr88cd14sSJXL9ktUqVKho6dKg6d+6s9evXS8r6gNisWTOnm12FChV022236eOPP9bXX3+thISEHMMYbTabvLy8nD6sJCQkuDQr46X8/f3VvHlzffnll05n206fPp3jdbMfL/uHNWOM3nvvvRz7LciZjit57a9Wbv3SqlUrBQUFacaMGTLG5LpdnTp1VKlSJX322WdObZKTk/XFF184ZmrMT48ePWSM0cGDB3O8D5o1a6ZGjRrlu73NZsvxgXnhwoU6ePBgntu48l61c/UM5KU1Zd/W7t1333V5H0Wtffv2krLOnGY3d+7cAu8rt9fTz89PHTt21IYNG9S4ceNc+9Ye7K/kNbYLDw9X//79de+992rnzp05hs5mN3DgQC1fvlxffvmlY8hvdlf7Xrwa3bp10++//64ffvihwNvWqVNHtWrV0qZNm3Ktu1mzZgoMDJSU+8/L5s2bcwyRzs7f31/dunXTc889p9TUVG3btk1S1uu1detW1ahRI9djZg9ml9sXUNZxxgwohVq2bKnp06dryJAhatq0qR577DE1aNBAaWlp2rBhg2bOnKmGDRvmer1UdmPGjHFcP/Diiy8qODhYn376qRYuXKjJkyc7hv/07NlTDRs2VLNmzVSxYkXt27dP06ZNU9WqVVWrVi0lJiaqY8eOuu+++1S3bl0FBgZqzZo1WrRoUZ5/2b3UgAEDNG/ePA0dOlSVK1fOcQ2Gfbr/IUOG6M4779SBAwf0z3/+U5GRkS4NbbrUP//5T8XGxjq+9y0jI0OvvPKK/P39HTOaSVnfC+fl5aV7771XzzzzjM6fP6/p06fnGLonZV0v9+WXX2r69Olq2rSp3NzcnAJodq6+9lfDlX4JCAjQ1KlT9fDDD+vmm2/WoEGDFB4ervj4eG3atElvv/223NzcNHnyZN1///3q0aOHBg8erJSUFL366qs6deqUXn755cvW0rp1az3yyCN66KGHtHbtWrVr107+/v46fPiwfvrpJzVq1EiPPfZYntv36NFDs2fPVt26ddW4cWOtW7dOr776ao6zIZd7r+alRo0a8vX11aeffqp69eopICBAUVFRuX7otKtbt65q1KihUaNGyRij4OBgffXVV8Vq6FZsbKxat26tp556SklJSWratKni4uL08ccfS5LTNYO5ceX1fOONN9SmTRu1bdtWjz32mKpVq6bTp08rPj5eX331lSOEFPQ1btGihXr06KHGjRurQoUK2r59uz755JN8/xDw6quv6pNPPtHf//53+fv7O13fVq5cOdWvX/+q34tXY/jw4Zo3b5569eqlUaNGqXnz5jp37pxWrFihHj16qGPHjvlu/+6776pbt27q2rWr+vfvr0qVKunEiRPavn271q9fr//85z+Ssn5e/vnPf2rMmDFq3769du7cqZdeekkxMTFOU9wPGjRIvr6+at26tSIjI5WQkKBJkyYpKCjI8fUGL730kpYuXapWrVpp2LBhqlOnjs6fP6+9e/fqm2++0YwZM1S5cmWX9gWUeVbNOgKg6G3cuNH069fPVKlSxXh5eRl/f3/TpEkT8+KLL5ojR4442lWtWtV07949131s2bLF9OzZ0wQFBRkvLy9z3XXX5Zg1berUqaZVq1YmNDTUeHl5mSpVqpiBAweavXv3GmOMOX/+vHn00UdN48aNTbly5Yyvr6+pU6eOGTNmjElOTnbpuWRkZJjo6GgjyTz33HO5tnn55ZdNtWrVjLe3t6lXr5557733cp2VzZVZGY0x5n//+59p3Lix4zm9/PLLue7vq6++Mtddd53x8fExlSpVMk8//bT59ttvjSSzbNkyR7sTJ06YO++805QvX97YbDan/SiXGfBcee3tszL+5z//cVqe13PKriD98s0335j27dsbf39/4+fnZ+rXr29eeeUVpzYLFiwwLVq0MD4+Psbf39906tTJ/Pzzz05t7K/f0aNHc63pww8/NC1atDD+/v7G19fX1KhRwzz44INm7dq1eT4PY4w5efKkGThwoAkLCzN+fn6mTZs2ZuXKlaZ9+/amffv2jnaXe6/m5/PPPzd169Y1np6eTv3Vr18/4+/vn+s2v/32m+ncubMJDAw0FSpUMHfddZfZv39/jv7Oaya/Bg0a5Nhnv379nGb3zG9Wxktf59yOc+LECfPQQw+Z8uXLGz8/P9O5c2ezevVqI8m88cYb+b4mrr6ee/bsMQMGDDCVKlUynp6epmLFiqZVq1Zm/PjxTu3yeo1zM2rUKNOsWTNToUIF4+3tbapXr26efPJJc+zYsRyvQ/bXTlKut+zvE2Ou/L14NX1pTNZ7+YknnjBVqlQxnp6eJiwszHTv3t3s2LHD8VpKMq+++mqux9+0aZO5++67TVhYmPH09DQRERHmpptuMjNmzHC0SUlJMSNHjjSVKlUyPj4+5oYbbjALFizIUc9HH31kOnbsaMLDw42Xl5eJiooyd999t9m8ebPTMY8ePWqGDRtmYmJijKenpwkODjZNmzY1zz33nDlz5kyB9gWUZTZj8hibAgAAyiT799L9/PPPatWqldXlAECZQDADAKAM+/zzz3Xw4EE1atRIbm5uWr16tV599VU1adLEMdU5AKDocY0ZAABlWGBgoObOnavx48crOTlZkZGR6t+/v8aPH291aQBQpnDGDAAAAAAsxnT5AAAAAGAxghkAAAAAWIxgBgAAAAAWY/KPy8jMzNShQ4cUGBgom81mdTkAAAAALGKM0enTpxUVFSU3t8I9x0Uwu4xDhw4pOjra6jIAAAAAFBMHDhxQ5cqVC3WfBLPLCAwMlJT14pcrV87iagAAAABYJSkpSdHR0Y6MUJgIZpdhH75Yrlw5ghkAAACAIrnEick/AAAAAMBiBDMAAAAAsBjBDAAAAAAsRjADAAAAAIsRzAAAAADAYgQzAAAAALAYwQwAAAAALEYwAwAAAACLEcwAAAAAwGIEMwAAAACwGMEMAAAAACxGMAMAAAAAixHMAAAAAMBiBDMAAAAAsBjBDABKkbOp6ao2aqGqjVqos6npVpcDAMBVKUu/1whmAJCHsvTLAAAAWItgBpQwJTEslMSagex4D6Mk4/0LlAwEMwAAABcQcAAUJYJZCcEvAwAAAKD08rC6AKC4OZuarvovLpYk/fZSV/l58WMCAABQFIwxSsswSs/MVFqGUVpGptLt/2YaJZ1Ps7rEa4ZPnAAAAEAJZIxReqbJCjKZFwONPdykZ2YqNf1i6EnPuBB+LrRNz8hUara2TsHIaX9ZbdMzzYX29jZGaemZF/efmam09FxqsdeYrTZHDZnG6pex2CCYAQAAoMwxxigjMysYZD9Lk5aZLcDkGlKyhR57YLnQJiukmGwhyJX9Ze0rPSNb6Lmwj7QLj3M7o2R/XBq5u9nk4WaTp7ubPNxtOnW2bJw1I5gBKLWMMUpJz9TZ1AydTU3X+bSMC/czdC41Q+fS7PfTs/5Ny1pub3M65eIvgkc/WSdPdzfZbDa5u0luNpvcbDbZbFn33d0u3nez/+uW7f6Ftu4Xlmdv626zyXahjZtNF7a7uG32Y7jZlG/btIxMR83f/faXfL08HPV6uLnJzS3reO5uWXV4XNje3c2+/6xl9vVZ9Uoebm6O++7ZtvFwy6odQNmTaT974kqQyXQ+83IxaNiX2c/u5LK/S4NTnkEmt/07B5mL+8+q0ZTCXONmkzzc3eTpZpOnh5s83Nzk6W6Th3tW0PF0ywo7Hu5u8nLP+t3gWHdhuafbhX/tyy7swx6UPN3dHMHJvo2Hm01eF46X1ca+XW5tnPd1sSabPN3c5OZ28fdK9ktMSjuCGQDL2MeVn0vN0Nm0dJcDU1bAutjeqU1aetY+UjN0Ni2j0H7p/rjrWOHs6BoaNnfjNTlO9sDpfiHYuWULeu4Xgp09FNrb2cOg+yXhMHtwdLcp93bZw2Se+7OHULescJp937asDwDZ9+cIqo52crTLHnjjdh+Xr6e7ZJNsygrONmUF5ov3JV267sJy+za65LEtx/4KsI9c1l3YPGeN2drJcVwXa7y0HaHcZdmHnKVnG8LldD/DOTRkZAsk9vsZmcZxBibn/rIts6/PNDqXbdKwf8zfIhnlehYn1zNAjkBkH6Z28QxQaR2B5nUhMOQMGs4hxcM9W1hxswcd+7ZXFmSy2uW+f88LQckeXi4NU/Y27m78XJZUBDMAReJsarp2H0lW/NHT2n74tGN57LSVOp92MThlXKPf7F4ebvL1dJefl7t8vS786+kuXy8P+V1Y7uPl7rhvP9M08ZsdkqTxtzWUp7tNGZlSpjEyxijTZN3PNFl/Oc7Mtsy+PiMzl7bGXGh/SVv7/cxL2l6638xsbS+pIS0jU7/sOSFJahJdXkZZ6zMys90uHD/jwr7SMzMdzysjM2td+iXt8gu4xkjpxqjUfkq7xMCP1lpdQrGTb/BT3uFO2R9fuk65hdOc+3Ec35VjXLIPXbo8R7DNvo2cfg76vLtaRhevm8nIvBhe7PcdwerCz15xsGDDoSLdvz245B1k7MsuE2TyOIuTtf7iPjw9LncGyJX9XQw/7owCgIUIZgCuyonkVMUfOXPxdvSMdh85o4OnzuXafv+Js7ku93CzOQKTn5fHhdB0MUDZA5Wvp0e2+9mDlvM2F4NX1r8e7gX/dpCzqemOYNb7hkolYobO7EM+Ph3UotBqtge0jAsBNCvAXQh12cKiI9Rla2e/n34h9GVcEgqz2imfdheWXbK/rG2yQmX2EJk9hOYdSu3bGqdQmnt4zRouZf8DQ62wANlsWR/SjbLOhBhJuuRx1vqsUGv/QJ/rOtnXmxz7zNrWhf071ue+j6J2sYbsByseQaSobDmYWCj78XTPCgP24OB+IaC4uzmHBfuZkOzBJ2ud8xmTrMDj3MZIen/lHknSkzfXkp+XR8GDTF5ngLIHI0INcFWK/6cMAJYzxuhw4nmn8GW/fyI5Nc/tQvy9VCMsQNVC/PTvtX9KkuYMbK4K/l4XQpWHI0h5XkFwwrXj5maTm2zydLe6EmtkD7z/Hdq6RIT0SxmTR/CTyRYc8wl3+awzF1Jj7qHTxf1nr+PStgWp85LAW6A6dcnzvGRdSlqGY4jwv+5rIn9vD6cA5AhH9rDjdvG6Hnv4cref6XF3c1wzWtTOpqY7gtmgdtVL5PsXKAv4yUSR4jvBSpb0jEztO3HWEbp2ZzsDlpyaked2lcr7qmZYgPOtYoAq+HtJynof2IPZDVUr8D4ALGAfLnjhkZWllFhns12r1bFuGP+XAdeAn5eH9r7c3eoyrgn+RwHKoPNpGdqd7ayX/bb3eHKeU+96uNlULdRfNSs6B7DqFf35cAIAAHCV+DQFlGKJZ9MUf/R0jmvA/jx5Ls/rTnw93VUjLGcAqxriz3BDAACAIkIwA0o4Y4z+Skq5ELxOZ7v+K1nHzqTkuV0FP09H6KqRLYRFBfk6fX8IAAAAih7BDCjGjDE6k5Ku42dSdTw5RcfOpOpw4sXZDu+ZuVp7jibrdEp6nvuICvJRjUuu/aoZFqCQAO9r8RQAAADgAoIZcI2dT8vQ8eRUnTiTqmPJKVmh60yKjien6tiZFEcIy/o3VanpmXnua/OfWdM1u7vZVDXY72IAuxC+aoQFKMCbH/MrVZYuOAYAANbiExtwldIzMnXybJpTmDqeLWAdyxa8jp9J1Zl8zm7lxd/LXSEB3goJ8FJ5P08t23FUkvR6n+vUICpI1UL85eXB9V8gTALIif8XgJKBYAbkY92+k0pOSdexM6k6cSFwHXMKXqk6eTa1wF/g6uluU4h/VtAKCfBWqL+X436Iv5dCA7ydHvtk+/Ko7F9B0LVBRImYEZEPBSjpeA9D4n0AoGgV/090l3jnnXf06quv6vDhw2rQoIGmTZumtm3b5tp2+fLl6tixY47l27dvV926dYu6VJQgxhj9cSxZP+06puU7jziW9/3gV5e2t9mkCn5eCskWsLLC1oWAZQ9hF5aV8/G4Jl8qCgAAgJKhRAWzefPmafjw4XrnnXfUunVrvfvuu+rWrZt+++03ValSJc/tdu7cqXLlyjkeV6xY8VqUi2Lu1NlU/Rx/XCt3HdXKXcd08NS5HG2ig31VMcA7K2gFeCnY/2LICs0Wuir4ecqDqeQBAABwhUpUMHvttdc0cOBAPfzww5KkadOmafHixZo+fbomTZqU53ZhYWEqX778NaoSxVVaRqY27D+llbuO6sddx7T5z1NOQxC93N30t5gKahETrNeW7pIkLR7erkQMFQQAAEDJVmI+caampmrdunUaNWqU0/IuXbpo1apV+W7bpEkTnT9/XvXr19fzzz+f6/BGu5SUFKWkXPzup6SkpKsrHJYxxmjPsWSt3HVMK3cdU9zuY0pOzXBqUzs8QG1rVVTbWqFqERMiXy93nU1NdwQzAAAA4FooMcHs2LFjysjIUHh4uNPy8PBwJSQk5LpNZGSkZs6cqaZNmyolJUWffPKJOnXqpOXLl6tdu3a5bjNp0iSNGzeu0OvHtZF4Nk0/7z6WdVbs95zDE4P9vdSmZqja1gpV21oVFRHkY1GlAAAAwEUlJpjZXTphgjEmz0kU6tSpozp16jget2zZUgcOHNCUKVPyDGajR4/WiBEjHI+TkpIUHR1dCJWjKKRlZGrjgVNa+fvF4YmZlwxPbFatguOsWP3IcnJzY9INAAAAFC8lJpiFhobK3d09x9mxI0eO5DiLlp8bb7xRc+bMyXO9t7e3vL29r7hOFC1jjPYeP+uYsCNu9/Ec3wtWK+zC8MTaoWoRE8w1YgAAACj2SswnVi8vLzVt2lRLly7V7bff7li+dOlS9erVy+X9bNiwQZGRkUVRIopI4tk0rdp9TD/uyhqi+OdJ5+GJFfw81ebCGbG2tUIVGeRrUaUAAADAlSkxwUySRowYob59+6pZs2Zq2bKlZs6cqf379+vRRx+VlDUM8eDBg/r4448lZc3aWK1aNTVo0ECpqamaM2eOvvjiC33xxRdWPg1cRlpGpjYdOOUIYpsOOA9P9HS3qWnVrOGJ7WpVVIMohicCAACgZCtRwaxPnz46fvy4XnrpJR0+fFgNGzbUN998o6pVq0qSDh8+rP379zvap6amauTIkTp48KB8fX3VoEEDLVy4ULfccotVTwF52Hc8OSuI/X5UcbuP6/QlwxNrhgWoba1QtatVUc1jguXvXaLeugAAAEC+Styn2yFDhmjIkCG5rps9e7bT42eeeUbPPPPMNagKBZV4Lk1x2YYnHjjhPDyxvJ+n2tTMCmJtaoUqqjzDEwEAAFB6lbhghpJrw/6T+nXPSa3cdVQb8xme2LZWqBpEBcmd4YkAAAAoIwhmKFKnz6c57t///q9O62pU9M+6Tqx21pc7MzwRAAAAZRWfhFGkFmw45Lgf5OupNrVC1a5WqNrUqqhKDE8EAAAAJBHMUMSW/vaX4/5Pz3ZUoI+nhdWUDn5eHtr7cnerywAAAEAhcrO6AJRex86kaP3+k47HXDMGAAAA5I5ghiLz3W9/OU3wAQAAACB3BDMUmcXbEqwuAQAAACgRuMYMReL0+TT9HH/c6jKuCNdwAQAA4FrjjBmKxLKdR5WakamYUH+rSwEAAACKPYIZioR9GGOnemEWVwIAAAAUfwQzFLrzaRlavuOIJKlz/XCLqwEAAACKP4IZCt3P8ceUnJqhyCAfNYwqZ3U5AAAAQLFHMEOhW7Q1axhj1wYRstn47jIAAADgcghmKFTpGZn6bvtfkqQuDRjGCAAAALiCYIZCtWbvSZ08m6YKfp5qXi3Y6nIAAACAEoFghkJln43x5nrh8nDn7QUAAAC4gk/OKDTGGEcw69ogwuJqAAAAgJKDYIZCs/nPRB1OPC8/L3e1qRVqdTkAAABAiUEwQ6Gxny3rWCdMPp7uFlcDAAAAlBwEMxSaRReCGbMxAgAAAAVDMEOhiD9yWn8cTZaXu5tuqhtmdTkAAABAiUIwQ6FYvC3ru8ta1QxRoI+nxdUAAAAAJQvBDIVi0dasYYyxzMYIAAAAFBjBDFft4Klz2nIwUW426eb6XF8GAAAAFJSH1QWg5Ft84WxZs2rBCg3wdlrn5+WhvS93t6IsAAAAoMTgjBmuGl8qDQAAAFwdghmuyvEzKVqz94QkqQvDGAEAAIArQjDDVflu+1/KNFLDSuUUHexndTkAAABAiUQww1Wxz8bYtT7DGAEAAIArRTDDFTt9Pk0/xx+XJMU2JJgBAAAAV4pghiu2fOdRpWZkqnqov2qGBVhdDgAAAFBiEcxwxRbZZ2NsGCGbzWZxNQAAAEDJRTDDFTmflqHlO45IYpp8AAAA4GoRzHBFfo4/puTUDEWU81HjSkFWlwMAAACUaAQzXJGLXyodLjc3hjECAAAAV4NghgJLz8jU0t/+ksQwRgAAAKAwEMxQYGv2ntTJs2kq7+ep5jHBVpcDAAAAlHgEMxSYfRjjzfXC5eHOWwgAAAC4WnyqRoEYY7TkQjCLZRgjAAAAUCgIZiiQLQcTdSjxvPy83NWmVqjV5QAAAAClAsEMBbJoa9bZsg51KsrH093iagAAAIDSgWCGArk4TT7DGAEAAIDCQjCDy+KPnNbuo8nydLepY90wq8sBAAAASo0SF8zeeecdxcTEyMfHR02bNtXKlSvzbb9ixQo1bdpUPj4+ql69umbMmHGNKi19Fm/L+u6y1jVDVc7H0+JqAAAAgNKjRAWzefPmafjw4Xruuee0YcMGtW3bVt26ddP+/ftzbb9nzx7dcsstatu2rTZs2KB//OMfGjZsmL744otrXHnpwDBGAAAAoGiUqGD22muvaeDAgXr44YdVr149TZs2TdHR0Zo+fXqu7WfMmKEqVapo2rRpqlevnh5++GENGDBAU6ZMucaVl3wHT53T5j8TZbNJneuHW10OAAAAUKqUmGCWmpqqdevWqUuXLk7Lu3TpolWrVuW6TVxcXI72Xbt21dq1a5WWlpbrNikpKUpKSnK6QY7vLvtb1WCFBnhbXA0AAABQupSYYHbs2DFlZGQoPNz5bE14eLgSEhJy3SYhISHX9unp6Tp27Fiu20yaNElBQUGOW3R0dOE8gRLOPk1+lwacLQMAAAAKW4kJZnY2m83psTEmx7LLtc9tud3o0aOVmJjouB04cOAqKy75jp9J0Zq9JyRxfRkAAABQFDysLsBVoaGhcnd3z3F27MiRIznOitlFRETk2t7Dw0MhISG5buPt7S1vb4bqZffd9r+UaaQGUeUUHexndTkAAABAqVNizph5eXmpadOmWrp0qdPypUuXqlWrVrlu07JlyxztlyxZombNmsnTk+neXWWfJj+Ws2UAAABAkSgxwUySRowYoffff18ffvihtm/frieffFL79+/Xo48+KilrGOKDDz7oaP/oo49q3759GjFihLZv364PP/xQH3zwgUaOHGnVUyhxzqSk66ddWdfjdW1IMAMAAACKQokZyihJffr00fHjx/XSSy/p8OHDatiwob755htVrVpVknT48GGn7zSLiYnRN998oyeffFL/+te/FBUVpTfffFN33HGHVU+hxFm244hSMzJVPdRftcICrC4HAAAAKJVKVDCTpCFDhmjIkCG5rps9e3aOZe3bt9f69euLuKrSy/6l0l0aROQ7yQoAAACAK1eihjLi2jqflqFlO45IkroyTT4AAABQZAhmyNOq3ceUnJqhiHI+uq5yeavLAQAAAEotghnylP1Lpd3cGMYIAAAAFBWCGXKVnpGp77ZnDWNkmnwAAACgaBHMkKu1+07qRHKqyvt5qnlMsNXlAAAAAKUawQy5sg9j7FQ3XB7uvE0AAACAosQnbuRgjNGSC9Pkx/Kl0gAAAECRI5ghhy0HE3Uo8bz8vNzVtlao1eUAAAAApR7BDDnYv1S6Q52K8vF0t7gaAAAAoPQjmCEH+/VlXZmNEQAAALgmCGZwEn/ktHYfTZanu00d64ZZXQ4AAABQJhDM4GTxtr8kSa1qhKqcj6fF1QAAAABlA8EMTuzXlzGMEQAAALh2CGZwOHjqnDb/mSibTepcP9zqcgAAAIAyg2AGB/t3lzWrWkEVA70trgYAAAAoOwhmcGAYIwAAAGANghkkScfPpOjXPSckEcwAAACAa41gBknS99uPKNNIDaLKKTrYz+pyAAAAgDKFYAZJ0iKGMQIAAACWIZhBZ1LS9dOuY5IIZgAAAIAVCGbQ8p1HlJqRqZhQf9UOD7C6HAAAAKDMIZhBi7ZmDWPs0iBcNpvN4moAAACAsodgVsadT8vQsh1HJEmxDGMEAAAALEEwK+NW7T6m5NQMhZfz1nWVy1tdDgAAAFAmEczKuMVb/5KUNemHmxvDGAEAAAArEMzKsPSMTC3dfjGYAQAAALAGwawMW7vvpE4kpyrI11PNY4KtLgcAAAAoswhmZdjiC18qfXO9cHm681YAAAAArMKn8TLKGKMl2+zDGMMtrgYAAAAo2whmZdTWg0k6eOqcfD3d1a52RavLAQAAAMo0glkZtWjbYUlShzoV5ePpbnE1AAAAQNlGMCujFl8YxhjbkNkYAQAAAKsRzMqg+CNnFH/kjDzdbepYN8zqcgAAAIAyj2BWBtlnY2xZI1TlfDwtrgYAAAAAwawMWnIhmMXypdIAAABAsUAwK2MOnTqnTX8mymaTOtdnmnwAAACgOCCYlTH2s2XNqlZQxUBvi6sBAAAAIBHMypxFF4JZV4YxAgAAAMUGwawMOZGcql/3nJBEMAMAAACKE4JZGfLdb38p00j1I8spOtjP6nIAAAAAXEAwK0MWM4wRAAAAKJYIZmXEmZR0rYw/JkmKbUgwAwAAAIoTglkZsXznEaWmZ6paiJ9qhwdYXQ4AAACAbAhmZcTibX9Jkro2jJDNZrO4GgAAAADZlZhgdvLkSfXt21dBQUEKCgpS3759derUqXy36d+/v2w2m9PtxhtvvDYFFyMp6RlatuOIJK4vAwAAAIojD6sLcNV9992nP//8U4sWLZIkPfLII+rbt6+++uqrfLeLjY3VrFmzHI+9vLyKtM7iaFX8cZ1JSVdYoLeur1ze6nIAAAAAXKJEBLPt27dr0aJFWr16tVq0aCFJeu+999SyZUvt3LlTderUyXNbb29vRUS4fpYoJSVFKSkpjsdJSUlXXngxsWjrxdkY3dwYxggAAAAUNyViKGNcXJyCgoIcoUySbrzxRgUFBWnVqlX5brt8+XKFhYWpdu3aGjRokI4cOZJv+0mTJjmGSwYFBSk6OrpQnoNVMjKNlm6/cH0ZwxgBAACAYqlEBLOEhASFhYXlWB4WFqaEhIQ8t+vWrZs+/fRT/fDDD5o6darWrFmjm266yemM2KVGjx6txMREx+3AgQOF8hyssmbvCZ1ITlWQr6daVA+2uhwAAAAAubB0KOPYsWM1bty4fNusWbNGknKdSdAYk+8Mg3369HHcb9iwoZo1a6aqVatq4cKF6t27d67beHt7y9vb25XySwT7l0p3qhcmT/cSkcMBAACAMsfSYDZ06FDdc889+bapVq2aNm/erL/++ivHuqNHjyo8PNzl40VGRqpq1aratWtXgWstiYwxWnJhmvxYhjECAAAAxZalwSw0NFShoaGXbdeyZUslJibq119/VfPmzSVJv/zyixITE9WqVSuXj3f8+HEdOHBAkZGRV1xzSbL1YJIOnjonX093tatd0epyAAAAAOShRIxtq1evnmJjYzVo0CCtXr1aq1ev1qBBg9SjRw+nGRnr1q2r+fPnS5LOnDmjkSNHKi4uTnv37tXy5cvVs2dPhYaG6vbbb7fqqVxT9mGM7WtXlI+nu8XVAAAAAMhLiQhmkvTpp5+qUaNG6tKli7p06aLGjRvrk08+cWqzc+dOJSYmSpLc3d21ZcsW9erVS7Vr11a/fv1Uu3ZtxcXFKTAw0IqncM0tuhDMYhsyjBEAAAAozkrE95hJUnBwsObMmZNvG2OM476vr68WL15c1GUVW/FHzij+yBl5uNnUsW7OGS0BAAAAFB8l5owZCsY+jLFVzVAF+XpaXA0AAACA/BDMSqklF4JZ1wauz1oJAAAAwBpXHcySkpK0YMECbd++vTDqQSE4dOqcNv2ZKJtN6lyfYAYAAAAUdwUOZnfffbfefvttSdK5c+fUrFkz3X333WrcuLG++OKLQi8QBWc/W9a0SgWFBfpYXA0AAACAyylwMPvxxx/Vtm1bSdL8+fNljNGpU6f05ptvavz48YVeIApu8YUvle7Kl0oDAAAAJUKBg1liYqKCg4MlSYsWLdIdd9whPz8/de/eXbt27Sr0AlEwJ5JT9cue45IIZgAAAEBJUeBgFh0drbi4OCUnJ2vRokXq0qWLJOnkyZPy8WHYnNW+2/6XMo1UL7KcqoT4WV0OAAAAABcU+HvMhg8frvvvv18BAQGqUqWKOnToIClriGOjRo0Kuz4U0OKtF75UmrNlAAAAQIlR4GA2ZMgQNW/eXAcOHFDnzp3l5pZ10q169epcY2axMynpWhl/TJLUtSGzMQIAAAAlRYGDmSQ1a9ZMjRs31p49e1SjRg15eHioe/fuhV0bCmjFzqNKTc9UtRA/1QkPtLocAAAAAC4q8DVmZ8+e1cCBA+Xn56cGDRpo//79kqRhw4bp5ZdfLvQC4bpFji+VjpDNZrO4GgAAAACuKnAwGz16tDZt2qTly5c7TfZx8803a968eYVaHFyXkp6hZTuOSJK6cH0ZAAAAUKIUeCjjggULNG/ePN14441OZ2Xq16+v3bt3F2pxcN2q+OM6k5KusEBvNYkub3U5AAAAAAqgwGfMjh49qrCwsBzLk5OTGT5nocUXhjF2aRAuNzf6AQAAAChJChzM/va3v2nhwoWOx/Yw9t5776lly5aFVxlclpFptPS3vyRJsQ0iLa4GAAAAQEEVeCjjpEmTFBsbq99++03p6el64403tG3bNsXFxWnFihVFUSMuY+3eEzqenKogX0+1qB5sdTkAAAAACqjAZ8xatWqlVatW6ezZs6pRo4aWLFmi8PBwxcXFqWnTpkVRIy5j8bass2Wd6oXJ073AXQoAAADAYgU6Y5aWlqZHHnlEL7zwgj766KOiqgkFYIxxXF/WldkYAQAAgBKpQKdXPD09NX/+/KKqBVdg26EkHTx1Tj6ebmpXq6LV5QAAAAC4AgUe93b77bdrwYIFRVAKrsSirVlnyzrUDpOvl7vF1QAAAAC4EgWe/KNmzZr65z//qVWrVqlp06by9/d3Wj9s2LBCKw6X5xjG2DDc4koAAAAAXKkCB7P3339f5cuX17p167Ru3TqndTabjWB2De0+eka7jpyRh5tNN9UlmAEAAAAlVYGD2Z49e4qiDlwB+9myljVCFOTraXE1AAAAAK7UVc2tboyRMaawakEB2afJZzZGAAAAoGS7omD28ccfq1GjRvL19ZWvr68aN26sTz75pLBrQz4OJ57TpgOnZLNJXeozjBEAAAAoyQo8lPG1117TCy+8oKFDh6p169Yyxujnn3/Wo48+qmPHjunJJ58sijpxiSUXzpbdUKWCwsr5WFwNAAAAgKtR4GD21ltvafr06XrwwQcdy3r16qUGDRpo7NixBLNrxD5NfizDGAEAAIASr8BDGQ8fPqxWrVrlWN6qVSsdPny4UIpC/k4mp+rXvSckcX0ZAAAAUBoUOJjVrFlT//73v3MsnzdvnmrVqlUoRSF/y3YeVUamUb3IcqoS4md1OQAAAACuUoGHMo4bN059+vTRjz/+qNatW8tms+mnn37S999/n2tgQ+H7frt9NkYm/QAAAABKgwKfMbvjjjv0yy+/KDQ0VAsWLNCXX36p0NBQ/frrr7r99tuLokZc4ufdxyUxjBEAAAAoLQp8xkySmjZtqjlz5hR2LXBRanqmqob4qW5EoNWlAAAAACgEBT5j9s0332jx4sU5li9evFjffvttoRSFy+vaIEI2m83qMgAAAAAUggIHs1GjRikjIyPHcmOMRo0aVShF4fIYxggAAACUHgUOZrt27VL9+vVzLK9bt67i4+MLpSjkr2Kgt5pEl7e6DAAAAACFpMDBLCgoSH/88UeO5fHx8fL39y+UopC/TnXD5ObGMEYAAACgtChwMLv11ls1fPhw7d6927EsPj5eTz31lG699dZCLQ4XZWYax/2b64VZWAkAAACAwlbgYPbqq6/K399fdevWVUxMjGJiYlSvXj2FhIRoypQpRVEjJKczZH+LCbawEgAAAACFrcDT5QcFBWnVqlVaunSpNm3aJF9fXzVu3Fjt2rUrivqQC0/3AudpAAAAAMXYFX2Pmc1mU5cuXdSlSxdJ0qlTpwqzJgAAAAAoUwp86uWVV17RvHnzHI/vvvtuhYSEqFKlStq0aVOhFgcAAAAAZUGBg9m7776r6OhoSdLSpUu1dOlSffvtt+rWrZuefvrpQi8QAAAAAEq7Ag9lPHz4sCOYff3117r77rvVpUsXVatWTS1atCj0AgEAAACgtCvwGbMKFSrowIEDkqRFixbp5ptvliQZY5SRkVG41WUzYcIEtWrVSn5+fipfvrxL2xhjNHbsWEVFRcnX11cdOnTQtm3biqxGAAAAALgSBQ5mvXv31n333afOnTvr+PHj6tatmyRp48aNqlmzZqEXaJeamqq77rpLjz32mMvbTJ48Wa+99prefvttrVmzRhEREercubNOnz5dZHUCAAAAQEEVeCjj66+/rmrVqunAgQOaPHmyAgICJGUNcRwyZEihF2g3btw4SdLs2bNdam+M0bRp0/Tcc8+pd+/ekqSPPvpI4eHh+uyzzzR48OCiKhUAAAAACqTAwczT01MjR47MsXz48OGFUU+h2bNnjxISEhxT+kuSt7e32rdvr1WrVuUZzFJSUpSSkuJ4nJSUVOS1AgAAACjbSu03FSckJEiSwsPDnZaHh4c71uVm0qRJCgoKctzsE50AAAAAQFGxNJiNHTtWNpst39vatWuv6hg2m83psTEmx7LsRo8ercTERMfNPtEJAAAAABSVAg9lLExDhw7VPffck2+batWqXdG+IyIiJGWdOYuMjHQsP3LkSI6zaNl5e3vL29v7io4JAAAAAFfC5WCWnp4uD4/CzXGhoaEKDQ0t1H3axcTEKCIiQkuXLlWTJk0kZc3suGLFCr3yyitFckwAAAAAuBIuD2WMjIzUyJEjtX379qKsJ0/79+/Xxo0btX//fmVkZGjjxo3auHGjzpw542hTt25dzZ8/X1LWEMbhw4dr4sSJmj9/vrZu3ar+/fvLz89P9913nyXPAQAAAABy4/IpsBEjRmj27Nl6/fXX1bx5cz388MPq06ePY7r8ovbiiy/qo48+cjy2nwVbtmyZOnToIEnauXOnEhMTHW2eeeYZnTt3TkOGDNHJkyfVokULLVmyRIGBgdekZgAAAABwhc0YYwqywcqVK/Xhhx/q//7v/yRJd955px5++GG1bt26SAq0WlJSkoKCgpSYmKhy5cpZVsfZ1HTVf3GxJOm3l7rKz8vSywMBAACAMqcos0GBZ2Vs27atZs2apYSEBE2bNk3x8fFq27at6tSpo8mTJxdqcQAAAABQFlzxdPn+/v4aOHCgVq5cqa+++krHjh3T6NGjC7M2AAAAACgTrjiYnT17VrNmzVK7du106623KiQkRBMmTCjM2gAAAACgTCjwhUorV67UrFmz9H//93/KyMjQnXfeqfHjx6tdu3ZFUR8AAAAAlHouB7OJEydq9uzZ2r17t5o1a6ZXX31V9957r6UTYgAAAABAaeByMHv99df1wAMPaODAgWrYsGFR1gQAAAAAZYrLwezQoUPy9PQsyloAAAAAoExyefKPlStXqn79+kpKSsqxLjExUQ0aNNDKlSsLtTgAAAAAKAtcDmbTpk3ToEGDcr2mLCgoSIMHD9Zrr71WqMUBAAAAQFngcjDbtGmTYmNj81zfpUsXrVu3rlCKAgAAAICyxOVg9tdff+V7jZmHh4eOHj1aKEUBAAAAQFnicjCrVKmStmzZkuf6zZs3KzIyslCKAgAAAICyxOVgdsstt+jFF1/U+fPnc6w7d+6cxowZox49ehRqcQAAAABQFrg8Xf7zzz+vL7/8UrVr19bQoUNVp04d2Ww2bd++Xf/617+UkZGh5557rihrBQAAAIBSyeVgFh4erlWrVumxxx7T6NGjZYyRJNlsNnXt2lXvvPOOwsPDi6xQAAAAACitXA5mklS1alV98803OnnypOLj42WMUa1atVShQoWiqg8AAAAASr0CBTO7ChUq6G9/+1th1wIAAAAAZZLLk38AAAAAAIoGwQwAAAAALEYwAwAAAACLEcwAAAAAwGIEMwAAAACwGMEMAAAAACxGMAMAAAAAixHMAAAAAMBiBDMAAAAAsBjBDAAAAAAsRjADAAAAAIsRzAAAAADAYgQzAAAAALAYwQwAAAAALEYwAwAAAACLEcwAAAAAwGIEMwAAAACwGMEMAAAAACxGMAMAAAAAixHMAAAAAMBiBDMAAAAAsBjBDAAAAAAsRjADAAAAAIsRzAAAAADAYgQzAAAAALAYwQwAAAAALFZigtmECRPUqlUr+fn5qXz58i5t079/f9lsNqfbjTfeWLSFAgAAAEABlZhglpqaqrvuukuPPfZYgbaLjY3V4cOHHbdvvvmmiCoEAAAAgCvjYXUBrho3bpwkafbs2QXaztvbWxEREUVQEQAAAAAUjhJzxuxKLV++XGFhYapdu7YGDRqkI0eO5Ns+JSVFSUlJTjcAAAAAKEqlOph169ZNn376qX744QdNnTpVa9as0U033aSUlJQ8t5k0aZKCgoIct+jo6GtYMQAAAICyyNJgNnbs2ByTc1x6W7t27RXvv0+fPurevbsaNmyonj176ttvv9Xvv/+uhQsX5rnN6NGjlZiY6LgdOHDgio8PAAAAAK6w9BqzoUOH6p577sm3TbVq1QrteJGRkapatap27dqVZxtvb295e3sX2jEBAAAA4HIsDWahoaEKDQ29Zsc7fvy4Dhw4oMjIyGt2TAAAAAC4nBJzjdn+/fu1ceNG7d+/XxkZGdq4caM2btyoM2fOONrUrVtX8+fPlySdOXNGI0eOVFxcnPbu3avly5erZ8+eCg0N1e23327V0wAAAACAHErMdPkvvviiPvroI8fjJk2aSJKWLVumDh06SJJ27typxMRESZK7u7u2bNmijz/+WKdOnVJkZKQ6duyoefPmKTAw8JrXDwAAAAB5KTHBbPbs2Zf9DjNjjOO+r6+vFi9eXMRVAQAAAMDVKzFDGQEAAACgtCKYAQAAAIDFCGYAAAAAYDGCGQAAAABYjGAGAAAAABYjmAEAAACAxQhmAAAAAGAxghkAAAAAWIxgBgAAAAAWI5gBAAAAgMUIZgAAAABgMYIZAAAAAFiMYAYAAAAAFiOYAQAAAIDFCGYAAAAAYDGCGQAAAABYjGAGAAAAABYjmAEAAACAxQhmAAAAAGAxghkAAAAAWIxgBgAAAAAWI5gBAAAAgMUIZgAAAABgMYIZAAAAAFiMYAYAAAAAFiOYAQAAAIDFCGYAAAAAYDGCGQAAAABYjGAGAAAAABYjmAEAAACAxQhmAAAAAGAxghkAAAAAWIxgBgAAAAAWI5gBAAAAgMUIZgAAAABgMYIZAAAAAFiMYAYAAAAAFiOYAQAAAIDFCGYAAAAAYDGCGQAAAABYjGAGAAAAABYjmAEAAACAxQhmAAAAAGCxEhHM9u7dq4EDByomJka+vr6qUaOGxowZo9TU1Hy3M8Zo7NixioqKkq+vrzp06KBt27Zdo6oBAAAAwDUlIpjt2LFDmZmZevfdd7Vt2za9/vrrmjFjhv7xj3/ku93kyZP12muv6e2339aaNWsUERGhzp076/Tp09eocgAAAAC4PJsxxlhdxJV49dVXNX36dP3xxx+5rjfGKCoqSsOHD9ezzz4rSUpJSVF4eLheeeUVDR482KXjJCUlKSgoSImJiSpXrlyh1V9QZ1PTVf/FxZKk317qKj8vD8tqAQAAAMqioswGJeKMWW4SExMVHByc5/o9e/YoISFBXbp0cSzz9vZW+/bttWrVqjy3S0lJUVJSktMNAAAAAIpSiQxmu3fv1ltvvaVHH300zzYJCQmSpPDwcKfl4eHhjnW5mTRpkoKCghy36OjowikaAAAAAPJgaTAbO3asbDZbvre1a9c6bXPo0CHFxsbqrrvu0sMPP3zZY9hsNqfHxpgcy7IbPXq0EhMTHbcDBw5c2ZMDAAAAABdZeqHS0KFDdc899+Tbplq1ao77hw4dUseOHdWyZUvNnDkz3+0iIiIkZZ05i4yMdCw/cuRIjrNo2Xl7e8vb29uF6gEAAACgcFgazEJDQxUaGupS24MHD6pjx45q2rSpZs2aJTe3/E/2xcTEKCIiQkuXLlWTJk0kSampqVqxYoVeeeWVq64dAAAAAApLibjG7NChQ+rQoYOio6M1ZcoUHT16VAkJCTmuFatbt67mz58vKWsI4/DhwzVx4kTNnz9fW7duVf/+/eXn56f77rvPiqcBAAAAALkqEXOuL1myRPHx8YqPj1flypWd1mWf7X/nzp1KTEx0PH7mmWd07tw5DRkyRCdPnlSLFi20ZMkSBQYGXrPaAQAAAOBySuz3mF0rfI8ZAAAAAInvMQMAAACAUo1gBgAAAAAWI5gBAAAAgMUIZgAAAABgMYIZAAAAAFiMYAYAAAAAFiOYAQAAAIDFCGYAAAAAYDGCGQAAAABYjGAGAAAAABYjmAEAAACAxQhmAAAAAGAxghkAAAAAWIxgBgAAAAAWI5gBAAAAgMUIZgAAAABgMYIZAAAAAFiMYAYAAAAAFiOYAQAAAIDFCGYAAAAAYDGCGQAAAABYjGAGAAAAABYjmAEAAACAxQhmAAAAAGAxghkAAAAAWIxgBgAAAAAWI5gBAAAAgMUIZgAAAABgMYIZAAAAAFiMYAYAAAAAFiOYAQAAAIDFCGYAAAAAYDGCGQAAAABYjGAGAAAAABYjmAEAAACAxQhmAAAAAGAxghkAAAAAWIxgBgAAAAAWI5gBAAAAgMUIZgAAAABgMYIZAAAAAFiMYAYAAAAAFiOYAQAAAIDFCGYAAAAAYLESEcz27t2rgQMHKiYmRr6+vqpRo4bGjBmj1NTUfLfr37+/bDab0+3GG2+8RlUDAAAAgGs8rC7AFTt27FBmZqbeffdd1axZU1u3btWgQYOUnJysKVOm5LttbGysZs2a5Xjs5eVV1OUCAAAAQIGUiGAWGxur2NhYx+Pq1atr586dmj59+mWDmbe3tyIiIoq6RAAAAAC4YiUimOUmMTFRwcHBl223fPlyhYWFqXz58mrfvr0mTJigsLCwPNunpKQoJSXF8TgpKalQ6r1afl4e2vtyd6vLAAAAAFAESsQ1ZpfavXu33nrrLT366KP5tuvWrZs+/fRT/fDDD5o6darWrFmjm266ySl4XWrSpEkKCgpy3KKjowu7fAAAAABwYjPGGKsOPnbsWI0bNy7fNmvWrFGzZs0cjw8dOqT27durffv2ev/99wt0vMOHD6tq1aqaO3euevfunWub3M6YRUdHKzExUeXKlSvQ8QAAAACUHklJSQoKCiqSbGDpUMahQ4fqnnvuybdNtWrVHPcPHTqkjh07qmXLlpo5c2aBjxcZGamqVatq165debbx9vaWt7d3gfcNAAAAAFfK0mAWGhqq0NBQl9oePHhQHTt2VNOmTTVr1iy5uRV8FObx48d14MABRUZGFnhbAAAAACgqJeIas0OHDqlDhw6Kjo7WlClTdPToUSUkJCghIcGpXd26dTV//nxJ0pkzZzRy5EjFxcVp7969Wr58uXr27KnQ0FDdfvvtVjwNAAAAAMhViZiVccmSJYqPj1d8fLwqV67stC77JXI7d+5UYmKiJMnd3V1btmzRxx9/rFOnTikyMlIdO3bUvHnzFBgYeE3rBwAAAID8WDr5R0lQlBf4AQAAACg5ijIblIihjAAAAABQmhHMAAAAAMBiBDMAAAAAsBjBDAAAAAAsRjADAAAAAIsRzAAAAADAYgQzAAAAALAYwQwAAAAALEYwAwAAAACLeVhdQHFnjJGU9S3fAAAAAMoueyawZ4TCRDC7jNOnT0uSoqOjLa4EAAAAQHFw+vRpBQUFFeo+baYo4l4pkpmZqUOHDikwMFA2m83qckq9pKQkRUdH68CBAypXrpzV5eAK0Y8lH31YOtCPpQP9WPLRh6WDvR9/++031alTR25uhXtVGGfMLsPNzU2VK1e2uowyp1y5cvzHVQrQjyUffVg60I+lA/1Y8tGHpUOlSpUKPZRJTP4BAAAAAJYjmAEAAACAxQhmKFa8vb01ZswYeXt7W10KrgL9WPLRh6UD/Vg60I8lH31YOhR1PzL5BwAAAABYjDNmAAAAAGAxghkAAAAAWIxgBgAAAAAWI5gBAAAAgMUIZrDcpEmTZLPZNHz4cMcyY4zGjh2rqKgo+fr6qkOHDtq2bZt1RSJXBw8e1AMPPKCQkBD5+fnp+uuv17p16xzr6cfiLT09Xc8//7xiYmLk6+ur6tWr66WXXlJmZqajDX1Y/Pz444/q2bOnoqKiZLPZtGDBAqf1rvRZSkqK/v73vys0NFT+/v669dZb9eeff17DZ4H8+jEtLU3PPvusGjVqJH9/f0VFRenBBx/UoUOHnPZBP1rvcj+P2Q0ePFg2m03Tpk1zWk4/WsuVPty+fbtuvfVWBQUFKTAwUDfeeKP279/vWF9YfUgwg6XWrFmjmTNnqnHjxk7LJ0+erNdee01vv/221qxZo4iICHXu3FmnT5+2qFJc6uTJk2rdurU8PT317bff6rffftPUqVNVvnx5Rxv6sXh75ZVXNGPGDL399tvavn27Jk+erFdffVVvvfWWow19WPwkJyfruuuu09tvv53relf6bPjw4Zo/f77mzp2rn376SWfOnFGPHj2UkZFxrZ5GmZdfP549e1br16/XCy+8oPXr1+vLL7/U77//rltvvdWpHf1ovcv9PNotWLBAv/zyi6KionKsox+tdbk+3L17t9q0aaO6detq+fLl2rRpk1544QX5+Pg42hRaHxrAIqdPnza1atUyS5cuNe3btzdPPPGEMcaYzMxMExERYV5++WVH2/Pnz5ugoCAzY8YMi6rFpZ599lnTpk2bPNfTj8Vf9+7dzYABA5yW9e7d2zzwwAPGGPqwJJBk5s+f73jsSp+dOnXKeHp6mrlz5zraHDx40Li5uZlFixZds9px0aX9mJtff/3VSDL79u0zxtCPxVFe/fjnn3+aSpUqma1bt5qqVaua119/3bGOfixecuvDPn36OH4v5qYw+5AzZrDM448/ru7du+vmm292Wr5nzx4lJCSoS5cujmXe3t5q3769Vq1ada3LRB7+97//qVmzZrrrrrsUFhamJk2a6L333nOspx+LvzZt2uj777/X77//LknatGmTfvrpJ91yyy2S6MOSyJU+W7dundLS0pzaREVFqWHDhvRrMZaYmCibzeYYlUA/lgyZmZnq27evnn76aTVo0CDHevqxeMvMzNTChQtVu3Ztde3aVWFhYWrRooXTcMfC7EOCGSwxd+5crV+/XpMmTcqxLiEhQZIUHh7utDw8PNyxDtb7448/NH36dNWqVUuLFy/Wo48+qmHDhunjjz+WRD+WBM8++6zuvfde1a1bV56enmrSpImGDx+ue++9VxJ9WBK50mcJCQny8vJShQoV8myD4uX8+fMaNWqU7rvvPpUrV04S/VhSvPLKK/Lw8NCwYcNyXU8/Fm9HjhzRmTNn9PLLLys2NlZLlizR7bffrt69e2vFihWSCrcPPQqtcsBFBw4c0BNPPKElS5Y4jc+9lM1mc3psjMmxDNbJzMxUs2bNNHHiRElSkyZNtG3bNk2fPl0PPvigox39WHzNmzdPc+bM0WeffaYGDRpo48aNGj58uKKiotSvXz9HO/qw5LmSPqNfi6e0tDTdc889yszM1DvvvHPZ9vRj8bFu3Tq98cYbWr9+fYH7hH4sHuyTYfXq1UtPPvmkJOn666/XqlWrNGPGDLVv3z7Pba+kDzljhmtu3bp1OnLkiJo2bSoPDw95eHhoxYoVevPNN+Xh4eH4S++lf2U4cuRIjr8CwzqRkZGqX7++07J69eo5ZimKiIiQRD8WZ08//bRGjRqle+65R40aNVLfvn315JNPOs5k04cljyt9FhERodTUVJ08eTLPNige0tLSdPfdd2vPnj1aunSp42yZRD+WBCtXrtSRI0dUpUoVx+edffv26amnnlK1atUk0Y/FXWhoqDw8PC77eaew+pBghmuuU6dO2rJlizZu3Oi4NWvWTPfff782btyo6tWrKyIiQkuXLnVsk5qaqhUrVqhVq1YWVo7sWrdurZ07dzot+/3331W1alVJUkxMDP1YzJ09e1Zubs6/Btzd3R1/IaQPSx5X+qxp06by9PR0anP48GFt3bqVfi1G7KFs165d+u677xQSEuK0nn4s/vr27avNmzc7fd6JiorS008/rcWLF0uiH4s7Ly8v/e1vf8v3805h9iFDGXHNBQYGqmHDhk7L/P39FRIS4lg+fPhwTZw4UbVq1VKtWrU0ceJE+fn56b777rOiZOTiySefVKtWrTRx4kTdfffd+vXXXzVz5kzNnDlTkhzfTUc/Fl89e/bUhAkTVKVKFTVo0EAbNmzQa6+9pgEDBkiiD4urM2fOKD4+3vF4z5492rhxo4KDg1WlSpXL9llQUJAGDhyop556SiEhIQoODtbIkSPVqFGjHJMxoejk149RUVG68847tX79en399dfKyMhwnAUNDg6Wl5cX/VhMXO7n8dJA7enpqYiICNWpU0cSP4/FweX68Omnn1afPn3Url07dezYUYsWLdJXX32l5cuXSyrkPizQHI5AEck+Xb4xWVM+jxkzxkRERBhvb2/Trl07s2XLFusKRK6++uor07BhQ+Pt7W3q1q1rZs6c6bSefizekpKSzBNPPGGqVKlifHx8TPXq1c1zzz1nUlJSHG3ow+Jn2bJlRlKOW79+/YwxrvXZuXPnzNChQ01wcLDx9fU1PXr0MPv377fg2ZRd+fXjnj17cl0nySxbtsyxD/rRepf7ebzUpdPlG0M/Ws2VPvzggw9MzZo1jY+Pj7nuuuvMggULnPZRWH1oM8aYAkdLAAAAAECh4RozAAAAALAYwQwAAAAALEYwAwAAAACLEcwAAAAAwGIEMwAAAACwGMEMAAAAACxGMAMAAAAAixHMAAAAAMBiBDMAQLHXoUMHDR8+3OX2e/fulc1m08aNG4uspuKif//+uu2226wuAwBwlWzGGGN1EQCA0sFms+W7vl+/fpo9e3aB93vixAl5enoqMDDQpfYZGRk6evSoQkND5eHhUeDjXSv9+/fXqVOntGDBgiveR2JioowxKl++fKHVBQC49orvbysAQIlz+PBhx/158+bpxRdf1M6dOx3LfH19ndqnpaXJ09PzsvsNDg4uUB3u7u6KiIgo0DYlVVBQkNUlAAAKAUMZAQCFJiIiwnELCgqSzWZzPD5//rzKly+vf//73+rQoYN8fHw0Z84cHT9+XPfee68qV64sPz8/NWrUSJ9//rnTfi8dylitWjVNnDhRAwYMUGBgoKpUqaKZM2c61l86lHH58uWy2Wz6/vvv1axZM/n5+alVq1ZOoVGSxo8fr7CwMAUGBurhhx/WqFGjdP311+f5fE+ePKn7779fFStWlK+vr2rVqqVZs2Y51h88eFB9+vRRhQoVFBISol69emnv3r2SpLFjx+qjjz7Sf//7X9lsNtlsNi1fvjzX4/zf//2fGjVqJF9fX4WEhOjmm29WcnKyJOehjPbnfemtQ4cOjn2tWrVK7dq1k6+vr6KjozVs2DDHvgAA1iGYAQCuqWeffVbDhg3T9u3b1bVrV50/f15NmzbV119/ra1bt+qRRx5R37599csvv+S7n6lTp6pZs2basGGDhgwZoscee0w7duzId5vnnntOU6dO1dq1a+Xh4aEBAwY41n366aeaMGGCXnnlFa1bt05VqlTR9OnT893fCy+8oN9++03ffvuttm/frunTpys0NFSSdPbsWXXs2FEBAQH68ccf9dNPPykgIECxsbFKTU3VyJEjdffddys2NlaHDx/W4cOH1apVqxzHOHz4sO69914NGDBA27dv1/Lly9W7d2/ldiVCdHS0Y1+HDx/Whg0bFBISonbt2kmStmzZoq5du6p3797avHmz5s2bp59++klDhw7N93kCAK4BAwBAEZg1a5YJCgpyPN6zZ4+RZKZNm3bZbW+55Rbz1FNPOR63b9/ePPHEE47HVatWNQ888IDjcWZmpgkLCzPTp093OtaGDRuMMcYsW7bMSDLfffedY5uFCxcaSebcuXPGGGNatGhhHn/8cac6Wrduba677ro86+zZs6d56KGHcl33wQcfmDp16pjMzEzHspSUFOPr62sWL15sjDGmX79+plevXnm/EMaYdevWGUlm7969ua7Pax/nzp0zLVq0MD169DAZGRnGGGP69u1rHnnkEad2K1euNG5ubo7XAQBgDc6YAQCuqWbNmjk9zsjI0IQJE9S4cWOFhIQoICBAS5Ys0f79+/PdT+PGjR337UMmjxw54vI2kZGRkuTYZufOnWrevLlT+0sfX+qxxx7T3Llzdf311+uZZ57RqlWrHOvWrVun+Ph4BQYGKiAgQAEBAQoODtb58+e1e/fufPeb3XXXXadOnTqpUaNGuuuuu/Tee+/p5MmTl91u4MCBOn36tD777DO5ubk5apo9e7ajnoCAAHXt2lWZmZnas2ePyzUBAAofk38AAK4pf39/p8dTp07V66+/rmnTpqlRo0by9/fX8OHDlZqamu9+Lp00xGazKTMz0+Vt7DNIZt/m0lklzWUmLu7WrZv27dunhQsX6rvvvlOnTp30+OOPa8qUKcrMzFTTpk316aef5tiuYsWK+e43O3d3dy1dulSrVq3SkiVL9NZbb+m5557TL7/8opiYmFy3GT9+vBYtWqRff/3VaSbLzMxMDR48WMOGDcuxTZUqVVyuCQBQ+DhjBgCw1MqVK9WrVy898MADuu6661S9enXt2rXrmtdRp04d/frrr07L1q5de9ntKlasqP79+2vOnDmaNm2aYxKSG264Qbt27VJYWJhq1qzpdLPPpOjl5aWMjIzLHsNms6l169YaN26cNmzYIC8vL82fPz/Xtl988YVeeukl/fvf/1aNGjWc1t1www3atm1bjnpq1qwpLy+vy9YBACg6BDMAgKVq1qzpOCO0fft2DR48WAkJCde8jr///e/64IMP9NFHH2nXrl0aP368Nm/enO93s7344ov673//q/j4eG3btk1ff/216tWrJ0m6//77FRoaql69emnlypXas2ePVqxYoSeeeEJ//vmnpKzZJTdv3qydO3fq2LFjSktLy3GMX375RRMnTtTatWu1f/9+ffnllzp69KjjONlt3bpVDz74oJ599lk1aNBACQkJSkhI0IkTJyRlTbwSFxenxx9/XBs3btSuXbv0v//9T3//+98L4yUEAFwFghkAwFIvvPCCbrjhBnXt2lUdOnRQRESEY/r3a+n+++/X6NGjNXLkSN1www3as2eP+vfvLx8fnzy38fLy0ujRo9W4cWO1a9dO7u7umjt3riTJz89PP/74o6pUqaLevXurXr16GjBggM6dO6dy5cpJkgYNGqQ6deqoWbNmqlixon7++eccxyhXrpx+/PFH3XLLLapdu7aef/55TZ06Vd26dcvRdu3atTp79qzGjx+vyMhIx613796Ssq6xW7FihXbt2qW2bduqSZMmeuGFFxzX2wEArGMzlxtADwBAGdW5c2dFRETok08+sboUAEApx+QfAAAo63vHZsyYoa5du8rd3V2ff/65vvvuOy1dutTq0gAAZQBnzAAAkHTu3Dn17NlT69evV0pKiurUqaPnn3/eMQwQAICiRDADAAAAAIsx+QcAAAAAWIxgBgAAAAAWI5gBAAAAgMUIZgAAAABgMYIZAAAAAFiMYAYAAAAAFiOYAQAAAIDFCGYAAAAAYLH/B+gDieNqjF2DAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplots(figsize=(10, 5))\n",
+    "plt.errorbar(train_size, test_scores_mean, yerr=test_scores_std)\n",
+    "plt.xlabel('Training set size')\n",
+    "plt.ylabel('CV scores')\n",
+    "plt.title('Cross-validation score as training set size increases');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This shows that you seem to have plenty of data. There's an initial rapid improvement in model scores as one would expect, but it's essentially levelled off by around a sample size of 40-50."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.13 Save best model object from pipeline<a id='4.13_Save_best_model_object_from_pipeline'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 28#\n",
+    "#This may not be \"production grade ML deployment\" practice, but adding some basic\n",
+    "#information to your saved models can save your bacon in development.\n",
+    "#Just what version model have you just loaded to reuse? What version of `sklearn`\n",
+    "#created it? When did you make it?\n",
+    "#Assign the pandas version number (`pd.__version__`) to the `pandas_version` attribute,\n",
+    "#the numpy version (`np.__version__`) to the `numpy_version` attribute,\n",
+    "#the sklearn version (`sklearn_version`) to the `sklearn_version` attribute,\n",
+    "#and the current datetime (`datetime.datetime.now()`) to the `build_datetime` attribute\n",
+    "#Let's call this model version '1.0'\n",
+    "best_model = rf_grid_cv.best_estimator_\n",
+    "best_model.version = '1.0'\n",
+    "best_model.pandas_version = pd.__version__\n",
+    "best_model.numpy_version = np.__version__\n",
+    "best_model.sklearn_version = sklearn_version\n",
+    "best_model.X_columns = [col for col in X_train.columns]\n",
+    "best_model.build_datetime = datetime.datetime.now()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Directory ../models was created.\n",
+      "Writing file.  \"../models\\ski_resort_pricing_model.pkl\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "# save the model\n",
+    "\n",
+    "modelpath = '../models'\n",
+    "save_file(best_model, 'ski_resort_pricing_model.pkl', modelpath)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4.14 Summary<a id='4.14_Summary'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q: 1** Write a summary of the work in this notebook. Capture the fact that you gained a baseline idea of performance by simply taking the average price and how well that did. Then highlight that you built a linear model and the features that found. Comment on the estimate of its performance from cross-validation and whether its performance on the test split was consistent with this estimate. Also highlight that a random forest regressor was tried, what preprocessing steps were found to be best, and again what its estimated performance via cross-validation was and whether its performance on the test set was consistent with that. State which model you have decided to use going forwards and why. This summary should provide a quick overview for someone wanting to know quickly why the given model was chosen for the next part of the business problem to help guide important business decisions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**A: 1** Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We began this phase of our process by fashioning a baseline using the average price to assess our modeling. Beforehand, we partitioned our data into training and testing sets to facilitate cross-validation.\n",
+    "Using Mean Absolute error as a rough metric, we see that the average price model produces estimates that we can expect to be off by around 19 dollars based on an MAE of 19.13. Turning to Mean Squared Error, a standard metric for machine learning, we get a higher value of around 24 dollars, after taking the square root.\n",
+    "\n",
+    "Turning now to our first attempt at modeling the pricing using the additional data for the 32 features in our data set, we make use of linear regression to assess the fitness. Based on this arguably unrefined analysis, we use the same metrics as before to assess the fit of our modeling and find that this new information results in predictions that are roughly 9 to 12 dollars within the actual price, MAE vs.  square root of MSE, representing a notable improvement from simply using the mean. As our variance explained by the data decreased significantly from training to testing, we intuit that our model is likely overfitting and attempt to identify a more parsimonious set of factors to use in our model.\n",
+    "\n",
+    "Employing cross-validation to identify the number of factors optimal for our modeling and then determining which among our original 32 are most informative to our analysis, we landed on a regression model incorporating 8 factors: vertical drop, snow making area, total chairs, fast Quads, Runs,  Longest run, trams, and skiable terrain area. \n",
+    "We also use cross-validation to assess the performance of our test models compared to our training models. Based on the r^2, MAE and MSE, we believe the performance to be consistent across data sets.\n",
+    "\n",
+    "We also used a Random Forest approach to explore how different mixes of factors might influence pricing in order to identify the most useful subset of our initial 32.  In preprocessing, we determined that imputing with the median helps, but scaling our features does not, leading us to incorporate the former and not the latter. Again we used cross-validation to compare training and test models and our r^2, MAE and MSE values provide support that our outcomes are consistent across the sets. \n",
+    "\n",
+    "Encouragingly, the top four features identified in the random forest regression are in common with our linear model: fastQuads, Runs, Snow Making area, and vertical drop.\n",
+    "\n",
+    "The random forest model has a lower cross-validation mean absolute error by almost $1. It also exhibits less variability. For these reasons, we will proceed with the random forest model to round out our analysis.\n",
+    "\n",
+    "Using cross-validation again we are able to assess the value of potential additional data. While it is a given that additional information is intuitively helpful, our analysis suggests the addition of further data only adds very marginal improvement to the modeling. Given the inherent costs of additional data collection and processing, we would not advise additional data collection at this time.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Notebooks/05_modeling_DM.ipynb b/Notebooks/05_modeling_DM.ipynb
new file mode 100644
index 000000000..4e4008174
--- /dev/null
+++ b/Notebooks/05_modeling_DM.ipynb
@@ -0,0 +1,1449 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 5 Modeling<a id='5_Modeling'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.1 Contents<a id='5.1_Contents'></a>\n",
+    "* [5 Modeling](#5_Modeling)\n",
+    "  * [5.1 Contents](#5.1_Contents)\n",
+    "  * [5.2 Introduction](#5.2_Introduction)\n",
+    "  * [5.3 Imports](#5.3_Imports)\n",
+    "  * [5.4 Load Model](#5.4_Load_Model)\n",
+    "  * [5.5 Load Data](#5.5_Load_Data)\n",
+    "  * [5.6 Refit Model On All Available Data (excluding Big Mountain)](#5.6_Refit_Model_On_All_Available_Data_(excluding_Big_Mountain))\n",
+    "  * [5.7 Calculate Expected Big Mountain Ticket Price From The Model](#5.7_Calculate_Expected_Big_Mountain_Ticket_Price_From_The_Model)\n",
+    "  * [5.8 Big Mountain Resort In Market Context](#5.8_Big_Mountain_Resort_In_Market_Context)\n",
+    "    * [5.8.1 Ticket price](#5.8.1_Ticket_price)\n",
+    "    * [5.8.2 Vertical drop](#5.8.2_Vertical_drop)\n",
+    "    * [5.8.3 Snow making area](#5.8.3_Snow_making_area)\n",
+    "    * [5.8.4 Total number of chairs](#5.8.4_Total_number_of_chairs)\n",
+    "    * [5.8.5 Fast quads](#5.8.5_Fast_quads)\n",
+    "    * [5.8.6 Runs](#5.8.6_Runs)\n",
+    "    * [5.8.7 Longest run](#5.8.7_Longest_run)\n",
+    "    * [5.8.8 Trams](#5.8.8_Trams)\n",
+    "    * [5.8.9 Skiable terrain area](#5.8.9_Skiable_terrain_area)\n",
+    "  * [5.9 Modeling scenarios](#5.9_Modeling_scenarios)\n",
+    "    * [5.9.1 Scenario 1](#5.9.1_Scenario_1)\n",
+    "    * [5.9.2 Scenario 2](#5.9.2_Scenario_2)\n",
+    "    * [5.9.3 Scenario 3](#5.9.3_Scenario_3)\n",
+    "    * [5.9.4 Scenario 4](#5.9.4_Scenario_4)\n",
+    "  * [5.10 Summary](#5.10_Summary)\n",
+    "  * [5.11 Further work](#5.11_Further_work)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.2 Introduction<a id='5.2_Introduction'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook, we now take our model for ski resort ticket price and leverage it to gain some insights into what price Big Mountain's facilities might actually support as well as explore the sensitivity of changes to various resort parameters. Note that this relies on the implicit assumption that all other resorts are largely setting prices based on how much people value certain facilities. Essentially this assumes prices are set by a free market.\n",
+    "\n",
+    "We can now use our model to gain insight into what Big Mountain's ideal ticket price could/should be, and how that might change under various scenarios."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.3 Imports<a id='5.3_Imports'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import pickle\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn import __version__ as sklearn_version\n",
+    "from sklearn.model_selection import cross_validate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.4 Load Model<a id='5.4_Load_Model'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This isn't exactly production-grade, but a quick check for development\n",
+    "# These checks can save some head-scratching in development when moving from\n",
+    "# one python environment to another, for example\n",
+    "expected_model_version = '1.0'\n",
+    "model_path = '../models/ski_resort_pricing_model.pkl'\n",
+    "if os.path.exists(model_path):\n",
+    "    with open(model_path, 'rb') as f:\n",
+    "        model = pickle.load(f)\n",
+    "    if model.version != expected_model_version:\n",
+    "        print(\"Expected model version doesn't match version loaded\")\n",
+    "    if model.sklearn_version != sklearn_version:\n",
+    "        print(\"Warning: model created under different sklearn version\")\n",
+    "else:\n",
+    "    print(\"Expected model not found\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.5 Load Data<a id='5.5_Load_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ski_data = pd.read_csv('../data/ski_data_step3_features.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "big_mountain = ski_data[ski_data.Name == 'Big Mountain Resort']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>124</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Big Mountain Resort</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Montana</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Montana</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>6817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>2353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>4464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>3.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>3000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>72</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>81</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <td>1.12278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "      <td>8.16104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_skiable_area_ac_state_ratio</th>\n",
+       "      <td>0.140121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_days_open_state_ratio</th>\n",
+       "      <td>0.129338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_terrain_park_state_ratio</th>\n",
+       "      <td>0.148148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>resort_night_skiing_state_ratio</th>\n",
+       "      <td>0.84507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs_runs_ratio</th>\n",
+       "      <td>0.133333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs_skiable_ratio</th>\n",
+       "      <td>0.00466667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads_runs_ratio</th>\n",
+       "      <td>0.0285714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads_skiable_ratio</th>\n",
+       "      <td>0.001</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    124\n",
+       "Name                                Big Mountain Resort\n",
+       "Region                                          Montana\n",
+       "state                                           Montana\n",
+       "summit_elev                                        6817\n",
+       "vertical_drop                                      2353\n",
+       "base_elev                                          4464\n",
+       "trams                                                 0\n",
+       "fastSixes                                             0\n",
+       "fastQuads                                             3\n",
+       "quad                                                  2\n",
+       "triple                                                6\n",
+       "double                                                0\n",
+       "surface                                               3\n",
+       "total_chairs                                         14\n",
+       "Runs                                                105\n",
+       "TerrainParks                                          4\n",
+       "LongestRun_mi                                       3.3\n",
+       "SkiableTerrain_ac                                  3000\n",
+       "Snow Making_ac                                      600\n",
+       "daysOpenLastYear                                    123\n",
+       "yearsOpen                                            72\n",
+       "averageSnowfall                                     333\n",
+       "AdultWeekend                                         81\n",
+       "projectedDaysOpen                                   123\n",
+       "NightSkiing_ac                                      600\n",
+       "resorts_per_state                                    12\n",
+       "resorts_per_100kcapita                          1.12278\n",
+       "resorts_per_100ksq_mile                         8.16104\n",
+       "resort_skiable_area_ac_state_ratio             0.140121\n",
+       "resort_days_open_state_ratio                   0.129338\n",
+       "resort_terrain_park_state_ratio                0.148148\n",
+       "resort_night_skiing_state_ratio                 0.84507\n",
+       "total_chairs_runs_ratio                        0.133333\n",
+       "total_chairs_skiable_ratio                   0.00466667\n",
+       "fastQuads_runs_ratio                          0.0285714\n",
+       "fastQuads_skiable_ratio                           0.001"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "big_mountain.T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.6 Refit Model On All Available Data (excluding Big Mountain)<a id='5.6_Refit_Model_On_All_Available_Data_(excluding_Big_Mountain)'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This next step requires some careful thought. We want to refit the model using all available data. But should we include Big Mountain data? On the one hand, we are _not_ trying to estimate model performance on a previously unseen data sample, so theoretically including Big Mountain data should be fine. One might first think that including Big Mountain in the model training would, if anything, improve model performance in predicting Big Mountain's ticket price. But here's where our business context comes in. The motivation for this entire project is based on the sense that Big Mountain needs to adjust its pricing. One way to phrase this problem: we want to train a model to predict Big Mountain's ticket price based on data from _all the other_ resorts! We don't want Big Mountain's current price to bias this. We want to calculate a price based only on its competitors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = ski_data.loc[ski_data.Name != \"Big Mountain Resort\", model.X_columns]\n",
+    "y = ski_data.loc[ski_data.Name != \"Big Mountain Resort\", 'AdultWeekend']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(276, 276)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(X), len(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
+       "                ('standardscaler', None),\n",
+       "                ('randomforestregressor',\n",
+       "                 RandomForestRegressor(n_estimators=69, random_state=47))])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cv_results = cross_validate(model, X, y, scoring='neg_mean_absolute_error', cv=5, n_jobs=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([-12.09690217,  -9.30247694, -11.41595784,  -8.10096706,\n",
+       "       -11.04942819])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cv_results['test_score']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10.393146442687748, 1.4712769116280346)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mae_mean, mae_std = np.mean(-1 * cv_results['test_score']), np.std(-1 * cv_results['test_score'])\n",
+    "mae_mean, mae_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These numbers will inevitably be different to those in the previous step that used a different training data set. They should, however, be consistent. It's important to appreciate that estimates of model performance are subject to the noise and uncertainty of data!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.7 Calculate Expected Big Mountain Ticket Price From The Model<a id='5.7_Calculate_Expected_Big_Mountain_Ticket_Price_From_The_Model'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_bm = ski_data.loc[ski_data.Name == \"Big Mountain Resort\", model.X_columns]\n",
+    "y_bm = ski_data.loc[ski_data.Name == \"Big Mountain Resort\", 'AdultWeekend']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bm_pred = model.predict(X_bm).item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_bm = y_bm.values.item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Big Mountain Resort modelled price is $95.87, actual price is $81.00.\n",
+      "Even with the expected mean absolute error of $10.39, this suggests there is room for an increase.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Big Mountain Resort modelled price is ${bm_pred:.2f}, actual price is ${y_bm:.2f}.')\n",
+    "print(f'Even with the expected mean absolute error of ${mae_mean:.2f}, this suggests there is room for an increase.')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This result should be looked at optimistically and doubtfully! The validity of our model lies in the assumption that other resorts accurately set their prices according to what the market (the ticket-buying public) supports. The fact that our resort seems to be charging that much less that what's predicted suggests our resort might be undercharging. \n",
+    "But if ours is mispricing itself, are others? It's reasonable to expect that some resorts will be \"overpriced\" and some \"underpriced.\" Or if resorts are pretty good at pricing strategies, it could be that our model is simply lacking some key data? Certainly we know nothing about operating costs, for example, and they would surely help."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.8 Big Mountain Resort In Market Context<a id='5.8_Big_Mountain_Resort_In_Market_Context'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Features that came up as important in the modeling (not just our final, random forest model) included:\n",
+    "* vertical_drop\n",
+    "* Snow Making_ac\n",
+    "* total_chairs\n",
+    "* fastQuads\n",
+    "* Runs\n",
+    "* LongestRun_mi\n",
+    "* trams\n",
+    "* SkiableTerrain_ac"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A handy glossary of skiing terms can be found on the [ski.com](https://www.ski.com/ski-glossary) site. Some potentially relevant contextual information is that vertical drop, although nominally the height difference from the summit to the base, is generally taken from the highest [_lift-served_](http://verticalfeet.com/) point."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's often useful to define custom functions for visualizing data in meaningful ways. The function below takes a feature name as an input and plots a histogram of the values of that feature. It then marks where Big Mountain sits in the distribution by marking Big Mountain's value with a vertical line using `matplotlib`'s [axvline](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.axvline.html) function. It also performs a little cleaning up of missing values and adds descriptive labels and a title."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 1#\n",
+    "#Add code to the `plot_compare` function that displays a vertical, dashed line\n",
+    "#on the histogram to indicate Big Mountain's position in the distribution\n",
+    "#Hint: plt.axvline() plots a vertical line, its position for 'feature1'\n",
+    "#would be `big_mountain['feature1'].values, we'd like a red line, which can be\n",
+    "#specified with c='r', a dashed linestyle is produced by ls='--',\n",
+    "#and it's nice to give it a slightly reduced alpha value, such as 0.8.\n",
+    "#Don't forget to give it a useful label (e.g. 'Big Mountain') so it's listed\n",
+    "#in the legend.\n",
+    "def plot_compare(feat_name, description, state=None, figsize=(10, 5)):\n",
+    "    \"\"\"Graphically compare distributions of features.\n",
+    "    \n",
+    "    Plot histogram of values for all resorts and reference line to mark\n",
+    "    Big Mountain's position.\n",
+    "    \n",
+    "    Arguments:\n",
+    "    feat_name - the feature column name in the data\n",
+    "    description - text description of the feature\n",
+    "    state - select a specific state (None for all states)\n",
+    "    figsize - (optional) figure size\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    plt.subplots(figsize=figsize)\n",
+    "    # quirk that hist sometimes objects to NaNs, sometimes doesn't\n",
+    "    # filtering only for finite values tidies this up\n",
+    "    if state is None:\n",
+    "        ski_x = ski_data[feat_name]\n",
+    "    else:\n",
+    "        ski_x = ski_data.loc[ski_data.state == state, feat_name]\n",
+    "    ski_x = ski_x[np.isfinite(ski_x)]\n",
+    "    plt.hist(ski_x, bins=30)\n",
+    "    plt.___(x=big_mountain[feat_name].___, c=___, ls=___, alpha=0.8, label=___)\n",
+    "    plt.xlabel(description)\n",
+    "    plt.ylabel('frequency')\n",
+    "    plt.title(description + ' distribution for resorts in market share')\n",
+    "    plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.8.1 Ticket price<a id='5.8.1_Ticket_price'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Look at where Big Mountain sits overall amongst all resorts for price and for just other resorts in Montana."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('AdultWeekend', 'Adult weekend ticket price ($)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('AdultWeekend', 'Adult weekend ticket price ($) - Montana only', state='Montana')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.8.2 Vertical drop<a id='5.8.2_Vertical_drop'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('vertical_drop', 'Vertical drop (feet)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Big Mountain is doing well for vertical drop, but there are still quite a few resorts with a greater drop."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.8.3 Snow making area<a id='5.8.3_Snow_making_area'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('Snow Making_ac', 'Area covered by snow makers (acres)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Big Mountain is very high up the league table of snow making area."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.8.4 Total number of chairs<a id='5.8.4_Total_number_of_chairs'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('total_chairs', 'Total number of chairs')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Big Mountain has amongst the highest number of total chairs, resorts with more appear to be outliers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.8.5 Fast quads<a id='5.8.5_Fast_quads'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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4xz8G4Oabb2avvfZarrp23nlnNttsM3qvtf2KV7yCtdZaq68xA5x33nkPxLo0Z511FkcffXTfj99r3OaQVdUCYEF7/+4k1wIbAy8Hdm43Oxk4D3h3W35qVd0H/DrJDcD2wEXjFaMG1P77dx2BJKnHwx/+cK644goAvve973HYYYfxwx/+kI022ogzzjhjuetbZ511uPDCC9lxxx258847WbBgQb9DBpqEbK211uI5z3nOUrfbc8892XPPPcclhiETMocsyUxgO+CnwAZtsjaUtD223Wxj4Kae3ea3ZZIkaUDcddddrLtuMxtp3rx5bLPNNgDcc889vPrVr2bWrFm85jWvYYcddhj1ajv77LMPp556KgBnnnkmr3rVqx5YV1W8613vYptttuFpT3sap512GtAkV7vvvvsD2x100EGcdNJJQHNln/e///08/elP52lPexrXXXcd8+bN49Of/jQf+9jHmD17Nj/60Y/45je/yQ477MB2223HrrvuysKFCwE46aSTOKg9Kfkb3vAGDj74YJ7znOew5ZZbrlDCOZJx/5VlkrWArwLvqKq7ljKuOtKKeshGyQHAAQCbbbZZv8LUIDn44Ob2uOO6jUOSBMCf//xnZs+ezb333suCBQv4/ve//5BtPvnJT7Luuusyd+5crr76ambPnj1qfbvssgtvfvObWbx4MaeeeionnHACRx55JNAkaFdccQVXXnklt912G8985jPZaaedlhnj+uuvz+WXX84nP/lJPvKRj3DiiSdy4IEHstZaa3HIIYcA8Pvf/56f/OQnJOHEE0/kmGOO4dhjj31IXQsWLOCCCy7guuuuY88991zuYdmRjGtClmR1mmTsS1V1Zlu8MMmGVbUgyYbALW35fGDTnt03AW4eXmdVnQCcADBnzpyHJGzjYeZ7vr3Sdcw7+mV9iEQAeK4dSRrdAQc8tOyFL4S9927+fg79U9trjz2a5c47H3ri7RNOWOYhe4csL7roIv7+7/+eq6++eoltLrjgAt7+9rcDsM022zBr1qxR65s2bRo77rgjp512Gn/+85+ZOXPmEvXsu+++TJs2jQ022IDnP//5XHLJJTzqUY9aaoxDvWzPeMYzOPPMM0fcZv78+bzmNa9hwYIF/OUvfxn1tBWveMUreNjDHsZTnvKUB3rRVtZ4/soywOeAa6vqoz2rzgL2a+/vB3yjp3yfJGsm2QLYCrh4vOKTJEn99+xnP5vbbruNW2+9dYny3kn6Y7HPPvvwtre9jVe/+tVjqme11Vbjr3/96wOPh592Ys011wSaZG/RokUj1vG2t72Ngw46iKuuuorPfOYzo566YqiupcWzvMazh+y5wOuBq5Jc0Za9FzgaOD3Jm4DfAHsDVNXPkpwOXEPzC823VtXicYxPkqSpZ2k9WtOnL339OuuMqUdsaa677joWL17Meuutxz333PNA+Y477sjpp5/OC17wAq655hquuuqqpdbzvOc9j8MOO4x99913ifKddtqJz3zmM+y3337ccccdnH/++Xz4wx/m/vvv55prruG+++7j3nvv5dxzz2XHHXdc6jHWXntt7rrrrgce/+EPf2DjjZvp6yeffPLyPvWVMp6/sryAkeeFAewyyj5HAUeNV0ySJKn/huaQQdNjdPLJJzNt2pJnrvqnf/on9ttvP2bNmsV2223HrFmzePSjHz1qnUkemNvV65WvfCUXXXQR2267LUk45phjeNzjHgfwwI8GttpqK7bbbrtlxr3HHnuw11578Y1vfINPfOITHHHEEey9995svPHGPOtZz+LXv/718jTDSkm/utq6MGfOnBrtFxr95ByySea//7u5ff3ru41DkiaBa6+9lic/+cldh7FMixcv5v7772f69On88pe/ZJddduHnP/85a6yxRtehjYuRXpckl1XVnJG291qWGjwmYpI0cO655x5e8IIXcP/991NVfOpTn5qyydiKMCGTJEnjbu211x71vGPy4uIaRAccMPLPuiVJGlAmZJIkDbhBng8+Fa3I62FCJknSAJs+fTq33367SdkkUVXcfvvtTJ8+fbn2cw6ZJEkDbJNNNmH+/PkPORGrujN9+nQ22WST5drHhEySpAG2+uqrj3qJHw0OEzINnhe+sOsIJEnqKxMyDZ699+46AkmS+spJ/Ro8997bLJIkTRH2kGnwHHxwc7uSF8CVJGmysIdMkiSpYyZkkiRJHTMhkyRJ6pgJmSRJUsec1K/Bs8ceXUcgSVJfmZBp8JiQSZKmGIcsNXjuvLNZJEmaIuwh0+A59NDm1vOQSZKmCHvIJEmSOmZCJkmS1DETMkmSpI6NW0KW5PNJbklydU/ZaUmuaJd5Sa5oy2cm+XPPuk+PV1ySJEmTzXhO6j8JOB74r6GCqnrN0P0kxwJ/6Nn+l1U1exzj0VSx115dRyBJUl+NW0JWVecnmTnSuiQBXg38zXgdX1PYbrt1HYEkSX3V1Ryy5wELq+oXPWVbJPm/JD9M8ryO4tIgWLiwWSRJmiK6Og/ZvsApPY8XAJtV1e1JngF8PclTq+qu4TsmOQA4AGCzzTabkGA1ybzvfc2t5yGTJE0RE95DlmQ14FXAaUNlVXVfVd3e3r8M+CWw9Uj7V9UJVTWnqubMmDFjIkKWJEkaV10MWe4KXFdV84cKksxIMq29vyWwFfCrDmKTJEmacON52otTgIuAJyaZn+RN7ap9WHK4EmAnYG6SK4EzgAOr6o7xik2SJGkyGc9fWe47SvkbRij7KvDV8YpFkiRpMvPi4ho8r3td1xFIktRXJmQaPDvt1HUEkiT1ldey1OC58cZmkSRpirCHTIPnqKOaW89DJkmaIuwhkyRJ6pgJmSRJUsdMyCRJkjpmQiZJktQxJ/Vr8Oy/f9cRSJLUVyZkGjzbb991BJIk9ZVDlho8P/95s0iSNEXYQ6bB85GPNLeeh0ySNEXYQyZJktQxEzJJkqSOmZBJkiR1zIRMkiSpY07q1+A56KCuI5Akqa9MyDR4Zs3qOgJJkvrKIUsNnrlzm0WSpCnCHjINnuOPb249D5kkaYqwh0ySJKljJmSSJEkdMyGTJEnqmAmZJElSx8YtIUvy+SS3JLm6p+yIJL9NckW7vLRn3WFJbkhyfZIXjVdcmgIOOaRZJEmaIsbzV5YnAccD/zWs/GNV9ZHegiRPAfYBngpsBPxvkq2ravE4xqdBtfXWXUcgSVJfjVsPWVWdD9wxxs1fDpxaVfdV1a+BG4Dtxys2DbiLL24WSZKmiC7mkB2UZG47pLluW7YxcFPPNvPbMumhTjyxWSRJmiImOiH7FPB4YDawADi2Lc8I29ZIFSQ5IMmlSS699dZbxydKSZKkCTShCVlVLayqxVX1V+CzPDgsOR/YtGfTTYCbR6njhKqaU1VzZsyYMb4BS5IkTYAJTciSbNjz8JXA0C8wzwL2SbJmki2ArQAnCUmSpFXCuP3KMskpwM7A+knmA+8Hdk4ym2Y4ch7wFoCq+lmS04FrgEXAW/2FpSRJWlWMW0JWVfuOUPy5pWx/FHDUeMWjKeTww7uOQJKkvhrP85BJ42PzzbuOQJKkvvLSSRo855/fLJIkTRH2kGnwfPGLze1OO3UbhyRJfWIPmSRJUsdMyCRJkjpmQiZJktQxEzJJkqSOOalfg+fII7uOQJKkvjIh0+DZYIOuI5Akqa8cstTgOfvsZpEkaYqwh0yD54wzmtvddus2DkmS+sQeMkmSpI6ZkEmSJHXMhEySJKljJmSSJEkdc1K/Bs8xx3QdgSRJfWVCpsGzzjpdRyBJUl85ZKnB881vNoskSVOECZkGjwmZJGmKMSGTJEnqmAmZJElSx0zIJEmSOmZCJkmS1LFxO+1Fks8DuwO3VNU2bdmHgT2AvwC/BP6hqu5MMhO4Fri+3f0nVXXgeMWmAXfccV1HIElSX41nD9lJwIuHlZ0DbFNVs4CfA4f1rPtlVc1uF5MxjW769GaRJGmKGLeErKrOB+4YVnZ2VS1qH/4E2GS8jq8p7CtfaRZJkqaILueQvRH4bs/jLZL8X5IfJnleV0FpAJxzTrNIkjRFdHLppCSHA4uAL7VFC4DNqur2JM8Avp7kqVV11wj7HgAcALDZZptNVMiSJEnjZsJ7yJLsRzPZ/7VVVQBVdV9V3d7ev4xmwv/WI+1fVSdU1ZyqmjNjxoyJCluSJGncTGhCluTFwLuBPavqnp7yGUmmtfe3BLYCfjWRsUmSJHVlPE97cQqwM7B+kvnA+2l+VbkmcE4SePD0FjsBH0yyCFgMHFhVd4xYsSRJ0hSzzIQsyaXAF4AvV9Xvx1pxVe07QvHnRtn2q8BXx1q3VnEnnNB1BJIk9dVYhiz3ATYCLklyapIXpe3ekiRJ0spbZkJWVTdU1eE0k+y/DHwe+E2SDyR5zHgHKD3Ef/93s0iSNEWMaVJ/klnAscCHaYYW9wLuAr4/fqFJo/jRj5pFkqQpYixzyC4D7qSZ//WeqrqvXfXTJM8dz+AkSZJWBWP5leXeVTXiKSiq6lV9jkeSJGmVM5Yhy/2TrDP0IMm6ST40jjFJkiStUsaSkL2kqu4cetCe+uKl4xeStAzTpzeLJElTxFiGLKclWXNo7liSh9Oc3FXqxnHHdR2BJEl9NZaE7IvAuUm+ABTwRuDkcY1KkiRpFbLMhKyqjklyFbALEODIqvreuEcmjebEE5vb/ffvNg5JkvpkTNeyrKrvAt8d51iksbn44ubWhEySNEUsc1J/klcl+UWSPyS5K8ndSe6aiOAkSZJWBWPpITsG2KOqrh3vYCRJklZFYzntxUKTMUmSpPEzlh6yS5OcBnwdGLpsElV15rhFJS3NOussextJkgbIWBKyRwH3ALv1lBVgQqZuHHNM1xFIktRXYzntxT9MRCCSJEmrqrH8ynLrJOcmubp9PCvJ/xv/0KRRHH98s0iSNEWMZVL/Z4HDgPsBqmousM94BiUt1dy5zSJJ0hQxloTsEVV18bCyReMRjCRJ0qpoLAnZbUkeTzORnyR7AQvGNSpJkqRVyFh+ZflW4ATgSUl+C/waeN24RiVJkrQKGcuvLH8F7JrkkcDDquru8Q9LWooNNug6AkmS+mqZCVmSfxn2GICq+uA4xSQt3ZFHdh2BJEl9NZY5ZH/qWRYDLwFmLmunJJ9PcsvQ6TLassckOae9WPk5SdbtWXdYkhuSXJ/kRcv9TCRJkgbUMhOyqjq2ZzkK2BnYeAx1nwS8eFjZe4Bzq2or4Nz2MUmeQnMqjae2+3wyybSxPgmtYo49tlkkSZoixtJDNtwjgC2XtVFVnQ/cMaz45cDJ7f2TgVf0lJ9aVfdV1a+BG4DtVyA2rQquv75ZJEmaIsYyh+wq2lNeANOAGcCKzh/boKoWAFTVgiSPbcs3Bn7Ss918xtYLJ0mSNPDGctqL3XvuLwIWVlW/TwybEcpqhDKSHAAcALDZZpv1OQxJkqSJN5Yhy7t7lj8Dj2on5z8myWOW83gLk2wI0N7e0pbPBzbt2W4T4OaRKqiqE6pqTlXNmTFjxnIeXpIkafIZS0J2OXAr8HPgF+39y9rl0uU83lnAfu39/YBv9JTvk2TNJFsAWwHDL9ckNTbfvFkkSZoixjJk+T/AWVX1HYAkLwF2rap3Lm2nJKfQ/CJz/STzgfcDRwOnJ3kT8Btgb4Cq+lmS04FraIZF31pVi1fsKWnKO/zwriOQJKmvxpKQPbOqDhx6UFXfTbLMM3NW1b6jrNpllO2PAo4aQzySJElTylgSstuS/D/gizQT7V8H3D6uUUlLc1Sbt9tTJkmaIsYyh2xfmlNdfK1dZrRlUjduvLFZJEmaIsZycfE7gLcnWauq/jgBMUmSJK1SltlDluQ5Sa6hmXBPkm2TfHLcI5MkSVpFjGXI8mPAi2jnjVXVlcBO4xmUJEnSqmQsk/qpqpuSJU6m7ykp1J0nPrHrCCRJ6quxJGQ3JXkOUEnWAA4Grh3fsKSleOdST4EnSdLAGcuQ5YHAW2ku9j0fmN0+liRJUh8stYcsyTTg41X12gmKR1q2972vuT1ymecnliRpICw1IauqxUlmJFmjqv4yUUFJS7VwYdcRSJLUV2OZQzYPuDDJWcCfhgqr6qPjFZQkSdKqZNQ5ZEn+u737GuBb7bZr9yySJEnqg99RpVAAABCGSURBVKX1kD0jyebAb4BPTFA8kiRJq5ylJWSfBv4H2AK4tKc8NBcZ33Ic45JGN2tW1xFIktRXoyZkVXUccFyST1XVP05gTNLSHXRQ1xFIktRXyzwPmcmYJEnS+BrLiWGlyeXQQ5tFkqQpYkzXspQmlTvv7DoCSZL6yh4ySZKkjpmQSZIkdcyETJIkqWPOIdPg2X77riOQJKmvTMg0ePbfv+sIJEnqqwlPyJI8ETitp2hL4F+AdYA3A7e25e+tqu9McHiSJEkTbsITsqq6HpgNkGQa8Fvga8A/AB+rqo9MdEwaMAcf3Nwed1y3cUiS1CddD1nuAvyyqm5M0nEoGhj33tt1BJIk9VXXv7LcBzil5/FBSeYm+XySdbsKSpIkaSJ1lpAlWQPYE/hKW/Qp4PE0w5kLgGNH2e+AJJcmufTWW28daRNJkqSB0mUP2UuAy6tqIUBVLayqxVX1V+CzwIjnNqiqE6pqTlXNmTFjxgSGK0mSND66nEO2Lz3DlUk2rKoF7cNXAld3EpUmv+c9r+sIJEnqq04SsiSPAF4IvKWn+Jgks4EC5g1bJz3o9a/vOgJJkvqqk4Ssqu4B1htW5resJElaJXX9K0tp+R1wQLNIkjRFmJBJkiR1zIRMkiSpYyZkkiRJHTMhkyRJ6ljX17KUlt8LX9h1BJIk9ZUJmQbP3nt3HYEkSX3lkKUGz733NoskSVOEPWQaPAcf3NyecEK3cUiS1Cf2kEmSJHXMhEySJKljJmSSJEkdMyGTJEnqmJP6NXj22KPrCCRJ6isTMg0eEzJJ0hTjkKUGz513NoskSVOEPWQaPIce2tx6HjJJ0hRhD5kkSVLHTMgkSZI6ZkImSZLUMRMySZKkjjmpX4Nnr726jkCSpL4yIdPg2W23riOQJKmvHLLU4Fm4sFkkSZoiOukhSzIPuBtYDCyqqjlJHgOcBswE5gGvrqrfdxGfJrn3va+59TxkkqQposseshdU1eyqmtM+fg9wblVtBZzbPpYkSZryJtOQ5cuBk9v7JwOv6DAWSZKkCdNVQlbA2UkuS3JAW7ZBVS0AaG8f21FskiRJE6qrX1k+t6puTvJY4Jwk1411xzaBOwBgs802G6/4JEmSJkwnCVlV3dze3pLka8D2wMIkG1bVgiQbAreMsu8JwAkAc+bMqYmKWZPI617XdQSSJPXVhA9ZJnlkkrWH7gO7AVcDZwH7tZvtB3xjomPTgNhpp2aRJGmK6KKHbAPga0mGjv/lqvqfJJcApyd5E/AbYO8OYtMguPHG5nbzzbuNQ5KkPpnwhKyqfgVsO0L57cAuEx2PBtBRRzW3nodMkjRFTKbTXkiSJK2STMgkSZI6ZkImSZLUMRMySZKkjnV1Ylhpxe2/f9cRSJLUVyZkGjzbb991BJIk9ZVDlho8P/95s0iSNEXYQ6bB85GPNLeeh0ySNEXYQyZJktQxEzJJkqSOmZBJkiR1zIRMkiSpY07q1+A56KCuI5Akqa9MyDR4Zs3qOgJJkvrKIUsNnrlzm0WSpCnCHjINnuOPb249D5kkaYqwh0ySJKljJmSSJEkdMyGTJEnqmAmZJElSx5zUr8FzyCFdRyBJUl+ZkGnwbL111xFIktRXJmQDZOZ7vr1S+887+mV9iqRjF1/c3G6/fbdxSJLUJxM+hyzJpkl+kOTaJD9L8va2/Igkv01yRbu8dKJj04A48cRmkSRpiuiih2wR8M6qujzJ2sBlSc5p132sqj7SQUySJEmdmfCErKoWAAva+3cnuRbYeKLjkCRJmiw6Pe1FkpnAdsBP26KDksxN8vkk63YWmCRJ0gTqbFJ/krWArwLvqKq7knwKOBKo9vZY4I0j7HcAcADAZpttNnEBC1j5HxbAFPpxgSRJfdJJQpZkdZpk7EtVdSZAVS3sWf9Z4Fsj7VtVJwAnAMyZM6fGP1pNOocf3nUEkiT11YQnZEkCfA64tqo+2lO+YTu/DOCVwNUTHZsGxOabdx2BJEl91UUP2XOB1wNXJbmiLXsvsG+S2TRDlvOAt3QQmwbB+ec3tzvt1G0ckiT1SRe/srwAyAirvjPRsWhAffGLza0JmSRpivDi4pIkSR0zIZMkSeqYCZkkSVLHTMgkSZI61tmJYaUVduSRXUcgSVJfmZBp8GywQdcRSJLUVw5ZavCcfXazSJI0RdhDpsFzxhnN7W67dRuHJEl9Yg+ZJElSx0zIJEmSOmZCJkmS1DETMkmSpI45qV+D55hjuo5AkqS+MiHT4FlnHWa+59srXc28o1/Wh2AkSVp5Dllq8Hzzm+xyw0+7jkKSpL4xIdPg+eY32eWGi7uOQpKkvjEhkyRJ6pgJmSRJUsdMyCRJkjpmQiZJktQxEzINnuOO4wO7vqXrKCRJ6hsTMg2e6dO5b7U1uo5CkqS+MSHT4PnKV3jpdRd0HYUkSX3jmfo1eM45hx3n/YbvPGnHriPxigGSpL6YdAlZkhcD/wFMA06sqqM7Dkma9EwMJWmwTaqELMk04D+BFwLzgUuSnFVV13QbmaSxMDGUpBUzqRIyYHvghqr6FUCSU4GXAyZkkgbKZElOJ0scmnym0ntjKjyXyTapf2Pgpp7H89sySZKkKStV1XUMD0iyN/Ciqtq/ffx6YPuqelvPNgcAB7QPnwhcPwGhrQ/cNgHHGQS2xZJsjyXZHg+yLZZkeyzJ9njQqtQWm1fVjJFWTLYhy/nApj2PNwFu7t2gqk4ATpjIoJJcWlVzJvKYk5VtsSTbY0m2x4NsiyXZHkuyPR5kWzQm25DlJcBWSbZIsgawD3BWxzFJkiSNq0nVQ1ZVi5IcBHyP5rQXn6+qn3UcliRJ0riaVAkZQFV9B/hO13EMM6FDpJOcbbEk22NJtseDbIsl2R5Lsj0eZFswySb1S5IkrYom2xwySZKkVY4J2VIkeXGS65PckOQ9XcfTpSSbJvlBkmuT/CzJ27uOqWtJpiX5vyTf6jqWriVZJ8kZSa5r3yPP7jqmLiX55/ZzcnWSU5JM7zqmiZTk80luSXJ1T9ljkpyT5Bft7bpdxjhRRmmLD7eflblJvpZknS5jnEgjtUfPukOSVJL1u4itayZko+i5jNNLgKcA+yZ5SrdRdWoR8M6qejLwLOCtq3h7ALwduLbrICaJ/wD+p6qeBGzLKtwuSTYGDgbmVNU2ND9Q2qfbqCbcScCLh5W9Bzi3qrYCzm0frwpO4qFtcQ6wTVXNAn4OHDbRQXXoJB7aHiTZlOayib+Z6IAmCxOy0T1wGaeq+gswdBmnVVJVLaiqy9v7d9N84a6yV1FIsgnwMuDErmPpWpJHATsBnwOoqr9U1Z3dRtW51YCHJ1kNeATDzqc41VXV+cAdw4pfDpzc3j8ZeMWEBtWRkdqiqs6uqkXtw5/QnHNzlTDKewPgY8ChwCo7sd2EbHRexmkUSWYC2wE/7TaSTn2c5o/HX7sOZBLYErgV+EI7hHtikkd2HVRXquq3wEdo/tNfAPyhqs7uNqpJYYOqWgDNP3jAYzuOZ7J4I/DdroPoUpI9gd9W1ZVdx9IlE7LRZYSyVTZzH5JkLeCrwDuq6q6u4+lCkt2BW6rqsq5jmSRWA54OfKqqtgP+xKozHPUQ7dyolwNbABsBj0zyum6j0mSU5HCa6SBf6jqWriR5BHA48C9dx9I1E7LRLfMyTquaJKvTJGNfqqozu46nQ88F9kwyj2Yo+2+SfLHbkDo1H5hfVUM9pmfQJGirql2BX1fVrVV1P3Am8JyOY5oMFibZEKC9vaXjeDqVZD9gd+C1tWqff+rxNP+8XNn+Td0EuDzJ4zqNqgMmZKPzMk49koRmjtC1VfXRruPpUlUdVlWbVNVMmvfF96tqle0BqarfATcleWJbtAtwTYchde03wLOSPKL93OzCKvwjhx5nAfu19/cDvtFhLJ1K8mLg3cCeVXVP1/F0qaquqqrHVtXM9m/qfODp7d+VVYoJ2SjaCZdDl3G6Fjh9Fb+M03OB19P0Bl3RLi/tOihNGm8DvpRkLjAb+NeO4+lM21N4BnA5cBXN39lV6kzkSU4BLgKemGR+kjcBRwMvTPILml/THd1ljBNllLY4HlgbOKf9W/rpToOcQKO0h/BM/ZIkSZ2zh0ySJKljJmSSJEkdMyGTJEnqmAmZJElSx0zIJEmSOmZCJmmlJKkkx/Y8PiTJEX2q+6Qke/WjrmUcZ+8k1yb5wQjrPpzkZ0k+vJx1zkzyd/2LcszHPS/JnIk+rqSVY0ImaWXdB7wqyfpdB9IrybTl2PxNwD9V1QtGWPcWmhNVvms5Q5gJTHhCJmkwmZBJWlmLaE58+s/DVwzv4Uryx/Z25yQ/THJ6kp8nOTrJa5NcnOSqJI/vqWbXJD9qt9u93X9a23N1SZK5Sd7SU+8PknyZ5qSsw+PZt63/6iT/3pb9C7Aj8OnhvWBJzgIeCfw0yWuS7JHkp+1F1P83yQbtds/vOWHy/yVZm+bEp89ry/55WL1JcnySa5J8O8l3htopybyh5DbJnCTntfe3T/Ljtv4fD10ZIcnDk5zatsNpwMN72uik9rleNTwGSZPLal0HIGlK+E9gbpJjlmOfbYEnA3cAvwJOrKrtk7yd5sz/72i3mwk8n+aadz9I8gTg74E/VNUzk6wJXJjk7Hb77YFtqurXvQdLshHw78AzgN8DZyd5RVV9MMnfAIdU1aW9+1TVnkn+WFWz2zrWBZ5VVZVkf+BQ4J3AIcBbq+rCJGsB99JcYP2Qqtp9hOf+SuCJwNOADWguNfX5ZbTXdcBOVbUoya40V0P4W+AfgXuqalaSWTRXCIDmigkbV9U2bezrLKN+SR0yIZO00qrqriT/BRwM/HmMu11SVQsAkvwSGEqorgJ6hw5Pr6q/Ar9I8ivgScBuwKye3rdHA1sBfwEuHp6MtZ4JnFdVt7bH/BKwE/D1McYLzYWPT0tzcew1gKHjXAh8tK3zzKqan2Rp9ewEnFJVi4Gbk3x/DMd+NHBykq2AAlbvqes4gKqa216+Cpokd8sknwC+zYPtK2kScshSUr98nGYu1iN7yhbR/p1Jk6Gs0bPuvp77f+15/FeW/Gdx+PXdCgjwtqqa3S5bVNVQwvGnUeJbaoY0Rp8Ajq+qp9HMLZsOUFVHA/vTDBf+JMmTxlDXaNete6DNhupvHQn8oO3x2mPYuofUVVW/p+mFPA94K3DiGGKS1BETMkl9UVV3AKfTJGVD5tEMEQK8nAd7dZbH3kke1s4r2xK4Hvge8I9JVgdIsnWSRy6tEuCnwPOTrN9O+N8X+OFyxvJo4Lft/f2GCpM8vqquqqp/By6l6cW7m+YC0iM5H9innee1IUv2CM7jwTb721GO/YZhdb22jWMbYFZ7f33gYVX1VeB9wNPH/CwlTTgTMkn9dCzQ+2vLz9IkQRcDOzB679XSXE+TOH0XOLCq7qXp7bkGuDzJ1cBnWMYUjHZ49DDgB8CVwOVV9Y3ljOUI4CtJfgTc1lP+jnby/JU0Q7bfBeYCi5JcOcKE+q8Bv6AZnv0USyaGHwD+oz3G4p7yY4B/S3Ih0PsL0k8Ba7VDlYcCF7flGwPnJbkCOKl97pImqVSN1msuSZoISU4CvlVVZ3Qdi6Ru2EMmSZLUMXvIJEmSOmYPmSRJUsdMyCRJkjpmQiZJktQxEzJJkqSOmZBJkiR1zIRMkiSpY/8/043/G+TO9dgAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('fastQuads', 'Number of fast quads')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Most resorts have no fast quads. Big Mountain has 3, which puts it high up that league table. There are some values  much higher, but they are rare."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.8.6 Runs<a id='5.8.6_Runs'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('Runs', 'Total number of runs')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Big Mountain compares well for the number of runs. There are some resorts with more, but not many."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.8.7 Longest run<a id='5.8.7_Longest_run'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('LongestRun_mi', 'Longest run length (miles)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Big Mountain has one of the longest runs. Although it is just over half the length of the longest, the longer ones are rare."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.8.8 Trams<a id='5.8.8_Trams'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('trams', 'Number of trams')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The vast majority of resorts, such as Big Mountain, have no trams."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.8.9 Skiable terrain area<a id='5.8.9_Skiable_terrain_area'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_compare('SkiableTerrain_ac', 'Skiable terrain area (acres)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Big Mountain is amongst the resorts with the largest amount of skiable terrain."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.9 Modeling scenarios<a id='5.9_Modeling_scenarios'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Big Mountain Resort has been reviewing potential scenarios for either cutting costs or increasing revenue (from ticket prices). Ticket price is not determined by any set of parameters; the resort is free to set whatever price it likes. However, the resort operates within a market where people pay more for certain facilities, and less for others. Being able to sense how facilities support a given ticket price is valuable business intelligence. This is where the utility of our model comes in.\n",
+    "\n",
+    "The business has shortlisted some options:\n",
+    "1. Permanently closing down up to 10 of the least used runs. This doesn't impact any other resort statistics.\n",
+    "2. Increase the vertical drop by adding a run to a point 150 feet lower down but requiring the installation of an additional chair lift to bring skiers back up, without additional snow making coverage\n",
+    "3. Same as number 2, but adding 2 acres of snow making cover\n",
+    "4. Increase the longest run by 0.2 mile to boast 3.5 miles length, requiring an additional snow making coverage of 4 acres\n",
+    "\n",
+    "The expected number of visitors over the season is 350,000 and, on average, visitors ski for five days. Assume the provided data includes the additional lift that Big Mountain recently installed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "expected_visitors = 350_000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <th>total_chairs</th>\n",
+       "      <th>fastQuads</th>\n",
+       "      <th>Runs</th>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <th>trams</th>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>2353</td>\n",
+       "      <td>600.0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>3</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     vertical_drop  Snow Making_ac  total_chairs  fastQuads   Runs  \\\n",
+       "124           2353           600.0            14          3  105.0   \n",
+       "\n",
+       "     LongestRun_mi  trams  SkiableTerrain_ac  \n",
+       "124            3.3      0             3000.0  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_feats = ['vertical_drop', 'Snow Making_ac', 'total_chairs', 'fastQuads', \n",
+    "             'Runs', 'LongestRun_mi', 'trams', 'SkiableTerrain_ac']\n",
+    "big_mountain[all_feats]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 2#\n",
+    "#In this function, copy the Big Mountain data into a new data frame\n",
+    "#(Note we use .copy()!)\n",
+    "#And then for each feature, and each of its deltas (changes from the original),\n",
+    "#create the modified scenario dataframe (bm2) and make a ticket price prediction\n",
+    "#for it. The difference between the scenario's prediction and the current\n",
+    "#prediction is then calculated and returned.\n",
+    "#Complete the code to increment each feature by the associated delta\n",
+    "def predict_increase(features, deltas):\n",
+    "    \"\"\"Increase in modelled ticket price by applying delta to feature.\n",
+    "    \n",
+    "    Arguments:\n",
+    "    features - list, names of the features in the ski_data dataframe to change\n",
+    "    deltas - list, the amounts by which to increase the values of the features\n",
+    "    \n",
+    "    Outputs:\n",
+    "    Amount of increase in the predicted ticket price\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    bm2 = X_bm.copy()\n",
+    "    for f, d in zip(features, deltas):\n",
+    "        bm2[___] += ___\n",
+    "    return model.predict(bm2).item() - model.predict(X_bm).item()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.9.1 Scenario 1<a id='5.9.1_Scenario_1'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Close up to 10 of the least used runs. The number of runs is the only parameter varying."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[i for i in range(-1, -11, -1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "runs_delta = [i for i in range(-1, -11, -1)]\n",
+    "price_deltas = [predict_increase(['Runs'], [delta]) for delta in runs_delta]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.0,\n",
+       " -0.4057971014492807,\n",
+       " -0.6666666666666714,\n",
+       " -0.6666666666666714,\n",
+       " -0.6666666666666714,\n",
+       " -1.2608695652173907,\n",
+       " -1.2608695652173907,\n",
+       " -1.2608695652173907,\n",
+       " -1.7101449275362341,\n",
+       " -1.8115942028985472]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "price_deltas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 3#\n",
+    "#Create two plots, side by side, for the predicted ticket price change (delta) for each\n",
+    "#condition (number of runs closed) in the scenario and the associated predicted revenue\n",
+    "#change on the assumption that each of the expected visitors buys 5 tickets\n",
+    "#There are two things to do here:\n",
+    "#1 - use a list comprehension to create a list of the number of runs closed from `runs_delta`\n",
+    "#2 - use a list comprehension to create a list of predicted revenue changes from `price_deltas`\n",
+    "runs_closed = [-1 * ___ for ___ in runs_delta] #1\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
+    "fig.subplots_adjust(wspace=0.5)\n",
+    "ax[0].plot(runs_closed, price_deltas, 'o-')\n",
+    "ax[0].set(xlabel='Runs closed', ylabel='Change ($)', title='Ticket price')\n",
+    "revenue_deltas = [5 * expected_visitors * ___ for ___ in ___] #2\n",
+    "ax[1].plot(runs_closed, revenue_deltas, 'o-')\n",
+    "ax[1].set(xlabel='Runs closed', ylabel='Change ($)', title='Revenue');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model says closing one run makes no difference. Closing 2 and 3 successively reduces support for ticket price and so revenue. If Big Mountain closes down 3 runs, it seems they may as well close down 4 or 5 as there's no further loss in ticket price. Increasing the closures down to 6 or more leads to a large drop. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.9.2 Scenario 2<a id='5.9.2_Scenario_2'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this scenario, Big Mountain is adding a run, increasing the vertical drop by 150 feet, and installing an additional chair lift."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 4#\n",
+    "#Call `predict_increase` with a list of the features 'Runs', 'vertical_drop', and 'total_chairs'\n",
+    "#and associated deltas of 1, 150, and 1\n",
+    "ticket2_increase = ___(['Runs', ___, ___], [1, ___, ___])\n",
+    "revenue2_increase = 5 * expected_visitors * ticket2_increase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This scenario increases support for ticket price by $1.99\n",
+      "Over the season, this could be expected to amount to $3474638\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'This scenario increases support for ticket price by ${ticket2_increase:.2f}')\n",
+    "print(f'Over the season, this could be expected to amount to ${revenue2_increase:.0f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.9.3 Scenario 3<a id='5.9.3_Scenario_3'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this scenario, you are repeating the previous one but adding 2 acres of snow making."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 5#\n",
+    "#Repeat scenario 2 conditions, but add an increase of 2 to `Snow Making_ac`\n",
+    "ticket3_increase = predict_increase(['Runs', 'vertical_drop', 'total_chairs', ___], [1, 150, 1, ___])\n",
+    "revenue3_increase = 5 * expected_visitors * ticket3_increase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This scenario increases support for ticket price by $1.99\n",
+      "Over the season, this could be expected to amount to $3474638\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'This scenario increases support for ticket price by ${ticket3_increase:.2f}')\n",
+    "print(f'Over the season, this could be expected to amount to ${revenue3_increase:.0f}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Such a small increase in the snow making area makes no difference!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.9.4 Scenario 4<a id='5.9.4_Scenario_4'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This scenario calls for increasing the longest run by .2 miles and guaranteeing its snow coverage by adding 4 acres of snow making capability."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Code task 6#\n",
+    "#Predict the increase from adding 0.2 miles to `LongestRun_mi` and 4 to `Snow Making_ac`\n",
+    "predict_increase([___, ___], [___, ___])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No difference whatsoever. Although the longest run feature was used in the linear model, the random forest model (the one we chose because of its better performance) only has longest run way down in the feature importance list. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.10 Summary<a id='5.10_Summary'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q: 1** Write a summary of the results of modeling these scenarios. Start by starting the current position; how much does Big Mountain currently charge? What does your modelling suggest for a ticket price that could be supported in the marketplace by Big Mountain's facilities? How would you approach suggesting such a change to the business leadership? Discuss the additional operating cost of the new chair lift per ticket (on the basis of each visitor on average buying 5 day tickets) in the context of raising prices to cover this. For future improvements, state which, if any, of the modeled scenarios you'd recommend for further consideration. Suggest how the business might test, and progress, with any run closures."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**A: 1** Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5.11 Further work<a id='5.11_Further_work'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q: 2** What next? Highlight any deficiencies in the data that hampered or limited this work. The only price data in our dataset were ticket prices. You were provided with information about the additional operating cost of the new chair lift, but what other cost information would be useful? Big Mountain was already fairly high on some of the league charts of facilities offered, but why was its modeled price so much higher than its current price? Would this mismatch come as a surprise to the business executives? How would you find out? Assuming the business leaders felt this model was useful, how would the business make use of it? Would you expect them to come to you every time they wanted to test a new combination of parameters in a scenario? We hope you would have better things to do, so how might this model be made available for business analysts to use and explore?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**A: 2** Your answer here"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.9"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/data/ski_data_cleaned.csv b/data/ski_data_cleaned.csv
new file mode 100644
index 000000000..4259e45d8
--- /dev/null
+++ b/data/ski_data_cleaned.csv
@@ -0,0 +1,278 @@
+Name,Region,state,summit_elev,vertical_drop,base_elev,trams,fastSixes,fastQuads,quad,triple,double,surface,total_chairs,Runs,TerrainParks,LongestRun_mi,SkiableTerrain_ac,Snow Making_ac,daysOpenLastYear,yearsOpen,averageSnowfall,AdultWeekend,projectedDaysOpen,NightSkiing_ac
+Alyeska Resort,Alaska,Alaska,3939,2500,250,1,0,2,2,0,0,2,7,76.0,2.0,1.0,1610.0,113.0,150.0,60.0,669.0,85.0,150.0,550.0
+Eaglecrest Ski Area,Alaska,Alaska,2600,1540,1200,0,0,0,0,0,4,0,4,36.0,1.0,2.0,640.0,60.0,45.0,44.0,350.0,53.0,90.0,
+Hilltop Ski Area,Alaska,Alaska,2090,294,1796,0,0,0,0,1,0,2,3,13.0,1.0,1.0,30.0,30.0,150.0,36.0,69.0,34.0,152.0,30.0
+Arizona Snowbowl,Arizona,Arizona,11500,2300,9200,0,1,0,2,2,1,2,8,55.0,4.0,2.0,777.0,104.0,122.0,81.0,260.0,89.0,122.0,
+Sunrise Park Resort,Arizona,Arizona,11100,1800,9200,0,0,1,2,3,1,0,7,65.0,2.0,1.2,800.0,80.0,115.0,49.0,250.0,78.0,104.0,80.0
+Yosemite Ski & Snowboard Area,Northern California,California,7800,600,7200,0,0,0,0,1,3,1,5,10.0,2.0,0.4,88.0,,110.0,84.0,300.0,47.0,107.0,
+Dodge Ridge,Sierra Nevada,California,8200,1600,6600,0,0,0,1,2,5,4,12,67.0,5.0,2.0,862.0,,,69.0,350.0,78.0,140.0,
+Donner Ski Ranch,Sierra Nevada,California,8012,750,7031,0,0,0,0,1,5,2,8,52.0,2.0,1.5,505.0,60.0,163.0,82.0,400.0,75.0,170.0,
+Mammoth Mountain Ski Area,Sierra Nevada,California,11053,3100,7953,3,2,9,1,6,4,0,25,154.0,7.0,3.0,3500.0,700.0,243.0,66.0,400.0,159.0,,
+Mt. Shasta Ski Park,Sierra Nevada,California,6890,1435,5500,0,0,0,0,3,0,1,4,32.0,2.0,1.1,425.0,225.0,140.0,34.0,300.0,59.0,130.0,
+Mountain High,Sierra Nevada,California,8200,1600,6600,0,0,2,2,2,5,3,14,59.0,1.0,1.6,290.0,275.0,118.0,95.0,108.0,84.0,150.0,73.0
+Mt. Baldy,Sierra Nevada,California,8600,2100,6500,0,0,0,0,0,4,0,4,26.0,,2.5,400.0,80.0,175.0,67.0,178.0,69.0,200.0,
+Ski China Peak,Sierra Nevada,California,8709,1679,7030,0,0,0,1,4,2,4,11,45.0,1.0,2.2,1400.0,150.0,140.0,62.0,300.0,83.0,144.0,
+Snow Valley,Sierra Nevada,California,7841,1041,6800,0,0,0,0,5,6,1,12,28.0,6.0,1.2,240.0,188.0,111.0,82.0,160.0,79.0,143.0,164.0
+Soda Springs,Sierra Nevada,California,7352,652,6700,0,0,0,0,1,1,2,4,18.0,,0.4,200.0,20.0,150.0,83.0,400.0,50.0,144.0,
+Sugar Bowl Resort,Sierra Nevada,California,8383,1500,6883,1,0,5,3,1,0,2,12,105.0,3.0,3.0,1650.0,375.0,151.0,80.0,500.0,125.0,150.0,
+Tahoe Donner,Sierra Nevada,California,7350,600,6750,0,0,0,1,1,0,3,5,14.0,2.0,1.0,120.0,,150.0,48.0,400.0,69.0,144.0,
+Arapahoe Basin Ski Area,Colorado,Colorado,13050,2530,10780,0,0,1,2,1,2,3,9,145.0,3.0,1.5,1428.0,125.0,230.0,73.0,350.0,85.0,233.0,
+Aspen / Snowmass,Colorado,Colorado,12510,4406,8104,3,1,15,4,3,5,9,40,336.0,10.0,5.3,5517.0,658.0,138.0,72.0,300.0,179.0,138.0,
+Copper Mountain Resort,Colorado,Colorado,12313,2738,9712,1,2,4,0,4,4,9,24,150.0,6.0,1.7,2527.0,364.0,164.0,47.0,300.0,158.0,164.0,
+Purgatory,Colorado,Colorado,10822,2029,8793,0,1,2,0,3,3,3,12,101.0,9.0,1.3,1605.0,250.0,130.0,54.0,260.0,89.0,130.0,
+Howelsen Hill,Colorado,Colorado,7136,440,6696,0,0,0,0,0,1,3,4,17.0,1.0,6.0,50.0,25.0,100.0,104.0,150.0,25.0,100.0,10.0
+Loveland,Colorado,Colorado,13010,2210,10800,0,0,1,3,3,2,1,10,94.0,1.0,2.0,1800.0,240.0,205.0,82.0,422.0,79.0,184.0,
+Monarch Mountain,Colorado,Colorado,11952,1162,10790,0,0,0,1,0,4,2,7,64.0,2.0,1.0,800.0,,143.0,80.0,350.0,89.0,136.0,
+Powderhorn,Colorado,Colorado,9850,1650,8200,0,0,1,0,0,2,2,5,42.0,2.0,1.5,1600.0,42.0,111.0,53.0,250.0,71.0,110.0,
+Silverton Mountain,Colorado,Colorado,13487,3087,10400,0,0,0,0,0,1,0,1,,,1.5,1819.0,,175.0,17.0,400.0,79.0,181.0,
+Cooper,Colorado,Colorado,11700,1200,10500,0,0,0,0,1,1,2,4,41.0,1.0,1.0,400.0,,130.0,74.0,260.0,56.0,130.0,
+Ski Granby Ranch,Colorado,Colorado,9202,1000,8202,0,0,2,0,1,1,1,5,40.0,1.0,0.6,406.0,170.0,116.0,36.0,220.0,84.0,92.0,100.0
+Sunlight Mountain Resort,Colorado,Colorado,9895,2010,7885,0,0,0,0,1,2,0,3,67.0,1.0,2.5,680.0,30.0,100.0,53.0,250.0,65.0,135.0,
+Telluride,Colorado,Colorado,13150,4425,8725,2,0,6,1,2,2,4,17,148.0,3.0,4.6,2000.0,220.0,131.0,47.0,280.0,139.0,137.0,
+Wolf Creek Ski Area,Colorado,Colorado,11904,1604,10300,0,0,3,1,2,1,3,10,120.0,,2.0,1600.0,5.0,130.0,80.0,430.0,72.0,150.0,
+Mohawk Mountain,Connecticut,Connecticut,1600,650,950,0,0,0,0,5,0,3,8,25.0,,1.5,107.0,100.0,,72.0,92.0,65.0,110.0,64.0
+Mount Southington Ski Area,Connecticut,Connecticut,525,425,100,0,0,0,0,2,2,3,7,14.0,2.0,0.3,51.0,51.0,63.0,55.0,80.0,60.0,95.0,51.0
+Powder Ridge Park,Connecticut,Connecticut,720,550,170,0,0,0,0,1,2,2,5,19.0,4.0,0.5,80.0,68.0,80.0,60.0,80.0,55.0,100.0,40.0
+Ski Sundown,Connecticut,Connecticut,1075,625,450,0,0,0,0,3,0,2,5,16.0,2.0,1.0,70.0,70.0,84.0,50.0,45.0,62.0,95.0,66.0
+Woodbury Ski Area,Connecticut,Connecticut,730,300,430,0,0,0,0,0,1,4,5,12.0,2.0,0.2,50.0,50.0,126.0,57.0,70.0,42.0,180.0,35.0
+Bogus Basin,Idaho,Idaho,7582,1800,5800,0,0,3,0,1,3,4,11,91.0,3.0,1.5,2600.0,,134.0,77.0,250.0,64.0,130.0,165.0
+Brundage Mountain Resort,Idaho,Idaho,7640,1800,5840,0,0,1,0,4,0,1,6,51.0,2.0,3.2,1920.0,2.0,126.0,58.0,320.0,70.0,,
+Kelly Canyon Ski Area,Idaho,Idaho,6600,1000,5600,0,0,0,0,0,4,2,6,51.0,1.0,1.3,740.0,,,62.0,200.0,42.0,,
+Lookout Pass Ski Area,Idaho,Idaho,5650,1150,4500,0,0,0,0,1,3,0,4,35.0,2.0,1.5,540.0,,113.0,84.0,400.0,47.0,140.0,
+Magic Mountain Ski Area,Idaho,Idaho,7200,700,6500,0,0,0,0,0,1,2,3,11.0,,1.5,280.0,,65.0,81.0,180.0,32.0,70.0,
+Pebble Creek Ski Area,Idaho,Idaho,8560,2200,6360,0,0,0,0,3,0,0,3,54.0,2.0,1.3,1100.0,30.0,85.0,70.0,250.0,47.0,91.0,30.0
+Schweitzer,Idaho,Idaho,6400,2400,4000,0,1,2,0,1,3,2,9,92.0,3.0,2.1,2900.0,47.0,136.0,56.0,300.0,81.0,,100.0
+Silver Mountain,Idaho,Idaho,6300,2200,4100,1,0,0,1,2,2,1,7,80.0,2.0,2.5,1600.0,225.0,130.0,29.0,300.0,62.0,193.0,20.0
+Soldier Mountain Ski Area,Idaho,Idaho,7200,1400,5800,0,0,0,0,0,2,1,3,36.0,,0.4,1142.0,,60.0,71.0,,43.0,,
+Tamarack Resort,Idaho,Idaho,7700,2800,4900,0,0,2,2,0,0,2,6,48.0,3.0,1.5,1020.0,200.0,,15.0,300.0,71.0,150.0,
+Chestnut Mountain Resort,Illinois,Illinois,1040,475,565,0,0,0,2,4,0,3,9,22.0,3.0,0.2,139.0,139.0,87.0,60.0,50.0,55.0,112.0,139.0
+Ski Snowstar Winter Sports Park,Illinois,Illinois,790,262,528,0,0,0,2,0,2,2,6,15.0,1.0,0.8,28.0,28.0,56.0,38.0,38.0,35.0,86.0,28.0
+Villa Olivia,Illinois,Illinois,500,180,320,0,0,0,1,0,0,6,7,7.0,1.0,0.1,15.0,15.0,,53.0,25.0,40.0,70.0,15.0
+Paoli Peaks,Indiana,Indiana,900,300,600,0,0,1,1,3,1,2,8,15.0,2.0,0.4,65.0,65.0,75.0,41.0,18.0,45.0,80.0,65.0
+Perfect North Slopes,Indiana,Indiana,800,400,400,0,0,0,2,3,0,6,11,23.0,2.0,1.0,100.0,100.0,82.0,39.0,24.0,52.0,90.0,100.0
+Mt. Crescent Ski Area,Iowa,Iowa,1500,300,1200,0,0,0,1,0,1,0,2,11.0,1.0,0.2,50.0,50.0,,58.0,30.0,39.0,,50.0
+Seven Oaks,Iowa,Iowa,975,275,800,0,0,0,0,2,0,2,4,11.0,2.0,1.0,35.0,35.0,100.0,22.0,40.0,40.0,100.0,35.0
+Sundown Mountain,Iowa,Iowa,1059,475,584,0,0,0,1,1,2,2,6,21.0,2.0,0.6,55.0,55.0,,46.0,45.0,46.0,,55.0
+Big Squaw Mountain Ski Resort,Maine,Maine,3200,660,1750,0,0,0,0,1,0,0,1,29.0,,0.8,,,67.0,6.0,,30.0,58.0,
+Camden Snow Bowl,Maine,Maine,1080,850,150,0,0,0,0,1,1,1,3,26.0,2.0,1.0,100.0,48.0,68.0,83.0,69.0,43.0,70.0,48.0
+Lost Valley,Maine,Maine,495,240,255,0,0,0,0,0,2,2,4,22.0,2.0,0.3,45.0,45.0,87.0,58.0,50.0,55.0,104.0,45.0
+Mt. Abram Ski Resort,Maine,Maine,2250,1150,1050,0,0,0,0,0,2,3,5,54.0,1.0,0.5,640.0,175.0,120.0,59.0,125.0,49.0,120.0,
+New Hermon Mountain,Maine,Maine,450,350,100,0,0,0,0,0,1,2,3,20.0,,1.9,70.0,70.0,102.0,55.0,90.0,32.0,117.0,45.0
+Shawnee Peak,Maine,Maine,1900,1350,600,0,0,0,1,2,1,2,6,43.0,3.0,0.8,239.0,234.0,97.0,81.0,110.0,75.0,103.0,110.0
+Sugarloaf,Maine,Maine,4237,2820,1417,0,0,2,3,1,5,2,13,162.0,4.0,3.5,1240.0,618.0,159.0,68.0,200.0,99.0,155.0,
+Sunday River,Maine,Maine,3140,2340,800,1,0,4,5,3,1,1,15,135.0,5.0,3.0,870.0,552.0,165.0,60.0,167.0,105.0,169.0,140.0
+Wisp,Maryland,Maryland,3115,700,2415,0,0,0,2,5,0,5,12,34.0,3.0,1.5,172.0,118.0,121.0,64.0,100.0,79.0,120.0,118.0
+Berkshire East,Massachusetts,Massachusetts,1720,1180,540,0,0,0,2,1,1,2,6,47.0,2.0,2.0,180.0,165.0,120.0,68.0,120.0,68.0,120.0,80.0
+Blandford Ski Area,Massachusetts,Massachusetts,1685,465,1035,0,0,0,0,0,3,2,5,29.0,2.0,0.5,132.0,70.0,,83.0,50.0,45.0,,70.0
+Blue Hills Ski Area,Massachusetts,Massachusetts,635,309,326,0,0,0,0,0,1,3,4,16.0,1.0,,60.0,60.0,,19.0,,45.0,,
+Bousquet Ski Area,Massachusetts,Massachusetts,1875,750,1125,0,0,0,0,0,3,2,5,23.0,1.0,1.0,200.0,98.0,,19.0,83.0,49.0,,100.0
+Bradford Ski Area,Massachusetts,Massachusetts,1548,248,1300,0,0,0,0,2,0,8,10,15.0,1.0,0.3,48.0,48.0,,71.0,,55.0,,
+Jiminy Peak,Massachusetts,Massachusetts,2380,1150,1230,0,1,0,2,3,1,2,9,45.0,3.0,2.0,167.0,163.0,121.0,71.0,90.0,81.0,120.0,104.0
+Nashoba Valley,Massachusetts,Massachusetts,440,240,200,0,0,0,0,3,1,7,11,17.0,2.0,0.5,52.0,52.0,112.0,55.0,55.0,58.0,126.0,52.0
+Otis Ridge Ski Area,Massachusetts,Massachusetts,1700,400,1300,0,0,0,0,0,1,3,4,11.0,1.0,1.0,60.0,55.0,106.0,73.0,70.0,40.0,106.0,35.0
+Ski Butternut,Massachusetts,Massachusetts,1800,1000,800,0,0,0,3,1,1,6,11,22.0,2.0,1.5,110.0,110.0,107.0,56.0,115.0,60.0,110.0,
+Wachusett Mountain Ski Area,Massachusetts,Massachusetts,2006,1000,1006,0,0,3,0,1,0,4,8,27.0,2.0,1.5,112.0,112.0,,57.0,100.0,71.0,120.0,104.0
+Alpine Valley Ski Area,Michigan,Michigan,500,240,126,0,0,0,1,2,5,6,14,25.0,3.0,0.2,100.0,100.0,,57.0,20.0,47.0,,100.0
+Apple Mountain,Michigan,Michigan,820,220,600,0,0,0,1,0,0,5,6,12.0,,,80.0,42.0,,58.0,52.0,35.0,,80.0
+Big Powderhorn Mountain,Michigan,Michigan,1800,600,1200,0,0,0,0,0,9,1,10,45.0,2.0,1.0,253.0,228.0,100.0,55.0,214.0,69.0,108.0,
+Bittersweet Ski Area,Michigan,Michigan,850,350,450,0,0,0,1,7,0,4,12,20.0,2.0,0.2,100.0,100.0,80.0,36.0,90.0,48.0,,100.0
+Big Snow Resort - Blackjack,Michigan,Michigan,850,465,385,0,0,0,0,0,4,2,6,26.0,2.0,1.0,170.0,86.0,95.0,42.0,210.0,65.0,115.0,
+Boyne Highlands,Michigan,Michigan,1290,552,745,0,0,1,3,4,0,2,10,55.0,4.0,1.2,435.0,400.0,97.0,56.0,140.0,98.0,120.0,150.0
+Caberfae Peaks,Michigan,Michigan,1569,485,1060,0,0,0,1,2,1,1,5,34.0,2.0,1.2,200.0,200.0,118.0,82.0,140.0,49.0,130.0,150.0
+Cannonsburg,Michigan,Michigan,1100,250,850,0,0,0,1,1,1,7,10,21.0,5.0,0.1,100.0,,100.0,54.0,100.0,37.0,100.0,
+Crystal Mountain,Michigan,Michigan,1132,375,757,0,0,1,3,2,0,2,8,58.0,3.0,0.3,102.0,96.0,120.0,63.0,132.0,64.0,135.0,56.0
+Big Snow Resort - Indianhead Mountain,Michigan,Michigan,1935,638,1297,0,0,0,1,1,5,2,9,32.0,2.0,1.0,240.0,150.0,120.0,60.0,204.0,49.0,120.0,
+Mont Ripley,Michigan,Michigan,1140,440,700,0,0,0,0,0,2,2,4,25.0,2.0,0.8,112.0,112.0,114.0,83.0,275.0,49.0,100.0,100.0
+Mount Bohemia,Michigan,Michigan,1500,900,600,0,0,0,0,1,1,0,2,,,2.3,585.0,,83.0,19.0,273.0,68.0,100.0,
+Mt. Brighton,Michigan,Michigan,1330,230,1100,0,0,0,2,3,0,8,13,25.0,5.0,0.1,130.0,130.0,111.0,59.0,60.0,59.0,100.0,130.0
+Mt. Holiday Ski Area,Michigan,Michigan,440,200,240,0,0,0,0,0,2,2,4,12.0,,0.1,45.0,45.0,100.0,70.0,120.0,34.0,90.0,45.0
+Mount Holly,Michigan,Michigan,1105,350,755,0,0,1,2,3,1,6,13,19.0,,0.1,100.0,100.0,,63.0,42.0,45.0,102.0,100.0
+Mulligan's Hollow Ski Bowl,Michigan,Michigan,700,130,570,0,0,0,0,0,0,5,5,6.0,,0.2,10.0,10.0,,19.0,60.0,20.0,,10.0
+Norway Mountain,Michigan,Michigan,1335,500,835,0,0,0,0,1,2,3,6,17.0,1.0,1.4,186.0,186.0,110.0,45.0,100.0,45.0,110.0,40.0
+Nubs Nob Ski Area,Michigan,Michigan,1338,427,911,0,0,0,3,4,2,1,10,53.0,3.0,0.9,248.0,248.0,133.0,61.0,135.0,85.0,130.0,160.0
+Pine Mountain,Michigan,Michigan,1650,500,1150,0,0,0,0,1,2,1,4,28.0,1.0,0.5,160.0,160.0,110.0,80.0,60.0,45.0,126.0,80.0
+Schuss Mountain at Shanty Creek,Michigan,Michigan,1125,450,675,0,0,0,5,0,0,3,8,42.0,3.0,1.0,70.0,70.0,94.0,57.0,160.0,78.0,111.0,70.0
+Ski Brule,Michigan,Michigan,1860,500,1360,0,0,0,0,0,5,7,12,17.0,3.0,1.0,150.0,150.0,164.0,62.0,150.0,49.0,165.0,40.0
+Snow Snake Mountain Ski Area,Michigan,Michigan,1230,210,1020,0,0,0,0,1,0,5,6,12.0,2.0,0.0,40.0,40.0,,72.0,,35.0,,40.0
+Swiss Valley,Michigan,Michigan,1200,225,975,0,0,0,2,1,0,4,7,11.0,2.0,0.1,60.0,60.0,89.0,51.0,60.0,42.0,80.0,60.0
+The Homestead,Michigan,Michigan,900,320,580,0,0,0,0,2,1,2,5,15.0,1.0,0.2,16.0,16.0,47.0,34.0,150.0,50.0,42.0,16.0
+Timber Ridge,Michigan,Michigan,850,250,600,0,0,0,1,1,2,4,8,16.0,2.0,0.3,50.0,50.0,80.0,58.0,,45.0,,50.0
+Afton Alps,Minnesota,Minnesota,1530,350,1180,0,0,0,1,3,14,4,22,48.0,5.0,0.5,250.0,250.0,135.0,56.0,60.0,60.0,135.0,250.0
+Andes Tower Hills Ski Area,Minnesota,Minnesota,1620,290,1330,0,0,0,1,2,0,3,6,15.0,2.0,0.2,35.0,35.0,100.0,38.0,55.0,45.0,110.0,35.0
+Buck Hill,Minnesota,Minnesota,1225,309,919,0,0,0,2,1,0,5,8,16.0,,0.2,45.0,45.0,115.0,65.0,60.0,47.0,112.0,45.0
+Buena Vista Ski Area,Minnesota,Minnesota,1510,230,1280,0,0,0,0,2,2,2,6,17.0,,0.3,30.0,30.0,57.0,70.0,78.0,44.0,60.0,30.0
+Coffee Mill Ski & Snowboard Resort,Minnesota,Minnesota,1150,425,725,0,0,0,0,0,2,1,3,14.0,1.0,1.0,40.0,35.0,57.0,39.0,48.0,37.0,56.0,35.0
+Elm Creek Winter Recreation Area,Minnesota,Minnesota,928,60,868,0,0,0,0,0,0,3,3,3.0,,1.0,15.0,20.0,105.0,13.0,45.0,17.0,102.0,15.0
+Giants Ridge Resort,Minnesota,Minnesota,1972,500,1472,0,0,1,1,1,2,2,7,35.0,2.0,0.8,202.0,202.0,120.0,35.0,85.0,58.0,125.0,121.0
+Hyland Ski & Snowboard Area,Minnesota,Minnesota,1075,175,900,0,0,0,2,1,0,5,8,14.0,1.0,1.0,35.0,35.0,110.0,61.0,55.0,35.34,115.0,35.0
+Lutsen Mountains,Minnesota,Minnesota,1688,825,800,1,1,0,0,1,4,1,8,62.0,2.0,2.0,393.0,231.0,135.0,71.0,120.0,84.0,127.0,
+Mount Kato Ski Area,Minnesota,Minnesota,540,240,300,0,0,0,5,0,3,2,10,19.0,4.0,1.0,55.0,55.0,115.0,43.0,50.0,46.0,120.0,50.0
+Powder Ridge Ski Area,Minnesota,Minnesota,790,300,500,0,0,0,1,0,2,3,6,15.0,4.0,,60.0,60.0,97.0,58.0,45.0,48.0,113.0,60.0
+Spirit Mountain,Minnesota,Minnesota,1320,700,620,0,0,1,1,2,1,2,7,22.0,3.0,1.0,175.0,175.0,100.0,45.0,100.0,59.0,125.0,144.0
+Welch Village,Minnesota,Minnesota,1060,360,700,0,0,0,3,1,4,2,10,50.0,1.0,0.8,125.0,125.0,114.0,54.0,45.0,60.0,122.0,100.0
+Wild Mountain Ski & Snowboard Area,Minnesota,Minnesota,1113,300,813,0,0,0,4,0,0,4,8,26.0,4.0,0.9,100.0,100.0,130.0,47.0,50.0,55.0,140.0,100.0
+Hidden Valley Ski Area,Missouri,Missouri,2566,310,2316,0,0,0,2,2,0,3,7,17.0,,0.1,30.0,30.0,,37.0,26.0,49.0,,17.0
+Snow Creek,Missouri,Missouri,1100,300,800,0,0,0,0,2,1,2,5,14.0,2.0,0.3,30.0,30.0,69.0,33.0,20.0,47.0,85.0,30.0
+Blacktail Mountain Ski Area,Montana,Montana,6676,1440,5236,0,0,0,0,1,2,1,4,27.0,,0.7,1000.0,,,21.0,250.0,42.0,,
+Bridger Bowl,Montana,Montana,8700,2600,6100,0,0,0,1,6,1,3,11,105.0,2.0,1.5,2000.0,100.0,122.0,64.0,350.0,63.0,133.0,
+Discovery Ski Area,Montana,Montana,8150,2380,5770,0,0,0,0,5,2,1,8,74.0,1.0,1.5,2400.0,25.0,116.0,46.0,225.0,49.0,116.0,
+Great Divide,Montana,Montana,7330,1580,5750,0,0,0,0,0,5,1,6,110.0,6.0,3.0,1600.0,150.0,94.0,78.0,180.0,48.0,100.0,100.0
+Lost Trail - Powder Mtn,Montana,Montana,8200,1800,6400,0,0,0,0,0,5,3,8,69.0,2.0,2.5,1800.0,,84.0,81.0,325.0,46.0,80.0,
+Maverick Mountain,Montana,Montana,8520,2020,6500,0,0,0,0,0,1,1,2,22.0,,1.3,255.0,,,83.0,160.0,39.0,,
+Montana Snowbowl,Montana,Montana,7600,2600,5000,0,0,0,0,0,2,2,4,37.0,,1.2,950.0,20.0,,58.0,300.0,50.0,,10.0
+Red Lodge Mountain,Montana,Montana,9416,2400,7016,0,0,2,0,1,3,1,7,70.0,2.0,2.5,1635.0,496.0,142.0,59.0,250.0,67.0,136.0,
+Showdown Montana,Montana,Montana,8200,1400,6800,0,0,0,0,1,2,1,4,36.0,1.0,1.8,640.0,,86.0,83.0,250.0,47.0,85.0,
+Teton Pass Ski Resort,Montana,Montana,7200,1010,6190,0,0,0,0,0,1,2,3,43.0,1.0,3.0,330.0,,40.0,54.0,250.0,39.0,150.0,
+Big Mountain Resort,Montana,Montana,6817,2353,4464,0,0,3,2,6,0,3,14,105.0,4.0,3.3,3000.0,600.0,123.0,72.0,333.0,81.0,123.0,600.0
+Diamond Peak,Sierra Nevada,Nevada,8540,1840,6700,0,0,1,2,0,3,1,7,30.0,3.0,2.5,655.0,492.0,100.0,53.0,300.0,99.0,122.0,
+Elko SnoBowl,Nevada,Nevada,7000,700,6300,0,0,0,0,0,1,1,2,10.0,,1.0,60.0,2.0,19.0,23.0,24.0,20.0,30.0,
+Lee Canyon,Nevada,Nevada,11289,860,8510,0,0,0,2,1,0,0,3,24.0,1.0,0.3,195.0,50.0,144.0,56.0,161.0,70.0,150.0,
+Mt. Rose - Ski Tahoe,Sierra Nevada,Nevada,9700,1800,8260,0,2,0,2,2,0,2,8,65.0,5.0,2.5,1200.0,330.0,152.0,55.0,350.0,135.0,150.0,
+Attitash,New Hampshire,New Hampshire,2350,1750,600,0,0,2,1,3,2,1,9,68.0,3.0,3.0,311.0,240.0,115.0,54.0,120.0,89.0,130.0,
+Black Mountain,New Hampshire,New Hampshire,2350,1100,1250,0,0,0,0,1,1,3,5,45.0,,1.6,143.0,120.0,110.0,84.0,125.0,59.0,107.0,
+Bretton Woods,New Hampshire,New Hampshire,3100,1500,1600,0,0,4,1,1,0,3,9,63.0,2.0,2.0,464.0,427.0,180.0,46.0,200.0,99.0,180.0,45.0
+Cannon Mountain,New Hampshire,New Hampshire,4080,2180,1900,1,0,1,2,3,1,3,11,97.0,3.0,2.3,285.0,192.0,124.0,81.0,160.0,79.0,143.0,
+Crotched Mountain,New Hampshire,New Hampshire,2066,1016,1050,0,0,1,1,1,1,1,5,25.0,3.0,1.2,100.0,100.0,105.0,16.0,105.0,69.0,100.0,100.0
+Dartmouth Skiway,New Hampshire,New Hampshire,1943,969,974,0,0,0,1,0,1,2,4,28.0,1.0,1.1,107.0,54.0,104.0,63.0,100.0,50.0,105.0,
+Gunstock,New Hampshire,New Hampshire,2300,1400,900,0,0,1,2,2,0,1,6,55.0,4.0,1.5,227.0,176.0,106.0,82.0,120.0,92.0,,60.0
+King Pine,New Hampshire,New Hampshire,850,350,500,0,0,0,0,3,0,3,6,17.0,2.0,0.3,48.0,45.0,105.0,57.0,120.0,58.0,107.0,23.0
+Mount Sunapee,New Hampshire,New Hampshire,2743,1510,1233,0,0,2,1,2,1,4,10,66.0,4.0,0.8,232.0,215.0,130.0,71.0,100.0,93.0,136.0,
+Pats Peak,New Hampshire,New Hampshire,1460,770,690,0,0,0,0,4,2,5,11,28.0,3.0,1.5,115.0,115.0,109.0,56.0,100.0,72.0,112.0,93.0
+Ragged Mountain Resort,New Hampshire,New Hampshire,2250,1250,1000,0,1,1,0,1,0,3,6,57.0,3.0,0.7,250.0,200.0,,54.0,100.0,84.0,140.0,
+Waterville Valley,New Hampshire,New Hampshire,4004,2020,1984,0,0,2,0,2,3,4,11,62.0,4.0,1.9,265.0,220.0,142.0,54.0,148.0,93.0,142.0,
+Whaleback Mountain,New Hampshire,New Hampshire,1800,700,1100,0,0,0,0,0,1,3,4,30.0,1.0,1.0,85.0,60.0,105.0,64.0,110.0,45.0,105.0,55.0
+Wildcat Mountain,New Hampshire,New Hampshire,4062,2112,1950,0,0,1,0,3,0,1,5,48.0,,2.8,225.0,200.0,156.0,61.0,200.0,89.0,150.0,
+Mountain Creek Resort,New Jersey,New Jersey,1480,1040,440,1,0,2,2,1,1,3,10,46.0,3.0,2.0,167.0,167.0,90.0,54.0,65.0,79.99,100.0,167.0
+Angel Fire Resort,New Mexico,New Mexico,10677,2077,8600,0,0,2,0,0,3,2,7,81.0,3.0,3.0,560.0,230.0,101.0,53.0,210.0,77.0,101.0,50.0
+Enchanted Forest Ski Area,New Mexico,New Mexico,10078,400,9820,0,0,0,0,0,0,0,0,33.0,,2.5,600.0,,130.0,34.0,240.0,20.0,140.0,
+Pajarito Mountain Ski Area,New Mexico,New Mexico,10441,1410,9031,0,0,0,1,1,3,1,6,45.0,2.0,0.6,750.0,35.0,89.0,62.0,163.0,49.0,117.0,
+Red River,New Mexico,New Mexico,10350,1600,8750,0,0,0,1,3,1,2,7,63.0,3.0,2.5,209.0,,110.0,60.0,214.0,79.0,,
+Sandia Peak,New Mexico,New Mexico,10378,1700,8678,0,0,0,0,0,4,1,5,39.0,1.0,2.0,200.0,30.0,32.0,82.0,100.0,55.0,38.0,
+Sipapu Ski Resort,New Mexico,New Mexico,9255,1055,8200,0,0,0,1,2,0,3,6,42.0,4.0,0.5,200.0,140.0,127.0,67.0,190.0,47.0,143.0,
+Ski Apache,New Mexico,New Mexico,11500,1900,9600,1,0,0,2,6,0,2,11,55.0,3.0,2.0,750.0,270.0,133.0,58.0,185.0,74.0,124.0,
+Ski Santa Fe,New Mexico,New Mexico,12075,1725,10350,0,0,0,1,2,2,2,7,83.0,1.0,3.0,660.0,275.0,107.0,73.0,225.0,80.0,130.0,
+Taos Ski Valley,New Mexico,New Mexico,12481,3281,9200,1,0,1,3,4,1,4,14,111.0,1.0,5.0,1294.0,647.0,137.0,64.0,300.0,110.0,136.0,
+Belleayre,New York,New York,3429,1404,2025,1,0,1,1,1,2,2,8,50.0,2.0,2.2,175.0,168.0,154.0,70.0,130.0,72.0,150.0,
+Brantling Ski Slopes,New York,New York,850,250,600,0,0,0,0,0,0,5,5,10.0,,0.1,20.0,16.0,,19.0,110.0,32.0,,
+Bristol Mountain,New York,New York,2200,1200,1000,0,0,2,1,1,1,1,6,34.0,3.0,2.0,160.0,148.0,129.0,55.0,60.0,76.0,129.0,154.0
+Buffalo Ski Club Ski Area,New York,New York,3429,500,2025,0,0,0,0,0,2,4,6,43.0,1.0,,225.0,150.0,,12.0,,50.0,,100.0
+Catamount,New York,New York,2000,1000,1000,0,0,0,1,1,2,3,7,36.0,5.0,2.0,133.0,130.0,100.0,80.0,108.0,69.0,90.0,55.0
+Dry Hill Ski Area,New York,New York,950,300,650,0,0,0,0,0,1,2,3,7.0,1.0,0.2,35.0,26.0,,55.0,125.0,35.0,,26.0
+Gore Mountain,New York,New York,3600,2537,998,1,0,2,2,3,2,4,14,110.0,7.0,4.5,439.0,338.0,142.0,55.0,150.0,88.0,,15.0
+Greek Peak,New York,New York,2100,952,1148,0,0,0,1,1,4,2,8,56.0,4.0,1.5,220.0,184.0,110.0,62.0,122.0,63.2,113.0,175.0
+Holiday Mountain,New York,New York,1550,400,1150,0,0,0,1,0,1,2,4,9.0,,0.4,37.0,37.0,75.0,60.0,50.0,42.0,85.0,37.0
+Holiday Valley,New York,New York,2250,750,1500,0,0,3,8,0,0,2,13,60.0,5.0,1.0,290.0,266.0,116.0,62.0,180.0,78.0,129.0,189.0
+Holimont Ski Area,New York,New York,2260,700,1560,0,0,1,1,2,3,1,8,53.0,3.0,1.5,135.0,135.0,110.0,57.0,180.0,75.0,119.0,
+Hunt Hollow Ski Club,New York,New York,2030,825,1000,0,0,0,0,1,1,1,3,19.0,1.0,1.0,400.0,400.0,,52.0,130.0,58.0,75.0,400.0
+Hunter Mountain,New York,New York,3200,1600,1600,0,2,1,2,2,2,4,13,67.0,4.0,2.0,320.0,320.0,148.0,59.0,120.0,89.0,155.0,
+Kissing Bridge,New York,New York,1700,550,1150,0,0,0,2,1,4,3,10,39.0,5.0,0.5,700.0,550.0,103.0,59.0,120.0,60.0,100.0,650.0
+Labrador Mt.,New York,New York,1825,700,1125,0,0,0,0,1,2,1,4,23.0,1.0,1.0,250.0,237.0,,62.0,125.0,59.0,100.0,180.0
+Maple Ski Ridge,New York,New York,1200,450,750,0,0,0,0,1,1,1,3,10.0,,0.3,25.0,25.0,,57.0,,38.0,,20.0
+McCauley Mountain Ski Center,New York,New York,2250,633,1563,0,0,0,0,0,1,4,5,23.0,1.0,0.3,70.0,55.0,105.0,61.0,200.0,30.0,105.0,
+Mount Peter Ski Area,New York,New York,1250,450,750,0,0,0,1,0,2,2,5,14.0,1.0,1.0,69.0,69.0,100.0,83.0,50.0,54.0,100.0,69.0
+Oak Mountain,New York,New York,2400,650,1750,0,0,0,1,0,0,3,4,22.0,1.0,1.2,46.0,18.0,,71.0,120.0,40.0,,12.0
+Peek'n Peak,New York,New York,1800,400,1400,0,0,0,0,8,0,2,10,27.0,4.0,2.4,110.0,110.0,110.0,55.0,225.0,63.0,,110.0
+Plattekill Mountain,New York,New York,3500,1100,2400,0,0,0,0,1,1,2,4,38.0,1.0,2.0,110.0,75.0,65.0,26.0,175.0,67.0,65.0,
+Royal Mountain Ski Area,New York,New York,1800,550,1250,0,0,0,0,0,3,0,3,14.0,,0.3,35.0,28.0,,63.0,90.0,45.0,,
+Snow Ridge,New York,New York,2000,650,1350,0,0,0,0,0,4,2,6,21.0,2.0,0.8,130.0,65.0,73.0,74.0,230.0,48.0,100.0,40.0
+Song Mountain,New York,New York,1940,700,1240,0,0,0,0,1,1,3,5,24.0,,0.4,93.0,70.0,90.0,55.0,125.0,59.0,122.0,70.0
+Swain,New York,New York,1970,650,1320,0,0,0,3,0,1,1,5,35.0,3.0,1.0,130.0,90.0,102.0,72.0,120.0,59.0,100.0,80.0
+Thunder Ridge,New York,New York,1270,500,770,0,0,0,0,1,2,3,6,30.0,,0.4,100.0,100.0,121.0,60.0,,57.0,121.0,100.0
+Titus Mountain,New York,New York,2025,1200,825,0,0,0,0,2,6,2,10,50.0,3.0,2.0,200.0,150.0,101.0,59.0,150.0,49.0,100.0,70.0
+Toggenburg Mountain,New York,New York,2000,700,1300,0,0,0,0,1,1,3,5,22.0,2.0,0.4,85.0,,,66.0,130.0,55.0,122.0,73.0
+West Mountain,New York,New York,1470,1010,460,0,0,0,0,1,2,2,5,29.0,1.0,0.6,124.0,105.0,,58.0,80.0,59.0,120.0,105.0
+Whiteface Mountain Resort,New York,New York,4650,3430,1220,1,0,1,1,2,5,2,12,86.0,5.0,2.1,288.0,220.0,122.0,61.0,168.0,96.0,141.0,
+Willard Mountain,New York,New York,1415,505,910,0,0,0,0,0,2,3,5,16.0,,0.4,50.0,35.0,85.0,19.0,80.0,46.0,120.0,35.0
+Windham Mountain,New York,New York,3100,1600,1500,0,1,2,0,3,1,5,12,54.0,6.0,2.0,285.0,280.0,123.0,59.0,105.0,95.0,130.0,56.0
+Woods Valley Ski Area,New York,New York,1400,500,900,0,0,0,0,0,2,4,6,21.0,,0.3,25.0,16.0,,55.0,180.0,39.0,,15.0
+Appalachian Ski Mountain,North Carolina,North Carolina,4000,365,3635,0,0,0,2,0,1,2,5,12.0,3.0,0.5,27.0,27.0,100.0,57.0,50.0,64.0,100.0,27.0
+Cataloochee Ski Area,North Carolina,North Carolina,5400,740,4660,0,0,0,1,1,1,2,5,18.0,2.0,1.0,50.0,50.0,141.0,58.0,50.0,70.0,108.0,50.0
+Sapphire Valley,North Carolina,North Carolina,3450,200,3200,0,0,0,1,0,0,2,3,,1.0,1.0,8.0,8.0,53.0,55.0,24.0,43.0,60.0,8.0
+Beech Mountain Resort,North Carolina,North Carolina,5506,830,4675,0,0,0,3,0,3,2,8,17.0,1.0,1.0,95.0,95.0,98.0,52.0,31.0,68.0,,95.0
+Sugar Mountain Resort,North Carolina,North Carolina,5300,1200,4100,0,1,0,0,1,4,2,8,21.0,1.0,1.5,125.0,125.0,114.0,50.0,77.0,75.0,120.0,95.0
+Wolf Ridge Ski Resort,North Carolina,North Carolina,4700,720,4000,0,0,0,1,0,1,2,4,15.0,1.0,0.6,65.0,65.0,,49.0,65.0,65.0,100.0,60.0
+Alpine Valley,Ohio,Ohio,1500,230,1260,0,0,0,1,2,1,1,5,11.0,1.0,0.2,72.0,72.0,105.0,53.0,120.0,43.0,,72.0
+Boston Mills,Ohio,Ohio,871,264,631,0,0,0,0,4,2,2,8,7.0,2.0,0.3,40.0,40.0,92.0,56.0,51.0,44.0,110.0,40.0
+Brandywine,Ohio,Ohio,871,240,631,0,0,0,2,7,2,5,16,11.0,2.0,0.3,85.0,85.0,92.0,56.0,51.0,44.0,110.0,85.0
+Mad River Mountain,Ohio,Ohio,1460,300,1160,0,0,0,1,2,3,6,12,20.0,4.0,0.5,144.0,144.0,99.0,57.0,36.0,44.0,90.0,144.0
+Snow Trails,Ohio,Ohio,1475,301,1174,0,0,0,0,4,2,3,9,17.0,3.0,0.2,80.0,80.0,101.0,58.0,50.0,52.0,70.0,80.0
+Anthony Lakes Mountain Resort,Oregon,Oregon,8000,900,7100,0,0,0,0,1,0,2,3,21.0,2.0,1.5,1100.0,,75.0,56.0,300.0,40.0,80.0,
+Cooper Spur,Mt. Hood,Oregon,4000,350,3500,0,0,0,0,0,1,1,2,10.0,,0.1,50.0,,78.0,66.0,100.0,39.0,90.0,
+Hoodoo Ski Area,Oregon,Oregon,5703,1035,4668,0,0,0,3,1,1,0,5,34.0,,0.4,806.0,,80.0,81.0,350.0,59.0,108.0,200.0
+Mt. Ashland,Oregon,Oregon,7533,1150,6383,0,0,0,0,2,2,1,5,23.0,2.0,1.0,220.0,,94.0,55.0,300.0,52.0,92.0,40.0
+Mt. Bachelor,Oregon,Oregon,9065,3365,5700,0,0,8,0,3,0,0,11,101.0,5.0,4.0,4318.0,20.0,185.0,61.0,462.0,99.0,185.0,
+Mt. Hood Skibowl,Mt. Hood,Oregon,5100,1500,3600,0,0,0,0,0,4,5,9,65.0,2.0,3.0,960.0,29.0,125.0,82.0,300.0,70.0,144.0,317.0
+Willamette Pass,Oregon,Oregon,6683,1563,5120,0,1,0,0,3,0,1,5,29.0,,2.1,555.0,60.0,3.0,78.0,430.0,60.0,100.0,
+Bear Creek Mountain Resort,Pennsylvania,Pennsylvania,1100,510,600,0,0,0,3,1,0,2,6,23.0,3.0,1.0,86.0,86.0,91.0,52.0,30.0,60.0,90.0,86.0
+Ski Big Bear,Pennsylvania,Pennsylvania,1250,650,600,0,0,0,0,0,4,2,6,18.0,1.0,1.5,26.0,26.0,75.0,43.0,69.0,62.0,75.0,26.0
+Big Boulder,Pennsylvania,Pennsylvania,2175,600,1700,0,0,0,0,2,5,1,8,16.0,8.0,,55.0,55.0,76.0,72.0,50.0,65.0,95.0,55.0
+Blue Knob,Pennsylvania,Pennsylvania,3146,1072,2074,0,0,0,0,2,2,2,6,34.0,1.0,2.0,100.0,84.0,87.0,56.0,120.0,68.0,105.0,42.0
+Blue Mountain Resort,Pennsylvania,Pennsylvania,1600,1082,460,0,1,1,1,1,3,9,16,39.0,5.0,1.2,164.0,164.0,122.0,42.0,33.0,65.0,112.0,164.0
+Camelback Mountain Resort,Pennsylvania,Pennsylvania,2100,800,1250,0,0,2,0,3,5,6,16,37.0,5.0,1.0,166.0,166.0,100.0,56.0,50.0,70.0,100.0,160.0
+Elk Mountain Ski Resort,Pennsylvania,Pennsylvania,2693,1000,1693,0,0,0,1,0,5,1,7,27.0,2.0,0.7,180.0,146.0,,60.0,60.0,69.0,100.0,90.0
+Jack Frost,Pennsylvania,Pennsylvania,2000,600,1400,0,0,0,1,2,5,1,9,20.0,1.0,1.0,100.0,100.0,96.0,47.0,50.0,65.0,105.0,
+Liberty,Pennsylvania,Pennsylvania,1190,620,570,0,0,0,5,0,0,3,8,16.0,3.0,1.0,100.0,100.0,107.0,54.0,31.0,77.0,97.0,100.0
+Mount Pleasant of Edinboro,Pennsylvania,Pennsylvania,1540,340,1200,0,0,0,0,1,0,1,2,10.0,,0.5,40.0,35.0,75.0,48.0,100.0,33.0,90.0,35.0
+Roundtop Mountain Resort,Pennsylvania,Pennsylvania,1400,600,800,0,0,0,3,2,0,3,8,20.0,2.0,0.4,103.0,103.0,,55.0,30.0,73.0,,100.0
+Seven Springs,Pennsylvania,Pennsylvania,2994,750,2240,0,2,0,3,5,0,4,14,33.0,7.0,1.2,285.0,285.0,99.0,87.0,135.0,87.0,115.0,200.0
+Shawnee Mountain Ski Area,Pennsylvania,Pennsylvania,1350,700,650,0,0,1,1,0,4,4,10,23.0,2.0,1.6,125.0,125.0,100.0,44.0,50.0,65.0,122.0,120.0
+Ski Sawmill,Pennsylvania,Pennsylvania,2215,515,1700,0,0,0,0,1,1,3,5,14.0,1.0,0.1,15.0,13.0,,50.0,24.0,44.0,90.0,15.0
+Tussey Mountain,Pennsylvania,Pennsylvania,1750,520,1230,0,0,0,1,0,1,3,5,8.0,1.0,0.3,38.0,30.0,100.0,39.0,41.0,45.0,100.0,30.0
+Whitetail Resort,Pennsylvania,Pennsylvania,1800,935,865,0,0,1,3,0,2,2,8,23.0,2.0,1.0,120.0,120.0,116.0,28.0,40.0,71.0,100.0,120.0
+Deer Mountain Ski Resort,South Dakota,South Dakota,6850,940,6040,0,0,0,0,1,1,2,4,63.0,2.0,1.6,500.0,50.0,69.0,51.0,200.0,45.0,81.0,
+Terry Peak Ski Area,South Dakota,South Dakota,7100,1100,5900,0,0,3,0,1,0,1,5,30.0,1.0,1.2,450.0,225.0,114.0,65.0,150.0,58.0,120.0,
+Ober Gatlinburg Ski Resort,Tennessee,Tennessee,3300,600,2700,0,0,0,2,0,1,1,4,10.0,1.0,1.0,,,83.0,44.0,35.0,65.0,94.0,
+Alta Ski Area,Salt Lake City,Utah,11068,2538,8530,0,0,3,0,1,2,0,6,116.0,,1.3,2614.0,140.0,150.0,81.0,545.0,116.0,140.0,
+Beaver Mountain,Utah,Utah,8600,1600,7232,0,0,0,0,1,3,1,5,48.0,2.0,0.8,464.0,,120.0,81.0,400.0,50.0,120.0,
+Brian Head Resort,Utah,Utah,10970,1548,9600,0,0,1,0,6,1,0,8,71.0,2.0,0.6,650.0,216.0,149.0,54.0,360.0,59.0,148.0,
+Brighton Resort,Salt Lake City,Utah,10500,1745,8755,0,0,3,1,1,0,2,7,66.0,4.0,1.2,1050.0,200.0,138.0,83.0,500.0,85.0,138.0,200.0
+Deer Valley Resort,Salt Lake City,Utah,9570,3000,6570,1,0,13,0,5,2,0,21,103.0,,2.8,2026.0,660.0,,39.0,300.0,169.0,,
+Eagle Point,Utah,Utah,10600,1500,9100,0,0,0,1,1,2,1,5,40.0,1.0,0.9,650.0,,,9.0,400.0,60.0,109.0,
+Powder Mountain,Utah,Utah,9422,2522,6900,0,0,1,4,1,0,3,9,167.0,2.0,3.5,8464.0,,120.0,47.0,500.0,88.0,146.0,300.0
+Snowbasin,Utah,Utah,9350,2900,6450,3,1,2,0,3,0,2,11,107.0,4.0,3.5,3000.0,625.0,143.0,79.0,300.0,115.0,138.0,
+Snowbird,Salt Lake City,Utah,11000,3240,7760,1,0,6,0,0,4,3,14,170.0,1.0,2.5,2500.0,,188.0,48.0,500.0,125.0,180.0,2.0
+Solitude Mountain Resort,Salt Lake City,Utah,10488,2494,7994,0,0,4,2,1,1,1,9,80.0,,3.0,1200.0,150.0,161.0,62.0,500.0,119.0,148.0,
+Sundance,Utah,Utah,8250,2150,6100,0,0,0,2,2,0,1,5,45.0,1.0,0.6,450.0,112.0,128.0,50.0,320.0,80.0,129.0,
+Nordic Valley Resort,Utah,Utah,6400,960,5440,0,0,0,0,0,2,2,4,23.0,1.0,0.4,140.0,84.0,105.0,51.0,300.0,50.0,105.0,140.0
+Bolton Valley,Vermont,Vermont,3150,1704,1446,0,0,0,2,0,3,1,6,71.0,3.0,0.6,300.0,90.0,133.0,53.0,300.0,79.0,132.0,50.0
+Bromley Mountain,Vermont,Vermont,3284,1334,1950,0,0,1,1,0,4,3,9,47.0,1.0,2.5,178.0,153.0,,83.0,168.0,91.0,152.0,
+Burke Mountain,Vermont,Vermont,3267,2011,1210,0,0,2,1,0,0,3,6,50.0,3.0,2.2,178.0,125.0,110.0,62.0,217.0,73.0,125.0,
+Jay Peak,Vermont,Vermont,3968,2153,1815,1,0,1,3,1,1,2,9,79.0,2.0,3.0,385.0,300.0,155.0,64.0,349.0,89.0,160.0,
+Killington Resort,Vermont,Vermont,4241,3050,1165,3,1,5,4,3,1,5,22,155.0,6.0,6.0,1515.0,600.0,192.0,61.0,250.0,119.0,,
+Magic Mountain,Vermont,Vermont,2850,1500,1350,0,0,0,0,0,3,3,6,50.0,1.0,1.6,205.0,95.0,76.0,59.0,150.0,74.0,80.0,
+Pico Mountain,Vermont,Vermont,3967,1967,2000,0,0,2,0,2,2,1,7,59.0,1.0,4.0,260.0,156.0,,82.0,250.0,81.0,,
+Smugglers' Notch Resort,Vermont,Vermont,3640,2610,1030,0,0,0,0,0,6,2,8,78.0,6.0,3.0,1000.0,192.0,136.0,63.0,280.0,79.0,135.0,
+Sugarbush,Vermont,Vermont,4083,2600,1483,0,0,5,5,2,1,3,16,111.0,4.0,3.0,581.0,406.0,150.0,61.0,250.0,119.0,156.0,
+Suicide Six,Vermont,Vermont,1200,650,550,0,0,0,0,0,2,1,3,24.0,,0.4,100.0,50.0,100.0,85.0,90.0,75.0,106.0,
+Bryce Resort,Virginia,Virginia,1750,500,1250,0,0,0,1,0,1,5,7,8.0,,0.4,25.0,25.0,100.0,54.0,30.0,68.0,95.0,20.0
+49 Degrees North,Washington,Washington,5774,1851,3932,0,0,0,1,0,5,1,7,89.0,1.0,2.0,2325.0,,101.0,48.0,301.0,62.0,135.0,250.0
+Bluewood,Washington,Washington,5670,1125,4545,0,0,0,0,2,0,1,3,24.0,2.0,2.0,355.0,,70.0,40.0,300.0,47.0,110.0,
+Crystal Mountain,Washington,Washington,7012,3100,4400,1,2,2,1,2,2,0,10,57.0,1.0,2.5,2600.0,10.0,,57.0,486.0,99.0,,
+Mt. Baker,Washington,Washington,5000,1500,3500,0,0,0,8,0,0,2,10,38.0,,0.7,1000.0,,143.0,66.0,663.0,60.01,165.0,
+Mt. Spokane Ski and Snowboard Park,Washington,Washington,5889,2000,4200,0,0,0,0,1,5,1,7,52.0,3.0,0.6,1704.0,,100.0,81.0,300.0,59.0,103.0,45.0
+The Summit at Snoqualmie,Washington,Washington,3865,1025,2840,0,0,3,3,4,10,7,27,112.0,5.0,0.8,1994.0,5.0,120.0,82.0,428.0,95.0,140.0,541.0
+White Pass,Washington,Washington,6550,2050,4500,0,0,2,1,1,2,2,8,45.0,2.0,2.5,1402.0,30.0,148.0,67.0,400.0,69.0,144.0,90.0
+Canaan Valley Resort,West Virginia,West Virginia,4280,850,3430,0,0,0,1,2,0,1,4,47.0,1.0,1.2,95.0,75.0,,48.0,160.0,68.0,93.0,
+Snowshoe Mountain Resort,West Virginia,West Virginia,4848,1500,3348,0,0,3,2,6,0,3,14,60.0,5.0,1.5,257.0,257.0,125.0,46.0,180.0,87.0,138.0,86.0
+Timberline Four Seasons,West Virginia,West Virginia,4265,1000,3268,0,0,0,0,2,1,1,4,40.0,1.0,2.0,100.0,100.0,97.0,37.0,150.0,92.0,115.0,27.0
+Winterplace Ski Resort,West Virginia,West Virginia,3600,603,2997,0,0,0,2,3,2,2,9,27.0,2.0,1.2,90.0,90.0,120.0,36.0,100.0,72.0,120.0,74.0
+Alpine Valley Resort,Wisconsin,Wisconsin,1040,388,820,0,0,3,0,3,1,5,12,21.0,3.0,0.2,90.0,90.0,100.0,55.0,80.0,65.0,120.0,90.0
+Bruce Mound,Wisconsin,Wisconsin,1375,375,1000,0,0,0,0,1,0,4,5,12.0,2.0,0.5,40.0,30.0,42.0,71.0,42.0,25.0,42.0,30.0
+Cascade Mountain,Wisconsin,Wisconsin,1280,460,820,0,0,2,2,3,2,3,12,47.0,4.0,1.1,175.0,175.0,120.0,57.0,56.0,64.0,120.0,
+Christie Mountain,Wisconsin,Wisconsin,1650,350,1300,0,0,0,0,0,1,5,6,30.0,4.0,0.8,45.0,41.0,92.0,43.0,48.0,38.0,120.0,35.0
+Devils Head,Wisconsin,Wisconsin,995,500,495,0,0,0,3,1,6,2,12,27.0,1.0,1.0,260.0,260.0,110.0,48.0,45.0,65.0,135.0,200.0
+Grand Geneva,Wisconsin,Wisconsin,1086,211,875,0,0,0,0,0,3,3,6,20.0,1.0,0.2,30.0,30.0,90.0,25.0,25.0,49.0,93.0,30.0
+Granite Peak Ski Area,Wisconsin,Wisconsin,1942,700,1242,0,1,2,0,2,0,2,7,75.0,4.0,0.6,220.0,160.0,136.0,82.0,75.0,92.0,135.0,200.0
+Mount La Crosse,Wisconsin,Wisconsin,1110,516,594,0,0,0,0,0,3,1,4,19.0,1.0,0.4,100.0,100.0,115.0,60.0,40.0,56.0,100.0,90.0
+Nordic Mountain,Wisconsin,Wisconsin,1137,265,872,0,0,0,0,1,1,5,7,18.0,4.0,1.0,60.0,60.0,68.0,43.0,80.0,47.0,90.0,60.0
+Sunburst,Wisconsin,Wisconsin,1100,214,866,0,0,0,0,0,3,6,9,13.0,4.0,0.5,37.0,37.0,99.0,59.0,50.0,44.0,115.0,37.0
+Trollhaugen,Wisconsin,Wisconsin,1200,260,920,0,0,0,2,0,1,5,8,24.0,4.0,0.5,86.0,86.0,130.0,69.0,50.0,54.0,120.0,86.0
+Tyrol Basin,Wisconsin,Wisconsin,1160,300,860,0,0,0,0,3,0,2,5,18.0,5.0,0.5,32.0,32.0,112.0,61.0,41.0,48.0,103.0,32.0
+Whitecap Mountain,Wisconsin,Wisconsin,1750,400,1295,0,0,0,1,0,4,0,5,43.0,1.0,1.0,400.0,300.0,105.0,57.0,200.0,60.0,118.0,
+Wilmot Mountain,Wisconsin,Wisconsin,1030,230,800,0,0,0,3,2,2,3,10,23.0,2.0,0.5,135.0,135.0,125.0,81.0,70.0,66.0,139.0,135.0
+Grand Targhee Resort,Wyoming,Wyoming,9920,2270,7851,0,0,2,2,0,0,1,5,95.0,1.0,2.7,2602.0,,152.0,50.0,500.0,90.0,152.0,
+Hogadon Basin,Wyoming,Wyoming,8000,640,7400,0,0,0,0,0,1,1,2,28.0,1.0,0.6,92.0,32.0,121.0,61.0,80.0,48.0,95.0,
+Sleeping Giant Ski Resort,Wyoming,Wyoming,7428,810,6619,0,0,0,0,1,1,1,3,48.0,1.0,1.0,184.0,18.0,61.0,81.0,310.0,42.0,77.0,
+Snow King Resort,Wyoming,Wyoming,7808,1571,6237,0,0,0,1,1,1,0,3,32.0,2.0,1.0,400.0,250.0,121.0,80.0,300.0,59.0,123.0,110.0
+Snowy Range Ski & Recreation Area,Wyoming,Wyoming,9663,990,8798,0,0,0,0,1,3,1,5,33.0,2.0,0.7,75.0,30.0,131.0,59.0,250.0,49.0,,
+White Pine Ski Area,Wyoming,Wyoming,9500,1100,8400,0,0,0,0,2,0,0,2,25.0,,0.4,370.0,,,81.0,150.0,49.0,,
diff --git a/data/ski_data_step3_features.csv b/data/ski_data_step3_features.csv
new file mode 100644
index 000000000..6895fd09a
--- /dev/null
+++ b/data/ski_data_step3_features.csv
@@ -0,0 +1,278 @@
+Name,Region,state,summit_elev,vertical_drop,base_elev,trams,fastSixes,fastQuads,quad,triple,double,surface,total_chairs,Runs,TerrainParks,LongestRun_mi,SkiableTerrain_ac,Snow Making_ac,daysOpenLastYear,yearsOpen,averageSnowfall,AdultWeekend,projectedDaysOpen,NightSkiing_ac,resorts_per_state,resorts_per_100kcapita,resorts_per_100ksq_mile,resort_skiable_area_ac_state_ratio,resort_days_open_state_ratio,resort_terrain_park_state_ratio,resort_night_skiing_state_ratio,total_chairs_runs_ratio,total_chairs_skiable_ratio,fastQuads_runs_ratio,fastQuads_skiable_ratio
+Alyeska Resort,Alaska,Alaska,3939,2500,250,1,0,2,2,0,0,2,7,76.0,2.0,1.0,1610.0,113.0,150.0,60.0,669.0,85.0,150.0,550.0,3,0.4100909718472548,0.45086746901037594,0.706140350877193,0.43478260869565216,0.5,0.9482758620689655,0.09210526315789473,0.004347826086956522,0.02631578947368421,0.0012422360248447205
+Eaglecrest Ski Area,Alaska,Alaska,2600,1540,1200,0,0,0,0,0,4,0,4,36.0,1.0,2.0,640.0,60.0,45.0,44.0,350.0,53.0,90.0,,3,0.4100909718472548,0.45086746901037594,0.2807017543859649,0.13043478260869565,0.25,,0.1111111111111111,0.00625,0.0,0.0
+Hilltop Ski Area,Alaska,Alaska,2090,294,1796,0,0,0,0,1,0,2,3,13.0,1.0,1.0,30.0,30.0,150.0,36.0,69.0,34.0,152.0,30.0,3,0.4100909718472548,0.45086746901037594,0.013157894736842105,0.43478260869565216,0.25,0.05172413793103448,0.23076923076923078,0.1,0.0,0.0
+Arizona Snowbowl,Arizona,Arizona,11500,2300,9200,0,1,0,2,2,1,2,8,55.0,4.0,2.0,777.0,104.0,122.0,81.0,260.0,89.0,122.0,,2,0.027477369981550318,1.7545398719185894,0.49270767279644895,0.5147679324894515,0.6666666666666666,,0.14545454545454545,0.010296010296010296,0.0,0.0
+Sunrise Park Resort,Arizona,Arizona,11100,1800,9200,0,0,1,2,3,1,0,7,65.0,2.0,1.2,800.0,80.0,115.0,49.0,250.0,78.0,104.0,80.0,2,0.027477369981550318,1.7545398719185894,0.507292327203551,0.48523206751054854,0.3333333333333333,1.0,0.1076923076923077,0.00875,0.015384615384615385,0.00125
+Yosemite Ski & Snowboard Area,Northern California,California,7800,600,7200,0,0,0,0,1,3,1,5,10.0,2.0,0.4,88.0,,110.0,84.0,300.0,47.0,107.0,,21,0.05314811064920341,12.828736369467608,0.0033913981809773393,0.04017531044558072,0.024691358024691357,,0.5,0.056818181818181816,0.0,0.0
+Dodge Ridge,Sierra Nevada,California,8200,1600,6600,0,0,0,1,2,5,4,12,67.0,5.0,2.0,862.0,,,69.0,350.0,78.0,140.0,,21,0.05314811064920341,12.828736369467608,0.033220286727300756,,0.06172839506172839,,0.1791044776119403,0.013921113689095127,0.0,0.0
+Donner Ski Ranch,Sierra Nevada,California,8012,750,7031,0,0,0,0,1,5,2,8,52.0,2.0,1.5,505.0,60.0,163.0,82.0,400.0,75.0,170.0,,21,0.05314811064920341,12.828736369467608,0.019462000924926778,0.059532505478451424,0.024691358024691357,,0.15384615384615385,0.015841584158415842,0.0,0.0
+Mammoth Mountain Ski Area,Sierra Nevada,California,11053,3100,7953,3,2,9,1,6,4,0,25,154.0,7.0,3.0,3500.0,700.0,243.0,66.0,400.0,159.0,,,21,0.05314811064920341,12.828736369467608,0.1348851549252351,0.0887509130752374,0.08641975308641975,,0.16233766233766234,0.007142857142857143,0.05844155844155844,0.0025714285714285713
+Mt. Shasta Ski Park,Sierra Nevada,California,6890,1435,5500,0,0,0,0,3,0,1,4,32.0,2.0,1.1,425.0,225.0,140.0,34.0,300.0,59.0,130.0,,21,0.05314811064920341,12.828736369467608,0.016378911669492832,0.05113221329437546,0.024691358024691357,,0.125,0.009411764705882352,0.0,0.0
+Mountain High,Sierra Nevada,California,8200,1600,6600,0,0,2,2,2,5,3,14,59.0,1.0,1.6,290.0,275.0,118.0,95.0,108.0,84.0,150.0,73.0,21,0.05314811064920341,12.828736369467608,0.01117619855094805,0.04309715120525932,0.012345679012345678,0.12436115843270869,0.23728813559322035,0.04827586206896552,0.03389830508474576,0.006896551724137931
+Mt. Baldy,Sierra Nevada,California,8600,2100,6500,0,0,0,0,0,4,0,4,26.0,,2.5,400.0,80.0,175.0,67.0,178.0,69.0,200.0,,21,0.05314811064920341,12.828736369467608,0.015415446277169724,0.06391526661796933,,,0.15384615384615385,0.01,0.0,0.0
+Ski China Peak,Sierra Nevada,California,8709,1679,7030,0,0,0,1,4,2,4,11,45.0,1.0,2.2,1400.0,150.0,140.0,62.0,300.0,83.0,144.0,,21,0.05314811064920341,12.828736369467608,0.05395406197009404,0.05113221329437546,0.012345679012345678,,0.24444444444444444,0.007857142857142858,0.0,0.0
+Snow Valley,Sierra Nevada,California,7841,1041,6800,0,0,0,0,5,6,1,12,28.0,6.0,1.2,240.0,188.0,111.0,82.0,160.0,79.0,143.0,164.0,21,0.05314811064920341,12.828736369467608,0.009249267766301835,0.04054054054054054,0.07407407407407407,0.27938671209540034,0.42857142857142855,0.05,0.0,0.0
+Soda Springs,Sierra Nevada,California,7352,652,6700,0,0,0,0,1,1,2,4,18.0,,0.4,200.0,20.0,150.0,83.0,400.0,50.0,144.0,,21,0.05314811064920341,12.828736369467608,0.007707723138584862,0.0547845142439737,,,0.2222222222222222,0.02,0.0,0.0
+Sugar Bowl Resort,Sierra Nevada,California,8383,1500,6883,1,0,5,3,1,0,2,12,105.0,3.0,3.0,1650.0,375.0,151.0,80.0,500.0,125.0,150.0,,21,0.05314811064920341,12.828736369467608,0.06358871589332511,0.05514974433893353,0.037037037037037035,,0.11428571428571428,0.007272727272727273,0.047619047619047616,0.0030303030303030303
+Tahoe Donner,Sierra Nevada,California,7350,600,6750,0,0,0,1,1,0,3,5,14.0,2.0,1.0,120.0,,150.0,48.0,400.0,69.0,144.0,,21,0.05314811064920341,12.828736369467608,0.004624633883150917,0.0547845142439737,0.024691358024691357,,0.35714285714285715,0.041666666666666664,0.0,0.0
+Arapahoe Basin Ski Area,Colorado,Colorado,13050,2530,10780,0,0,1,2,1,2,3,9,145.0,3.0,1.5,1428.0,125.0,230.0,73.0,350.0,85.0,233.0,,22,0.3820282784277661,21.13474359713336,0.032690810860308596,0.07059545733578883,0.04054054054054054,,0.06206896551724138,0.0063025210084033615,0.006896551724137931,0.0007002801120448179
+Aspen / Snowmass,Colorado,Colorado,12510,4406,8104,3,1,15,4,3,5,9,40,336.0,10.0,5.3,5517.0,658.0,138.0,72.0,300.0,179.0,138.0,,22,0.3820282784277661,21.13474359713336,0.12629916212627626,0.0423572744014733,0.13513513513513514,,0.11904761904761904,0.0072503172013775605,0.044642857142857144,0.0027188689505165853
+Copper Mountain Resort,Colorado,Colorado,12313,2738,9712,1,2,4,0,4,4,9,24,150.0,6.0,1.7,2527.0,364.0,164.0,47.0,300.0,158.0,164.0,,22,0.3820282784277661,21.13474359713336,0.05784991529691864,0.05033763044812769,0.08108108108108109,,0.16,0.009497427779976256,0.02666666666666667,0.0015829046299960427
+Purgatory,Colorado,Colorado,10822,2029,8793,0,1,2,0,3,3,3,12,101.0,9.0,1.3,1605.0,250.0,130.0,54.0,260.0,89.0,130.0,,22,0.3820282784277661,21.13474359713336,0.03674282313080903,0.03990178023327195,0.12162162162162163,,0.1188118811881188,0.007476635514018692,0.019801980198019802,0.0012461059190031153
+Howelsen Hill,Colorado,Colorado,7136,440,6696,0,0,0,0,0,1,3,4,17.0,1.0,6.0,50.0,25.0,100.0,104.0,150.0,25.0,100.0,10.0,22,0.3820282784277661,21.13474359713336,0.0011446362346046427,0.030693677102516883,0.013513513513513514,0.02336448598130841,0.23529411764705882,0.08,0.0,0.0
+Loveland,Colorado,Colorado,13010,2210,10800,0,0,1,3,3,2,1,10,94.0,1.0,2.0,1800.0,240.0,205.0,82.0,422.0,79.0,184.0,,22,0.3820282784277661,21.13474359713336,0.041206904445767134,0.06292203806015961,0.013513513513513514,,0.10638297872340426,0.005555555555555556,0.010638297872340425,0.0005555555555555556
+Monarch Mountain,Colorado,Colorado,11952,1162,10790,0,0,0,1,0,4,2,7,64.0,2.0,1.0,800.0,,143.0,80.0,350.0,89.0,136.0,,22,0.3820282784277661,21.13474359713336,0.018314179753674283,0.04389195825659914,0.02702702702702703,,0.109375,0.00875,0.0,0.0
+Powderhorn,Colorado,Colorado,9850,1650,8200,0,0,1,0,0,2,2,5,42.0,2.0,1.5,1600.0,42.0,111.0,53.0,250.0,71.0,110.0,,22,0.3820282784277661,21.13474359713336,0.036628359507348565,0.03406998158379374,0.02702702702702703,,0.11904761904761904,0.003125,0.023809523809523808,0.000625
+Silverton Mountain,Colorado,Colorado,13487,3087,10400,0,0,0,0,0,1,0,1,,,1.5,1819.0,,175.0,17.0,400.0,79.0,181.0,,22,0.3820282784277661,21.13474359713336,0.041641866214916896,0.053713934929404544,,,,0.0005497526113249038,,0.0
+Cooper,Colorado,Colorado,11700,1200,10500,0,0,0,0,1,1,2,4,41.0,1.0,1.0,400.0,,130.0,74.0,260.0,56.0,130.0,,22,0.3820282784277661,21.13474359713336,0.009157089876837141,0.03990178023327195,0.013513513513513514,,0.0975609756097561,0.01,0.0,0.0
+Ski Granby Ranch,Colorado,Colorado,9202,1000,8202,0,0,2,0,1,1,1,5,40.0,1.0,0.6,406.0,170.0,116.0,36.0,220.0,84.0,92.0,100.0,22,0.3820282784277661,21.13474359713336,0.009294446224989698,0.03560466543891958,0.013513513513513514,0.2336448598130841,0.125,0.012315270935960592,0.05,0.0049261083743842365
+Sunlight Mountain Resort,Colorado,Colorado,9895,2010,7885,0,0,0,0,1,2,0,3,67.0,1.0,2.5,680.0,30.0,100.0,53.0,250.0,65.0,135.0,,22,0.3820282784277661,21.13474359713336,0.01556705279062314,0.030693677102516883,0.013513513513513514,,0.04477611940298507,0.004411764705882353,0.0,0.0
+Telluride,Colorado,Colorado,13150,4425,8725,2,0,6,1,2,2,4,17,148.0,3.0,4.6,2000.0,220.0,131.0,47.0,280.0,139.0,137.0,,22,0.3820282784277661,21.13474359713336,0.0457854493841857,0.040208717004297116,0.04054054054054054,,0.11486486486486487,0.0085,0.04054054054054054,0.003
+Wolf Creek Ski Area,Colorado,Colorado,11904,1604,10300,0,0,3,1,2,1,3,10,120.0,,2.0,1600.0,5.0,130.0,80.0,430.0,72.0,150.0,,22,0.3820282784277661,21.13474359713336,0.036628359507348565,0.03990178023327195,,,0.08333333333333333,0.00625,0.025,0.001875
+Mohawk Mountain,Connecticut,Connecticut,1600,650,950,0,0,0,0,5,0,3,8,25.0,,1.5,107.0,100.0,,72.0,92.0,65.0,110.0,64.0,5,0.14024151833321272,90.20386072523904,0.2988826815642458,,,0.25,0.32,0.07476635514018691,0.0,0.0
+Mount Southington Ski Area,Connecticut,Connecticut,525,425,100,0,0,0,0,2,2,3,7,14.0,2.0,0.3,51.0,51.0,63.0,55.0,80.0,60.0,95.0,51.0,5,0.14024151833321272,90.20386072523904,0.1424581005586592,0.17847025495750707,0.2,0.19921875,0.5,0.13725490196078433,0.0,0.0
+Powder Ridge Park,Connecticut,Connecticut,720,550,170,0,0,0,0,1,2,2,5,19.0,4.0,0.5,80.0,68.0,80.0,60.0,80.0,55.0,100.0,40.0,5,0.14024151833321272,90.20386072523904,0.22346368715083798,0.22662889518413598,0.4,0.15625,0.2631578947368421,0.0625,0.0,0.0
+Ski Sundown,Connecticut,Connecticut,1075,625,450,0,0,0,0,3,0,2,5,16.0,2.0,1.0,70.0,70.0,84.0,50.0,45.0,62.0,95.0,66.0,5,0.14024151833321272,90.20386072523904,0.19553072625698323,0.23796033994334279,0.2,0.2578125,0.3125,0.07142857142857142,0.0,0.0
+Woodbury Ski Area,Connecticut,Connecticut,730,300,430,0,0,0,0,0,1,4,5,12.0,2.0,0.2,50.0,50.0,126.0,57.0,70.0,42.0,180.0,35.0,5,0.14024151833321272,90.20386072523904,0.13966480446927373,0.35694050991501414,0.2,0.13671875,0.4166666666666667,0.1,0.0,0.0
+Bogus Basin,Idaho,Idaho,7582,1800,5800,0,0,3,0,1,3,4,11,91.0,3.0,1.5,2600.0,,134.0,77.0,250.0,64.0,130.0,165.0,12,0.6714920833881253,14.359391640440833,0.15857526225908758,0.11795774647887323,0.1111111111111111,0.39759036144578314,0.12087912087912088,0.004230769230769231,0.03296703296703297,0.001153846153846154
+Brundage Mountain Resort,Idaho,Idaho,7640,1800,5840,0,0,1,0,4,0,1,6,51.0,2.0,3.2,1920.0,2.0,126.0,58.0,320.0,70.0,,,12,0.6714920833881253,14.359391640440833,0.11710173212978775,0.11091549295774648,0.07407407407407407,,0.11764705882352941,0.003125,0.0196078431372549,0.0005208333333333333
+Kelly Canyon Ski Area,Idaho,Idaho,6600,1000,5600,0,0,0,0,0,4,2,6,51.0,1.0,1.3,740.0,,,62.0,200.0,42.0,,,12,0.6714920833881253,14.359391640440833,0.0451329592583557,,0.037037037037037035,,0.11764705882352941,0.008108108108108109,0.0,0.0
+Lookout Pass Ski Area,Idaho,Idaho,5650,1150,4500,0,0,0,0,1,3,0,4,35.0,2.0,1.5,540.0,,113.0,84.0,400.0,47.0,140.0,,12,0.6714920833881253,14.359391640440833,0.032934862161502806,0.0994718309859155,0.07407407407407407,,0.11428571428571428,0.007407407407407408,0.0,0.0
+Magic Mountain Ski Area,Idaho,Idaho,7200,700,6500,0,0,0,0,0,1,2,3,11.0,,1.5,280.0,,65.0,81.0,180.0,32.0,70.0,,12,0.6714920833881253,14.359391640440833,0.017077335935594046,0.05721830985915493,,,0.2727272727272727,0.010714285714285714,0.0,0.0
+Pebble Creek Ski Area,Idaho,Idaho,8560,2200,6360,0,0,0,0,3,0,0,3,54.0,2.0,1.3,1100.0,30.0,85.0,70.0,250.0,47.0,91.0,30.0,12,0.6714920833881253,14.359391640440833,0.0670895340326909,0.07482394366197183,0.07407407407407407,0.07228915662650602,0.05555555555555555,0.0027272727272727275,0.0,0.0
+Schweitzer,Idaho,Idaho,6400,2400,4000,0,1,2,0,1,3,2,9,92.0,3.0,2.1,2900.0,47.0,136.0,56.0,300.0,81.0,,100.0,12,0.6714920833881253,14.359391640440833,0.17687240790436692,0.11971830985915492,0.1111111111111111,0.24096385542168675,0.09782608695652174,0.003103448275862069,0.021739130434782608,0.000689655172413793
+Silver Mountain,Idaho,Idaho,6300,2200,4100,1,0,0,1,2,2,1,7,80.0,2.0,2.5,1600.0,225.0,130.0,29.0,300.0,62.0,193.0,20.0,12,0.6714920833881253,14.359391640440833,0.09758477677482313,0.11443661971830986,0.07407407407407407,0.04819277108433735,0.0875,0.004375,0.0,0.0
+Soldier Mountain Ski Area,Idaho,Idaho,7200,1400,5800,0,0,0,0,0,2,1,3,36.0,,0.4,1142.0,,60.0,71.0,,43.0,,,12,0.6714920833881253,14.359391640440833,0.06965113442303,0.0528169014084507,,,0.08333333333333333,0.002626970227670753,0.0,0.0
+Tamarack Resort,Idaho,Idaho,7700,2800,4900,0,0,2,2,0,0,2,6,48.0,3.0,1.5,1020.0,200.0,,15.0,300.0,71.0,150.0,,12,0.6714920833881253,14.359391640440833,0.062210295193949744,,0.1111111111111111,,0.125,0.0058823529411764705,0.041666666666666664,0.00196078431372549
+Chestnut Mountain Resort,Illinois,Illinois,1040,475,565,0,0,0,2,4,0,3,9,22.0,3.0,0.2,139.0,139.0,87.0,60.0,50.0,55.0,112.0,139.0,4,0.03156610245678186,6.906792830749041,0.7277486910994765,0.3936651583710407,0.5,0.7277486910994765,0.4090909090909091,0.06474820143884892,0.0,0.0
+Ski Snowstar Winter Sports Park,Illinois,Illinois,790,262,528,0,0,0,2,0,2,2,6,15.0,1.0,0.8,28.0,28.0,56.0,38.0,38.0,35.0,86.0,28.0,4,0.03156610245678186,6.906792830749041,0.14659685863874344,0.25339366515837103,0.16666666666666666,0.14659685863874344,0.4,0.21428571428571427,0.0,0.0
+Villa Olivia,Illinois,Illinois,500,180,320,0,0,0,1,0,0,6,7,7.0,1.0,0.1,15.0,15.0,,53.0,25.0,40.0,70.0,15.0,4,0.03156610245678186,6.906792830749041,0.07853403141361257,,0.16666666666666666,0.07853403141361257,1.0,0.4666666666666667,0.0,0.0
+Paoli Peaks,Indiana,Indiana,900,300,600,0,0,1,1,3,1,2,8,15.0,2.0,0.4,65.0,65.0,75.0,41.0,18.0,45.0,80.0,65.0,2,0.02970788680522722,5.491488193300384,0.3939393939393939,0.47770700636942676,0.5,0.3939393939393939,0.5333333333333333,0.12307692307692308,0.06666666666666667,0.015384615384615385
+Perfect North Slopes,Indiana,Indiana,800,400,400,0,0,0,2,3,0,6,11,23.0,2.0,1.0,100.0,100.0,82.0,39.0,24.0,52.0,90.0,100.0,2,0.02970788680522722,5.491488193300384,0.6060606060606061,0.5222929936305732,0.5,0.6060606060606061,0.4782608695652174,0.11,0.0,0.0
+Mt. Crescent Ski Area,Iowa,Iowa,1500,300,1200,0,0,0,1,0,1,0,2,11.0,1.0,0.2,50.0,50.0,,58.0,30.0,39.0,,50.0,3,0.0950850535804277,5.3311534839088015,0.35714285714285715,,0.2,0.35714285714285715,0.18181818181818182,0.04,0.0,0.0
+Seven Oaks,Iowa,Iowa,975,275,800,0,0,0,0,2,0,2,4,11.0,2.0,1.0,35.0,35.0,100.0,22.0,40.0,40.0,100.0,35.0,3,0.0950850535804277,5.3311534839088015,0.25,1.0,0.4,0.25,0.36363636363636365,0.11428571428571428,0.0,0.0
+Sundown Mountain,Iowa,Iowa,1059,475,584,0,0,0,1,1,2,2,6,21.0,2.0,0.6,55.0,55.0,,46.0,45.0,46.0,,55.0,3,0.0950850535804277,5.3311534839088015,0.39285714285714285,,0.4,0.39285714285714285,0.2857142857142857,0.10909090909090909,0.0,0.0
+Big Squaw Mountain Ski Resort,Maine,Maine,3200,660,1750,0,0,0,0,1,0,0,1,29.0,,0.8,,,67.0,6.0,,30.0,58.0,,9,0.6695372456130432,25.438100621820237,,0.07745664739884393,,,0.034482758620689655,,0.0,
+Camden Snow Bowl,Maine,Maine,1080,850,150,0,0,0,0,1,1,1,3,26.0,2.0,1.0,100.0,48.0,68.0,83.0,69.0,43.0,70.0,48.0,9,0.6695372456130432,25.438100621820237,0.03109452736318408,0.07861271676300578,0.11764705882352941,0.12371134020618557,0.11538461538461539,0.03,0.0,0.0
+Lost Valley,Maine,Maine,495,240,255,0,0,0,0,0,2,2,4,22.0,2.0,0.3,45.0,45.0,87.0,58.0,50.0,55.0,104.0,45.0,9,0.6695372456130432,25.438100621820237,0.013992537313432836,0.10057803468208093,0.11764705882352941,0.11597938144329897,0.18181818181818182,0.08888888888888889,0.0,0.0
+Mt. Abram Ski Resort,Maine,Maine,2250,1150,1050,0,0,0,0,0,2,3,5,54.0,1.0,0.5,640.0,175.0,120.0,59.0,125.0,49.0,120.0,,9,0.6695372456130432,25.438100621820237,0.19900497512437812,0.13872832369942195,0.058823529411764705,,0.09259259259259259,0.0078125,0.0,0.0
+New Hermon Mountain,Maine,Maine,450,350,100,0,0,0,0,0,1,2,3,20.0,,1.9,70.0,70.0,102.0,55.0,90.0,32.0,117.0,45.0,9,0.6695372456130432,25.438100621820237,0.021766169154228857,0.11791907514450867,,0.11597938144329897,0.15,0.04285714285714286,0.0,0.0
+Shawnee Peak,Maine,Maine,1900,1350,600,0,0,0,1,2,1,2,6,43.0,3.0,0.8,239.0,234.0,97.0,81.0,110.0,75.0,103.0,110.0,9,0.6695372456130432,25.438100621820237,0.07431592039800995,0.11213872832369942,0.17647058823529413,0.28350515463917525,0.13953488372093023,0.02510460251046025,0.0,0.0
+Sugarloaf,Maine,Maine,4237,2820,1417,0,0,2,3,1,5,2,13,162.0,4.0,3.5,1240.0,618.0,159.0,68.0,200.0,99.0,155.0,,9,0.6695372456130432,25.438100621820237,0.3855721393034826,0.1838150289017341,0.23529411764705882,,0.08024691358024691,0.010483870967741936,0.012345679012345678,0.0016129032258064516
+Sunday River,Maine,Maine,3140,2340,800,1,0,4,5,3,1,1,15,135.0,5.0,3.0,870.0,552.0,165.0,60.0,167.0,105.0,169.0,140.0,9,0.6695372456130432,25.438100621820237,0.27052238805970147,0.1907514450867052,0.29411764705882354,0.36082474226804123,0.1111111111111111,0.017241379310344827,0.02962962962962963,0.004597701149425287
+Wisp,Maryland,Maryland,3115,700,2415,0,0,0,2,5,0,5,12,34.0,3.0,1.5,172.0,118.0,121.0,64.0,100.0,79.0,120.0,118.0,1,0.016540736525916026,8.060615831049493,1.0,1.0,1.0,1.0,0.35294117647058826,0.06976744186046512,0.0,0.0
+Berkshire East,Massachusetts,Massachusetts,1720,1180,540,0,0,0,2,1,1,2,6,47.0,2.0,2.0,180.0,165.0,120.0,68.0,120.0,68.0,120.0,80.0,11,0.1595936918707181,104.22588592003032,0.15437392795883362,0.17883755588673622,0.1111111111111111,0.137221269296741,0.1276595744680851,0.03333333333333333,0.0,0.0
+Blandford Ski Area,Massachusetts,Massachusetts,1685,465,1035,0,0,0,0,0,3,2,5,29.0,2.0,0.5,132.0,70.0,,83.0,50.0,45.0,,70.0,11,0.1595936918707181,104.22588592003032,0.11320754716981132,,0.1111111111111111,0.12006861063464837,0.1724137931034483,0.03787878787878788,0.0,0.0
+Blue Hills Ski Area,Massachusetts,Massachusetts,635,309,326,0,0,0,0,0,1,3,4,16.0,1.0,,60.0,60.0,,19.0,,45.0,,,11,0.1595936918707181,104.22588592003032,0.051457975986277875,,0.05555555555555555,,0.25,0.06666666666666667,0.0,0.0
+Bousquet Ski Area,Massachusetts,Massachusetts,1875,750,1125,0,0,0,0,0,3,2,5,23.0,1.0,1.0,200.0,98.0,,19.0,83.0,49.0,,100.0,11,0.1595936918707181,104.22588592003032,0.17152658662092624,,0.05555555555555555,0.17152658662092624,0.21739130434782608,0.025,0.0,0.0
+Bradford Ski Area,Massachusetts,Massachusetts,1548,248,1300,0,0,0,0,2,0,8,10,15.0,1.0,0.3,48.0,48.0,,71.0,,55.0,,,11,0.1595936918707181,104.22588592003032,0.0411663807890223,,0.05555555555555555,,0.6666666666666666,0.20833333333333334,0.0,0.0
+Jiminy Peak,Massachusetts,Massachusetts,2380,1150,1230,0,1,0,2,3,1,2,9,45.0,3.0,2.0,167.0,163.0,121.0,71.0,90.0,81.0,120.0,104.0,11,0.1595936918707181,104.22588592003032,0.1432246998284734,0.18032786885245902,0.16666666666666666,0.1783876500857633,0.2,0.05389221556886228,0.0,0.0
+Nashoba Valley,Massachusetts,Massachusetts,440,240,200,0,0,0,0,3,1,7,11,17.0,2.0,0.5,52.0,52.0,112.0,55.0,55.0,58.0,126.0,52.0,11,0.1595936918707181,104.22588592003032,0.044596912521440824,0.16691505216095381,0.1111111111111111,0.08919382504288165,0.6470588235294118,0.21153846153846154,0.0,0.0
+Otis Ridge Ski Area,Massachusetts,Massachusetts,1700,400,1300,0,0,0,0,0,1,3,4,11.0,1.0,1.0,60.0,55.0,106.0,73.0,70.0,40.0,106.0,35.0,11,0.1595936918707181,104.22588592003032,0.051457975986277875,0.15797317436661698,0.05555555555555555,0.060034305317324184,0.36363636363636365,0.06666666666666667,0.0,0.0
+Ski Butternut,Massachusetts,Massachusetts,1800,1000,800,0,0,0,3,1,1,6,11,22.0,2.0,1.5,110.0,110.0,107.0,56.0,115.0,60.0,110.0,,11,0.1595936918707181,104.22588592003032,0.09433962264150944,0.15946348733233978,0.1111111111111111,,0.5,0.1,0.0,0.0
+Wachusett Mountain Ski Area,Massachusetts,Massachusetts,2006,1000,1006,0,0,3,0,1,0,4,8,27.0,2.0,1.5,112.0,112.0,,57.0,100.0,71.0,120.0,104.0,11,0.1595936918707181,104.22588592003032,0.09605488850771869,,0.1111111111111111,0.1783876500857633,0.2962962962962963,0.07142857142857142,0.1111111111111111,0.026785714285714284
+Alpine Valley Ski Area,Michigan,Michigan,500,240,126,0,0,0,1,2,5,6,14,25.0,3.0,0.2,100.0,100.0,,57.0,20.0,47.0,,100.0,28,0.28036848830417815,28.951341067477305,0.022696323195642305,,0.047619047619047616,0.051387461459403906,0.56,0.14,0.0,0.0
+Apple Mountain,Michigan,Michigan,820,220,600,0,0,0,1,0,0,5,6,12.0,,,80.0,42.0,,58.0,52.0,35.0,,80.0,28,0.28036848830417815,28.951341067477305,0.018157058556513846,,,0.041109969167523124,0.5,0.075,0.0,0.0
+Big Powderhorn Mountain,Michigan,Michigan,1800,600,1200,0,0,0,0,0,9,1,10,45.0,2.0,1.0,253.0,228.0,100.0,55.0,214.0,69.0,108.0,,28,0.28036848830417815,28.951341067477305,0.057421697684975036,0.041858518208455424,0.031746031746031744,,0.2222222222222222,0.039525691699604744,0.0,0.0
+Bittersweet Ski Area,Michigan,Michigan,850,350,450,0,0,0,1,7,0,4,12,20.0,2.0,0.2,100.0,100.0,80.0,36.0,90.0,48.0,,100.0,28,0.28036848830417815,28.951341067477305,0.022696323195642305,0.033486814566764334,0.031746031746031744,0.051387461459403906,0.6,0.12,0.0,0.0
+Big Snow Resort - Blackjack,Michigan,Michigan,850,465,385,0,0,0,0,0,4,2,6,26.0,2.0,1.0,170.0,86.0,95.0,42.0,210.0,65.0,115.0,,28,0.28036848830417815,28.951341067477305,0.03858374943259192,0.03976559229803265,0.031746031746031744,,0.23076923076923078,0.03529411764705882,0.0,0.0
+Boyne Highlands,Michigan,Michigan,1290,552,745,0,0,1,3,4,0,2,10,55.0,4.0,1.2,435.0,400.0,97.0,56.0,140.0,98.0,120.0,150.0,28,0.28036848830417815,28.951341067477305,0.09872900590104403,0.04060276266220176,0.06349206349206349,0.07708119218910586,0.18181818181818182,0.022988505747126436,0.01818181818181818,0.0022988505747126436
+Caberfae Peaks,Michigan,Michigan,1569,485,1060,0,0,0,1,2,1,1,5,34.0,2.0,1.2,200.0,200.0,118.0,82.0,140.0,49.0,130.0,150.0,28,0.28036848830417815,28.951341067477305,0.04539264639128461,0.0493930514859774,0.031746031746031744,0.07708119218910586,0.14705882352941177,0.025,0.0,0.0
+Cannonsburg,Michigan,Michigan,1100,250,850,0,0,0,1,1,1,7,10,21.0,5.0,0.1,100.0,,100.0,54.0,100.0,37.0,100.0,,28,0.28036848830417815,28.951341067477305,0.022696323195642305,0.041858518208455424,0.07936507936507936,,0.47619047619047616,0.1,0.0,0.0
+Crystal Mountain,Michigan,Michigan,1132,375,757,0,0,1,3,2,0,2,8,58.0,3.0,0.3,102.0,96.0,120.0,63.0,132.0,64.0,135.0,56.0,28,0.28036848830417815,28.951341067477305,0.02315024965955515,0.05023022185014651,0.047619047619047616,0.02877697841726619,0.13793103448275862,0.0784313725490196,0.017241379310344827,0.00980392156862745
+Big Snow Resort - Indianhead Mountain,Michigan,Michigan,1935,638,1297,0,0,0,1,1,5,2,9,32.0,2.0,1.0,240.0,150.0,120.0,60.0,204.0,49.0,120.0,,28,0.28036848830417815,28.951341067477305,0.05447117566954154,0.05023022185014651,0.031746031746031744,,0.28125,0.0375,0.0,0.0
+Mont Ripley,Michigan,Michigan,1140,440,700,0,0,0,0,0,2,2,4,25.0,2.0,0.8,112.0,112.0,114.0,83.0,275.0,49.0,100.0,100.0,28,0.28036848830417815,28.951341067477305,0.02541988197911938,0.04771871075763918,0.031746031746031744,0.051387461459403906,0.16,0.03571428571428571,0.0,0.0
+Mount Bohemia,Michigan,Michigan,1500,900,600,0,0,0,0,1,1,0,2,,,2.3,585.0,,83.0,19.0,273.0,68.0,100.0,,28,0.28036848830417815,28.951341067477305,0.1327734906945075,0.034742570113018,,,,0.003418803418803419,,0.0
+Mt. Brighton,Michigan,Michigan,1330,230,1100,0,0,0,2,3,0,8,13,25.0,5.0,0.1,130.0,130.0,111.0,59.0,60.0,59.0,100.0,130.0,28,0.28036848830417815,28.951341067477305,0.029505220154335,0.046462955211385513,0.07936507936507936,0.06680369989722508,0.52,0.1,0.0,0.0
+Mt. Holiday Ski Area,Michigan,Michigan,440,200,240,0,0,0,0,0,2,2,4,12.0,,0.1,45.0,45.0,100.0,70.0,120.0,34.0,90.0,45.0,28,0.28036848830417815,28.951341067477305,0.010213345438039038,0.041858518208455424,,0.023124357656731757,0.3333333333333333,0.08888888888888889,0.0,0.0
+Mount Holly,Michigan,Michigan,1105,350,755,0,0,1,2,3,1,6,13,19.0,,0.1,100.0,100.0,,63.0,42.0,45.0,102.0,100.0,28,0.28036848830417815,28.951341067477305,0.022696323195642305,,,0.051387461459403906,0.6842105263157895,0.13,0.05263157894736842,0.01
+Mulligan's Hollow Ski Bowl,Michigan,Michigan,700,130,570,0,0,0,0,0,0,5,5,6.0,,0.2,10.0,10.0,,19.0,60.0,20.0,,10.0,28,0.28036848830417815,28.951341067477305,0.0022696323195642307,,,0.0051387461459403904,0.8333333333333334,0.5,0.0,0.0
+Norway Mountain,Michigan,Michigan,1335,500,835,0,0,0,0,1,2,3,6,17.0,1.0,1.4,186.0,186.0,110.0,45.0,100.0,45.0,110.0,40.0,28,0.28036848830417815,28.951341067477305,0.04221516114389469,0.046044370029300966,0.015873015873015872,0.020554984583761562,0.35294117647058826,0.03225806451612903,0.0,0.0
+Nubs Nob Ski Area,Michigan,Michigan,1338,427,911,0,0,0,3,4,2,1,10,53.0,3.0,0.9,248.0,248.0,133.0,61.0,135.0,85.0,130.0,160.0,28,0.28036848830417815,28.951341067477305,0.05628688152519292,0.05567182921724571,0.047619047619047616,0.08221993833504625,0.18867924528301888,0.04032258064516129,0.0,0.0
+Pine Mountain,Michigan,Michigan,1650,500,1150,0,0,0,0,1,2,1,4,28.0,1.0,0.5,160.0,160.0,110.0,80.0,60.0,45.0,126.0,80.0,28,0.28036848830417815,28.951341067477305,0.03631411711302769,0.046044370029300966,0.015873015873015872,0.041109969167523124,0.14285714285714285,0.025,0.0,0.0
+Schuss Mountain at Shanty Creek,Michigan,Michigan,1125,450,675,0,0,0,5,0,0,3,8,42.0,3.0,1.0,70.0,70.0,94.0,57.0,160.0,78.0,111.0,70.0,28,0.28036848830417815,28.951341067477305,0.015887426236949616,0.039347007115948095,0.047619047619047616,0.03597122302158273,0.19047619047619047,0.11428571428571428,0.0,0.0
+Ski Brule,Michigan,Michigan,1860,500,1360,0,0,0,0,0,5,7,12,17.0,3.0,1.0,150.0,150.0,164.0,62.0,150.0,49.0,165.0,40.0,28,0.28036848830417815,28.951341067477305,0.03404448479346346,0.06864796986186689,0.047619047619047616,0.020554984583761562,0.7058823529411765,0.08,0.0,0.0
+Snow Snake Mountain Ski Area,Michigan,Michigan,1230,210,1020,0,0,0,0,1,0,5,6,12.0,2.0,0.0,40.0,40.0,,72.0,,35.0,,40.0,28,0.28036848830417815,28.951341067477305,0.009078529278256923,,0.031746031746031744,0.020554984583761562,0.5,0.15,0.0,0.0
+Swiss Valley,Michigan,Michigan,1200,225,975,0,0,0,2,1,0,4,7,11.0,2.0,0.1,60.0,60.0,89.0,51.0,60.0,42.0,80.0,60.0,28,0.28036848830417815,28.951341067477305,0.013617793917385384,0.03725408120552533,0.031746031746031744,0.030832476875642344,0.6363636363636364,0.11666666666666667,0.0,0.0
+The Homestead,Michigan,Michigan,900,320,580,0,0,0,0,2,1,2,5,15.0,1.0,0.2,16.0,16.0,47.0,34.0,150.0,50.0,42.0,16.0,28,0.28036848830417815,28.951341067477305,0.003631411711302769,0.019673503557974047,0.015873015873015872,0.008221993833504625,0.3333333333333333,0.3125,0.0,0.0
+Timber Ridge,Michigan,Michigan,850,250,600,0,0,0,1,1,2,4,8,16.0,2.0,0.3,50.0,50.0,80.0,58.0,,45.0,,50.0,28,0.28036848830417815,28.951341067477305,0.011348161597821153,0.033486814566764334,0.031746031746031744,0.025693730729701953,0.5,0.16,0.0,0.0
+Afton Alps,Minnesota,Minnesota,1530,350,1180,0,0,0,1,3,14,4,22,48.0,5.0,0.5,250.0,250.0,135.0,56.0,60.0,60.0,135.0,250.0,14,0.24824314777985515,16.103800496917273,0.16025641025641027,0.09060402684563758,0.1724137931034483,0.24509803921568626,0.4583333333333333,0.088,0.0,0.0
+Andes Tower Hills Ski Area,Minnesota,Minnesota,1620,290,1330,0,0,0,1,2,0,3,6,15.0,2.0,0.2,35.0,35.0,100.0,38.0,55.0,45.0,110.0,35.0,14,0.24824314777985515,16.103800496917273,0.022435897435897436,0.06711409395973154,0.06896551724137931,0.03431372549019608,0.4,0.17142857142857143,0.0,0.0
+Buck Hill,Minnesota,Minnesota,1225,309,919,0,0,0,2,1,0,5,8,16.0,,0.2,45.0,45.0,115.0,65.0,60.0,47.0,112.0,45.0,14,0.24824314777985515,16.103800496917273,0.028846153846153848,0.07718120805369127,,0.04411764705882353,0.5,0.17777777777777778,0.0,0.0
+Buena Vista Ski Area,Minnesota,Minnesota,1510,230,1280,0,0,0,0,2,2,2,6,17.0,,0.3,30.0,30.0,57.0,70.0,78.0,44.0,60.0,30.0,14,0.24824314777985515,16.103800496917273,0.019230769230769232,0.03825503355704698,,0.029411764705882353,0.35294117647058826,0.2,0.0,0.0
+Coffee Mill Ski & Snowboard Resort,Minnesota,Minnesota,1150,425,725,0,0,0,0,0,2,1,3,14.0,1.0,1.0,40.0,35.0,57.0,39.0,48.0,37.0,56.0,35.0,14,0.24824314777985515,16.103800496917273,0.02564102564102564,0.03825503355704698,0.034482758620689655,0.03431372549019608,0.21428571428571427,0.075,0.0,0.0
+Elm Creek Winter Recreation Area,Minnesota,Minnesota,928,60,868,0,0,0,0,0,0,3,3,3.0,,1.0,15.0,20.0,105.0,13.0,45.0,17.0,102.0,15.0,14,0.24824314777985515,16.103800496917273,0.009615384615384616,0.07046979865771812,,0.014705882352941176,1.0,0.2,0.0,0.0
+Giants Ridge Resort,Minnesota,Minnesota,1972,500,1472,0,0,1,1,1,2,2,7,35.0,2.0,0.8,202.0,202.0,120.0,35.0,85.0,58.0,125.0,121.0,14,0.24824314777985515,16.103800496917273,0.1294871794871795,0.08053691275167785,0.06896551724137931,0.11862745098039215,0.2,0.034653465346534656,0.02857142857142857,0.0049504950495049506
+Hyland Ski & Snowboard Area,Minnesota,Minnesota,1075,175,900,0,0,0,2,1,0,5,8,14.0,1.0,1.0,35.0,35.0,110.0,61.0,55.0,35.34,115.0,35.0,14,0.24824314777985515,16.103800496917273,0.022435897435897436,0.0738255033557047,0.034482758620689655,0.03431372549019608,0.5714285714285714,0.22857142857142856,0.0,0.0
+Lutsen Mountains,Minnesota,Minnesota,1688,825,800,1,1,0,0,1,4,1,8,62.0,2.0,2.0,393.0,231.0,135.0,71.0,120.0,84.0,127.0,,14,0.24824314777985515,16.103800496917273,0.2519230769230769,0.09060402684563758,0.06896551724137931,,0.12903225806451613,0.020356234096692113,0.0,0.0
+Mount Kato Ski Area,Minnesota,Minnesota,540,240,300,0,0,0,5,0,3,2,10,19.0,4.0,1.0,55.0,55.0,115.0,43.0,50.0,46.0,120.0,50.0,14,0.24824314777985515,16.103800496917273,0.035256410256410256,0.07718120805369127,0.13793103448275862,0.049019607843137254,0.5263157894736842,0.18181818181818182,0.0,0.0
+Powder Ridge Ski Area,Minnesota,Minnesota,790,300,500,0,0,0,1,0,2,3,6,15.0,4.0,,60.0,60.0,97.0,58.0,45.0,48.0,113.0,60.0,14,0.24824314777985515,16.103800496917273,0.038461538461538464,0.06510067114093959,0.13793103448275862,0.058823529411764705,0.4,0.1,0.0,0.0
+Spirit Mountain,Minnesota,Minnesota,1320,700,620,0,0,1,1,2,1,2,7,22.0,3.0,1.0,175.0,175.0,100.0,45.0,100.0,59.0,125.0,144.0,14,0.24824314777985515,16.103800496917273,0.11217948717948718,0.06711409395973154,0.10344827586206896,0.1411764705882353,0.3181818181818182,0.04,0.045454545454545456,0.005714285714285714
+Welch Village,Minnesota,Minnesota,1060,360,700,0,0,0,3,1,4,2,10,50.0,1.0,0.8,125.0,125.0,114.0,54.0,45.0,60.0,122.0,100.0,14,0.24824314777985515,16.103800496917273,0.08012820512820513,0.07651006711409396,0.034482758620689655,0.09803921568627451,0.2,0.08,0.0,0.0
+Wild Mountain Ski & Snowboard Area,Minnesota,Minnesota,1113,300,813,0,0,0,4,0,0,4,8,26.0,4.0,0.9,100.0,100.0,130.0,47.0,50.0,55.0,140.0,100.0,14,0.24824314777985515,16.103800496917273,0.0641025641025641,0.087248322147651,0.13793103448275862,0.09803921568627451,0.3076923076923077,0.08,0.0,0.0
+Hidden Valley Ski Area,Missouri,Missouri,2566,310,2316,0,0,0,2,2,0,3,7,17.0,,0.1,30.0,30.0,,37.0,26.0,49.0,,17.0,2,0.03258694032744661,2.8691523089503206,0.5,,,0.3617021276595745,0.4117647058823529,0.23333333333333334,0.0,0.0
+Snow Creek,Missouri,Missouri,1100,300,800,0,0,0,0,2,1,2,5,14.0,2.0,0.3,30.0,30.0,69.0,33.0,20.0,47.0,85.0,30.0,2,0.03258694032744661,2.8691523089503206,0.5,1.0,1.0,0.6382978723404256,0.35714285714285715,0.16666666666666666,0.0,0.0
+Blacktail Mountain Ski Area,Montana,Montana,6676,1440,5236,0,0,0,0,1,2,1,4,27.0,,0.7,1000.0,,,21.0,250.0,42.0,,,12,1.1227776020838753,8.161044613710555,0.046707146193367584,,,,0.14814814814814814,0.004,0.0,0.0
+Bridger Bowl,Montana,Montana,8700,2600,6100,0,0,0,1,6,1,3,11,105.0,2.0,1.5,2000.0,100.0,122.0,64.0,350.0,63.0,133.0,,12,1.1227776020838753,8.161044613710555,0.09341429238673517,0.12828601472134596,0.07407407407407407,,0.10476190476190476,0.0055,0.0,0.0
+Discovery Ski Area,Montana,Montana,8150,2380,5770,0,0,0,0,5,2,1,8,74.0,1.0,1.5,2400.0,25.0,116.0,46.0,225.0,49.0,116.0,,12,1.1227776020838753,8.161044613710555,0.1120971508640822,0.12197686645636173,0.037037037037037035,,0.10810810810810811,0.0033333333333333335,0.0,0.0
+Great Divide,Montana,Montana,7330,1580,5750,0,0,0,0,0,5,1,6,110.0,6.0,3.0,1600.0,150.0,94.0,78.0,180.0,48.0,100.0,100.0,12,1.1227776020838753,8.161044613710555,0.07473143390938813,0.09884332281808622,0.2222222222222222,0.14084507042253522,0.05454545454545454,0.00375,0.0,0.0
+Lost Trail - Powder Mtn,Montana,Montana,8200,1800,6400,0,0,0,0,0,5,3,8,69.0,2.0,2.5,1800.0,,84.0,81.0,325.0,46.0,80.0,,12,1.1227776020838753,8.161044613710555,0.08407286314806166,0.08832807570977919,0.07407407407407407,,0.11594202898550725,0.0044444444444444444,0.0,0.0
+Maverick Mountain,Montana,Montana,8520,2020,6500,0,0,0,0,0,1,1,2,22.0,,1.3,255.0,,,83.0,160.0,39.0,,,12,1.1227776020838753,8.161044613710555,0.011910322279308735,,,,0.09090909090909091,0.00784313725490196,0.0,0.0
+Montana Snowbowl,Montana,Montana,7600,2600,5000,0,0,0,0,0,2,2,4,37.0,,1.2,950.0,20.0,,58.0,300.0,50.0,,10.0,12,1.1227776020838753,8.161044613710555,0.044371788883699206,,,0.014084507042253521,0.10810810810810811,0.004210526315789474,0.0,0.0
+Red Lodge Mountain,Montana,Montana,9416,2400,7016,0,0,2,0,1,3,1,7,70.0,2.0,2.5,1635.0,496.0,142.0,59.0,250.0,67.0,136.0,,12,1.1227776020838753,8.161044613710555,0.076366184026156,0.14931650893796003,0.07407407407407407,,0.1,0.004281345565749235,0.02857142857142857,0.0012232415902140672
+Showdown Montana,Montana,Montana,8200,1400,6800,0,0,0,0,1,2,1,4,36.0,1.0,1.8,640.0,,86.0,83.0,250.0,47.0,85.0,,12,1.1227776020838753,8.161044613710555,0.029892573563755253,0.0904311251314406,0.037037037037037035,,0.1111111111111111,0.00625,0.0,0.0
+Teton Pass Ski Resort,Montana,Montana,7200,1010,6190,0,0,0,0,0,1,2,3,43.0,1.0,3.0,330.0,,40.0,54.0,250.0,39.0,150.0,,12,1.1227776020838753,8.161044613710555,0.015413358243811303,0.04206098843322818,0.037037037037037035,,0.06976744186046512,0.00909090909090909,0.0,0.0
+Big Mountain Resort,Montana,Montana,6817,2353,4464,0,0,3,2,6,0,3,14,105.0,4.0,3.3,3000.0,600.0,123.0,72.0,333.0,81.0,123.0,600.0,12,1.1227776020838753,8.161044613710555,0.14012143858010276,0.12933753943217666,0.14814814814814814,0.8450704225352113,0.13333333333333333,0.004666666666666667,0.02857142857142857,0.001
+Diamond Peak,Sierra Nevada,Nevada,8540,1840,6700,0,0,1,2,0,3,1,7,30.0,3.0,2.5,655.0,492.0,100.0,53.0,300.0,99.0,122.0,,4,0.12986355236552954,3.61755236407047,0.3104265402843602,0.24096385542168675,0.3333333333333333,,0.23333333333333334,0.010687022900763359,0.03333333333333333,0.0015267175572519084
+Elko SnoBowl,Nevada,Nevada,7000,700,6300,0,0,0,0,0,1,1,2,10.0,,1.0,60.0,2.0,19.0,23.0,24.0,20.0,30.0,,4,0.12986355236552954,3.61755236407047,0.02843601895734597,0.04578313253012048,,,0.2,0.03333333333333333,0.0,0.0
+Lee Canyon,Nevada,Nevada,11289,860,8510,0,0,0,2,1,0,0,3,24.0,1.0,0.3,195.0,50.0,144.0,56.0,161.0,70.0,150.0,,4,0.12986355236552954,3.61755236407047,0.0924170616113744,0.3469879518072289,0.1111111111111111,,0.125,0.015384615384615385,0.0,0.0
+Mt. Rose - Ski Tahoe,Sierra Nevada,Nevada,9700,1800,8260,0,2,0,2,2,0,2,8,65.0,5.0,2.5,1200.0,330.0,152.0,55.0,350.0,135.0,150.0,,4,0.12986355236552954,3.61755236407047,0.5687203791469194,0.36626506024096384,0.5555555555555556,,0.12307692307692308,0.006666666666666667,0.0,0.0
+Attitash,New Hampshire,New Hampshire,2350,1750,600,0,0,2,1,3,2,1,9,68.0,3.0,3.0,311.0,240.0,115.0,54.0,120.0,89.0,130.0,,16,1.1767206413715856,171.14129853460264,0.09074992704989787,0.062263129399025445,0.06976744186046512,,0.1323529411764706,0.028938906752411574,0.029411764705882353,0.006430868167202572
+Black Mountain,New Hampshire,New Hampshire,2350,1100,1250,0,0,0,0,1,1,3,5,45.0,,1.6,143.0,120.0,110.0,84.0,125.0,59.0,107.0,,16,1.1767206413715856,171.14129853460264,0.04172745841844178,0.05955603681645912,,,0.1111111111111111,0.03496503496503497,0.0,0.0
+Bretton Woods,New Hampshire,New Hampshire,3100,1500,1600,0,0,4,1,1,0,3,9,63.0,2.0,2.0,464.0,427.0,180.0,46.0,200.0,99.0,180.0,45.0,16,1.1767206413715856,171.14129853460264,0.13539538955354538,0.09745533297238766,0.046511627906976744,0.1196808510638298,0.14285714285714285,0.01939655172413793,0.06349206349206349,0.008620689655172414
+Cannon Mountain,New Hampshire,New Hampshire,4080,2180,1900,1,0,1,2,3,1,3,11,97.0,3.0,2.3,285.0,192.0,124.0,81.0,160.0,79.0,143.0,,16,1.1767206413715856,171.14129853460264,0.083163116428363,0.06713589604764483,0.06976744186046512,,0.1134020618556701,0.03859649122807018,0.010309278350515464,0.0035087719298245615
+Crotched Mountain,New Hampshire,New Hampshire,2066,1016,1050,0,0,1,1,1,1,1,5,25.0,3.0,1.2,100.0,100.0,105.0,16.0,105.0,69.0,100.0,100.0,16,1.1767206413715856,171.14129853460264,0.029180040852057193,0.0568489442338928,0.06976744186046512,0.26595744680851063,0.2,0.05,0.04,0.01
+Dartmouth Skiway,New Hampshire,New Hampshire,1943,969,974,0,0,0,1,0,1,2,4,28.0,1.0,1.1,107.0,54.0,104.0,63.0,100.0,50.0,105.0,,16,1.1767206413715856,171.14129853460264,0.031222643711701196,0.056307525717379535,0.023255813953488372,,0.14285714285714285,0.037383177570093455,0.0,0.0
+Gunstock,New Hampshire,New Hampshire,2300,1400,900,0,0,1,2,2,0,1,6,55.0,4.0,1.5,227.0,176.0,106.0,82.0,120.0,92.0,,60.0,16,1.1767206413715856,171.14129853460264,0.06623869273416982,0.057390362750406064,0.09302325581395349,0.1595744680851064,0.10909090909090909,0.02643171806167401,0.01818181818181818,0.004405286343612335
+King Pine,New Hampshire,New Hampshire,850,350,500,0,0,0,0,3,0,3,6,17.0,2.0,0.3,48.0,45.0,105.0,57.0,120.0,58.0,107.0,23.0,16,1.1767206413715856,171.14129853460264,0.014006419608987453,0.0568489442338928,0.046511627906976744,0.061170212765957445,0.35294117647058826,0.125,0.0,0.0
+Mount Sunapee,New Hampshire,New Hampshire,2743,1510,1233,0,0,2,1,2,1,4,10,66.0,4.0,0.8,232.0,215.0,130.0,71.0,100.0,93.0,136.0,,16,1.1767206413715856,171.14129853460264,0.06769769477677269,0.07038440714672442,0.09302325581395349,,0.15151515151515152,0.04310344827586207,0.030303030303030304,0.008620689655172414
+Pats Peak,New Hampshire,New Hampshire,1460,770,690,0,0,0,0,4,2,5,11,28.0,3.0,1.5,115.0,115.0,109.0,56.0,100.0,72.0,112.0,93.0,16,1.1767206413715856,171.14129853460264,0.03355704697986577,0.05901461829994586,0.06976744186046512,0.2473404255319149,0.39285714285714285,0.09565217391304348,0.0,0.0
+Ragged Mountain Resort,New Hampshire,New Hampshire,2250,1250,1000,0,1,1,0,1,0,3,6,57.0,3.0,0.7,250.0,200.0,,54.0,100.0,84.0,140.0,,16,1.1767206413715856,171.14129853460264,0.07295010213014298,,0.06976744186046512,,0.10526315789473684,0.024,0.017543859649122806,0.004
+Waterville Valley,New Hampshire,New Hampshire,4004,2020,1984,0,0,2,0,2,3,4,11,62.0,4.0,1.9,265.0,220.0,142.0,54.0,148.0,93.0,142.0,,16,1.1767206413715856,171.14129853460264,0.07732710825795155,0.0768814293448836,0.09302325581395349,,0.1774193548387097,0.04150943396226415,0.03225806451612903,0.007547169811320755
+Whaleback Mountain,New Hampshire,New Hampshire,1800,700,1100,0,0,0,0,0,1,3,4,30.0,1.0,1.0,85.0,60.0,105.0,64.0,110.0,45.0,105.0,55.0,16,1.1767206413715856,171.14129853460264,0.024803034724248614,0.0568489442338928,0.023255813953488372,0.14627659574468085,0.13333333333333333,0.047058823529411764,0.0,0.0
+Wildcat Mountain,New Hampshire,New Hampshire,4062,2112,1950,0,0,1,0,3,0,1,5,48.0,,2.8,225.0,200.0,156.0,61.0,200.0,89.0,150.0,,16,1.1767206413715856,171.14129853460264,0.06565509191712868,0.0844612885760693,,,0.10416666666666667,0.022222222222222223,0.020833333333333332,0.0044444444444444444
+Mountain Creek Resort,New Jersey,New Jersey,1480,1040,440,1,0,2,2,1,1,3,10,46.0,3.0,2.0,167.0,167.0,90.0,54.0,65.0,79.99,100.0,167.0,2,0.022516969351027167,22.927891780350798,0.8789473684210526,0.5294117647058824,0.75,0.9226519337016574,0.21739130434782608,0.059880239520958084,0.043478260869565216,0.011976047904191617
+Angel Fire Resort,New Mexico,New Mexico,10677,2077,8600,0,0,2,0,0,3,2,7,81.0,3.0,3.0,560.0,230.0,101.0,53.0,210.0,77.0,101.0,50.0,9,0.4292195500920676,7.401924500370097,0.10721807390388666,0.10455486542443064,0.16666666666666666,1.0,0.08641975308641975,0.0125,0.024691358024691357,0.0035714285714285713
+Enchanted Forest Ski Area,New Mexico,New Mexico,10078,400,9820,0,0,0,0,0,0,0,0,33.0,,2.5,600.0,,130.0,34.0,240.0,20.0,140.0,,9,0.4292195500920676,7.401924500370097,0.11487650775416428,0.13457556935817805,,,0.0,0.0,0.0,0.0
+Pajarito Mountain Ski Area,New Mexico,New Mexico,10441,1410,9031,0,0,0,1,1,3,1,6,45.0,2.0,0.6,750.0,35.0,89.0,62.0,163.0,49.0,117.0,,9,0.4292195500920676,7.401924500370097,0.14359563469270534,0.09213250517598344,0.1111111111111111,,0.13333333333333333,0.008,0.0,0.0
+Red River,New Mexico,New Mexico,10350,1600,8750,0,0,0,1,3,1,2,7,63.0,3.0,2.5,209.0,,110.0,60.0,214.0,79.0,,,9,0.4292195500920676,7.401924500370097,0.04001531686770055,0.11387163561076605,0.16666666666666666,,0.1111111111111111,0.03349282296650718,0.0,0.0
+Sandia Peak,New Mexico,New Mexico,10378,1700,8678,0,0,0,0,0,4,1,5,39.0,1.0,2.0,200.0,30.0,32.0,82.0,100.0,55.0,38.0,,9,0.4292195500920676,7.401924500370097,0.03829216925138809,0.033126293995859216,0.05555555555555555,,0.1282051282051282,0.025,0.0,0.0
+Sipapu Ski Resort,New Mexico,New Mexico,9255,1055,8200,0,0,0,1,2,0,3,6,42.0,4.0,0.5,200.0,140.0,127.0,67.0,190.0,47.0,143.0,,9,0.4292195500920676,7.401924500370097,0.03829216925138809,0.13146997929606624,0.2222222222222222,,0.14285714285714285,0.03,0.0,0.0
+Ski Apache,New Mexico,New Mexico,11500,1900,9600,1,0,0,2,6,0,2,11,55.0,3.0,2.0,750.0,270.0,133.0,58.0,185.0,74.0,124.0,,9,0.4292195500920676,7.401924500370097,0.14359563469270534,0.13768115942028986,0.16666666666666666,,0.2,0.014666666666666666,0.0,0.0
+Ski Santa Fe,New Mexico,New Mexico,12075,1725,10350,0,0,0,1,2,2,2,7,83.0,1.0,3.0,660.0,275.0,107.0,73.0,225.0,80.0,130.0,,9,0.4292195500920676,7.401924500370097,0.1263641585295807,0.11076604554865424,0.05555555555555555,,0.08433734939759036,0.010606060606060607,0.0,0.0
+Taos Ski Valley,New Mexico,New Mexico,12481,3281,9200,1,0,1,3,4,1,4,14,111.0,1.0,5.0,1294.0,647.0,137.0,64.0,300.0,110.0,136.0,,9,0.4292195500920676,7.401924500370097,0.24775033505648095,0.14182194616977226,0.05555555555555555,,0.12612612612612611,0.010819165378670788,0.009009009009009009,0.0007727975270479134
+Belleayre,New York,New York,3429,1404,2025,1,0,1,1,1,2,2,8,50.0,2.0,2.2,175.0,168.0,154.0,70.0,130.0,72.0,150.0,,33,0.16963475221837276,60.48941435248832,0.03173739571998549,0.06459731543624161,0.027777777777777776,,0.16,0.045714285714285714,0.02,0.005714285714285714
+Brantling Ski Slopes,New York,New York,850,250,600,0,0,0,0,0,0,5,5,10.0,,0.1,20.0,16.0,,19.0,110.0,32.0,,,33,0.16963475221837276,60.48941435248832,0.003627130939426913,,,,0.5,0.25,0.0,0.0
+Bristol Mountain,New York,New York,2200,1200,1000,0,0,2,1,1,1,1,6,34.0,3.0,2.0,160.0,148.0,129.0,55.0,60.0,76.0,129.0,154.0,33,0.16963475221837276,60.48941435248832,0.029017047515415305,0.054110738255033555,0.041666666666666664,0.054301833568406205,0.17647058823529413,0.0375,0.058823529411764705,0.0125
+Buffalo Ski Club Ski Area,New York,New York,3429,500,2025,0,0,0,0,0,2,4,6,43.0,1.0,,225.0,150.0,,12.0,,50.0,,100.0,33,0.16963475221837276,60.48941435248832,0.040805223068552776,,0.013888888888888888,0.03526093088857546,0.13953488372093023,0.02666666666666667,0.0,0.0
+Catamount,New York,New York,2000,1000,1000,0,0,0,1,1,2,3,7,36.0,5.0,2.0,133.0,130.0,100.0,80.0,108.0,69.0,90.0,55.0,33,0.16963475221837276,60.48941435248832,0.024120420747188974,0.04194630872483222,0.06944444444444445,0.019393511988716503,0.19444444444444445,0.05263157894736842,0.0,0.0
+Dry Hill Ski Area,New York,New York,950,300,650,0,0,0,0,0,1,2,3,7.0,1.0,0.2,35.0,26.0,,55.0,125.0,35.0,,26.0,33,0.16963475221837276,60.48941435248832,0.006347479143997099,,0.013888888888888888,0.009167842031029619,0.42857142857142855,0.08571428571428572,0.0,0.0
+Gore Mountain,New York,New York,3600,2537,998,1,0,2,2,3,2,4,14,110.0,7.0,4.5,439.0,338.0,142.0,55.0,150.0,88.0,,15.0,33,0.16963475221837276,60.48941435248832,0.07961552412042075,0.05956375838926174,0.09722222222222222,0.005289139633286318,0.12727272727272726,0.03189066059225513,0.01818181818181818,0.004555808656036446
+Greek Peak,New York,New York,2100,952,1148,0,0,0,1,1,4,2,8,56.0,4.0,1.5,220.0,184.0,110.0,62.0,122.0,63.2,113.0,175.0,33,0.16963475221837276,60.48941435248832,0.03989844033369604,0.04614093959731544,0.05555555555555555,0.06170662905500705,0.14285714285714285,0.03636363636363636,0.0,0.0
+Holiday Mountain,New York,New York,1550,400,1150,0,0,0,1,0,1,2,4,9.0,,0.4,37.0,37.0,75.0,60.0,50.0,42.0,85.0,37.0,33,0.16963475221837276,60.48941435248832,0.00671019223793979,0.031459731543624164,,0.01304654442877292,0.4444444444444444,0.10810810810810811,0.0,0.0
+Holiday Valley,New York,New York,2250,750,1500,0,0,3,8,0,0,2,13,60.0,5.0,1.0,290.0,266.0,116.0,62.0,180.0,78.0,129.0,189.0,33,0.16963475221837276,60.48941435248832,0.05259339862169024,0.04865771812080537,0.06944444444444445,0.06664315937940761,0.21666666666666667,0.04482758620689655,0.05,0.010344827586206896
+Holimont Ski Area,New York,New York,2260,700,1560,0,0,1,1,2,3,1,8,53.0,3.0,1.5,135.0,135.0,110.0,57.0,180.0,75.0,119.0,,33,0.16963475221837276,60.48941435248832,0.024483133841131665,0.04614093959731544,0.041666666666666664,,0.1509433962264151,0.05925925925925926,0.018867924528301886,0.007407407407407408
+Hunt Hollow Ski Club,New York,New York,2030,825,1000,0,0,0,0,1,1,1,3,19.0,1.0,1.0,400.0,400.0,,52.0,130.0,58.0,75.0,400.0,33,0.16963475221837276,60.48941435248832,0.07254261878853827,,0.013888888888888888,0.14104372355430184,0.15789473684210525,0.0075,0.0,0.0
+Hunter Mountain,New York,New York,3200,1600,1600,0,2,1,2,2,2,4,13,67.0,4.0,2.0,320.0,320.0,148.0,59.0,120.0,89.0,155.0,,33,0.16963475221837276,60.48941435248832,0.05803409503083061,0.06208053691275168,0.05555555555555555,,0.19402985074626866,0.040625,0.014925373134328358,0.003125
+Kissing Bridge,New York,New York,1700,550,1150,0,0,0,2,1,4,3,10,39.0,5.0,0.5,700.0,550.0,103.0,59.0,120.0,60.0,100.0,650.0,33,0.16963475221837276,60.48941435248832,0.12694958287994196,0.04320469798657718,0.06944444444444445,0.22919605077574048,0.2564102564102564,0.014285714285714285,0.0,0.0
+Labrador Mt.,New York,New York,1825,700,1125,0,0,0,0,1,2,1,4,23.0,1.0,1.0,250.0,237.0,,62.0,125.0,59.0,100.0,180.0,33,0.16963475221837276,60.48941435248832,0.04533913674283642,,0.013888888888888888,0.06346967559943582,0.17391304347826086,0.016,0.0,0.0
+Maple Ski Ridge,New York,New York,1200,450,750,0,0,0,0,1,1,1,3,10.0,,0.3,25.0,25.0,,57.0,,38.0,,20.0,33,0.16963475221837276,60.48941435248832,0.004533913674283642,,,0.007052186177715092,0.3,0.12,0.0,0.0
+McCauley Mountain Ski Center,New York,New York,2250,633,1563,0,0,0,0,0,1,4,5,23.0,1.0,0.3,70.0,55.0,105.0,61.0,200.0,30.0,105.0,,33,0.16963475221837276,60.48941435248832,0.012694958287994197,0.044043624161073824,0.013888888888888888,,0.21739130434782608,0.07142857142857142,0.0,0.0
+Mount Peter Ski Area,New York,New York,1250,450,750,0,0,0,1,0,2,2,5,14.0,1.0,1.0,69.0,69.0,100.0,83.0,50.0,54.0,100.0,69.0,33,0.16963475221837276,60.48941435248832,0.012513601741022852,0.04194630872483222,0.013888888888888888,0.024330042313117067,0.35714285714285715,0.07246376811594203,0.0,0.0
+Oak Mountain,New York,New York,2400,650,1750,0,0,0,1,0,0,3,4,22.0,1.0,1.2,46.0,18.0,,71.0,120.0,40.0,,12.0,33,0.16963475221837276,60.48941435248832,0.008342401160681901,,0.013888888888888888,0.004231311706629055,0.18181818181818182,0.08695652173913043,0.0,0.0
+Peek'n Peak,New York,New York,1800,400,1400,0,0,0,0,8,0,2,10,27.0,4.0,2.4,110.0,110.0,110.0,55.0,225.0,63.0,,110.0,33,0.16963475221837276,60.48941435248832,0.01994922016684802,0.04614093959731544,0.05555555555555555,0.038787023977433006,0.37037037037037035,0.09090909090909091,0.0,0.0
+Plattekill Mountain,New York,New York,3500,1100,2400,0,0,0,0,1,1,2,4,38.0,1.0,2.0,110.0,75.0,65.0,26.0,175.0,67.0,65.0,,33,0.16963475221837276,60.48941435248832,0.01994922016684802,0.02726510067114094,0.013888888888888888,,0.10526315789473684,0.03636363636363636,0.0,0.0
+Royal Mountain Ski Area,New York,New York,1800,550,1250,0,0,0,0,0,3,0,3,14.0,,0.3,35.0,28.0,,63.0,90.0,45.0,,,33,0.16963475221837276,60.48941435248832,0.006347479143997099,,,,0.21428571428571427,0.08571428571428572,0.0,0.0
+Snow Ridge,New York,New York,2000,650,1350,0,0,0,0,0,4,2,6,21.0,2.0,0.8,130.0,65.0,73.0,74.0,230.0,48.0,100.0,40.0,33,0.16963475221837276,60.48941435248832,0.023576351106274936,0.030620805369127518,0.027777777777777776,0.014104372355430184,0.2857142857142857,0.046153846153846156,0.0,0.0
+Song Mountain,New York,New York,1940,700,1240,0,0,0,0,1,1,3,5,24.0,,0.4,93.0,70.0,90.0,55.0,125.0,59.0,122.0,70.0,33,0.16963475221837276,60.48941435248832,0.016866158868335146,0.037751677852348994,,0.02468265162200282,0.20833333333333334,0.053763440860215055,0.0,0.0
+Swain,New York,New York,1970,650,1320,0,0,0,3,0,1,1,5,35.0,3.0,1.0,130.0,90.0,102.0,72.0,120.0,59.0,100.0,80.0,33,0.16963475221837276,60.48941435248832,0.023576351106274936,0.04278523489932886,0.041666666666666664,0.028208744710860368,0.14285714285714285,0.038461538461538464,0.0,0.0
+Thunder Ridge,New York,New York,1270,500,770,0,0,0,0,1,2,3,6,30.0,,0.4,100.0,100.0,121.0,60.0,,57.0,121.0,100.0,33,0.16963475221837276,60.48941435248832,0.018135654697134566,0.05075503355704698,,0.03526093088857546,0.2,0.06,0.0,0.0
+Titus Mountain,New York,New York,2025,1200,825,0,0,0,0,2,6,2,10,50.0,3.0,2.0,200.0,150.0,101.0,59.0,150.0,49.0,100.0,70.0,33,0.16963475221837276,60.48941435248832,0.03627130939426913,0.04236577181208054,0.041666666666666664,0.02468265162200282,0.2,0.05,0.0,0.0
+Toggenburg Mountain,New York,New York,2000,700,1300,0,0,0,0,1,1,3,5,22.0,2.0,0.4,85.0,,,66.0,130.0,55.0,122.0,73.0,33,0.16963475221837276,60.48941435248832,0.015415306492564382,,0.027777777777777776,0.025740479548660086,0.22727272727272727,0.058823529411764705,0.0,0.0
+West Mountain,New York,New York,1470,1010,460,0,0,0,0,1,2,2,5,29.0,1.0,0.6,124.0,105.0,,58.0,80.0,59.0,120.0,105.0,33,0.16963475221837276,60.48941435248832,0.022488211824446862,,0.013888888888888888,0.03702397743300423,0.1724137931034483,0.04032258064516129,0.0,0.0
+Whiteface Mountain Resort,New York,New York,4650,3430,1220,1,0,1,1,2,5,2,12,86.0,5.0,2.1,288.0,220.0,122.0,61.0,168.0,96.0,141.0,,33,0.16963475221837276,60.48941435248832,0.05223068552774755,0.051174496644295304,0.06944444444444445,,0.13953488372093023,0.041666666666666664,0.011627906976744186,0.003472222222222222
+Willard Mountain,New York,New York,1415,505,910,0,0,0,0,0,2,3,5,16.0,,0.4,50.0,35.0,85.0,19.0,80.0,46.0,120.0,35.0,33,0.16963475221837276,60.48941435248832,0.009067827348567283,0.03565436241610738,,0.01234132581100141,0.3125,0.1,0.0,0.0
+Windham Mountain,New York,New York,3100,1600,1500,0,1,2,0,3,1,5,12,54.0,6.0,2.0,285.0,280.0,123.0,59.0,105.0,95.0,130.0,56.0,33,0.16963475221837276,60.48941435248832,0.051686615886833515,0.051593959731543626,0.08333333333333333,0.019746121297602257,0.2222222222222222,0.042105263157894736,0.037037037037037035,0.007017543859649123
+Woods Valley Ski Area,New York,New York,1400,500,900,0,0,0,0,0,2,4,6,21.0,,0.3,25.0,16.0,,55.0,180.0,39.0,,15.0,33,0.16963475221837276,60.48941435248832,0.004533913674283642,,,0.005289139633286318,0.2857142857142857,0.24,0.0,0.0
+Appalachian Ski Mountain,North Carolina,North Carolina,4000,365,3635,0,0,0,2,0,1,2,5,12.0,3.0,0.5,27.0,27.0,100.0,57.0,50.0,64.0,100.0,27.0,6,0.057207779800390615,11.148479161634366,0.07297297297297298,0.1976284584980237,0.3333333333333333,0.08059701492537313,0.4166666666666667,0.18518518518518517,0.0,0.0
+Cataloochee Ski Area,North Carolina,North Carolina,5400,740,4660,0,0,0,1,1,1,2,5,18.0,2.0,1.0,50.0,50.0,141.0,58.0,50.0,70.0,108.0,50.0,6,0.057207779800390615,11.148479161634366,0.13513513513513514,0.27865612648221344,0.2222222222222222,0.14925373134328357,0.2777777777777778,0.1,0.0,0.0
+Sapphire Valley,North Carolina,North Carolina,3450,200,3200,0,0,0,1,0,0,2,3,,1.0,1.0,8.0,8.0,53.0,55.0,24.0,43.0,60.0,8.0,6,0.057207779800390615,11.148479161634366,0.021621621621621623,0.10474308300395258,0.1111111111111111,0.023880597014925373,,0.375,,0.0
+Beech Mountain Resort,North Carolina,North Carolina,5506,830,4675,0,0,0,3,0,3,2,8,17.0,1.0,1.0,95.0,95.0,98.0,52.0,31.0,68.0,,95.0,6,0.057207779800390615,11.148479161634366,0.25675675675675674,0.19367588932806323,0.1111111111111111,0.2835820895522388,0.47058823529411764,0.08421052631578947,0.0,0.0
+Sugar Mountain Resort,North Carolina,North Carolina,5300,1200,4100,0,1,0,0,1,4,2,8,21.0,1.0,1.5,125.0,125.0,114.0,50.0,77.0,75.0,120.0,95.0,6,0.057207779800390615,11.148479161634366,0.33783783783783783,0.22529644268774704,0.1111111111111111,0.2835820895522388,0.38095238095238093,0.064,0.0,0.0
+Wolf Ridge Ski Resort,North Carolina,North Carolina,4700,720,4000,0,0,0,1,0,1,2,4,15.0,1.0,0.6,65.0,65.0,,49.0,65.0,65.0,100.0,60.0,6,0.057207779800390615,11.148479161634366,0.17567567567567569,,0.1111111111111111,0.1791044776119403,0.26666666666666666,0.06153846153846154,0.0,0.0
+Alpine Valley,Ohio,Ohio,1500,230,1260,0,0,0,1,2,1,1,5,11.0,1.0,0.2,72.0,72.0,105.0,53.0,120.0,43.0,,72.0,5,0.042774892848893416,11.154240842368269,0.171021377672209,0.2147239263803681,0.08333333333333333,0.171021377672209,0.45454545454545453,0.06944444444444445,0.0,0.0
+Boston Mills,Ohio,Ohio,871,264,631,0,0,0,0,4,2,2,8,7.0,2.0,0.3,40.0,40.0,92.0,56.0,51.0,44.0,110.0,40.0,5,0.042774892848893416,11.154240842368269,0.09501187648456057,0.18813905930470348,0.16666666666666666,0.09501187648456057,1.1428571428571428,0.2,0.0,0.0
+Brandywine,Ohio,Ohio,871,240,631,0,0,0,2,7,2,5,16,11.0,2.0,0.3,85.0,85.0,92.0,56.0,51.0,44.0,110.0,85.0,5,0.042774892848893416,11.154240842368269,0.20190023752969122,0.18813905930470348,0.16666666666666666,0.20190023752969122,1.4545454545454546,0.18823529411764706,0.0,0.0
+Mad River Mountain,Ohio,Ohio,1460,300,1160,0,0,0,1,2,3,6,12,20.0,4.0,0.5,144.0,144.0,99.0,57.0,36.0,44.0,90.0,144.0,5,0.042774892848893416,11.154240842368269,0.342042755344418,0.20245398773006135,0.3333333333333333,0.342042755344418,0.6,0.08333333333333333,0.0,0.0
+Snow Trails,Ohio,Ohio,1475,301,1174,0,0,0,0,4,2,3,9,17.0,3.0,0.2,80.0,80.0,101.0,58.0,50.0,52.0,70.0,80.0,5,0.042774892848893416,11.154240842368269,0.19002375296912113,0.2065439672801636,0.25,0.19002375296912113,0.5294117647058824,0.1125,0.0,0.0
+Anthony Lakes Mountain Resort,Oregon,Oregon,8000,900,7100,0,0,0,0,1,0,2,3,21.0,2.0,1.5,1100.0,,75.0,56.0,300.0,40.0,80.0,,10,0.23709396768930827,10.164770936886937,0.09342619330728724,0.0635593220338983,0.09090909090909091,,0.14285714285714285,0.0027272727272727275,0.0,0.0
+Cooper Spur,Mt. Hood,Oregon,4000,350,3500,0,0,0,0,0,1,1,2,10.0,,0.1,50.0,,78.0,66.0,100.0,39.0,90.0,,10,0.23709396768930827,10.164770936886937,0.004246645150331238,0.06610169491525424,,,0.2,0.04,0.0,0.0
+Hoodoo Ski Area,Oregon,Oregon,5703,1035,4668,0,0,0,3,1,1,0,5,34.0,,0.4,806.0,,80.0,81.0,350.0,59.0,108.0,200.0,10,0.23709396768930827,10.164770936886937,0.06845591982333957,0.06779661016949153,,0.1774622892635315,0.14705882352941177,0.00620347394540943,0.0,0.0
+Mt. Ashland,Oregon,Oregon,7533,1150,6383,0,0,0,0,2,2,1,5,23.0,2.0,1.0,220.0,,94.0,55.0,300.0,52.0,92.0,40.0,10,0.23709396768930827,10.164770936886937,0.018685238661457448,0.07966101694915254,0.09090909090909091,0.0354924578527063,0.21739130434782608,0.022727272727272728,0.0,0.0
+Mt. Bachelor,Oregon,Oregon,9065,3365,5700,0,0,8,0,3,0,0,11,101.0,5.0,4.0,4318.0,20.0,185.0,61.0,462.0,99.0,185.0,,10,0.23709396768930827,10.164770936886937,0.36674027518260577,0.15677966101694915,0.22727272727272727,,0.10891089108910891,0.0025474756831866605,0.07920792079207921,0.0018527095877721167
+Mt. Hood Skibowl,Mt. Hood,Oregon,5100,1500,3600,0,0,0,0,0,4,5,9,65.0,2.0,3.0,960.0,29.0,125.0,82.0,300.0,70.0,144.0,317.0,10,0.23709396768930827,10.164770936886937,0.08153558688635977,0.1059322033898305,0.09090909090909091,0.28127772848269744,0.13846153846153847,0.009375,0.0,0.0
+Willamette Pass,Oregon,Oregon,6683,1563,5120,0,1,0,0,3,0,1,5,29.0,,2.1,555.0,60.0,3.0,78.0,430.0,60.0,100.0,,10,0.23709396768930827,10.164770936886937,0.047137761168676746,0.002542372881355932,,,0.1724137931034483,0.009009009009009009,0.0,0.0
+Bear Creek Mountain Resort,Pennsylvania,Pennsylvania,1100,510,600,0,0,0,3,1,0,2,6,23.0,3.0,1.0,86.0,86.0,91.0,52.0,30.0,60.0,90.0,86.0,19,0.14841443778775315,41.255916967038694,0.045550847457627115,0.06481481481481481,0.06382978723404255,0.056282722513089,0.2608695652173913,0.06976744186046512,0.0,0.0
+Ski Big Bear,Pennsylvania,Pennsylvania,1250,650,600,0,0,0,0,0,4,2,6,18.0,1.0,1.5,26.0,26.0,75.0,43.0,69.0,62.0,75.0,26.0,19,0.14841443778775315,41.255916967038694,0.013771186440677966,0.053418803418803416,0.02127659574468085,0.017015706806282723,0.3333333333333333,0.23076923076923078,0.0,0.0
+Big Boulder,Pennsylvania,Pennsylvania,2175,600,1700,0,0,0,0,2,5,1,8,16.0,8.0,,55.0,55.0,76.0,72.0,50.0,65.0,95.0,55.0,19,0.14841443778775315,41.255916967038694,0.02913135593220339,0.05413105413105413,0.1702127659574468,0.03599476439790576,0.5,0.14545454545454545,0.0,0.0
+Blue Knob,Pennsylvania,Pennsylvania,3146,1072,2074,0,0,0,0,2,2,2,6,34.0,1.0,2.0,100.0,84.0,87.0,56.0,120.0,68.0,105.0,42.0,19,0.14841443778775315,41.255916967038694,0.05296610169491525,0.06196581196581197,0.02127659574468085,0.0274869109947644,0.17647058823529413,0.06,0.0,0.0
+Blue Mountain Resort,Pennsylvania,Pennsylvania,1600,1082,460,0,1,1,1,1,3,9,16,39.0,5.0,1.2,164.0,164.0,122.0,42.0,33.0,65.0,112.0,164.0,19,0.14841443778775315,41.255916967038694,0.08686440677966102,0.0868945868945869,0.10638297872340426,0.10732984293193717,0.41025641025641024,0.0975609756097561,0.02564102564102564,0.006097560975609756
+Camelback Mountain Resort,Pennsylvania,Pennsylvania,2100,800,1250,0,0,2,0,3,5,6,16,37.0,5.0,1.0,166.0,166.0,100.0,56.0,50.0,70.0,100.0,160.0,19,0.14841443778775315,41.255916967038694,0.08792372881355932,0.07122507122507123,0.10638297872340426,0.10471204188481675,0.43243243243243246,0.0963855421686747,0.05405405405405406,0.012048192771084338
+Elk Mountain Ski Resort,Pennsylvania,Pennsylvania,2693,1000,1693,0,0,0,1,0,5,1,7,27.0,2.0,0.7,180.0,146.0,,60.0,60.0,69.0,100.0,90.0,19,0.14841443778775315,41.255916967038694,0.09533898305084745,,0.0425531914893617,0.058900523560209424,0.25925925925925924,0.03888888888888889,0.0,0.0
+Jack Frost,Pennsylvania,Pennsylvania,2000,600,1400,0,0,0,1,2,5,1,9,20.0,1.0,1.0,100.0,100.0,96.0,47.0,50.0,65.0,105.0,,19,0.14841443778775315,41.255916967038694,0.05296610169491525,0.06837606837606838,0.02127659574468085,,0.45,0.09,0.0,0.0
+Liberty,Pennsylvania,Pennsylvania,1190,620,570,0,0,0,5,0,0,3,8,16.0,3.0,1.0,100.0,100.0,107.0,54.0,31.0,77.0,97.0,100.0,19,0.14841443778775315,41.255916967038694,0.05296610169491525,0.07621082621082621,0.06382978723404255,0.06544502617801047,0.5,0.08,0.0,0.0
+Mount Pleasant of Edinboro,Pennsylvania,Pennsylvania,1540,340,1200,0,0,0,0,1,0,1,2,10.0,,0.5,40.0,35.0,75.0,48.0,100.0,33.0,90.0,35.0,19,0.14841443778775315,41.255916967038694,0.0211864406779661,0.053418803418803416,,0.022905759162303665,0.2,0.05,0.0,0.0
+Roundtop Mountain Resort,Pennsylvania,Pennsylvania,1400,600,800,0,0,0,3,2,0,3,8,20.0,2.0,0.4,103.0,103.0,,55.0,30.0,73.0,,100.0,19,0.14841443778775315,41.255916967038694,0.05455508474576271,,0.0425531914893617,0.06544502617801047,0.4,0.07766990291262135,0.0,0.0
+Seven Springs,Pennsylvania,Pennsylvania,2994,750,2240,0,2,0,3,5,0,4,14,33.0,7.0,1.2,285.0,285.0,99.0,87.0,135.0,87.0,115.0,200.0,19,0.14841443778775315,41.255916967038694,0.15095338983050846,0.07051282051282051,0.14893617021276595,0.13089005235602094,0.42424242424242425,0.04912280701754386,0.0,0.0
+Shawnee Mountain Ski Area,Pennsylvania,Pennsylvania,1350,700,650,0,0,1,1,0,4,4,10,23.0,2.0,1.6,125.0,125.0,100.0,44.0,50.0,65.0,122.0,120.0,19,0.14841443778775315,41.255916967038694,0.06620762711864407,0.07122507122507123,0.0425531914893617,0.07853403141361257,0.43478260869565216,0.08,0.043478260869565216,0.008
+Ski Sawmill,Pennsylvania,Pennsylvania,2215,515,1700,0,0,0,0,1,1,3,5,14.0,1.0,0.1,15.0,13.0,,50.0,24.0,44.0,90.0,15.0,19,0.14841443778775315,41.255916967038694,0.007944915254237288,,0.02127659574468085,0.00981675392670157,0.35714285714285715,0.3333333333333333,0.0,0.0
+Tussey Mountain,Pennsylvania,Pennsylvania,1750,520,1230,0,0,0,1,0,1,3,5,8.0,1.0,0.3,38.0,30.0,100.0,39.0,41.0,45.0,100.0,30.0,19,0.14841443778775315,41.255916967038694,0.020127118644067795,0.07122507122507123,0.02127659574468085,0.01963350785340314,0.625,0.13157894736842105,0.0,0.0
+Whitetail Resort,Pennsylvania,Pennsylvania,1800,935,865,0,0,1,3,0,2,2,8,23.0,2.0,1.0,120.0,120.0,116.0,28.0,40.0,71.0,100.0,120.0,19,0.14841443778775315,41.255916967038694,0.0635593220338983,0.08262108262108261,0.0425531914893617,0.07853403141361257,0.34782608695652173,0.06666666666666667,0.043478260869565216,0.008333333333333333
+Deer Mountain Ski Resort,South Dakota,South Dakota,6850,940,6040,0,0,0,0,1,1,2,4,63.0,2.0,1.6,500.0,50.0,69.0,51.0,200.0,45.0,81.0,,2,0.22607581000136776,2.593495513252762,0.5263157894736842,0.3770491803278688,0.6666666666666666,,0.06349206349206349,0.008,0.0,0.0
+Terry Peak Ski Area,South Dakota,South Dakota,7100,1100,5900,0,0,3,0,1,0,1,5,30.0,1.0,1.2,450.0,225.0,114.0,65.0,150.0,58.0,120.0,,2,0.22607581000136776,2.593495513252762,0.47368421052631576,0.6229508196721312,0.3333333333333333,,0.16666666666666666,0.011111111111111112,0.1,0.006666666666666667
+Ober Gatlinburg Ski Resort,Tennessee,Tennessee,3300,600,2700,0,0,0,2,0,1,1,4,10.0,1.0,1.0,,,83.0,44.0,35.0,65.0,94.0,,1,0.014643059321669063,2.3728170083523157,,1.0,1.0,,0.4,,0.0,
+Alta Ski Area,Salt Lake City,Utah,11068,2538,8530,0,0,3,0,1,2,0,6,116.0,,1.3,2614.0,140.0,150.0,81.0,545.0,116.0,140.0,,13,0.4054950189615709,15.312673003757494,0.08568244394912809,0.09715025906735751,,,0.05172413793103448,0.0022953328232593728,0.02586206896551724,0.0011476664116296864
+Beaver Mountain,Utah,Utah,8600,1600,7232,0,0,0,0,1,3,1,5,48.0,2.0,0.8,464.0,,120.0,81.0,400.0,50.0,120.0,,13,0.4054950189615709,15.312673003757494,0.015209125475285171,0.07772020725388601,0.07692307692307693,,0.10416666666666667,0.010775862068965518,0.0,0.0
+Brian Head Resort,Utah,Utah,10970,1548,9600,0,0,1,0,6,1,0,8,71.0,2.0,0.6,650.0,216.0,149.0,54.0,360.0,59.0,148.0,,13,0.4054950189615709,15.312673003757494,0.02130588698046414,0.09650259067357513,0.07692307692307693,,0.11267605633802817,0.012307692307692308,0.014084507042253521,0.0015384615384615385
+Brighton Resort,Salt Lake City,Utah,10500,1745,8755,0,0,3,1,1,0,2,7,66.0,4.0,1.2,1050.0,200.0,138.0,83.0,500.0,85.0,138.0,200.0,13,0.4054950189615709,15.312673003757494,0.03441720204536515,0.08937823834196891,0.15384615384615385,0.3115264797507788,0.10606060606060606,0.006666666666666667,0.045454545454545456,0.002857142857142857
+Deer Valley Resort,Salt Lake City,Utah,9570,3000,6570,1,0,13,0,5,2,0,21,103.0,,2.8,2026.0,660.0,,39.0,300.0,169.0,,,13,0.4054950189615709,15.312673003757494,0.06640881080372361,,,,0.20388349514563106,0.010365251727541954,0.1262135922330097,0.006416584402764067
+Eagle Point,Utah,Utah,10600,1500,9100,0,0,0,1,1,2,1,5,40.0,1.0,0.9,650.0,,,9.0,400.0,60.0,109.0,,13,0.4054950189615709,15.312673003757494,0.02130588698046414,,0.038461538461538464,,0.125,0.007692307692307693,0.0,0.0
+Powder Mountain,Utah,Utah,9422,2522,6900,0,0,1,4,1,0,3,9,167.0,2.0,3.5,8464.0,,120.0,47.0,500.0,88.0,146.0,300.0,13,0.4054950189615709,15.312673003757494,0.27743542677330535,0.07772020725388601,0.07692307692307693,0.4672897196261682,0.05389221556886228,0.0010633270321361058,0.005988023952095809,0.00011814744801512288
+Snowbasin,Utah,Utah,9350,2900,6450,3,1,2,0,3,0,2,11,107.0,4.0,3.5,3000.0,625.0,143.0,79.0,300.0,115.0,138.0,,13,0.4054950189615709,15.312673003757494,0.09833486298675757,0.09261658031088082,0.15384615384615385,,0.102803738317757,0.0036666666666666666,0.018691588785046728,0.0006666666666666666
+Snowbird,Salt Lake City,Utah,11000,3240,7760,1,0,6,0,0,4,3,14,170.0,1.0,2.5,2500.0,,188.0,48.0,500.0,125.0,180.0,2.0,13,0.4054950189615709,15.312673003757494,0.08194571915563131,0.12176165803108809,0.038461538461538464,0.003115264797507788,0.08235294117647059,0.0056,0.03529411764705882,0.0024
+Solitude Mountain Resort,Salt Lake City,Utah,10488,2494,7994,0,0,4,2,1,1,1,9,80.0,,3.0,1200.0,150.0,161.0,62.0,500.0,119.0,148.0,,13,0.4054950189615709,15.312673003757494,0.03933394519470303,0.10427461139896373,,,0.1125,0.0075,0.05,0.0033333333333333335
+Sundance,Utah,Utah,8250,2150,6100,0,0,0,2,2,0,1,5,45.0,1.0,0.6,450.0,112.0,128.0,50.0,320.0,80.0,129.0,,13,0.4054950189615709,15.312673003757494,0.014750229448013635,0.08290155440414508,0.038461538461538464,,0.1111111111111111,0.011111111111111112,0.0,0.0
+Nordic Valley Resort,Utah,Utah,6400,960,5440,0,0,0,0,0,2,2,4,23.0,1.0,0.4,140.0,84.0,105.0,51.0,300.0,50.0,105.0,140.0,13,0.4054950189615709,15.312673003757494,0.004588960272715353,0.06800518134715026,0.038461538461538464,0.21806853582554517,0.17391304347826086,0.02857142857142857,0.0,0.0
+Bolton Valley,Vermont,Vermont,3150,1704,1446,0,0,0,2,0,3,1,6,71.0,3.0,0.6,300.0,90.0,133.0,53.0,300.0,79.0,132.0,50.0,15,2.4038885300862676,155.99001663893512,0.04144218814753419,0.07484524479459764,0.06,1.0,0.08450704225352113,0.02,0.0,0.0
+Bromley Mountain,Vermont,Vermont,3284,1334,1950,0,0,1,1,0,4,3,9,47.0,1.0,2.5,178.0,153.0,,83.0,168.0,91.0,152.0,,15,2.4038885300862676,155.99001663893512,0.02458903163420362,,0.02,,0.19148936170212766,0.05056179775280899,0.02127659574468085,0.0056179775280898875
+Burke Mountain,Vermont,Vermont,3267,2011,1210,0,0,2,1,0,0,3,6,50.0,3.0,2.2,178.0,125.0,110.0,62.0,217.0,73.0,125.0,,15,2.4038885300862676,155.99001663893512,0.02458903163420362,0.061902082160945414,0.06,,0.12,0.033707865168539325,0.04,0.011235955056179775
+Jay Peak,Vermont,Vermont,3968,2153,1815,1,0,1,3,1,1,2,9,79.0,2.0,3.0,385.0,300.0,155.0,64.0,349.0,89.0,160.0,,15,2.4038885300862676,155.99001663893512,0.05318414145600221,0.08722566122678672,0.04,,0.11392405063291139,0.023376623376623377,0.012658227848101266,0.0025974025974025974
+Killington Resort,Vermont,Vermont,4241,3050,1165,3,1,5,4,3,1,5,22,155.0,6.0,6.0,1515.0,600.0,192.0,61.0,250.0,119.0,,,15,2.4038885300862676,155.99001663893512,0.20928305014504767,0.1080472706809229,0.12,,0.14193548387096774,0.014521452145214522,0.03225806451612903,0.0033003300330033004
+Magic Mountain,Vermont,Vermont,2850,1500,1350,0,0,0,0,0,3,3,6,50.0,1.0,1.6,205.0,95.0,76.0,59.0,150.0,74.0,80.0,,15,2.4038885300862676,155.99001663893512,0.028318828567481698,0.04276871131119865,0.02,,0.12,0.02926829268292683,0.0,0.0
+Pico Mountain,Vermont,Vermont,3967,1967,2000,0,0,2,0,2,2,1,7,59.0,1.0,4.0,260.0,156.0,,82.0,250.0,81.0,,,15,2.4038885300862676,155.99001663893512,0.0359165630611963,,0.02,,0.11864406779661017,0.026923076923076925,0.03389830508474576,0.007692307692307693
+Smugglers' Notch Resort,Vermont,Vermont,3640,2610,1030,0,0,0,0,0,6,2,8,78.0,6.0,3.0,1000.0,192.0,136.0,63.0,280.0,79.0,135.0,,15,2.4038885300862676,155.99001663893512,0.1381406271584473,0.07653348339898705,0.12,,0.10256410256410256,0.008,0.0,0.0
+Sugarbush,Vermont,Vermont,4083,2600,1483,0,0,5,5,2,1,3,16,111.0,4.0,3.0,581.0,406.0,150.0,61.0,250.0,119.0,156.0,,15,2.4038885300862676,155.99001663893512,0.08025970437905788,0.08441193021947102,0.08,,0.14414414414414414,0.027538726333907058,0.04504504504504504,0.008605851979345954
+Suicide Six,Vermont,Vermont,1200,650,550,0,0,0,0,0,2,1,3,24.0,,0.4,100.0,50.0,100.0,85.0,90.0,75.0,106.0,,15,2.4038885300862676,155.99001663893512,0.01381406271584473,0.056274620146314014,,,0.125,0.03,0.0,0.0
+Bryce Resort,Virginia,Virginia,1750,500,1250,0,0,0,1,0,1,5,7,8.0,,0.4,25.0,25.0,100.0,54.0,30.0,68.0,95.0,20.0,4,0.04686299684881493,9.35125657510228,0.09293680297397769,0.273224043715847,,0.14814814814814814,0.875,0.28,0.0,0.0
+49 Degrees North,Washington,Washington,5774,1851,3932,0,0,0,1,0,5,1,7,89.0,1.0,2.0,2325.0,,101.0,48.0,301.0,62.0,135.0,250.0,10,0.13132160885254723,14.02563886785043,0.15166340508806261,0.09882583170254403,0.047619047619047616,0.12518778167250877,0.07865168539325842,0.003010752688172043,0.0,0.0
+Bluewood,Washington,Washington,5670,1125,4545,0,0,0,0,2,0,1,3,24.0,2.0,2.0,355.0,,70.0,40.0,300.0,47.0,110.0,,10,0.13132160885254723,14.02563886785043,0.023157208088714937,0.0684931506849315,0.09523809523809523,,0.125,0.008450704225352112,0.0,0.0
+Crystal Mountain,Washington,Washington,7012,3100,4400,1,2,2,1,2,2,0,10,57.0,1.0,2.5,2600.0,10.0,,57.0,486.0,99.0,,,10,0.13132160885254723,14.02563886785043,0.16960208741030658,,0.047619047619047616,,0.17543859649122806,0.0038461538461538464,0.03508771929824561,0.0007692307692307692
+Mt. Baker,Washington,Washington,5000,1500,3500,0,0,0,8,0,0,2,10,38.0,,0.7,1000.0,,143.0,66.0,663.0,60.01,165.0,,10,0.13132160885254723,14.02563886785043,0.06523157208088715,0.13992172211350293,,,0.2631578947368421,0.01,0.0,0.0
+Mt. Spokane Ski and Snowboard Park,Washington,Washington,5889,2000,4200,0,0,0,0,1,5,1,7,52.0,3.0,0.6,1704.0,,100.0,81.0,300.0,59.0,103.0,45.0,10,0.13132160885254723,14.02563886785043,0.1111545988258317,0.09784735812133072,0.14285714285714285,0.022533800701051578,0.1346153846153846,0.004107981220657277,0.0,0.0
+The Summit at Snoqualmie,Washington,Washington,3865,1025,2840,0,0,3,3,4,10,7,27,112.0,5.0,0.8,1994.0,5.0,120.0,82.0,428.0,95.0,140.0,541.0,10,0.13132160885254723,14.02563886785043,0.13007175472928897,0.11741682974559686,0.23809523809523808,0.27090635953930897,0.24107142857142858,0.01354062186559679,0.026785714285714284,0.0015045135406218655
+White Pass,Washington,Washington,6550,2050,4500,0,0,2,1,1,2,2,8,45.0,2.0,2.5,1402.0,30.0,148.0,67.0,400.0,69.0,144.0,90.0,10,0.13132160885254723,14.02563886785043,0.09145466405740378,0.14481409001956946,0.09523809523809523,0.045067601402103155,0.17777777777777778,0.005706134094151213,0.044444444444444446,0.0014265335235378032
+Canaan Valley Resort,West Virginia,West Virginia,4280,850,3430,0,0,0,1,2,0,1,4,47.0,1.0,1.2,95.0,75.0,,48.0,160.0,68.0,93.0,,4,0.22319597666932456,16.50846058605035,0.1752767527675277,,0.1111111111111111,,0.0851063829787234,0.042105263157894736,0.0,0.0
+Snowshoe Mountain Resort,West Virginia,West Virginia,4848,1500,3348,0,0,3,2,6,0,3,14,60.0,5.0,1.5,257.0,257.0,125.0,46.0,180.0,87.0,138.0,86.0,4,0.22319597666932456,16.50846058605035,0.474169741697417,0.3654970760233918,0.5555555555555556,0.45989304812834225,0.23333333333333334,0.054474708171206226,0.05,0.011673151750972763
+Timberline Four Seasons,West Virginia,West Virginia,4265,1000,3268,0,0,0,0,2,1,1,4,40.0,1.0,2.0,100.0,100.0,97.0,37.0,150.0,92.0,115.0,27.0,4,0.22319597666932456,16.50846058605035,0.18450184501845018,0.28362573099415206,0.1111111111111111,0.1443850267379679,0.1,0.04,0.0,0.0
+Winterplace Ski Resort,West Virginia,West Virginia,3600,603,2997,0,0,0,2,3,2,2,9,27.0,2.0,1.2,90.0,90.0,120.0,36.0,100.0,72.0,120.0,74.0,4,0.22319597666932456,16.50846058605035,0.16605166051660517,0.3508771929824561,0.2222222222222222,0.39572192513368987,0.3333333333333333,0.1,0.0,0.0
+Alpine Valley Resort,Wisconsin,Wisconsin,1040,388,820,0,0,3,0,3,1,5,12,21.0,3.0,0.2,90.0,90.0,100.0,55.0,80.0,65.0,120.0,90.0,15,0.2576242169511926,22.90216196408941,0.05142857142857143,0.06583278472679395,0.075,0.08450704225352113,0.5714285714285714,0.13333333333333333,0.14285714285714285,0.03333333333333333
+Bruce Mound,Wisconsin,Wisconsin,1375,375,1000,0,0,0,0,1,0,4,5,12.0,2.0,0.5,40.0,30.0,42.0,71.0,42.0,25.0,42.0,30.0,15,0.2576242169511926,22.90216196408941,0.022857142857142857,0.027649769585253458,0.05,0.028169014084507043,0.4166666666666667,0.125,0.0,0.0
+Cascade Mountain,Wisconsin,Wisconsin,1280,460,820,0,0,2,2,3,2,3,12,47.0,4.0,1.1,175.0,175.0,120.0,57.0,56.0,64.0,120.0,,15,0.2576242169511926,22.90216196408941,0.1,0.07899934167215274,0.1,,0.2553191489361702,0.06857142857142857,0.0425531914893617,0.011428571428571429
+Christie Mountain,Wisconsin,Wisconsin,1650,350,1300,0,0,0,0,0,1,5,6,30.0,4.0,0.8,45.0,41.0,92.0,43.0,48.0,38.0,120.0,35.0,15,0.2576242169511926,22.90216196408941,0.025714285714285714,0.06056616194865043,0.1,0.03286384976525822,0.2,0.13333333333333333,0.0,0.0
+Devils Head,Wisconsin,Wisconsin,995,500,495,0,0,0,3,1,6,2,12,27.0,1.0,1.0,260.0,260.0,110.0,48.0,45.0,65.0,135.0,200.0,15,0.2576242169511926,22.90216196408941,0.14857142857142858,0.07241606319947334,0.025,0.18779342723004694,0.4444444444444444,0.046153846153846156,0.0,0.0
+Grand Geneva,Wisconsin,Wisconsin,1086,211,875,0,0,0,0,0,3,3,6,20.0,1.0,0.2,30.0,30.0,90.0,25.0,25.0,49.0,93.0,30.0,15,0.2576242169511926,22.90216196408941,0.017142857142857144,0.05924950625411455,0.025,0.028169014084507043,0.3,0.2,0.0,0.0
+Granite Peak Ski Area,Wisconsin,Wisconsin,1942,700,1242,0,1,2,0,2,0,2,7,75.0,4.0,0.6,220.0,160.0,136.0,82.0,75.0,92.0,135.0,200.0,15,0.2576242169511926,22.90216196408941,0.12571428571428572,0.08953258722843976,0.1,0.18779342723004694,0.09333333333333334,0.031818181818181815,0.02666666666666667,0.00909090909090909
+Mount La Crosse,Wisconsin,Wisconsin,1110,516,594,0,0,0,0,0,3,1,4,19.0,1.0,0.4,100.0,100.0,115.0,60.0,40.0,56.0,100.0,90.0,15,0.2576242169511926,22.90216196408941,0.05714285714285714,0.07570770243581304,0.025,0.08450704225352113,0.21052631578947367,0.04,0.0,0.0
+Nordic Mountain,Wisconsin,Wisconsin,1137,265,872,0,0,0,0,1,1,5,7,18.0,4.0,1.0,60.0,60.0,68.0,43.0,80.0,47.0,90.0,60.0,15,0.2576242169511926,22.90216196408941,0.03428571428571429,0.04476629361421988,0.1,0.056338028169014086,0.3888888888888889,0.11666666666666667,0.0,0.0
+Sunburst,Wisconsin,Wisconsin,1100,214,866,0,0,0,0,0,3,6,9,13.0,4.0,0.5,37.0,37.0,99.0,59.0,50.0,44.0,115.0,37.0,15,0.2576242169511926,22.90216196408941,0.021142857142857144,0.065174456879526,0.1,0.03474178403755868,0.6923076923076923,0.24324324324324326,0.0,0.0
+Trollhaugen,Wisconsin,Wisconsin,1200,260,920,0,0,0,2,0,1,5,8,24.0,4.0,0.5,86.0,86.0,130.0,69.0,50.0,54.0,120.0,86.0,15,0.2576242169511926,22.90216196408941,0.04914285714285714,0.08558262014483213,0.1,0.08075117370892018,0.3333333333333333,0.09302325581395349,0.0,0.0
+Tyrol Basin,Wisconsin,Wisconsin,1160,300,860,0,0,0,0,3,0,2,5,18.0,5.0,0.5,32.0,32.0,112.0,61.0,41.0,48.0,103.0,32.0,15,0.2576242169511926,22.90216196408941,0.018285714285714287,0.07373271889400922,0.125,0.03004694835680751,0.2777777777777778,0.15625,0.0,0.0
+Whitecap Mountain,Wisconsin,Wisconsin,1750,400,1295,0,0,0,1,0,4,0,5,43.0,1.0,1.0,400.0,300.0,105.0,57.0,200.0,60.0,118.0,,15,0.2576242169511926,22.90216196408941,0.22857142857142856,0.06912442396313365,0.025,,0.11627906976744186,0.0125,0.0,0.0
+Wilmot Mountain,Wisconsin,Wisconsin,1030,230,800,0,0,0,3,2,2,3,10,23.0,2.0,0.5,135.0,135.0,125.0,81.0,70.0,66.0,139.0,135.0,15,0.2576242169511926,22.90216196408941,0.07714285714285714,0.08229098090849243,0.05,0.1267605633802817,0.43478260869565216,0.07407407407407407,0.0,0.0
+Grand Targhee Resort,Wyoming,Wyoming,9920,2270,7851,0,0,2,2,0,0,1,5,95.0,1.0,2.7,2602.0,,152.0,50.0,500.0,90.0,152.0,,8,1.3822679215355613,8.17887192908918,0.3988962133987429,0.2122905027932961,0.07142857142857142,,0.05263157894736842,0.001921598770176787,0.021052631578947368,0.0007686395080707148
+Hogadon Basin,Wyoming,Wyoming,8000,640,7400,0,0,0,0,0,1,1,2,28.0,1.0,0.6,92.0,32.0,121.0,61.0,80.0,48.0,95.0,,8,1.3822679215355613,8.17887192908918,0.01410393990495171,0.16899441340782123,0.07142857142857142,,0.07142857142857142,0.021739130434782608,0.0,0.0
+Sleeping Giant Ski Resort,Wyoming,Wyoming,7428,810,6619,0,0,0,0,1,1,1,3,48.0,1.0,1.0,184.0,18.0,61.0,81.0,310.0,42.0,77.0,,8,1.3822679215355613,8.17887192908918,0.02820787980990342,0.08519553072625698,0.07142857142857142,,0.0625,0.016304347826086956,0.0,0.0
+Snow King Resort,Wyoming,Wyoming,7808,1571,6237,0,0,0,1,1,1,0,3,32.0,2.0,1.0,400.0,250.0,121.0,80.0,300.0,59.0,123.0,110.0,8,1.3822679215355613,8.17887192908918,0.06132147784761613,0.16899441340782123,0.14285714285714285,1.0,0.09375,0.0075,0.0,0.0
+Snowy Range Ski & Recreation Area,Wyoming,Wyoming,9663,990,8798,0,0,0,0,1,3,1,5,33.0,2.0,0.7,75.0,30.0,131.0,59.0,250.0,49.0,,,8,1.3822679215355613,8.17887192908918,0.011497777096428024,0.1829608938547486,0.14285714285714285,,0.15151515151515152,0.06666666666666667,0.0,0.0
+White Pine Ski Area,Wyoming,Wyoming,9500,1100,8400,0,0,0,0,2,0,0,2,25.0,,0.4,370.0,,,81.0,150.0,49.0,,,8,1.3822679215355613,8.17887192908918,0.05672236700904492,,,,0.08,0.005405405405405406,0.0,0.0
diff --git a/data/state_summary.csv b/data/state_summary.csv
new file mode 100644
index 000000000..53979a710
--- /dev/null
+++ b/data/state_summary.csv
@@ -0,0 +1,36 @@
+state,resorts_per_state,state_total_skiable_area_ac,state_total_days_open,state_total_terrain_parks,state_total_nightskiing_ac,state_population,state_area_sq_miles
+Alaska,3,2280.0,345.0,4.0,580.0,731545,665384
+Arizona,2,1577.0,237.0,6.0,80.0,7278717,113990
+California,21,25948.0,2738.0,81.0,587.0,39512223,163695
+Colorado,22,43682.0,3258.0,74.0,428.0,5758736,104094
+Connecticut,5,358.0,353.0,10.0,256.0,3565278,5543
+Idaho,12,16396.0,1136.0,27.0,415.0,1787065,83569
+Illinois,4,191.0,221.0,6.0,191.0,12671821,57914
+Indiana,2,165.0,157.0,4.0,165.0,6732219,36420
+Iowa,3,140.0,100.0,5.0,140.0,3155070,56273
+Maine,9,3216.0,865.0,17.0,388.0,1344212,35380
+Maryland,1,172.0,121.0,3.0,118.0,6045680,12406
+Massachusetts,11,1166.0,671.0,18.0,583.0,6892503,10554
+Michigan,28,4406.0,2389.0,63.0,1946.0,9986857,96714
+Minnesota,14,1560.0,1490.0,29.0,1020.0,5639632,86936
+Missouri,2,60.0,69.0,2.0,47.0,6137428,69707
+Montana,12,21410.0,951.0,27.0,710.0,1068778,147040
+Nevada,4,2110.0,415.0,9.0,0.0,3080156,110572
+New Hampshire,16,3427.0,1847.0,43.0,376.0,1359711,9349
+New Jersey,2,190.0,170.0,4.0,181.0,8882190,8723
+New Mexico,9,5223.0,966.0,18.0,50.0,2096829,121590
+New York,33,5514.0,2384.0,72.0,2836.0,19453561,54555
+North Carolina,6,370.0,506.0,9.0,335.0,10488084,53819
+Ohio,5,421.0,489.0,12.0,421.0,11689100,44826
+Oregon,10,11774.0,1180.0,22.0,1127.0,4217737,98379
+Pennsylvania,19,1888.0,1404.0,47.0,1528.0,12801989,46054
+Rhode Island,1,30.0,100.0,1.0,30.0,1059361,1545
+South Dakota,2,950.0,183.0,3.0,0.0,884659,77116
+Tennessee,1,0.0,83.0,1.0,0.0,6829174,42144
+Utah,13,30508.0,1544.0,26.0,642.0,3205958,84897
+Vermont,15,7239.0,1777.0,50.0,50.0,623989,9616
+Virginia,4,269.0,366.0,4.0,135.0,8535519,42775
+Washington,10,15330.0,1022.0,21.0,1997.0,7614893,71298
+West Virginia,4,542.0,342.0,9.0,187.0,1792147,24230
+Wisconsin,15,1750.0,1519.0,40.0,1065.0,5822434,65496
+Wyoming,8,6523.0,716.0,14.0,110.0,578759,97813
diff --git a/models/ski_resort_pricing_model.pkl b/models/ski_resort_pricing_model.pkl
new file mode 100644
index 000000000..e858bf70f
Binary files /dev/null and b/models/ski_resort_pricing_model.pkl differ