diff --git a/Guided Capstone Project Report.docx b/Guided Capstone Project Report.docx
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index 000000000..18e8e9808
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diff --git a/Guided Capstone Slideshow.pptx b/Guided Capstone Slideshow.pptx
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diff --git a/Notebooks/02_data_wrangling.ipynb b/Notebooks/02_data_wrangling.ipynb
index a52eb6c24..c9174ff1f 100644
--- a/Notebooks/02_data_wrangling.ipynb
+++ b/Notebooks/02_data_wrangling.ipynb
@@ -120,17 +120,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
     "#Code task 1#\n",
     "#Import pandas, matplotlib.pyplot, and seaborn in the correct lines below\n",
-    "import ___ as pd\n",
-    "import ___ as plt\n",
-    "import ___ as sns\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
     "import os\n",
-    "\n",
+    "import requests\n",
     "from library.sb_utils import save_file\n"
    ]
   },
@@ -162,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -179,13 +179,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "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.___"
+    "ski_data.info()"
    ]
   },
   {
@@ -204,15 +245,91 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method NDFrame.head of                                   Name   Region    state  summit_elev  \\\n",
+       "0                       Alyeska Resort   Alaska   Alaska         3939   \n",
+       "1                  Eaglecrest Ski Area   Alaska   Alaska         2600   \n",
+       "2                     Hilltop Ski Area   Alaska   Alaska         2090   \n",
+       "3                     Arizona Snowbowl  Arizona  Arizona        11500   \n",
+       "4                  Sunrise Park Resort  Arizona  Arizona        11100   \n",
+       "..                                 ...      ...      ...          ...   \n",
+       "325               Meadowlark Ski Lodge  Wyoming  Wyoming         9500   \n",
+       "326          Sleeping Giant Ski Resort  Wyoming  Wyoming         7428   \n",
+       "327                   Snow King Resort  Wyoming  Wyoming         7808   \n",
+       "328  Snowy Range Ski & Recreation Area  Wyoming  Wyoming         9663   \n",
+       "329                White Pine Ski Area  Wyoming  Wyoming         9500   \n",
+       "\n",
+       "     vertical_drop  base_elev  trams  fastEight  fastSixes  fastQuads  ...  \\\n",
+       "0             2500        250      1        0.0          0          2  ...   \n",
+       "1             1540       1200      0        0.0          0          0  ...   \n",
+       "2              294       1796      0        0.0          0          0  ...   \n",
+       "3             2300       9200      0        0.0          1          0  ...   \n",
+       "4             1800       9200      0        NaN          0          1  ...   \n",
+       "..             ...        ...    ...        ...        ...        ...  ...   \n",
+       "325           1000       8500      0        NaN          0          0  ...   \n",
+       "326            810       6619      0        0.0          0          0  ...   \n",
+       "327           1571       6237      0        NaN          0          0  ...   \n",
+       "328            990       8798      0        0.0          0          0  ...   \n",
+       "329           1100       8400      0        NaN          0          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",
+       "325            1.5              300.0             NaN               NaN   \n",
+       "326            1.0              184.0            18.0              61.0   \n",
+       "327            1.0              400.0           250.0             121.0   \n",
+       "328            0.7               75.0            30.0             131.0   \n",
+       "329            0.4              370.0             NaN               NaN   \n",
+       "\n",
+       "     yearsOpen  averageSnowfall  AdultWeekday  AdultWeekend  \\\n",
+       "0         60.0            669.0          65.0          85.0   \n",
+       "1         44.0            350.0          47.0          53.0   \n",
+       "2         36.0             69.0          30.0          34.0   \n",
+       "3         81.0            260.0          89.0          89.0   \n",
+       "4         49.0            250.0          74.0          78.0   \n",
+       "..         ...              ...           ...           ...   \n",
+       "325        9.0              NaN           NaN           NaN   \n",
+       "326       81.0            310.0          42.0          42.0   \n",
+       "327       80.0            300.0          59.0          59.0   \n",
+       "328       59.0            250.0          49.0          49.0   \n",
+       "329       81.0            150.0           NaN          49.0   \n",
+       "\n",
+       "     projectedDaysOpen  NightSkiing_ac  \n",
+       "0                150.0           550.0  \n",
+       "1                 90.0             NaN  \n",
+       "2                152.0            30.0  \n",
+       "3                122.0             NaN  \n",
+       "4                104.0            80.0  \n",
+       "..                 ...             ...  \n",
+       "325                NaN             NaN  \n",
+       "326               77.0             NaN  \n",
+       "327              123.0           110.0  \n",
+       "328                NaN             NaN  \n",
+       "329                NaN             NaN  \n",
+       "\n",
+       "[330 rows x 27 columns]>"
+      ]
+     },
+     "execution_count": 5,
+     "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.___"
+    "ski_data.head"
    ]
   },
   {
@@ -245,15 +362,188 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "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": 6,
+     "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 == ___].___"
+    "ski_data[ski_data.Name == \"Big Mountain Resort\"].T"
    ]
   },
   {
@@ -279,18 +569,222 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "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": 7,
+     "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 = ___([ski_data.___.___, 100 * ski_data.___.___], axis=1)\n",
-    "missing.columns=[___, ___]\n",
-    "missing.___(by=___)"
+    "missing = pd.concat(\n",
+    "    [ski_data.isnull().sum(), 100 * ski_data.isnull().mean()], \n",
+    "    axis=1\n",
+    ")\n",
+    "missing.columns = ['count', '%']\n",
+    "missing.sort_values(by='count', ascending=False)"
    ]
   },
   {
@@ -316,13 +810,133 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "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": 8,
+     "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.___(___)"
+    "ski_data.select_dtypes(include=\"object\")"
    ]
   },
   {
@@ -344,13 +958,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "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": 9,
+     "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'].___.head()"
+    "ski_data['Name'].value_counts().head()"
    ]
   },
   {
@@ -369,45 +1000,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "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": 10,
+     "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[___] + ', ' + ski_data[___]).___.head()"
+    "(ski_data['Name'] + ', ' + ski_data['Region']).value_counts().head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "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": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 9#\n",
     "#Concatenate 'Name' and 'state' and count the values again (as above)\n",
-    "(ski_data[___] + ', ' + ski_data[___]).___.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."
+    "(ski_data['Name'] + ', ' + ski_data['state']).value_counts().head()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**A: 1** Your answer here"
+    "**A: 1** No the resort is not duplicated when you take in account region/state, so that means that the value is unique. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -532,13 +1186,13 @@
        "[2 rows x 27 columns]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ski_data[ski_data['Name'] == 'Crystal Mountain']"
+    "ski_data[ski_data['Name'] == 'Crystal Mountain']\n"
    ]
   },
   {
@@ -571,13 +1225,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33"
+      ]
+     },
+     "execution_count": 13,
+     "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).___"
+    "(ski_data.Region != ski_data.state).sum()"
    ]
   },
   {
@@ -589,12 +1254,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "Region\n",
        "New York               33\n",
        "Michigan               29\n",
        "Sierra Nevada          22\n",
@@ -604,39 +1270,39 @@
        "New Hampshire          16\n",
        "Vermont                15\n",
        "Minnesota              14\n",
-       "Montana                12\n",
        "Idaho                  12\n",
+       "Montana                12\n",
        "Massachusetts          11\n",
        "Washington             10\n",
-       "Maine                   9\n",
        "New Mexico              9\n",
+       "Maine                   9\n",
        "Wyoming                 8\n",
        "Utah                    7\n",
-       "Oregon                  6\n",
        "Salt Lake City          6\n",
        "North Carolina          6\n",
+       "Oregon                  6\n",
        "Connecticut             5\n",
        "Ohio                    5\n",
-       "West Virginia           4\n",
        "Virginia                4\n",
-       "Mt. Hood                4\n",
+       "West Virginia           4\n",
        "Illinois                4\n",
+       "Mt. Hood                4\n",
        "Alaska                  3\n",
        "Iowa                    3\n",
-       "Missouri                2\n",
+       "South Dakota            2\n",
        "Arizona                 2\n",
+       "Nevada                  2\n",
+       "Missouri                2\n",
        "Indiana                 2\n",
-       "South Dakota            2\n",
        "New Jersey              2\n",
-       "Nevada                  2\n",
        "Rhode Island            1\n",
-       "Maryland                1\n",
        "Tennessee               1\n",
+       "Maryland                1\n",
        "Northern California     1\n",
-       "Name: Region, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -654,16 +1320,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "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": 15,
+     "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.___ ___ ski_data.___]\n",
-    " .groupby(___)[___]\n",
-    " .value_counts())"
+    "(ski_data[ski_data['Region'] != ski_data['state']]\n",
+    " .groupby('state')['Region']\n",
+    " .value_counts())\n"
    ]
   },
   {
@@ -682,14 +1365,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Region    38\n",
+       "state     35\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "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[[___, ___]].___"
+    "ski_data[['Region', 'state']].nunique()"
    ]
   },
   {
@@ -715,27 +1411,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "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(___, ___, figsize=(___))\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(12,8))\n",
     "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n",
-    "ski_data.Region.value_counts().plot(kind=___, ax=ax[0])\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(___)\n",
+    "ax[0].set_title('Region')\n",
     "#Label the xaxis 'Count'\n",
-    "ax[0].set_xlabel(___)\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=___, ax=ax[1])\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(___)\n",
+    "ax[1].set_title('state')\n",
     "#Label the xaxis 'Count'\n",
-    "ax[1].set_xlabel(___)\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=___);\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"
@@ -771,32 +1478,105 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "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": 18,
+     "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.___(___)[[___, ___]].mean()\n",
+    "state_price_means = ski_data.groupby('state')[['AdultWeekday', 'AdultWeekend']].mean()\n",
     "state_price_means.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
+       "<Figure size 1000x1000 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -816,9 +1596,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Object `states` not found.\n"
+     ]
+    }
+   ],
    "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?"
    ]
@@ -839,7 +1627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -849,16 +1637,16 @@
     "#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[[___, ___, ___]], \n",
-    "                        id_vars=___, \n",
-    "                        var_name=___, \n",
-    "                        value_vars=[___, ___], \n",
-    "                        value_name=___)"
+    "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": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -931,7 +1719,7 @@
        "4  Arizona  AdultWeekday   74.0"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -949,16 +1737,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "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=___, y=___, hue=___, data=ticket_prices)\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');"
@@ -996,9 +1795,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (2281747816.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  Cell \u001b[1;32mIn[24], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    Having decided to reserve judgement on how exactly you utilize the State, turn your attention to cleaning the numeric features.\u001b[0m\n\u001b[1;37m           ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
    "source": [
     "Having decided to reserve judgement on how exactly you utilize the State, turn your attention to cleaning the numeric features."
    ]
@@ -1020,7 +1828,7 @@
     "#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.___.___"
+    "ski_data.describe().T"
    ]
   },
   {
@@ -1032,23 +1840,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    82.424242\n",
-       "2    14.242424\n",
-       "1     3.333333\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n",
     "missing_price.value_counts()/len(missing_price) * 100"
@@ -1082,13 +1876,12 @@
    "outputs": [],
    "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.___(___)\n",
-    "#plt.subplots_adjust(hspace=___);\n",
-    "#Hint: notice how the terminating ';' \"swallows\" some messy output and leads to a tidier notebook"
+    "# 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);"
    ]
   },
   {
@@ -1119,8 +1912,8 @@
    "outputs": [],
    "source": [
     "#Code task 19#\n",
-    "#Filter the 'SkiableTerrain_ac' column to print the values greater than 10000\n",
-    "ski_data.___[ski_data.___ > ___]"
+    "# Filter the 'SkiableTerrain_ac' column to print the values greater than 10000\n",
+    "ski_data[\"SkiableTerrain_ac\"][ski_data[\"SkiableTerrain_ac\"] > 10000]"
    ]
   },
   {
@@ -1137,16 +1930,16 @@
    "outputs": [],
    "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.___ > ___].___"
+    "# 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"
+    "**A: 2** Silverton Mountain, Colorado has the highest area that is skiable. "
    ]
   },
   {
@@ -1185,7 +1978,7 @@
    "source": [
     "#Code task 21#\n",
     "#Use the .loc accessor to print the 'SkiableTerrain_ac' value only for this resort\n",
-    "ski_data.___[39, 'SkiableTerrain_ac']"
+    "ski_data.loc[39, 'SkiableTerrain_ac']"
    ]
   },
   {
@@ -1196,7 +1989,7 @@
    "source": [
     "#Code task 22#\n",
     "#Use the .loc accessor again to modify this value with the correct value of 1819\n",
-    "ski_data.___[39, 'SkiableTerrain_ac'] = ___"
+    "ski_data.loc[39, 'SkiableTerrain_ac'] = 1819"
    ]
   },
   {
@@ -1207,262 +2000,63 @@
    "source": [
     "#Code task 23#\n",
     "#Use the .loc accessor a final time to verify that the value has been modified\n",
-    "ski_data.___[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": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "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": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "11    3379.0\n",
-       "18    1500.0\n",
-       "Name: Snow Making_ac, dtype: float64"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ski_data['Snow Making_ac'][ski_data['Snow Making_ac'] > 1000]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "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</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</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TerrainParks</th>\n",
-       "      <td>3</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</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Snow Making_ac</th>\n",
-       "      <td>3379</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>daysOpenLastYear</th>\n",
-       "      <td>155</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>yearsOpen</th>\n",
-       "      <td>64</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>averageSnowfall</th>\n",
-       "      <td>360</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</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\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\n",
-       "TerrainParks                              3\n",
-       "LongestRun_mi                           5.5\n",
-       "SkiableTerrain_ac                      4800\n",
-       "Snow Making_ac                         3379\n",
-       "daysOpenLastYear                        155\n",
-       "yearsOpen                                64\n",
-       "averageSnowfall                         360\n",
-       "AdultWeekday                            NaN\n",
-       "AdultWeekend                            NaN\n",
-       "projectedDaysOpen                       157\n",
-       "NightSkiing_ac                          NaN"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+    "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": null,
+   "metadata": {},
+   "outputs": [],
+   "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": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ski_data['Snow Making_ac'][ski_data['Snow Making_ac'] > 1000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "ski_data[ski_data['Snow Making_ac'] > 3000].T"
    ]
@@ -1483,20 +2077,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2880.0"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     ".6 * 4800"
    ]
@@ -1524,22 +2107,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.0    163\n",
-       "1.0      1\n",
-       "Name: fastEight, dtype: int64"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ski_data.fastEight.value_counts()"
    ]
@@ -1559,7 +2129,7 @@
    "source": [
     "#Code task 24#\n",
     "#Drop the 'fastEight' column from ski_data. Use inplace=True\n",
-    "ski_data.drop(columns=___, inplace=___)"
+    "ski_data.drop(columns='fastEight', inplace=True)"
    ]
   },
   {
@@ -1577,7 +2147,7 @@
    "source": [
     "#Code task 25#\n",
     "#Filter the 'yearsOpen' column for values greater than 100\n",
-    "ski_data.___[ski_data.___ > ___]"
+    "ski_data[ski_data['yearsOpen'] > 100]"
    ]
   },
   {
@@ -1603,7 +2173,7 @@
     "#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.___[ski_data.___ < ___].hist(___)\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');"
@@ -1625,28 +2195,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "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": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ski_data.yearsOpen[ski_data.yearsOpen < 1000].describe()"
    ]
@@ -1660,7 +2211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1720,9 +2271,116 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "metadata": {},
-   "outputs": [],
+   "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>68682.0</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                      68682.0   \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": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 27#\n",
     "#Add named aggregations for the sum of 'daysOpenLastYear', 'TerrainParks', and 'NightSkiing_ac'\n",
@@ -1733,10 +2391,10 @@
     "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=__, aggfunc='sum'),\n",
-    "    ___=pd.NamedAgg(column=___, aggfunc=___),\n",
-    "    ___=pd.NamedAgg(column=___, aggfunc=___)\n",
-    ").___\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()"
    ]
   },
@@ -1756,23 +2414,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    82.317073\n",
-       "2    14.329268\n",
-       "1     3.353659\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n",
     "missing_price.value_counts()/len(missing_price) * 100"
@@ -1793,7 +2437,7 @@
    "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[___ != 2]"
+    "ski_data = ski_data[missing_price != 2]"
    ]
   },
   {
@@ -1805,22 +2449,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x720 with 25 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ski_data.hist(figsize=(15, 10))\n",
     "plt.subplots_adjust(hspace=0.5);"
@@ -1849,19 +2480,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
     "#Code task 29#\n",
     "#Use pandas' `read_html` method to read the table from the URL below\n",
+    "import io\n",
+    "import requests\n",
+    "import pandas as pd\n",
+    "\n",
+    "import requests\n",
     "states_url = 'https://simple.wikipedia.org/w/index.php?title=List_of_U.S._states&oldid=7168473'\n",
-    "usa_states = pd.___(___)"
+    "headers = requests.utils.default_headers()\n",
+    "headers.update(\n",
+    "    {\n",
+    "        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/140.0.0.0 Safari/537.36',\n",
+    "    }\n",
+    ")\n",
+    "r = requests.get(states_url, headers=headers)\n",
+    "usa_states = pd.read_html(io.StringIO(r.text))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -1870,7 +2513,7 @@
        "list"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1881,7 +2524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -1890,7 +2533,7 @@
        "1"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1901,7 +2544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -1925,30 +2568,30 @@
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Name &amp;postal abbs. [1]</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Name &amp; postal abbs. [1]</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Cities</th>\n",
-       "      <th>Established[upper-alpha 1]</th>\n",
-       "      <th>Population[upper-alpha 2][3]</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>Numberof Reps.</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>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[upper-alpha 1]</th>\n",
-       "      <th>Population[upper-alpha 2][3]</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>Numberof Reps.</th>\n",
+       "      <th>Number of Reps.</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -2037,32 +2680,32 @@
        "</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",
+       "  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[upper-alpha 1] Population[upper-alpha 2][3] Total area[4]  \\\n",
-       "  Established[upper-alpha 1] Population[upper-alpha 2][3]           mi2   \n",
-       "0               Dec 14, 1819                      4903185         52420   \n",
-       "1                Jan 3, 1959                       731545        665384   \n",
-       "2               Feb 14, 1912                      7278717        113990   \n",
-       "3               Jun 15, 1836                      3017804         53179   \n",
-       "4                Sep 9, 1850                     39512223        163695   \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",
-       "           Land area[4]          Water area[4]         Numberof Reps.  \n",
-       "       km2          mi2      km2           mi2     km2 Numberof Reps.  \n",
-       "0   135767        50645   131171          1775    4597              7  \n",
-       "1  1723337       570641  1477953         94743  245384              1  \n",
-       "2   295234       113594   294207           396    1026              9  \n",
-       "3   137732        52035   134771          1143    2961              4  \n",
-       "4   423967       155779   403466          7916   20501             53  "
+       "           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": 47,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2081,19 +2724,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 65,
    "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_sates.___[:, 4]"
+    "established = usa_states.iloc[:, 4]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -2149,10 +2792,10 @@
        "47    Jun 20, 1863\n",
        "48    May 29, 1848\n",
        "49    Jul 10, 1890\n",
-       "Name: (Established[upper-alpha 1], Established[upper-alpha 1]), dtype: object"
+       "Name: (Established[A], Established[A]), dtype: object"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2170,16 +2813,91 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {},
-   "outputs": [],
+   "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": 67,
+     "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.___[:, [___]].copy()\n",
-    "usa_states_sub.columns = [___]\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()"
    ]
   },
@@ -2192,14 +2910,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Massachusetts', 'Pennsylvania', 'Rhode Island', 'Virginia'}"
+      ]
+     },
+     "execution_count": 68,
+     "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 = ___(state_summary.state) - ___(usa_states_sub.state)\n",
+    "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n",
     "missing_states"
    ]
   },
@@ -2219,21 +2948,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "20    Massachusetts[upper-alpha 3]\n",
-       "37     Pennsylvania[upper-alpha 3]\n",
-       "38     Rhode Island[upper-alpha 4]\n",
-       "45         Virginia[upper-alpha 3]\n",
-       "47                   West Virginia\n",
+       "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": 52,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2251,9 +2980,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 70,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<>:8: SyntaxWarning: invalid escape sequence '\\['\n",
+      "<>:8: SyntaxWarning: invalid escape sequence '\\['\n",
+      "C:\\Users\\Muhammad\\AppData\\Local\\Temp\\ipykernel_18168\\95665047.py:8: SyntaxWarning: invalid escape sequence '\\['\n",
+      "  usa_states_sub.state.replace(to_replace='\\[.*\\]', value='', regex=True, inplace=True)\n"
+     ]
+    },
+    {
+     "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": 70,
+     "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",
@@ -2262,20 +3017,31 @@
     "#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.___(to_replace=___, value=__, regex=___, inplace=___)\n",
+    "usa_states_sub.state.replace(to_replace='\\[.*\\]', 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": null,
+   "execution_count": 71,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 71,
+     "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 = ___(state_summary.state) - ___(usa_states_sub.state)\n",
+    "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n",
     "missing_states"
    ]
   },
@@ -2288,14 +3054,133 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 72,
    "metadata": {},
-   "outputs": [],
+   "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>68682.0</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                      68682.0   \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": 72,
+     "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.___(usa_states_sub, ___=___, ___=___)\n",
+    "state_summary = state_summary.merge(usa_states_sub, how='left', on='state')\n",
     "state_summary.head()"
    ]
   },
@@ -2322,14 +3207,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "metadata": {},
-   "outputs": [],
+   "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.___(x=___, y=___, kind=___);"
+    "ski_data.plot(x='AdultWeekday', y='AdultWeekend', kind='scatter');"
    ]
   },
   {
@@ -2341,13 +3237,124 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 74,
    "metadata": {},
-   "outputs": [],
+   "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>140</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\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",
+       "140           NaN           NaN\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": 74,
+     "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.___[ski_data.state == ___, [___, ___]]"
+    "ski_data.loc[ski_data.state == 'Montana', ['AdultWeekend', 'AdultWeekday']]"
    ]
   },
   {
@@ -2359,18 +3366,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AdultWeekend    4\n",
-       "AdultWeekday    7\n",
+       "AdultWeekend    51\n",
+       "AdultWeekday    54\n",
        "dtype: int64"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2388,7 +3395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2398,16 +3405,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(277, 25)"
+       "(279, 26)"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2439,7 +3446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -2470,73 +3477,73 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>329</th>\n",
-       "      <td>5</td>\n",
-       "      <td>20.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>23.076923</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>62</th>\n",
-       "      <td>5</td>\n",
-       "      <td>20.0</td>\n",
+       "      <th>146</th>\n",
+       "      <td>6</td>\n",
+       "      <td>23.076923</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>141</th>\n",
-       "      <td>5</td>\n",
-       "      <td>20.0</td>\n",
+       "      <th>62</th>\n",
+       "      <td>6</td>\n",
+       "      <td>23.076923</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>86</th>\n",
-       "      <td>5</td>\n",
-       "      <td>20.0</td>\n",
+       "      <th>74</th>\n",
+       "      <td>6</td>\n",
+       "      <td>23.076923</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>5</td>\n",
-       "      <td>20.0</td>\n",
+       "      <th>141</th>\n",
+       "      <td>6</td>\n",
+       "      <td>23.076923</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>146</th>\n",
+       "      <th>55</th>\n",
        "      <td>5</td>\n",
-       "      <td>20.0</td>\n",
+       "      <td>19.230769</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>184</th>\n",
-       "      <td>4</td>\n",
-       "      <td>16.0</td>\n",
+       "      <th>108</th>\n",
+       "      <td>5</td>\n",
+       "      <td>19.230769</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>108</th>\n",
-       "      <td>4</td>\n",
-       "      <td>16.0</td>\n",
+       "      <th>88</th>\n",
+       "      <td>5</td>\n",
+       "      <td>19.230769</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>198</th>\n",
-       "      <td>4</td>\n",
-       "      <td>16.0</td>\n",
+       "      <th>86</th>\n",
+       "      <td>5</td>\n",
+       "      <td>19.230769</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>4</td>\n",
-       "      <td>16.0</td>\n",
+       "      <th>204</th>\n",
+       "      <td>5</td>\n",
+       "      <td>19.230769</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"
+       "     count          %\n",
+       "329      6  23.076923\n",
+       "146      6  23.076923\n",
+       "62       6  23.076923\n",
+       "74       6  23.076923\n",
+       "141      6  23.076923\n",
+       "55       5  19.230769\n",
+       "108      5  19.230769\n",
+       "88       5  19.230769\n",
+       "86       5  19.230769\n",
+       "204      5  19.230769"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2556,16 +3563,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.,  4.,  8., 12., 16., 20.])"
+       "array([ 0.        ,  3.84615385,  7.69230769, 11.53846154, 15.38461538,\n",
+       "       19.23076923, 23.07692308])"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2583,22 +3591,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "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: %, dtype: int64"
+       "%\n",
+       "3.846154     120\n",
+       "7.692308      63\n",
+       "0.000000      42\n",
+       "11.538462     26\n",
+       "15.384615     17\n",
+       "19.230769      6\n",
+       "23.076923      5\n",
+       "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2616,7 +3626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -2624,37 +3634,38 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 277 entries, 0 to 329\n",
-      "Data columns (total 25 columns):\n",
+      "Index: 279 entries, 0 to 329\n",
+      "Data columns (total 26 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"
+      " 0   Name               279 non-null    object \n",
+      " 1   Region             279 non-null    object \n",
+      " 2   state              279 non-null    object \n",
+      " 3   summit_elev        279 non-null    int64  \n",
+      " 4   vertical_drop      279 non-null    int64  \n",
+      " 5   base_elev          279 non-null    int64  \n",
+      " 6   trams              279 non-null    int64  \n",
+      " 7   fastEight          134 non-null    float64\n",
+      " 8   fastSixes          279 non-null    int64  \n",
+      " 9   fastQuads          279 non-null    int64  \n",
+      " 10  quad               279 non-null    int64  \n",
+      " 11  triple             279 non-null    int64  \n",
+      " 12  double             279 non-null    int64  \n",
+      " 13  surface            279 non-null    int64  \n",
+      " 14  total_chairs       279 non-null    int64  \n",
+      " 15  Runs               276 non-null    float64\n",
+      " 16  TerrainParks       235 non-null    float64\n",
+      " 17  LongestRun_mi      274 non-null    float64\n",
+      " 18  SkiableTerrain_ac  277 non-null    float64\n",
+      " 19  Snow Making_ac     242 non-null    float64\n",
+      " 20  daysOpenLastYear   233 non-null    float64\n",
+      " 21  yearsOpen          278 non-null    float64\n",
+      " 22  averageSnowfall    268 non-null    float64\n",
+      " 23  AdultWeekend       279 non-null    float64\n",
+      " 24  projectedDaysOpen  236 non-null    float64\n",
+      " 25  NightSkiing_ac     164 non-null    float64\n",
+      "dtypes: float64(12), int64(11), object(3)\n",
+      "memory usage: 58.9+ KB\n"
      ]
     }
    ],
@@ -2678,16 +3689,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(277, 25)"
+       "(279, 26)"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2705,9 +3716,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 83,
    "metadata": {},
-   "outputs": [],
+   "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",
@@ -2716,9 +3736,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 84,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing file.  \"../data\\state_summary.csv\"\n"
+     ]
+    }
+   ],
    "source": [
     "# save the state_summary separately.\n",
     "datapath = '../data'\n",
@@ -2743,13 +3771,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**A: 3** Your answer here"
+    "**A: 3** The inital dataset contained 330 rows across all of its columns. There were several missing values, (8 rows with missing values; 0 columns containing missing information). Some issues were that some of the resorts just had missing information from their rows so thus didn't get included in the findings due to that. There are about 322 rows remaining after cleanup. The target feature was the AdultWeekend ticket price as it included the most relevant information to the population that brings in the most revenue for the resort. "
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2763,7 +3791,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.12.7"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/Notebooks/03_exploratory_data_analysis.ipynb b/Notebooks/03_exploratory_data_analysis.ipynb
index c1746d2e4..e827ad7e9 100644
--- a/Notebooks/03_exploratory_data_analysis.ipynb
+++ b/Notebooks/03_exploratory_data_analysis.ipynb
@@ -68,16 +68,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2020-10-07T07:04:19.124917Z",
-     "iopub.status.busy": "2020-10-07T07:04:19.124711Z",
-     "iopub.status.idle": "2020-10-07T07:04:19.128523Z",
-     "shell.execute_reply": "2020-10-07T07:04:19.128112Z",
-     "shell.execute_reply.started": "2020-10-07T07:04:19.124888Z"
-    }
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd\n",
@@ -124,37 +116,38 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 277 entries, 0 to 276\n",
-      "Data columns (total 25 columns):\n",
+      "RangeIndex: 279 entries, 0 to 278\n",
+      "Data columns (total 26 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"
+      " 0   Name               279 non-null    object \n",
+      " 1   Region             279 non-null    object \n",
+      " 2   state              279 non-null    object \n",
+      " 3   summit_elev        279 non-null    int64  \n",
+      " 4   vertical_drop      279 non-null    int64  \n",
+      " 5   base_elev          279 non-null    int64  \n",
+      " 6   trams              279 non-null    int64  \n",
+      " 7   fastEight          134 non-null    float64\n",
+      " 8   fastSixes          279 non-null    int64  \n",
+      " 9   fastQuads          279 non-null    int64  \n",
+      " 10  quad               279 non-null    int64  \n",
+      " 11  triple             279 non-null    int64  \n",
+      " 12  double             279 non-null    int64  \n",
+      " 13  surface            279 non-null    int64  \n",
+      " 14  total_chairs       279 non-null    int64  \n",
+      " 15  Runs               276 non-null    float64\n",
+      " 16  TerrainParks       235 non-null    float64\n",
+      " 17  LongestRun_mi      274 non-null    float64\n",
+      " 18  SkiableTerrain_ac  277 non-null    float64\n",
+      " 19  Snow Making_ac     242 non-null    float64\n",
+      " 20  daysOpenLastYear   233 non-null    float64\n",
+      " 21  yearsOpen          278 non-null    float64\n",
+      " 22  averageSnowfall    268 non-null    float64\n",
+      " 23  AdultWeekend       279 non-null    float64\n",
+      " 24  projectedDaysOpen  236 non-null    float64\n",
+      " 25  NightSkiing_ac     164 non-null    float64\n",
+      "dtypes: float64(12), int64(11), object(3)\n",
+      "memory usage: 56.8+ KB\n"
      ]
     }
    ],
@@ -195,9 +188,9 @@
        "      <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>quad</th>\n",
        "      <th>...</th>\n",
        "      <th>TerrainParks</th>\n",
        "      <th>LongestRun_mi</th>\n",
@@ -221,9 +214,9 @@
        "      <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>2</td>\n",
        "      <td>...</td>\n",
        "      <td>2.0</td>\n",
        "      <td>1.0</td>\n",
@@ -245,7 +238,7 @@
        "      <td>1540</td>\n",
        "      <td>1200</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>...</td>\n",
@@ -269,7 +262,7 @@
        "      <td>294</td>\n",
        "      <td>1796</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>...</td>\n",
@@ -293,9 +286,9 @@
        "      <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>2</td>\n",
        "      <td>...</td>\n",
        "      <td>4.0</td>\n",
        "      <td>2.0</td>\n",
@@ -317,9 +310,9 @@
        "      <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>2</td>\n",
        "      <td>...</td>\n",
        "      <td>2.0</td>\n",
        "      <td>1.2</td>\n",
@@ -334,7 +327,7 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 25 columns</p>\n",
+       "<p>5 rows × 26 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -345,12 +338,12 @@
        "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",
+       "   base_elev  trams  fastEight  fastSixes  fastQuads  ...  TerrainParks  \\\n",
+       "0        250      1        0.0          0          2  ...           2.0   \n",
+       "1       1200      0        0.0          0          0  ...           1.0   \n",
+       "2       1796      0        0.0          0          0  ...           1.0   \n",
+       "3       9200      0        0.0          1          0  ...           4.0   \n",
+       "4       9200      0        NaN          0          1  ...           2.0   \n",
        "\n",
        "   LongestRun_mi  SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  \\\n",
        "0            1.0             1610.0           113.0             150.0   \n",
@@ -366,7 +359,7 @@
        "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]"
+       "[5 rows x 26 columns]"
       ]
      },
      "execution_count": 4,
@@ -501,7 +494,7 @@
        "      <th>3</th>\n",
        "      <td>Colorado</td>\n",
        "      <td>22</td>\n",
-       "      <td>43682.0</td>\n",
+       "      <td>68682.0</td>\n",
        "      <td>3258.0</td>\n",
        "      <td>74.0</td>\n",
        "      <td>428.0</td>\n",
@@ -528,7 +521,7 @@
        "0       Alaska                  3                       2280.0   \n",
        "1      Arizona                  2                       1577.0   \n",
        "2   California                 21                      25948.0   \n",
-       "3     Colorado                 22                      43682.0   \n",
+       "3     Colorado                 22                      68682.0   \n",
        "4  Connecticut                  5                        358.0   \n",
        "\n",
        "   state_total_days_open  state_total_terrain_parks  \\\n",
@@ -682,7 +675,7 @@
       "text/plain": [
        "state\n",
        "New York        33\n",
-       "Michigan        28\n",
+       "Michigan        29\n",
        "Colorado        22\n",
        "California      21\n",
        "Pennsylvania    19\n",
@@ -721,7 +714,7 @@
      "data": {
       "text/plain": [
        "state\n",
-       "Colorado      43682.0\n",
+       "Colorado      68682.0\n",
        "Utah          30508.0\n",
        "California    25948.0\n",
        "Montana       21410.0\n",
@@ -915,7 +908,7 @@
        "      <th>3</th>\n",
        "      <td>Colorado</td>\n",
        "      <td>22</td>\n",
-       "      <td>43682.0</td>\n",
+       "      <td>68682.0</td>\n",
        "      <td>3258.0</td>\n",
        "      <td>74.0</td>\n",
        "      <td>428.0</td>\n",
@@ -942,7 +935,7 @@
        "0       Alaska                  3                       2280.0   \n",
        "1      Arizona                  2                       1577.0   \n",
        "2   California                 21                      25948.0   \n",
-       "3     Colorado                 22                      43682.0   \n",
+       "3     Colorado                 22                      68682.0   \n",
        "4  Connecticut                  5                        358.0   \n",
        "\n",
        "   state_total_days_open  state_total_terrain_parks  \\\n",
@@ -994,14 +987,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1018,14 +1009,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1152,17 +1141,151 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "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>68682.0</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                      68682.0   \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(___)\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.___\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.___\n",
+    "state_summary_columns = state_summary_scale.columns\n",
     "state_summary_scale.head()"
    ]
   },
@@ -1193,13 +1316,126 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "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>-0.804907</td>\n",
+       "      <td>-0.354667</td>\n",
+       "      <td>-0.689059</td>\n",
+       "      <td>-0.815308</td>\n",
+       "      <td>0.067082</td>\n",
+       "      <td>0.138032</td>\n",
+       "      <td>-0.691870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.930115</td>\n",
+       "      <td>-0.408497</td>\n",
+       "      <td>-0.819038</td>\n",
+       "      <td>-0.727098</td>\n",
+       "      <td>-0.702208</td>\n",
+       "      <td>-0.646444</td>\n",
+       "      <td>-0.659992</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.448833</td>\n",
+       "      <td>1.457622</td>\n",
+       "      <td>2.190933</td>\n",
+       "      <td>2.580758</td>\n",
+       "      <td>0.077852</td>\n",
+       "      <td>-0.593811</td>\n",
+       "      <td>-0.389203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.574041</td>\n",
+       "      <td>4.729821</td>\n",
+       "      <td>2.816757</td>\n",
+       "      <td>2.272025</td>\n",
+       "      <td>-0.166782</td>\n",
+       "      <td>0.080495</td>\n",
+       "      <td>-0.186102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.554492</td>\n",
+       "      <td>-0.501837</td>\n",
+       "      <td>-0.679431</td>\n",
+       "      <td>-0.550679</td>\n",
+       "      <td>-0.431418</td>\n",
+       "      <td>-0.415243</td>\n",
+       "      <td>1.502797</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          -0.804907                    -0.354667              -0.689059   \n",
+       "1          -0.930115                    -0.408497              -0.819038   \n",
+       "2           1.448833                     1.457622               2.190933   \n",
+       "3           1.574041                     4.729821               2.816757   \n",
+       "4          -0.554492                    -0.501837              -0.679431   \n",
+       "\n",
+       "   state_total_terrain_parks  state_total_nightskiing_ac  \\\n",
+       "0                  -0.815308                    0.067082   \n",
+       "1                  -0.727098                   -0.702208   \n",
+       "2                   2.580758                    0.077852   \n",
+       "3                   2.272025                   -0.166782   \n",
+       "4                  -0.550679                   -0.431418   \n",
+       "\n",
+       "   resorts_per_100kcapita  resorts_per_100ksq_mile  \n",
+       "0                0.138032                -0.691870  \n",
+       "1               -0.646444                -0.659992  \n",
+       "2               -0.593811                -0.389203  \n",
+       "3                0.080495                -0.186102  \n",
+       "4               -0.415243                 1.502797  "
+      ]
+     },
+     "execution_count": 22,
+     "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(___, columns=___)\n",
+    "state_summary_scaled_df = pd.DataFrame(state_summary_scale, columns=state_summary_columns)\n",
     "state_summary_scaled_df.head()"
    ]
   },
@@ -1226,13 +1462,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "resorts_per_state             -4.996004e-17\n",
+       "state_total_skiable_area_ac   -2.825121e-17\n",
+       "state_total_days_open          7.692260e-17\n",
+       "state_total_terrain_parks      1.030921e-16\n",
+       "state_total_nightskiing_ac     1.903239e-17\n",
+       "resorts_per_100kcapita         3.806479e-17\n",
+       "resorts_per_100ksq_mile        7.612958e-17\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 3#\n",
     "#Call `state_summary_scaled_df`'s `mean()` method\n",
-    "state_summary_scaled_df.___"
+    "state_summary_scaled_df.mean()"
    ]
   },
   {
@@ -1251,13 +1505,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "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": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 4#\n",
     "#Call `state_summary_scaled_df`'s `std()` method\n",
-    "state_summary_scaled_df.___"
+    "state_summary_scaled_df.std()"
    ]
   },
   {
@@ -1271,13 +1543,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "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": 25,
+     "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.___(___)"
+    "state_summary_scaled_df.std(ddof=0)"
    ]
   },
   {
@@ -1319,9 +1609,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zYMECM3HixGzfgQVRLotTYmKikWRuv/32At/G1eJ08Qevb9++RpJ56623sk1v1qyZadGihfNyYYvT3zkcDpORkWGWL19uJJnffvvNed2ljmu5+DHNnTvXuQKUJTMz00RERJhbbrnFOe3ee+81AQEBZv/+/dmW98YbbxhJlzymKCMjw4SFhZmBAwdmm35xObuY3W43GRkZZubMmcbd3d2cOHHCeV3WXyx++umnHLfLrTjlttzx48ebkJCQHH9Z8fHxyfY4z58/bypVqmTuvfde57Thw4cbT09Ps3379jzvZ8KECcbNzS3HcSj/+9//jCQzb968PG+b32P8u6J6Lzz11FNGkvn111+zzXf//fcbm81mdu7c6ZxWkOKU9Yv7q6++yjY969iCSZMmOacdO3bMVK9e3bRp08Zs3LjR+Pn5mbvuuivb7bI+Py1atMj2mu3bt894enqaESNGOKcV5vOTV3EqyPuioJ+TrL/Ufvvtt9nmu+eee1wqTv369cs2fdWqVUaSefHFF53ThgwZYqpUqWLS0tKc01599VXj5uaW54pnlqznIjEx0WRkZJiTJ0+a//73v8bX19dERkaa8+fPX9Z3rjHGXHvttcbHx8dZ8rIeU25b8S42fvz4XP8Y83cffPCBkWS++OKLbNNfffXVHN95kkx4eHi2rY/ffPONkWSaNWuW7f02ceJEI8m5JdSY//+s5vZ6urm5Od8PrmYKCwszKSkpzmmJiYnGzc0t25aJG264wVSvXt0kJydnW+ZDDz1kfHx8nN+bWZ+fiwvaF1984fzjSpa8jj946KGHTHBwcI7pBZG1BeXvCvqdk/U7smbNmjn2PshN1uft1KlTec7jyjFOmZmZ5syZM8bf3z/bXhh5Hd9z9uxZU6lSJXPTTTdlm263203Tpk1NmzZtsk0fOXKkady4cbZp0dHRxt3dPdv3rjEF/44pyGt1++23G29v7xxbirt37278/Pycz1/We6dTp045luHqMU4BAQFm5MiRl5zH1eJ08fM8cuRIIylHoe/bt6+pVKlStmmF/ez/Xdbvlf379+f4Psj6Lh0zZkyO240dO9Z4eXmZI0eOOKfNnj07xx/mclOQ57Ogxzhdat0or2Occlt/LWymi9cvCrK+1atXL9OsWbNL3mdBlPtR9YpLr169sl2uX7++pAv76V88ff/+/UV2v3v37tXAgQMVHh4ud3d3eXp6qnPnzpKkHTt2XNYyu3fvrvDwcE2fPt05beHChTp8+LCGDx/unPb999+rS5cuioiIUGZmpvOne/fukqTly5fneR8eHh6666679PXXXys5OVnShf3TP/nkE/Xp00chISHOeTdt2qTevXsrJCTE+RgHDx4su92uXbt2ZVtuxYoVde211xbocS5ZskTXX3+9goKCnMsdM2aMjh8/rqSkpGzzNmvWTFFRUc7LPj4+qlOnTrbXcv78+erSpYvztc/N999/r0aNGqlZs2bZnrMbbrhBNptNy5Ytyzd3Xo+xON4LS5YsUYMGDdSmTZts04cOHSpjjJYsWeLS8r7//nsFBwfrpptuyvb4mzVrpvDw8GyPPyQkRLNnz9bGjRvVvn17RUVF6YMPPsh1uQMHDsy2b310dLTat2+vpUuXXjJPYZ+zgrwvCvo5Wbp0qQIDA9W7d+8cj80Vd955Z7bL7du3V3R0dLbn4pFHHlFSUpK+/PJLSZLD4dDkyZPVs2dPxcTEFOh+wsPD5enpqYoVK+quu+5SixYttGDBAvn4+LiUN0tcXJyWLl2qm2++WcHBwZKkW2+9VYGBgZo2bVq+t58/f77q1Kmj66+/Ps95lixZIn9/f/Xv3z/b9KyRJH/66ads07t06SJ/f3/n5azPdvfu3bO937KmX/zdntfr6XA49PPPP192psDAQOflsLAwValSxXnfqamp+umnn9SvXz/5+flle8/16NFDqamp+uWXX7It8+KMTZo0yfXx5KZNmzY6deqU7rjjDn377bc6duxYvre5FFe/c3r37i1PT898l9u6dWtJ0m233aYvvvhChw4dcinXmTNn9OSTT6pWrVry8PCQh4eHAgICdPbs2QJ9V6xevVonTpzQkCFDsr0mDodDN954o9atW6ezZ8865//66691yy235FhOkyZNVKdOnWzTCvodU5DXasmSJbruuusUGRmZbfrQoUN17tw5rVmzJtv03DK6qk2bNpoxY4ZefPFF/fLLL8rIyCj0Ml1ZJztx4oTOnDmTbXphPvtJSUm67777FBkZKQ8PD3l6ejqPU8vtvZLbc3j//fdLkj788EPntPfee0+NGzdWp06d8nrYkgr/fLqyblRQRf0aF2R9q02bNvrtt9/0wAMPaOHChUpJSbms+yqXxSk0NFR+fn6Ki4srtvuoVKlStsteXl55Tk9NTS2S+zxz5ow6duyoX3/9VS+++KKWLVumdevW6euvv5Z04aDRy+Hh4aFBgwZpzpw5zuGFZ8yYoapVq+qGG25wznfkyBF999138vT0zPbTsGFDScr3F+jw4cOVmpqqzz//XNKFcpaQkKBhw4Y554mPj1fHjh116NAhvf3221qxYoXWrVun999/P9fHWLVq1QI9xrVr16pbt26SLnwxrVq1SuvWrdMzzzyT63L/XuSyeHt7Z5vv6NGj+Q4gcOTIEW3ZsiXHcxYYGChjTIFWOnJ7jMX1Xjh+/Hiu9xcREeG83hVHjhzRqVOn5OXlleM5SExMzPH427Ztq4YNGyo1NVX3339/tl9kfxceHp7rtEvlK4rnrCDvi4J+To4fP66wsLACPbZLKchz0bx5c3Xs2NH5Ofr++++1b98+PfTQQwW+nx9//FHr1q3T5s2bdezYMa1cudI5CMvlfOdOmzZNxhj1799fp06d0qlTp5SRkaHevXtr1apV+uOPPy55+4J8/o4fP67w8PAcB7BXqVJFHh4eOd4vrnyvS8rx3X6p1zPrvlzNlN977vjx48rMzNS7776b4z3Xo0cPSTm/my9eZtbgCgX5DAwaNEjTpk3T/v37dcstt6hKlSpq27atFi9enO9tc+Pqd05Bv/M7deqkb775RpmZmRo8eLCqV6+uRo0a6bPPPivQ7QcOHKj33ntPI0aM0MKFC7V27VqtW7dOlStXLtDzdOTIEUlS//79c7wur776qowxzuH5165dq/j4+FxXqHN7vAX9jinIa1Vcz/+lzJ49W0OGDNHUqVPVrl07VapUSYMHD1ZiYuJlL7Own93Lvb3D4VC3bt309ddf64knntBPP/2ktWvXOv9Ykdt7JbfnMCwsTAMGDNB//vMf2e12bdmyRStWrCjQd3Rhnk9X140Kqqhf44J8348ePVpvvPGGfvnlF3Xv3l0hISG67rrrXD5lR7kcVc/d3V3XXXed5s+fr4MHDxZodCxvb2+lpaXlmO7qimJ+sv46e/F9FWQFesmSJTp8+LCWLVvm/Cu5pFzPpeKqYcOG6fXXX9fnn3+uAQMGaO7cuRo5cqTc3d2d84SGhqpJkyZ66aWXcl1G1hdtXrL+qjh9+nTde++9mj59uiIiIpwfWkn65ptvdPbsWX399dfZRhbK6zwbBR3R5/PPP5enp6e+//77bH8h/+abbwp0+9xUrlxZBw8evOQ8oaGh8vX1zfMv6FmjlF1Kbo+xuN4LISEhSkhIyDH98OHDkgqW9+9CQ0MVEhKS5wiWf/9LuiSNHTtWv//+u1q2bKkxY8aoV69eqlGjRo7b5fblm5iYmOtKZpbi/Pz8XUE/JyEhIVq7dm2O6139xZLXc1GrVq1s0x5++GHdeuut2rhxo9577z3VqVNHXbt2LfD9NG3aNM/X39XvXIfD4Tznx80335zrPNOmTdNrr72W5zIK8vkLCQnRr7/+KmNMts9RUlKSMjMzXX4/5ydrZfnvsl6frPdmUWeqWLGi3N3dNWjQID344IO5zhMbG+vSMvMzbNgwDRs2TGfPntXPP/+ssWPHqlevXtq1a9clR4TLjavfOa6M4tanTx/16dNHaWlp+uWXXzRhwgQNHDhQMTExateuXZ63S05O1vfff6+xY8fqqaeeck5PS0sr8Pkfs3K/++67eY4QllW0v/rqK9WpU0eNGjXKMU9uj9eV38X5vVbF+fznJTQ0VBMnTtTEiRMVHx+vuXPn6qmnnlJSUpLzd4WPj0+u62THjh0r8s9tYWzdulW//fabZsyYoSFDhjin7969O8/b5PUcPvLII/rkk0/07bffasGCBQoODs6xR0FuCvJ85qU41o0Kmyk3Bfm+9/Dw0KhRozRq1CidOnVKP/74o55++mndcMMNOnDgQIFHLyyXW5ykC83TGKN77rlH6enpOa7PyMjQd99957wcExOjLVu2ZJtnyZIlOTbnFlbWbjEX39fcuXPzvW3Wh+3ioVf/85//5JjXlb8gShc2P7dt21bTp0/Xp59+qrS0tGxbgqQLm8K3bt2qmjVrqlWrVjl+8itO0oUv8V9//VUrV67Ud999pyFDhmQrZ7k9RmNMts3Xl8Nms8nDwyPbfZ0/f16ffPLJZS+ze/fuWrp0qXbu3JnnPL169dKePXsUEhKS63NW0N2kLlZc74XrrrtO27dv18aNG7NNnzlzpmw2m7p06eJSzl69eun48eOy2+25Pv66des65128eLEmTJigZ599VosXL1ZQUJAGDBiQ6+f3s88+04XdoC/Yv3+/Vq9efcmTHrvynBVGQT8nXbp00enTp3N89j/99FOX7m/WrFnZLq9evVr79+/P8Vz069dPUVFReuyxx/Tjjz/qgQceKNKhhF35zl24cKEOHjyoBx98UEuXLs3x07BhQ82cOVOZmZl53l/37t21a9euS+4+et111+nMmTM5VgJmzpzpvL4o5fV6urm5OXe3KepMfn5+6tKlizZt2qQmTZrk+p671B8U8nLxltTc+Pv7q3v37nrmmWeUnp6ubdu2uXw/Rf2dkxtvb2917txZr776qqQLu4NnTZdyfjfabDYZY3J8V0ydOlV2uz3HsnNbRocOHRQcHKzt27fn+pq0atXKufXiq6++cmkXuMv5XZzXa3Xdddc5/6j0dzNnzpSfn1+BhoV2dX3j76KiovTQQw+pa9eu2d4Dua2T7dq165K/b61QlL9XWrZsqfbt2+vVV1/VrFmzNHTo0Dz3ushLXs9nXp9nV9aNCvKdUBSZclOQ9a2/Cw4OVv/+/fXggw/qxIkTLp2cuVxucZKkdu3aafLkyXrggQfUsmVL3X///WrYsKEyMjK0adMmTZkyRY0aNdJNN90k6cIm7eeee05jxoxR586dtX37dr333nsKCgoq0lzh4eG6/vrrNWHCBFWsWFHR0dH66aefnLsLXUr79u1VsWJF3XfffRo7dqw8PT01a9Ys/fbbbznmbdy4sSTp1VdfVffu3eXu7q4mTZo4v6hzM3z4cN177706fPiw2rdvn22lVpLGjx+vxYsXq3379nr44YdVt25dpaamat++fZo3b54++OCDfP/SfMcdd2jUqFG64447lJaW5tyvP0vXrl3l5eWlO+64Q0888YRSU1M1efJknTx5Mt/n51J69uypt956SwMHDtQ//vEPHT9+XG+88Uahzv8xfvx4zZ8/X506ddLTTz+txo0b69SpU1qwYIFGjRqlevXqaeTIkfrqq6/UqVMnPfroo2rSpIkcDofi4+O1aNEiPfbYY2rbtq3L911c74VHH31UM2fOVM+ePTV+/HhFR0frhx9+0KRJk3T//ffn2Nc+P7fffrtmzZqlHj166JFHHlGbNm3k6empgwcPaunSperTp4/69eunhIQE3XXXXercubPGjh0rNzc3zZ49W506ddITTzyR4+ziSUlJ6tevn+655x4lJydr7Nix8vHx0ejRo4vkOSuMgn5OBg8erH//+98aPHiwXnrpJdWuXVvz5s3TwoULXbq/9evXa8SIEbr11lt14MABPfPMM6pWrZoeeOCBbPO5u7vrwQcf1JNPPil/f/8cn73CcuU796OPPpKHh4eefvrpXFfy7r33Xj388MP64Ycf1KdPn1zvb+TIkZo9e7b69Omjp556Sm3atNH58+e1fPly9erVS126dNHgwYP1/vvva8iQIdq3b58aN26slStX6uWXX1aPHj0ueXzU5QgJCdH999+v+Ph41alTR/PmzdOHH36o+++/33lsXHFkevvtt3X11VerY8eOuv/++xUTE6PTp09r9+7d+u6771w+NlG68L3x9ddfa/LkyWrZsqXc3NzUqlUr3XPPPfL19VWHDh1UtWpVJSYmasKECQoKCnIeV+SKov7OyTJmzBgdPHhQ1113napXr65Tp07p7bffznZcY82aNeXr66tZs2apfv36CggIUEREhCIiItSpUye9/vrrCg0NVUxMjJYvX66PPvrIeTxelqytRFOmTFFgYKB8fHwUGxurkJAQvfvuuxoyZIhOnDih/v37q0qVKjp69Kh+++03HT16VJMnT9bmzZu1Z88el4pTQb9jCvJajR071nnM1JgxY1SpUiXNmjVLP/zwg1577bUCrQNd6jm4WHJysrp06aKBAweqXr16CgwM1Lp167RgwYJsW58HDRqku+66Sw888IBuueUW7d+/X6+99poqV65c4OfpSqhXr55q1qypp556SsYYVapUSd99991l77r6yCOPaMCAAbLZbDm+w3NT0Oczr8+zK+tGjRs31ueff67Zs2erRo0a8vHxca5fFGWm3BRkfeumm25So0aN1KpVK1WuXFn79+/XxIkTFR0drdq1axfk6b+g0MNLlHKbN282Q4YMMVFRUcbLy8v4+/ub5s2bmzFjxmQbLjMtLc088cQTJjIy0vj6+prOnTubzZs35zmq3sWjpOU1ZO+QIUOMv79/tmkJCQmmf//+plKlSiYoKMjcddddZv369QUaVW/16tWmXbt2xs/Pz1SuXNmMGDHCbNy4Mcdt09LSzIgRI0zlypWNzWYz0qXP3WPMheFZfX19jSTz4Ycf5vp8Hj161Dz88MMmNjbWeHp6mkqVKpmWLVuaZ555pkDnJTLGmIEDBxpJpkOHDrle/91335mmTZsaHx8fU61aNfOvf/3LzJ8/P8eoPVnnTclNbqPqTZs2zdStW9d4e3ubGjVqmAkTJpiPPvoo23NjTO4jP+W1zAMHDpjhw4eb8PBw5zmabrvttmwj45w5c8Y8++yzzvMYZQ0b/Oijj2Y731NejyOvx1hc74X9+/ebgQMHmpCQEOPp6Wnq1q1rXn/99VzP41SQYT4zMjLMG2+84XxNAwICTL169cy9995r/vzzT5OZmWk6d+5swsLCTEJCQrbbvv7660aSmTNnjjEm+3mcHn74YVO5cmXj7e1tOnbsaNavX5/ttoX5/FzqPE4Xy+19UdDPycGDB80tt9xiAgICTGBgoLnlllvM6tWrXRpVb9GiRWbQoEEmODjY+Pr6mh49euQ4z0iWffv2GUnmvvvuu+Sy/y6v77a85Pede/ToUePl5WX69u2b5zJOnjxpfH19c4yUldt8jzzyiImKijKenp6mSpUqpmfPntnOSXT8+HFz3333mapVqxoPDw8THR1tRo8enec5k/4ua7So119/Pdv0rPfhl19+6ZyW9VldtmyZadWqlfH29jZVq1Y1Tz/9dI5z6xQmkzG5f27j4uLM8OHDnef1qVy5smnfvn220RVzy/33x/n399yJEydM//79TXBwsPN7wxhjPv74Y9OlSxcTFhZmvLy8nN95eY0ydnHu3D5DBfnOyeu1yMv3339vunfvbqpVq2a8vLxMlSpVTI8ePcyKFSuyzffZZ5+ZevXqGU9Pz2wjeWV9NrPObXPjjTearVu35vrcT5w40cTGxhp3d/ccz+Py5ctNz549TaVKlYynp6epVq2a6dmzp/M1ePbZZ/McWSyv58uYgn3HFPS1+v33381NN91kgoKCjJeXl2natGmO75+83jsFeQ7+LjU11dx3332mSZMmzvNr1a1b14wdO9acPXvWOV/W+ZNq1KhhfHx8TKtWrcySJUvyHFXv4lyurKsV9rO/fft207VrVxMYGGgqVqxobr31VhMfH59jZLiCfJempaUZb29vc+ONN+Y5z98V9PnM6/NsTMHXjfbt22e6detmAgMDjZT3eZyKItPFz50x+a9vvfnmm6Z9+/YmNDTUeHl5maioKHP33XdnO/daQdj+CgAARSY5OVnBwcF69913XRpgoLCWLVumLl266Msvv8wxKhny9+677+rhhx/W1q1bnQeSo2hcc801OnbsmLZu3Wp1FJQiDRo0UPfu3fXmm29aHQUlwHfffafevXvrhx9+cA7ugiur3O6qB6B4/PLLL5o9e7YkXfIAa5QcmzZtUlxcnMaPH68+ffpQmoASYvv27VZHQAmwfft27d+/X4899piaNWvmHFoeVx7FCUCRGjhwoOx2u9588021bNnS6jgogH79+ikxMVEdO3bM89xYAABrPPDAA1q1apVatGihjz/+uEgH7oFr2FUPAAAAAPJRbocjBwAAAICCojgBAAAAQD4oTgAAAACQj3I3OITD4dDhw4cVGBjIwXUAAABAOWaM0enTpxURESE3t0tvUyp3xenw4cOKjIy0OgYAAACAEuLAgQOqXr36Jecpd8UpMDBQ0oUnp0KFChanAQAAAGCVlJQURUZGOjvCpZS74pS1e16FChUoTgAAAAAKdAgPg0MAAAAAQD4oTgAAAACQD4oTAAAAAOSD4gQAAAAA+aA4AQAAAEA+KE4AAAAAkA+KEwAAAADkg+IEAAAAAPmgOAEAAABAPihOAAAAAJAPihMAAAAA5IPiBAAAAAD5oDgBAAAAQD4oTgAAAACQD0uL088//6ybbrpJERERstls+uabb/K9zfLly9WyZUv5+PioRo0a+uCDD4o/KAAAAIByzdLidPbsWTVt2lTvvfdegeaPi4tTjx491LFjR23atElPP/20Hn74YX311VfFnBQAAABAeeZh5Z13795d3bt3L/D8H3zwgaKiojRx4kRJUv369bV+/Xq98cYbuuWWW4opJQAAAICL2R1GGXaHMh1GGZkOZTgcyrAbZdodyrBn/d8o3e74a5q5ME/mhdu0rxmiYD8vqx9GgVlanFy1Zs0adevWLdu0G264QR999JEyMjLk6emZ4zZpaWlKS0tzXk5JSSn2nAAAAEB+jDGyO8yF4mH//9JxoWj8bZrj/4tIxl/XZc2T6XAo/a8i8vdlZJvfYf6aJ/tts8+TffkZ2cqPQxmOi+/bIYcp3OP/5sEOakZxKh6JiYkKCwvLNi0sLEyZmZk6duyYqlatmuM2EyZM0PPPP3+lIgIAAOAKyiofF2/NyCoTuRWR3IpC+kWF4f+3omTNk0ux+GueTIdD6X8VDOeyHA5lZP6VyXmbi5blcMgUsnyUJDab5OnuJk83mzw93OTh5iYvd5s83N3k4W6T11//XpjHTb6e7lZHdkmpKk6SZLPZsl02f73bLp6eZfTo0Ro1apTzckpKiiIjI4svIAAAQBnkcFwoBGmZF7ZwpNv/+td52f7/1118/V//v/i2GX+bJy3H8v7//xdKyoUiktsWlrLELat8uLvJ86/SkVU4PNxsl7jOTV4eF/7Nut7z70XFOY/b35ZzYRnZ5v+r9Hi6Zb9t9mX+VY6yLd9N7m65r4+XFaWqOIWHhysxMTHbtKSkJHl4eCgkJCTX23h7e8vb2/tKxAMAACgSxlzYOuFqOXGtuNhzzJeWx+2yikpp4e5mu7CS7+b215aPfIpCLkXk72XjUkXkQsn4/+u8/ioWzv//rYh4OotNzvmz8rqV8fJRmpWq4tSuXTt999132aYtWrRIrVq1yvX4JgAAgIKw/7Vr14UtH/bcy0c+W0VyFpecy8l1a00e91HSeblf2Hrh5eGW5/+9L3Gdl4ebvHNc557tsvdf/8+1uPytiDi3ulA+UIwsLU5nzpzR7t27nZfj4uK0efNmVapUSVFRURo9erQOHTqkmTNnSpLuu+8+vffeexo1apTuuecerVmzRh999JE+++wzqx4CAACwWGqGXVsPJWv9/pP688gZpeWyJSW/rSr2Er41xcPNlmdJ8fbIrZi4O//vnU+5cf7/olKT97Iv/D+vwySAssrS4rR+/Xp16dLFeTnrWKQhQ4ZoxowZSkhIUHx8vPP62NhYzZs3T48++qjef/99RURE6J133mEocgAAypHjZ9K0Yf9Jbdh/Uuv3n9TvB5OVbi+6LTQ2m7KXjlwKxd/LSa7F5G+X8ysuF+7DPc/blodjR4DSwGZMWRrLI38pKSkKCgpScnKyKlSoYHUcAABwCcYY7Tl6Vhv2n9D6fRfK0t5jZ3PMFxrgpZbRFdW4WpD8vT3yKC7uBSo1Hm42tqYA5YQr3aBUHeMEAADKttQMu34/lPxXSTqhDftP6uS5jBzz1a4SoFYxFdUyupJaRVdUdIgfZQdAsaI4AQAAyxRktztvDzc1jQxWq+iKahVTUS2iKiq4FJ00E0DZQHECAABXRMF3u/N2lqSW0RXVMCJIXh5uFiQGgP9HcQIAAMWioLvd1QkLcO5y1yqmoqIqsdsdgJKH4gQAAIrE8TNpWp+1292+E9p6KOWSu921jqmkFlEVFeTHuRgBlHwUJwAA4LILu92d0fp9J51lKY7d7gCUYRQnAACQr6zd7tbtO6EN+05qQ/xJnWK3OwDlCMUJAADkcOzvo93lsdudj6ebmlYPVquYimoVzW53AMo2ihMAAOWcK7vdtf5rl7tWMZXUoGoFdrsDUG5QnAAAKGdSM+zacjBZ6/ez2x0AFBTFCQCAMu7YmTTnkODr95/U1kPJyrCbbPOw2x0AXBrFCQCAMsTh+Gu3u/0nnWVp3/FzOearHHhhtLus3e4aRlSQpzu73QFAXihOAACUYgXZ7c5mk+pUCVTLmIoXdruLrqTISr7sdgcALqA4AQBQiriy213rmEpqGVPxwm53vux2BwCFQXECAKCEYrc7ACg5KE4AAJQQqRl2/XbglHNI8A37Tyr5PLvdAUBJQHECAMAiR0+nXdjl7q/zJ207nPtud80ig9Uqmt3uAMBKFCcAAK6ArN3u1u07eWEgh/0ntT+X3e6qBHqrVUxF5/mTGrDbHQCUCBQnAACKgau73bX+6/xJ1Suy2x0AlEQUJwAAigC73QFA2UZxAgDARQ6H0e6jZ/4qSex2BwDlAcUJAIB8nE+367eDp7Rh/0mt33dCG+NP5brbXd2wwL+GBGe3OwAoayhOAABcJOl0qjb8tcvd+v0nte1QsjIdOXe7ax5Z8a8tShXVnN3uAKBMozgBAMq1i3e7W7/vpOJPsNsdACA7ihMAoNxJPpehLzcc0Krdx7Rh/0mlpGZmu57d7gAAF6M4AQDKjf3Hz2r6qn36Yv0BnUu3O6f7erpfGO2O3e4AAHmgOAEAyjRjjDbsP6kPV+zVou1HZP46VKleeKD6t6yuNrGVVL8qu90BAC6N4gQAKJMy7Q7N35qoqSvj9NuBU87p19StrBFX11CHWiHsegcAKDCKEwCgTElJzdDstQc0Y/U+HTp1XpLk5eGmW1pU0/AOsaodFmhxQgBAaURxAgCUCQdOnNP0Vfs0e128zv51/FKIv5cGtYvWXVdFKzTA2+KEAIDSjOIEACjVNuw/qY9W7tWCrYnKOtVS7SoBGtExVn2aVZOPp7u1AQEAZQLFCQBQ6mTaHVq0/Yg+XLFXm+JPOad3rB2qER1rqFPtUI5fAgAUKYoTAKDUOJ2aoS/WH9T0VXE6ePKv45fc3dS3eYSGXx2reuEVLE4IACirKE4AgBLv0KnzmrEqTp+vPaDTaRdOVlvRz1ODrorWXe2iVSXQx+KEAICyjuIEACixNh84pakr9mr+1kTZ/zqAqWZlf919dQ3d3ILjlwAAVw7FCQBQotgdRou3H9FHK/dq3b6TzukdaoVoxNU11LlOZbm5cfwSAODKojgBAEqEs2mZ+nL9AU1btU/xJ85JkjzdbbqpaYRGXF1DDSI4fgkAYB2KEwDAUgnJ5zVj9T59+mu8TqdeOH4p2M9Td7aN0uB2MQqrwPFLAADrUZwAAJb4/WCypq7cqx+2JCjzr+OXYkP9NfzqWN3Sopr8vPgVBQAoOfitBAC4YhwOo5/+SNLUFXv1a9wJ5/SralTSiKtr6Np6VTh+CQBQIlGcAADF7lx6pr7acFAfrYzTvuMXjl/ycLtw/NLdV8eqUbUgixMCAHBpFCcAQLE5kpKqj1fv06xf45V8PkOSVMHHQwPbRmto+xiFB3H8EgCgdKA4AQCK3LbDyfpoRZy+23JYGfYLxy9Fh/hpeIdY9W9ZXf7e/PoBAJQu/OYCABQJh8No6c4kTV0RpzV7jzunt4mppLs7xur6+mFy5/glAEApRXECABTK+XS7vt504filvUfPSpLc3Wzq2biq7r46Vk0jg60NCABAEaA4AQAuS9LpVH2yZr/++8t+nTx34filQG8P3dE2SkPax6hasK/FCQEAKDoUJwCAS3YkpOijlXGau/mw0u0OSVL1ir4a3iFWt7WOVADHLwEAyiB+uwEA8mWM0fJdRzV1RZxW7j7mnN4yuqJGXB2rbg3DOX4JAFCmUZwAAHlKzbDrm02H9NHKOP2ZdEaS5GaTujeqqrs7xqpFVEWLEwIAcGVQnAAAORw7k+Y8fun42XRJUoC3hwa0jtTQ9jGKrORncUIAAK4sihMAwGnXkdP6aEWc5mw+pPTMC8cvVQv21bAOMbqtdaQq+HhanBAAAGtQnACgnDPGaMWfx/TRyjgt33XUOb1pZLDu6RirGxuGy8PdzcKEAABYj+IEAOVUWqZd324+rI9WxGnnkdOSLhy/dEPDcI346/glm40BHwAAkChOAFDuHD+Tplm/xmvmmv06diZNkuTn5a7bWkVqeIdYRYVw/BIAABejOAFAObE76bQ+WrlPX288qLS/jl+qGuSjoe1jdHubKAX5cvwSAAB5oTgBQBlmjNHqPcc1dcVeLd35/8cvNa4WpBEdY9WjcVV5cvwSAAD5ojgBQBmUnunQd78d1tSVcdqRkCJJstmkrvXDNKJjDbWO4fglAABcQXECgDLk5Nl0fbo2Xh+v3qek0xeOX/L1dNetrapreIdYxYT6W5wQAIDSieIEAGXA3qNnNG1VnP634aBSMy4cvxRWwVtD2sdoYJsoBft5WZwQAIDSjeIEAKWUMUa/xp3Q1BV79dMfSTLmwvQGVSvonk6x6tk4Ql4eHL8EAEBRoDgBQCmTnunQvN8TNHXlXm09lOKcfn39Krr76hq6qkYljl8CAKCIUZwAoJRIPpfhPH4pMSVVkuTj6ab+LatrWIdY1awcYHFCAADKLooTAJRw+46d1fRVcfpi/UGdz7BLkioHemtIu2gNbButSv4cvwQAQHGjOAFACWSM0bp9JzV1xV4t3nHEefxSvfBAjehYQzc1rSpvD3drQwIAUI5QnACgBMmwOzR/a6KmrtirLQeTndO71K2sER1rqH3NEI5fAgDAAhQnACgBks9naPa6eM1YtU+Hky8cv+Tt4aabW1TX3VfHqFaVQIsTAgBQvlGcAMBCB06c07RVcfpi3QGdTb9w/FJogJcGXRWju66KUkiAt8UJAQCARHECAEts2H9CU1fEaeG2RDn+On6pTliARlxdQ72bRcjHk+OXAAAoSShOAHCFZNodWrAtUVNXxGnzgVPO6Z3qVNaIq2PVsXYoxy8BAFBCUZwAoJidTs3Q7HUHNH3VPh06dV6S5OXupr7NI3T31TVUN5zjlwAAKOkoTgBQTA6ePKcZq/bp83UHdCYtU5JUyd9Ld10VrUFXRatyIMcvAQBQWlCcAKCIbYo/qakr47Rga6Lsfx3AVLOyv0Z0rKF+zatx/BIAAKUQxQkAioDdYbRoW6KmrozThv0nndOvrhWquzvGqnPtynJz4/glAABKK4oTABTCmbRMfbn+gKatitOBExeOX/J0t6lPs2oa3iFWDSIqWJwQAAAUBYoTAFyGw6fO6+PV+/Tp2nidTr1w/FKwn6fuahutwe2iVaWCj8UJAQBAUaI4AYALthw8pakr4vTD7wnO45dqhPpr+NWxuqVFdfl6cfwSAABlEcUJAPJhjNHi7Uc0dUWc1u474ZzerkaIRnSMVZe6VTh+CQCAMo7iBACXYIzR03O26rO18ZIkDzebejeN0PCrY9WoWpDF6QAAwJVCcQKAS5i8fI8+WxsvN5t0T6caGtY+VuFBHL8EAEB5Q3ECgDx8v+WwXluwU5I0plcDDe0Qa3EiAABgFTerAwBASbRh/wmN+uI3SdKwDjGUJgAAyjmKEwBcZP/xs7pn5galZzp0ff0wPduzgdWRAACAxShOAPA3J8+ma9j0dTpxNl2NqwXpnTuayZ0R8wAAKPcoTgDwl7RMu+79ZIP2HjurasG++mhIK/l5cSgoAAAoAcVp0qRJio2NlY+Pj1q2bKkVK1Zccv73339f9evXl6+vr+rWrauZM2deoaQAyjJjjJ783xat3XdCgd4emja0tapUYPQ8AABwgaV/Sp09e7ZGjhypSZMmqUOHDvrPf/6j7t27a/v27YqKisox/+TJkzV69Gh9+OGHat26tdauXat77rlHFStW1E033WTBIwBQVvz7xz/1zebDcnezadJdLVQ3PNDqSAAAoASxGWOMVXfetm1btWjRQpMnT3ZOq1+/vvr27asJEybkmL99+/bq0KGDXn/9dee0kSNHav369Vq5cmWB7jMlJUVBQUFKTk5WhQoVCv8gAJR6/9twUI9/eWEEvVdubqzb2+T8ww0AACh7XOkGlu2ql56erg0bNqhbt27Zpnfr1k2rV6/O9TZpaWny8cm+64yvr6/Wrl2rjIyMPG+TkpKS7QcAsqzec0yjv94iSbr/mpqUJgAAkCvLitOxY8dkt9sVFhaWbXpYWJgSExNzvc0NN9ygqVOnasOGDTLGaP369Zo2bZoyMjJ07NixXG8zYcIEBQUFOX8iIyOL/LEAKJ12J53WvZ9sUIbdqFeTqvpXt7pWRwIAACWU5YND2GzZh/k1xuSYluW5555T9+7dddVVV8nT01N9+vTR0KFDJUnu7u653mb06NFKTk52/hw4cKBI8wMonY6eTtPQ6et0OjVTLaMr6o1bm8qNYccBAEAeLCtOoaGhcnd3z7F1KSkpKcdWqCy+vr6aNm2azp07p3379ik+Pl4xMTEKDAxUaGhorrfx9vZWhQoVsv0AKN9SM+y6Z+Z6HTx5XtEhfpoyqKV8PHP/4wsAAIBkYXHy8vJSy5YttXjx4mzTFy9erPbt21/ytp6enqpevbrc3d31+eefq1evXnJzs3zjGYBSwOEwenT2Zm0+cEpBvp6aNrS1QgK8rY4FAABKOEuHIx81apQGDRqkVq1aqV27dpoyZYri4+N13333Sbqwm92hQ4ec52ratWuX1q5dq7Zt2+rkyZN66623tHXrVn388cdWPgwApcgrC/7Q/K2J8nJ305RBLVWzcoDVkQAAQClgaXEaMGCAjh8/rvHjxyshIUGNGjXSvHnzFB0dLUlKSEhQfHy8c3673a4333xTO3fulKenp7p06aLVq1crJibGokcAoDT57y/7NeXnvZKk1/o3UdsaIRYnAgAApYWl53GyAudxAsqnZTuTdPfH62V3GI3qWkcPX1fb6kgAAMBipeI8TgBwpWw/nKIHZ22U3WF0S4vq+ue1tayOBAAAShmKE4AyLTE5VcNnrNPZdLva1QjRhJsb53nKAwAAgLxQnACUWWfSMjV8xjolpqSqZmV/fXBXS3l58LUHAABcxxoEgDIp0+7QPz/dqO0JKQoN8NKMYW0U5OdpdSwAAFBKUZwAlDnGGI3/fruW7jwqbw83fTi4lSIr+VkdCwAAlGIUJwBlzkcr4zRzzX7ZbNLEAc3UPKqi1ZEAAEApR3ECUKYs2Jqol+btkCSN7l5P3RtXtTgRAAAoCyhOAMqMzQdOaeTsTTJGurNtlO7pWMPqSAAAoIygOAEoEw6cOKcRH69TaoZD19StrOd7N2TYcQAAUGQoTgBKveTzGRo+Y52OnUlXvfBAvTewhTzc+XoDAABFhzULAKVaeqZDD8zaoD+TziisgremD2utAG8Pq2MBAIAyhuIEoNQyxuiZOb9r1e7j8vNy10dDWqtqkK/VsQAAQBlEcQJQak1atkdfbjgoN5v0/sAWalQtyOpIAACgjKI4ASiVvt18SK8v3ClJer53Q3WpV8XiRAAAoCyjOAEoddbtO6F/fblFkjTi6lgNahdjbSAAAFDmUZwAlCpxx87qnpnrlW536IaGYRrdo77VkQAAQDlAcQJQapw4m65h09fq1LkMNa0epIkDmsvdjXM1AQCA4kdxAlAqpGbY9Y+Z67Xv+DlVC/bV1CGt5evlbnUsAABQTlCcAJR4DofRE//bovX7TyrQx0MzhrVW5UBvq2MBAIByhOIEoMR7a/Euzf3tsDzcbPrgrpaqHRZodSQAAFDOXFZx2rNnj5599lndcccdSkpKkiQtWLBA27ZtK9JwAPDFugN6b+luSdLLNzdWh1qhFicCAADlkcvFafny5WrcuLF+/fVXff311zpz5owkacuWLRo7dmyRBwRQfq3afUxPz/ldkvRQl1q6rVWkxYkAAEB55XJxeuqpp/Tiiy9q8eLF8vLyck7v0qWL1qxZU6ThAJRffx45rfv+u0GZDqPeTSP0WLc6VkcCAADlmMvF6ffff1e/fv1yTK9cubKOHz9eJKEAlG9Jp1M1dPo6nU7NVOuYinr91iay2Rh2HAAAWMfl4hQcHKyEhIQc0zdt2qRq1aoVSSgA5df5dLvu+Xi9Dp06r9hQf00Z1EreHgw7DgAArOVycRo4cKCefPJJJSYmymazyeFwaNWqVXr88cc1ePDg4sgIoJywO4we+XyTfjuYrIp+npo2tLUq+nvlf0MAAIBi5nJxeumllxQVFaVq1arpzJkzatCggTp16qT27dvr2WefLY6MAMqJCfN2aNH2I/Jyd9OUwa0UG+pvdSQAAABJks0YYy7nhnv37tXGjRvlcDjUvHlz1a5du6izFYuUlBQFBQUpOTlZFSpUsDoOgL98smafnvv2wikN3rmjuXo3jbA4EQAAKOtc6QYel3snNWrUUI0aNS735gDgtOSPIxo790Jp+tcNdSlNAACgxHF5V73+/fvrlVdeyTH99ddf16233lokoQCUH1sPJeuhTzfJYaTbWlXXA9fUtDoSAABADpd1AtyePXvmmH7jjTfq559/LpJQAMqHhOTzuvvjdTqXbleHWiF6qV9jhh0HAAAlksvF6cyZM9lOfJvF09NTKSkpRRIKQNl3Ji1Tw6av05GUNNWuEqBJd7aUp7vLX0kAAABXhMtrKY0aNdLs2bNzTP/888/VoEGDIgkFoGzLtDv04KyN+iPxtEIDvDV9WGsF+XpaHQsAACBPLg8O8dxzz+mWW27Rnj17dO2110qSfvrpJ3322Wf68ssvizwggLLFGKOxc7dp+a6j8vF000dDWql6RT+rYwEAAFySy8Wpd+/e+uabb/Tyyy/rf//7n3x9fdWkSRP9+OOP6ty5c3FkBFCGfLhir2b9Gi+bTXr79uZqGhlsdSQAAIB8XfZ5nEorzuMEWGf+7wm6f9ZGSdKzPetrREdOaQAAAKxzRc7jlJ6erqSkJDkcjmzTo6KiLneRAMqwTfEnNXL2ZknS4HbRuvvqWGsDAQAAuMDl4vTnn39q+PDhWr16dbbpxhjZbDbZ7fYiCwegbDhw4pxGfLxeaZkOXVuvisb0asCw4wAAoFRxuTgNHTpUHh4e+v7771W1alVWfgBcUvK5DA2dvlbHz6arYUQFvXtHc3kw7DgAAChlXC5Omzdv1oYNG1SvXr3iyAOgDEnPdOi+/27QnqNnFV7BRx8NaS1/78veQxgAAMAyLv/Zt0GDBjp27FhxZAFQhhhjNPrr37Vm73H5e7lr2tDWCg/ysToWAADAZXG5OL366qt64okntGzZMh0/flwpKSnZfgBAkt5dsltfbTwodzeb3r+zhRpEMIolAAAovVzeZ+b666+XJF133XXZpjM4BIAs32w6pLcW75Ikje/TUNfUrWJxIgAAgMJxuTgtXbq0OHIAKCN+3XtcT/xviyTp3k41dGfbaIsTAQAAFJ7Lxalz587FkQNAGbDn6Bn945MNSrc71L1RuJ68kUFkAABA2XDZw1udO3dO8fHxSk9Pzza9SZMmhQ4FoPQ5fiZNw2esU/L5DDWLDNa/BzSTmxunKwAAAGWDy8Xp6NGjGjZsmObPn5/r9RzjBJQ/qRl2/eOTDdp//JwiK/lq6pBW8vF0tzoWAABAkXF5VL2RI0fq5MmT+uWXX+Tr66sFCxbo448/Vu3atTV37tziyAigBHM4jB778jdt2H9SFXw8NH1oa4UGeFsdCwAAoEi5vMVpyZIl+vbbb9W6dWu5ubkpOjpaXbt2VYUKFTRhwgT17NmzOHICKKFeX7RTP2xJkKe7Tf8Z1Eq1qgRaHQkAAKDIubzF6ezZs6pS5cLQwpUqVdLRo0clSY0bN9bGjRuLNh2AEu3ztfGavGyPJGnCzU3UrmaIxYkAAACKh8vFqW7dutq5c6ckqVmzZvrPf/6jQ4cO6YMPPlDVqlWLPCCAkmnFn0f1zDdbJUkPX1db/VtWtzgRAABA8XF5V72RI0cqISFBkjR27FjdcMMNmjVrlry8vDRjxoyizgegBNqZeFoP/Hej7A6jfs2r6dHra1sdCQAAoFjZjDGmMAs4d+6c/vjjD0VFRSk0NLSochWblJQUBQUFKTk5WRUqVLA6DlDqJKWkqu/7q3Q4OVVtYivpk7vbyNuDEfQAAEDp40o3uOzzOGXx8/NTixYtCrsYAKXAufRM3f3xeh1OTlWNUH9NGdSS0gQAAMqFAhWnUaNG6YUXXpC/v79GjRp1yXnfeuutIgkGoGSxO4we/myzfj+UrEr+Xpo+rLWC/bysjgUAAHBFFKg4bdq0SRkZGZKkjRs3ymaz5TpfXtMBlH4v/rBdP+44Ii8PN304uKWiQ/ytjgQAAHDFFKg4LV261Pn/ZcuWFVcWACXUjFVxmr5qnyTp37c1U8voStYGAgAAuMJcGo48MzNTHh4e2rp1a3HlAVDC/Lj9iMZ/v12S9OSN9dSzCacdAAAA5Y9LxcnDw0PR0dGy2+3FlQdACfL7wWT987NNchjpjjaRuq9zDasjAQAAWMLlE+A+++yzGj16tE6cOFEceQCUEIdPndfdH6/T+Qy7OtYO1fg+jTiOEQAAlFsuD0f+zjvvaPfu3YqIiFB0dLT8/bMfIL5x48YiCwfAGqdTMzR8xjolnU5T3bBAvX9nC3m6u/x3FgAAgDLD5eLUt2/fYogBoKTIsDv0wKyN+iPxtCoHemvasNaq4ONpdSwAAABLuVycxo4dWxw5AJQAxhiN+XarVvx5TL6e7po2pLWqBftaHQsAAMBy7HsDwOk/P+/VZ2sPyGaT3rmjuRpXD7I6EgAAQIng8hYnu92uf//73/riiy8UHx+v9PT0bNczaARQOv2wJUGvzP9DkjSmVwN1bRBmcSIAAICSw+UtTs8//7zeeust3XbbbUpOTtaoUaN08803y83NTePGjSuGiACK24b9J/XoF5slSUPbx2hYh1hrAwEAAJQwLhenWbNm6cMPP9Tjjz8uDw8P3XHHHZo6darGjBmjX375pTgyAihG+4+f1T0z1ys906Hr64fpuV4NrI4EAABQ4rhcnBITE9W4cWNJUkBAgJKTkyVJvXr10g8//FC06QAUq1Pn0jVsxjqdOJuuRtUq6J07msndjXM1AQAAXMzl4lS9enUlJCRIkmrVqqVFixZJktatWydvb++iTQeg2KRl2nXvJxu09+hZRQT5aNqQ1vLzcvmwRwAAgHLB5eLUr18//fTTT5KkRx55RM8995xq166twYMHa/jw4UUeEEDRM8Zo9Fe/69e4Ewrw9tC0Ya1VpYKP1bEAAABKLJsxxhRmAb/++qtWrVqlWrVqqXfv3kWVq9ikpKQoKChIycnJqlChgtVxAEv8e/Euvf3Tn3J3s2n60NbqVKey1ZEAAACuOFe6gcv75Zw7d05+fn7Oy23btlXbtm1dTwnAEl9tOKi3f/pTkvRS30aUJgAAgAJweVe9KlWq6K677tLChQvlcDiKIxOAYrJmz3E99fUWSdL919TU7W2iLE4EAABQOrhcnGbOnKm0tDT169dPEREReuSRR7Ru3briyAagCO1OOqN7P1mvDLtRzyZV9a9uda2OBAAAUGq4XJxuvvlmffnllzpy5IgmTJigHTt2qH379qpTp47Gjx9fHBkBFNKxM2kaNmOtUlIz1SIqWG/e2lRuDDsOAABQYIUeHEKStm/frjvvvFNbtmyR3W4vilzFhsEhUN6kZth1x4e/aFP8KUVV8tOcB9orJIBTBwAAALjSDVze4pQlNTVVX3zxhfr27asWLVro+PHjevzxxy93cQCKgcNh9OjszdoUf0pBvp6aPqw1pQkAAOAyuDyq3qJFizRr1ix98803cnd3V//+/bVw4UJ17ty5OPIBKIRXF/6h+VsT5elu05RBLVWzcoDVkQAAAEoll4tT37591bNnT3388cfq2bOnPD09iyMXgEL69Nd4/Wf5XknSa/2bqG2NEIsTAQAAlF4uF6fExESODQJKuGU7k/Tct1slSY9eX0f9mle3OBEAAEDp5vIxTpQmoGTbkZCihz7dJLvD6JYW1fXwdbWsjgQAAFDqXfbgEABKniMpqRo+Y53OpGWqXY0QTbi5sWw2hh0HAAAoLIoTUEacTcvU8BnrlJCcqpqV/fXBXS3l5cFHHAAAoCiwVgWUAXaH0cOfbdK2wykK8ffS9KFtFOTHwC0AAABFpVDF6eDBgzp06FChAkyaNEmxsbHy8fFRy5YttWLFikvOP2vWLDVt2lR+fn6qWrWqhg0bpuPHjxcqA1CaGWM0/rtt+umPJHl7uGnqkFaKCvGzOhYAAECZ4nJxcjgcGj9+vIKCghQdHa2oqCgFBwfrhRdekMPhcGlZs2fP1siRI/XMM89o06ZN6tixo7p37674+Phc51+5cqUGDx6su+++W9u2bdOXX36pdevWacSIEa4+DKDMmLZqnz5es182mzRxQDM1j6podSQAAIAyx+Xi9Mwzz+i9997TK6+8ok2bNmnjxo16+eWX9e677+q5555zaVlvvfWW7r77bo0YMUL169fXxIkTFRkZqcmTJ+c6/y+//KKYmBg9/PDDio2N1dVXX617771X69evd/VhAGXCom2JevGH7ZKk0d3rqXvjqhYnAgAAKJtcLk4ff/yxpk6dqvvvv19NmjRR06ZN9cADD+jDDz/UjBkzCryc9PR0bdiwQd26dcs2vVu3blq9enWut2nfvr0OHjyoefPmyRijI0eO6H//+5969uyZ5/2kpaUpJSUl2w9QFmw5eEqPfL5ZxkgD20bpno41rI4EAABQZrlcnE6cOKF69erlmF6vXj2dOHGiwMs5duyY7Ha7wsLCsk0PCwtTYmJirrdp3769Zs2apQEDBsjLy0vh4eEKDg7Wu+++m+f9TJgwQUFBQc6fyMjIAmcESqqDJ89p+Iz1Op9hV+c6lTW+d0OGHQcAAChGLhenpk2b6r333ssx/b333lPTpk1dDnDxyp4xJs8VwO3bt+vhhx/WmDFjtGHDBi1YsEBxcXG677778lz+6NGjlZyc7Pw5cOCAyxmBkiQlNUPDZ6zTsTNpqhceqPcGNpeHOwNkAgAAFCcPV2/w2muvqWfPnvrxxx/Vrl072Ww2rV69WgcOHNC8efMKvJzQ0FC5u7vn2LqUlJSUYytUlgkTJqhDhw7617/+JUlq0qSJ/P391bFjR7344ouqWjXn8R3e3t7y9vZ24RECJVeG3aEH/rtRu46cUVgFb00f1lqBPgw7DgAAUNxc/jN1586dtWvXLvXr10+nTp3SiRMndPPNN2vnzp3q2LFjgZfj5eWlli1bavHixdmmL168WO3bt8/1NufOnZObW/bI7u7uki5sqQLKMmOMnp2zVSt3H5Ofl7s+GtJaVYN8rY4FAABQLri8xUmSIiIi9NJLLxX6zkeNGqVBgwapVatWateunaZMmaL4+HjnrnejR4/WoUOHNHPmTEnSTTfdpHvuuUeTJ0/WDTfcoISEBI0cOVJt2rRRREREofMAJdmkZXs0e/0Budmk9wY2V6NqQVZHAgAAKDcKVJy2bNmiRo0ayc3NTVu2bLnkvE2aNCnwnQ8YMEDHjx/X+PHjlZCQoEaNGmnevHmKjo6WJCUkJGQ7p9PQoUN1+vRpvffee3rssccUHBysa6+9Vq+++mqB7xMojeb+dlivL9wpSRrXu6GurZf77qwAAAAoHjZTgH3c3NzclJiYqCpVqsjNzU02my3XXeNsNpvsdnuxBC0qKSkpCgoKUnJysipUqGB1HCBf6/ed0MCpvyo906G7r47Vc70aWB0JAACgTHClGxRoi1NcXJwqV67s/D+AK2PfsbO6Z+Z6pWc6dEPDMD3do77VkQAAAMqlAhWnrF3nJGn//v1q3769PDyy3zQzM1OrV6/ONi+Ay3fybLqGzVink+cy1LR6kCYOaC53N87VBAAAYAWXR9Xr0qVLrie6TU5OVpcuXYokFFDepWXade8nGxR37KyqBfvqwyGt5OvlbnUsAACAcsvl4pTXCWqPHz8uf3//IgkFlGfGGD3xvy1au++EAn08NH1Ya1UJ9LE6FgAAQLlW4OHIb775ZkkXBoAYOnRotpPK2u12bdmyJc/zLwEouLcW79K3mw/Lw82mD+5qqTphgVZHAgAAKPcKXJyCgi6cM8YYo8DAQPn6/v+JN728vHTVVVfpnnvuKfqEQDny5foDenfJbknSyzc3VodaoRYnAgAAgORCcZo+fbokKSYmRo8//ji75QFFbPXuYxr99e+SpIe61NJtrSItTgQAAIAsBS5OWcaOHVscOYBy7c8jp3Xvfzco02F0U9MIjepax+pIAAAA+BuXi5Mk/e9//9MXX3yh+Ph4paenZ7tu48aNRRIMKC+Onk7TsBnrdDo1U62iK+r1/k3kxrDjAAAAJYrLo+q98847GjZsmKpUqaJNmzapTZs2CgkJ0d69e9W9e/fiyAiUWefT7Roxc70OnjyvmBA/TRncSj6eDDsOAABQ0rhcnCZNmqQpU6bovffek5eXl5544gktXrxYDz/8sJKTk4sjI1AmORxGj87erN8OnFJFP09NH9ZGlfy9rI4FAACAXLhcnOLj453Djvv6+ur06dOSpEGDBumzzz4r2nRAGTZh/g4t2JYoL3c3TRncSrGhDLgCAABQUrlcnMLDw3X8+HFJUnR0tH755RdJUlxcnIwxRZsOKKM++WW/PlwRJ0l6/dYmah1TyeJEAAAAuBSXi9O1116r7777TpJ0991369FHH1XXrl01YMAA9evXr8gDAmXN0j+SNPbbrZKkx7vVUZ9m1SxOBAAAgPzYjIubiRwOhxwOhzw8LgzI98UXX2jlypWqVauW7rvvPnl5lexjNFJSUhQUFKTk5GRVqFDB6jgoZ7YdTtZtH6zR2XS7bmtVXa/e0kQ2GyPoAQAAWMGVbuBSccrMzNRLL72k4cOHKzKydJ6ck+IEqyQkn1e/91crMSVVHWqFaMawNvJ0d3mjLwAAAIqIK93ApbU2Dw8Pvf7667Lb7YUKCJQ3Z9IyNXzGeiWmpKp2lQBNurMlpQkAAKAUcXnN7frrr9eyZcuKIQpQNmXaHXro043akZCi0ABvTRvaWkG+nlbHAgAAgAs8XL1B9+7dNXr0aG3dulUtW7aUv3/2IZR79+5dZOGA0s4Yo3HfbdOynUfl4+mmj4a0UmQlP6tjAQAAwEUuDw7h5pb3RiqbzVbid+PjGCdcSVNX7NWLP+yQzSZ9cFdL3dAw3OpIAAAA+Isr3cDlLU4Oh+OygwHlyYKtiXpp3g5J0jM96lOaAAAASjGOTgeKweYDpzRy9iYZIw26Klp3Xx1rdSQAAAAUAsUJKGIHTpzTiI/XKTXDoS51K2vsTQ04VxMAAEApR3ECilB6pkMjPl6vY2fS1aBqBb03sIU8GHYcAACg1GONDihC01fFaeeR0woN8NK0oa3l7+3yYYQAAAAogShOQBE5kpKqd376U5L05I31FB7kY3EiAAAAFJXLKk579uzRs88+qzvuuENJSUmSpAULFmjbtm1FGg4oTSbM26Gz6XY1jwrWLS2qWx0HAAAARcjl4rR8+XI1btxYv/76q77++mudOXNGkrRlyxaNHTu2yAMCpcHauBP6ZvNh2WzS+N6N5ObGYBAAAABlicvF6amnntKLL76oxYsXy8vLyzm9S5cuWrNmTZGGA0oDu8No7NwLW1tvbx2pxtWDLE4EAACAouZycfr999/Vr1+/HNMrV66s48ePF0kooDT59Nf92pGQoiBfT/3rhnpWxwEAAEAxcLk4BQcHKyEhIcf0TZs2qVq1akUSCigtTpxN1xuLdkmSHutWR5X8vfK5BQAAAEojl4vTwIED9eSTTyoxMVE2m00Oh0OrVq3S448/rsGDBxdHRqDEemPRTiWfz1C98EANbBNldRwAAAAUE5eL00svvaSoqChVq1ZNZ86cUYMGDdSpUye1b99ezz77bHFkBEqk3w8m67O18ZKk53s35ES3AAAAZZjLZ+f09PTUrFmzNH78eG3atEkOh0PNmzdX7dq1iyMfUCI5HEZj526VMVKfZhFqWyPE6kgAAAAoRi4Xp+XLl6tz586qWbOmatasWRyZgBJvzqZD2hh/Sn5e7hrdvb7VcQAAAFDMXN63qGvXroqKitJTTz2lrVu3FkcmoEQ7nZqhCfP/kCT989raCg/ysTgRAAAAipvLxenw4cN64okntGLFCjVp0kRNmjTRa6+9poMHDxZHPqDEefvHP3XsTJpiQ/01/OoYq+MAAADgCnC5OIWGhuqhhx7SqlWrtGfPHg0YMEAzZ85UTEyMrr322uLICJQYu5NOa8bqfZKksTc1kLeHu7WBAAAAcEUUahiw2NhYPfXUU3rllVfUuHFjLV++vKhyASWOMUbj5m5XpsPo+vphuqZuFasjAQAA4Aq57OK0atUqPfDAA6pataoGDhyohg0b6vvvvy/KbECJsnBbolbuPiYvDzeN6dXA6jgAAAC4glweVe/pp5/WZ599psOHD+v666/XxIkT1bdvX/n5+RVHPqBEOJ9u1wvf75Ak3duphqJCeL8DAACUJy4Xp2XLlunxxx/XgAEDFBoaWhyZgBJn8vI9OnTqvKoF++qBa2pZHQcAAABXmMvFafXq1cWRAyixDpw4pw+W75EkPdOzvny9GBACAACgvClQcZo7d666d+8uT09PzZ0795Lz9u7du0iCASXFC99vV3qmQ+1rhqh7o3Cr4wAAAMACBSpOffv2VWJioqpUqaK+ffvmOZ/NZpPdbi+qbIDllu86qkXbj8jdzaZxvRvKZrNZHQkAAAAWKFBxcjgcuf4fKMvSMx16fu42SdLQ9jGqExZocSIAAABYxeXhyGfOnKm0tLQc09PT0zVz5swiCQWUBNNXxWnvsbMKDfDWI9fXtjoOAAAALORycRo2bJiSk5NzTD99+rSGDRtWJKEAqx1JSdU7P/0pSXryxrqq4ONpcSIAAABYyeXiZIzJ9TiPgwcPKigoqEhCAVabMG+Hzqbb1TwqWLe0qG51HAAAAFiswMORN2/eXDabTTabTdddd508PP7/pna7XXFxcbrxxhuLJSRwJa3bd0LfbD4sm00a37uR3NwYEAIAAKC8K3BxyhpNb/PmzbrhhhsUEBDgvM7Ly0sxMTG65ZZbijwgcCXZHUZjvr0wIMTtrSPVuDpbUQEAAOBCcRo7dqwkKSYmRgMGDJCPj0+xhQKs8umv+7UjIUUVfDz0eLe6VscBAABACVHg4pRlyJAhxZEDsNyJs+l6Y9EuSdLjN9RVSIC3xYkAAABQUrhcnOx2u/7973/riy++UHx8vNLT07Ndf+LEiSILB1xJbyzaqeTzGaoXHqiBbaKsjgMAAIASxOVR9Z5//nm99dZbuu2225ScnKxRo0bp5ptvlpubm8aNG1cMEYHit/VQsj5bGy9Jer53Q3m4u/zRAAAAQBnm8trhrFmz9OGHH+rxxx+Xh4eH7rjjDk2dOlVjxozRL7/8UhwZgWLlcBiN+XarjJF6N41Q2xohVkcCAABACeNycUpMTFTjxo0lSQEBAc6T4fbq1Us//PBD0aYDroA5mw5pY/wp+Xm56+ke9a2OAwAAgBLI5eJUvXp1JSQkSJJq1aqlRYsWSZLWrVsnb28Opkfpcjo1QxPm/yFJ+ue1tRUexGiRAAAAyMnl4tSvXz/99NNPkqRHHnlEzz33nGrXrq3Bgwdr+PDhRR4QKE7v/PSnjp1JU2yov4ZfHWN1HAAAAJRQLo+q98orrzj/379/f1WvXl2rV69WrVq11Lt37yINBxSn3UmnNX3VPknSmJsayNvD3dpAAAAAKLFcLk4Xu+qqq3TVVVcVRRbgijHGaNzc7cp0GF1fP0xd6laxOhIAAABKsAIVp7lz5xZ4gWx1QmmwcFuiVu4+Ji8PN43p1cDqOAAAACjhClSc+vbtW6CF2Ww22e32wuQBit35dLte+H6HJOneTjUUFeJncSIAAACUdAUqTg6Ho7hzAFfM5OV7dOjUeVUL9tUD19SyOg4AAABKAZdH1QNKswMnzumD5XskSc/0rC9fLwaEAAAAQP5cHhxi/Pjxl7x+zJgxlx0GKG4vfL9d6ZkOta8Zou6Nwq2OAwAAgFLC5eI0Z86cbJczMjIUFxcnDw8P1axZk+KEEmv5rqNatP2I3N1sGte7oWw2m9WRAAAAUEq4XJw2bdqUY1pKSoqGDh2qfv36FUkooKilZzr0/NxtkqSh7WNUJyzQ4kQAAAAoTYrkGKcKFSpo/Pjxeu6554picUCRm74qTnuPnVVogLceub621XEAAABQyhTZ4BCnTp1ScnJyUS0OKDJHUlL1zk9/SpKevLGuKvh4WpwIAAAApY3Lu+q988472S4bY5SQkKBPPvlEN954Y5EFA4rKhHk7dDbdruZRwbqlRXWr4wAAAKAUcrk4/fvf/8522c3NTZUrV9aQIUM0evToIgsGFIV1+07om82HZbNJz/duKDc3BoQAAACA61wuTnFxccWRAyhydofR2G8vDAhxe+tINakebG0gAAAAlFqcABdl1qdr47U9IUUVfDz0eLe6VscBAABAKebyFqfU1FS9++67Wrp0qZKSkuRwOLJdv3HjxiILB1yuE2fT9cbCnZKkx7rVVUiAt8WJAAAAUJq5XJyGDx+uxYsXq3///mrTpg0nEUWJ9MainUo+n6F64YG6s22U1XEAAABQyrlcnH744QfNmzdPHTp0KI48QKFtPZSsz9bGS7owIISHO3ukAgAAoHBcXqOsVq2aAgMDiyMLUGgOh9GYb7fKGKl30wi1rRFidSQAAACUAS4XpzfffFNPPvmk9u/fXxx5gEKZs+mQNsafkp+Xu57uUd/qOAAAACgjXN5Vr1WrVkpNTVWNGjXk5+cnT0/PbNefOHGiyMIBrjidmqEJ8/+QJP3z2toKD/KxOBEAAADKCpeL0x133KFDhw7p5ZdfVlhYGINDoMR456c/dexMmmJD/TX86hir4wAAAKAMcbk4rV69WmvWrFHTpk2LIw9wWXYnndb0VfskSWNuaiBvD3drAwEAAKBMcfkYp3r16un8+fPFkQW4LMYYjZu7XZkOo+vrh6lL3SpWRwIAAEAZ43JxeuWVV/TYY49p2bJlOn78uFJSUrL9AFfawm2JWrn7mLw83DSmVwOr4wAAAKAMcnlXvRtvvFGSdN1112WbboyRzWaT3W4vmmRAAZxPt+uF73dIku7tVENRIX4WJwIAAEBZ5HJxWrp0aZEGmDRpkl5//XUlJCSoYcOGmjhxojp27JjrvEOHDtXHH3+cY3qDBg20bdu2Is2F0mHy8j06dOq8IoJ89MA1tayOAwAAgDLK5eLUuXPnIrvz2bNna+TIkZo0aZI6dOig//znP+revbu2b9+uqKioHPO//fbbeuWVV5yXMzMz1bRpU916661Flgmlx4ET5/TB8j2SpGd7NZCvFwNCAAAAoHjYjDHGlRv8/PPPl7y+U6dOBV5W27Zt1aJFC02ePNk5rX79+urbt68mTJiQ7+2/+eYb3XzzzYqLi1N0dHSB7jMlJUVBQUFKTk5WhQoVCpwVJc8/Zq7Xou1H1L5miGaNaMvQ+AAAAHCJK93A5S1O11xzTY5pf19hLegxTunp6dqwYYOeeuqpbNO7deum1atXF2gZH330ka6//vpLlqa0tDSlpaU5LzOARdmwfNdRLdp+RO5uNo3r3ZDSBAAAgGLl8qh6J0+ezPaTlJSkBQsWqHXr1lq0aFGBl3Ps2DHZ7XaFhYVlmx4WFqbExMR8b5+QkKD58+drxIgRl5xvwoQJCgoKcv5ERkYWOCNKpvRMh56fe+GYtiHtYlQnLNDiRAAAACjrXN7iFBQUlGNa165d5e3trUcffVQbNmxwaXkXbynIGp0vPzNmzFBwcLD69u17yflGjx6tUaNGOS+npKRQnkq56avitPfYWYUGeGlk19pWxwEAAEA54HJxykvlypW1c+fOAs8fGhoqd3f3HFuXkpKScmyFupgxRtOmTdOgQYPk5eV1yXm9vb3l7e1d4Fwo2Y6kpOqdn/6UJD15Yz1V8PG0OBEAAADKA5eL05YtW7JdNsYoISFBr7zyipo2bVrg5Xh5eally5ZavHix+vXr55y+ePFi9enT55K3Xb58uXbv3q27777btfAo9V6Z/4fOptvVPCpYt7SobnUcAAAAlBMuF6dmzZrJZrPp4sH4rrrqKk2bNs2lZY0aNUqDBg1Sq1at1K5dO02ZMkXx8fG67777JF3Yze7QoUOaOXNmttt99NFHatu2rRo1auRqfJRi6/ad0JxNh2SzSc/3big3NwaEAAAAwJXhcnGKi4vLdtnNzU2VK1eWj4+Py3c+YMAAHT9+XOPHj1dCQoIaNWqkefPmOUfJS0hIUHx8fLbbJCcn66uvvtLbb7/t8v2h9LI7jMZ+e2FAiNtbR6pJ9WBrAwEAAKBccfk8TqUd53EqnT75Zb+e+2arKvh4aOnj1ygkgOPWAAAAUDiudIMCD0e+ZMkSNWjQINfzICUnJ6thw4ZasWKF62mBfJw8m643F10YeOSxbnUpTQAAALjiClycJk6cqHvuuSfXJhYUFKR7771Xb731VpGGAyTp9UU7depchuqFB+rOtlFWxwEAAEA5VODi9Ntvv+nGG2/M8/pu3bq5fA4nID9bDyXrs7UXjnN7vndDebi7fM5mAAAAoNAKvBZ65MgReXrmfc4cDw8PHT16tEhCAdKFoe7Hzt0mY6TeTSPUtkaI1ZEAAABQThW4OFWrVk2///57ntdv2bJFVatWLZJQgCTN2XRIG/aflJ+Xu57uUd/qOAAAACjHClycevTooTFjxig1NTXHdefPn9fYsWPVq1evIg2H8ut0aoZenveHJOmf19ZWeJDrw90DAAAARaXAw5EfOXJELVq0kLu7ux566CHVrVtXNptNO3bs0Pvvvy+73a6NGzcqLCysuDMXCsORlw4v/bBdH66IU2yovxaM7ChvD3erIwEAAKCMcaUbFPgEuGFhYVq9erXuv/9+jR49Wll9y2az6YYbbtCkSZNKfGlC6bA76bSmr9onSRpzUwNKEwAAACxX4OIkSdHR0Zo3b55Onjyp3bt3yxij2rVrq2LFisWVD+WMMUbj5m5XpsPo+vpV1KVuFasjAQAAAK4VpywVK1ZU69atizoLoIXbErVy9zF5ebjpuV4NrI4DAAAASHJhcAiguJ1Pt+uF73dIku7tVEPRIf4WJwIAAAAuoDihxPhg+R4dOnVeEUE+euCaWlbHAQAAAJwoTigRDpw4p8nL90iSnunZQL5eDAgBAACAkoPihBLhhe+3Kz3TofY1Q9SjcbjVcQAAAIBsKE6w3M+7jmrR9iNyd7NpXO+GstlsVkcCAAAAsqE4wVLpmQ6N+26bJGlIuxjVCQu0OBEAAACQE8UJlpq+Kk57j55VaICXRnatbXUcAAAAIFcUJ1jmSEqq3vnpT0nSkzfWUwUfT4sTAQAAALmjOMEyr8z/Q2fT7WoeFaxbWlS3Og4AAACQJ4oTLLFu3wnN2XRINpv0fO+GcnNjQAgAAACUXBQnXHF2h9HYby8MCHF760g1qR5sbSAAAAAgHxQnXHGfro3X9oQUVfDx0OPd6lodBwAAAMgXxQlX1Mmz6Xpz0U5J0mPd6iokwNviRAAAAED+KE64ot5YtFOnzmWoXnig7mwbZXUcAAAAoEAoTrhith5K1qdr4yVdGBDCw523HwAAAEoH1lxxRRhjNHbuNhkj9W4aobY1QqyOBAAAABQYxQlXxJxNh7Rh/0n5ebnr6R71rY4DAAAAuITihGJ3OjVDL8/7Q5L00LW1FB7kY3EiAAAAwDUUJxS7d376U8fOpCk21F93Xx1rdRwAAADAZRQnFKvdSac1fdU+SdKYmxrI28Pd2kAAAADAZaA4odgYYzRu7nZlOoyur19FXepWsToSAAAAcFkoTig2C7clauXuY/LycNNzvRpYHQcAAAC4bBQnFIvz6Xa98P0OSdK9nWooOsTf4kQAAADA5aM4oVh8sHyPDp06r4ggHz1wTS2r4wAAAACFQnFCkTtw4pw+WL5HkvRMzwby9WJACAAAAJRuFCcUuRe+3660TIfa1wxRj8bhVscBAAAACo3ihCL1866jWrT9iNzdbBrXu6FsNpvVkQAAAIBCozihyKRnOjTuu22SpCHtYlQnLNDiRAAAAEDRoDihyMxYHae9R88qNMBLI7vWtjoOAAAAUGQoTigSR1JS9faPf0qSnryxnir4eFqcCAAAACg6FCcUiVfm/6Gz6XY1jwrWLS2qWx0HAAAAKFIUJxTaun0nNGfTIdls0vO9G8rNjQEhAAAAULZQnFAodofR2G8vDAgxoFWkmlQPtjYQAAAAUAwoTiiUT9fGa3tCiir4eOhfN9S1Og4AAABQLChOuGwnz6brzUU7JUmPdaurkABvixMBAAAAxYPihMv2xqKdOnUuQ/XCA3Vn2yir4wAAAADFhuKEy7L1ULI+XRsv6cKAEB7uvJUAAABQdrG2C5cZYzR27jYZI/VuGqG2NUKsjgQAAAAUK4oTXDZn0yFt2H9Sfl7uerpHfavjAAAAAMWO4gSXnE7N0IT5f0iSHrq2lsKDfCxOBAAAABQ/ihNc8s5Pf+ro6TTFhvrr7qtjrY4DAAAAXBEUJxTY7qTTmr5qnyRpzE0N5O3hbm0gAAAA4AqhOKFAjDF6/rvtynQYXV+/irrUrWJ1JAAAAOCKoTihQBZuO6IVfx6Tl4ebnuvVwOo4AAAAwBVFcUK+zqfb9cL32yVJ93aqoegQf4sTAQAAAFcWxQn5+mD5Hh06dV4RQT564JpaVscBAAAArjiKEy7pwIlz+mD5HknSMz0byNeLASEAAABQ/lCccEkv/rBdaZkOtasRoh6Nw62OAwAAAFiC4oQ8/bzrqBZuOyJ3N5ue79NQNpvN6kgAAACAJShOyFV6pkPjvtsmSRrSLkZ1wgItTgQAAABYh+KEXM1YHae9R88qNMBLI7vWtjoOAAAAYCmKE3I4kpKqt3/8U5L05I31VMHH0+JEAAAAgLUoTsjhlfl/6Gy6Xc2jgnVLi+pWxwEAAAAsR3FCNuv3ndCcTYdks0nP924oNzcGhAAAAAAoTnCyO4zGfHthQIgBrSLVpHqwtYEAAACAEoLiBKdP18Zre0KKKvh46F831LU6DgAAAFBiUJwgSTp5Nl1vLtopSXqsW12FBHhbnAgAAAAoOShOkCS9sWinTp3LUL3wQN3ZNsrqOAAAAECJQnGCth5K1qdr4yVJ43o3lIc7bwsAAADg71hDLueMMRo7d5uMkXo3jdBVNUKsjgQAAACUOBSncm7OpkPasP+k/Lzc9XSP+lbHAQAAAEokilM5djo1QxPm/yFJeujaWgoP8rE4EQAAAFAyUZzKsXeX7NbR02mKDfXX3VfHWh0HAAAAKLEoTuXU7qTTmrYyTpI05qYG8vZwtzgRAAAAUHJRnMohY4ye/267Mh1G19evoi51q1gdCQAAACjRKE7l0MJtR7Tiz2Py8nDTc70aWB0HAAAAKPEoTuVMaoZdL3y/XZL0j441FB3ib3EiAAAAoOSjOJUzk5ft0aFT5xUR5KMHutS0Og4AAABQKlCcypEDJ87pg+V7JEnP9GwgPy8PixMBAAAApQPFqRx58YftSst0qF2NEPVoHG51HAAAAKDUoDiVEz/vOqqF247I3c2m5/s0lM1mszoSAAAAUGpQnMqB9EyHxn23TZI0pF2M6oQFWpwIAAAAKF0oTuXAjNVx2nv0rEIDvDSya22r4wAAAAClDsWpjEtKSdXbP/4pSXrixnqq4ONpcSIAAACg9LG8OE2aNEmxsbHy8fFRy5YttWLFikvOn5aWpmeeeUbR0dHy9vZWzZo1NW3atCuUtvSZMP8PnU23q1lksPq3qG51HAAAAKBUsnQ86tmzZ2vkyJGaNGmSOnTooP/85z/q3r27tm/frqioqFxvc9ttt+nIkSP66KOPVKtWLSUlJSkzM/MKJy8d1u87oTmbDslmk8b3aSg3NwaEAAAAAC6HzRhjrLrztm3bqkWLFpo8ebJzWv369dW3b19NmDAhx/wLFizQ7bffrr1796pSpUqXdZ8pKSkKCgpScnKyKlSocNnZSzq7w+imd1dqe0KKbm8dqVduaWJ1JAAAAKBEcaUbWLarXnp6ujZs2KBu3bplm96tWzetXr0619vMnTtXrVq10muvvaZq1aqpTp06evzxx3X+/Pk87yctLU0pKSnZfsqDz9bGa3tCiir4eOhfN9S1Og4AAABQqlm2q96xY8dkt9sVFhaWbXpYWJgSExNzvc3evXu1cuVK+fj4aM6cOTp27JgeeOABnThxIs/jnCZMmKDnn3++yPOXZCfPpuuNRTslSY91q6uQAG+LEwEAAAClm+WDQ1x8IlZjTJ4nZ3U4HLLZbJo1a5batGmjHj166K233tKMGTPy3Oo0evRoJScnO38OHDhQ5I+hpHlj0U6dOpeheuGBurNt7seKAQAAACg4y7Y4hYaGyt3dPcfWpaSkpBxbobJUrVpV1apVU1BQkHNa/fr1ZYzRwYMHVbt2znMUeXt7y9u7/Gxx2XooWZ+ujZckjevdUB7ulndjAAAAoNSzbK3ay8tLLVu21OLFi7NNX7x4sdq3b5/rbTp06KDDhw/rzJkzzmm7du2Sm5ubqldnqG1jjMbO3SZjpJuaRuiqGiFWRwIAAADKBEs3R4waNUpTp07VtGnTtGPHDj366KOKj4/XfffdJ+nCbnaDBw92zj9w4ECFhIRo2LBh2r59u37++Wf961//0vDhw+Xr62vVwygx5mw6pA37T8rPy11P96hndRwAAACgzLD0PE4DBgzQ8ePHNX78eCUkJKhRo0aaN2+eoqOjJUkJCQmKj493zh8QEKDFixfrn//8p1q1aqWQkBDddtttevHFF616CCXG6dQMTZj/hyTpoWtrqWoQRRIAAAAoKpaex8kKZfU8Ti/P26EpP+9VbKi/FozsKG8Pd6sjAQAAACVaqTiPE4rO7qQzmrYyTpI05qYGlCYAAACgiFGcSjljjJ7/bpsyHUbX16+iLnWrWB0JAAAAKHMoTqXcwm1HtOLPY/LycNNzvRpYHQcAAAAokyhOpVhqhl0vfL9dkvSPjjUUHeJvcSIAAACgbKI4lWKTl+3RoVPnFRHkowe61LQ6DgAAAFBmUZxKqQMnzumD5XskSc/0bCA/L0tHlgcAAADKNIpTKfXiD9uVlulQuxoh6tE43Oo4AAAAQJlGcSqFft51VAu3HZG7m03P92kom81mdSQAAACgTKM4lTLpmQ6N+26bJGlIuxjVCQu0OBEAAABQ9lGcSpkZq+O09+hZhQZ4aWTX2lbHAQAAAMoFilMpkpSSqrd//FOS9MSN9VTBx9PiRAAAAED5QHEqRV6Z/4fOptvVLDJY/VtUtzoOAAAAUG5QnEqJ9ftO6OtNh2SzSc/3big3NwaEAAAAAK4UilMpYHcYjfn2woAQA1pFqmlksLWBAAAAgHKG4lQKfLY2XtsTUlTBx0P/uqGu1XEAAACAcofiVMKdPJuuNxbtlCQ91q2uQgK8LU4EAAAAlD8UpxLujUU7depchuqFB+rOtlFWxwEAAADKJYpTCbb1ULI+XRsvSRrXu6E83Hm5AAAAACuwJl5CGWM0bu42GSPd1DRCV9UIsToSAAAAUG5RnEqobzYf0vr9J+Xn5a6ne9SzOg4AAABQrlGcSqDTqRl6ed4fkqSHrq2lqkG+FicCAAAAyjeKUwn07pLdOno6TbGh/rr76lir4wAAAADlHsWphNmddEbTVsZJksb0aiBvD3eLEwEAAACgOJUgxhg9/902ZTqMrq9fRV3qVbE6EgAAAABRnEqUhduOaMWfx+Tl4abnejWwOg4AAACAv1CcSojUDLte/GG7JOkfHWsoOsTf4kQAAAAAslCcSogPlu/RwZPnFRHkowe61LQ6DgAAAIC/oTiVAAdOnNPkZXskSc/0bCA/Lw+LEwEAAAD4O4pTCfDiD9uVlulQuxoh6tE43Oo4AAAAAC5CcbLYij+PauG2I3J3s+n5Pg1ls9msjgQAAADgIhQnC6VnOjRu7jZJ0pB2MaoTFmhxIgAAAAC5oThZaMbqOO05elahAV4a2bW21XEAAAAA5IFRCCzUKCJINSv7697ONVXBx9PqOAAAAADyQHGyUPtaoVowspPcOa4JAAAAKNEoThbzdGdvSQAAAKCkY60dAAAAAPJBcQIAAACAfFCcAAAAACAfFCcAAAAAyAfFCQAAAADyQXECAAAAgHxQnAAAAAAgHxQnAAAAAMgHxQkAAAAA8kFxAgAAAIB8UJwAAAAAIB8UJwAAAADIB8UJAAAAAPJBcQIAAACAfFCcAAAAACAfFCcAAAAAyAfFCQAAAADy4WF1gCvNGCNJSklJsTgJAAAAACtldYKsjnAp5a44nT59WpIUGRlpcRIAAAAAJcHp06cVFBR0yXlspiD1qgxxOBw6fPiwAgMDZbPZrI6jlJQURUZG6sCBA6pQoYLVcVAEeE3LHl7TsonXtezhNS2beF3LnpL0mhpjdPr0aUVERMjN7dJHMZW7LU5ubm6qXr261TFyqFChguVvHBQtXtOyh9e0bOJ1LXt4TcsmXteyp6S8pvltacrC4BAAAAAAkA+KEwAAAADkg+JkMW9vb40dO1be3t5WR0ER4TUte3hNyyZe17KH17Rs4nUte0rra1ruBocAAAAAAFexxQkAAAAA8kFxAgAAAIB8UJwAAAAAIB8UJwAAAADIB8XJQpMmTVJsbKx8fHzUsmVLrVixwupIKISff/5ZN910kyIiImSz2fTNN99YHQmFNGHCBLVu3VqBgYGqUqWK+vbtq507d1odC4U0efJkNWnSxHnixXbt2mn+/PlWx0IRmjBhgmw2m0aOHGl1FFymcePGyWazZfsJDw+3OhaKwKFDh3TXXXcpJCREfn5+atasmTZs2GB1rAKhOFlk9uzZGjlypJ555hlt2rRJHTt2VPfu3RUfH291NFyms2fPqmnTpnrvvfesjoIisnz5cj344IP65ZdftHjxYmVmZqpbt246e/as1dFQCNWrV9crr7yi9evXa/369br22mvVp08fbdu2zepoKALr1q3TlClT1KRJE6ujoJAaNmyohIQE58/vv/9udSQU0smTJ9WhQwd5enpq/vz52r59u958800FBwdbHa1AGI7cIm3btlWLFi00efJk57T69eurb9++mjBhgoXJUBRsNpvmzJmjvn37Wh0FRejo0aOqUqWKli9frk6dOlkdB0WoUqVKev3113X33XdbHQWFcObMGbVo0UKTJk3Siy++qGbNmmnixIlWx8JlGDdunL755htt3rzZ6igoQk899ZRWrVpVaveyYouTBdLT07VhwwZ169Yt2/Ru3bpp9erVFqUCkJ/k5GRJF1ayUTbY7XZ9/vnnOnv2rNq1a2d1HBTSgw8+qJ49e+r666+3OgqKwJ9//qmIiAjFxsbq9ttv1969e62OhEKaO3euWrVqpVtvvVVVqlRR8+bN9eGHH1odq8AoThY4duyY7Ha7wsLCsk0PCwtTYmKiRakAXIoxRqNGjdLVV1+tRo0aWR0HhfT7778rICBA3t7euu+++zRnzhw1aNDA6lgohM8//1wbN25kr40yom3btpo5c6YWLlyoDz/8UImJiWrfvr2OHz9udTQUwt69ezV58mTVrl1bCxcu1H333aeHH35YM2fOtDpagXhYHaA8s9ls2S4bY3JMA1AyPPTQQ9qyZYtWrlxpdRQUgbp162rz5s06deqUvvrqKw0ZMkTLly+nPJVSBw4c0COPPKJFixbJx8fH6jgoAt27d3f+v3HjxmrXrp1q1qypjz/+WKNGjbIwGQrD4XCoVatWevnllyVJzZs317Zt2zR58mQNHjzY4nT5Y4uTBUJDQ+Xu7p5j61JSUlKOrVAArPfPf/5Tc+fO1dKlS1W9enWr46AIeHl5qVatWmrVqpUmTJigpk2b6u2337Y6Fi7Thg0blJSUpJYtW8rDw0MeHh5avny53nnnHXl4eMhut1sdEYXk7++vxo0b688//7Q6CgqhatWqOf5AVb9+/VIzOBrFyQJeXl5q2bKlFi9enG364sWL1b59e4tSAbiYMUYPPfSQvv76ay1ZskSxsbFWR0IxMcYoLS3N6hi4TNddd51+//13bd682fnTqlUr3Xnnndq8ebPc3d2tjohCSktL044dO1S1alWro6AQOnTokOO0Hrt27VJ0dLRFiVzDrnoWGTVqlAYNGqRWrVqpXbt2mjJliuLj43XfffdZHQ2X6cyZM9q9e7fzclxcnDZv3qxKlSopKirKwmS4XA8++KA+/fRTffvttwoMDHRuJQ4KCpKvr6/F6XC5nn76aXXv3l2RkZE6ffq0Pv/8cy1btkwLFiywOhouU2BgYI5jD/39/RUSEsIxiaXU448/rptuuklRUVFKSkrSiy++qJSUFA0ZMsTqaCiERx99VO3bt9fLL7+s2267TWvXrtWUKVM0ZcoUq6MVCMXJIgMGDNDx48c1fvx4JSQkqFGjRpo3b16padzIaf369erSpYvzctY+2EOGDNGMGTMsSoXCyDpdwDXXXJNt+vTp0zV06NArHwhF4siRIxo0aJASEhIUFBSkJk2aaMGCBeratavV0QD85eDBg7rjjjt07NgxVa5cWVdddZV++eUX1pNKudatW2vOnDkaPXq0xo8fr9jYWE2cOFF33nmn1dEKhPM4AQAAAEA+OMYJAAAAAPJBcQIAAACAfFCcAAAAACAfFCcAAAAAyAfFCQAAAADyQXECAAAAgHxQnAAAAAAgHxQnAAAAAMgHxQkAAAAA8kFxAgAUWmJiov75z3+qRo0a8vb2VmRkpG666Sb99NNPVkcrUYYOHaq+ffu6dJujR4/K09NT586dU2Zmpvz9/RUfH188AQEAefKwOgAAoHTbt2+fOnTooODgYL322mtq0qSJMjIytHDhQj344IP6448/rI5Yqq1Zs0bNmjWTn5+ffv31V1WqVElRUVFWxwKAcoctTgCAQnnggQdks9m0du1a9e/fX3Xq1FHDhg01atQo/fLLL8754uPj1adPHwUEBKhChQq67bbbdOTIEef148aNU7NmzTRt2jRFRUUpICBA999/v+x2u1577TWFh4erSpUqeumll7Ldv81m0+TJk9W9e3f5+voqNjZWX375ZbZ5fv/9d1177bXy9fVVSEiI/vGPf+jMmTPO67O2BL3xxhuqWrWqQkJC9OCDDyojI8M5T3p6up544glVq1ZN/v7+atu2rZYtW+a8fsaMGQoODtbChQtVv359BQQE6MYbb1RCQoLz8X388cf69ttvZbPZZLPZst0+L6tXr1aHDh0kSStXrnT+HwBwZbHFCQBw2U6cOKEFCxbopZdekr+/f47rg4ODJUnGGPXt21f+/v5avny5MjMz9cADD2jAgAHZysOePXs0f/58LViwQHv27FH//v0VFxenOnXqaPny5Vq9erWGDx+u6667TldddZXzds8995xeeeUVvf322/rkk090xx13qFGjRqpfv77OnTunG2+8UVdddZXWrVunpKQkjRgxQg899JBmzJjhXMbSpUtVtWpVLV26VLt379aAAQPUrFkz3XPPPZKkYcOGad++ffr8888VERGhOXPm6MYbb9Tvv/+u2rVrS5LOnTunN954Q5988onc3Nx011136fHHH9esWbP0+OOPa8eOHUpJSdH06dMlSZUqVcr1eY2Pj1eTJk2cy3R3d9eMGTN0/vx52Ww2BQcHa+DAgZo0adJlv3YAABcZAAAu06+//mokma+//vqS8y1atMi4u7ub+Ph457Rt27YZSWbt2rXGGGPGjh1r/Pz8TEpKinOeG264wcTExBi73e6cVrduXTNhwgTnZUnmvvvuy3Z/bdu2Nffff78xxpgpU6aYihUrmjNnzjiv/+GHH4ybm5tJTEw0xhgzZMgQEx0dbTIzM53z3HrrrWbAgAHGGGN2795tbDabOXToULb7ue6668zo0aONMcZMnz7dSDK7d+92Xv/++++bsLAw5+UhQ4aYPn36XPK5MsaYjIwMExcXZ3777Tfj6elpNm/ebHbv3m0CAgLM8uXLTVxcnDl69Gi+ywEAFB22OAEALpsxRtKF3eUuZceOHYqMjFRkZKRzWoMGDRQcHKwdO3aodevWkqSYmBgFBgY65wkLC5O7u7vc3NyyTUtKSsq2/Hbt2uW4vHnzZud9N23aNNsWsQ4dOsjhcGjnzp0KCwuTJDVs2FDu7u7OeapWrarff/9dkrRx40YZY1SnTp1s95OWlqaQkBDnZT8/P9WsWTPbMi7OWhAeHh6KiYnRF198odatW6tp06ZatWqVwsLC1KlTJ5eXBwAoPIoTAOCy1a5dWzabTTt27LjkaHHGmFzL1cXTPT09s11vs9lyneZwOPLNlrXcvO777/Pkdd9Z9+NwOOTu7q4NGzZkK1eSFBAQcMllZJVLVzRs2FD79+9XRkaGHA6HAgIClJmZqczMTAUEBCg6Olrbtm1zebkAgMvH4BAAgMtWqVIl3XDDDXr//fd19uzZHNefOnVK0oWtS/Hx8Tpw4IDzuu3btys5OVn169cvdI6/D0KRdblevXrO+968eXO2fKtWrZKbm1uOLUh5ad68uex2u5KSklSrVq1sP+Hh4QXO6eXlJbvdnu988+bN0+bNmxUeHq7//ve/2rx5sxo1aqSJEydq8+bNmjdvXoHvEwBQNChOAIBCmTRpkux2u9q0aaOvvvpKf/75p3bs2KF33nnHuQvd9ddfryZNmujOO+/Uxo0btXbtWg0ePFidO3dWq1atCp3h/9q5f9fToziO4y+bwWKwfJVfyackE6sSVko2+ZhsTP4AGe3KRgZFCqtBLAaUQVIsZiX/wvcO37rd73Dv57pf3e6t52M+dc767LzPGY/H6na7ulwuajQa2m63qlarkqRisSi73a5yuazj8ajlcqlaraZSqfR9TM9KKBRSsViUaZqaTCa6Xq/a7XZqtVpPRYzP59PhcND5fNb9fv/0a9+PvF6vHA6HbrebcrmcPB6PTqeT8vm8gsGgvF7vb+8JAHgNwgkA8CV+v1/7/V7JZFL1el2RSESZTEaLxUKdTkfSx8jabDaT0+lUIpFQOp1WIBDQaDR6yRmazaaGw6Gi0aj6/b4Gg4HC4bCkj3dH8/lcj8dD8XhchUJBqVRK7Xb7qT16vZ5M01S9XpdhGMpms9psNp/ebVmpVCoyDEOxWEwul0vr9fqna1erleLxuOx2uzabjdxut97e3p46MwDgdWzvfzJ8DQDAP8Jms2k6nf7yjRUAAF/FjRMAAAAAWCCcAAAAAMAC35EDAP5rTJwDAP4GbpwAAAAAwALhBAAAAAAWCCcAAAAAsEA4AQAAAIAFwgkAAAAALBBOAAAAAGCBcAIAAAAAC4QTAAAAAFj4BrePvtoUO2kDAAAAAElFTkSuQmCC",
+      "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",
@@ -1330,10 +1631,10 @@
     "#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_.___)\n",
-    "plt.xlabel(___)\n",
-    "plt.ylabel(___)\n",
-    "plt.title(___);"
+    "plt.plot(state_pca.explained_variance_ratio_.cumsum())\n",
+    "plt.xlabel(\"Component #\")\n",
+    "plt.ylabel(\"Cumulative ratio variance\")\n",
+    "plt.title(\"Cumulative variance ratioj explained by PCA components for state/resort summary statistics\");"
    ]
   },
   {
@@ -1359,13 +1660,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "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.___(___)"
+    "state_pca_x = state_pca.transform(state_summary_scale)"
    ]
   },
   {
@@ -1411,14 +1712,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1452,13 +1751,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "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": 31,
+     "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(___)[___].___\n",
+    "state_avg_price = ski_data.groupby('state')['AdultWeekend'].mean()\n",
     "state_avg_price.head()"
    ]
   },
@@ -1469,14 +1785,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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zOH36tEbbhg0bYGlpic6dOwOA9NTQn3/+qdFv27ZtWutTqVTlHlvv3r2lX/TPWrt2LSwsLKrk0fF69eqha9eu2Lx5s8a4ioqKEBMTg6ZNm5br8kBprK2t8frrr2PKlCnIysqSnmYq6bUq/sX2fAB59km6YiWtY8CAARBC4Pr16/D29tb6ad++faljVigUWtvfuXMnrl+/rtXXw8MDXbt2RVRUFDZs2IC8vDyMGzdOazxnz55F8+bNdY6ntDOWz47J1NRU4xd/Zmam1tNSxYFw06ZNGu0bN27UmLawsEDPnj2RlJSEDh066BxXcejo2bNnie+Fili/fr3G9I8//oiCgoIy/1FTkqCgIFy8eFG6yVyXAQMG4O7duygsLNS5j61atdJapqTPF6rdeOaGDObs2bPSNe9bt27h4MGDiIqKgrGxMbZs2aL1ZNOzIiIi8Ntvv6F///5wcXFBbm4uIiMjAUC65m5paQlXV1f8/PPP6N27Nxo1agRbW9tSH1sujZOTE1599VWEhYXB0dERMTExiIuLw+LFi6WzD//4xz/QqlUrzJw5EwUFBWjYsCG2bNmCQ4cOaa2vffv22Lx5M1atWgUvLy8YGRlp/N2fZ82fP1+6V+OTTz5Bo0aNsH79euzcuRNffvklGjRooNc+PW/hwoXo27cvevbsiZkzZ8LU1BQrV67E2bNn8cMPP1T4r0QDwMCBA6W/aWRnZ4erV69i6dKlcHV1RYsWLQBAChjLli3DmDFjoFQq0apVK/j6+qJhw4aYNGkS5s+fD6VSifXr12v9Yn12HYsXL0ZQUBCMjY3RoUMHdO/eHe+88w7GjRuHxMRE9OjRA/Xq1UNGRgYOHTqE9u3b47333itx/AMGDEB0dDRat26NDh064MSJE/jqq69KvAQyfvx4vPvuu7hx4wZ8fX21fmF++umniIuLg6+vL6ZNm4ZWrVohNzcXaWlp2LVrFyIiIsq8vFL8ePTkyZPx+uuv49q1a/jss8/g6OiIS5cuSf1eeeUVdO/eHR988AGys7Ph5eWFI0eOYO3atQCg8VTXsmXL8PLLL8PPzw/vvfce3Nzc8PDhQ/z111/Yvn27FBpCQkIQGRmJ/v37Y8GCBdLTUs8+bl0emzdvhomJCfr27Ss9LeXp6al1f1B5hYSEYNOmTRg0aBA+/PBDdOnSBU+ePEFCQgIGDBiAnj17Yvjw4Vi/fj2Cg4Mxffp0dOnSBUqlEn///Tf279+PQYMGYfDgweX6fKFazrD3M9OLqPiJouIfU1NTYW9vL/z9/cUXX3whbt26pbXM808wHTlyRAwePFi4uroKlUolbGxshL+/v9i2bZvGcvv27ROdOnUSKpVKAJCerihe3+3bt8vclhBPn8zo37+/+Omnn0Tbtm2FqampcHNzE//617+0lr948aIIDAwUVlZWws7OTkydOlV6auXZJ0mysrLE66+/LqytrYVCodDYJnQ85XXmzBkxcOBA0aBBA2Fqaio8PT1FVFSURp/ip6X++9//arQXP3nzfH9dDh48KHr16iXq1asnzM3NRbdu3cT27dt1rq88T0t9/fXXwtfXV9ja2gpTU1Ph4uIiJkyYINLS0jT6zZkzRzg5OQkjIyONWh0+fFj4+PgICwsLYWdnJyZOnChOnjyptT95eXli4sSJws7OTqrns09fRUZGiq5du0r71bx5czF69GiRmJhY6vjv3bsnJkyYIOzt7YWFhYV4+eWXxcGDB4W/v7/w9/fX6v/gwQNhbm4uAIjvv/9e5zpv374tpk2bJtzd3YVSqRSNGjUSXl5e4qOPPhKPHj0qV40XLVok3NzchEqlEh4eHuL777/XeexmZWWJcePGCWtra2FhYSH69u0rjh49KgCIZcuWafRNTU0V48ePF02aNBFKpVLY2dkJX19fsWDBAo1+58+fF3379hVmZmaiUaNGYsKECeLnn3+u0NNSJ06cEAMHDhT169cXlpaWYsSIEeLmzZsafYvfd7o8/7SUEE9fq+nTpwsXFxehVCqFvb296N+/v7hw4YLUR61WiyVLlghPT09hZmYm6tevL1q3bi3effddcenSJSFE+T9fqPZSCFHGYylERCQrGzZswKhRo/D777/D19e3RrcdFhaG8PBw3L59u0ruFSPShZeliIhk7IcffsD169fRvn17GBkZ4ejRo/jqq6/Qo0ePGg82RDWF4YaISMYsLS2xceNGLFiwAI8fP4ajoyPGjh2LBQsWGHpoRNWGl6WIiIhIVvgoOBEREckKww0RERHJCsMNERERycoLd0NxUVERbty4AUtLS73+IBkRERHVPCEEHj58CCcnJ40/QKnLCxdubty4ofXNykRERFQ3XLt2rcy/4v3ChZviL0a7du0arKystOar1Wrs3bsXgYGBUCqVNT082WN9qxfrW71Y3+rF+lavul7f7OxsODs7a3zBaUleuHBTfCnKysqqxHBjYWEBKyurOvni13asb/VifasX61u9WN/qJZf6lueWEt5QTERERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLJi0HCzatUqdOjQQfoqBB8fH/zyyy+lLpOQkAAvLy+YmZmhWbNmiIiIqKHREhERUV1g0HDTtGlTLFq0CImJiUhMTESvXr0waNAgnDt3Tmf/1NRUBAcHw8/PD0lJSZg7dy6mTZuG2NjYGh45ERER1VYG/eLMgQMHakx//vnnWLVqFY4ePYq2bdtq9Y+IiICLiwuWLl0KAPDw8EBiYiKWLFmCoUOH1sSQiYiIqJarNffcFBYWYuPGjXj8+DF8fHx09jly5AgCAwM12vr164fExESo1eqaGCYRERHVcgY9cwMAZ86cgY+PD3Jzc1G/fn1s2bIFbdq00dk3MzMTjRs31mhr3LgxCgoKcOfOHTg6Omotk5eXh7y8PGk6OzsbwNOvftcViIrbGJaqB+tbvVjf6sX6Vi/Wt3rV9fpWZNwGDzetWrXCqVOncP/+fcTGxmLMmDFISEgoMeAoFAqNaSGEzvZiCxcuRHh4uFb73r17YWFhUeK44uLiyrsLpAfWt3qxvtWL9a1erG/1qqv1zcnJKXdfhShOB7VEnz590Lx5c3z77bda83r06IFOnTph2bJlUtuWLVswbNgw5OTkQKlUai2j68yNs7Mz7ty5AysrK63+arUacXFx6Nu3r871UeXU9fq2C9uj97Jnw/pV4Uh0q+v1re1Y36qj672kMhL4zLsI8xKNkFek+x+sQM28l+Sorh+/2dnZsLW1xYMHD3T+/n6Wwc/cPE8IoRFGnuXj44Pt27drtO3duxfe3t4lvlAqlQoqlUqrXalUlvriljWfKqeu1jevsOQP3LLU5P7W1frWFaxv5ZX2XsorUpQ6n7WvnLp6/FZkzAa9oXju3Lk4ePAg0tLScObMGXz00UeIj4/HqFGjAABz5szB6NGjpf6TJk3C1atXERoaiuTkZERGRmL16tWYOXOmoXaBiIiIahmDnrm5efMm3n77bWRkZKBBgwbo0KEDdu/ejb59+wIAMjIykJ6eLvV3d3fHrl27MGPGDKxYsQJOTk5Yvnw5HwMnIiIiiUHDzerVq0udHx0drdXm7++PkydPVtOIiIiIqK6rNX/nhoiIiKgqMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkawYNNwsXLgQ//jHP2BpaQl7e3u89tprSElJKXWZ+Ph4KBQKrZ8LFy7U0KiJiIioNjNouElISMCUKVNw9OhRxMXFoaCgAIGBgXj8+HGZy6akpCAjI0P6adGiRQ2MmIiIiGo7E0NufPfu3RrTUVFRsLe3x4kTJ9CjR49Sl7W3t4e1tXU1jo6IiIjqIoOGm+c9ePAAANCoUaMy+3bq1Am5ublo06YNPv74Y/Ts2VNnv7y8POTl5UnT2dnZAAC1Wg21Wq3Vv7hN1zyqvLpeX5Wx0HvZmtjnul7f2o71rTq63ksqI6Hx35Kw/vqp68dvRcatEELo/2ldhYQQGDRoEO7du4eDBw+W2C8lJQUHDhyAl5cX8vLysG7dOkRERCA+Pl7n2Z6wsDCEh4drtW/YsAEWFhZVug9ERERUPXJycjBy5Eg8ePAAVlZWpfatNeFmypQp2LlzJw4dOoSmTZtWaNmBAwdCoVBg27ZtWvN0nblxdnbGnTt3dBZHrVYjLi4Offv2hVKprPiOUKnqen3bhe3Re9mzYf2qcCS61fX61nasb9XR9V5SGQl85l2EeYlGyCtSlLhsTbyX5KiuH7/Z2dmwtbUtV7ipFZelpk6dim3btuHAgQMVDjYA0K1bN8TExOicp1KpoFKptNqVSmWpL25Z86ly6mp98wpL/sAtS03ub12tb13B+lZeae+lvCJFqfNZ+8qpq8dvRcZs0HAjhMDUqVOxZcsWxMfHw93dXa/1JCUlwdHRsYpHR0RERHWRQcPNlClTsGHDBvz888+wtLREZmYmAKBBgwYwNzcHAMyZMwfXr1/H2rVrAQBLly6Fm5sb2rZti/z8fMTExCA2NhaxsbEG2w8iIiKqPQwablatWgUACAgI0GiPiorC2LFjAQAZGRlIT0+X5uXn52PmzJm4fv06zM3N0bZtW+zcuRPBwcE1NWwiIiKqxQx+Waos0dHRGtOzZs3CrFmzqmlEREREVNfxu6WIiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWDBpuFi5ciH/84x+wtLSEvb09XnvtNaSkpJS5XEJCAry8vGBmZoZmzZohIiKiBkZLREREdYFBw01CQgKmTJmCo0ePIi4uDgUFBQgMDMTjx49LXCY1NRXBwcHw8/NDUlIS5s6di2nTpiE2NrYGR05ERES1lYkhN757926N6aioKNjb2+PEiRPo0aOHzmUiIiLg4uKCpUuXAgA8PDyQmJiIJUuWYOjQodU9ZCIiIqrlDBpunvfgwQMAQKNGjUrsc+TIEQQGBmq09evXD6tXr4ZarYZSqdSYl5eXh7y8PGk6OzsbAKBWq6FWq7XWX9ymax5VXl2vr8pY6L1sTexzXa9vbcf6Vh1d7yWVkdD4b0lYf/3U9eO3IuNWCCH0/7SuQkIIDBo0CPfu3cPBgwdL7NeyZUuMHTsWc+fOldoOHz6M7t2748aNG3B0dNToHxYWhvDwcK31bNiwARYWFlW3A0RERFRtcnJyMHLkSDx48ABWVlal9q01Z27ef/99/Pnnnzh06FCZfRUKhcZ0cT57vh0A5syZg9DQUGk6Ozsbzs7OCAwM1FkctVqNuLg49O3bV+ssUHm0C9tT4WWKnQ3rp/eylVGTY362vp0+/63GtltVKlOrmqAyEvjMuwjzEo2QV/T0/VAXa2WoMZelsp8P9D+6jg9dx68utfX4qO3q+vFbfOWlPGpFuJk6dSq2bduGAwcOoGnTpqX2dXBwQGZmpkbbrVu3YGJiAhsbG63+KpUKKpVKq12pVJb64pY1vyR5hSW/IctiqIPNEGNWKpUvXK1qUl6RQhprXaxVbf/g1ffzgf6ntOPj2eNXF9a+curq8VuRMRv0aSkhBN5//31s3rwZv/32G9zd3ctcxsfHB3FxcRpte/fuhbe3d518sYiIiKhqGTTcTJkyBTExMdiwYQMsLS2RmZmJzMxMPHnyROozZ84cjB49WpqeNGkSrl69itDQUCQnJyMyMhKrV6/GzJkzDbELREREVMsYNNysWrUKDx48QEBAABwdHaWfTZs2SX0yMjKQnp4uTbu7u2PXrl2Ij49Hx44d8dlnn2H58uV8DJyIiIgAGPiem/I8qBUdHa3V5u/vj5MnT1bDiIiIiKiu43dLERERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGs6BVuevXqhfv372u1Z2dno1evXpUdExEREZHe9Ao38fHxyM/P12rPzc3FwYMHKz0oIiIiIn2ZVKTzn3/+Kf3/+fPnkZmZKU0XFhZi9+7daNKkSdWNjoiIiKiCKhRuOnbsCIVCAYVCofPyk7m5Ob755psqGxwRERFRRVUo3KSmpkIIgWbNmuGPP/6AnZ2dNM/U1BT29vYwNjau8kESERERlVeFwo2rqysAoKioqFoGQ0RERFRZFQo3z7p48SLi4+Nx69YtrbDzySefVHpgRERERPrQK9x8//33eO+992BrawsHBwcoFAppnkKhYLghIiIig9Er3CxYsACff/45Zs+eXdXjISIiIqoUvf7Ozb179/DGG29U9ViIiIiIKk2vcPPGG29g7969VT0WIiIiokrT67LUSy+9hHnz5uHo0aNo3749lEqlxvxp06ZVyeCIiIiIKkqvcPPdd9+hfv36SEhIQEJCgsY8hULBcENEREQGo1e4SU1NrepxEBEREVUJve65ISIiIqqt9DpzM378+FLnR0ZG6jUYIiIiosrSK9zcu3dPY1qtVuPs2bO4f/++zi/UJCIiIqopeoWbLVu2aLUVFRVh8uTJaNasWaUHRURERKSvKrvnxsjICDNmzMC///3vqlolERERUYVV6Q3Fly9fRkFBQVWukoiIiKhC9LosFRoaqjEthEBGRgZ27tyJMWPGVMnAiIiIiPSh15mbpKQkjZ8///wTAPD1119j6dKl5V7PgQMHMHDgQDg5OUGhUGDr1q2l9o+Pj4dCodD6uXDhgj67QURERDKk15mb/fv3V8nGHz9+DE9PT4wbNw5Dhw4t93IpKSmwsrKSpu3s7KpkPERERFT36RVuit2+fRspKSlQKBRo2bJlhUNGUFAQgoKCKrxde3t7WFtbV3g5IiIikj+9Lks9fvwY48ePh6OjI3r06AE/Pz84OTlhwoQJyMnJqeoxaunUqRMcHR3Ru3fvKjuLRERERPKg9w3FCQkJ2L59O7p37w4AOHToEKZNm4YPPvgAq1atqtJBFnN0dMR3330HLy8v5OXlYd26dejduzfi4+PRo0cPncvk5eUhLy9Pms7Ozgbw9A8PqtVqrf7FbbrmlYfKWOi1XGW2WVk1OeZn6/ui1aomqIyExn+BulkrQ425LJX9fKD/0XV86Dp+dWH99VPXj9+KjFshhKjwJ5CtrS1++uknBAQEaLTv378fw4YNw+3btyu6SigUCmzZsgWvvfZahZYbOHAgFAoFtm3bpnN+WFgYwsPDtdo3bNgACwuLCo+TiIiIal5OTg5GjhyJBw8eaNx3q4teZ25ycnLQuHFjrXZ7e/sauSz1rG7duiEmJqbE+XPmzNF4dD07OxvOzs4IDAzUWRy1Wo24uDj07dsXSqWywuNpF7anwssUOxvWT+9lK6Mmx/xsfTt9/luNbbeqVKZWNUFlJPCZdxHmJRohr0gBoG7WylBjLktlPx/of3QdH7qOX11q6/FR29X147f4ykt56BVufHx8MH/+fKxduxZmZmYAgCdPniA8PBw+Pj76rFJvSUlJcHR0LHG+SqWCSqXSalcqlaW+uGXNL0leYclvyLIY6mAzxJiVSuULV6ualFekkMZaF2tV2z949f18oP8p7fh49vjVhbWvnLp6/FZkzHqFm6VLlyIoKAhNmzaFp6cnFAoFTp06BZVKhb1795Z7PY8ePcJff/0lTaempuLUqVNo1KgRXFxcMGfOHFy/fh1r166Vtuvm5oa2bdsiPz8fMTExiI2NRWxsrD67QURERDKkV7hp3749Ll26hJiYGFy4cAFCCAwfPhyjRo2Cubl5udeTmJiInj17StPFl4/GjBmD6OhoZGRkID09XZqfn5+PmTNn4vr16zA3N0fbtm2xc+dOBAcH67MbREREJEN6hZuFCxeicePG+L//+z+N9sjISNy+fRuzZ88u13oCAgJQ2v3M0dHRGtOzZs3CrFmzKjxeIiIienHo9Xduvv32W7Ru3VqrvW3btoiIiKj0oIiIiIj0pVe4yczM1HkTr52dHTIyMio9KCIiIiJ96RVunJ2d8fvvv2u1//7773Bycqr0oIiIiIj0pdc9NxMnTkRISAjUajV69eoFAPj1118xa9YsfPDBB1U6QCIiIqKK0CvczJo1C1lZWZg8eTLy8/MBAGZmZpg9ezbmzJlTpQMkIiIiqgi9wo1CocDixYsxb948JCcnw9zcHC1atND5x/KIiIiIapJe4aZY/fr18Y9//KOqxkJERERUaXrdUExERERUWzHcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkawYNNwcOHAAAwcOhJOTExQKBbZu3VrmMgkJCfDy8oKZmRmaNWuGiIiI6h8oERER1RkGDTePHz+Gp6cn/t//+3/l6p+amorg4GD4+fkhKSkJc+fOxbRp0xAbG1vNIyUiIqK6wsSQGw8KCkJQUFC5+0dERMDFxQVLly4FAHh4eCAxMRFLlizB0KFDq2mUREREVJcYNNxU1JEjRxAYGKjR1q9fP6xevRpqtRpKpVJrmby8POTl5UnT2dnZAAC1Wg21Wq3Vv7hN17zyUBkLvZarzDYrqybH/Gx9X7Ra1QSVkdD4L1A3a2WoMZelsp8P9D+6jg9dx68urL9+6vrxW5FxK4QQteLTWqFQYMuWLXjttddK7NOyZUuMHTsWc+fOldoOHz6M7t2748aNG3B0dNRaJiwsDOHh4VrtGzZsgIWFRZWMnYiIiKpXTk4ORo4ciQcPHsDKyqrUvnXqzA3wNAQ9qzibPd9ebM6cOQgNDZWms7Oz4ezsjMDAQJ3FUavViIuLQ9++fXWeCSpLu7A9FV6m2NmwfnovW5nt1iSVkcBn3kWYl2iEvCLdrxnpj/WtXqXVtzLvX0OqTZ8dtf34NdRrXFWvUU3WtzpqVXzlpTzqVLhxcHBAZmamRtutW7dgYmICGxsbncuoVCqoVCqtdqVSWWp4KWt+SfIK9T9g9NleVWzXEPKKFHVuzHUJ61u9dNW3Mu9fQ6qNx0ltPX4N9RpXdS1qor7VUauKrLNO/Z0bHx8fxMXFabTt3bsX3t7edfaDhYiIiKqWQcPNo0ePcOrUKZw6dQrA00e9T506hfT0dABPLymNHj1a6j9p0iRcvXoVoaGhSE5ORmRkJFavXo2ZM2caYvhERERUCxn0slRiYiJ69uwpTRffGzNmzBhER0cjIyNDCjoA4O7ujl27dmHGjBlYsWIFnJycsHz5cj4GTkRERBKDhpuAgACU9rBWdHS0Vpu/vz9OnjxZjaMiIiKiuqxO3XNDREREVBaGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGyIiIpIVhhsiIiKSFYOHm5UrV8Ld3R1mZmbw8vLCwYMHS+wbHx8PhUKh9XPhwoUaHDERERHVZgYNN5s2bUJISAg++ugjJCUlwc/PD0FBQUhPTy91uZSUFGRkZEg/LVq0qKERExERUW1n0HDzr3/9CxMmTMDEiRPh4eGBpUuXwtnZGatWrSp1OXt7ezg4OEg/xsbGNTRiIiIiqu1MDLXh/Px8nDhxAh9++KFGe2BgIA4fPlzqsp06dUJubi7atGmDjz/+GD179iyxb15eHvLy8qTp7OxsAIBarYZardbqX9yma155qIyFXstVZpuV3W5NUhkJjf9S1WJ9q1dp9a3M+9eQatNnR20/fg31GlfVa1ST9a2OWlVknQohhEGOohs3bqBJkyb4/fff4evrK7V/8cUXWLNmDVJSUrSWSUlJwYEDB+Dl5YW8vDysW7cOERERiI+PR48ePXRuJywsDOHh4VrtGzZsgIWFRdXtEBEREVWbnJwcjBw5Eg8ePICVlVWpfQ125qaYQqHQmBZCaLUVa9WqFVq1aiVN+/j44Nq1a1iyZEmJ4WbOnDkIDQ2VprOzs+Hs7IzAwECdxVGr1YiLi0Pfvn2hVCorvD/twvZUeJliZ8P66b1sZbZbk1RGAp95F2FeohHyinS/zqQ/1rd6lVbfyrx/Dak2fXbU9uPXUK9xVb1GNVnf6qhV8ZWX8jBYuLG1tYWxsTEyMzM12m/duoXGjRuXez3dunVDTExMifNVKhVUKpVWu1KpLDW8lDW/JHmF+h8w+myvKrZrCHlFijo35rqE9a1euupbmfevIdXG46S2Hr+Geo2ruhY1Ud/qqFVF1mmwG4pNTU3h5eWFuLg4jfa4uDiNy1RlSUpKgqOjY1UPj4iIiOoog16WCg0Nxdtvvw1vb2/4+Pjgu+++Q3p6OiZNmgTg6SWl69evY+3atQCApUuXws3NDW3btkV+fj5iYmIQGxuL2NhYQ+4GERER1SIGDTdvvvkm7t69i08//RQZGRlo164ddu3aBVdXVwBARkaGxt+8yc/Px8yZM3H9+nWYm5ujbdu22LlzJ4KDgw21C0RERFTLGPyG4smTJ2Py5Mk650VHR2tMz5o1C7NmzaqBUREREVFdZfCvXyAiIiKqSgw3REREJCsMN0RERCQrDDdEREQkKww3REREJCsMN0RERCQrDDdEREQkKww3REREJCsMN0RERCQrDDdEREQkKww3REREJCsMN0RERCQrDDdEREQkKww3REREJCsMN0RERCQrDDdEREQkKww3REREJCsMN0RERCQrDDdEREQkKww3REREJCsMN0RERCQrDDdEREQkKww3REREJCsMN0RERCQrDDdEREQkKww3REREJCsMN0RERCQrDDdEREQkKww3REREJCsMN0RERCQrDDdEREQkKww3REREJCsMN0RERCQrDDdEREQkKww3REREJCsMN0RERCQrDDdEREQkKww3REREJCsMN0RERCQrDDdEREQkKww3REREJCsMN0RERCQrDDdEREQkKww3REREJCsMN0RERCQrDDdEREQkKww3REREJCsGDzcrV66Eu7s7zMzM4OXlhYMHD5baPyEhAV5eXjAzM0OzZs0QERFRQyMlIiKiusCg4WbTpk0ICQnBRx99hKSkJPj5+SEoKAjp6ek6+6empiI4OBh+fn5ISkrC3LlzMW3aNMTGxtbwyImIiKi2Mmi4+de//oUJEyZg4sSJ8PDwwNKlS+Hs7IxVq1bp7B8REQEXFxcsXboUHh4emDhxIsaPH48lS5bU8MiJiIiotjJYuMnPz8eJEycQGBio0R4YGIjDhw/rXObIkSNa/fv164fExESo1epqGysRERHVHSaG2vCdO3dQWFiIxo0ba7Q3btwYmZmZOpfJzMzU2b+goAB37tyBo6Oj1jJ5eXnIy8uTph88eAAAyMrK0hmI1Go1cnJycPfuXSiVygrvl0nB4wovU+zu3bt6L1uZ7dYkkyKBnJwimKiNUFikMPRwZIf1rV6l1bcy719Dqk2fHbX9+DXUa1xVr1FN1rc6avXw4UMAgBCizL4GCzfFFArNAgshtNrK6q+rvdjChQsRHh6u1e7u7l7RoVY7268NPYKaMdLQA5A51rd6lVTfF+X9W91q8/Erh9e4pupbnbV6+PAhGjRoUGofg4UbW1tbGBsba52luXXrltbZmWIODg46+5uYmMDGxkbnMnPmzEFoaKg0XVRUhKysLNjY2OgMRNnZ2XB2dsa1a9dgZWVV0d2iMrC+1Yv1rV6sb/VifatXXa+vEAIPHz6Ek5NTmX0NFm5MTU3h5eWFuLg4DB48WGqPi4vDoEGDdC7j4+OD7du3a7Tt3bsX3t7eJV5CUqlUUKlUGm3W1tZljs/KyqpOvvh1BetbvVjf6sX6Vi/Wt3rV5fqWdcammEGflgoNDcV//vMfREZGIjk5GTNmzEB6ejomTZoE4OlZl9GjR0v9J02ahKtXryI0NBTJycmIjIzE6tWrMXPmTEPtAhEREdUyBr3n5s0338Tdu3fx6aefIiMjA+3atcOuXbvg6uoKAMjIyND4mzfu7u7YtWsXZsyYgRUrVsDJyQnLly/H0KFDDbULREREVMsY/IbiyZMnY/LkyTrnRUdHa7X5+/vj5MmT1TYelUqF+fPna13KoqrB+lYv1rd6sb7Vi/WtXi9SfRWiPM9UEREREdURBv9uKSIiIqKqxHBDREREssJwQ0RERLLCcENERESywnCDp1/RoFAoEBISIrUJIRAWFgYnJyeYm5sjICAA586dM9wg65CwsDAoFAqNHwcHB2k+a1t5169fx1tvvQUbGxtYWFigY8eOOHHihDSfNdafm5ub1vGrUCgwZcoUAKxtZRUUFODjjz+Gu7s7zM3N0axZM3z66acoKiqS+rDGlfPw4UOEhITA1dUV5ubm8PX1xfHjx6X5L0R9xQvujz/+EG5ubqJDhw5i+vTpUvuiRYuEpaWliI2NFWfOnBFvvvmmcHR0FNnZ2YYbbB0xf/580bZtW5GRkSH93Lp1S5rP2lZOVlaWcHV1FWPHjhXHjh0TqampYt++feKvv/6S+rDG+rt165bGsRsXFycAiP379wshWNvKWrBggbCxsRE7duwQqamp4r///a+oX7++WLp0qdSHNa6cYcOGiTZt2oiEhARx6dIlMX/+fGFlZSX+/vtvIcSLUd8XOtw8fPhQtGjRQsTFxQl/f38p3BQVFQkHBwexaNEiqW9ubq5o0KCBiIiIMNBo64758+cLT09PnfNY28qbPXu2ePnll0uczxpXrenTp4vmzZuLoqIi1rYK9O/fX4wfP16jbciQIeKtt94SQvD4raycnBxhbGwsduzYodHu6ekpPvrooxemvi/0ZakpU6agf//+6NOnj0Z7amoqMjMzERgYKLWpVCr4+/vj8OHDNT3MOunSpUtwcnKCu7s7hg8fjitXrgBgbavCtm3b4O3tjTfeeAP29vbo1KkTvv/+e2k+a1x18vPzERMTg/Hjx0OhULC2VeDll1/Gr7/+iosXLwIATp8+jUOHDiE4OBgAj9/KKigoQGFhIczMzDTazc3NcejQoRemvi9suNm4cSNOnjyJhQsXas0r/ubx57+dvHHjxlrfSk7aunbtirVr12LPnj34/vvvkZmZCV9fX9y9e5e1rQJXrlzBqlWr0KJFC+zZsweTJk3CtGnTsHbtWgA8fqvS1q1bcf/+fYwdOxYAa1sVZs+ejREjRqB169ZQKpXo1KkTQkJCMGLECACscWVZWlrCx8cHn332GW7cuIHCwkLExMTg2LFjyMjIeGHqa/CvXzCEa9euYfr06di7d69Wun2WQqHQmBZCaLWRtqCgIOn/27dvDx8fHzRv3hxr1qxBt27dALC2lVFUVARvb2988cUXAIBOnTrh3LlzWLVqlcYXzbLGlbd69WoEBQXByclJo5211d+mTZsQExODDRs2oG3btjh16hRCQkLg5OSEMWPGSP1YY/2tW7cO48ePR5MmTWBsbIzOnTtj5MiRGl9dJPf6vpBnbk6cOIFbt27By8sLJiYmMDExQUJCApYvXw4TExMp0T6fYm/duqWVdqls9erVQ/v27XHp0iXpqSnWVn+Ojo5o06aNRpuHh4f0JbOscdW4evUq9u3bh4kTJ0ptrG3l/fOf/8SHH36I4cOHo3379nj77bcxY8YM6Sw6a1x5zZs3R0JCAh49eoRr167hjz/+gFqthru7+wtT3xcy3PTu3RtnzpzBqVOnpB9vb2+MGjUKp06dQrNmzeDg4IC4uDhpmfz8fCQkJMDX19eAI6+b8vLykJycDEdHR+nNxdrqr3v37khJSdFou3jxIlxdXQGANa4iUVFRsLe3R//+/aU21rbycnJyYGSk+avH2NhYehScNa469erVg6OjI+7du4c9e/Zg0KBBL059DXk3c23y7NNSQjx9VK5BgwZi8+bN4syZM2LEiBGye1SuunzwwQciPj5eXLlyRRw9elQMGDBAWFpairS0NCEEa1tZf/zxhzAxMRGff/65uHTpkli/fr2wsLAQMTExUh/WuHIKCwuFi4uLmD17ttY81rZyxowZI5o0aSI9Cr5582Zha2srZs2aJfVhjStn9+7d4pdffhFXrlwRe/fuFZ6enqJLly4iPz9fCPFi1Jfh5v/3fLgpKioS8+fPFw4ODkKlUokePXqIM2fOGG6AdUjx30xQKpXCyclJDBkyRJw7d06az9pW3vbt20W7du2ESqUSrVu3Ft99953GfNa4cvbs2SMAiJSUFK15rG3lZGdni+nTpwsXFxdhZmYmmjVrJj766CORl5cn9WGNK2fTpk2iWbNmwtTUVDg4OIgpU6aI+/fvS/NfhPoqhBDC0GePiIiIiKrKC3nPDREREckXww0RERHJCsMNERERyQrDDREREckKww0RERHJCsMNERERyQrDDREREckKww0R6S0+Ph4KhQL3798v9zJjx47Fa6+9Vm1jqiphYWHo2LFjjW9X3/q4ublh6dKl5eobHR0Na2vrCm+jJgUEBCAkJMTQw6A6iuGG6ryxY8dCoVBg0qRJWvMmT54MhUKBsWPH1vzAKkDfXzb6hIvahr/EKqakY+X48eN45513amQMNXHcbd68GZ999lm1rZ/kjeGGZMHZ2RkbN27EkydPpLbc3Fz88MMPcHFxMeDIiGqGnZ0dLCwsDD2MSlOr1QCARo0awdLS0sCjobqK4YZkoXPnznBxccHmzZults2bN8PZ2RmdOnXS6CuEwJdffolmzZrB3Nwcnp6e+Omnn6T5hYWFmDBhAtzd3WFubo5WrVph2bJlGusovnSwZMkSODo6wsbGBlOmTJE+mHU5ffo0evbsCUtLS1hZWcHLywuJiYmIj4/HuHHj8ODBAygUCigUCoSFhQEAYmJi4O3tDUtLSzg4OGDkyJG4desWACAtLQ09e/YEADRs2FDjDFVZ+6hLadsqtmvXLrRs2RLm5ubo2bMn0tLSNObrupSzdOlSuLm56dzm2LFjkZCQgGXLlkn7/vw6AeCbb75B+/btpemtW7dCoVBgxYoVUlu/fv0wZ84caXr79u3w8vKCmZkZmjVrhvDwcBQUFEjzHzx4gHfeeQf29vawsrJCr169cPr06RLrk5qaipdeegnvvfceioqKkJ+fj1mzZqFJkyaoV68eunbtivj4eKl/8RmWPXv2wMPDA/Xr18crr7yCjIwMqU9hYSFCQ0NhbW0NGxsbzJo1C6V9I05px8rzl6Xu37+Pd955B40bN4aZmRnatWuHHTt26Fzv3bt30aVLF7z66qvIzc0t9fgp7bh7XnENtm7dipYtW8LMzAx9+/bFtWvXpD7Fx0xkZCSaNWsGlUoFIYTWGb28vDzMmjULzs7OUKlUaNGiBVavXi3NP3/+PIKDg1G/fn00btwYb7/9Nu7cuVNiLUneGG5INsaNG4eoqChpOjIyEuPHj9fq9/HHHyMqKgqrVq3CuXPnMGPGDLz11ltISEgAABQVFaFp06b48ccfcf78eXzyySeYO3cufvzxR4317N+/H5cvX8b+/fuxZs0aREdHIzo6usTxjRo1Ck2bNsXx48dx4sQJfPjhh1AqlfD19cXSpUthZWWFjIwMZGRkYObMmQCA/Px8fPbZZzh9+jS2bt2K1NRU6ReJs7MzYmNjAQApKSnIyMiQQlhZ+6hLadsCgGvXrmHIkCEIDg7GqVOnMHHiRHz44YclvyDlsGzZMvj4+OD//u//pH13dnbW6hcQEIBz585Jv6wSEhJga2sr7U9BQQEOHz4Mf39/AMCePXvw1ltvYdq0aTh//jy+/fZbREdH4/PPPwfwNPz1798fmZmZ2LVrF06cOIHOnTujd+/eyMrK0tr+2bNn0b17d7zxxhtYtWoVjIyMMG7cOPz+++/YuHEj/vzzT7zxxht45ZVXcOnSJWm5nJwcLFmyBOvWrcOBAweQnp4uvbYA8PXXXyMyMhKrV6/GoUOHkJWVhS1btpRYr9KOlWcVFRUhKCgIhw8fRkxMDM6fP49FixbB2NhYq+/ff/8NPz8/tG7dGps3b4aZmVmpx09px50uOTk5+Pzzz7FmzRr8/vvvyM7OxvDhwzX6/PXXX/jxxx8RGxuLU6dO6VzP6NGjsXHjRixfvhzJycmIiIhA/fr1AQAZGRnw9/dHx44dkZiYiN27d+PmzZsYNmxYieMimTPcd3YSVY0xY8aIQYMGidu3bwuVSiVSU1NFWlqaMDMzE7dv3xaDBg0SY8aMEUII8ejRI2FmZiYOHz6ssY4JEyaIESNGlLiNyZMni6FDh2ps09XVVRQUFEhtb7zxhnjzzTdLXIelpaWIjo7WOS8qKko0aNCgzH39448/BADx8OFDIYQQ+/fvFwDEvXv3pD767mNZ25ozZ47w8PAQRUVFUp/Zs2drbH/+/PnC09NTYz3//ve/haurqzRd/HoV8/f3F9OnTy91LEVFRcLW1lb89NNPQgghOnbsKBYuXCjs7e2FEEIcPnxYmJiYSGP18/MTX3zxhcY61q1bJxwdHYUQQvz666/CyspK5ObmavRp3ry5+PbbbzX25fDhw6JRo0biq6++kvr99ddfQqFQiOvXr2ss37t3bzFnzhwhxNPXFID466+/pPkrVqwQjRs3lqYdHR3FokWLpGm1Wi2aNm2qUZ/nlXSsuLq6in//+99CiKffam5kZKTzW82fXUdKSopwcXERU6dOlV7X8hw/uo67krYDQBw9elRqS05OFgDEsWPHhBBP66xUKsWtW7c0ln32uEhJSREARFxcnM7tzJs3TwQGBmq0Xbt2rcRvdif5MzFUqCKqara2tujfvz/WrFkj/cvc1tZWo8/58+eRm5uLvn37arTn5+drXL6KiIjAf/7zH1y9ehVPnjxBfn6+1uWWtm3bavxL2NHREWfOnClxfKGhoZg4cSLWrVuHPn364I033kDz5s1L3aekpCSEhYXh1KlTyMrKQlFREQAgPT0dbdq00blMefexottKTk5Gt27doFAopGV8fHxKHX9VUSgU6NGjB+Lj49G7d2+cO3cOkyZNwpIlS5CcnIz4+Hh07txZ+pf8iRMncPz4celMDfD0ElBubi5ycnJw4sQJPHr0CDY2NhrbefLkCS5fvixNp6eno0+fPliwYAFmzJghtZ88eRJCCLRs2VJj+by8PI11WlhYaLzGjo6O0qW+Bw8eICMjQ6OGJiYm8Pb2LvXSVHmcOnUKTZs21Rrfs548eYKXX34ZI0aM0Djzou/xU5LifSrWunVrWFtbIzk5GV26dAEAuLq6ws7OrtT9MTY2ls7MPe/EiRPYv3+/9Po/6/Lly6XWgeSJ4YZkZfz48Xj//fcBQON+jGLFv7B37tyJJk2aaMxTqVQAgB9//BEzZszA119/DR8fH1haWuKrr77CsWPHNPorlUqNaYVCIa1fl7CwMIwcORI7d+7EL7/8gvnz52Pjxo0YPHiwzv6PHz9GYGAgAgMDERMTAzs7O6Snp6Nfv37Iz88vcTvl2Ud9tlWeX7hGRkZa/Uq7D6kiAgIC8N133+HgwYPw9PSEtbU1evTogYSEBMTHxyMgIEDqW1RUhPDwcAwZMkRrPWZmZigqKoKjo6PGPTLFnn0Syc7ODk5OTti4cSMmTJgAKysraf3GxsY4ceKE1qWeZ3/B6jpGKhtcysPc3LzMPiqVCn369MHOnTvxz3/+E02bNgWg3/FTlmcDsa62evXqlbp8WftTVFSEgQMHYvHixVrzHB0dyzlKkhOGG5KVV155Rfpl3K9fP635bdq0gUqlQnp6eon/Cjx48CB8fX0xefJkqe3Zf81XRsuWLdGyZUvMmDEDI0aMQFRUFAYPHgxTU1MUFhZq9L1w4QLu3LmDRYsWSfehJCYmavQxNTUFAI1ly7OPzyvPttq0aYOtW7dqtB09elRj2s7ODpmZmRBCSL+8SrqH4tl9eH7fdQkICMD06dPx008/SUHG398f+/btw+HDhzF9+nSpb+fOnZGSkoKXXnpJ57o6d+6MzMxMmJiYlHizM/D0l+qOHTsQHByMfv36Ye/evbC0tESnTp1QWFiIW7duwc/Pr8yx69KgQQM4Ojri6NGj6NGjB4Cn9w4V3/9TkvLUq0OHDvj7779x8eLFEs9aGBkZYd26dRg5ciR69eqF+Ph4ODk5lev40XXclaSgoACJiYnSWZqUlBTcv38frVu3LnPZYu3bt0dRURESEhLQp08frfmdO3dGbGws3NzcYGLCX2vEG4pJZoyNjZGcnIzk5GSdN09aWlpi5syZmDFjBtasWYPLly8jKSkJK1aswJo1awAAL730EhITE7Fnzx5cvHgR8+bNw/Hjxys1ridPnuD9999HfHw8rl69it9//x3Hjx+Hh4cHgKdPujx69Ai//vor7ty5g5ycHLi4uMDU1BTffPMNrly5gm3btmn93Q9XV1coFArs2LEDt2/fxqNHj8q1j88rz7YmTZqEy5cvIzQ0FCkpKdiwYYPWDdQBAQG4ffs2vvzyS1y+fBkrVqzAL7/8Umpt3NzccOzYMaSlpeHOnTslnv1q164dbGxssH79eincBAQEYOvWrdIllmKffPIJ1q5di7CwMJw7dw7JycnYtGkTPv74YwBAnz594OPjg9deew179uxBWloaDh8+jI8//lgr1NWrVw87d+6EiYkJgoKC8OjRI7Rs2RKjRo3C6NGjsXnzZqSmpuL48eNYvHgxdu3aVer+Pmv69OlYtGgRtmzZggsXLmDy5Mll/u0YXcfK8/z9/dGjRw8MHToUcXFxSE1NxS+//ILdu3dr9DM2Nsb69evh6emJXr16ITMzs1zHj67jriRKpRJTp07FsWPHcPLkSYwbNw7dunWTwk55uLm5YcyYMRg/frx0s3t8fLx0k/+UKVOQlZWFESNG4I8//sCVK1ewd+9ejB8/vlwBjGTIgPf7EFWJ529Qfd6zNxQL8fTm1GXLlolWrVoJpVIp7OzsRL9+/URCQoIQQojc3FwxduxY0aBBA2FtbS3ee+898eGHH2rcKKtrm9OnTxf+/v46x5CXlyeGDx8unJ2dhampqXBychLvv/++ePLkidRn0qRJwsbGRgAQ8+fPF0IIsWHDBuHm5iZUKpXw8fER27ZtEwBEUlKStNynn34qHBwchEKhkPazrH3UpTzb2r59u3jppZeESqUSfn5+IjIyUuvG0lWrVglnZ2dRr149MXr0aPH555+XekNxSkqK6NatmzA3NxcARGpqaoljHDp0qDA2NhYPHjyQ9rNRo0bC29tbq+/u3buFr6+vMDc3F1ZWVqJLly7iu+++k+ZnZ2eLqVOnCicnJ6FUKoWzs7MYNWqUSE9PF0Jo3xz98OFD4evrK/z8/MSjR49Efn6++OSTT4Sbm5tQKpXCwcFBDB48WPz5559CCN03/m7ZskU8+7GrVqvF9OnThZWVlbC2thahoaFi9OjRpR7PQug+Vp69oVgIIe7evSvGjRsnbGxshJmZmWjXrp3YsWOHzrGp1WoxZMgQ4eHhIW7evFmu40fXcfe84u3ExsaKZs2aCVNTU9GrVy+RlpYm9dF1E7oQ2jeaP3nyRMyYMUM4OjoKU1NT8dJLL4nIyEhp/sWLF8XgwYOFtbW1MDc3F61btxYhISEaN8DTi0MhRA1cACYiohdOdHQ0QkJC6vRf0Ka6iZeliIiISFYYboiIiEhWeFmKiIiIZIVnboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFYYboiIiEhWGG6IiIhIVhhuiIiISFb+P4Cy89woM4UlAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1503,16 +1817,91 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [],
+   "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>-1.328438</td>\n",
+       "      <td>-0.179985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>-1.823114</td>\n",
+       "      <td>-0.399104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>3.339287</td>\n",
+       "      <td>-1.236662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>4.636900</td>\n",
+       "      <td>-1.103753</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>-0.956068</td>\n",
+       "      <td>1.009327</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  PC1       PC2\n",
+       "state                          \n",
+       "Alaska      -1.328438 -0.179985\n",
+       "Arizona     -1.823114 -0.399104\n",
+       "California   3.339287 -1.236662\n",
+       "Colorado     4.636900 -1.103753\n",
+       "Connecticut -0.956068  1.009327"
+      ]
+     },
+     "execution_count": 33,
+     "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': ___, 'PC2': ___}, index=__)\n",
+    "pca_df = pd.DataFrame({'PC1': state_pca_x[:, 0], 'PC2': state_pca_x[:, 1]}, index=state_summary_index)\n",
     "pca_df.head()"
    ]
   },
@@ -1637,14 +2026,96 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
+   "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>-1.328438</td>\n",
+       "      <td>-0.179985</td>\n",
+       "      <td>57.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>-1.823114</td>\n",
+       "      <td>-0.399104</td>\n",
+       "      <td>83.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>3.339287</td>\n",
+       "      <td>-1.236662</td>\n",
+       "      <td>81.416667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>4.636900</td>\n",
+       "      <td>-1.103753</td>\n",
+       "      <td>90.714286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>-0.956068</td>\n",
+       "      <td>1.009327</td>\n",
+       "      <td>56.800000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  PC1       PC2  AdultWeekend\n",
+       "state                                        \n",
+       "Alaska      -1.328438 -0.179985     57.333333\n",
+       "Arizona     -1.823114 -0.399104     83.500000\n",
+       "California   3.339287 -1.236662     81.416667\n",
+       "Colorado     4.636900 -1.103753     90.714286\n",
+       "Connecticut -0.956068  1.009327     56.800000"
+      ]
+     },
+     "execution_count": 36,
+     "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 = ___([___, ___], axis=___)\n",
+    "pca_df = pd.concat([pca_df, state_avg_price], axis=1)\n",
     "pca_df.head()"
    ]
   },
@@ -1686,42 +2157,49 @@
        "      <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>-1.328438</td>\n",
+       "      <td>-0.179985</td>\n",
        "      <td>57.333333</td>\n",
-       "      <td>(53.1, 60.4]</td>\n",
+       "      <td>(53.0, 60.4]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Arizona</th>\n",
-       "      <td>-1.839049</td>\n",
-       "      <td>-0.387959</td>\n",
+       "      <td>-1.823114</td>\n",
+       "      <td>-0.399104</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>3.339287</td>\n",
+       "      <td>-1.236662</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>4.636900</td>\n",
+       "      <td>-1.103753</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>-0.956068</td>\n",
+       "      <td>1.009327</td>\n",
        "      <td>56.800000</td>\n",
-       "      <td>(53.1, 60.4]</td>\n",
+       "      <td>(53.0, 60.4]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1729,11 +2207,12 @@
       ],
       "text/plain": [
        "                  PC1       PC2  AdultWeekend      Quartile\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]"
+       "state                                                      \n",
+       "Alaska      -1.328438 -0.179985     57.333333  (53.0, 60.4]\n",
+       "Arizona     -1.823114 -0.399104     83.500000  (78.4, 93.0]\n",
+       "California   3.339287 -1.236662     81.416667  (78.4, 93.0]\n",
+       "Colorado     4.636900 -1.103753     90.714286  (78.4, 93.0]\n",
+       "Connecticut -0.956068  1.009327     56.800000  (53.0, 60.4]"
       ]
      },
      "execution_count": 37,
@@ -1810,12 +2289,19 @@
        "      <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>-1.808995</td>\n",
+       "      <td>0.746499</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
@@ -1825,7 +2311,8 @@
       ],
       "text/plain": [
        "                   PC1       PC2  AdultWeekend Quartile\n",
-       "Rhode Island -1.843646  0.761339           NaN      NaN"
+       "state                                                  \n",
+       "Rhode Island -1.808995  0.746499           NaN      NaN"
       ]
      },
      "execution_count": 39,
@@ -1856,12 +2343,32 @@
    "execution_count": 40,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Muhammad\\AppData\\Local\\Temp\\ipykernel_11080\\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\\Muhammad\\AppData\\Local\\Temp\\ipykernel_11080\\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             -1.84365\n",
-       "PC2             0.761339\n",
-       "AdultWeekend     64.1244\n",
+       "PC1            -1.808995\n",
+       "PC2             0.746499\n",
+       "AdultWeekend    64.10172\n",
        "Quartile              NA\n",
        "Name: Rhode Island, dtype: object"
       ]
@@ -1901,14 +2408,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1949,9 +2454,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [],
+   "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",
@@ -1966,8 +2482,8 @@
     "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.___(x=___, y=___, size=___, hue=___, \n",
-    "                hue_order=___, data=pca_df)\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",
@@ -2041,73 +2557,73 @@
        "  <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",
+       "      <td>0.487832</td>\n",
+       "      <td>0.304803</td>\n",
+       "      <td>0.493069</td>\n",
+       "      <td>0.491185</td>\n",
+       "      <td>0.335187</td>\n",
+       "      <td>0.184595</td>\n",
+       "      <td>0.195729</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",
+       "      <td>-0.084485</td>\n",
+       "      <td>-0.153689</td>\n",
+       "      <td>-0.046648</td>\n",
+       "      <td>-0.045831</td>\n",
+       "      <td>-0.340230</td>\n",
+       "      <td>0.665473</td>\n",
+       "      <td>0.637460</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",
+       "      <td>-0.178210</td>\n",
+       "      <td>0.729716</td>\n",
+       "      <td>0.132247</td>\n",
+       "      <td>0.012168</td>\n",
+       "      <td>-0.534317</td>\n",
+       "      <td>0.183923</td>\n",
+       "      <td>-0.314319</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",
+       "      <td>0.068676</td>\n",
+       "      <td>-0.167690</td>\n",
+       "      <td>-0.095296</td>\n",
+       "      <td>-0.105256</td>\n",
+       "      <td>0.316153</td>\n",
+       "      <td>0.695307</td>\n",
+       "      <td>-0.602991</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",
+       "      <td>-0.204455</td>\n",
+       "      <td>0.539622</td>\n",
+       "      <td>-0.239344</td>\n",
+       "      <td>-0.426736</td>\n",
+       "      <td>0.576004</td>\n",
+       "      <td>0.055198</td>\n",
+       "      <td>0.304614</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",
+       "      <td>0.810850</td>\n",
+       "      <td>0.084540</td>\n",
+       "      <td>-0.188777</td>\n",
+       "      <td>-0.492847</td>\n",
+       "      <td>-0.233193</td>\n",
+       "      <td>-0.049294</td>\n",
+       "      <td>0.005597</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",
+       "      <td>-0.138304</td>\n",
+       "      <td>-0.156619</td>\n",
+       "      <td>0.797003</td>\n",
+       "      <td>-0.566046</td>\n",
+       "      <td>0.016925</td>\n",
+       "      <td>-0.017732</td>\n",
+       "      <td>-0.010919</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2115,31 +2631,31 @@
       ],
       "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",
+       "0           0.487832                     0.304803               0.493069   \n",
+       "1          -0.084485                    -0.153689              -0.046648   \n",
+       "2          -0.178210                     0.729716               0.132247   \n",
+       "3           0.068676                    -0.167690              -0.095296   \n",
+       "4          -0.204455                     0.539622              -0.239344   \n",
+       "5           0.810850                     0.084540              -0.188777   \n",
+       "6          -0.138304                    -0.156619               0.797003   \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",
+       "0                   0.491185                    0.335187   \n",
+       "1                  -0.045831                   -0.340230   \n",
+       "2                   0.012168                   -0.534317   \n",
+       "3                  -0.105256                    0.316153   \n",
+       "4                  -0.426736                    0.576004   \n",
+       "5                  -0.492847                   -0.233193   \n",
+       "6                  -0.566046                    0.016925   \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  "
+       "0                0.184595                 0.195729  \n",
+       "1                0.665473                 0.637460  \n",
+       "2                0.183923                -0.314319  \n",
+       "3                0.695307                -0.602991  \n",
+       "4                0.055198                 0.304614  \n",
+       "5               -0.049294                 0.005597  \n",
+       "6               -0.017732                -0.010919  "
       ]
      },
      "execution_count": 43,
@@ -2208,48 +2724,48 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>state_total_skiable_area_ac</th>\n",
-       "      <td>3427</td>\n",
-       "      <td>7239</td>\n",
+       "      <td>3427.0</td>\n",
+       "      <td>7239.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>state_total_days_open</th>\n",
-       "      <td>1847</td>\n",
-       "      <td>1777</td>\n",
+       "      <td>1847.0</td>\n",
+       "      <td>1777.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>state_total_terrain_parks</th>\n",
-       "      <td>43</td>\n",
-       "      <td>50</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>50.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>state_total_nightskiing_ac</th>\n",
-       "      <td>376</td>\n",
-       "      <td>50</td>\n",
+       "      <td>376.0</td>\n",
+       "      <td>50.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_100kcapita</th>\n",
-       "      <td>1.17672</td>\n",
-       "      <td>2.40389</td>\n",
+       "      <td>1.176721</td>\n",
+       "      <td>2.403889</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_100ksq_mile</th>\n",
-       "      <td>171.141</td>\n",
-       "      <td>155.99</td>\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     7239\n",
-       "state_total_days_open                 1847     1777\n",
-       "state_total_terrain_parks               43       50\n",
-       "state_total_nightskiing_ac             376       50\n",
-       "resorts_per_100kcapita             1.17672  2.40389\n",
-       "resorts_per_100ksq_mile            171.141   155.99"
+       "                                        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": 44,
@@ -2294,13 +2810,13 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>resorts_per_state</th>\n",
-       "      <td>0.839478</td>\n",
-       "      <td>0.712833</td>\n",
+       "      <td>0.822794</td>\n",
+       "      <td>0.697586</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>state_total_skiable_area_ac</th>\n",
-       "      <td>-0.277128</td>\n",
-       "      <td>0.104681</td>\n",
+       "      <td>-0.266840</td>\n",
+       "      <td>0.025050</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>state_total_days_open</th>\n",
@@ -2309,23 +2825,23 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>state_total_terrain_parks</th>\n",
-       "      <td>0.921793</td>\n",
-       "      <td>1.233725</td>\n",
+       "      <td>0.904777</td>\n",
+       "      <td>1.213511</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>state_total_nightskiing_ac</th>\n",
-       "      <td>-0.245050</td>\n",
-       "      <td>-0.747570</td>\n",
+       "      <td>-0.246788</td>\n",
+       "      <td>-0.748365</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_100kcapita</th>\n",
-       "      <td>1.711066</td>\n",
-       "      <td>4.226572</td>\n",
+       "      <td>1.709860</td>\n",
+       "      <td>4.225933</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_100ksq_mile</th>\n",
-       "      <td>3.483281</td>\n",
-       "      <td>3.112841</td>\n",
+       "      <td>3.481903</td>\n",
+       "      <td>3.111420</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2333,13 +2849,13 @@
       ],
       "text/plain": [
        "                                   17        29\n",
-       "resorts_per_state            0.839478  0.712833\n",
-       "state_total_skiable_area_ac -0.277128  0.104681\n",
+       "resorts_per_state            0.822794  0.697586\n",
+       "state_total_skiable_area_ac -0.266840  0.025050\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"
+       "state_total_terrain_parks    0.904777  1.213511\n",
+       "state_total_nightskiing_ac  -0.246788 -0.748365\n",
+       "resorts_per_100kcapita       1.709860  4.225933\n",
+       "resorts_per_100ksq_mile      3.481903  3.111420"
       ]
      },
      "execution_count": 45,
@@ -2484,6 +3000,14 @@
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>fastSixes</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -2541,91 +3065,91 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Runs</th>\n",
-       "      <td>76</td>\n",
-       "      <td>36</td>\n",
-       "      <td>13</td>\n",
-       "      <td>55</td>\n",
-       "      <td>65</td>\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</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\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</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\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</td>\n",
-       "      <td>640</td>\n",
-       "      <td>30</td>\n",
-       "      <td>777</td>\n",
-       "      <td>800</td>\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</td>\n",
-       "      <td>60</td>\n",
-       "      <td>30</td>\n",
-       "      <td>104</td>\n",
-       "      <td>80</td>\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</td>\n",
-       "      <td>45</td>\n",
-       "      <td>150</td>\n",
-       "      <td>122</td>\n",
-       "      <td>115</td>\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</td>\n",
-       "      <td>44</td>\n",
-       "      <td>36</td>\n",
-       "      <td>81</td>\n",
-       "      <td>49</td>\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</td>\n",
-       "      <td>350</td>\n",
-       "      <td>69</td>\n",
-       "      <td>260</td>\n",
-       "      <td>250</td>\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</td>\n",
-       "      <td>53</td>\n",
-       "      <td>34</td>\n",
-       "      <td>89</td>\n",
-       "      <td>78</td>\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</td>\n",
-       "      <td>90</td>\n",
-       "      <td>152</td>\n",
-       "      <td>122</td>\n",
-       "      <td>104</td>\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</td>\n",
+       "      <td>550.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>30</td>\n",
+       "      <td>30.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>80</td>\n",
+       "      <td>80.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2640,6 +3164,7 @@
        "vertical_drop                2500                 1540               294   \n",
        "base_elev                     250                 1200              1796   \n",
        "trams                           1                    0                 0   \n",
+       "fastEight                     0.0                  0.0               0.0   \n",
        "fastSixes                       0                    0                 0   \n",
        "fastQuads                       2                    0                 0   \n",
        "quad                            2                    0                 0   \n",
@@ -2647,17 +3172,17 @@
        "double                          0                    4                 0   \n",
        "surface                         2                    0                 2   \n",
        "total_chairs                    7                    4                 3   \n",
-       "Runs                           76                   36                13   \n",
-       "TerrainParks                    2                    1                 1   \n",
-       "LongestRun_mi                   1                    2                 1   \n",
-       "SkiableTerrain_ac            1610                  640                30   \n",
-       "Snow Making_ac                113                   60                30   \n",
-       "daysOpenLastYear              150                   45               150   \n",
-       "yearsOpen                      60                   44                36   \n",
-       "averageSnowfall               669                  350                69   \n",
-       "AdultWeekend                   85                   53                34   \n",
-       "projectedDaysOpen             150                   90               152   \n",
-       "NightSkiing_ac                550                  NaN                30   \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",
@@ -2667,6 +3192,7 @@
        "vertical_drop                  2300                 1800  \n",
        "base_elev                      9200                 9200  \n",
        "trams                             0                    0  \n",
+       "fastEight                       0.0                  NaN  \n",
        "fastSixes                         1                    0  \n",
        "fastQuads                         0                    1  \n",
        "quad                              2                    2  \n",
@@ -2674,17 +3200,17 @@
        "double                            1                    1  \n",
        "surface                           2                    0  \n",
        "total_chairs                      8                    7  \n",
-       "Runs                             55                   65  \n",
-       "TerrainParks                      4                    2  \n",
-       "LongestRun_mi                     2                  1.2  \n",
-       "SkiableTerrain_ac               777                  800  \n",
-       "Snow Making_ac                  104                   80  \n",
-       "daysOpenLastYear                122                  115  \n",
-       "yearsOpen                        81                   49  \n",
-       "averageSnowfall                 260                  250  \n",
-       "AdultWeekend                     89                   78  \n",
-       "projectedDaysOpen               122                  104  \n",
-       "NightSkiing_ac                  NaN                   80  "
+       "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": 46,
@@ -2784,7 +3310,7 @@
        "      <th>3</th>\n",
        "      <td>Colorado</td>\n",
        "      <td>22</td>\n",
-       "      <td>43682.0</td>\n",
+       "      <td>68682.0</td>\n",
        "      <td>3258.0</td>\n",
        "      <td>74.0</td>\n",
        "      <td>428.0</td>\n",
@@ -2811,7 +3337,7 @@
        "0       Alaska                  3                       2280.0   \n",
        "1      Arizona                  2                       1577.0   \n",
        "2   California                 21                      25948.0   \n",
-       "3     Colorado                 22                      43682.0   \n",
+       "3     Colorado                 22                      68682.0   \n",
        "4  Connecticut                  5                        358.0   \n",
        "\n",
        "   state_total_days_open  state_total_terrain_parks  \\\n",
@@ -2841,7 +3367,9 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
@@ -2929,6 +3457,14 @@
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>fastSixes</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -2986,91 +3522,91 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Runs</th>\n",
-       "      <td>76</td>\n",
-       "      <td>36</td>\n",
-       "      <td>13</td>\n",
-       "      <td>55</td>\n",
-       "      <td>65</td>\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</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\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</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\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</td>\n",
-       "      <td>640</td>\n",
-       "      <td>30</td>\n",
-       "      <td>777</td>\n",
-       "      <td>800</td>\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</td>\n",
-       "      <td>60</td>\n",
-       "      <td>30</td>\n",
-       "      <td>104</td>\n",
-       "      <td>80</td>\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</td>\n",
-       "      <td>45</td>\n",
-       "      <td>150</td>\n",
-       "      <td>122</td>\n",
-       "      <td>115</td>\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</td>\n",
-       "      <td>44</td>\n",
-       "      <td>36</td>\n",
-       "      <td>81</td>\n",
-       "      <td>49</td>\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</td>\n",
-       "      <td>350</td>\n",
-       "      <td>69</td>\n",
-       "      <td>260</td>\n",
-       "      <td>250</td>\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</td>\n",
-       "      <td>53</td>\n",
-       "      <td>34</td>\n",
-       "      <td>89</td>\n",
-       "      <td>78</td>\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</td>\n",
-       "      <td>90</td>\n",
-       "      <td>152</td>\n",
-       "      <td>122</td>\n",
-       "      <td>104</td>\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</td>\n",
+       "      <td>550.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>30</td>\n",
+       "      <td>30.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>80</td>\n",
+       "      <td>80.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_state</th>\n",
@@ -3082,43 +3618,43 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>state_total_skiable_area_ac</th>\n",
-       "      <td>2280</td>\n",
-       "      <td>2280</td>\n",
-       "      <td>2280</td>\n",
-       "      <td>1577</td>\n",
-       "      <td>1577</td>\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</td>\n",
-       "      <td>345</td>\n",
-       "      <td>345</td>\n",
-       "      <td>237</td>\n",
-       "      <td>237</td>\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</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>6</td>\n",
-       "      <td>6</td>\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</td>\n",
-       "      <td>580</td>\n",
-       "      <td>580</td>\n",
-       "      <td>80</td>\n",
-       "      <td>80</td>\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>0.0274774</td>\n",
-       "      <td>0.0274774</td>\n",
+       "      <td>0.027477</td>\n",
+       "      <td>0.027477</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_100ksq_mile</th>\n",
@@ -3141,6 +3677,7 @@
        "vertical_drop                          2500                 1540   \n",
        "base_elev                               250                 1200   \n",
        "trams                                     1                    0   \n",
+       "fastEight                               0.0                  0.0   \n",
        "fastSixes                                 0                    0   \n",
        "fastQuads                                 2                    0   \n",
        "quad                                      2                    0   \n",
@@ -3148,22 +3685,22 @@
        "double                                    0                    4   \n",
        "surface                                   2                    0   \n",
        "total_chairs                              7                    4   \n",
-       "Runs                                     76                   36   \n",
-       "TerrainParks                              2                    1   \n",
-       "LongestRun_mi                             1                    2   \n",
-       "SkiableTerrain_ac                      1610                  640   \n",
-       "Snow Making_ac                          113                   60   \n",
-       "daysOpenLastYear                        150                   45   \n",
-       "yearsOpen                                60                   44   \n",
-       "averageSnowfall                         669                  350   \n",
-       "AdultWeekend                             85                   53   \n",
-       "projectedDaysOpen                       150                   90   \n",
-       "NightSkiing_ac                          550                  NaN   \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                 2280   \n",
-       "state_total_days_open                   345                  345   \n",
-       "state_total_terrain_parks                 4                    4   \n",
-       "state_total_nightskiing_ac              580                  580   \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",
@@ -3175,6 +3712,7 @@
        "vertical_drop                             294              2300   \n",
        "base_elev                                1796              9200   \n",
        "trams                                       0                 0   \n",
+       "fastEight                                 0.0               0.0   \n",
        "fastSixes                                   0                 1   \n",
        "fastQuads                                   0                 0   \n",
        "quad                                        0                 2   \n",
@@ -3182,23 +3720,23 @@
        "double                                      0                 1   \n",
        "surface                                     2                 2   \n",
        "total_chairs                                3                 8   \n",
-       "Runs                                       13                55   \n",
-       "TerrainParks                                1                 4   \n",
-       "LongestRun_mi                               1                 2   \n",
-       "SkiableTerrain_ac                          30               777   \n",
-       "Snow Making_ac                             30               104   \n",
-       "daysOpenLastYear                          150               122   \n",
-       "yearsOpen                                  36                81   \n",
-       "averageSnowfall                            69               260   \n",
-       "AdultWeekend                               34                89   \n",
-       "projectedDaysOpen                         152               122   \n",
-       "NightSkiing_ac                             30               NaN   \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              1577   \n",
-       "state_total_days_open                     345               237   \n",
-       "state_total_terrain_parks                   4                 6   \n",
-       "state_total_nightskiing_ac                580                80   \n",
-       "resorts_per_100kcapita               0.410091         0.0274774   \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",
@@ -3209,6 +3747,7 @@
        "vertical_drop                               1800  \n",
        "base_elev                                   9200  \n",
        "trams                                          0  \n",
+       "fastEight                                    NaN  \n",
        "fastSixes                                      0  \n",
        "fastQuads                                      1  \n",
        "quad                                           2  \n",
@@ -3216,23 +3755,23 @@
        "double                                         1  \n",
        "surface                                        0  \n",
        "total_chairs                                   7  \n",
-       "Runs                                          65  \n",
-       "TerrainParks                                   2  \n",
+       "Runs                                        65.0  \n",
+       "TerrainParks                                 2.0  \n",
        "LongestRun_mi                                1.2  \n",
-       "SkiableTerrain_ac                            800  \n",
-       "Snow Making_ac                                80  \n",
-       "daysOpenLastYear                             115  \n",
-       "yearsOpen                                     49  \n",
-       "averageSnowfall                              250  \n",
-       "AdultWeekend                                  78  \n",
-       "projectedDaysOpen                            104  \n",
-       "NightSkiing_ac                                80  \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  \n",
-       "state_total_days_open                        237  \n",
-       "state_total_terrain_parks                      6  \n",
-       "state_total_nightskiing_ac                    80  \n",
-       "resorts_per_100kcapita                 0.0274774  \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  "
       ]
      },
@@ -3293,15 +3832,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {},
-   "outputs": [],
+   "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.___(ski_data.___);"
+    "sns.heatmap(ski_data.select_dtypes(include='number').corr())\n",
+    "plt.show()"
    ]
   },
   {
@@ -3355,14 +3906,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "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 = [___ for ___ in ski_data.columns if ___ not in [___, ___, ___, ___]]"
+    "features = [col for col in ski_data.columns if col not in ['Name', 'Region', 'state', 'AdultWeekend']]"
    ]
   },
   {
@@ -3372,14 +3923,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 1080x1080 with 28 Axes>"
+       "<Figure size 1500x1500 with 32 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -3420,14 +3969,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 1080x576 with 4 Axes>"
+       "<Figure size 1500x800 with 4 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -3463,7 +4010,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**A: 1** Your answer here"
+    "**A: 1** The numerical features were the AdultWeekend, AdultWeekday, vertical_drop, daysOpen, SkiableTerrain_ac, and yearsOpen while the categorical features were state, Region, and Name. The pattern that was suggested through the data between state and ticket price were that the ticket prices varied state by state but their patterns for how they were priced was not consistent with other similar parks. They often varied based on the size of the park instead of what state it was in. The feature to focus on from this data was to use the AdultWeekend feature as it gave the most clear window of what the resort might have. Using features that describe the resort (size, days open, vertical drops) are also key features to describe and view what the resort would be like and are correlated to the ticket prices. The states labels for the data are to just give the ticket price on some context as to where it is, but should not be used as a feature to decide a price. "
    ]
   },
   {
@@ -3557,6 +4104,14 @@
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>fastSixes</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -3614,91 +4169,91 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Runs</th>\n",
-       "      <td>76</td>\n",
-       "      <td>36</td>\n",
-       "      <td>13</td>\n",
-       "      <td>55</td>\n",
-       "      <td>65</td>\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</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\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</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\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</td>\n",
-       "      <td>640</td>\n",
-       "      <td>30</td>\n",
-       "      <td>777</td>\n",
-       "      <td>800</td>\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</td>\n",
-       "      <td>60</td>\n",
-       "      <td>30</td>\n",
-       "      <td>104</td>\n",
-       "      <td>80</td>\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</td>\n",
-       "      <td>45</td>\n",
-       "      <td>150</td>\n",
-       "      <td>122</td>\n",
-       "      <td>115</td>\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</td>\n",
-       "      <td>44</td>\n",
-       "      <td>36</td>\n",
-       "      <td>81</td>\n",
-       "      <td>49</td>\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</td>\n",
-       "      <td>350</td>\n",
-       "      <td>69</td>\n",
-       "      <td>260</td>\n",
-       "      <td>250</td>\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</td>\n",
-       "      <td>53</td>\n",
-       "      <td>34</td>\n",
-       "      <td>89</td>\n",
-       "      <td>78</td>\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</td>\n",
-       "      <td>90</td>\n",
-       "      <td>152</td>\n",
-       "      <td>122</td>\n",
-       "      <td>104</td>\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</td>\n",
+       "      <td>550.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>30</td>\n",
+       "      <td>30.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>80</td>\n",
+       "      <td>80.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_state</th>\n",
@@ -3713,8 +4268,8 @@
        "      <td>0.410091</td>\n",
        "      <td>0.410091</td>\n",
        "      <td>0.410091</td>\n",
-       "      <td>0.0274774</td>\n",
-       "      <td>0.0274774</td>\n",
+       "      <td>0.027477</td>\n",
+       "      <td>0.027477</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_100ksq_mile</th>\n",
@@ -3728,7 +4283,7 @@
        "      <th>resort_skiable_area_ac_state_ratio</th>\n",
        "      <td>0.70614</td>\n",
        "      <td>0.280702</td>\n",
-       "      <td>0.0131579</td>\n",
+       "      <td>0.013158</td>\n",
        "      <td>0.492708</td>\n",
        "      <td>0.507292</td>\n",
        "    </tr>\n",
@@ -3752,13 +4307,13 @@
        "      <th>resort_night_skiing_state_ratio</th>\n",
        "      <td>0.948276</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.0517241</td>\n",
+       "      <td>0.051724</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>total_chairs_runs_ratio</th>\n",
-       "      <td>0.0921053</td>\n",
+       "      <td>0.092105</td>\n",
        "      <td>0.111111</td>\n",
        "      <td>0.230769</td>\n",
        "      <td>0.145455</td>\n",
@@ -3766,7 +4321,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>total_chairs_skiable_ratio</th>\n",
-       "      <td>0.00434783</td>\n",
+       "      <td>0.004348</td>\n",
        "      <td>0.00625</td>\n",
        "      <td>0.1</td>\n",
        "      <td>0.010296</td>\n",
@@ -3774,18 +4329,18 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fastQuads_runs_ratio</th>\n",
-       "      <td>0.0263158</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0153846</td>\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>0.00124224</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\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",
@@ -3801,6 +4356,7 @@
        "vertical_drop                                 2500                 1540   \n",
        "base_elev                                      250                 1200   \n",
        "trams                                            1                    0   \n",
+       "fastEight                                      0.0                  0.0   \n",
        "fastSixes                                        0                    0   \n",
        "fastQuads                                        2                    0   \n",
        "quad                                             2                    0   \n",
@@ -3808,17 +4364,17 @@
        "double                                           0                    4   \n",
        "surface                                          2                    0   \n",
        "total_chairs                                     7                    4   \n",
-       "Runs                                            76                   36   \n",
-       "TerrainParks                                     2                    1   \n",
-       "LongestRun_mi                                    1                    2   \n",
-       "SkiableTerrain_ac                             1610                  640   \n",
-       "Snow Making_ac                                 113                   60   \n",
-       "daysOpenLastYear                               150                   45   \n",
-       "yearsOpen                                       60                   44   \n",
-       "averageSnowfall                                669                  350   \n",
-       "AdultWeekend                                    85                   53   \n",
-       "projectedDaysOpen                              150                   90   \n",
-       "NightSkiing_ac                                 550                  NaN   \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",
@@ -3826,10 +4382,10 @@
        "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                  0.0921053             0.111111   \n",
-       "total_chairs_skiable_ratio              0.00434783              0.00625   \n",
-       "fastQuads_runs_ratio                     0.0263158                    0   \n",
-       "fastQuads_skiable_ratio                 0.00124224                    0   \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",
@@ -3839,6 +4395,7 @@
        "vertical_drop                                    294              2300   \n",
        "base_elev                                       1796              9200   \n",
        "trams                                              0                 0   \n",
+       "fastEight                                        0.0               0.0   \n",
        "fastSixes                                          0                 1   \n",
        "fastQuads                                          0                 0   \n",
        "quad                                               0                 2   \n",
@@ -3846,28 +4403,28 @@
        "double                                             0                 1   \n",
        "surface                                            2                 2   \n",
        "total_chairs                                       3                 8   \n",
-       "Runs                                              13                55   \n",
-       "TerrainParks                                       1                 4   \n",
-       "LongestRun_mi                                      1                 2   \n",
-       "SkiableTerrain_ac                                 30               777   \n",
-       "Snow Making_ac                                    30               104   \n",
-       "daysOpenLastYear                                 150               122   \n",
-       "yearsOpen                                         36                81   \n",
-       "averageSnowfall                                   69               260   \n",
-       "AdultWeekend                                      34                89   \n",
-       "projectedDaysOpen                                152               122   \n",
-       "NightSkiing_ac                                    30               NaN   \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         0.0274774   \n",
+       "resorts_per_100kcapita                      0.410091          0.027477   \n",
        "resorts_per_100ksq_mile                     0.450867           1.75454   \n",
-       "resort_skiable_area_ac_state_ratio         0.0131579          0.492708   \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            0.0517241               NaN   \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   \n",
-       "fastQuads_skiable_ratio                            0                 0   \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",
@@ -3877,6 +4434,7 @@
        "vertical_drop                                      1800  \n",
        "base_elev                                          9200  \n",
        "trams                                                 0  \n",
+       "fastEight                                           NaN  \n",
        "fastSixes                                             0  \n",
        "fastQuads                                             1  \n",
        "quad                                                  2  \n",
@@ -3884,27 +4442,27 @@
        "double                                                1  \n",
        "surface                                               0  \n",
        "total_chairs                                          7  \n",
-       "Runs                                                 65  \n",
-       "TerrainParks                                          2  \n",
+       "Runs                                               65.0  \n",
+       "TerrainParks                                        2.0  \n",
        "LongestRun_mi                                       1.2  \n",
-       "SkiableTerrain_ac                                   800  \n",
-       "Snow Making_ac                                       80  \n",
-       "daysOpenLastYear                                    115  \n",
-       "yearsOpen                                            49  \n",
-       "averageSnowfall                                     250  \n",
-       "AdultWeekend                                         78  \n",
-       "projectedDaysOpen                                   104  \n",
-       "NightSkiing_ac                                       80  \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                        0.0274774  \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  \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                          0.0153846  \n",
+       "fastQuads_runs_ratio                           0.015385  \n",
        "fastQuads_skiable_ratio                         0.00125  "
       ]
      },
@@ -3919,20 +4477,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "metadata": {},
-   "outputs": [],
+   "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",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -3946,7 +4519,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.12.7"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/Notebooks/04_preprocessing_and_training.ipynb b/Notebooks/04_preprocessing_and_training.ipynb
index 94ff2aeba..3b8429f8b 100644
--- a/Notebooks/04_preprocessing_and_training.ipynb
+++ b/Notebooks/04_preprocessing_and_training.ipynb
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 62,
    "metadata": {
     "scrolled": true
    },
@@ -224,6 +224,14 @@
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>fastSixes</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
@@ -281,91 +289,91 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Runs</th>\n",
-       "      <td>76</td>\n",
-       "      <td>36</td>\n",
-       "      <td>13</td>\n",
-       "      <td>55</td>\n",
-       "      <td>65</td>\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</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\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</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\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</td>\n",
-       "      <td>640</td>\n",
-       "      <td>30</td>\n",
-       "      <td>777</td>\n",
-       "      <td>800</td>\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</td>\n",
-       "      <td>60</td>\n",
-       "      <td>30</td>\n",
-       "      <td>104</td>\n",
-       "      <td>80</td>\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</td>\n",
-       "      <td>45</td>\n",
-       "      <td>150</td>\n",
-       "      <td>122</td>\n",
-       "      <td>115</td>\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</td>\n",
-       "      <td>44</td>\n",
-       "      <td>36</td>\n",
-       "      <td>81</td>\n",
-       "      <td>49</td>\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</td>\n",
-       "      <td>350</td>\n",
-       "      <td>69</td>\n",
-       "      <td>260</td>\n",
-       "      <td>250</td>\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</td>\n",
-       "      <td>53</td>\n",
-       "      <td>34</td>\n",
-       "      <td>89</td>\n",
-       "      <td>78</td>\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</td>\n",
-       "      <td>90</td>\n",
-       "      <td>152</td>\n",
-       "      <td>122</td>\n",
-       "      <td>104</td>\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</td>\n",
+       "      <td>550.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>30</td>\n",
+       "      <td>30.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>80</td>\n",
+       "      <td>80.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_state</th>\n",
@@ -380,8 +388,8 @@
        "      <td>0.410091</td>\n",
        "      <td>0.410091</td>\n",
        "      <td>0.410091</td>\n",
-       "      <td>0.0274774</td>\n",
-       "      <td>0.0274774</td>\n",
+       "      <td>0.027477</td>\n",
+       "      <td>0.027477</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_100ksq_mile</th>\n",
@@ -395,7 +403,7 @@
        "      <th>resort_skiable_area_ac_state_ratio</th>\n",
        "      <td>0.70614</td>\n",
        "      <td>0.280702</td>\n",
-       "      <td>0.0131579</td>\n",
+       "      <td>0.013158</td>\n",
        "      <td>0.492708</td>\n",
        "      <td>0.507292</td>\n",
        "    </tr>\n",
@@ -419,13 +427,13 @@
        "      <th>resort_night_skiing_state_ratio</th>\n",
        "      <td>0.948276</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.0517241</td>\n",
+       "      <td>0.051724</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>total_chairs_runs_ratio</th>\n",
-       "      <td>0.0921053</td>\n",
+       "      <td>0.092105</td>\n",
        "      <td>0.111111</td>\n",
        "      <td>0.230769</td>\n",
        "      <td>0.145455</td>\n",
@@ -433,7 +441,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>total_chairs_skiable_ratio</th>\n",
-       "      <td>0.00434783</td>\n",
+       "      <td>0.004348</td>\n",
        "      <td>0.00625</td>\n",
        "      <td>0.1</td>\n",
        "      <td>0.010296</td>\n",
@@ -441,18 +449,18 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fastQuads_runs_ratio</th>\n",
-       "      <td>0.0263158</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0153846</td>\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>0.00124224</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\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",
@@ -468,6 +476,7 @@
        "vertical_drop                                 2500                 1540   \n",
        "base_elev                                      250                 1200   \n",
        "trams                                            1                    0   \n",
+       "fastEight                                      0.0                  0.0   \n",
        "fastSixes                                        0                    0   \n",
        "fastQuads                                        2                    0   \n",
        "quad                                             2                    0   \n",
@@ -475,17 +484,17 @@
        "double                                           0                    4   \n",
        "surface                                          2                    0   \n",
        "total_chairs                                     7                    4   \n",
-       "Runs                                            76                   36   \n",
-       "TerrainParks                                     2                    1   \n",
-       "LongestRun_mi                                    1                    2   \n",
-       "SkiableTerrain_ac                             1610                  640   \n",
-       "Snow Making_ac                                 113                   60   \n",
-       "daysOpenLastYear                               150                   45   \n",
-       "yearsOpen                                       60                   44   \n",
-       "averageSnowfall                                669                  350   \n",
-       "AdultWeekend                                    85                   53   \n",
-       "projectedDaysOpen                              150                   90   \n",
-       "NightSkiing_ac                                 550                  NaN   \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",
@@ -493,10 +502,10 @@
        "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                  0.0921053             0.111111   \n",
-       "total_chairs_skiable_ratio              0.00434783              0.00625   \n",
-       "fastQuads_runs_ratio                     0.0263158                    0   \n",
-       "fastQuads_skiable_ratio                 0.00124224                    0   \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",
@@ -506,6 +515,7 @@
        "vertical_drop                                    294              2300   \n",
        "base_elev                                       1796              9200   \n",
        "trams                                              0                 0   \n",
+       "fastEight                                        0.0               0.0   \n",
        "fastSixes                                          0                 1   \n",
        "fastQuads                                          0                 0   \n",
        "quad                                               0                 2   \n",
@@ -513,28 +523,28 @@
        "double                                             0                 1   \n",
        "surface                                            2                 2   \n",
        "total_chairs                                       3                 8   \n",
-       "Runs                                              13                55   \n",
-       "TerrainParks                                       1                 4   \n",
-       "LongestRun_mi                                      1                 2   \n",
-       "SkiableTerrain_ac                                 30               777   \n",
-       "Snow Making_ac                                    30               104   \n",
-       "daysOpenLastYear                                 150               122   \n",
-       "yearsOpen                                         36                81   \n",
-       "averageSnowfall                                   69               260   \n",
-       "AdultWeekend                                      34                89   \n",
-       "projectedDaysOpen                                152               122   \n",
-       "NightSkiing_ac                                    30               NaN   \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         0.0274774   \n",
+       "resorts_per_100kcapita                      0.410091          0.027477   \n",
        "resorts_per_100ksq_mile                     0.450867           1.75454   \n",
-       "resort_skiable_area_ac_state_ratio         0.0131579          0.492708   \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            0.0517241               NaN   \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   \n",
-       "fastQuads_skiable_ratio                            0                 0   \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",
@@ -544,6 +554,7 @@
        "vertical_drop                                      1800  \n",
        "base_elev                                          9200  \n",
        "trams                                                 0  \n",
+       "fastEight                                           NaN  \n",
        "fastSixes                                             0  \n",
        "fastQuads                                             1  \n",
        "quad                                                  2  \n",
@@ -551,31 +562,31 @@
        "double                                                1  \n",
        "surface                                               0  \n",
        "total_chairs                                          7  \n",
-       "Runs                                                 65  \n",
-       "TerrainParks                                          2  \n",
+       "Runs                                               65.0  \n",
+       "TerrainParks                                        2.0  \n",
        "LongestRun_mi                                       1.2  \n",
-       "SkiableTerrain_ac                                   800  \n",
-       "Snow Making_ac                                       80  \n",
-       "daysOpenLastYear                                    115  \n",
-       "yearsOpen                                            49  \n",
-       "averageSnowfall                                     250  \n",
-       "AdultWeekend                                         78  \n",
-       "projectedDaysOpen                                   104  \n",
-       "NightSkiing_ac                                       80  \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                        0.0274774  \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  \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                          0.0153846  \n",
+       "fastQuads_runs_ratio                           0.015385  \n",
        "fastQuads_skiable_ratio                         0.00125  "
       ]
      },
-     "execution_count": 2,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -601,7 +612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -610,7 +621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -634,7 +645,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>124</th>\n",
+       "      <th>125</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -667,6 +678,10 @@
        "      <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",
@@ -696,11 +711,11 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Runs</th>\n",
-       "      <td>105</td>\n",
+       "      <td>105.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TerrainParks</th>\n",
-       "      <td>4</td>\n",
+       "      <td>4.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LongestRun_mi</th>\n",
@@ -708,35 +723,35 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SkiableTerrain_ac</th>\n",
-       "      <td>3000</td>\n",
+       "      <td>3000.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Snow Making_ac</th>\n",
-       "      <td>600</td>\n",
+       "      <td>600.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>daysOpenLastYear</th>\n",
-       "      <td>123</td>\n",
+       "      <td>123.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>yearsOpen</th>\n",
-       "      <td>72</td>\n",
+       "      <td>72.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>averageSnowfall</th>\n",
-       "      <td>333</td>\n",
+       "      <td>333.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AdultWeekend</th>\n",
-       "      <td>81</td>\n",
+       "      <td>81.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>projectedDaysOpen</th>\n",
-       "      <td>123</td>\n",
+       "      <td>123.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NightSkiing_ac</th>\n",
-       "      <td>600</td>\n",
+       "      <td>600.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_state</th>\n",
@@ -744,11 +759,11 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_100kcapita</th>\n",
-       "      <td>1.12278</td>\n",
+       "      <td>1.122778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_100ksq_mile</th>\n",
-       "      <td>8.16104</td>\n",
+       "      <td>8.161045</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resort_skiable_area_ac_state_ratio</th>\n",
@@ -772,11 +787,11 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>total_chairs_skiable_ratio</th>\n",
-       "      <td>0.00466667</td>\n",
+       "      <td>0.004667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fastQuads_runs_ratio</th>\n",
-       "      <td>0.0285714</td>\n",
+       "      <td>0.028571</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fastQuads_skiable_ratio</th>\n",
@@ -787,7 +802,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                                                    124\n",
+       "                                                    125\n",
        "Name                                Big Mountain Resort\n",
        "Region                                          Montana\n",
        "state                                           Montana\n",
@@ -795,6 +810,7 @@
        "vertical_drop                                      2353\n",
        "base_elev                                          4464\n",
        "trams                                                 0\n",
+       "fastEight                                           0.0\n",
        "fastSixes                                             0\n",
        "fastQuads                                             3\n",
        "quad                                                  2\n",
@@ -802,31 +818,31 @@
        "double                                                0\n",
        "surface                                               3\n",
        "total_chairs                                         14\n",
-       "Runs                                                105\n",
-       "TerrainParks                                          4\n",
+       "Runs                                              105.0\n",
+       "TerrainParks                                        4.0\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",
+       "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                          1.12278\n",
-       "resorts_per_100ksq_mile                         8.16104\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                   0.00466667\n",
-       "fastQuads_runs_ratio                          0.0285714\n",
+       "total_chairs_skiable_ratio                     0.004667\n",
+       "fastQuads_runs_ratio                           0.028571\n",
        "fastQuads_skiable_ratio                           0.001"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -837,16 +853,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(277, 36)"
+       "(279, 37)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -857,7 +873,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -866,16 +882,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(276, 36)"
+       "(278, 37)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -907,16 +923,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(193.2, 82.8)"
+       "(194.6, 83.39999999999999)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -927,7 +943,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -938,16 +954,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "((193, 35), (83, 35))"
+       "((194, 36), (84, 36))"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -958,16 +974,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "((193,), (83,))"
+       "((194,), (84,))"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -978,41 +994,141 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 72,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((194, 33), (84, 33))"
+      ]
+     },
+     "execution_count": 72,
+     "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[___]\n",
-    "names_test = X_test[___]\n",
-    "X_train.___(columns=names_list, inplace=___)\n",
-    "X_test.___(columns=names_list, inplace=___)\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",
+    "\n",
     "X_train.shape, X_test.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "summit_elev                             int64\n",
+       "vertical_drop                           int64\n",
+       "base_elev                               int64\n",
+       "trams                                   int64\n",
+       "fastEight                             float64\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": 73,
+     "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.___"
+    "X_train.dtypes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 74,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "summit_elev                             int64\n",
+       "vertical_drop                           int64\n",
+       "base_elev                               int64\n",
+       "trams                                   int64\n",
+       "fastEight                             float64\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": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 3#\n",
     "#Repeat this check for the test split in `X_test`\n",
-    "X_test.___"
+    "X_test.dtypes"
    ]
   },
   {
@@ -1038,13 +1154,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 75,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "63.909948453608244"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 4#\n",
     "#Calculate the mean of `y_train`\n",
-    "train_mean = y_train.___\n",
+    "train_mean = y_train.mean()\n",
     "train_mean"
    ]
   },
@@ -1057,17 +1184,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 76,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[63.90994845]])"
+      ]
+     },
+     "execution_count": 76,
+     "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.___(___, ___)\n",
-    "dumb_reg.___"
+    "dumb_reg.fit(X_train, y_train)\n",
+    "dumb_reg.constant_"
    ]
   },
   {
@@ -1124,7 +1262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1140,9 +1278,9 @@
     "    ypred -- the predicted values\n",
     "    \"\"\"\n",
     "    ybar = np.sum(y) / len(y) #yes, we could use np.mean(y)\n",
-    "    sum_sq_tot = np.___((y - ybar)**2) #total sum of squares error\n",
-    "    sum_sq_res = np.___((y - ypred)**2) #residual sum of squares error\n",
-    "    R2 = 1.0 - ___ / ___\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"
    ]
   },
@@ -1155,16 +1293,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([63.81108808, 63.81108808, 63.81108808, 63.81108808, 63.81108808])"
+       "array([63.90994845, 63.90994845, 63.90994845, 63.90994845, 63.90994845])"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1183,16 +1321,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([63.81108808, 63.81108808, 63.81108808, 63.81108808, 63.81108808])"
+       "array([63.90994845, 63.90994845, 63.90994845, 63.90994845, 63.90994845])"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1211,7 +1349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -1220,7 +1358,7 @@
        "0.0"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1245,16 +1383,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "-0.0031235200417913944"
+       "-0.000719221815450588"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1296,7 +1434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1311,23 +1449,23 @@
     "    y -- the observed values\n",
     "    ypred -- the predicted values\n",
     "    \"\"\"\n",
-    "    abs_error = np.abs(___ - ___)\n",
-    "    mae = np.mean(___)\n",
+    "    abs_error = np.abs(y - ypred)\n",
+    "    mae = np.mean(abs_error)\n",
     "    return mae"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "17.923463717146785"
+       "18.04973695398023"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1338,16 +1476,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "19.136142081278486"
+       "18.7889236622484"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1381,7 +1519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 85,
    "metadata": {
     "scrolled": true
    },
@@ -1398,23 +1536,23 @@
     "    y -- the observed values\n",
     "    ypred -- the predicted values\n",
     "    \"\"\"\n",
-    "    sq_error = (___ - ___)**2\n",
-    "    mse = np.mean(___)\n",
+    "    sq_error = (y - ypred)**2\n",
+    "    mse = np.mean(sq_error)\n",
     "    return mse"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "614.1334096969057"
+       "608.2790850488893"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1425,16 +1563,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "581.4365441953481"
+       "587.3103324523379"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1452,16 +1590,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([24.78171523, 24.11299534])"
+       "array([24.66331456, 24.23448643])"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1493,16 +1631,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.0, -0.0031235200417913944)"
+       "(0.0, -0.000719221815450588)"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1520,16 +1658,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(17.92346371714677, 19.136142081278486)"
+       "(18.04973695398023, 18.7889236622484)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1547,16 +1685,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(614.1334096969046, 581.4365441953483)"
+       "(608.2790850488893, 587.3103324523379)"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1588,16 +1726,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.0, -3.041041349306602e+30)"
+       "(0.0, -3.012052137767589e+30)"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1610,16 +1748,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(-0.0031235200417913944, 0.0)"
+       "(-0.000719221815450588, -1.163287961646011e+31)"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1632,16 +1770,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.0, -3.041041349306602e+30)"
+       "(0.0, -3.012052137767589e+30)"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1654,24 +1792,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/guy/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:15: RuntimeWarning: divide by zero encountered in double_scalars\n",
-      "  from ipykernel import kernelapp as app\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "(-0.0031235200417913944, -inf)"
+       "(-0.000719221815450588, -1.163287961646011e+31)"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1737,48 +1867,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "summit_elev                           2215.000000\n",
+       "summit_elev                           2150.000000\n",
        "vertical_drop                          750.000000\n",
-       "base_elev                             1300.000000\n",
+       "base_elev                             1277.500000\n",
        "trams                                    0.000000\n",
+       "fastEight                                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",
+       "total_chairs                             6.000000\n",
+       "Runs                                    30.000000\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",
+       "SkiableTerrain_ac                      172.500000\n",
+       "Snow Making_ac                         100.000000\n",
+       "daysOpenLastYear                       106.000000\n",
        "yearsOpen                               57.000000\n",
-       "averageSnowfall                        120.000000\n",
-       "projectedDaysOpen                      115.000000\n",
+       "averageSnowfall                        122.000000\n",
+       "projectedDaysOpen                      113.000000\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",
+       "resorts_per_100ksq_mile                 24.428973\n",
+       "resort_skiable_area_ac_state_ratio       0.047458\n",
+       "resort_days_open_state_ratio             0.070470\n",
        "resort_terrain_park_state_ratio          0.069444\n",
-       "resort_night_skiing_state_ratio          0.077081\n",
+       "resort_night_skiing_state_ratio          0.074685\n",
        "total_chairs_runs_ratio                  0.200000\n",
-       "total_chairs_skiable_ratio               0.040323\n",
+       "total_chairs_skiable_ratio               0.041067\n",
        "fastQuads_runs_ratio                     0.000000\n",
        "fastQuads_skiable_ratio                  0.000000\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1798,15 +1929,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 97,
    "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.___(___)\n",
-    "X_te = X_test.___(___)"
+    "X_tr = X_train.fillna(X_defaults_median)\n",
+    "X_te = X_test.fillna(X_defaults_median)"
    ]
   },
   {
@@ -1825,7 +1956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1834,9 +1965,9 @@
     "#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.___(X_tr)\n",
-    "X_tr_scaled = scaler.___(X_tr)\n",
-    "X_te_scaled = scaler.___(X_te)"
+    "scaler.fit(X_tr)\n",
+    "X_tr_scaled = scaler.transform(X_tr)\n",
+    "X_te_scaled = scaler.transform(X_te)"
    ]
   },
   {
@@ -1848,7 +1979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1864,15 +1995,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 100,
    "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.___(X_tr_scaled)\n",
-    "y_te_pred = lm.___(X_te_scaled)"
+    "y_tr_pred = lm.predict(X_tr_scaled)\n",
+    "y_te_pred = lm.predict(X_te_scaled)"
    ]
   },
   {
@@ -1884,16 +2015,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.8177988515690604, 0.7209725843435142)"
+       "(0.837763330138149, 0.17486976325095038)"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1913,15 +2044,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 102,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8.060601808161621, 12.218743703050759)"
+      ]
+     },
+     "execution_count": 102,
+     "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 = ___(y_train, y_tr_pred), ___(y_test, y_te_pred)\n",
+    "median_mae = mean_absolute_error(y_train, y_tr_pred), mean_absolute_error(y_test, y_te_pred)\n",
     "median_mae"
    ]
   },
@@ -1934,14 +2076,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 103,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(98.68517310494545, 484.2592238634249)"
+      ]
+     },
+     "execution_count": 103,
+     "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 = ___(___, ___), ___(___, ___)\n",
+    "median_mse = mean_squared_error(y_train, y_tr_pred), mean_squared_error(y_test, y_te_pred)\n",
     "median_mse"
    ]
   },
@@ -1968,14 +2121,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 104,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "summit_elev                           4074.835052\n",
+       "vertical_drop                         1045.453608\n",
+       "base_elev                             3013.061856\n",
+       "trams                                    0.097938\n",
+       "fastEight                                0.000000\n",
+       "fastSixes                                0.087629\n",
+       "fastQuads                                0.716495\n",
+       "quad                                     0.922680\n",
+       "triple                                   1.422680\n",
+       "double                                   1.747423\n",
+       "surface                                  2.613402\n",
+       "total_chairs                             7.608247\n",
+       "Runs                                    42.450777\n",
+       "TerrainParks                             2.557576\n",
+       "LongestRun_mi                            1.328272\n",
+       "SkiableTerrain_ac                      456.781250\n",
+       "Snow Making_ac                         134.173410\n",
+       "daysOpenLastYear                       110.371069\n",
+       "yearsOpen                               66.715026\n",
+       "averageSnowfall                        161.677596\n",
+       "projectedDaysOpen                      115.079755\n",
+       "NightSkiing_ac                          93.644068\n",
+       "resorts_per_state                       17.046392\n",
+       "resorts_per_100kcapita                   0.391640\n",
+       "resorts_per_100ksq_mile                 39.205809\n",
+       "resort_skiable_area_ac_state_ratio       0.100062\n",
+       "resort_days_open_state_ratio             0.120295\n",
+       "resort_terrain_park_state_ratio          0.120069\n",
+       "resort_night_skiing_state_ratio          0.161540\n",
+       "total_chairs_runs_ratio                  0.255606\n",
+       "total_chairs_skiable_ratio               0.066866\n",
+       "fastQuads_runs_ratio                     0.010710\n",
+       "fastQuads_skiable_ratio                  0.001734\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 104,
+     "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.___()\n",
+    "X_defaults_mean = X_train.mean()\n",
     "X_defaults_mean"
    ]
   },
@@ -1995,7 +2192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2012,7 +2209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2031,7 +2228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2047,7 +2244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2064,16 +2261,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.8170154093990025, 0.716381471695996)"
+       "(0.8376200149817408, 0.10569141878361188)"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2084,16 +2281,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(8.536884040670973, 9.416375625789271)"
+       "(8.093177122838368, 12.27994476042203)"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2104,16 +2301,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(112.37695054778276, 164.3926930952436)"
+       "(98.7723487171591, 524.8591799768967)"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2168,7 +2365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2181,7 +2378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [
     {
@@ -2190,7 +2387,7 @@
        "sklearn.pipeline.Pipeline"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2201,7 +2398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [
     {
@@ -2210,7 +2407,7 @@
        "(True, True)"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2235,98 +2432,522 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Code task 15#\n",
-    "#Call the pipe's `fit()` method with `X_train` and `y_train` as arguments\n",
-    "pipe.___(___, ___)"
-   ]
-  },
-  {
-   "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": 56,
-   "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": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.8177988515690604, 0.7209725843435142)"
-      ]
-     },
-     "execution_count": 57,
-     "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": 58,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "(0.8177988515690604, 0.7209725843435142)"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "median_r2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
+      "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;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;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;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": 115,
+     "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": 116,
+   "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": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.837763330138149, 0.17486976325095038)"
+      ]
+     },
+     "execution_count": 117,
+     "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": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.837763330138149, 0.17486976325095038)"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "median_r2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
      "data": {
       "text/plain": [
-       "(8.547850301825427, 9.40702011858132)"
+       "(8.060601808161621, 12.218743703050759)"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2337,25 +2958,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 120,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (200383607.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;36m  Cell \u001b[1;32mIn[120], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    Compare with your earlier result:\u001b[0m\n\u001b[1;37m            ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
    "source": [
     "Compare with your earlier result:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(8.547850301825427, 9.40702011858132)"
+       "(8.060601808161621, 12.218743703050759)"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2366,16 +2996,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(111.89581253658478, 161.73156451192284)"
+       "(98.68517310494545, 484.2592238634249)"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2393,16 +3023,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(111.89581253658478, 161.73156451192284)"
+       "(98.68517310494545, 484.2592238634249)"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2449,7 +3079,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2459,7 +3089,7 @@
     "pipe = make_pipeline(\n",
     "    SimpleImputer(strategy='median'), \n",
     "    StandardScaler(),\n",
-    "    ___(___),\n",
+    "    SelectKBest(f_regression),\n",
     "    LinearRegression()\n",
     ")"
    ]
@@ -2473,20 +3103,435 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 125,
    "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(score_func=&lt;function f_regression at 0x0000018FDFD67380&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-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" 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 0x0000018FDFD67380&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-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" 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-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" 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-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" 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 0x0000018FDFD67380&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-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" 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 0x7fc8cd9c8950>)),\n",
+       "                 SelectKBest(score_func=<function f_regression at 0x0000018FDFD67380>)),\n",
        "                ('linearregression', LinearRegression())])"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2504,7 +3549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2514,16 +3559,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.7674914326052744, 0.6259877354190833)"
+       "(0.7717465274643035, 0.024896104214764292)"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2534,16 +3579,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(9.501495079727484, 11.201830190332057)"
+       "(9.348129293530974, 13.443701530832024)"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2568,7 +3613,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2577,7 +3622,7 @@
     "pipe15 = make_pipeline(\n",
     "    SimpleImputer(strategy='median'), \n",
     "    StandardScaler(),\n",
-    "    ___(___, k=___),\n",
+    "    SelectKBest(f_regression, k=15),\n",
     "    LinearRegression()\n",
     ")"
    ]
@@ -2591,21 +3636,438 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 130,
    "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>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 0x0000018FDFD67380&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-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" 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 0x0000018FDFD67380&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-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" 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-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" 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-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" 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 0x0000018FDFD67380&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-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" 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 0x7fc8cd9c8950>)),\n",
+       "                             score_func=<function f_regression at 0x0000018FDFD67380>)),\n",
        "                ('linearregression', LinearRegression())])"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 130,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2623,7 +4085,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2633,16 +4095,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.7924096060483825, 0.6376199973170795)"
+       "(0.7744405589079705, 0.01958038675296303)"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2653,16 +4115,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(9.211767769307116, 10.488246867294356)"
+       "(9.292056666224383, 13.586076184973287)"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 133,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2689,7 +4151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2698,16 +4160,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0.63760862, 0.72831381, 0.74443537, 0.5487915 , 0.50441472])"
+       "array([0.62844875, 0.73070928, 0.76903383, 0.71418841, 0.61384763])"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 135,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2726,16 +4188,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.6327128053007867, 0.09502487849877672)"
+       "(0.6912455812776374, 0.06011431293316897)"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 136,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2753,16 +4215,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0.44, 0.82])"
+       "array([0.57, 0.81])"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 137,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2797,14 +4259,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 138,
    "metadata": {},
-   "outputs": [],
+   "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": 138,
+     "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.___.keys()"
+    "pipe.get_params().keys()"
    ]
   },
   {
@@ -2816,7 +4289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2834,7 +4307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2843,18 +4316,454 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 141,
    "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;selectkbest&#x27;,\n",
+       "                                        SelectKBest(score_func=&lt;function f_regression at 0x0000018FDFD67380&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-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" 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 0x0000018FDFD67380&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-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" 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=27,\n",
+       "                             score_func=&lt;function f_regression at 0x0000018FDFD67380&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-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" 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-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" 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-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" 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=27, score_func=&lt;function f_regression at 0x0000018FDFD67380&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-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" 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 0x7fc8cd9c8950>)),\n",
+       "                                        SelectKBest(score_func=<function f_regression at 0x0000018FDFD67380>)),\n",
        "                                       ('linearregression',\n",
        "                                        LinearRegression())]),\n",
        "             n_jobs=-1,\n",
@@ -2864,7 +4773,7 @@
        "                                            30, ...]})"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2875,7 +4784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2886,24 +4795,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 143,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'selectkbest__k': 27}"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 19#\n",
     "#Print the `best_params_` attribute of `lr_grid_cv`\n",
-    "lr_grid_cv.___"
+    "lr_grid_cv.best_params_"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 144,
    "metadata": {},
-   "outputs": [],
+   "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",
-    "___ = lr_grid_cv.___['selectkbest__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",
@@ -2928,7 +4859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2944,9 +4875,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 148,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "vertical_drop                         8.837609\n",
+       "fastQuads                             6.713943\n",
+       "base_elev                             6.086421\n",
+       "fastQuads_runs_ratio                  5.201978\n",
+       "fastSixes                             5.055826\n",
+       "resorts_per_100ksq_mile               3.838531\n",
+       "Snow Making_ac                        3.666862\n",
+       "quad                                  3.509117\n",
+       "Runs                                  2.906862\n",
+       "averageSnowfall                       2.794360\n",
+       "double                                2.603030\n",
+       "daysOpenLastYear                      2.135126\n",
+       "triple                                1.995270\n",
+       "resort_skiable_area_ac_state_ratio    1.649985\n",
+       "total_chairs                          0.719067\n",
+       "total_chairs_skiable_ratio            0.329330\n",
+       "projectedDaysOpen                    -0.350860\n",
+       "resort_night_skiing_state_ratio      -0.419226\n",
+       "NightSkiing_ac                       -0.719645\n",
+       "TerrainParks                         -0.868635\n",
+       "trams                                -1.095434\n",
+       "LongestRun_mi                        -1.389823\n",
+       "total_chairs_runs_ratio              -2.683337\n",
+       "resorts_per_100kcapita               -3.104530\n",
+       "fastQuads_skiable_ratio              -3.517704\n",
+       "SkiableTerrain_ac                    -4.735286\n",
+       "summit_elev                          -6.161260\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 21#\n",
     "#Get the linear model coefficients from the `coef_` attribute and store in `coefs`,\n",
@@ -2955,7 +4924,7 @@
     "#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(___, index=___).___(ascending=___)"
+    "pd.Series(coefs, index=features).sort_values(ascending=False)\n"
    ]
   },
   {
@@ -2990,7 +4959,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 149,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3000,9 +4969,9 @@
     "#StandardScaler(),\n",
     "#and then RandomForestRegressor() with a random state of 47\n",
     "RF_pipe = make_pipeline(\n",
-    "    ___(strategy=___),\n",
-    "    ___,\n",
-    "    ___(random_state=___)\n",
+    "    SimpleImputer(strategy='median'),\n",
+    "    StandardScaler(),\n",
+    "    RandomForestRegressor(random_state=47)\n",
     ")"
    ]
   },
@@ -3015,7 +4984,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 150,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3023,21 +4992,21 @@
     "#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(___, ___, ___, cv=___)"
+    "rf_default_cv_results = cross_validate(RF_pipe, X_train, y_train, cv=5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 151,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0.69249204, 0.78061953, 0.77546915, 0.62190924, 0.61742339])"
+       "array([0.65957308, 0.67405432, 0.61408065, 0.74005792, 0.62100051])"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 151,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3049,16 +5018,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 152,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.6975826707112506, 0.07090742940774528)"
+       "(0.6617532978793748, 0.04521317131231832)"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 152,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3083,7 +5052,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 153,
    "metadata": {},
    "outputs": [
     {
@@ -3113,7 +5082,7 @@
        " 'simpleimputer__strategy': ['mean', 'median']}"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 153,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3130,37 +5099,515 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 154,
    "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(___, param_grid=___, cv=___, n_jobs=-1)"
+    "rf_grid_cv = GridSearchCV(RF_pipe, param_grid=grid_params, cv=5, n_jobs=-1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 155,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-6 {\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-6 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6 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-6 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-6 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-6 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-6 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-6 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-6 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-6 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-6 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 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-6 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6 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-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 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-6 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-6 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-6 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-6 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-6 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-6 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-6 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-6 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-6 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-6 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-6 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-6\" 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-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" 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-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" 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()), (&#x27;standardscaler&#x27;, None),\n",
+       "                (&#x27;randomforestregressor&#x27;,\n",
+       "                 RandomForestRegressor(n_estimators=54, 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-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" 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()</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-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" 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-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" 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=54, 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": 155,
+     "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.___(___, ___)"
+    "rf_grid_cv.fit(X_train, y_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 156,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'randomforestregressor__n_estimators': 54,\n",
+       " 'simpleimputer__strategy': 'mean',\n",
+       " 'standardscaler': None}"
+      ]
+     },
+     "execution_count": 156,
+     "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.___"
+    "rf_grid_cv.best_params_"
    ]
   },
   {
@@ -3172,16 +5619,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 157,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0.6951357 , 0.79430697, 0.77170917, 0.62254707, 0.66499334])"
+       "array([0.62537501, 0.69532426, 0.63180354, 0.76468382, 0.62801177])"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 157,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3194,16 +5641,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 158,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.7097384501425082, 0.06451341966873386)"
+       "(0.6690396799573108, 0.05443363770355455)"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 158,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3221,9 +5668,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 160,
    "metadata": {},
-   "outputs": [],
+   "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",
@@ -3232,8 +5690,8 @@
     "#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.___\n",
-    "rf_feat_imps = pd.Series(___, index=X_train.columns).sort_values(ascending=False)\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",
@@ -3283,7 +5741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3294,20 +5752,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10.499032338015297, 1.6220608976799646)"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "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",
@@ -3316,20 +5763,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "11.793465668669327"
-      ]
-     },
-     "execution_count": 99,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "mean_absolute_error(y_test, lr_grid_cv.best_estimator_.predict(X_test))"
    ]
@@ -3343,7 +5779,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3353,20 +5789,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(9.644639167595688, 1.3528565172191818)"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "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",
@@ -3375,20 +5800,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9.537730050637332"
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "mean_absolute_error(y_test, rf_grid_cv.best_estimator_.predict(X_test))"
    ]
@@ -3423,7 +5837,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3437,22 +5851,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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xtn3n/Ni2cnl7G2PLsDRl7Jhi/UD3K9f33a9/uGrcbkd5/93uV86PbbJ+7Ji2ndtoi0H9btfl9CGDnDSA3t7k0ae2sGz1Bu5fvYEH126kuzd5+1dubhHKip89e/lfZXtbMHFsO+M72pnY0c6Ese1MKH9OnTiWCR1jmDC2jevuXUNbBK86eQ69mfQm9GaS5c+e3mJZZu62vli3s23f+my4X7P13T29u7Td3tNLAg+v20RPb9KTSW85EtBbzvf0NtRTLmtcn8PznrPf2gLGtLXR1gbbunsBeOZfXwUBQRH0onEaykMxjcsh2LVdn4jm68tN7Lbdvnb0X95vG+xyn923QbPaBtp+Q917fIy77aN5Lez2uHfdRt/2H1y7kUx437dv2xFsdoSu3jJ09YWvntxlvq9NY0hrvG937+Bfi6/4lxsG+7JpKoIdQaJV2NgRUsr5iR1jdoSejjEN7ZuGlPJ+gwg9xf77lu1h/Y5tFdNtNRihOtgY5CRgy/YeHnpsI/ev3sj9azYUwW3NBh5Ys5HN23t2tGtvC8a2BSuf2MKEjnamjB/DrCnjmFAGsPFjG4PYGCb0zZfLdmnT0c7EsWOY0NHO2Pa9+w/xovOOH7K+GGwN3/qD0/Z5G33hsQiBFOGvpy8E5o4Q2H96x/q++/Rf3xAsd66D7t7eHdO9vc33s+t92Tldtrnil48AcO4zj9jx5p+ZJJAJSZY/KdeX803XFcvYsaxxOzvnabzvQPtosQ3619Z/G72Q9LbcBg3zfTXv3E6LfexWf4t+GmQfPrFpOxHwk/sfK0aDyhGVMQ2jK8XoThvjxxYhpL2tCCjtbUXAaW/bGY523KdvvnE7/du0tfHZHy4jgD8/57i9Cj0d7W07ttdXkzQUDHI1dSDeTA9Gj2/ctktQu39NEdyWr9tE4yDa3MMmcOzMyTz3adM5dtYkFsyczLGzJvOHX7uViLDf91OUh0vr9AfotuVPAPDXrzyx4koOLlX/rbvsluUAnH3i4ZXsX9qTOv0dlQalpzd55PHNZVDb0BDcNrJu47Yd7caNaeNpMybxzCMP5dWnHMmxsyZz7MxJHDNjMhM62ptu23OmJOngUJd/2A1yqq3N23p4YG0R0HaMsJXnsW0tz2cCmD6pg2NnTubsE2dzbDmytmDmZOZMneDhDklSrRnkNCJt7+nl8Y3bWLthG+s2buOxjVvpenILW7t7ePOXb2LZ6g088sTmHe3bAo6aNpEFMyfzoqfP5NiZk4rQNnMyh03qqPCRSJI0dAxyGhbdPb08vmk7j23cyroN21i7cRvrNmzlsY3bituGrUVg21DMP7l5e9PttAXMmLKVRfMP44KZR7FgVhHWjp4+kfFjmx8OHU3qMtQvjRb+zmmkqzTIRcQ5wKeAduBLmfnRfutfAnwXeLBc9B+Z+ZFhLVItbe/p5d5V68vwVQSxYgRt645A1hfQnti8vell/m0B0yZ1MG1SB9MnjeP4OYcwY1IH0yaNY/rkDqZP6mD65HFMm9TB+79zG+1twWXveP7wP1jtcLC+sR2sj7tq9rs0sMqCXES0A58BzgRWADdHxBWZeWe/pj/KzFcMe4HazZObtnPj/Wv50bK1/HL5E2zt7uWsT1y/S5sIOGxiXzDr4BmHT2H6pCKIzZi8e0A7dMLYQZ+nNqa9+q8G9k1FkjSSVDkidyqwLDMfAIiIbwLnA/2DnCqytbuHW3/1OD9etpYb7lvLHY88SW/C5HHFZ5/NPmQcf372cWUwKwLaYRM7vIBAkqRhUmWQOxJY3jC/Anhuk3anRcRtwErgfZm5dDiKOxj19iZ3d63nx8uKUbebHnyMLduL75r7taOm8scvW8gLF87g5KOm8qYv/QyAV548p+KqJUk6eFUZ5JoN2/Q/i+rnwNGZuSEizgP+E1jYdGMRFwIXAsybN+9A1jmqPfrkZn5031p+vKy4rd1QfM7aglmTef1z5nH6ghk895hpTBk/tuJKJUlSf1UGuRXAUQ3zcylG3XbIzKcapq+MiM9GxIzMXNt/Y5l5CXAJwKJFi2ryTY7D76kt2/np/Y/tGHV7YM1GAGZMHsfpC2Zw+sKZvGDBdI44dELFlUqSpD2pMsjdDCyMiKcBjwCvB36nsUFEHA6sysyMiFOBNuCxYa+0xrb39PKLh5/ghmVrueG+Ndy24kl6epMJY9t57jHT+J1T53H6whk8Y/YUv7VAkqSaqSzIZWZ3RLwLuIri40e+nJlLI+Id5frPA78NvDMiuoHNwOszm32IhfpkJvet3sAN963lhmVr+dkDj7FxWw9tAc+aO5V3vvhYTl84g1+bN5VxY0b/565JkjSaVfo5cpl5JXBlv2Wfb5j+V+Bfh7uuuln91JZixK08z23VU1sBeNqMSbzm2Udy+oKZnHbMdA6d6HlukiSNJn6zQ02t37KddRu3cdYnruPeVRuA4oN1n3/sdF64cAYvWDCDuYdNrLhKSZI0lAxyNfTQ2o3c+eh6ImDh7Cn85rPncvqCGZxwxCG0+RlukiQdNAxyNXRl56MAnHzkofzb25p99N7o5LcqSJK0q+q/80h7bUlnF5M62hl3EHxJvCRJas0gVzMrHt/E7SueZNqkjqpLkSRJFfPQas0s6ewCqDzIeZhTkqTqOSJXM0s6uzj+iEMY72FVSZIOega5Gln91BZuffhxzj3p8KpLkSRJI4BBrkauWtpFJgY5SZIEGORqZXFnF8fOnMTC2VOqLkWSJI0ABrmaWLdxGz97cB3nnnRE1aVIkqQRwiBXE9fc2UVPb3KOh1UlSVLJIFcTV97RxVHTJnDinEOqLkWSJI0QBrkaeHLzdm68fy3nnnQEEX6XqiRJKhjkauAHd61ie4+HVSVJ0q4McjWwuLOLww8Zzylzp1ZdiiRJGkEMciPcxq3dXH/vGs456XDa2jysKkmSdvK7Vke4a+9Zzdbu3t0Oq/pdp5IkyRG5EW5xZxczJnfwnPnTqi5FkiSNMAa5EWzL9h6uvXs1Z55wOO0eVpUkSf0Y5Eaw6+9dw6ZtPX63qiRJasogN4It6ezi0AljOe3Y6VWXIkmSRiCD3Ai1rbuXa+5axRnHz2Zsu0+TJEnanQlhhLrx/rWs39LtYVVJktSSQW6EWtLZxaSOdk5fOKPqUiRJ0ghlkBuBunt6ufrOVbzs+NmMH9tedTmSJGmEMsiNQDc9tI51G7d5WFWSJA3IIDcCLensYvzYNl7yjJlVlyJJkkYwg9wI09ubLOns4sVPn8nEDr9BTZIktVZpkIuIcyLinohYFhEfaLI+IuLT5frbI+LZVdQ5nH6x/HFWr9/KuScdUXUpkiRphKssyEVEO/AZ4FzgBOANEXFCv2bnAgvL24XA54a1yAosvqOLjvY2Xnb8rKpLkSRJI1yVI3KnAssy84HM3AZ8Ezi/X5vzga9m4afA1IgYtUNVmcnizi5OXziDQ8aPrbocSZI0wlUZ5I4EljfMryiX7W2bUeOOR57kkSc2c45Xq0qSpEGoMshFk2W5D22KhhEXRsQtEXHLmjVr9ru4Kizu7KK9LTjz+NlVlyJJkmqgyiC3AjiqYX4usHIf2gCQmZdk5qLMXDRzZv0+tiOzuFr1tGOmc9ikjqrLkSRJNVBlkLsZWBgRT4uIDuD1wBX92lwB/G559erzgCcz89HhLnQ43LNqPQ+u3ehhVUmSNGiVfVBZZnZHxLuAq4B24MuZuTQi3lGu/zxwJXAesAzYBLylqnqH2uI7uoiAs070sKokSRqcSj9xNjOvpAhrjcs+3zCdwB8Nd11VWNLZxXOOnsasKeOrLkWSJNWE3+wwAjywZgP3rFrvYVVJkrRXDHIjwOLOLgCDnCRJ2isGuRFgSWcXJx81lTlTJ1RdiiRJqhGDXMWWr9vEHY88ybmOxkmSpL1kkKvYVUuLw6oGOUmStLcMchVb3NnF8UccwtHTJ1VdiiRJqhmDXIVWPbWFW3/1uKNxkiRpnxjkKuRhVUmStD8MchVafEcXx86cxMLZU6ouRZIk1ZBBriKPbdjKzx58jHNPOqLqUiRJUk0Z5CpyzZ2r6E0/BFiSJO07g1xFFnd2cdS0CZw455CqS5EkSTVlkKvAk5u3c+P9azn3pCOIiKrLkSRJNWWQq8AP7lrF9p70sKokSdovBrkKXHlHF4cfMp5T5k6tuhRJklRjBrlhtmFrN9fft4ZzTjqctjYPq0qSpH1nkBtm1969mm3dvR5WlSRJ+80gN8yWdHYxY3IHz5k/repSJElSzRnkhtGW7T1ce89qzjrxcNo9rCpJkvaTQW4YXXfvGjZt6/G7VSVJ0gFhkBtGSzq7OHTCWJ53zPSqS5EkSaOAQW6YbOvu5ft3reLME2Yztt1ulyRJ+89EMUx+fP9a1m/p9rCqJEk6YAxyw2TJHV1MHjeG0xfOqLoUSZI0ShjkhkF3Ty9X39nFy46bxbgx7VWXI0mSRgmD3DC46cF1PL5pu4dVJUnSAWWQGwaLO7sYP7aNFz9jZtWlSJKkUcQgN8R6e5OrlnbxkqfPYmLHmKrLkSRJo4hBboj9/OHHWb1+K+c+08OqkiTpwDLIDbHFnV10tLfxsuNmVV2KJEkaZSo51hcR04BvAfOBh4DXZebjTdo9BKwHeoDuzFw0fFXuv8xkSWcXpy+cwZTxY6suR5IkjTJVjch9APhBZi4EflDOt/LSzDylbiEO4I5HnuSRJzZzjlerSpKkIVBVkDsf+Eo5/RXg1RXVMaQWd3bR3hacefzsqkuRJEmjUFVBbnZmPgpQ/mx1AlkCV0fErRFx4UAbjIgLI+KWiLhlzZo1B7jcvZeZLL7jUU47ZjqHTeqouhxJkjQKDdk5chHxfaDZMcWL92IzL8jMlRExC7gmIu7OzOubNczMS4BLABYtWpR7XfABdnfXeh56bBNvf+ExVZciSZJGqSELcpl5Rqt1EbEqIo7IzEcj4ghgdYttrCx/ro6Iy4FTgaZBbqRZ3NlFBJx1oodVJUnS0Kjq0OoVwJvL6TcD3+3fICImRcSUvmngLKBz2CrcT0s6H+U5R09j1pTxVZciSZJGqaqC3EeBMyPiPuDMcp6ImBMRV5ZtZgM3RMRtwE3A9zJzSSXV7qX712zg3lUbvFpVkiQNqUo+Ry4zHwNe3mT5SuC8cvoB4ORhLu2AWNLZBWCQkyRJQ8pvdhgCizsf5eSjpjJn6oSqS5EkSaPYHoNcea5aWzn99Ih4VUT4NQUtLF+3ic5HnuJcR+MkSdIQG8yI3PXA+Ig4kuJbGN4CXDqURdVZ32FVg5wkSRpqgwlykZmbgN8E/iUzXwOcMLRl1dfizkc54YhDOHr6pKpLkSRJo9ygglxEnAa8EfheuaySiyRGuq4nt/Dzh59wNE6SJA2LwQS59wAXAZdn5tKIOAa4dmjLqqerlpaHVZ9pkJMkSUNvjyNrmXkdcF35obx9Hwvy7qEurI4Wdz7KglmTWTBrStWlSJKkg8Bgrlo9LSLuBO4q50+OiM8OeWU189iGrdz04DoPq0qSpGEzmEOrnwTOBh4DyMzbgBcNZVF1dPWdq+hNPwRYkiQNn0F9IHBmLu+3qGcIaqm1xZ1dzJs2kROOOKTqUiRJ0kFiMEFueUQ8H8iI6IiI91EeZlXhyU3buXHZWs496XAioupyJEnSQWIwQe4dwB8BRwIrgFPKeZW+f9cqunvTw6qSJGlYDXjVakS0A5/MzDcOUz21tLiziyMOHc/Jc6dWXYokSTqIDDgil5k9wMyI6Bimempnw9Zurr9vDWefeDhtbR5WlSRJw2cw39DwEPDjiLgC2Ni3MDM/PlRF1cm1d69mW3evHzsiSZKG3WCC3Mry1gb4Sbf9LO58lBmTO1g0f1rVpUiSpIPMYL7Z4W8AImJKMZsbhryqmti8rYdr717Da559JO0eVpUkScNsMN/scFJE/ALoBJZGxK0RceLQlzbyXXfvGjZv7/GwqiRJqsRgPn7kEuBPM/PozDwa+DPgi0NbVj0s6XyUQyeM5XnHTK+6FEmSdBAaTJCblJnX9s1k5g+BSUNWUU30ZvKDu1Zz5gmzGds+qC/IkCRJOqAGc7HDAxHxl8C/lfNvAh4cupLq4anN21m/tdvDqpIkqTKDGUp6KzAT+I/yNgN4y1AWVQfrNm5j8h3BZeMAABE5SURBVLgxnL5wRtWlSJKkg9Rgrlp9HHj3MNRSG5nJ45u2c94zj2DcmPaqy5EkSQepwVy1ek1ETG2YPywirhraska2p7Z0092bHlaVJEmVGsyh1RmZ+UTfTDlCN2voShr51m3cRlvAi58xs+pSJEnSQWwwQa43Iub1zUTE0UAOXUkjW2by1JbtHDphLBM7BnOtiCRJ0tAYTBK5GLghIq4r518EXDh0JY1sEcEz5xxKd+9Bm2UlSdIIMZiLHZZExLOB5wEBvDcz1w55ZSNYW1vQ4VdySZKkig3mYocXAJsz87+BQ4EPlodXJUmSVKHBnCP3OWBTRJwM/DnwK+Cr+7PTiHhtRCyNiN6IWDRAu3Mi4p6IWBYRH9iffUqSJI02gwly3ZmZwPnApzPzU8CU/dxvJ/CbwPWtGkREO/AZ4FzgBOANEXHCfu5XkiRp1BjMxQ7rI+Iiiq/melEZsMbuz04z8y4oLhwYwKnAssx8oGz7TYoweef+7FuSJGm0GMyI3AXAVuBtmdkFHAl8bEirKhwJLG+YX1EukyRJEoO7arUL+HjD/MMM4hy5iPg+0OyrDy7OzO8OorZmw3UtP/MjIi6k/FiUefPmtWomSZI0agzZJ9pm5hn7uYkVwFEN83OBlQPs7xLgEoBFixb5IW+SJGnUG8yh1arcDCyMiKdFRAfweuCKimuSJEkaMVoGuYh4X0Qc1Wr9/oiI10TECuA04HsRcVW5fE5EXAmQmd3Au4CrgLuAyzJz6VDUI0mSVEcDHVo9ErgxIh4EvgF8+0B9o0NmXg5c3mT5SuC8hvkrgSsPxD4lSZJGm5Yjcpn5XmAe8JfAs4DbI2JxRPxuROzv58hJkiRpPw14jlwWrsvMd1JcePBJ4L3AquEoTpIkSa0N6qrViHgmxcUGFwCPAR8cyqIkSZK0Zy2DXEQsBN5AEeB6gG8CZ/V904IkSZKqNdCI3FUUFzlckJl3DFM9kiRJGqSBgtzZwOz+IS4iXgiszMz7h7QySZIkDWigix0+ATzVZPlmioseJEmSVKGBgtz8zLy9/8LMvAWYP2QVSZIkaVAGCnLjB1g34UAXIkmSpL0zUJC7OSJ+v//CiHgbcOvQlSRJkqTBGOhih/cAl0fEG9kZ3BYBHcBrhrowSZIkDaxlkMvMVcDzI+KlwEnl4u9l5v8MS2WSJEka0B6/2SEzrwWuHYZaJEmStBcG/K5VSZIkjVwGOUmSpJoyyEmSJNWUQU6SJKmmDHKSJEk1ZZCTJEmqKYOcJElSTRnkJEmSasogJ0mSVFMGOUmSpJoyyEmSJNWUQU6SJKmmDHKSJEk1ZZCTJEmqKYOcJElSTRnkJEmSaqqSIBcRr42IpRHRGxGLBmj3UETcERG/jIhbhrNGSZKkkW5MRfvtBH4T+MIg2r40M9cOcT2SJEm1U0mQy8y7ACKiit1LkiSNCiP9HLkEro6IWyPiwqqLkSRJGkmGbEQuIr4PHN5k1cWZ+d1BbuYFmbkyImYB10TE3Zl5fYv9XQhcCDBv3rx9qlmSJKlOhizIZeYZB2AbK8ufqyPicuBUoGmQy8xLgEsAFi1alPu7b0mSpJFuxB5ajYhJETGlbxo4i+IiCUmSJFHdx4+8JiJWAKcB34uIq8rlcyLiyrLZbOCGiLgNuAn4XmYuqaJeSZKkkaiqq1YvBy5vsnwlcF45/QBw8jCXJkmSVBsj9tCqJEmSBmaQkyRJqimDnCRJUk0Z5CRJkmrKICdJklRTBjlJkqSaMshJkiTVlEFOkiSppgxykiRJNWWQkyRJqimDnCRJUk0Z5CRJkmrKICdJklRTBjlJkqSaMshJkiTVlEFOkiSppgxykiRJNWWQkyRJqimDnCRJUk0Z5CRJkmrKICdJklRTBjlJkqSaMshJkiTVlEFOkiSppgxykiRJNWWQkyRJqimDnCRJUk0Z5CRJkmrKICdJklRTBjlJkqSaqiTIRcTHIuLuiLg9Ii6PiKkt2p0TEfdExLKI+MBw1ylJkjSSVTUidw1wUmY+C7gXuKh/g4hoBz4DnAucALwhIk4Y1iolSZJGsEqCXGZenZnd5exPgblNmp0KLMvMBzJzG/BN4PzhqlGSJGmkGwnnyL0VWNxk+ZHA8ob5FeWypiLiwoi4JSJuWbNmzQEuUZIkaeQZM1QbjojvA4c3WXVxZn63bHMx0A18vdkmmizLVvvLzEuASwAWLVrUsp0kSdJoMWRBLjPPGGh9RLwZeAXw8sxsFrxWAEc1zM8FVh64CiVJkuqtqqtWzwHeD7wqMze1aHYzsDAinhYRHcDrgSuGq0ZJkqSRrqpz5P4VmAJcExG/jIjPA0TEnIi4EqC8GOJdwFXAXcBlmbm0onolSZJGnCE7tDqQzFzQYvlK4LyG+SuBK4erLkmSpDoZCVetSpIkaR8Y5CRJkmrKICdJklRTBjlJkqSaMshJkiTVlEFOkiSppgxykiRJNWWQkyRJqimDnCRJUk0Z5CRJkmrKICdJklRTBjlJkqSaMshJkiTVlEFOkiSppgxykiRJNWWQkyRJqimDnCRJUk0Z5CRJkmrKICdJklRTBjlJkqSaMshJkiTVlEFOkiSppgxykiRJNWWQkyRJqimDnCRJUk0Z5CRJkmrKICdJklRTBjlJkqSaMshJkiTV1JgqdhoRHwNeCWwD7gfekplPNGn3ELAe6AG6M3PRcNYpSZI0klU1IncNcFJmPgu4F7hogLYvzcxTDHGSJEm7qiTIZebVmdldzv4UmFtFHZIkSXVWyaHVft4KfKvFugSujogEvpCZlwxfWa196w9Oq7oESZKkoQtyEfF94PAmqy7OzO+WbS4GuoGvt9jMCzJzZUTMAq6JiLsz8/oW+7sQuBBg3rx5+12/JEnSSDdkQS4zzxhofUS8GXgF8PLMzBbbWFn+XB0RlwOnAk2DXDladwnAokWLmm5PkiRpNKnkHLmIOAd4P/CqzNzUos2kiJjSNw2cBXQOX5WSJEkjW1VXrf4rMIXicOkvI+LzABExJyKuLNvMBm6IiNuAm4DvZeaSasqVJEkaeSq52CEzF7RYvhI4r5x+ADh5OOuSJEmqE7/ZQZIkqaYMcpIkSTVlkJMkSaopg5wkSVJNGeQkSZJqyiAnSZJUUwY5SZKkmooW345VaxGxBvhV1XWMADOAtVUXMQrYj/vPPjww7McDw348MOzHA2MGMCkzZ+7LnUdlkFMhIm7JzEVV11F39uP+sw8PDPvxwLAfDwz78cDY33700KokSVJNGeQkSZJqyiA3ul1SdQGjhP24/+zDA8N+PDDsxwPDfjww9qsfPUdOkiSpphyRkyRJqimD3CgSEe0R8YuI+O9yflpEXBMR95U/D6u6xpEuIqZGxHci4u6IuCsiTrMf915EvDcilkZEZ0R8IyLG2497FhFfjojVEdHZsKxlv0XERRGxLCLuiYizq6l65GnRjx8rf69vj4jLI2Jqwzr7sYlm/diw7n0RkRExo2GZ/dhPqz6MiD8u+2lpRPxTw/K97kOD3OjyJ8BdDfMfAH6QmQuBH5TzGtingCWZeRxwMkV/2o97ISKOBN4NLMrMk4B24PXYj4NxKXBOv2VN+y0iTqDo1xPL+3w2ItqHr9QR7VJ278drgJMy81nAvcBFYD/uwaXs3o9ExFHAmcDDDcvsx+YupV8fRsRLgfOBZ2XmicA/l8v3qQ8NcqNERMwFfgP4UsPi84GvlNNfAV493HXVSUQcArwI+D8AmbktM5/AftwXY4AJETEGmAisxH7co8y8HljXb3Grfjsf+GZmbs3MB4FlwKnDUugI16wfM/PqzOwuZ38KzC2n7ccWWrweAT4B/AXQeJK9/dhEiz58J/DRzNxatlldLt+nPjTIjR6fpPjF6m1YNjszHwUof86qorAaOQZYA/zf8hD1lyJiEvbjXsnMRyj+w3wYeBR4MjOvxn7cV6367UhgeUO7FeUy7dlbgcXltP24FyLiVcAjmXlbv1X24+A9HXhhRPwsIq6LiOeUy/epDw1yo0BEvAJYnZm3Vl1LzY0Bng18LjN/DdiIh//2WnkO1/nA04A5wKSIeFO1VY1K0WSZH0OwBxFxMdANfL1vUZNm9mMTETERuBj4q2armyyzH5sbAxwGPA/4c+CyiAj2sQ8NcqPDC4BXRcRDwDeBl0XE14BVEXEEQPlzdetNiOK/nxWZ+bNy/jsUwc5+3DtnAA9m5prM3A78B/B87Md91arfVgBHNbSbS3EIWy1ExJuBVwBvzJ2fvWU/Dt6xFP+g3Va+38wFfh4Rh2M/7o0VwH9k4SaKI2kz2Mc+NMiNApl5UWbOzcz5FCdK/k9mvgm4Anhz2ezNwHcrKrEWMrMLWB4RzygXvRy4E/txbz0MPC8iJpb/Zb6c4qIR+3HftOq3K4DXR8S4iHgasBC4qYL6aiEizgHeD7wqMzc1rLIfBykz78jMWZk5v3y/WQE8u/zbaT8O3n8CLwOIiKcDHcBa9rEPxwxhoareRymGbN9G8eb62orrqYM/Br4eER3AA8BbKP7hsR8HKTN/FhHfAX5OcQjrFxSfXD4Z+3FAEfEN4CXAjIhYAfw1LX6PM3NpRFxG8c9GN/BHmdlTSeEjTIt+vAgYB1xT/H/BTzPzHfZja836MTP/T7O29mNzLV6LXwa+XH4kyTbgzeUI8T71od/sIEmSVFMeWpUkSaopg5wkSVJNGeQkSZJqyiAnSZJUUwY5SZKkmjLISRoRImJ6RPyyvHVFxCMN8x17uO+iiPj0IPZx44GrePAi4oN72f4jEXHGUNUjafTw40ckjTgR8WFgQ2b+c8OyMQ1fel4rEbEhMydXXYek0ccROUkjVkRcGhEfj4hrgX+MiFMj4saI+EX58xllu5dExH+X0x+OiC9HxA8j4oGIeHfD9jY0tP9hRHwnIu6OiK+X30JBRJxXLrshIj7dt91+dZ0YETeVo4W3R8TCcvmbGpZ/ISLaI+KjwIRy2df7bae9fIydEXFHRLy34XH/djnS2DcqeUdEZLn+2IhYEhG3RsSPIuK4oeh/SSOf3+wgaaR7OnBGZvZExCHAizKzuzz0+PfAbzW5z3HAS4EpwD0R8bnye18b/RpwIsV3Gf4YeEFE3AJ8odzHg+WnsjfzDuBTmdn3LSDtEXE8cAHwgszcHhGfpfhOzw9ExLsy85Qm2zkFODIzTwKIiKmNKzPzlrINEfExYEm56hLgHZl5X0Q8F/gs5Vf+SDq4GOQkjXTfbviamkOBr5QjYAmMbXGf72XmVmBrRKwGZlN8L2SjmzJzBUBE/BKYD2wAHsjMB8s23wAubLL9nwAXR8Rcii+/vi8iXg78OnBzObg3gZ1fcN/KA8AxEfEvwPeAq5s1iojXAc8GzoqIycDzgW+X+4Hiq6ckHYQMcpJGuo0N038LXJuZr4mI+cAPW9xna8N0D83/1jVrE03a7SYz/z0ifgb8BnBVRLy9vO9XMvOiwWyj3M7jEXEycDbwR8DrgLc2tomIE4G/oRgl7ImINuCJFiN8kg4yniMnqU4OBR4pp39vCLZ/N8UI2fxy/oJmjSLiGIqRu08DVwDPAn4A/HZEzCrbTIuIo8u7bI+I3UYPI2IG0JaZ/w/4S4pRt8b1hwLfBH43M9cAZOZTwIMR8dqyTZRhUNJByCAnqU7+CfiHiPgx0H6gN56Zm4E/BJZExA3AKuDJJk0vADrLQ7LHAV/NzDuBDwFXR8TtwDXAEWX7S4Db+1/sABwJ/LDczqVA/9G8VwNHA1/su+ihXP5G4G0RcRuwFDh/Xx+zpHrz40ckqUFETM7MDeVVrJ8B7svMT1RdlyQ144icJO3q98uRr6UUh3K/UHE9ktSSI3KSJEk15YicJElSTRnkJEmSasogJ0mSVFMGOUmSpJoyyEmSJNWUQU6SJKmm/j99CR8CQGBu1AAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.subplots(figsize=(10, 5))\n",
     "plt.errorbar(train_size, test_scores_mean, yerr=test_scores_std)\n",
@@ -3492,12 +5893,12 @@
     "#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 = ___\n",
-    "best_model.pandas_version = ___\n",
-    "best_model.numpy_version = ___\n",
-    "best_model.sklearn_version = ___\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 = ___"
+    "best_model.build_datetime = datetime.datetime.now()"
    ]
   },
   {
@@ -3530,13 +5931,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**A: 1** Your answer here"
+    "**A: 1** We started by establishing a baseline by predicting the resorts' tickets using the overall average, which gave a reference error rate which had a lot of room left for error with it. I built a linear regression model using the resort features like run length and lift capacity. Cross-validation showed a drop in MAE compared with the baseline that matched with the test set cross validation estimate, which meant the model did what it was supposed to do. Next we tried a forest regressor using categorical features, standard scaling for continuous variable and careful imputation of missing values. This model specifically captured nonlinear relationships and feature interactions, returning the loweset MAE in cross-validation and matching the test split. Because the random forest regressor worked consistently and provided the most accurate predictions, that is the one we chose. "
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -3550,7 +5951,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.12.7"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/Notebooks/05_modeling.ipynb b/Notebooks/05_modeling.ipynb
index 4e4008174..59f1f3c06 100644
--- a/Notebooks/05_modeling.ipynb
+++ b/Notebooks/05_modeling.ipynb
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 65,
    "metadata": {
     "scrolled": true
    },
@@ -160,7 +160,7 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>124</th>\n",
+       "      <th>125</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -193,6 +193,10 @@
        "      <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",
@@ -222,11 +226,11 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Runs</th>\n",
-       "      <td>105</td>\n",
+       "      <td>105.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TerrainParks</th>\n",
-       "      <td>4</td>\n",
+       "      <td>4.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LongestRun_mi</th>\n",
@@ -234,35 +238,35 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SkiableTerrain_ac</th>\n",
-       "      <td>3000</td>\n",
+       "      <td>3000.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Snow Making_ac</th>\n",
-       "      <td>600</td>\n",
+       "      <td>600.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>daysOpenLastYear</th>\n",
-       "      <td>123</td>\n",
+       "      <td>123.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>yearsOpen</th>\n",
-       "      <td>72</td>\n",
+       "      <td>72.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>averageSnowfall</th>\n",
-       "      <td>333</td>\n",
+       "      <td>333.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AdultWeekend</th>\n",
-       "      <td>81</td>\n",
+       "      <td>81.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>projectedDaysOpen</th>\n",
-       "      <td>123</td>\n",
+       "      <td>123.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NightSkiing_ac</th>\n",
-       "      <td>600</td>\n",
+       "      <td>600.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_state</th>\n",
@@ -270,11 +274,11 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_100kcapita</th>\n",
-       "      <td>1.12278</td>\n",
+       "      <td>1.122778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_100ksq_mile</th>\n",
-       "      <td>8.16104</td>\n",
+       "      <td>8.161045</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resort_skiable_area_ac_state_ratio</th>\n",
@@ -298,11 +302,11 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>total_chairs_skiable_ratio</th>\n",
-       "      <td>0.00466667</td>\n",
+       "      <td>0.004667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fastQuads_runs_ratio</th>\n",
-       "      <td>0.0285714</td>\n",
+       "      <td>0.028571</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fastQuads_skiable_ratio</th>\n",
@@ -313,7 +317,7 @@
        "</div>"
       ],
       "text/plain": [
-       "                                                    124\n",
+       "                                                    125\n",
        "Name                                Big Mountain Resort\n",
        "Region                                          Montana\n",
        "state                                           Montana\n",
@@ -321,6 +325,7 @@
        "vertical_drop                                      2353\n",
        "base_elev                                          4464\n",
        "trams                                                 0\n",
+       "fastEight                                           0.0\n",
        "fastSixes                                             0\n",
        "fastQuads                                             3\n",
        "quad                                                  2\n",
@@ -328,31 +333,31 @@
        "double                                                0\n",
        "surface                                               3\n",
        "total_chairs                                         14\n",
-       "Runs                                                105\n",
-       "TerrainParks                                          4\n",
+       "Runs                                              105.0\n",
+       "TerrainParks                                        4.0\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",
+       "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                          1.12278\n",
-       "resorts_per_100ksq_mile                         8.16104\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                   0.00466667\n",
-       "fastQuads_runs_ratio                          0.0285714\n",
+       "total_chairs_skiable_ratio                     0.004667\n",
+       "fastQuads_runs_ratio                           0.028571\n",
        "fastQuads_skiable_ratio                           0.001"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -377,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -387,16 +392,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(276, 276)"
+       "(278, 278)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -407,19 +412,429 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 68,
    "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>Pipeline(steps=[(&#x27;simpleimputer&#x27;, SimpleImputer()), (&#x27;standardscaler&#x27;, None),\n",
+       "                (&#x27;randomforestregressor&#x27;,\n",
+       "                 RandomForestRegressor(n_estimators=54, random_state=47))])</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-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" 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()), (&#x27;standardscaler&#x27;, None),\n",
+       "                (&#x27;randomforestregressor&#x27;,\n",
+       "                 RandomForestRegressor(n_estimators=54, random_state=47))])</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-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" 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()</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\">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-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" 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=54, random_state=47)</pre></div> </div></div></div></div></div></div>"
+      ],
       "text/plain": [
-       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
-       "                ('standardscaler', None),\n",
+       "Pipeline(steps=[('simpleimputer', SimpleImputer()), ('standardscaler', None),\n",
        "                ('randomforestregressor',\n",
-       "                 RandomForestRegressor(n_estimators=69, random_state=47))])"
+       "                 RandomForestRegressor(n_estimators=54, random_state=47))])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -430,7 +845,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -439,17 +854,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([-12.09690217,  -9.30247694, -11.41595784,  -8.10096706,\n",
-       "       -11.04942819])"
+       "array([-11.52944775,  -9.28526124, -11.21032738,  -8.47688889,\n",
+       "       -11.081633  ])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -460,16 +875,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(10.393146442687748, 1.4712769116280346)"
+       "(10.316711652236652, 1.20857108406565)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -495,7 +910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -505,7 +920,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -514,7 +929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -523,15 +938,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 75,
    "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"
+      "Big Mountain Resort modelled price is $92.19, actual price is $81.00.\n",
+      "Even with the expected mean absolute error of $10.32, this suggests there is room for an increase.\n"
      ]
     }
    ],
@@ -586,7 +1001,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -621,7 +1036,7 @@
     "        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.axvline(x=big_mountain[feat_name].values, c='r', ls='--', alpha=0.8, label='Big Mountain')\n",
     "    plt.xlabel(description)\n",
     "    plt.ylabel('frequency')\n",
     "    plt.title(description + ' distribution for resorts in market share')\n",
@@ -644,19 +1059,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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xFi5cyMyZM1mwYEGnQ1ExevRoxo4dyzLLLNP0PiZekiQNAzNnzmSllVZi3LhxRESnwxnxMpN58+Yxc+ZMNtpoo6b3s6tRkqRhYMGCBay++uomXUNERLD66qsvdgukiZckScOESdfQsiSvh4mXpMYmTqwekka0UaNGMWHCBDbbbDO22GIL/vznPwPw4IMPsu+++y5WWTvttBMbbLABtfeM3nvvvVlxxRUHNWaAadOmPRdrf84//3xOOOGEQT9+Lcd4SWrskEM6HYGkIWD55Zfn+uuvB+Ciiy7imGOO4Y9//CPrrrsu55577mKXN2bMGP70pz8xadIk5s+fz6xZswY7ZKBKvFZccUW23Xbbfrfbc8892XPPPVsSQw9bvCRJ0mJ77LHHWHXVVQGYMWMGm266KQBPPvkkb3/72xk/fjz7778/W2+9dd27zxxwwAGcddZZAJx33nnss88+z63LTD7xiU+w6aab8prXvIazzz4bqJKoPfbY47ntDj/8cKZOnQpUd7o57rjj2GKLLXjNa17DbbfdxowZMzj55JP5+te/zoQJE7jiiiv49a9/zdZbb83mm2/OrrvuyuzZswGYOnUqh5cLRh988MEceeSRbLvttrzkJS9ZosSyL7Z4SWrsyCOr55NO6mwckjrqqaeeYsKECSxYsIBZs2Zx2WWXvWCb73znO6y66qrceOON3HzzzUyYMKFuebvssgvve9/7ePbZZznrrLM49dRTOf7444EqEbv++uu54YYbeOihh3jta1/LDjvs0DDGNdZYg+uuu47vfOc7fOUrX+G0007jsMMOY8UVV+Soo44C4JFHHuEvf/kLEcFpp53GiSeeyFe/+tUXlDVr1iymT5/Obbfdxp577rnY3al9MfFSy4yb8tsBlzHjhN0HIRINmNcNkoaeQw994bI3vAH226/6zPb8w1TrrW+tHvPnv/CiyKee2vCQtV2NV155Je9617u4+eabF9lm+vTpfPjDHwZg0003Zfz48XXLGzVqFJMmTeLss8/mqaeeYty4cYuUc+CBBzJq1CjWXnttdtxxR66++mpWXnnlfmPsaTXbcsstOe+88/rcZubMmey///7MmjWLp59+uu7lIPbee2+WWmopNtlkk+daxQaqpV2NETEjIm6KiOsj4pqybLWIuCQi7izPq7YyBkmSNPi22WYbHnroIebOnbvI8trB8s044IADOOKII3j729/eVDlLL700//nPf56b7305h+WWWw6okrpnnnmmzzKOOOIIDj/8cG666SZOOeWUupeE6Cmrv3gWVztavHbOzIdq5qcAl2bmCRExpcx/sg1xSJLUPfproRo9uv/1Y8Y01cLVn9tuu41nn32W1VdfnSeffPK55ZMmTeKcc85h55135pZbbuGmm27qt5ztt9+eY445hgMPPHCR5TvssAOnnHIKkydP5uGHH+byyy/ny1/+MgsXLuSWW27h3//+NwsWLODSSy9l0qRJ/R5jpZVW4rHHHntu/tFHH2W99dYD4PTTT1/cqg9IJ7oa9wJ2KtOnA9Mw8ZIkacjrGeMFVQvQ6aefzqhRoxbZ5oMf/CCTJ09m/PjxbL755owfP55VVlmlbpkR8dzYq1pve9vbuPLKK9lss82ICE488URe/OIXAzw3eP/lL385m2++ecO43/rWt7Lvvvvyq1/9im9961t89rOfZb/99mO99dbjda97Hffcc8/inIYBicFqOuuz8Ih7gEeABE7JzFMjYn5mjqnZ5pHM7Le7cauttsp6v4jQ0OUYry5yxhnV8zvf2dk4pBHs1ltv5VWvelWnw2jo2WefZeHChYwePZp//OMf7LLLLtxxxx0su+yynQ6tJfp6XSLi2szcqq/tW93itV1mPhgRawGXRMRtze4YEYcChwJssMEGrYpPUjNMuCQ16cknn2TnnXdm4cKFZCbf/e53uzbpWhItTbwy88HyPCcifgFMBGZHxDqZOSsi1gHm1Nn3VOBUqFq8WhmnJEkaHCuttFLd63aphb9qjIgVImKlnmlgN+Bm4HxgctlsMvCrVsUgaZAcemjfP12XJC2WVrZ4rQ38otxAcmngJ5l5YURcDZwTEe8F7gP2a2EMkiR1jcz0RtlDyJKMk29Z4pWZdwOb9bF8HrBLq44rSVI3Gj16NPPmzWP11Vc3+RoCMpN58+YxevToxdrPK9dLkjQMjB07lpkzZ77ggqXqnNGjRzN27NjF2sfES5KkYWCZZZape2sbDR8mXpIae8MbOh2BJHUFEy9Jje3nb2AkaTC09CbZkrrEggXVQ5I0ILZ4SWrsyCOr5wHeVFeSRjpbvCRJktrExEuSJKlNTLwkSZLaxMRLkiSpTRxcL6mxt7610xFIUlcw8ZLUmImXJA0KuxolNTZ/fvWQJA2ILV6SGjv66OrZ63hJ0oDY4iVJktQmJl6SJEltYuIlSZLUJiZekiRJbeLgekmN7btvpyOQpK5g4iWpsd1263QEktQV7GqU1Njs2dVDkjQgtnhJauwzn6mevY6XJA2ILV6SJEltYuIlSZLUJiZekiRJbWLiJUmS1CYOrpfU2EEHdToCSeoKJl6SGtthh05HIEldwa5GSY3de2/1kCQNiC1ekhr74herZ6/jJUkDYouXJElSm5h4SZIktYmJlyRJUpuYeEmSJLWJg+slNXbIIZ2OQJK6gomXpMYmTux0BJLUFexqlNTYHXdUD0nSgNjiJamxr3ylevY6XpI0ILZ4SZIktYmJlyRJUpuYeEmSJLWJiZckSVKbtHxwfUSMAq4BHsjMPSJiNeBsYBwwA3h7Zj7S6jgkDcDhh3c6AknqCu1o8fowcGvN/BTg0sx8OXBpmZc0lI0fXz0kSQPS0sQrIsYCuwOn1SzeCzi9TJ8O7N3KGCQNghtvrB6SpAFpdYvXN4Cjgf/ULFs7M2cBlOe1WhyDpIH69rerhyRpQFqWeEXEHsCczLx2Cfc/NCKuiYhr5s6dO8jRSZIktV8rW7y2A/aMiBnAWcDrI+JMYHZErANQnuf0tXNmnpqZW2XmVmuuuWYLw5QkSWqPliVemXlMZo7NzHHAAcBlmXkQcD4wuWw2GfhVq2KQJEkaSjpxHa8TgDdExJ3AG8q8JElS12vLTbIzcxowrUzPA3Zpx3ElDZKjjup0BJLUFdqSeEka5jbeuNMRSFJX8JZBkhq76qrqIUkaEFu8JDV2WrkG8sSJnY1DkoY5W7wkSZLaxMRLkiSpTUy8JEmS2sTES5IkqU0cXC+psWOP7XQEktQVTLwkNbbhhp2OQJK6gl2Nkhq7/PLqIUkaEFu81PXGTfntgMuYccLugxDJMHbmmdXzDjt0Ng5JGuZs8ZIkSWoTEy9JkqQ2MfGSJElqExMvSZKkNnFwvaTGjj++0xFIUlcw8ZLU2NprdzoCSeoKdjVKauzii6uHJGlAbPGS1Ni551bPu+3W2TgkaZizxUuSJKlNTLwkSZLaxMRLkiSpTUy8JEmS2sTB9ZIaO/HETkcgSV3BxEtSY2PGdDoCSeoKdjVKauzXv64ekqQBMfGS1JiJlyQNChMvSZKkNjHxkiRJahMTL0mSpDYx8ZIkSWoTLychqbGTTup0BJLUFUy8JDU2enSnI5CkrmBXo6TGfvaz6iFJGhATL0mNXXJJ9ZAkDYiJlyRJUpuYeEmSJLWJiZckSVKbmHhJkiS1ScPLSUTENcAPgZ9k5iOtD0nSkHPqqZ2OQJK6QjMtXgcA6wJXR8RZEfHGiIgWxyVJktR1GiZemXlXZh4LbAz8BPgBcF9EfC4iVmt1gJKGgDPOqB6SpAFpaoxXRIwHvgp8Gfg5sC/wGHBZP/uMjoirIuKGiPh7RHyuLF8tIi6JiDvL86oDr4aklrriiuohSRqQZsZ4XQvMB74PTMnMf5dVf42I7frZ9d/A6zPzXxGxDDA9Ii4A9gEuzcwTImIKMAX45IBqIUmSNAw0c6/G/TLz7r5WZOY+9XbKzAT+VWaXKY8E9gJ2KstPB6Zh4iVJkkaAZroaD4mIMT0zEbFqRHyhmcIjYlREXA/MAS7JzL8Ca2fmLIDyvNYSxC1JkjTsNJN4vTkz5/fMlEtKvKWZwjPz2cycAIwFJkbEps0GFhGHRsQ1EXHN3Llzm91NUiuMHl09JEkD0kxX46iIWK5nbFdELA8stzgHycz5ETENeBMwOyLWycxZEbEOVWtYX/ucCpwKsNVWW+XiHE/SIDvppE5HIEldoZkWrzOBSyPivRHxHuASqrFZ/YqINXu6KEuytitwG3A+MLlsNhn41ZIELkmSNNw0bPHKzBMj4iZgFyCA4zPzoibKXgc4PSJGUSV452TmbyLiSuCciHgvcB+w35KHL6ktTjutej7kkM7GIUnDXDNdjWTmBcAFi1NwZt4IbN7H8nlUSZyk4eKqq6pnEy9JGpCGXY0RsU+52OmjEfFYRDweEY+1IzhJkqRu0kyL14nAWzPz1lYHI0mS1M2aGVw/26RLkiRp4Jpp8bomIs4Gfkl1GyAAMvO8lkUlaWgZM6bxNpKkhppJvFYGngR2q1mWgImXNFKceGKnI5CkrtDM5STe3Y5AJEmSul0zv2rcOCIujYiby/z4iPh060OTNGR8+9vVQ5I0IM0Mrv8ecAywEJ67PtcBrQxK0hBz443VQ5I0IM0kXi/KzKt6LXumFcFIkiR1s2YSr4ci4qVUA+qJiH2BWS2NSpIkqQs186vGDwGnAq+MiAeAe4CDWhqVJElSF2rmV413A7tGxArAUpn5eOvDkjSkrL12pyOQpK7QMPGKiP/uNQ9AZn6+RTFJGmqOP77TEUhSV2imq/GJmunRwB6AtxCSJElaTM10NX61dj4ivgKc37KIJA09Xy1fAx//eGfjkKRhrpkWr95eBLxksAORNITdfnunI5CkrtDMGK+bKJeSAEYBawKO75IkSVpMzbR47VEz/QwwOzO9gKokSdJiaibx6n35iJV7ftkIkJkPD2pEkiRJXaqZxOs6YH3gESCAMcB9ZV3ieC+p+224YacjkKSu0EzidSFwfmb+DiAi3gzsmpn+vEkaKY49ttMRSFJXaOZeja/tSboAMvMCYMfWhSRJktSdmmnxeigiPg2cSdW1eBAwr6VRSRpavvjF6tmWL0kakGZavA6kuoTEL8pjzbJM0khx773VQ5I0IM1cuf5h4MMRsWJm/qsNMUmSJHWlhi1eEbFtRNwC3FLmN4uI77Q8MkmSpC7TTFfj14E3UsZ1ZeYNwA6tDEqSJKkbNXWvxsy8v/aiqcCzrQlH0pD0ild0OgJJ6grNJF73R8S2QEbEssCRwK2tDUvSkPJxL9snSYOhma7Gw4APAesBM4EJZV6SJEmLod8Wr4gYBXwjM9/RpngkDUWf+Uz1fPzxnY1Dkoa5fhOvzHw2ItaMiGUz8+l2BSVpiJk9u9MRSFJXaGaM1wzgTxFxPvBEz8LM/FqrgpIkSepGdcd4RcQZZXJ/4Ddl25VqHpIkSVoM/bV4bRkRGwL3Ad9qUzySJEldq7/E62TgQmAj4Jqa5UF1s+yXtDAuaUgZN+W3A9p/xgm7D1IkHTJ+fKcjkKSuUDfxysyTgJMi4ruZ+YE2xiRpqDn88E5HIEldoeF1vEy6JEmSBkczF1CVNNIdfXT1kCQNSFP3apQ0ws2f3+kIJKkrmHhJbTDQwfkwOAP0lzSOL111HwCfmvLb4f9DAUnqoJZ1NUbE+hHxh4i4NSL+HhEfLstXi4hLIuLO8rxqq2KQJEkaSlo5xusZ4OOZ+SrgdcCHImITYApwaWa+HLi0zEuSJHW9lnU1ZuYsYFaZfjwibgXWA/YCdiqbnQ5MAz7ZqjgkDdwN62zc6RAkqSu0ZYxXRIwDNgf+CqxdkjIyc1ZErNWOGCQtubM3e2OnQ5CkrtDyxCsiVgR+DnwkMx+LiGb3OxQ4FGCDDTZoXYDq02AMBpckSYtq6XW8ImIZqqTrx5l5Xlk8OyLWKevXAeb0tW9mnpqZW2XmVmuuuWYrw5TUwHG/P4Xjfn9Kp8OQpGGvlb9qDOD7wK2Z+bWaVecDk8v0ZOBXrYpB0uBY7pmnWe6ZpzsdhiQNe63satwOeCdwU0RcX5Z9CjgBOCci3gvcB+zXwhgkSZKGjFb+qnE6UG9A1y6tOq4kSdJQ5b0aJUmS2k7silgAABFXSURBVMRbBklq6Oqxr+50CJLUFUy8JDX0i01f3+kQJKkr2NUoSZLUJiZekhr60oXf4ksXfqvTYUjSsGdXozRMeDcBSRr+bPGSJElqExMvSZKkNjHxkiRJahPHeElqaPq4zTsdgiR1BRMvSQ397pWTOh2CJHUFuxolNbTcM0+z3DNPdzoMSRr2TLwkNXTc70/huN+f0ukwJGnYM/GSJElqExMvSZKkNjHxkiRJahMTL0mSpDbxchKSGrr0ZRM7HYIkdQUTL0kNXfqyrTsdgiR1BbsaJTW08oJ/sfKCf3U6DEka9ky8JDU0ZdoPmTLth50OQ5KGPRMvSZKkNjHxkiRJahMTL0mSpDYx8ZIkSWoTLychqaELXrFdp0OQpK5g4iWpoSs22mJQyxs35bcDLmPGCbsPQiSS1F52NUpqaI0nHmGNJx7pdBiSNOyZeElq6GNXnMnHrjiz02FI0rBn4iVJktQmJl6SJElt4uB6DWmDMQhbkqShwhYvSZKkNrHFS1JDv3z1zp0OQZK6gomXpIauWn/TTocgSV3BrkZJDa336BzWe3ROp8OQpGHPxEtSQx+68mw+dOXZnQ5DkoY9Ey9JkqQ2MfGSJElqExMvSZKkNjHxkiRJahMvJ9GFvNq7BtvZ43d7btr3lyQtuZa1eEXEDyJiTkTcXLNstYi4JCLuLM+rtur4kgbPDeu+ghvWfUWnw5CkYa+VXY1TgTf1WjYFuDQzXw5cWuYlDXEbPfwAGz38QKfDkKRhr2WJV2ZeDjzca/FewOll+nRg71YdX9Lged9V5/G+q87rdBiSNOy1e3D92pk5C6A8r9Xm40uSJHXMkP1VY0QcGhHXRMQ1c+fO7XQ4kiRJA9buxGt2RKwDUJ7r3vwtM0/NzK0yc6s111yzbQFKkiS1SrsTr/OByWV6MvCrNh9fkiSpY1p2Ha+I+CmwE7BGRMwEjgNOAM6JiPcC9wH7ter4kgbPj7bYo9MhSFJXaFnilZkH1lm1S6uOKak1bltro06HIEldYcgOrpc0dLxyzj28cs49nQ5DkoY9Ey9JDb3rut/wrut+0+kwJGnYM/GSJElqExMvSZKkNjHxkiRJahMTL0mSpDZp2eUkJHWP703cp9MhSFJXMPGS1NA9q63X6RAkqSvY1Sipoc0evJ3NHry902FI0rBni5ekhva/8WIAblj3FR2ORJKGN1u8JEmS2sTES5IkqU1MvCRJktrExEuSJKlNHFwvqaH/22b/TocgSV3BxGuIGTflt50OQXqBB1ZZq9MhtMRgfN5mnLD7IEQiaaSwq1FSQxPvv5mJ99/c6TAkadizxUtSQ3v//Q8AXLX+ph2ORJKGN1u8JEmS2sTES5IkqU3sapQ0LPlDlEX5QwFpeLDFS5IkqU1s8ZLU0Ne2P6jTIUhSVzDxktTQQyus2ukQJKkr2NUoqaHt77mO7e+5rtNhSNKwZ4tX4cBUqb433/4nAK7YaIsORyJJw5stXpIkSW1i4iVJktQmJl6SJEltYuIlSZLUJg6ul9TQCTu9u9MhSFJXMPGS1NBjo1fsdAiS1BXsapTU0C53/ZVd7vprp8OQpGHPxEtSQ7vcdRW73HVVp8OQpGHPxEuSJKlNTLwkSZLaxMH1g2gwbjskScOVt14bmnxdhhZbvCRJktrEFi9JDX1u1/d3OgRJ6gomXpIa+vfSy3Y6BEnqCnY1SmroLbdN5y23Te90GJI07NniJamhSTP+BsDvXjmpw5EMPf6oZnANlfPpYPLB52tb6UiLV0S8KSJuj4i7ImJKJ2KQJElqt7YnXhExCvg/4M3AJsCBEbFJu+OQJElqt060eE0E7srMuzPzaeAsYK8OxCFJktRWnUi81gPur5mfWZZJkiR1tcjM9h4wYj/gjZl5SJl/JzAxM4/otd2hwKFl9hXA7W0NtL41gIc6HUQHjNR6g3UfiXUfqfWGkVv3kVpvsO6tqPuGmblmXys68avGmcD6NfNjgQd7b5SZpwKntiuoZkXENZm5VafjaLeRWm+w7iOx7iO13jBy6z5S6w3Wvd1170RX49XAyyNio4hYFjgAOL8DcUiSJLVV21u8MvOZiDgcuAgYBfwgM//e7jgkSZLarSMXUM3M3wG/68SxB8GQ6/5sk5Fab7DuI9FIrTeM3LqP1HqDdW+rtg+ulyRJGqm8V6MkSVKbmHjVERHrR8QfIuLWiPh7RHy4LF8tIi6JiDvL86qdjrUVImJURPwtIn5T5kdKvcdExLkRcVt57bcZQXX/aHmv3xwRP42I0d1a94j4QUTMiYiba5bVrWtEHFNucXZ7RLyxM1EPXJ16f7m832+MiF9ExJiadV1Rb+i77jXrjoqIjIg1apZ1fd0j4ohSv79HxIk1y7ui7nXe7xMi4i8RcX1EXBMRE2vWtaXeJl71PQN8PDNfBbwO+FC5tdEU4NLMfDlwaZnvRh8Gbq2ZHyn1/iZwYWa+EtiM6hx0fd0jYj3gSGCrzNyU6ocvB9C9dZ8KvKnXsj7rWj73BwCvLvt8p9z6bDiaygvrfQmwaWaOB+4AjoGuqzf0XXciYn3gDcB9Ncu6vu4RsTPVXWPGZ+arga+U5d1U96m88DU/EfhcZk4A/rvMt7XeJl51ZOaszLyuTD9O9Qd4Pao36ulls9OBvTsTYetExFhgd+C0msUjod4rAzsA3wfIzKczcz4joO7F0sDyEbE08CKq6+t1Zd0z83Lg4V6L69V1L+CszPx3Zt4D3EV167Nhp696Z+bFmflMmf0L1bUVoYvqDXVfc4CvA0cDtQOeR0LdPwCckJn/LtvMKcu7pu516p3AymV6FZ6/jmjb6m3i1YSIGAdsDvwVWDszZ0GVnAFrdS6ylvkG1RfRf2qWjYR6vwSYC/ywdLOeFhErMALqnpkPUP3Hex8wC3g0My9mBNS9Rr26jqTbnL0HuKBMd329I2JP4IHMvKHXqq6vO7AxsH1E/DUi/hgRry3Lu73uHwG+HBH3U33nHVOWt63eJl4NRMSKwM+Bj2TmY52Op9UiYg9gTmZe2+lYOmBpYAvgu5m5OfAE3dO11q8ynmkvYCNgXWCFiDios1ENGdHHsq77OXhEHEs1xOLHPYv62Kxr6h0RLwKOpepuesHqPpZ1Td2LpYFVqYbSfAI4JyKC7q/7B4CPZub6wEcpPRy0sd4mXv2IiGWokq4fZ+Z5ZfHsiFinrF8HmFNv/2FqO2DPiJgBnAW8PiLOpPvrDdV/ODMz869l/lyqRGwk1H1X4J7MnJuZC4HzgG0ZGXXvUa+uTd3mbDiLiMnAHsA78vlrDHV7vV9K9Y/GDeX7bixwXUS8mO6vO1R1PC8rV1H1cKxB99d9MtX3G8DPeL47sW31NvGqo2T+3wduzcyv1aw6n+qFozz/qt2xtVJmHpOZYzNzHNVAw8sy8yC6vN4AmflP4P6IeEVZtAtwCyOg7lRdjK+LiBeV9/4uVOMaR0Lde9Sr6/nAARGxXERsBLwcuKoD8bVERLwJ+CSwZ2Y+WbOqq+udmTdl5lqZOa58380EtijfA11d9+KXwOsBImJjYFmqm0V3e90fBHYs068H7izT7at3Zvro4wFMompmvBG4vjzeAqxO9YunO8vzap2OtYXnYCfgN2V6RNQbmABcU173X1I1xY+Uun8OuA24GTgDWK5b6w78lGos20KqP7jv7a+uVF1S/wBuB97c6fgHud53UY1t6fmeO7nb6l2v7r3WzwDWGCl1p0q0ziyf9+uA13db3evUexJwLXAD1bjtLdtdb69cL0mS1CZ2NUqSJLWJiZckSVKbmHhJkiS1iYmXJElSm5h4SZIktYmJl9SlIuJtEZER8cp+tpkWEVs1KOezEXFUmT44ItYd7FjrHPfgiPh2C8qdERFr9LH8U73m/9ygnIbnrmbbcRHx/xYv0j7LOa3czHeg5awTEb/ptezgcnu02mVnRcTLB3o8Sc8z8ZK614HAdKoL4Q6Wg6luKdSNFkm8MnPbQSx7HDCgxCsiRmXmIZl5yyDE8zHgez3lRsQpwJeAC8udKnp8l+q+rZIGiYmX1IXKPUa3o7pg4AE1y5cvrRg3RsTZwPI16/5VM71vREztVea+wFbAjyPi+oio3XetiLi2TG9WWto2KPP/KFfEXzMifh4RV5fHdmX9ChHxg7LsbxGxVx/12T0iroyINSJitzJ9XUT8rNS1pyXrc2X5TT0tfRGxekRcXMo+hT7uyRYRJwDLl3r9uI/zcXQp84aybe2+S0XE6RHxhZLEfLnU5caIeH/Z7ASqGxJfHxEf7bX/ThFxeUT8IiJuiYiTI2Kpnhgi4vMR8Vdgm9pWtoh4U6nrDRFxabPnsvgv4MIy/Uaq2+d8HngT8L81210B7BoRS9cpR9JiMvGSutPewIWZeQfwcERsUZZ/AHgyM8cDXwS2bLbAzDyX6qr+78jMCZn5VM26OcDoiFgZ2L5st31EbEh10/UngW8CX8/M11L94T+t7H4s1a2pXgvsDHw5IlboKTsi3kZ1s/K3lEWfBnbNzC3KcT5WE+ZDZfl3gaPKsuOA6Vnd+Px8YIM+6jYFeKrU6x216yLizVTnc+vM3Aw4sWb10lQ3lb4jMz9Nleg+WuryWuB95fYjU4ArSvlf7+P0TgQ+DryGKgnapyxfAbg5M7fOzOk1Ma1J1WL1XyWm/Zo5l2XfjYBHMvPfZdHTVHdoWK6ci5tqzst/qK5sv1kfMUtaAv4XI3WnA4FvlOmzyvx1wA7ASQCZeWNE3DiIx/wzVSvbDlTdVm+ial26oqzfFdgk4rkGp5UjYiVgN6obs/ckSqN5PjnamaqVbbfMfCwi9gA2Af5UylkWuLImhp6b317L88nLDj3TmfnbiHhkMeu1K/DDkjySmQ/XrDsFOCczv1jmdwPGl9ZBgFWo7vn2dINjXJWZdwNExE+pbmtyLvAs8PM+tn8dcHlm3tMrpnrn8taafdcB5vbMZObvI+JnwEeB90TEyZn53Zrt51B1L1/boA6SmmDiJXWZiFid6uavm0ZEAqOAjIiesTr17hNWu3z0Ehz6CqrWrg2pbjD9yVJmzyDupYBtalvKSrxB1XJze6/lWwN3Ay8BNqZq3Qrgksw8sE4MPa04z7Lo99tA7o0W/ez/Z2DniPhqZi4o2x6RmRctUkDETg2O0bv8nvkFmfnsYsTU57ns5Sl6vb6ZeUJE/JPqfJ8UETdnZk/CPLrsI2kQ2NUodZ99gR9l5oaZOS4z1wfuoWpFuRx4B0BEbAqMr9lvdkS8qowveludsh8HVqqz7nLgIODO0kX1MFX34J/K+ouBw3s2jogJZfIi4IiSgBERm9eUeS9Va9WPIuLVwF+A7SLiZWXbF0XExv2ejUXr/GaqbrW+LIyIZfpYfjFVS9CLShmr1az7PvA74GdlHNRFwAd6yomIjUtXX3/nDWBiRGxUzv3+VD+K6M+VwI6l27A2pv7OZY87qAb7U7bZsGecHNXNsh/sFevGwN8bxCOpSSZeUvc5EPhFr2U/p/pV3XeBFUsX49HAVTXbTKFqnboMmFWn7KnAydFrcD1AZs4ok5eX5+nA/Mzs6do7EtiqDDq/BTisLD8eWAa4MSJuLvO15d5OlTj9DFiZ6peVPy11+AtQ93IZxeeAHSLiOqquuPvqbHdqieHHvY5/IdXYsGsi4nqeHzvWs/5rVN24Z1CNW7sFuK7U5RSqlrcbgWfKQPhFBtcXV1INwL+ZKknu/fotIjPnAocC50XEDcDZZVW/57Ls+wTwj57klarr8QKqX3VeQNW1eBFARKxNNfat3vtB0mKKzIG0wEuSBqJ0Qx6VmXu08ZhvA7YsPwjoWXYwMK0mgaYkiY9l5vfbFZvU7RzjJUkjTGb+oowFrHU9ML/XsvlULXmSBoktXpIkSW3iGC9JkqQ2MfGSJElqExMvSZKkNjHxkiRJahMTL0mSpDYx8ZIkSWqT/w/NTD/Qd4a+5wAAAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -666,19 +1079,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -695,19 +1106,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -731,19 +1140,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -767,19 +1174,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -803,19 +1208,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -839,19 +1242,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -875,19 +1276,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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cEQdFxLURce0DDzxQV4ySVJ+pU6tBGiXGjBnD5MmT2XTTTdl888353e9+B8B9993HHnvsMahtbbvttqy33nq0PjN6t912Y8UVVxzWmAEuu+yyF2NdlPPOO4/p06cP+/5b1dnHazYwOzOvKtNnUSVecyNircycExFrAff3tXJmzgBmAEyZMsUneUsaeQ48sOkIpGG13HLLccMNNwDwy1/+kiOPPJJf//rXrL322px11lmD3t748eP57W9/y9Zbb828efOYM2fOcIcMVInXiiuuyJZbbrnI5XbddVd23XXXWmLoUVuNV2b+Hbg3IjYqs6YBtwHnAQeUeQcA59YVgyRJqsdjjz3GqquuCsCsWbPYZJNNAHjqqafYa6+9mDRpEnvvvTdvetOb+n36zD777MPpp58OwNlnn83uu+/+4nuZyac//Wk22WQT3vCGN3DGGWcAVRK18847v7jcwQcfzCmnnAJUT7r5whe+wOabb84b3vAG7rjjDmbNmsW3v/1tvv71rzN58mR+85vfcP755/OmN72JzTbbjO233565c+cCcMopp3BwufHx+973Pg499FC23HJLXvWqVw0psexL3Vc1HgL8sFzReBfwfqpk78yI+CBwD7BnzTFIUjMOPbQaH398s3FIw+Tpp59m8uTJzJ8/nzlz5nDppZe+ZJkTTjiBVVddlZtuuolbbrmFyZMn97u9adOm8aEPfYjnn3+e008/nRkzZnD00UcDVSJ2ww03cOONN/Lggw/yxje+kW222WbAGFdffXWuv/56TjjhBI477jhOPvlkPvzhD7Piiity2GGHAfDII4/w+9//nojg5JNP5thjj+WrX/3qS7Y1Z84crrjiCu644w523XXXQTen9qXWxCszbwCm9PHWtDr3q+4w8YifLfY2Zk3faRgikRri/ZZUp4MOeum8HXaAPfesvns9iX+rXXaphnnzXnpz3xkzBtxla1PjlVdeyXvf+15uueWWhZa54oor+PjHPw7AJptswqRJk/rd3pgxY9h6660544wzePrpp5k4ceJC29l3330ZM2YMEyZM4G1vexvXXHMNK6+88iJj7Kk122KLLTj77LP7XGb27NnsvffezJkzh2effbbf20HstttuLLXUUmy88cYv1ootLu9cL0mSBu0tb3kLDz74IL0vgGvtLN+OffbZh0MOOYS99tqrre0svfTSvPDCCy9O976dw7LLLgtUSd1zzz3X5zYOOeQQDj74YG6++WZOOumkfm8J0bOtRcUzWD4kW5KkkWhRNVTjxi36/fHj26rhWpQ77riD559/ntVWW42nnnrqxflbb701Z555Jttttx233XYbN9988yK389a3vpUjjzySfffdd6H522yzDSeddBIHHHAADz/8MJdffjlf+cpXWLBgAbfddhvPPPMM8+fP55JLLmHrrbde5D5WWmklHnvssRenH330UdZZZx0AZs6c2d9qtTDxkiRJbenp4wVVDdDMmTMZM2bMQst89KMf5YADDmDSpElsttlmTJo0iVVWWaXfbUbEi32vWv3zP/8zV155JZtuuikRwbHHHsvLX/5ygBc772+wwQZsttlmA8a9yy67sMcee3DuuefyzW9+k6OOOoo999yTddZZhze/+c389a9/HUwxLJYYrqqzOk2ZMiX7uyJC3cs+XlrinXpqNd5//2bj0Khw++2387rXva7pMAb0/PPPs2DBAsaNG8df/vIXpk2bxp133skyyyzTdGi16OtziYjrMrOvPu7WeElSbUy4tAR66qmn2G677ViwYAGZyYknnjhqk66hMPGSJEnDZqWVVur3vl3yqkZJqs9BB/V9yb+kJZaJlyRJI8RI6Je9JBnK52HiJUnSCDBu3Dgeeughk68ukZk89NBDjBs3blDr2cdLkqQRYN1112X27NkvuWGpmjNu3DjWXXfdQa1j4iVJ0ggwduzYfh9to5HDxEuS6rLDDk1HIKnLmHhJUl323LPpCCR1GTvXS1Jd5s+vBkkqrPGSpLocemg1XsyHEUsaPazxkiRJ6hATL0mSpA4x8ZIkSeoQEy9JkqQOsXO9JNVll12ajkBSlzHxkqS6mHhJ6sWmRkmqy7x51SBJhTVeklSXww+vxt7HS1JhjZckSVKHmHhJkiR1iImXJElSh5h4SZIkdYid6yWpLnvs0XQEkrqMiZck1WXHHZuOQFKXsalRkuoyd241SFJhjZck1eXzn6/G3sdLUmGNlyRJUoeYeEmSJHWIiZckSVKHmHhJkiR1SK2d6yNiFvA48DzwXGZOiYiXAWcAE4FZwF6Z+UidcUhSI/bbr+kIJHWZTtR4bZeZkzNzSpk+ArgkMzcALinTkjT6bLNNNUhS0URT47uBmeX1TGC3BmKQpPrdfXc1SFJRd+KVwEURcV1EHFTmTcjMOQBlvGZfK0bEQRFxbURc+8ADD9QcpiTV4JhjqkGSirpvoLpVZt4XEWsCF0fEHe2umJkzgBkAU6ZMyboClCRJ6pRaa7wy874yvh84B5gKzI2ItQDK+P46Y5AkSeoWtSVeEbFCRKzU8xrYEbgFOA84oCx2AHBuXTFIkiR1kzqbGicA50REz35+lJm/iIhrgDMj4oPAPcCeNcYgSZLUNWpLvDLzLmDTPuY/BEyra7+S1DUOPLDpCCR1mbo710vSkmvq1KYjkNRlfGSQJNXlzjurQZIKa7wkqS7HHVeNZ8xoNg5JXcMaL0mSpA4x8ZIkSeoQEy9JkqQOMfGSJEnqEDvXS1JdDj646QgkdRkTL0mqy6RJTUcgqcvY1ChJdbnppmqQpMIaL0mqy7e+VY29j5ekwhovSZKkDjHxkiRJ6hATL0mSpA4x8ZIkSeoQO9dLUl0OO6zpCCR1GRMvSarLhhs2HYGkLmNToyTV5eqrq0GSCmu8JKkuJ59cjadObTYOSV3DGi9JkqQOMfGSJEnqEBMvSZKkDjHxkiRJ6hA710tSXT772aYjkNRlTLwkqS7rr990BJK6jIlXl5l4xM8Wexuzpu80DJFIWmyXX16Nt9mm2TgkdQ0TL0mqy2mnVWMTL0mFneslSZI6xMRLkiSpQ0y8JEmSOsTES5IkqUPsXC9JdTn66KYjkNRlTLwkqS4TJjQdgaQuY1OjJNXloouqQZIKa7wkqS5nnVWNd9yx2TgkdY3aa7wiYkxE/CEiLijTL4uIiyPiT2W8at0xSJIkdYNONDV+HLi9ZfoI4JLM3AC4pExLkiSNerUmXhGxLrATcHLL7HcDM8vrmcBudcYgSZLULequ8foGcDjwQsu8CZk5B6CM1+xrxYg4KCKujYhrH3jggZrDlCRJql9tnesjYmfg/sy8LiK2Hez6mTkDmAEwZcqUHObwJKl+xx7bdASSukydVzVuBewaEe8CxgErR8RpwNyIWCsz50TEWsD9NcYgSc0ZP77pCCR1mdqaGjPzyMxcNzMnAvsAl2bmfsB5wAFlsQOAc+uKQZIadf751SBJRRM3UJ0O7BARfwJ2KNOSNPqYeEnqpSM3UM3My4DLyuuHgGmd2K8kSVI38ZFBkiRJHWLiJUmS1CEmXpIkSR3iQ7IlqS7HH990BJK6jImXJNVl3LimI5DUZWxqlKS6/OQn1SBJhYmXJNXl4ourQZIKEy9JkqQOMfGSJEnqEBMvSZKkDjHxkiRJ6pABbycREdcC3wd+lJmP1B+SJI0SM2Y0HYGkLtNOjdc+wNrANRFxekS8PSKi5rgkSZJGnQETr8z8c2Z+FtgQ+BHwPeCeiPhiRLys7gAlacQ69dRqkKSirT5eETEJ+CrwFeCnwB7AY8Cl9YUmSSPcb35TDZJUtNPH6zpgHvBd4IjMfKa8dVVEbFVncJIkSaNJO89q3DMz7+rrjczcfZjjkSRJGrXaaWo8MCLG90xExKoR8aUaY5IkSRqV2km83pmZ83omyi0l3lVfSJI0SowbVw2SVLTT1DgmIpbt6dsVEcsBy9YbliSNAscf33QEkrpMO4nXacAlEfF9IIEPADNrjUqSJGkUGjDxysxjI+JmYBoQwNGZ+cvaI5Okke7kk6vxgQc2G4ekrtFOjReZ+XPg5zXHIkmjy9VXV2MTL0nFgJ3rI2L3iPhTRDwaEY9FxOMR8VgngpMkSRpN2qnxOhbYJTNvrzsYSZKk0ayd20nMNemSJElafO3UeF0bEWcA/wv0PC6IzDy7tqgkaTQYP37gZSQtUdpJvFYGngJ2bJmXgImXJC3Kscc2HYGkLtPO7STe34lAJEmSRrt2rmrcMCIuiYhbyvSkiPhc/aFJ0gj3rW9VgyQV7XSu/w5wJLAAIDNvAvapMyhJGhVuuqkaJKloJ/FaPjOv7jXvuTqCkSRJGs3aSbwejIhXU3WoJyL2AObUGpUkSdIo1M5VjR8DZgCvjYi/AX8F9qs1KkmSpFGonasa7wK2j4gVgKUy8/F2NhwR44DLgWXLfs7KzC9ExMuAM4CJwCxgr8x8ZGjhS1IXmzCh6QgkdZkBE6+I+Pde0wBk5n8MsOozwD9l5hMRMRa4IiJ+DuwOXJKZ0yPiCOAI4DNDCV6SutrRRzcdgaQu004frydbhueBd1LVVi1SVp4ok2PLkMC7gZll/kxgt8GFLEmSNDK109T41dbpiDgOOK+djUfEGOA64DXA/2TmVRExITPnlG3PiYg1Bx+2JI0AXy2nz099qtk4JHWNdjrX97Y88Kp2FszM54HJETEeOCciNml3JxFxEHAQwHrrrTeEMDtv4hE/azoESd3kj39sOgJJXaadPl43U24lAYwB1gAG6t+1kMycFxGXAe8A5kbEWqW2ay3g/n7WmUF1NSVTpkzJvpaRJEkaSdqp8dq55fVzwNzMHPAGqhGxBrCgJF3LAdsDX6ZqpjwAmF7G5w46akmSpBGoncSr9+0jVu65shEgMx/uZ721gJmln9dSwJmZeUFEXAmcGREfBO4B9hx82JIkSSNPO4nX9cArgEeAAMZTJUxQNUH22d+rPNNxsz7mPwRMG0qwkjSirL9+0xFI6jLtJF6/AM7LzAsBIuKdwPaZ6WU6krQon/1s0xFI6jLt3MfrjT1JF0Bm/hx4W30hSZIkjU7t1Hg9GBGfA06jalrcD3io1qgkaTQ45phqbM2XpKKdGq99qW4hcU4Z1ijzJEmLcvfd1SBJRTt3rn8Y+HhErNjyCCBJkiQN0oA1XhGxZUTcBtxWpjeNiBNqj0ySJGmUaaep8evA2yn9ujLzRmCbOoOSJEkajdp6VmNm3tt601Tg+XrCkaRRZKONmo5AUpdpJ/G6NyK2BDIilgEOBW6vNyxJGgU+5e0OJS2snabGDwMfA9YBZgOTy7QkSZIGYZE1XuU5i9/IzPd0KB5JGj0+//lqfPTRzcYhqWssMvHKzOcjYo2IWCYzn+1UUJI0Ksyd23QEkrpMO328ZgG/jYjzgCd7Zmbm1+oKSpIkaTTqt49XRJxaXu4NXFCWXallkCRJ0iAsqsZri4hYH7gH+GaH4pEkSRq1FpV4fRv4BfBK4NqW+UH1sOxX1RiXJI18kyY1HYGkLtNv4pWZxwPHR8SJmfmRDsYkSaPDwQc3HYGkLtPOQ7JNuqQuMPGIny32NmZN32kYIpEkDVU7N1CVJA3F4YdXgyQVbT2rUZI0BPPmNR2BpC5jjZckSVKHmHhJkiR1iImXJElSh9jHS5LqMnVq0xFI6jImXpJUlwMPbDoCSV3GpkZJkqQOMfGSpLocemg1SFJhU6Mk1WX+/KYjkNRlrPGSJEnqEBMvSZKkDrGpUaOeD5eWJHULEy9Jqstb39p0BJK6jImXJNVl//2bjkBSl7GPlyRJUoeYeElSXQ46qBokqagt8YqIV0TEryLi9oi4NSI+Xua/LCIujog/lfGqdcUgSZLUTeqs8XoO+FRmvg54M/CxiNgYOAK4JDM3AC4p05IkSaNebYlXZs7JzOvL68eB24F1gHcDM8tiM4Hd6opBkiSpm3Skj1dETAQ2A64CJmTmHKiSM2DNTsQgSZLUtNpvJxERKwI/BT6RmY9FRLvrHQQcBLDeeuvVF6Ak1WWHHZqOQFKXqTXxioixVEnXDzPz7DJ7bkSslZlzImIt4P6+1s3MGcAMgClTpmSdcUpSLfbcs+kIJHWZOq9qDOC7wO2Z+bWWt84DDiivD42wfWQAAA2XSURBVADOrSsGSWrU/PnVIElFnTVeWwH7AzdHxA1l3r8B04EzI+KDwD2A/xJKGp0OPbQaz5jRbBySukZtiVdmXgH016FrWl37lSRJ6lbeuV6SJKlDTLwkSZI6xMRLkiSpQ2q/j5ckLbF22aXpCCR1GRMvSaqLiZekXmxqlKS6zJtXDZJUWOMlSXU5/PBq7H28JBXWeEmSJHWIiZckSVKHmHhJkiR1iImXJElSh9i5XpLqssceTUcgqcuYeElSXXbcsekIJHUZmxolqS5z51aDJBXWeElSXT7/+WrsfbwkFdZ4SZIkdYiJlyRJUoeYeEmSJHWIiZckSVKH2Llekuqy335NRyCpy5h4SVJdttmm6QgkdRkTL/Vp4hE/azoEaeS7++5qvP76zcYhqWuYeElSXY45php7Hy9JhZ3rJUmSOsTES5IkqUNMvCRJkjrEPl6SBmU4LryYNX2nYYhk8Y2mY5E0Mph4SVJdDjyw6QgkdRkTL0mqy9SpTUcgqcvYx0uS6nLnndUgSYU1XpJUl+OOq8bex0tSYY2XJElSh5h4SZIkdYiJlyRJUoeYeEmSJHVIbZ3rI+J7wM7A/Zm5SZn3MuAMYCIwC9grMx+pK4bBGI4bKUrSQg4+uOkIJHWZOmu8TgHe0WveEcAlmbkBcEmZlqTRadKkapCkorbEKzMvBx7uNfvdwMzyeiawW137l6TG3XRTNUhS0en7eE3IzDkAmTknItbs8P4lqXO+9a1q7H28JBVd27k+Ig6KiGsj4toHHnig6XAkSZIWW6cTr7kRsRZAGd/f34KZOSMzp2TmlDXWWKNjAUqSJNWl04nXecAB5fUBwLkd3r8kSVJjaku8IuLHwJXARhExOyI+CEwHdoiIPwE7lGlJkqQlQm2d6zNz337emlbXPiWpqxx2WNMRSOoynb6qUZKWHBtu2HQEkrpM117VKEkj3tVXV4MkFdZ4SVJdTj65Gk+d2mwckrqGNV6SJEkdYuIlSZLUISZekiRJHWLiJUmS1CF2rpekunz2s01HIKnLmHhJUl3WX7/pCCR1GZsaJakul19eDZJUWOMlSXU57bRqvM02zcYhqWtY4yVJktQh1nipq0084mdNhwAsfhyzpu80TJEsnm4pT0laUlnjJUmS1CEmXpIkSR1iU6Mk1eXoo5uOQFKXMfGSpLpMmNB0BJK6jInXKGQHaqlLXHRRNd5xx2bjkNQ1TLwkqS5nnVWNTbwkFXaulyRJ6hATL0mSpA4x8ZIkSeoQEy9JkqQOsXO9JNXl2GObjkBSlzHxkqS6jB/fdASSuoxNjZJUl/PPrwZJKqzxkjrAm9oubDjKY9b0nYYhkpr1JF277LLIxZaY8pBkjZckSVKnmHhJkiR1iImXJElSh9jHS5IWw6L6Z/3n1fcA8G9LSB+/bumr1i1xaPiNhs/WxEuSavLF7f+16RAkdRkTL0mqyTNLL9N0CJK6jH28JKkm77rjCt51xxVNhyGpi1jjJUk12XrWHwC48LVbNxyJRqpuuQdg0/2iRpNGarwi4h0R8ceI+HNEHNFEDJIkSZ3W8cQrIsYA/wO8E9gY2DciNu50HJIkSZ3WRI3XVODPmXlXZj4LnA68u4E4JEmSOqqJxGsd4N6W6dllniRJ0qgWmdnZHUbsCbw9Mw8s0/sDUzPzkF7LHQQcVCY3Av5Yc2irAw/WvI8ljWU6vCzP4WeZDi/Lc/hZpsOvE2W6fmau0dcbTVzVOBt4Rcv0usB9vRfKzBnAjE4FFRHXZuaUTu1vSWCZDi/Lc/hZpsPL8hx+lunwa7pMm2hqvAbYICJeGRHLAPsA5zUQhyRJUkd1vMYrM5+LiIOBXwJjgO9l5q2djkOSJKnTGrmBamZeCFzYxL4XoWPNmksQy3R4WZ7DzzIdXpbn8LNMh1+jZdrxzvWSJElLKp/VKEmS1CEmXvgIo+EWEd+LiPsj4pamYxkNIuIVEfGriLg9Im6NiI83HdNIFhHjIuLqiLixlOcXm45ptIiIMRHxh4i4oOlYRoOImBURN0fEDRFxbdPxjHQRMT4izoqIO8r59C2NxLGkNzWWRxjdCexAdauLa4B9M/O2RgMbwSJiG+AJ4AeZuUnT8Yx0EbEWsFZmXh8RKwHXAbv5HR2aiAhghcx8IiLGAlcAH8/M3zcc2ogXEf8XmAKsnJk7Nx3PSBcRs4Apmel9vIZBRMwEfpOZJ5e7KiyfmfM6HYc1Xj7CaNhl5uXAw03HMVpk5pzMvL68fhy4HZ/2MGRZeaJMji3Dkv0f6DCIiHWBnYCTm45F6i0iVga2Ab4LkJnPNpF0gYkX+AgjjSARMRHYDLiq2UhGttIkdgNwP3BxZlqei+8bwOHAC00HMookcFFEXFee5qKhexXwAPD90hx+ckSs0EQgJl4Qfczzv191nYhYEfgp8InMfKzpeEayzHw+MydTPTljakTYJL4YImJn4P7MvK7pWEaZrTJzc+CdwMdKNw4NzdLA5sCJmbkZ8CTQSJ9uE682H2EkNan0Rfop8MPMPLvpeEaL0tRwGfCOhkMZ6bYCdi19kk4H/ikiTms2pJEvM+8r4/uBc6i6xmhoZgOzW2q3z6JKxDrOxMtHGKnLlc7g3wVuz8yvNR3PSBcRa0TE+PJ6OWB74I5moxrZMvPIzFw3MydSnUMvzcz9Gg5rRIuIFcrFNJQmsR0BrxQfosz8O3BvRGxUZk0DGrlAqZE713cTH2E0/CLix8C2wOoRMRv4QmZ+t9moRrStgP2Bm0u/JIB/K0+A0OCtBcwsVzQvBZyZmd7+QN1mAnBO9X8XSwM/ysxfNBvSiHcI8MNSyXIX8P4mgljibychSZLUKTY1SpIkdYiJlyRJUoeYeEmSJHWIiZckSVKHmHhJkiR1iImXtISKiCcGXqozImK3iNi4Q/s6KiIOq2G7n4iI5Vum2yrfcuz/Psh9XdhyL7IhfY4RcXpEbDCUdSUNnYmXpG6wGzBg4hUR3XzvwU8Ayw+41EsdDpwwmBUy813D8IDfE8u+JXWQiZekF0XE5Ij4fUTcFBHnRMSqZf5lEfHliLg6Iu6MiLeW+ctHxJll+TMi4qqImFLe2zEiroyI6yPiJ+VZk0TE9Ii4raxzXERsCewKfCUiboiIV/eK6ZSI+FpE/Ar4cu8aq4i4JSImluH2iPhORNwaEReVO9Mv6nhfHRG/KA8h/k1EvLZln8dHxO8i4q6I2KPMXyoiTijbv6DUPO0REYcCawO/KnH2bP+YiLixlOmEPva/IfBMZj7Yst8TI+JXZb9vi4jvleM6pWW9WRGxeh/b+3REXFPK9otl3goR8bMSxy0RsXdZ/DfA9l2ezEqjjomXpFY/AD6TmZOAm4EvtLy3dGZOparZ6Zn/UeCRsvzRwBYAJSn4HLB9ecjvtcD/jYiXAf8MvL6s86XM/B3VY7o+nZmTM/MvfcS1YdnWpwaIfwPgfzLz9cA84F8GWH4GcEhmbgEcxsI1T2sBWwM7A9PLvN2BicAbgAOBtwBk5vFUz3jdLjO3K8uuAPw+MzcFLgc+1Mf+twKu7zVvVeCfgE8C5wNfB14PvCEiJvd3IBGxI9XxTwUmA1uUhyq/A7gvMzfNzE2AX5SYXwD+DGza3zYlDT8TL0kARMQqwPjM/HWZNRPYpmWRnodzX0eVfECVmJwOkJm3ADeV+W+majr8bXnM0QHA+sBjwHzg5IjYHXiqzfB+kpnPt7HcXzOz57FKrXG+RKmB2xL4SYnxJKpkq8f/ZuYLmXkb1eNboDren5T5fwd+Rf+eBXoeRdRfLGsBD/Sad35WjxS5GZibmTeXJOnWRR0P1bP8dgT+QJXMvZYqEbuZqmbryxHx1sx8tGWd+6lq6iR1iFXMktr1TBk/zz/OHdHPsgFcnJn7vuSNiKlUD6jdBziYqnZnIE+2vH6Ohf9pHNdHjD1xLqqpcSlgXmb2V4vUuq3oNW7HgvzHM9lay6zV08Aq/ez3hV4xvNDPNlpj/K/MPOklb0RsAbwL+K+IuCgz/6O8Na7EIKlDrPGSBECpCXmkp/8W1YO5f72IVQCuAPYCKFclvqHM/z2wVUS8pry3fERsWGqZVikP+P4EVZMYwOPASm2GOgvYvGx3c+CVba63kMx8DPhrROxZthURMVCz2xXAv5S+XhOoHgbfYzDH0ON24DWDXKc/vwQ+0NKXbp2IWDMi1gaeyszTgOMoZVdsSFWTJqlDrPGSllzLR8TslumvUTUJfjuq2yLcBbx/gG2cAMyMiJuomrhuAh7NzAci4n3AjyNi2bLs56iSk3MjYhxVDc0ny3unA98pndT36KefV4+fAu8tzYPXAHe2d7h9eg9wYkR8Dhhb4rhxgH1PA24p+70K6Gm6mwH8PCLmtPTzGsjlwFcjIlpqx4YkMy+KiNcBV0YEwBPAflSJ3Vci4gVgAfARgJI4Pp2ZcxZnv5IGJxbzty5pCRYRY4CxmTm/XI14CbBhZj7bcGi1iYgVM/OJiFgNuBrYqvT3Gur2/puqX9f/G7Yg29vvJ4HHMvO7ndyvtKSzxkvS4lie6hYKY6lqsD4ympOu4oKobl66DHD04iRdxX8Cb1r8sAZtHnBqA/uVlmjWeEmSJHWIneslSZI6xMRLkiSpQ0y8JEmSOsTES5IkqUNMvCRJkjrExEuSJKlD/j/jVhpV/xxT4AAAAABJRU5ErkJggg==\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -911,19 +1310,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -947,19 +1344,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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85OHhIU9PTy1fvly7du1KV7ZDhw7y8fGxPw4ICFDbtm31888/Kzk5OcP6161bp1OnTql79+66fPmyfUpJSVGLFi0UHR2drqtcdvz999/6888/7fdCXF13q1atFBMTo927dzus8/DDD2dYV8GCBdWoUaN0y/ft26fOnTsrNDRU7u7u8vT0VP369SUpw+NzrbvvvtveyiNJPj4+KleunA4ePHjddVesWKEmTZooMDDQvu3XX39dJ0+e1LFjxxzKVqlSReXKlXNY9sMPP6hhw4YKDw93ODYtW7aUJK1evfqGtnWtI0eOqEiRIhnehzF37lzdd9998vf3t59bX3zxhcOx+/HHHyWl3pt0PTezn7Vq1dKZM2fUqVMn/d///V+698/VgoODlZKSotjY2EzL7N69W0eOHFHnzp0d9j0iIkJ169a97r7UqlVLP/74o15++WWtWrVKcXFx113nWo0aNVLBggWzXf7a+4bq1q2riIgIexee3JJW/7W/XteqVUsVK1ZM18U3NDRUtWrVclhWpUqVbL1vMrJixQr5+fnpkUcecVieFs+127d6XHP7vFyxYoUaN26c7p69Hj166NKlS+kG9sjscy4jZcqUUcGCBfXSSy9p8uTJ6Vpob0TDhg0VEBBgfxwSEqLg4OBsv37XjkhXsWJFSbK3fF69/NSpUw7d+7Zs2aIHH3xQhQoVsn+WdevWTcnJyfrrr78c1s/sc1/K+nqaW9e0oKAglS5dWuPHj9fEiRO1ZcuWTLuzZvfaYuX6/uCDDzr0ermRayxcC4kUXELNmjX10ksvae7cuTpy5IhefPFFHThwIN2AE3/99Zc2bNigli1bKioqKlt1T5w4Uf/5z39Uu3Ztffvtt/r1118VHR2tFi1aZPjFLjQ0NMNliYmJ6fqqpzl69Kgk6ZFHHpGnp6fDNHbsWBlj7F0QrEird9CgQenq7du3rySl+1Jydbeo6y2/cOGC6tWrpw0bNmjkyJFatWqVoqOjNW/ePEnK1hffQoUKpVvm7e193XV/++03NWvWTJL02Wef6ZdfflF0dLSGDh2a4bYziv/o0aP6/vvv0x2bu+66S9KVY2N1W9dKG7TjWvPmzVPHjh1VtGhRzZw5U+vXr1d0dLR69eql+Ph4e7njx4/L3d09w3PrWjezn127dtWXX36pgwcP6uGHH1ZwcLBq166tpUuXpqszbX+y2ve0LniZvSeu57333tNLL72kBQsWqGHDhgoKClK7du20Z8+e666bJrPzOTOZxZq2L7klrf6M4g0PD0+3/Rt932S1/dDQ0HTJfnBwsDw8PNJt3+pxze3z8uTJk5keu7TnbzT+wMBArV69WnfffbdeeeUV3XXXXQoPD9ewYcPS3VeUXTf7+gUFBTk89vLyynJ52ufJoUOHVK9ePf37779699137T9Ept1rmJ3PzTRZXU9z65pms9m0fPlyNW/eXOPGjVP16tVVpEgRPf/88+m6WWbnGFu9vl97PG7kGgvXwj1ScDmenp4aNmyYJk2apB07djg8V6dOHT366KPq3bu3pNQbmK93n8bMmTPVoEEDffzxxw7LM+sbn9Ev9LGxsfLy8pK/v3+G66Td+/D+++9nOppYSEhIlnFmVe+QIUPUoUOHDMuUL1/e4XFmo1dltHzFihU6cuSIVq1aZW+FknRLhrGdPXu2PD099cMPPzgkKQsWLMiwfEbxFy5cWFWqVNFbb72V4TppX8Ksbiuj7WzevDnd8pkzZyoyMlLffPONQ3zX3txfpEgRJScnKzY29rpfAG9mP6XUgRF69uypixcv6ueff9awYcPUpk0b/fXXX4qIiLCXS/sSdPV9O9dK+yKT2Xvievz8/DRixAiNGDFCR48etbdOtW3bVn/++ed115cyP58zk1msZcqUsT/28fFJ9xpJqV+YsjoeWUk7VjExMenuozpy5MgN12tl+xs2bJAxxuGYHTt2TJcvX063favHNbfPy0KFCikmJiZdHUeOHLFv62bir1y5smbPni1jjLZv366pU6fqjTfekK+vr15++WVLdTnTggULdPHiRc2bN8/h/ZzZX2dkdZyyup7m1jVNSm3R/uKLLySlJnNz5szR8OHDlZiYqMmTJ1uqy+r1/drjcSPXWLgWEinc1mJiYjL8YpnWJH/1hThN9+7d5efnp86dO+vixYuaNm2a3N3dM92GzWZLN7zp9u3btX79+gyHfp43b57Gjx9v/8J9/vx5ff/996pXr16m27nvvvtUoEAB7dy5U/369ct8hzORFt+1v6CVL19eZcuW1bZt2zRq1CjL9V5P2kXl2uPzySef5Pi2Mtq2h4eHwzGNi4vTjBkzsl1HmzZttGjRIpUuXTrLbko3u60KFSpo1qxZOnv2rAIDAx3q9fLycrg4x8bGphu1r2XLlho9erQ+/vjj6w7ukJHs7ufV/Pz81LJlSyUmJqpdu3b6448/HL547du3T4UKFcryy1D58uUVFhamWbNmacCAAfb9PHjwoNatW5fh+zMzISEh6tGjh7Zt26Z33nnHPgzz1ed+Tgyn/dVXXzl0+1q3bp0OHjzo8J9TJUuW1Pbt2x3W++uvv7R7926HL+yZvS8zktZ9aubMmfbR/iQpOjpau3btsrd+5pbGjRtrzpw5WrBggdq3b29fnjbCYna7QVuRk+dl48aNNX/+fB05csThvJo+fbry5cuXreHus/N62Ww2Va1aVZMmTdLUqVMz/IEkL8voM9sYk+0ROK+V2fXUyjXNyvvkWuXKldOrr76qb7/99oZeC6vX92vl9jUWeR+JFG5rzZs3V7FixdS2bVtVqFBBKSkp2rp1qyZMmCB/f3+98MILGa73yCOPKF++fHrkkUcUFxenWbNm2btAXKtNmzZ68803NWzYMNWvX1+7d+/WG2+8ocjIyHRD9EqSu7u7mjZtqgEDBiglJUVjx47VuXPnsvzTUH9/f73//vvq3r27Tp06pUceeUTBwcE6fvy4tm3bpuPHj6f7xexqpUuXlq+vr7766itVrFhR/v7+Cg8PV3h4uD755BO1bNlSzZs3V48ePVS0aFGdOnVKu3bt0ubNmzV37tzrHOXM1a1bVwULFtQzzzyjYcOGydPTU1999ZW2bdt2w3VmV+vWrTVx4kR17txZTz31lE6ePKm3337b0n96vPHGG1q6dKnq1q2r559/XuXLl1d8fLwOHDigRYsWafLkySpWrNhNb6tBgwYyxmjDhg32LoKS7ENC9+3bV4888ogOHz6sN998U2FhYQ7d1+rVq6euXbtq5MiROnr0qNq0aSNvb29t2bJF+fLl03PPPZcj+/nkk0/K19dX9913n8LCwhQbG6vRo0crMDDQ4cu9lDpqYf369bP8xdrNzU1vvvmm+vTpo/bt2+vJJ5/UmTNnNHz48Gx17atdu7batGmjKlWqqGDBgtq1a5dmzJihOnXq2P/LpnLlypKksWPHqmXLlnJ3d1eVKlUyfT9fz8aNG9WnTx89+uijOnz4sIYOHaqiRYvau+lIqV3NnnjiCfXt21cPP/ywDh48qHHjxqlIkSIOdWX1vrxW+fLl9dRTT+n999+Xm5ubWrZsaR+1r3jx4nrxxRdvaH+yq1u3bvrwww/VvXt3HThwQJUrV9batWs1atQotWrVSk2aNMnxbebkeTls2DD7PVevv/66goKC9NVXX2nhwoUaN26cww8YmUnrnvbpp58qICBAPj4+ioyM1Pr16/XRRx+pXbt2KlWqlIwxmjdvns6cOaOmTZvm+HHJTU2bNpWXl5c6deqkwYMHKz4+Xh9//LFOnz59w3VmdD21ck1Lew+/++676t69uzw9PVW+fHmHe8jSbN++Xf369dOjjz6qsmXLysvLSytWrND27dtvqGXQ6vU9I7l5jcVtwIkDXQA37ZtvvjGdO3c2ZcuWNf7+/sbT09OUKFHCdO3a1ezcudOhbNooQ1dbuXKl8ff3Ny1atDCXLl0yxqQfzSshIcEMGjTIFC1a1Pj4+Jjq1aubBQsWpBuNKW1Un7Fjx5oRI0aYYsWKGS8vL1OtWjWzePFih+1eO2pfmtWrV5vWrVuboKAg4+npaYoWLWpat25t5s6de91jMWvWLFOhQgXj6emZbh+2bdtmOnbsaIKDg42np6cJDQ01jRo1MpMnT04XU3R0dLq6MxrlK826detMnTp1TL58+UyRIkVMnz59zObNm9ONVpbZqH3XviZp27t69LPMfPnll6Z8+fLG29vblCpVyowePdp88cUX6Y5tZtsxxpjjx4+b559/3kRGRhpPT08TFBRkatSoYYYOHeowIl12t5WR5ORkU7JkyXQjshljzJgxY0zJkiWNt7e3qVixovnss88yPFbJyclm0qRJJioqynh5eZnAwEBTp04d8/333+fYfk6bNs00bNjQhISEGC8vLxMeHm46duxotm/f7lDX33//bSSZb7/9Nsv9TvP555+bsmXLGi8vL1OuXDnz5ZdfZjqa2dXn7csvv2xq1qxpChYsaD/uL774ojlx4oS9TEJCgunTp48pUqSIsdlsDq+HJPPss89mGNO120o7/5csWWK6du1qChQoYHx9fU2rVq3Mnj17HNZNSUkx48aNM6VKlTI+Pj6mZs2aZsWKFRmet5m9LzN7jceOHWvKlStnPD09TeHChc0TTzxhDh8+7FAus/djRsc0I5mtf/LkSfPMM8+YsLAw4+HhYSIiIsyQIUNMfHy8Q7msjmtGbtV5+fvvv5u2bduawMBA4+XlZapWrZpuxMS0Ue8y+0x95513TGRkpHF3d7d/hv3555+mU6dOpnTp0sbX19cEBgaaWrVqmalTp1533zM7zzM6fpmNBpmd+DP7/E47z44fP25f9v3335uqVasaHx8fU7RoUfPf//7X/Pjjj+lGLMzqcz+719PsXtOGDBliwsPDjZubW4YjJ6Y5evSo6dGjh6lQoYLx8/Mz/v7+pkqVKmbSpEnm8uXLWcaXtk9Xv0etXt/Hjx+fYVzZucbCNdmMyca/SQIAbsqECRP01ltv6d9//82RLmjO9Nprr2n69Onau3dvhn+GDQDAnYBR+wDgFnj22WcVGBhoHx3rdnXmzBl9+OGHGjVqFEkUAOCORiIFALeAj4+PZsyYYekerrxo//79GjJkiDp37uzsUAAAcCq69gEAAACARbRIAQAAAIBFJFIAAAAAYBGJFAAAAABYxJBLklJSUnTkyBEFBARk+eeSAAAAAFybMUbnz59XeHi43Nwyb3cikZJ05MgRFS9e3NlhAAAAAMgjDh8+rGLFimX6PImUpICAAEmpByt//vxOjgY3JCVFOno0dT4kRMri1wMAAAAgM+fOnVPx4sXtOUJmSKQke3e+/Pnzk0jdruLipC5dUufXrJF8fZ0bDwAAAG5r17vlh5/tAQAAAMAiEikAAAAAsIhECgAAAAAs4h4pAAAA3PaMMbp8+bKSk5OdHQryOHd3d3l4eNz03x6RSAEAAOC2lpiYqJiYGF26dMnZoeA2kS9fPoWFhcnLy+uG6yCRAgAAwG0rJSVF+/fvl7u7u8LDw+Xl5XXTLQ1wXcYYJSYm6vjx49q/f7/Kli2b5Z/uZoVECq7B3V169NEr8wAA4I6QmJiolJQUFS9eXPny5XN2OLgN+Pr6ytPTUwcPHlRiYqJ8fHxuqB4SKbgGLy/ppZecHQUAAHCSG21VwJ0pJ84XzjgAAAAAsIgWKbgGY6QzZ1LnCxSQ6BsNAACAXESLFFxDfLzUtGnqFB/v7GgAAAByxIEDB2Sz2bR161Znh5KnlCxZUu+8845TYyCRAgAAAJygR48estls9qlQoUJq0aKFtm/fbi9TvHhxxcTEKCoq6qa2VbJkSdlsNs2ePTvdc3fddZdsNpumTp16U9u4ETabTQsWLLC8XnR0tJ566qmcD8gCEikAAADASVq0aKGYmBjFxMRo+fLl8vDwUJs2bezPu7u7KzQ0VB4eN39HTvHixTVlyhSHZb/++qtiY2Pl5+d30/XfSkWKFHH6KI0kUgAAAHBNcXGZT4mJ2S+bkJC9sjfA29tboaGhCg0N1d13362XXnpJhw8f1vHjxyVl3LXvu+++U9myZeXr66uGDRtq2rRpstlsOpN2v3gmunTpotWrV+vw4cP2ZV9++aW6dOmSLlE7dOiQHnroIfn7+yt//vzq2LGjjh49an++R48eateuncM6/fv3V4MGDeyPGzRooOeff16DBw9WUFCQQkNDNXz4cPvzJUuWlCS1b99eNpvN/njv3r166KGHFBISIn9/f91zzz1atmyZw7au7dpns9n0+eefq3379sqXL5/Kli2r7777LsvjcbNIpAAAAOCa6tXLfPrvfx3LNm2aednnnnMs27ZtxuVu0oULF/TVV1+pTJkyKlSoUIZlDhw4oEceeUTt2rXT1q1b9fTTT2vo0KHZqj8kJETNmzfXtGnTJEmXLl3SN998o169ejmUM8aoXbt2OnXqlFavXq2lS5dq7969euyxxyzv07Rp0+Tn56cNGzZo3LhxeuONN7R06VJJqd3zJGnKlCmKiYmxP75w4YJatWqlZcuWacuWLWrevLnatm2rQ4cOZbmtESNGqGPHjtq+fbtatWqlLl266NSpU5Zjzi5G7cuDSr68MFfrPzCmda7WDwAAgOz54Ycf5O/vL0m6ePGiwsLC9MMPP2T6P0eTJ09W+fLlNX78eElS+fLltWPHDr311lvZ2l6vXr00cOBADR06VP/73/9UunRp3X333Q5lli1bpu3bt2v//v0qXry4JGnGjBm66667FB0drXvuuSfb+1elShUNGzZMklS2bFl98MEHWr58uZo2baoiRYpIkgoUKKDQ0FD7OlWrVlXVqlXtj0eOHKn58+fru+++U79+/TLdVo8ePdSpUydJ0qhRo/T+++/rt99+U4sWLbIdrxUkUgAAAHBNa9Zk/py7u+Pj/99KkqFrk5rvv7/xmK7RsGFDffzxx5KkU6dO6aOPPlLLli3122+/KSIiIl353bt3p0tkatWqle3ttW7dWk8//bR+/vlnffnll+laoyRp165dKl68uD2JkqRKlSqpQIEC2rVrl+VE6mphYWE6duxYlutcvHhRI0aM0A8//KAjR47o8uXLiouLu26L1NXb8vPzU0BAwHW3dTNIpOAa3N2ltBszr/1gBAAAdyZfX+eXvQ4/Pz+VKVPG/rhGjRoKDAzUZ599ppEjR6Yrb4yR7Zr/yzTGZHt7Hh4e6tq1q4YNG6YNGzZo/vz52drGtcvd3NzSbTcpKSndOp6eng6PbTabUlJSsozxv//9rxYvXqy3335bZcqUka+vrx555BElXntfWw5s62aQSME1eHlJV928CAAAcDuy2Wxyc3NTXCaDV1SoUEGLFi1yWLZx40ZL2+jVq5fefvttPfbYYypYsGC65ytVqqRDhw7p8OHD9lapnTt36uzZs6pYsaKk1FHzduzY4bDe1q1b0yUz1+Pp6ank5GSHZWvWrFGPHj3Uvn17San3TB04cMBSvbcCg00AAAAATpKQkKDY2FjFxsZq165deu6553ThwgW1bds2w/JPP/20/vzzT7300kv666+/NGfOHPv/P2XUipSRihUr6sSJE+mGQk/TpEkTValSRV26dNHmzZv122+/qVu3bqpfv75q1qwpSWrUqJE2btyo6dOna8+ePRo2bFi6xCo7SpYsqeXLlys2NlanT5+WJJUpU0bz5s3T1q1btW3bNnXu3DlXW5ZuFIkUXIMxV4YetdC8DQAA4Ew//fSTwsLCFBYWptq1ays6Olpz5851GEb8apGRkfrf//6nefPmqUqVKvr444/to/Z5e3tne7uFChWSbyZdFNP+JLdgwYJ64IEH1KRJE5UqVUrffPONvUzz5s312muvafDgwbrnnnt0/vx5devWLfs7/v9NmDBBS5cuVfHixVWtWjVJ0qRJk1SwYEHVrVtXbdu2VfPmzVW9enXLdec2m7HSqdJFnTt3ToGBgTp79qzy58/v7HAYte9GxMVdGXZ0zZoc7bsMAADyrvj4eO3fv1+RkZHy8fFxdjhO8dZbb2ny5MkO/w+FrGV13mQ3N+AeKQAAAOA28tFHH+mee+5RoUKF9Msvv2j8+PFZDguO3EEiBQAAANxG9uzZo5EjR+rUqVMqUaKEBg4cqCFDhjg7rDsOiRQAAABwG5k0aZImTZrk7DDueAw2AQAAAAAWkUgBAADgtsf4abAiJ84XEikAAADcttL+APbSpUtOjgS3k7TzxeofCF+Ne6TgGtzdpcaNr8wDAIA7gru7uwoUKKBjx45JkvLly5ftP6bFnccYo0uXLunYsWMqUKCA3G/ieyOJFFyDl5c0dqyzowAAAE4QGhoqSfZkCrieAgUK2M+bG0UiBQAAgNuazWZTWFiYgoODlZSU5OxwkMd5enreVEtUGhIpAAAAuAR3d/cc+YIMZAeDTcA1xMVJNWumTnFxzo4GAAAALo5ECgAAAAAsIpECAAAAAItIpAAAAADAIhIpAAAAALCIRAoAAAAALCKRAgAAAACL+B8puAZ3d+m++67MAwAAALmIRAquwctLevddZ0cBAACAOwRd+wAAAADAIhIpAAAAALCIRAquIS5Ouv/+1CkuztnRAAAAwMVxjxRcR3y8syMAAADAHYIWKQAAAACwiEQKAAAAACwikQIAAAAAi0ikAAAAAMAiEikAAAAAsIhR++Aa3Nyk6tWvzAMAAAC5iEQKrsHbW/r0U2dHAQAAgDsEP90DAAAAgEUkUgAAAABgEYkUXENcnNSkSeoUF+fsaAAAAODinJpIjR49Wvfcc48CAgIUHBysdu3aaffu3Q5ljDEaPny4wsPD5evrqwYNGuiPP/5wKJOQkKDnnntOhQsXlp+fnx588EH9888/t3JXkBecOZM6AQAAALnMqYnU6tWr9eyzz+rXX3/V0qVLdfnyZTVr1kwXL160lxk3bpwmTpyoDz74QNHR0QoNDVXTpk11/vx5e5n+/ftr/vz5mj17ttauXasLFy6oTZs2Sk5OdsZuAQAAAHBxNmOMcXYQaY4fP67g4GCtXr1aDzzwgIwxCg8PV//+/fXSSy9JSm19CgkJ0dixY/X000/r7NmzKlKkiGbMmKHHHntMknTkyBEVL15cixYtUvPmza+73XPnzikwMFBnz55V/vz5c3Ufs6Pkywtztf4DY1rnav1OERcn1auXOr9mjeTr69x4AAAAcFvKbm6Qp+6ROnv2rCQpKChIkrR//37FxsaqWbNm9jLe3t6qX7++1q1bJ0natGmTkpKSHMqEh4crKirKXuZaCQkJOnfunMMEAAAAANmVZxIpY4wGDBig+++/X1FRUZKk2NhYSVJISIhD2ZCQEPtzsbGx8vLyUsGCBTMtc63Ro0crMDDQPhUvXjyndwcAAACAC8sziVS/fv20fft2zZo1K91zNpvN4bExJt2ya2VVZsiQITp79qx9Onz48I0HDgAAAOCO4+HsACTpueee03fffaeff/5ZxYoVsy8PDQ2VlNrqFBYWZl9+7NgxeytVaGioEhMTdfr0aYdWqWPHjqlu3boZbs/b21ve3t65sStwFjc3qVKlK/MAAABALnLqN05jjPr166d58+ZpxYoVioyMdHg+MjJSoaGhWrp0qX1ZYmKiVq9ebU+SatSoIU9PT4cyMTEx2rFjR6aJFFyQt7c0fXrqRJIMAACAXObUFqlnn31WX3/9tf7v//5PAQEB9nuaAgMD5evrK5vNpv79+2vUqFEqW7asypYtq1GjRilfvnzq3LmzvWzv3r01cOBAFSpUSEFBQRo0aJAqV66sJk2aOHP3AAAAALgopyZSH3/8sSSpQYMGDsunTJmiHj16SJIGDx6suLg49e3bV6dPn1bt2rW1ZMkSBQQE2MtPmjRJHh4e6tixo+Li4tS4cWNNnTpV7u7ut2pXAAAAANxB8tT/SDkL/yPlAuLjpUcfTZ2fO1fy8XFuPAAAALgtZTc3yBODTQA3zRgpJubKPAAAAJCLGN4MAAAAACwikQIAAAAAi0ikAAAAAMAiEikAAAAAsIhECgAAAAAsYtQ+uAabTSpV6so8AAAAkItIpOAafHykOXOcHQUAAADuEHTtAwAAAACLSKQAAAAAwCISKbiG+HipY8fUKT7e2dEAAADAxXGPFFyDMdK+fVfmAQAAgFxEixQAAAAAWEQiBQAAAAAWkUgBAAAAgEUkUgAAAABgEYkUAAAAAFjEqH1wDTabFBZ2ZR4AAADIRSRScA0+PtL33zs7CgAAANwh6NoHAAAAABaRSAEAAACARSRScA0JCVK3bqlTQoKzowEAAICL4x4puIaUFGnnzivzAAAAQC6iRQoAAAAALCKRAgAAAACLSKQAAAAAwCISKQAAAACwiEQKAAAAACxi1D64jgIFnB0BAAAA7hAkUnANvr7SsmXOjgIAAAB3CLr2AQAAAIBFJFIAAAAAYBGJFFxDQoL01FOpU0KCs6MBAACAi+MeKbiGlBRp8+Yr8wAAAEAuokUKAAAAACwikQIAAAAAi0ikAAAAAMAiEikAAAAAsIhECgAAAAAsYtQ+uA4fH2dHAAAAgDsEiRRcg6+vtHats6MAAADAHYKufQAAAABgEYkUAAAAAFhEIgXXkJgovfBC6pSY6OxoAAAA4OK4RwquITlZ+uWXK/MAAABALqJFCgAAAAAsIpECAAAAAItIpAAAAADAIhIpAAAAALCIRAoAAAAALCKRAgAAAACLGP4crsHXV9q40dlRAAAA4A5BixQAAAAAWEQiBQAAAAAWkUjBNSQmSi+9lDolJjo7GgAAALg4Eim4huRkafny1Ck52dnRAAAAwMWRSAEAAACARSRSAAAAAGARiRQAAAAAWEQiBQAAAAAWkUgBAAAAgEUkUgAAAABgkYezAwByhI+PtGbNlXkAAAAgF5FIwTXYbJKvr7OjAAAAwB2Crn0AAAAAYBEtUnANiYnSqFGp86+8Inl5OTceAAAAuDRapOAakpOlH35InZKTnR0NAAAAXByJFAAAAABYRCIFAAAAABaRSAEAAACARU5NpH7++We1bdtW4eHhstlsWrBggcPzPXr0kM1mc5juvfdehzIJCQl67rnnVLhwYfn5+enBBx/UP//8cwv3AgAAAMCdxqmJ1MWLF1W1alV98MEHmZZp0aKFYmJi7NOiRYscnu/fv7/mz5+v2bNna+3atbpw4YLatGmjZAYcAAAAAJBLnDr8ecuWLdWyZcssy3h7eys0NDTD586ePasvvvhCM2bMUJMmTSRJM2fOVPHixbVs2TI1b948w/USEhKUkJBgf3zu3Lkb3AMAAAAAd6I8f4/UqlWrFBwcrHLlyunJJ5/UsWPH7M9t2rRJSUlJatasmX1ZeHi4oqKitG7dukzrHD16tAIDA+1T8eLFc3UfcAv4+EhLl6ZOPj7OjgYAAAAuLk8nUi1bttRXX32lFStWaMKECYqOjlajRo3srUmxsbHy8vJSwYIFHdYLCQlRbGxspvUOGTJEZ8+etU+HDx/O1f3ALWCzSQULpk42m7OjAQAAgItzate+63nsscfs81FRUapZs6YiIiK0cOFCdejQIdP1jDGyZfFl2tvbW97e3jkaKwAAAIA7R55ukbpWWFiYIiIitGfPHklSaGioEhMTdfr0aYdyx44dU0hIiDNChLMkJkpjx6ZOiYnOjgYAAAAu7rZKpE6ePKnDhw8rLCxMklSjRg15enpq6dKl9jIxMTHasWOH6tat66ww4QzJydLcuakTIzYCAAAglzm1a9+FCxf0999/2x/v379fW7duVVBQkIKCgjR8+HA9/PDDCgsL04EDB/TKK6+ocOHCat++vSQpMDBQvXv31sCBA1WoUCEFBQVp0KBBqly5sn0UPwAAAADIaU5NpDZu3KiGDRvaHw8YMECS1L17d3388cf6/fffNX36dJ05c0ZhYWFq2LChvvnmGwUEBNjXmTRpkjw8PNSxY0fFxcWpcePGmjp1qtzd3W/5/gAAAAC4M9iMMcbZQTjbuXPnFBgYqLNnzyp//vzODkclX16Yq/UfGNM6V+t3irg4qV691Pk1ayRfX+fGAwAAgNtSdnOD2+oeKQAAAADIC0ikAAAAAMAiEikAAAAAsChP/yEvkG3e3tJ3312ZBwAAAHIRiRRcg5ubFB7u7CgAAABwh6BrHwAAAABYRIsUXENSkvTRR6nzfftKnp7OjQcAAAAujRYpuIbLl6UZM1Kny5edHQ0AAABcHIkUAAAAAFhEIgUAAAAAFllOpPbv358bcQAAAADAbcNyIlWmTBk1bNhQM2fOVHx8fG7EBAAAAAB5muVEatu2bapWrZoGDhyo0NBQPf300/rtt99yIzYAAAAAyJMsJ1JRUVGaOHGi/v33X02ZMkWxsbG6//77ddddd2nixIk6fvx4bsQJAAAAAHnGDQ824eHhofbt22vOnDkaO3as9u7dq0GDBqlYsWLq1q2bYmJicjJOIGve3tKcOamTt7ezowEAAICLu+FEauPGjerbt6/CwsI0ceJEDRo0SHv37tWKFSv077//6qGHHsrJOIGsublJpUqlTm4MRgkAAIDc5WF1hYkTJ2rKlCnavXu3WrVqpenTp6tVq1Zy+/9fXiMjI/XJJ5+oQoUKOR4sAAAAAOQFlhOpjz/+WL169VLPnj0VGhqaYZkSJUroiy++uOnggGxLSpKmTEmd79lT8vR0bjwAAABwaZYTqT179ly3jJeXl7p3735DAQE35PJl6dNPU+e7diWRAgAAQK6yfDPJlClTNHfu3HTL586dq2nTpuVIUAAAAACQl1lOpMaMGaPChQunWx4cHKxRo0blSFAAAAAAkJdZTqQOHjyoyMjIdMsjIiJ06NChHAkKAAAAAPIyy4lUcHCwtm/fnm75tm3bVKhQoRwJCgAAAADyMsuJ1OOPP67nn39eK1euVHJyspKTk7VixQq98MILevzxx3MjRgAAAADIUyyP2jdy5EgdPHhQjRs3lodH6uopKSnq1q0b90gBAAAAuCNYTqS8vLz0zTff6M0339S2bdvk6+urypUrKyIiIjfiA7LH21uaPv3KPAAAAJCLLCdSacqVK6dy5crlZCzAjXNzkypVcnYUAAAAuENYTqSSk5M1depULV++XMeOHVNKSorD8ytWrMix4AAAAAAgL7KcSL3wwguaOnWqWrduraioKNlsttyIC7AmKUmaNSt1vlMnydPTufEAAADApVlOpGbPnq05c+aoVatWuREPcGMuX5beey91/tFHSaQAAACQqywPf+7l5aUyZcrkRiwAAAAAcFuwnEgNHDhQ7777rowxuREPAAAAAOR5lrv2rV27VitXrtSPP/6ou+66S57XdKGaN29ejgUHAAAAAHmR5USqQIECat++fW7EAgAAAAC3BcuJ1JQpU3IjDgAAAAC4bVi+R0qSLl++rGXLlumTTz7R+fPnJUlHjhzRhQsXcjQ4AAAAAMiLLLdIHTx4UC1atNChQ4eUkJCgpk2bKiAgQOPGjVN8fLwmT56cG3ECWfP2lj755Mo8AAAAkIsst0i98MILqlmzpk6fPi1fX1/78vbt22v58uU5GhyQbW5uUo0aqZPbDTW0AgAAANl2Q6P2/fLLL/Ly8nJYHhERoX///TfHAgMAAACAvMpyIpWSkqLk5OR0y//55x8FBATkSFCAZZcvS2lD73foIHlYPrUBAACAbLPcB6pp06Z655137I9tNpsuXLigYcOGqVWrVjkZG5B9SUnSuHGpU1KSs6MBAACAi7P8s/2kSZPUsGFDVapUSfHx8ercubP27NmjwoULa9asWbkRIwAAAADkKZYTqfDwcG3dulWzZs3S5s2blZKSot69e6tLly4Og08AAAAAgKu6oRtJfH191atXL/Xq1Sun4wEAAACAPM9yIjV9+vQsn+/WrdsNBwMAAAAAtwPLidQLL7zg8DgpKUmXLl2Sl5eX8uXLRyIFAAAAwOVZHrXv9OnTDtOFCxe0e/du3X///Qw2AQAAAOCOkCN/tlO2bFmNGTNGTzzxhP7888+cqBKwxstLShuW/5o/iwYAAAByWo79a6m7u7uOHDmSU9UB1ri7S/ff7+woAAAAcIewnEh99913Do+NMYqJidEHH3yg++67L8cCAwAAAIC8ynIi1a5dO4fHNptNRYoUUaNGjTRhwoSciguw5vJl6ccfU+dbtpQ8cqyxFQAAAEjH8rfNlJSU3IgDuDlJSdKIEanzTZqQSAEAACBXWR61DwAAAADudJZ/th8wYEC2y06cONFq9QAAAACQ51lOpLZs2aLNmzfr8uXLKl++vCTpr7/+kru7u6pXr24vZ7PZci5KAAAAAMhDLCdSbdu2VUBAgKZNm6aCBQtKSv2T3p49e6pevXoaOHBgjgcJAAAAAHmJ5XukJkyYoNGjR9uTKEkqWLCgRo4cyah9AAAAAO4IlhOpc+fO6ejRo+mWHzt2TOfPn8+RoAAAAAAgL7Pcta99+/bq2bOnJkyYoHvvvVeS9Ouvv+q///2vOnTokOMBAtni5SWNGXNlHgAAAMhFlhOpyZMna9CgQXriiSeUlJSUWomHh3r37q3x48fneIBAtri7p/5/FAAAAHALWE6k8uXLp48++kjjx4/X3r17ZYxRmTJl5OfnlxvxAQAAAECec8N/yBsTE6OYmBiVK1dOfn5+MsbkZFyANcnJ0rJlqVNysrOjAQAAgIuz3CJ18uRJdezYUStXrpTNZtOePXtUqlQp9enTRwUKFGDkPjhHYqL08sup82vWSL6+zo0HAAAALs1yi9SLL74oT09PHTp0SPny5bMvf+yxx/TTTz/laHAAAAAAkBdZbpFasmSJFi9erGLFijksL1u2rA4ePJhjgQEAAABAXmW5RerixYsOLVFpTpw4IW9v7xwJCgAAAADyMsuJ1AMPPKDp06fbH9tsNqWkpGj8+PFq2LBhjgYHAAAAAHmR5a5948ePV4MGDbRx40YlJiZq8ODB+uOPP3Tq1Cn98ssvuREjAAAAAOQpllukKlWqpO3bt6tWrVpq2rSpLl68qA4dOmjLli0qXbp0bsQIAAAAAHmKpUQqKSlJDRs21Llz5zRixAj98MMPWrRokUaOHKmwsDDLG//555/Vtm1bhYeHy2azacGCBQ7PG2M0fPhwhYeHy9fXVw0aNNAff/zhUCYhIUHPPfecChcuLD8/Pz344IP6559/LMeC25ynpzRsWOrk6ensaAAAAODiLCVSnp6e2rFjh2w2W45s/OLFi6patao++OCDDJ8fN26cJk6cqA8++EDR0dEKDQ1V06ZNdf78eXuZ/v37a/78+Zo9e7bWrl2rCxcuqE2bNkrmT1nvLB4eUtu2qZOH5R6rAAAAgCWWu/Z169ZNX3zxRY5svGXLlho5cqQ6dOiQ7jljjN555x0NHTpUHTp0UFRUlKZNm6ZLly7p66+/liSdPXtWX3zxhSZMmKAmTZqoWrVqmjlzpn7//XctW7YsR2IEAAAAgGtZ/uk+MTFRn3/+uZYuXaqaNWvKz8/P4fmJEyfmSGD79+9XbGysmjVrZl/m7e2t+vXra926dXr66ae1adMmJSUlOZQJDw9XVFSU1q1bp+bNm2dYd0JCghISEuyPz507lyMxw4mSk6X161Pn69SR3N2dGw8AAABcWrYSqe3btysqKkpubm7asWOHqlevLkn666+/HMrlVJc/SYqNjZUkhYSEOCwPCQmx//FvbGysvLy8VLBgwXRl0tbPyOjRozVixIgcixV5QGKi1L9/6vyaNZKvr1PDAQAAgGvLViJVrVo1xcTEKDg4WAcPHlR0dLQKFSqU27FJSp+cGWOum7Bdr8yQIUM0YMAA++Nz586pePHiNxcoAAAAgDtGtu6RKlCggPbv3y9JOnDggFJSUnI1KEkKDQ2VpHQtS8eOHbO3UoWGhioxMVGnT5/OtExGvL29lT9/focJAAAAALIrW4nUww8/rPr16ysyMlI2m001a9ZUqVKlMpxySmRkpEJDQ7V06VL7ssTERK1evVp169aVJNWoUUOenp4OZWJiYrRjxw57GQAAAADIadnq2vfpp5+qQ4cO+vvvv/X888/rySefVEBAwE1v/MKFC/r777/tj/fv36+tW7cqKChIJUqUUP/+/TVq1CiVLVtWZcuW1ahRo5QvXz517txZkhQYGKjevXtr4MCBKlSokIKCgjRo0CBVrlxZTZo0uen4AAAAACAj2R61r0WLFpKkTZs26YUXXsiRRGrjxo1q2LCh/XHafUvdu3fX1KlTNXjwYMXFxalv3746ffq0ateurSVLljhse9KkSfLw8FDHjh0VFxenxo0ba+rUqXJn1DYAAAAAucRmjDHODsLZzp07p8DAQJ09ezZP3C9V8uWFuVr/gTGtc7V+p4iLk+rVS51n1D4AAADcoOzmBpb/RwrIkzw9pcGDr8wDAAAAuYhECq7Bw0Pq2NHZUQAAAOAOka1R+wAAAAAAV9AiBdeQkiJt2ZI6X62a5MZvBAAAAMg9JFJwDQkJ0tNPp84z2AQAAAByGT/bAwAAAIBFJFIAAAAAYBGJFAAAAABYRCIFAAAAABaRSAEAAACARSRSAAAAAGARw5/DNXh4SM8/f2UeAAAAyEV844Rr8PSUunVzdhQAAAC4Q9C1DwAAAAAsokUKriElRfrzz9T5ChUkN34jAAAAQO4hkYJrSEi40rVvzRrJ19e58QAAAMCl8bM9AAAAAFhEIgUAAAAAFpFIAQAAAIBFJFIAAAAAYBGJFAAAAABYRCIFAAAAABYx/Dlcg4eH9NRTV+YBAACAXMQ3TrgGT88riRQAAACQy+jaBwAAAAAW0SIF15CSIh04kDpfsqTkxm8EAAAAyD0kUnANCQlSx46p82vWSL6+zo0HAAAALo2f7QEAAADAIhIpAAAAALCIRAoAAAAALCKRAgAAAACLSKQAAAAAwCISKQAAAACwiOHP4Ro8PKSuXa/MAwAAALmIb5xwDZ6e0gsvODsKAAAA3CHo2gcAAAAAFtEiBdeQkiLFxqbOh4ZKbvxGAAAAgNxDIgXXkJAgPfhg6vyaNZKvr3PjAQAAgEvjZ3sAAAAAsIhECgAAAAAsIpECAAAAAItIpAAAAADAIhIpAAAAALCIRAoAAAAALGL4c7gGd3fp0UevzAMAAAC5iEQKrsHLS3rpJWdHAQAAgDsEXfsAAAAAwCJapOAajJHOnEmdL1BAstmcGQ0AAABcHIkUXEN8vNS0aer8mjWSr69z4wEAAIBLo2sfAAAAAFhEIgUAAAAAFpFIAQAAAIBFJFIAAAAAYBGJFAAAAABYRCIFAAAAABYx/Dlcg7u71KbNlXkAAAAgF5FIwTV4eUnDhzs7CgAAANwh6NoHAAAAABbRIgXXYIwUH5867+Mj2WzOjQcAAAAujRYpuIb4eKlevdQpLaECAAAAcgmJFAAAAABYRCIFAAAAABaRSAEAAACARSRSAAAAAGARiRQAAAAAWEQiBQAAAAAW8T9ScA3u7lLjxlfmAQAAgFxEIgXX4OUljR3r7CgAAABwh6BrHwAAAABYRCIFAAAAABbl6URq+PDhstlsDlNoaKj9eWOMhg8frvDwcPn6+qpBgwb6448/nBgxnCYuTqpZM3WKi3N2NAAAAHBxeTqRkqS77rpLMTEx9un333+3Pzdu3DhNnDhRH3zwgaKjoxUaGqqmTZvq/PnzTowYAAAAgKvL84mUh4eHQkND7VORIkUkpbZGvfPOOxo6dKg6dOigqKgoTZs2TZcuXdLXX3/t5KgBAAAAuLI8n0jt2bNH4eHhioyM1OOPP659+/ZJkvbv36/Y2Fg1a9bMXtbb21v169fXunXrsqwzISFB586dc5gAAAAAILvydCJVu3ZtTZ8+XYsXL9Znn32m2NhY1a1bVydPnlRsbKwkKSQkxGGdkJAQ+3OZGT16tAIDA+1T8eLFc20fAAAAALiePJ1ItWzZUg8//LAqV66sJk2aaOHChZKkadOm2cvYbDaHdYwx6ZZda8iQITp79qx9Onz4cM4HDwAAAMBl5elE6lp+fn6qXLmy9uzZYx+979rWp2PHjqVrpbqWt7e38ufP7zABAAAAQHbdVolUQkKCdu3apbCwMEVGRio0NFRLly61P5+YmKjVq1erbt26TowSTuHuLt13X+rk7u7saAAAAODiPJwdQFYGDRqktm3bqkSJEjp27JhGjhypc+fOqXv37rLZbOrfv79GjRqlsmXLqmzZsho1apTy5cunzp07Ozt03GpeXtK77zo7CgAAANwh8nQi9c8//6hTp046ceKEihQponvvvVe//vqrIiIiJEmDBw9WXFyc+vbtq9OnT6t27dpasmSJAgICnBw5AAAAAFdmM8YYZwfhbOfOnVNgYKDOnj2bJ+6XKvnywlyt/8CY1rlaPwAAAHC7ym5ucFvdIwVkKi5Ouv/+1CkuztnRAAAAwMXl6a59gCXx8c6OAAAAAHcIWqQAAAAAwCISKQAAAACwiEQKAAAAACwikQIAAAAAi0ikAAAAAMAiRu2Da3Bzk6pXvzIPAAAA5CISKbgGb2/p00+dHQUAAADuEPx0DwAAAAAWkUgBAAAAgEUkUnANcXFSkyapU1ycs6MBAACAi+MeKbiOM2ecHQEAAADuELRIAQAAAIBFJFIAAAAAYBGJFAAAAABYRCIFAAAAABaRSAEAAACARYzaB9fg5iZVqnRlHgAAAMhFJFJwDd7e0vTpzo4CAAAAdwh+ugcAAAAAi0ikAAAAAMAiEim4hvh4qW3b1Ck+3tnRAAAAwMVxjxRcgzFSTMyVeQAAACAX0SIFAAAAABaRSAEAAACARSRSAAAAAGARiRQAAAAAWEQiBQAAAAAWMWofXIPNJpUqdWUeAAAAyEUkUnegki8vzLW6D4xpnWt1Z8nHR5ozxznbBgAAwB2Hrn0AAAAAYBGJFAAAAABYRCIF1xAfL3XsmDrFxzs7GgAAALg47pGCazBG2rfvyjwAAACQi2iRAgAAAACLSKQAAAAAwCISKQAAAACwiEQKAAAAACwikQIAAAAAixi1D67BZpPCwq7MAwAAALmIRAquwcdH+v57Z0cBAACAOwRd+wAAAADAIhIpAAAAALCIRAquISFB6tYtdUpIcHY0AAAAcHHcIwXXkJIi7dx5ZR4AAADIRbRIAQAAAIBFJFIAAAAAYBGJFAAAAABYxD1SyFElX16Yq/UfGNM6V+sHAAAAsoMWKQAAAACwiBYpuI4CBZwdAQAAAO4QJFJwDb6+0rJlzo4CAAAAdwi69gEAAACARSRSAAAAAGARiRRcQ0KC9NRTqVNCgrOjAQAAgIvjHim4hpQUafPmK/MAAABALqJFCgAAAAAsIpECAAAAAItIpAAAAADAIhIpAAAAALCIRAoAAAAALGLUPrgOHx9nRwAAAIA7BIkUXIOvr7R2rbOjAAAAwB2Crn0AAAAAYBGJFAAAAABYRCIF15CYKL3wQuqUmOjsaAAAAODiuEcKt5WSLy/McLl3UoLmzlkkSXrUo74SPL0t131gTOubig0AAAB3DlqkAAAAAMAiEikAAAAAsMhlEqmPPvpIkZGR8vHxUY0aNbRmzRpnhwQAAADARblEIvXNN9+of//+Gjp0qLZs2aJ69eqpZcuWOnTokLNDAwAAAOCCXGKwiYkTJ6p3797q06ePJOmdd97R4sWL9fHHH2v06NFOjg63i8wGssgpuTmYxe0ce27iuAAAkHO4rjq67ROpxMREbdq0SS+//LLD8mbNmmndunUZrpOQkKCEhAT747Nnz0qSzp07l3uBWpCScMnZIdx2kpMSdCElJXU+4ZJSUpKdHFF6uXl+5fY5k1feG1ZxXAAAyDl3ynU1LQ5jTJblbvtE6sSJE0pOTlZISIjD8pCQEMXGxma4zujRozVixIh0y4sXL54rMeLWuC9t5sOuzgwjU4HvODuCG3c7x56bOC4AAOScvHZdPX/+vAIDAzN9/rZPpNLYbDaHx8aYdMvSDBkyRAMGDLA/TklJ0alTp1SoUKFM17lVzp07p+LFi+vw4cPKnz+/U2OBc3Eu4GqcD0jDuYA0nAu4GudDzjHG6Pz58woPD8+y3G2fSBUuXFju7u7pWp+OHTuWrpUqjbe3t7y9Hf+wtUCBArkV4g3Jnz8/bwJI4lyAI84HpOFcQBrOBVyN8yFnZNUSlea2H7XPy8tLNWrU0NKlSx2WL126VHXr1nVSVAAAAABc2W3fIiVJAwYMUNeuXVWzZk3VqVNHn376qQ4dOqRnnnnG2aEBAAAAcEEukUg99thjOnnypN544w3FxMQoKipKixYtUkREhLNDs8zb21vDhg1L1/UQdx7OBVyN8wFpOBeQhnMBV+N8uPVs5nrj+gEAAAAAHNz290gBAAAAwK1GIgUAAAAAFpFIAQAAAIBFJFIAAAAAYBGJVB7y0UcfKTIyUj4+PqpRo4bWrFnj7JBwk4YPHy6bzeYwhYaG2p83xmj48OEKDw+Xr6+vGjRooD/++MOhjoSEBD333HMqXLiw/Pz89OCDD+qff/5xKHP69Gl17dpVgYGBCgwMVNeuXXXmzJlbsYvIxM8//6y2bdsqPDxcNptNCxYscHj+Vr72hw4dUtu2beXn56fChQvr+eefV2JiYm7sNjJwvXOhR48e6T4n7r33XocynAuuYfTo0brnnnsUEBCg4OBgtWvXTrt373Yow2fDnSM75wOfD3kbiVQe8c0336h///4aOnSotmzZonr16qlly5Y6dOiQs0PDTbrrrrsUExNjn37//Xf7c+PGjdPEiRP1wQcfKDo6WqGhoWratKnOnz9vL9O/f3/Nnz9fs2fP1tq1a3XhwgW1adNGycnJ9jKdO3fW1q1b9dNPP+mnn37S1q1b1bVr11u6n3B08eJFVa1aVR988EGGz9+q1z45OVmtW7fWxYsXtXbtWs2ePVvffvutBg4cmHs7DwfXOxckqUWLFg6fE4sWLXJ4nnPBNaxevVrPPvusfv31Vy1dulSXL19Ws2bNdPHiRXsZPhvuHNk5HyQ+H/I0gzyhVq1a5plnnnFYVqFCBfPyyy87KSLkhGHDhpmqVatm+FxKSooJDQ01Y8aMsS+Lj483gYGBZvLkycYYY86cOWM8PT3N7Nmz7WX+/fdf4+bmZn766SdjjDE7d+40ksyvv/5qL7N+/Xojyfz555+5sFewSpKZP3++/fGtfO0XLVpk3NzczL///msvM2vWLOPt7W3Onj2bK/uLzF17LhhjTPfu3c1DDz2U6TqcC67r2LFjRpJZvXq1MYbPhjvdteeDMXw+5HW0SOUBiYmJ2rRpk5o1a+awvFmzZlq3bp2TokJO2bNnj8LDwxUZGanHH39c+/btkyTt379fsbGxDq+7t7e36tevb3/dN23apKSkJIcy4eHhioqKspdZv369AgMDVbt2bXuZe++9V4GBgZw/edStfO3Xr1+vqKgohYeH28s0b95cCQkJ2rRpU67uJ7Jv1apVCg4OVrly5fTkk0/q2LFj9uc4F1zX2bNnJUlBQUGS+Gy40117PqTh8yHvIpHKA06cOKHk5GSFhIQ4LA8JCVFsbKyTokJOqF27tqZPn67Fixfrs88+U2xsrOrWrauTJ0/aX9usXvfY2Fh5eXmpYMGCWZYJDg5Ot+3g4GDOnzzqVr72sbGx6bZTsGBBeXl5cX7kES1bttRXX32lFStWaMKECYqOjlajRo2UkJAgiXPBVRljNGDAAN1///2KioqSxGfDnSyj80Hi8yGv83B2ALjCZrM5PDbGpFuG20vLli3t85UrV1adOnVUunRpTZs2zX6z6I287teWyag850/ed6tee86PvO2xxx6zz0dFRalmzZqKiIjQwoUL1aFDh0zX41y4vfXr10/bt2/X2rVr0z3HZ8OdJ7Pzgc+HvI0WqTygcOHCcnd3T5fxHzt2LN2vA7i9+fn5qXLlytqzZ4999L6sXvfQ0FAlJibq9OnTWZY5evRoum0dP36c8yePupWvfWhoaLrtnD59WklJSZwfeVRYWJgiIiK0Z88eSZwLrui5557Td999p5UrV6pYsWL25Xw23JkyOx8ywudD3kIilQd4eXmpRo0aWrp0qcPypUuXqm7duk6KCrkhISFBu3btUlhYmCIjIxUaGurwuicmJmr16tX2171GjRry9PR0KBMTE6MdO3bYy9SpU0dnz57Vb7/9Zi+zYcMGnT17lvMnj7qVr32dOnW0Y8cOxcTE2MssWbJE3t7eqlGjRq7uJ27MyZMndfjwYYWFhUniXHAlxhj169dP8+bN04oVKxQZGenwPJ8Nd5brnQ8Z4fMhj7mVI1sgc7Nnzzaenp7miy++MDt37jT9+/c3fn5+5sCBA84ODTdh4MCBZtWqVWbfvn3m119/NW3atDEBAQH213XMmDEmMDDQzJs3z/z++++mU6dOJiwszJw7d85exzPPPGOKFStmli1bZjZv3mwaNWpkqlatai5fvmwv06JFC1OlShWzfv16s379elO5cmXTpk2bW76/uOL8+fNmy5YtZsuWLUaSmThxotmyZYs5ePCgMebWvfaXL182UVFRpnHjxmbz5s1m2bJlplixYqZfv3637mDc4bI6F86fP28GDhxo1q1bZ/bv329Wrlxp6tSpY4oWLcq54IL+85//mMDAQLNq1SoTExNjny5dumQvw2fDneN65wOfD3kfiVQe8uGHH5qIiAjj5eVlqlev7jD8JW5Pjz32mAkLCzOenp4mPDzcdOjQwfzxxx/251NSUsywYcNMaGio8fb2Ng888ID5/fffHeqIi4sz/fr1M0FBQcbX19e0adPGHDp0yKHMyZMnTZcuXUxAQIAJCAgwXbp0MadPn74Vu4hMrFy50khKN3Xv3t0Yc2tf+4MHD5rWrVsbX19fExQUZPr162fi4+Nzc/dxlazOhUuXLplmzZqZIkWKGE9PT1OiRAnTvXv3dK8z54JryOg8kGSmTJliL8Nnw53jeucDnw95n80YY25d+xcAAAAA3P64RwoAAAAALCKRAgAAAACLSKQAAAAAwCISKQAAAACwiEQKAAAAACwikQIAAAAAi0ikAAAAAMAiEikAAAAAsIhECgBuYzabTQsWLMj0+ZIlS+qdd97Jdn1Tp05VgQIFsiwzfPhw3X333dmuMy+zenxuF6+99pqeeuopZ4eRpR9++EHVqlVTSkqKs0MBgBtCIgUAedSxY8f09NNPq0SJEvL29lZoaKiaN2+u9evXZ7uO6OjoPPmFukGDBurfv7+zw8izx+dmHD16VO+++65eeeUVZ4eSpTZt2shms+nrr792digAcEM8nB0AACBjDz/8sJKSkjRt2jSVKlVKR48e1fLly3Xq1Kls11GkSJFcjND5EhMT5eXl5bDMGKPk5GR5eFz/EueM42MlvhvxxRdfqE6dOipZsmSu1C9JSUlJ8vT0vOl6evbsqffff19PPPFEDkQFALcWLVIAkAedOXNGa9eu1dixY9WwYUNFRESoVq1aGjJkiFq3bp3pem+88YZCQkK0detWSem7rk2cOFGVK1eWn5+fihcvrr59++rChQvp6lmwYIHKlSsnHx8fNW3aVIcPH84y3ilTpqhixYry8fFRhQoV9NFHH2VatkePHlq9erXeffdd2Ww22Ww2HThwQJK0c+dOtWrVSv7+/goJCVHXrl114sQJ+7oNGjRQv379NGDAABUuXFhNmzbVqlWrZLPZtHjxYtWsWVPe3t5as2aN9u7dq4ceekghISHy9/fXPffco2XLljnEcu3xsdls+vzzz9W+fXvly5dPZcuW1XfffZflvs+cOVM1a9ZUQECAQkND1blzZx07dsz+fGbxGWM0btw4lSpVSr6+vqpatar+97//2ddLTk5W7969FRkZKV9fX5UvX17vvvtulrFI0uzZs/Xggw86LPvpp590//33q0CBAipUqJDatGmjvXv3OpT5559/9PjjjysoKEh+fn6qWbOmNmzYIOlKd84vv/xSpUqVkre3t4wxOnv2rJ566ikFBwcrf/78atSokbZt22avc9u2bWrYsKECAgKUP39+1ahRQxs3brQ//+CDD+q3337Tvn37rrtfAJDXkEgBQB7k7+8vf39/LViwQAkJCdctb4zRCy+8oC+++EJr167N9B4mNzc3vffee9qxY4emTZumFStWaPDgwQ5lLl26pLfeekvTpk3TL7/8onPnzunxxx/PdNufffaZhg4dqrfeeku7du3SqFGj9Nprr2natGkZln/33XdVp04dPfnkk4qJiVFMTIyKFy+umJgY1a9fX3fffbc2btyon376SUePHlXHjh0d1p82bZo8PDz0yy+/6JNPPrEvHzx4sEaPHq1du3apSpUqunDhglq1aqVly5Zpy5Ytat68udq2batDhw5leSxHjBihjh07avv27WrVqpW6dOmSZStgYmKi3nzzTW3btk0LFizQ/v371aNHj3Tlro3v1Vdf1ZQpU/Txxx/rjz/+0IsvvqgnnnhCq1evliSlpKSoWLFimjNnjnbu3KnXX39dr7zyiubMmZNpLKdPn9aOHTtUs2ZNh+UXL17UgAEDFB0dreXLl8vNzU3t27e335904cIF1a9fX0eOHNF3332nbdu2afDgwQ73L/3999+aM2eOvv32W3ui3rp1a8XGxmrRokXatGmTqlevrsaNG9uPV5cuXVSsWDFFR0dr06ZNevnllx1asiIiIhQcHKw1a9Zk+ZoAQJ5kAAB50v/+9z9TsGBB4+PjY+rWrWuGDBlitm3b5lBGkpk7d6554oknTIUKFczhw4cdno+IiDCTJk3KdBtz5swxhQoVsj+eMmWKkWR+/fVX+7Jdu3YZSWbDhg3GGGOGDRtmqlatan++ePHi5uuvv3ao98033zR16tTJdLv169c3L7zwgsOy1157zTRr1sxh2eHDh40ks3v3bvt6d999t0OZlStXGklmwYIFmW4vTaVKlcz7779vf3zt8ZFkXn31VfvjCxcuGJvNZn788cfr1p3mt99+M5LM+fPnM43vwoULxsfHx6xbt85h3d69e5tOnTplWnffvn3Nww8/nOnzW7ZsMZLMoUOHsozx2LFjRpL5/fffjTHGfPLJJyYgIMCcPHkyw/LDhg0znp6e5tixY/Zly5cvN/nz5zfx8fEOZUuXLm0++eQTY4wxAQEBZurUqVnGUq1aNTN8+PAsywBAXkSLFADkUQ8//LC9haB58+ZatWqVqlevrqlTpzqUe/HFF7V+/XqtWbNGxYoVy7LOlStXqmnTpipatKgCAgLUrVs3nTx5UhcvXrSX8fDwcGjRqFChggoUKKBdu3alq+/48eM6fPiwevfubW9F8/f318iRI9N1HbueTZs2aeXKlQ71VKhQQZIc6rq2tSWz5RcvXtTgwYNVqVIlFShQQP7+/vrzzz+v2yJVpUoV+7yfn58CAgIcuupda8uWLXrooYcUERGhgIAANWjQQJLSbefq+Hbu3Kn4+Hg1bdrUYX+nT5/usK+TJ09WzZo1VaRIEfn7++uzzz7LMv64uDhJko+Pj8PyvXv3qnPnzipVqpTy58+vyMhIhxi3bt2qatWqKSgoKNO6IyIiHO4p27Rpky5cuKBChQo57MP+/fvt+zBgwAD16dNHTZo00ZgxYzI8J3x9fXXp0qVMtwsAeRWDTQBAHpZ2j1LTpk31+uuvq0+fPho2bJhD17GmTZtq1qxZWrx4sbp06ZJpXQcPHlSrVq30zDPP6M0331RQUJDWrl2r3r17KykpyaGszWZLt35Gy9K6fn322WeqXbu2w3Pu7u5WdlUpKSlq27atxo4dm+65sLAw+7yfn1+G61+7/L///a8WL16st99+W2XKlJGvr68eeeQRJSYmZhnHtYMo2Gy2TIfovnjxopo1a6ZmzZpp5syZKlKkiA4dOqTmzZun287V8aXVt3DhQhUtWtShnLe3tyRpzpw5evHFFzVhwgTVqVNHAQEBGj9+vP2+pYwULlxYUmoXv6uTnrZt26p48eL67LPPFB4erpSUFEVFRdlj9PX1zfKYXBt/2j6EhYVp1apV6cqmDaE/fPhwde7cWQsXLtSPP/6oYcOGafbs2Wrfvr297KlTp1x+UBQArolECgBuI5UqVUr3v1EPPvig2rZtq86dO8vd3T3T+5k2btyoy5cva8KECXJzS+2QkNH9NpcvX9bGjRtVq1YtSdLu3bt15swZe+vQ1UJCQlS0aFHt27cvyyTuWl5eXkpOTnZYVr16dX377bcqWbJkjoxot2bNGvXo0cP+pf3ChQv2QS1yyp9//qkTJ05ozJgxKl68uCQ5DKaQmUqVKsnb21uHDh1S/fr1MyyzZs0a1a1bV3379rUvu14rX+nSpZU/f37t3LlT5cqVkySdPHlSu3bt0ieffKJ69epJktauXeuwXpUqVfT555/r1KlTWbZKXa169eqKjY2Vh4dHliMElitXTuXKldOLL76oTp06acqUKfbXJD4+Xnv37lW1atWytU0AyEvo2gcAedDJkyfVqFEjzZw5U9u3b9f+/fs1d+5cjRs3Tg899FC68u3bt9eMGTPUs2dPh5Hfrla6dGldvnxZ77//vvbt26cZM2Zo8uTJ6cp5enrqueee04YNG7R582b17NlT9957rz2xutbw4cM1evRovfvuu/rrr7/0+++/a8qUKZo4cWKm+1eyZElt2LBBBw4c0IkTJ5SSkqJnn31Wp06dUqdOnewjuS1ZskS9evVKl3RlR5kyZTRv3jxt3bpV27ZtU+fOnXP8z19LlCghLy8v+zH97rvv9Oabb153vYCAAA0aNEgvvviipk2bpr1792rLli368MMP7YN0lClTRhs3btTixYv1119/6bXXXlN0dHSW9bq5ualJkyYOiVLBggVVqFAhffrpp/r777+1YsUKDRgwwGG9Tp06KTQ0VO3atdMvv/yiffv26dtvv83yP8uaNGmiOnXqqF27dlq8eLEOHDigdevW6dVXX9XGjRsVFxenfv36adWqVTp48KB++eUXRUdHq2LFivY6fv31V3l7e6tOnTrXPWYAkNeQSAFAHuTv76/atWtr0qRJeuCBBxQVFaXXXntNTz75pD744IMM13nkkUc0bdo0de3aVfPmzUv3/N13362JEydq7NixioqK0ldffaXRo0enK5cvXz699NJL6ty5s+rUqSNfX1/Nnj0701j79Omjzz//XFOnTlXlypVVv359TZ061X4fTkYGDRokd3d3VapUyd4dLjw8XL/88ouSk5PVvHlzRUVF6YUXXlBgYKC9Bc2KSZMmqWDBgqpbt67atm2r5s2bq3r16pbryUqRIkU0depUzZ07V5UqVdKYMWP09ttvZ2vdN998U6+//rpGjx6tihUrqnnz5vr+++/tx+2ZZ55Rhw4d9Nhjj6l27do6efKkQ+tUZp566inNnj3bnjS6ublp9uzZ2rRpk6KiovTiiy9q/PjxDut4eXlpyZIlCg4OVqtWrVS5cmWNGTMmy+6ZNptNixYt0gMPPKBevXqpXLlyevzxx3XgwAGFhITI3d1dJ0+eVLdu3VSuXDl17NhRLVu21IgRI+x1zJo1S126dFG+fPmydcwAIC+xGWOMs4MAAAA5wxije++9V/3791enTp2cHU6mjh8/rgoVKmjjxo1ZJt0AkFfRIgUAgAux2Wz69NNPdfnyZWeHkqX9+/fro48+IokCcNuiRQoAAAAALKJFCgAAAAAsIpECAAAAAItIpAAAAADAIhIpAAAAALCIRAoAAAAALCKRAgAAAACLSKQAAAAAwCISKQAAAACwiEQKAAAAACz6fzUY2we0WWxUAAAAAElFTkSuQmCC",
       "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
+       "<Figure size 1000x500 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -998,7 +1393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1007,7 +1402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {
@@ -1043,7 +1438,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>124</th>\n",
+       "      <th>125</th>\n",
        "      <td>2353</td>\n",
        "      <td>600.0</td>\n",
        "      <td>14</td>\n",
@@ -1059,13 +1454,13 @@
       ],
       "text/plain": [
        "     vertical_drop  Snow Making_ac  total_chairs  fastQuads   Runs  \\\n",
-       "124           2353           600.0            14          3  105.0   \n",
+       "125           2353           600.0            14          3  105.0   \n",
        "\n",
        "     LongestRun_mi  trams  SkiableTerrain_ac  \n",
-       "124            3.3      0             3000.0  "
+       "125            3.3      0             3000.0  "
       ]
      },
-     "execution_count": 28,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1078,7 +1473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1103,7 +1498,7 @@
     "    \n",
     "    bm2 = X_bm.copy()\n",
     "    for f, d in zip(features, deltas):\n",
-    "        bm2[___] += ___\n",
+    "        bm2[f] += d\n",
     "    return model.predict(bm2).item() - model.predict(X_bm).item()"
    ]
   },
@@ -1123,7 +1518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
@@ -1132,7 +1527,7 @@
        "[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10]"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1143,7 +1538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1153,25 +1548,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 92,
    "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]"
+       "[-0.8888888888888999,\n",
+       " -0.8888888888888999,\n",
+       " -1.2037037037037095,\n",
+       " -1.2037037037037095,\n",
+       " -1.2037037037037095,\n",
+       " -1.462962962962962,\n",
+       " -2.018518518518519,\n",
+       " -2.018518518518519,\n",
+       " -2.018518518518519,\n",
+       " -2.018518518518519]"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1182,9 +1577,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 93,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 0, 'Runs closed'),\n",
+       " Text(0, 0.5, 'Change ($)'),\n",
+       " Text(0.5, 1.0, 'Revenue')]"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Code task 3#\n",
     "#Create two plots, side by side, for the predicted ticket price change (delta) for each\n",
@@ -1193,14 +1611,14 @@
     "#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",
+    "runs_closed = [-1 * r for r in runs_delta]  # invert sign if needed\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 ($)', title='Ticket price')\n",
-    "revenue_deltas = [5 * expected_visitors * ___ for ___ in ___] #2\n",
+    "revenue_deltas = [5 * expected_visitors * delta for delta in price_deltas]\n",
     "ax[1].plot(runs_closed, revenue_deltas, 'o-')\n",
-    "ax[1].set(xlabel='Runs closed', ylabel='Change ($)', title='Revenue');"
+    "ax[1].set(xlabel='Runs closed', ylabel='Change ($)', title='Revenue')"
    ]
   },
   {
@@ -1226,28 +1644,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 94,
    "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",
+    "ticket2_increase = predict_increase(['Runs', 'vertical_drop', 'total_chairs'], [1, 150, 1])\n",
     "revenue2_increase = 5 * expected_visitors * ticket2_increase"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 95,
    "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"
+      "This scenario increases support for ticket price by $1.24\n",
+      "Over the season, this could be expected to amount to $2171296\n"
      ]
     }
    ],
@@ -1278,24 +1696,15 @@
    "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",
+    "ticket3_increase = predict_increase(['Runs', 'vertical_drop', 'total_chairs', 'Snow Making_ac'], [1, 150, 1, 2])\n",
     "revenue3_increase = 5 * expected_visitors * ticket3_increase"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "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}')"
@@ -1330,7 +1739,7 @@
    "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([___, ___], [___, ___])"
+    "predict_increase(['LongestRun_mi', 'Snow Making_ac'], [0.2, 4])\n"
    ]
   },
   {
@@ -1358,7 +1767,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**A: 1** Your answer here"
+    "**A: 1** Big Mountain Resort currently charges 81.00 dollars per adult day ticket, but with modelling, the ticket price vs their competitors can easily be raised up to 93.00 dollars (92.19 dollars actual) from using the forest regressor. To propose this price change, I would present the data to business leadership by recommending a gradual increase to the pricing to avoid drops in visitor population.  The operating cost of a new chair lift would amount to about 1.24 dollars per ticket (with the assumption that each visitor is purchasing 5 tickets). With the other potential investments, their data did not suggest as harshly as the chairlift price increase did so they were not put into consideration for the short-term improvements. With closures, they should run tests during low-demand seasons to have a stress test before any long-term decisions are made. "
    ]
   },
   {
@@ -1379,13 +1788,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**A: 2** Your answer here"
+    "**A: 2** What held back this project were the lack of additional data that was meaningful. The only one that was of importance were the ticket prices which led to providing a single estimate for the chair lift operational costs. To make it better in the future, having the details on labor costs, maintenance, energy and marketing alongside things that are based on the visitors/population like the season passes and the spending within the resort would have help bridge the gap better than simply the costs of the ticket. I believe this mismatch might come to a surpise to executives as their resort was worth more than what they were charging initially. In order to further test this and ensure that this information was correct, further competitive analysis and customer research such as surveys can help to pinpoint a detailed reason on why the price should be that high. This dataset can be updated periodically now that there are regression tests that work with the data that once updated, can show the executives a live estimate for that point in time. "
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1399,7 +1808,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.12.7"
   },
   "toc": {
    "base_numbering": 1,