diff --git a/Notebooks/02_data_wrangling.ipynb b/Notebooks/02_data_wrangling.ipynb
index a52eb6c24..a8ffe9873 100644
--- a/Notebooks/02_data_wrangling.ipynb
+++ b/Notebooks/02_data_wrangling.ipynb
@@ -120,18 +120,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "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",
-    "from library.sb_utils import save_file\n"
+    "import lxml\n",
+    "\n",
+    "from library.sb_utils import save_file'\n"
    ]
   },
   {
@@ -179,13 +181,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "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: 65.8+ 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()"
    ]
   },
   {
@@ -204,211 +247,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
-   "source": [
-    "#Code task 3#\n",
-    "#Call the head method on ski_data to print the first several rows of the data\n",
-    "ski_data.___"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The output above suggests you've made a good start getting the ski resort data organized. You have plausible column headings. You can already see you have a missing value in the `fastEight` column"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2.6 Explore The Data<a id='2.6_Explore_The_Data'></a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.6.1 Find Your Resort Of Interest<a id='2.6.1_Find_Your_Resort_Of_Interest'></a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Your resort of interest is called Big Mountain Resort. Check it's in the data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "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 == ___].___"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It's good that your resort doesn't appear to have any missing values."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.6.2 Number Of Missing Values By Column<a id='2.6.2_Number_Of_Missing_Values_By_Column'></a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Count the number of missing values in each column and sort them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "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=___)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`fastEight` has the most missing values, at just over 50%. Unfortunately, you see you're also missing quite a few of your desired target quantity, the ticket price, which is missing 15-16% of values. `AdultWeekday` is missing in a few more records than `AdultWeekend`. What overlap is there in these missing values? This is a question you'll want to investigate. You should also point out that `isnull()` is not the only indicator of missing data. Sometimes 'missingness' can be encoded, perhaps by a -1 or 999. Such values are typically chosen because they are \"obviously\" not genuine values. If you were capturing data on people's heights and weights but missing someone's height, you could certainly encode that as a 0 because no one has a height of zero (in any units). Yet such entries would not be revealed by `isnull()`. Here, you need a data dictionary and/or to spot such values as part of looking for outliers. Someone with a height of zero should definitely show up as an outlier!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.6.3 Categorical Features<a id='2.6.3_Categorical_Features'></a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So far you've examined only the numeric features. Now you inspect categorical ones such as resort name and state. These are discrete entities. 'Alaska' is a name. Although names can be sorted alphabetically, it makes no sense to take the average of 'Alaska' and 'Arizona'. Similarly, 'Alaska' is before 'Arizona' only lexicographically; it is neither 'less than' nor 'greater than' 'Arizona'. As such, they tend to require different handling than strictly numeric quantities. Note, a feature _can_ be numeric but also categorical. For example, instead of giving the number of `fastEight` lifts, a feature might be `has_fastEights` and have the value 0 or 1 to denote absence or presence of such a lift. In such a case it would not make sense to take an average of this or perform other mathematical calculations on it. Although you digress a little to make a point, month numbers are also, strictly speaking, categorical features. Yes, when a month is represented by its number (1 for January, 2 for Februrary etc.) it provides a convenient way to graph trends over a year. And, arguably, there is some logical interpretation of the average of 1 and 3 (January and March) being 2 (February). However, clearly December of one years precedes January of the next and yet 12 as a number is not less than 1. The numeric quantities in the section above are truly numeric; they are the number of feet in the drop, or acres or years open or the amount of snowfall etc."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Code task 6#\n",
-    "#Use ski_data's `select_dtypes` method to select columns of dtype 'object'\n",
-    "ski_data.___(___)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You saw earlier on that these three columns had no missing values. But are there any other issues with these columns? Sensible questions to ask here include:\n",
-    "\n",
-    "* Is `Name` (or at least a combination of Name/Region/State) unique?\n",
-    "* Is `Region` always the same as `state`?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.6.3.1 Unique Resort Names<a id='2.6.3.1_Unique_Resort_Names'></a>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Code task 7#\n",
-    "#Use pandas' Series method `value_counts` to find any duplicated resort names\n",
-    "ski_data['Name'].___.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You have a duplicated resort name: Crystal Mountain."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q: 1** Is this resort duplicated if you take into account Region and/or state as well?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "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()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "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."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**A: 1** Your answer here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -456,57 +298,1012 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>104</th>\n",
-       "      <td>Crystal Mountain</td>\n",
-       "      <td>Michigan</td>\n",
-       "      <td>Michigan</td>\n",
-       "      <td>1132</td>\n",
-       "      <td>375</td>\n",
-       "      <td>757</td>\n",
-       "      <td>0</td>\n",
+       "      <th>0</th>\n",
+       "      <td>Alyeska Resort</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>3939</td>\n",
+       "      <td>2500</td>\n",
+       "      <td>250</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>102.0</td>\n",
-       "      <td>96.0</td>\n",
-       "      <td>120.0</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>132.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>135.0</td>\n",
-       "      <td>56.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1610.0</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>669.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>550.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>295</th>\n",
-       "      <td>Crystal Mountain</td>\n",
-       "      <td>Washington</td>\n",
-       "      <td>Washington</td>\n",
-       "      <td>7012</td>\n",
-       "      <td>3100</td>\n",
-       "      <td>4400</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
+       "      <th>1</th>\n",
+       "      <td>Eaglecrest Ski Area</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>2600</td>\n",
+       "      <td>1540</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>...</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>2600.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>486.0</td>\n",
-       "      <td>99.0</td>\n",
-       "      <td>99.0</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>90.0</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2 rows × 27 columns</p>\n",
-       "</div>"
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hilltop Ski Area</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>2090</td>\n",
+       "      <td>294</td>\n",
+       "      <td>1796</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arizona Snowbowl</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>11500</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>777.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sunrise Park Resort</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>11100</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>800.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>115.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>250.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Name   Region    state  summit_elev  vertical_drop  \\\n",
+       "0       Alyeska Resort   Alaska   Alaska         3939           2500   \n",
+       "1  Eaglecrest Ski Area   Alaska   Alaska         2600           1540   \n",
+       "2     Hilltop Ski Area   Alaska   Alaska         2090            294   \n",
+       "3     Arizona Snowbowl  Arizona  Arizona        11500           2300   \n",
+       "4  Sunrise Park Resort  Arizona  Arizona        11100           1800   \n",
+       "\n",
+       "   base_elev  trams  fastEight  fastSixes  fastQuads  ...  LongestRun_mi  \\\n",
+       "0        250      1        0.0          0          2  ...            1.0   \n",
+       "1       1200      0        0.0          0          0  ...            2.0   \n",
+       "2       1796      0        0.0          0          0  ...            1.0   \n",
+       "3       9200      0        0.0          1          0  ...            2.0   \n",
+       "4       9200      0        NaN          0          1  ...            1.2   \n",
+       "\n",
+       "   SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  yearsOpen  \\\n",
+       "0             1610.0           113.0             150.0       60.0   \n",
+       "1              640.0            60.0              45.0       44.0   \n",
+       "2               30.0            30.0             150.0       36.0   \n",
+       "3              777.0           104.0             122.0       81.0   \n",
+       "4              800.0            80.0             115.0       49.0   \n",
+       "\n",
+       "   averageSnowfall  AdultWeekday  AdultWeekend  projectedDaysOpen  \\\n",
+       "0            669.0          65.0          85.0              150.0   \n",
+       "1            350.0          47.0          53.0               90.0   \n",
+       "2             69.0          30.0          34.0              152.0   \n",
+       "3            260.0          89.0          89.0              122.0   \n",
+       "4            250.0          74.0          78.0              104.0   \n",
+       "\n",
+       "   NightSkiing_ac  \n",
+       "0           550.0  \n",
+       "1             NaN  \n",
+       "2            30.0  \n",
+       "3             NaN  \n",
+       "4            80.0  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 3#\n",
+    "#Call the head method on ski_data to print the first several rows of the data\n",
+    "ski_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output above suggests you've made a good start getting the ski resort data organized. You have plausible column headings. You can already see you have a missing value in the `fastEight` column"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.6 Explore The Data<a id='2.6_Explore_The_Data'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.6.1 Find Your Resort Of Interest<a id='2.6.1_Find_Your_Resort_Of_Interest'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Your resort of interest is called Big Mountain Resort. Check it's in the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>151</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Big Mountain Resort</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Montana</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Montana</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>6817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>2353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>4464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>105.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>3.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>3000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>600.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>72.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>333.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <td>81.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>81.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>123.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>600.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   151\n",
+       "Name               Big Mountain Resort\n",
+       "Region                         Montana\n",
+       "state                          Montana\n",
+       "summit_elev                       6817\n",
+       "vertical_drop                     2353\n",
+       "base_elev                         4464\n",
+       "trams                                0\n",
+       "fastEight                          0.0\n",
+       "fastSixes                            0\n",
+       "fastQuads                            3\n",
+       "quad                                 2\n",
+       "triple                               6\n",
+       "double                               0\n",
+       "surface                              3\n",
+       "total_chairs                        14\n",
+       "Runs                             105.0\n",
+       "TerrainParks                       4.0\n",
+       "LongestRun_mi                      3.3\n",
+       "SkiableTerrain_ac               3000.0\n",
+       "Snow Making_ac                   600.0\n",
+       "daysOpenLastYear                 123.0\n",
+       "yearsOpen                         72.0\n",
+       "averageSnowfall                  333.0\n",
+       "AdultWeekday                      81.0\n",
+       "AdultWeekend                      81.0\n",
+       "projectedDaysOpen                123.0\n",
+       "NightSkiing_ac                   600.0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 4#\n",
+    "#Filter the ski_data dataframe to display just the row for our resort with the name 'Big Mountain Resort'\n",
+    "#Hint: you will find that the transpose of the row will give a nicer output. DataFrame's do have a\n",
+    "#transpose method, but you can access this conveniently with the `T` property.\n",
+    "ski_data[ski_data.Name == 'Big Mountain Resort'].T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's good that your resort doesn't appear to have any missing values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.6.2 Number Of Missing Values By Column<a id='2.6.2_Number_Of_Missing_Values_By_Column'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Count the number of missing values in each column and sort them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</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>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>surface</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>Region</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.303030</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>Runs</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.212121</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>averageSnowfall</th>\n",
+       "      <td>14</td>\n",
+       "      <td>4.242424</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>projectedDaysOpen</th>\n",
+       "      <td>47</td>\n",
+       "      <td>14.242424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</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>AdultWeekend</th>\n",
+       "      <td>51</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <td>54</td>\n",
+       "      <td>16.363636</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>fastEight</th>\n",
+       "      <td>166</td>\n",
+       "      <td>50.303030</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   count          %\n",
+       "Name                   0   0.000000\n",
+       "total_chairs           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",
+       "surface                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",
+       "Region                 0   0.000000\n",
+       "yearsOpen              1   0.303030\n",
+       "SkiableTerrain_ac      3   0.909091\n",
+       "Runs                   4   1.212121\n",
+       "LongestRun_mi          5   1.515152\n",
+       "averageSnowfall       14   4.242424\n",
+       "Snow Making_ac        46  13.939394\n",
+       "projectedDaysOpen     47  14.242424\n",
+       "TerrainParks          51  15.454545\n",
+       "daysOpenLastYear      51  15.454545\n",
+       "AdultWeekend          51  15.454545\n",
+       "AdultWeekday          54  16.363636\n",
+       "NightSkiing_ac       143  43.333333\n",
+       "fastEight            166  50.303030"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 5#\n",
+    "#Count (using `.sum()`) the number of missing values (`.isnull()`) in each column of \n",
+    "#ski_data as well as the percentages (using `.mean()` instead of `.sum()`).\n",
+    "#Order them (increasing or decreasing) using sort_values\n",
+    "#Call `pd.concat` to present these in a single table (DataFrame) with the helpful column names 'count' and '%'\n",
+    "missing = pd.concat([ski_data.isnull().sum(), 100 * ski_data.isnull().mean()], axis=1)\n",
+    "missing.columns=['count', '%']\n",
+    "missing.sort_values(by='count')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`fastEight` has the most missing values, at just over 50%. Unfortunately, you see you're also missing quite a few of your desired target quantity, the ticket price, which is missing 15-16% of values. `AdultWeekday` is missing in a few more records than `AdultWeekend`. What overlap is there in these missing values? This is a question you'll want to investigate. You should also point out that `isnull()` is not the only indicator of missing data. Sometimes 'missingness' can be encoded, perhaps by a -1 or 999. Such values are typically chosen because they are \"obviously\" not genuine values. If you were capturing data on people's heights and weights but missing someone's height, you could certainly encode that as a 0 because no one has a height of zero (in any units). Yet such entries would not be revealed by `isnull()`. Here, you need a data dictionary and/or to spot such values as part of looking for outliers. Someone with a height of zero should definitely show up as an outlier!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.6.3 Categorical Features<a id='2.6.3_Categorical_Features'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So far you've examined only the numeric features. Now you inspect categorical ones such as resort name and state. These are discrete entities. 'Alaska' is a name. Although names can be sorted alphabetically, it makes no sense to take the average of 'Alaska' and 'Arizona'. Similarly, 'Alaska' is before 'Arizona' only lexicographically; it is neither 'less than' nor 'greater than' 'Arizona'. As such, they tend to require different handling than strictly numeric quantities. Note, a feature _can_ be numeric but also categorical. For example, instead of giving the number of `fastEight` lifts, a feature might be `has_fastEights` and have the value 0 or 1 to denote absence or presence of such a lift. In such a case it would not make sense to take an average of this or perform other mathematical calculations on it. Although you digress a little to make a point, month numbers are also, strictly speaking, categorical features. Yes, when a month is represented by its number (1 for January, 2 for Februrary etc.) it provides a convenient way to graph trends over a year. And, arguably, there is some logical interpretation of the average of 1 and 3 (January and March) being 2 (February). However, clearly December of one years precedes January of the next and yet 12 as a number is not less than 1. The numeric quantities in the section above are truly numeric; they are the number of feet in the drop, or acres or years open or the amount of snowfall etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>state</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alyeska Resort</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Eaglecrest Ski Area</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hilltop Ski Area</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arizona Snowbowl</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sunrise Park Resort</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>325</th>\n",
+       "      <td>Meadowlark Ski Lodge</td>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Wyoming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>326</th>\n",
+       "      <td>Sleeping Giant Ski Resort</td>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Wyoming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>327</th>\n",
+       "      <td>Snow King Resort</td>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Wyoming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>328</th>\n",
+       "      <td>Snowy Range Ski &amp; Recreation Area</td>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Wyoming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>White Pine Ski Area</td>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Wyoming</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>330 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  Name   Region    state\n",
+       "0                       Alyeska Resort   Alaska   Alaska\n",
+       "1                  Eaglecrest Ski Area   Alaska   Alaska\n",
+       "2                     Hilltop Ski Area   Alaska   Alaska\n",
+       "3                     Arizona Snowbowl  Arizona  Arizona\n",
+       "4                  Sunrise Park Resort  Arizona  Arizona\n",
+       "..                                 ...      ...      ...\n",
+       "325               Meadowlark Ski Lodge  Wyoming  Wyoming\n",
+       "326          Sleeping Giant Ski Resort  Wyoming  Wyoming\n",
+       "327                   Snow King Resort  Wyoming  Wyoming\n",
+       "328  Snowy Range Ski & Recreation Area  Wyoming  Wyoming\n",
+       "329                White Pine Ski Area  Wyoming  Wyoming\n",
+       "\n",
+       "[330 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 6#\n",
+    "#Use ski_data's `select_dtypes` method to select columns of dtype 'object'\n",
+    "ski_data.select_dtypes('object')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You saw earlier on that these three columns had no missing values. But are there any other issues with these columns? Sensible questions to ask here include:\n",
+    "\n",
+    "* Is `Name` (or at least a combination of Name/Region/State) unique?\n",
+    "* Is `Region` always the same as `state`?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.3.1 Unique Resort Names<a id='2.6.3.1_Unique_Resort_Names'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Crystal Mountain    2\n",
+       "Alyeska Resort      1\n",
+       "Brandywine          1\n",
+       "Boston Mills        1\n",
+       "Alpine Valley       1\n",
+       "Name: Name, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 7#\n",
+    "#Use pandas' Series method `value_counts` to find any duplicated resort names\n",
+    "ski_data['Name'].value_counts().head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You have a duplicated resort name: Crystal Mountain."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q: 1** Is this resort duplicated if you take into account Region and/or state as well?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Alyeska Resort, Alaska    1\n",
+       "Snow Trails, Ohio         1\n",
+       "Brandywine, Ohio          1\n",
+       "Boston Mills, Ohio        1\n",
+       "Alpine Valley, Ohio       1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 8#\n",
+    "#Concatenate the string columns 'Name' and 'Region' and count the values again (as above)\n",
+    "(ski_data['Name'] + ', ' + ski_data['Region']).value_counts().head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Alyeska Resort, Alaska    1\n",
+       "Snow Trails, Ohio         1\n",
+       "Brandywine, Ohio          1\n",
+       "Boston Mills, Ohio        1\n",
+       "Alpine Valley, Ohio       1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 9#\n",
+    "#Concatenate 'Name' and 'state' and count the values again (as above)\n",
+    "(ski_data['Name'] + ', ' + ski_data['state']).value_counts().head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**NB** because you know `value_counts()` sorts descending, you can use the `head()` method and know the rest of the counts must be 1."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**A: 1** Your answer here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Region</th>\n",
+       "      <th>state</th>\n",
+       "      <th>summit_elev</th>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <th>base_elev</th>\n",
+       "      <th>trams</th>\n",
+       "      <th>fastEight</th>\n",
+       "      <th>fastSixes</th>\n",
+       "      <th>fastQuads</th>\n",
+       "      <th>...</th>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>Crystal Mountain</td>\n",
+       "      <td>Michigan</td>\n",
+       "      <td>Michigan</td>\n",
+       "      <td>1132</td>\n",
+       "      <td>375</td>\n",
+       "      <td>757</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>56.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>Crystal Mountain</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>7012</td>\n",
+       "      <td>3100</td>\n",
+       "      <td>4400</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>2600.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>486.0</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>99.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 27 columns</p>\n",
+       "</div>"
       ],
       "text/plain": [
        "                 Name      Region       state  summit_elev  vertical_drop  \\\n",
@@ -571,13 +1368,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33"
+      ]
+     },
+     "execution_count": 12,
+     "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()"
    ]
   },
   {
@@ -604,34 +1412,34 @@
        "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"
       ]
@@ -654,15 +1462,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "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: Region, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "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",
+    "(ski_data[ski_data.Region != ski_data.state]\n",
+    " .groupby('state')['Region']\n",
     " .value_counts())"
    ]
   },
@@ -682,14 +1507,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Region    38\n",
+       "state     35\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "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 +1553,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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R9viaSfoKyrKfxjOgzzPEHQjYzvZ9Pals+7eSbqYE/7hM0ldtX91UZWdgRWBD2y9Leoh5M9sd+7xUbzpq+wTgBIAlVhrn3lwbEdHKIjGQaBUJw/YkytsjbN8IrN50+vu1fCJlBuI19YE0jqblUX1pqznah+1TKFPTERExwGzPlrQ3cKGkX3ZR9T7KW/5/sv2/wBeAaylBNMZKWs32nyl75BquAPaStJdtS1rf9u2d3UDSu4EHbB8jaVVK8I/mgcQY4G91ELEl3SSvq1GonpO0me3rKQORiIgFYpEYSAwUSR+mRG460nbmhSMiRgjbt9flQjsBD3dS56UajvscSYsBtwLH254jaQ/gUkmzgcnAsvWyHwJHAdMlvYESZrarsLCfBb4g6WXKkqufdDh/BvB7STOAdsogpju7ASdLMnBlD+pnZjsiBoTKcs8YSdra2tze3j7U3YiIEUDSVNttQ92PGF7yHImInurqObKobbaOiIiIiIgBsNAsbZL0FuCqevg2yka0J+vxxjVHQ0REREREDICFZiBh+2lK5mhqFKRZtg8byj4NlhmPzmTsAZ0HhHoo614jIiIiYpAt1EubarbPayVNlXSFpJVq+SRJP6vZRe+XtHkt31XS+ZIul/QnST9vamsrSTfWbKPnSBpdyw+RdHfNUHpYLfuMpDtrptHratkoSYdKurXW/WpT2/s3lR+8IH9HERFR1Hw+0+rPXyU92nT8xqHuX0TEcLPQzEi0IOAXwLa2n5S0A/BjYPd6fjHbG0v6V+AHwIdr+XhKxtM5wH2SfgG8SAnj+mHbL9REQt+UdBzwKWCNGvZv+drGgcC/2H60qexLwEzbG0laArhB0pWUULLjgI1rny+WtEVTcqGIiFgAMrM9T2a2I6InFuYZiSWAtYE/1Iyj3wfe0XT+/PrfqZSMpg1X2Z5p+yXgbkoM7/cDa1L+8T8N+GItnwm8BPy3pE8Ds2sbNwATJX0FGFXLtgJ2qdffDLyFMoDYqv7cDtxGyaA6ruOXSUbSiIgFLzPbERGdW9hnJO6yvUkn5xsZQjtmNG2V7VTAH2w3JyEqN5E2Bv4Z2B74BvAh23tKeh8lc+lUSRvWNvayfUWH6/8F+KntX3f1ZZKRNCJigcvMdkREFxbmgcQcYEVJm9i+UdLiwOq27+pDWzcBx6lmO5W0DLAy8BiwtO3LJN0APACgkv30ZuBmSR8DVqFkP/2apKtrxtLVgUdr+Q8lnWF7lqSVgZdt/62zziSRUETEAtE8sw1lhvnxpvNdzmwDSGrMbC/PvJltgDcCNzL/zPYlwCW1jcbM9u+a7rMVsK6k7evxGF4/sw0wupbPN5BQSaq3B8Co5Vbsze8hIqKlhXkg8SplluAYSWMo3/UooNcDifomalfgzPoWCMqbpeeBiyQtSXkL9M167lBJ42rZVcAdwHTKg+Y2lafIk8AnbV8p6b3AjfXhMgv4PNDpQCIiIhaIzGxHRHRhoRxI2D6o6XCLFucnNH1+ivomyfZEYGLTua2bPl8NbNTidhu3aP/TrboFfLf+dKx/NHB0i2siImLoLLQz2xERA2GhHEhEREQMgIV2ZjtLZCNiIMjO7OZI09bW5vb29qHuRkSMAJKm2m4b6n7E8JLnSET0VFfPkWEd/lXSXJVEQHdK+n0jcoWkCXVTWl/b7dX1ksZKurObOrP62p/e3isiIiIiYqgN96VNL9oeDyDpVODfKKH3FmlJJBQRERERQ21Yz0h0cCNlY1rDaEnnSrpX0hl1vSiS/lnS7ZJmSDq5sRZV0kdr3duA1zZDS1qm1rulXrdtV52QtFatO60m/hnX4fxoSVepJBya0WivzjTcI+lESXdJulLSUvXchipJh+6gDJYiIiIiIoa1ETGQkDSKEhrv4qbi9YF9KHG53w18oG5WmwjsYHsdyozL12r5icAngA2BtzW18z3gatsbA1tSNrgt00V39gSOrjMlbcAjHc6/BHzK9ga1vcMbgxxKXO/jbK8FPAdsV8tPoYT0W6/bX0ZERERExDAw3Jc2LSVpGmUm4h7gD03nbrH9CECtM5YS/eJB2/fXOo3lUJNq+Z9q/dOpSXkoSXy2kbRfPV4SWLXer5Ubge9JegdwfqPNJgJ+ImkLSsSPlYG31nMP2p5WP08FxtZ9H8s3ZSD9DfCxjjdNIqGIiJ6R9D3gc5QcDq8CX62hVHvbzgTgH7an1OOJwCW2z+3murnADGBx4BXgNOBI2692c6/9msOOd3OPfYATbM/uSf2Oulsi250soY0IGP4zEo09Eu+k/AO9edlPq4Q/fSFgO9vj68+qtjsbRGD7t8A2wIvAZZI+1KHKzsCKwIa1709QBif96rPtE2y32W4btfSYnl4WEbFIkbQJsDWwge11gQ8DD/exuQnApn247sX6PFkL+Ajl5dAP+tiHzuwDLD3AbUZE9MpwH0gAUN+47A18S1JX//i+j/KW/5/q8ReAa4F7a/lqtbw5s+gVwF5NeyzW76ovkt4NPGD7GOAiYN0OVcYAf6vJgrakDIK6+m7PAc9J2qwW7dxV/YiI6NJKwFO250BJOmr7MehyD91Dklaon9skTZI0lrKUdd+6J27z2v4WkqZIekDS9t11piaF2wP4hoqxkibXfXS3SXrdQEXSRrWfq7Xqs6S9gbcD10i6pl7zK0ntdQ/ewf38HUZE9MhwX9r0Gtu3S5pOGQS0fLtk+yVJuwHn1AHHrcDxtufUpUGXSpoNTAaWrZf9kJJgaLqkNwAPUt5mdeazwBckvQz8FfhJh/NnAL+XNANopwxiurMbcLIkA1d2VzmJhCIiOnUlcKCk+4E/AmfbvrZpD90/275f0mnA1yh//1/H9kOSjgdm2T4MQNKXKAOVzYA1KPv2ulzmVNt6oO71+3+UJHEfqc+rccCZlP121HtsCvwC2LbWndSxz7aPkvRNYEvbT9VLv2f7mXqfqySta3t6cz+yRDYiBtqwHkjYHt3h+BNNh5Oayr/R9Pkqykbsjm1dTvnD37H8ReCr3fTjIWDt+vkQ4JDO+lr/qG/SSVNrN9U/rOnzVKB5o/W3u+pPRES0ZnuWpA2BzSkBL86WdABwO6330B3Vy1tcWPc63C3prd3Wfr3FgWMljacscV296dx7gROArWw/Jmm9XvT5s3WgsBhlsLMmJRP2a2yfUNtniZXGJRttRPTbsB5IRERE9JbtuZSXTZPq7PAXKQOJzrzCvKW+S3ZRD+bf66ZOazVXKkti51JmGH5A2Tu3Xr3nS01VH6/3Xx94rCdt1/bfBewHbGT72bopvLvvERHRbyNij0RERERPSHpPh/w+44G/0PkeOoCHKKHBYV5YbiiRAJelHyStCBwPHGvblH10j9dZjS8Ao5qqPwd8HPhpjeLUVZ+b+7Yc8AIws86SvC7yX0TEYMiMRERELExGA7+oobVfAf4X2KOzPXT1moOB/5b0Q5qWzQK/B86tiUX36kUfGqHLG+FffwMcUc/9EjhP0i7A5ZQBwGtsPyFpa+B/gN0pe+ha9fkE4HJJj9neUtLtlD15DwM3dNfB7LWLiIGg8oJk5KoblI+w/a16vB8w2vZBA9T+WErc8LW7q7ugtLW1ub29fai7EREjgKSpttu6rxmLkjxHIqKnunqOLAwzEnOAT0v6aVP0iiEjaTHbrwzmPZJIKCIiIiKG2sKwR+IVyhTvvh1PSFpR0nmSbq0/H6jlMyQtX2N6P12nmJF0mqSPdHYjSaMkHVrbmi7pq7V8Qo0LfjElkscyki6VdIekOyXtUOttKOlaSVMlXSFppRon/Lame4xrPo6IiIiIGI4WhoEEwHHAzpI6pnw+GjjS9kaUDXQn1fIbgA8AawEPUMIEQgnbOqWL+3wJmFnb2wj4So2WAbAB8O+2Vwc+Cjxme726JOpySYtTYoNvb3tD4GTgx7b/TNkgN762sxtwSscbS9qjJhtqnzt7Zg9+JRERERERg2ehGEjY/jtwGiX7dbMPU+J1T6MkDlpO0mhKQrot6s+vgHUkrQw8a/sFOrcVsEtt72bgLUAjOsgtth+sn2cAH5H0M0mb254JvIeSR+IP9frvA++o9U8CdquJhHYAftviO55gu81226ilO46XIiJiQZNkSac3HS8m6UlJl9TjbWoOiwXdrz0bM+0REYNpYdgj0XAUcBvzv81/A/B+281xupF0HSWpz6rA94BPAdtTBhhdEbCX7Ss6tDeBpsgbNQPpBsC/Aj+SdBVwAXCX7VbJ6s6jxBa/Gphq++lu+hEREUPvBWBtSUvV5KYfAR5tnLR9MeUl1gJT9+kd31297LWLiIGw0AwkbD8j6XeU5Ucn1+IrKSH7DgWQNN72NNsPS1oBeKPtByRdT0nm841WbTe5AviapKttvyxpdZoeGg2S3g48Y/t0Sc8BX6Zkw15R0ia2b6xLnVa3fVcNS3gFZXbkS91914Tti4gYNi6j5H44F9gJOJO6XFbSrkCb7W9I+gzlhdFcyhLZLSStRXn59UbKi6/tbP9J0jcpoV8BTrJ9VMcIgs0RCiVNAqYBmwFnSloWmGX7sEH/9hGxSFsoljY1ORxYoel4b6Ctboy+G9iz6dzNwP3182RgZeD6Fm0uxrxMpicBdwO3SboT+DWtB2PrALfUJUw/AH5k+x+UWY+fSbqD8kd/06ZrzgBepQx+IiJiZDgL2FHSksC6lGdLKwcC/2J7PWCbWrYncLTt8UAb8IikDSl75d4HvJ+yF2/9HvTjjXX56+F9/yoREb0z4mckbI9u+vwEsHTT8VOUPQetrvtC0+cpdD6oWgv4c633KvDd+tNsEk1JjOrSpys61MH2NMq+jFY2A06xPbeT8xERMczYnl5nC3aizE505gZgYp05P7+W3Qh8T9I7gPPrbMRmwAWN/XqSzqfMcHS3ROrs7voqaQ9gD4BRy63YXfWIiG6N+IHEYJL0X8C2wK6DfJ8LgNWADw3mfSIiYlBcDBwGTKAE4Xgd23tKeh9lGdRUSRva/q2km2vZZY2Q4p14hflfeC3Z4XxXgUIafTiBEi6dJVYaN7Kz0UbEsLCwLW0aULYPrCFcbx/k+3zK9rrDIaFeRET02snAwbZndFZB0mq2b7Z9IPAksIqkdwMP2D4GuIiyNGoy8ElJS0tahhIMZDLwBPD/JL1F0hLA1oP8nSIiupUZiUrSrOZlUj2oPwHYz/bWkrYB1rR9yGD1LyIihifbjwDHdFPtUEnjKNH/rgLuAL4DfEHSy8BfgZ/UwCETgVvqdSc1XmbVWfJbKEE+7u1PnxO0IyIGguzMbkL/BhKD1qlOLLHSOK/0xaP6fH3C9kUsOiRNtd021P2I4aWtrc3t7e1D3Y2IGAG6eo5kaVMHkiZImiTpXEn3SjpDkuq5j9ay24BPN12zq6Rj6+dPSLpZ0u2S/ijprbX8IEkn17YfkLR30/UXSpoq6a66GS4iIiIiYljLQKK19YF9gDWBdwMfqKH9TgQ+AWwIvK2Ta6+nJMFbnxIW8NtN59YA/gXYGPhBzSUBsLvtDSnh//aW1HKzXkRERETEcJE9Eq3dUte8UnNBjAVmAQ/a/lMtP50aRq+DdwBnS1qJkmTowaZzl9qeA8yR9DfgrcAjlMHDp2qdVYBxwHzZrRO2LyIiIiKGk8xItDan6fNcejfg+gVwrO11gK8yf4i+17Vb91p8GNikJiq6ndeH9cP2CTXZUNuopcf0ojsREREREQMvMxI9dy8wtobw+zMl+VArYygRNQC+2IN2xwDP2p4taQ1KJtOIiBiBJH0SuAB4r+2WkZUkTbG96QLtWAczHp3J2AMu7fP1CdoREZCBRI/ZfqkuL7pU0mxKXO9lW1Q9CDhH0rPA1cC7umn6cmBPSfcA9wE3ddeXhO2LiBi2dqLsldsJ+EHzCUmL2X5lqAcREREDJeFfR6CE7YuInkr41wVH0mjKC6Etgd/bfk9dvvpD4FlgDdurN8KN17wQ29TLVwSutL2bpG8Cu9fyk2wfJWks8D+UQcqmlJnvbW2/KOkrlD10bwT+F/iC7dld9TVhxCOipxL+NSIiYvBtC1xu+37gaUkb1vINgH+3vXpzZdsH2h4PTACeAY6t1+wGvI+y1PUrktavl4wDjrO9FvAcsF0tP9/2RnWf3T3Al1p1TtIektoltc+dPXNAvnBELNoykIiIiBgYO1HCflP/29hLd4vtB1tdUPMUnQ4cYXsqsBlwge0XbM8Czgc2r9UftD2tfp5KiSgIsLakyZJmADsDa7W6V4J2RMRAyx6JiIiIfpL0ZuBDwDqSDIwCDFwKvNDFpQcBj9g+pQe36Rj5b6n6eSLwSdt3SNqVMsMRETHoFtmBRP1Df4btz9fjxYDHgZttby1pG2BN24cs4H7tCcy2fVpndRJtIyJi2Nke+I3trzYKJF3LvNmE15H0CUr47y2biicDEyUdAgj4FPCFbu69LPB4TXK6M/MiB3YqQTsiYiAssgMJyhuitSUtZftF4CM0/fG1fTFw8YLsUI3ocfyCvGdERAyInYCfdSg7D/ga8OdOrvkmsDJwS1nhxMW2D5Q0Ebil1jnJ9u11s3Vn/hO4GXiy/rdVRMGIiAG3KA8kAC4DPg6cS3kInEl9e1Snh9tsf0PSZyhh/OYCM21vIWkt4BRKlIw3ANvZ/lMX0TYusb12bXs/YLTtgyRNAqZR1sWeKWlZYJbtwwb920dExICwvWWLsmOAY1qUj+7smlp+BHBEh7KHgLWbjg9r+vwr4Fd97HpERJ8t6putzwJ2lLQksC7lTU4rBwL/UiNiNEL17QkcXSNutAGPdBNtoytvrBvgDu+sQqJtRERERMRwskgPJGxPp0S92IkyO9GZGyhrVr9C2UAHcCPwXUnfAd5Zl0d1FW2jK2f3oK+JthERERERw8YiPZCoLgYOoyxrasn2nsD3gVWAqZLeYvu3lNmJF4HLJH2oi3u8wvy/6yU7nO8qokdERERExLCzqO+RADgZeM72jJqB9HUkrWb7ZuBmSR8DVpE0BnjA9jGSVqUsjbqO1tE2ngD+n6S3ALOArYHL+9rhRNuIiBg4NYrfEba/VY9f28c2QO2PpWmfXETEwmKRH0jYfoQWm+E6OFTSOMrg4CrgDuA7wBckvQz8FfiJ7WdaRdsAkPRftfxR4N4B/yIREdFXc4BPS/qp7aeGujM1gt8rg3mPhBGPiIGwyA4kGlEzOpRNAibVzxMpSX6w/ekWTRxSfzq28bpoG7W8s+gdEzocH9Rd3yMiYkC9ApwA7At8r/mEpBWB44FVa9E+tm+oWaQ3B2YCTwH72j5N0mmUfBJ/aHUjSaMoz44JwBLAcbZ/XWfEfwg8C6xRA3X8DngHZW/eD22fXYN6HAGMrvfdFVgaOMf2BvUe44CzG8cREYMleyQiIiLgOGDnumy12dHAkbY3ArYDTqrlNwAfANYCHmBeYI1NgCld3OdLlDDiGwEbUaL7vaue2wD4d9urAx8FHrO9Xl0SdXlNOPcLYHvbG1KW5v7Y9p+BmZLG13Z2o4Qnj4gYVIvsjERERESD7b/X2YS9KUE0Gj4MrFkTxgEsJ2k0JQP1FsBfKDkc9pC0MvCs7a4CaGwFrCtp+3o8BhgH/AO4xfaDtXwGcLikn1H2V0yWtDYll8Qfan9GAY/X+icBu9VcRjsAG3e8saQ9gD0ARi23Yg9+KxERXcuMBGWjnaTDm473k3TQIN/zIUkrDOY9IiKiV46izBgs01T2BuD9tsfXn5VreO/rKLMQm1OWxD4JbE8ZYHRFwF5N7b3L9pX13GsDENv3U2YoZgA/knRgvfaupmvXsb1VveQ84GOUYB5TbT/d8cYJIx4RAy0zEsWw2mjXnWySi4gYeDVgxu8og4mTa/GVwF7AoQCSxtueZvvh+jLojbYfkHQ9sB/wjW5ucwXwNUlX235Z0uqUIBzzkfR24Bnbp0t6DvgyZW/FipI2sX1jXeq0uu27bL8k6QrK7MiX+vmriIjokQwkil5ttKMko3sAGG/7uVrvT5SEdBtTck68EXga2Nn2EzX065nAyvV6Nd3jQkqOiiUp2bJPGITvGBER3Tuc+QcDewPHSZpOeWZeB+xZz93MvCSlk4GfAte3aHMxygsrKEuQxgK3qaxPehL4ZItr1qFEDHwVeBn4mu1/1CVRx9S9HItRZlHuqtecQQk7fmWL9uZvPGHEI2IAZCAxz3HAdEk/71De2Gh3fc0XcYXt90q6iPIH+xRJ7wP+UgcM11OmwS3py8C3gW8BPwCut/1fkj7O/G+Mdq9vwpYCbpV0Xqtp6YiIGHjNUfxsP0GJgtQ4foqy56DVdV9o+jyFzpcLrwX8udZ7Ffhu/Wk2qf402ruCMnvR8Z7TKHszWtkMOMX23E7OR0QMqAwkqj5stDsbOJASGWPHegwlVN/ZklaizEo0Ns5tAXy63utSSc823WNvSZ+qn1ehbLybbyCRTXIRESNPzSG0LSVM62De5wJgNeBDg3mfiIhm2Ww9v6Po+Ua7G4F/qkufPgmcX+v/AjjW9jrAVynLlTpVY4d/GNjE9nrA7a2uySa5iIiRx/aBNYTr7YN8n0/ZXnck7POLiIVHBhJNbD9DSQDUvOyosdEOKBvtal0DF1ASA93TtBRpDPM2zn2xqZ3rgM/VNj4GvKmp/rO2Z0taA3j/AH6liIiIiIhBkaVNr9ebjXZnA7cy/5T1QcA5denS1UAj0dDBwJmS7qIkK/q/Wn45sKeke4D7gJu662A2yUVEDD+SZjXvt+hB/QnAfra3lrQNsKbtQwarfxERAy0DCfq10a6dpuhLtewi4KIWdZ+mJCJq5WO973VERCwsbF8MXLyg7pcw4hExELK0KSIiYgBJmiBpkqRzJd0r6Ywa6hVJH61lt1EDcNTyXSUdWz9/QtLNkm6X9EdJb63lB0k6ubb9gKS9m66/UNJUSXfV4BwREYMuA4mIiIiBtz4l79CawLuBD0haEjgR+ASwIfC2Tq5thBFfHziLEka8YQ3gXyg5i35Qk9JBCSO+IdBGiQT4loH9OhERr5eBRBNJn5Tkuum5szpTFmSfIiJiRLrF9iM1b8Q0ShK6NYAHbf+pBuw4vZNr3wFcIWkGsD8lD0XDpbbn1GW3fwPeWsv3lnQHZZ9dI4z4fCTtIaldUvvc2TP7/w0jYpGXPRLz24nyJmgnSgK510hazPYrtjcdkp41ydrWiIhhb07T57n07nn7C+AI2xfXDdkHddVuhzDisyVNopMw4sAJAEusNM696E9EREuZkahqkrnNKKFfd6xlEyRNlnQxcHctm1X/+1+SptWfRyWdUsu/KenO+rNPLRsr6R5JJ9b1q1fWLNZI+oqkWyXdIek8SUu/rnMREbEwuBcYK2m1erxTJ/U6CyPemYQRj4ghkRmJebYFLrd9v6SnJW1YyzcA1rb9YHNl2wcCB0paHpgMHFuv2Q14HyWa082SrgWepUwz72T7K5J+B2xHmdY+3/aJAJJ+RBnI/GKQv2tERCxgtl+qG6EvlTSb8uxYtkXVg2gdRrwzCSMeEUMiA4l5dgKOrp/PqseXUNa5PtjqghqF43TKFPRUSf8OXGD7hXr+fGBzSki/B21Pq5dOpayXBVi7DiCWB0YDV3Ryrz2APQBGLbdin79kREQMjkYocduTgElN5d9o+nw5Za9Ex2snAhPr587CiB/U4XjtpsOEEY+IBS4DCUDSm4EPAetIMjAKMHAp8EIXlx4EPGL7lB7cpuO61qXq54nAJ23fIWlXYEKri7O2NSIiIiKGk+yRKLYHfmP7nbbH2l4FeJAym9CSpE9QNrft3VQ8GfikpKUlLQN8qpZ1ZVng8RrCb+f+fImIiIiIiAUlMxLFTsDPOpSdB3wN+HMn13wTWBm4peYZutj2gZImArfUOifZvl3S2C7u/Z/AzcCT9b+t1svOJ2tbIyIiImKoqYSyjpGkra3N7e3tQ92NiBgBJE213TbU/YjhJc+RiOiprp4jmZGIiIgYQpJmNTZqLyj9zUfUleQqilh0ZI9ERERERET0WgYSERERw4CKQ2tC0xmSdqjlx0napn6+QNLJ9fPukn5cP18oaWpNerrH0H2LiFiUDLuBhKTv1T+E02vW6Pf1sZ0JkjZtOp4oafseXDe33veumm36W5K6/D3Ve13Si77tkwzWERHRwaeB8cB6lKiAh0paiRL9rxFFcGVgzfp5c+C6+nl32xsCbcDekt6yoDodEYuuYbVHQtImwNbABrbnSFoBeGMfm5sAzAKm9PK6F22Pr/35f8BvgeWAH/SxH63sQ0lkN7svF/d3bWvWr0ZEDEubAWfangs8IelaYCPKQGIfSWsCdwNvqgOMTZgXgnxvSZ+qn1cBxgFPNzeexKYRMdCG24zESsBTtucA2H7K9mMAkv5Z0u11uvdkSUvU8ofqgANJbZIm1XCrewL71tmFxpucLSRNkfRAT2YnbP+N8kf3G3XKeaykyZJuqz+bdrxG0ka1n6u16rOkvYG3A9dIuqZe8ytJ7XUW5OB+/g4jImIhYvtRYHngo5QZiMnAZ4FZtp+XNIEyg7GJ7fWA24ElW7Rzgu02222jlh6zgHofEQuz4TaQuBJYRdL9kn4p6YMAkpakZIDewfY6lJmUr3XWiO2HgOOBI22Pt91ICrcS5Y3P1sAhPemQ7Qcoma7/H/A34CO2NwB2AI5prlsHFscD2wKPtuqz7WOAx4AtbW9ZL/1eDau1LvBBSev2pG8REbFQmQzsIGmUpBWBLZiXl+gmymx2YyCxH/MSno4BnrU9W9IawPsXaK8jYpE1rJY22Z4laUPKus8tgbMlHUB5u/Kg7ftr1VOBfwOO6uUtLrT9KnC3pLf2oYuLA8dKGg/MBVZvOvde4ARgK9uPSVqvF33+bJ1yXowy2FkTmN5cIVPSERELvQsoy5XuAAx82/Zf67nJlOfL/0r6C/Bm5g0kLgf2lHQPcB9l0NGlJDaNiIEwrAYSAHVt6CRgkqQZwBcpA4nOvMK8mZXXTeV2MKfps3rSH0nvpgwa/kbZJ/EEZSPcG4CXmqo+Xu+/PmXGoUckvYvyZmkj28/WzNgtp6QpAxWWWGlcsghGRCwkGjkkXDLE7l9/Otb5b+C/6+eXgWWazs0BPrZAOhsR0WRYLW2S9B5J45qKxgN/obxhGSvpn2r5F4Br6+eHgA3r5+2arn0eWLaf/VmRslTp2PoHfgzweJ3V+AJlyVPDc8DHgZ/W9apd9bm5b8sBLwAz6yxJHgYRERERMewNtxmJ0cAvJC1PmWn4X2AP2y9J2g04R9JiwK2Uf+ADHAz8t6QfUmYyGn4PnCtpW2CvXvRhKUnTKMuYXgF+AxxRz/0SOE/SLpSp5BeaL7T9hKStgf8Bdgc66/MJwOWSHrO9paTbgXuBh4EbuutgpqQjIiIiYqipvGiPkaStrc3t7e1D3Y2IGAEkTa3BHCJek+dIRPRUV8+RYbW0KSIiYmEi6ZOSXKMpUcOI39nHtmYNbO8iIvpnuC1tioiIWJjsBFxf/zuQiU37pb+JTbuSpKcRi47MSPRT3hBFREQrkkZTchd9CdixxfmWSU4lrSTpuppQ9c6mpKqN61aQdKOkj/ckUWpExGDJjMQINJhvkiBvkyIiBsi2wOW275f0dM2T9HTT+UaS05dqxMIzgTbgc8AVtn8saRSwdOOCGt3vYuD7tv8gaelO2oiIGHSZkRggKg6tb49mSNqhlh8naZv6+QJJJ9fPu0v6cf18oaSpku6qieciImLk2wk4q34+qx43Wxw4seZMOoeSjBRKlL/dJB0ErGP7+ab6V1ES1f2hmzZeR9Iektoltc+dPbN/3ywigsxIDKRPU/JerAesANwq6TpK5tHNKW+QVqZkrqaWNR4wu9t+RtJS9brzbDe/tYqIiBFE0puBDwHrSDIl75CB45qq7UuLJKe2r5O0BSU30URJR9g+jRKSfCrwL8zLS9SyjVaS2DQiBlpmJAbOZsCZtufafoLyR34j6kBC0prA3cATklYCNgGm1Gv3lnQHcBOwCjCuY+N5kxQRMaJsD/zG9jttj7W9CvAg5W98Q8skp5LeCTxh+0TgJGCDWt+UHEVrSPpOV21ERCwImZEYZLYfrQn2PgpcB7wZ+Cwwy/bzNQv2h4FNbM+WNAlYskU7eZMUETFy7AT8rEPZecB/NB13luR0ArC/pJeBWcAujQtsz5W0E3CxpOe7aKNLSWwaEQMhA4mBMxn4qqRTKYOFLYD967mbgH0o09xvAc6tP1DeJj1bBxFrAO9fkJ2OiIiBZ3vLFmXHAMc0Hf8JWLepyndq+anAqS2uH13/O4eyvKnhdW1ERCwIGUgMnAsoy5XuoEw/f9v2X+u5ycBWtv9X0l8oA43J9dzlwJ6S7gHuoww6upQ3SREREREx1DKQ6KemN0SmzEDs36LOfwP/XT+/DCzTdG4O8LEF0tmIiIiIiAGSzdYREREREdFrGUhERERERESvZWlTREREP0i6BjjE9hVNZfsA/w782vYhvWjr7cAxtrfvpt5lwOdsP9eXPs94dCZjD7i0L5f220PZ4xex0MiMRDckfVKSa0QlJI2VdGcf25o1sL2LiIhh4Exgxw5lOwJfbDWIkNTpSzzbj3U3iKj1/rWvg4iIiIGSGYnu7QRcX//7gyHuCzD4b5LytigiolfOBX4k6Y22/yFpLPB2YDVJn7X9DUkTKVmn1wdukHQccAYl+MZFwD62R9drL7G9tqRdgW2ApYHVgAtsfxtA0kNAm+2nJF1ISXS3JHB0zTsUETHoMiPRBUmjKRmrv8Tr3zY1ZicmS7qt/mxay1eSdJ2kaZLulLR5h+tWkHSjpI931kZERIwMtp8BbmFeBL4dgd9RQoE3ewewqe1vAkdT/tG/DvBIF82PB3YA1gF2kLRKizq7294QaAP2lvSWVg1J2kNSu6T2ubNn9uzLRUR0IQOJrm0LXG77fuBpSRt2OP834CO2N6D8oW8kGvoccIXt8cB6wLTGBZLeClwKHGj70i7aiIiIkaN5edOO9bijc2zPrZ83Ac6pn3/bRbtX2Z5p+yXgbuCdLersLekOSh6iVYBxrRqyfYLtNttto5Ye0/W3iYjogSxt6tpOlLdGAGfV42Obzi8OHCtpPDAXWL2W3wqcLGlx4ELb05rqXwX8m+1ru2ljPpL2APYAGLXciv39XhERMbAuAo6UtAGwtO2pktbpUOeFPrQ7p+nzXDo8tyVNAD4MbGJ7tqRJlCVOERGDLgOJTkh6M/AhYB1JBkZRpqmPa6q2L/AEZdbhDZT1r9i+TtIWwMeBiZKOsH0a8AowFfgX4Nqu2uiornk9AWCJlcZ1nC6PiIghZHtWjd50Mq1nIzq6CdgOOJsWS2d7YQzwbB1ErAG8vycXrbPyGNqzHy4i+ikDic5tD/zG9lcbBZKupUwbN4wBHrH9qqQvUgYbSHpnLT9R0hLABsBplIHI7sA5kr5j+2edtdGVPAAiIoalM4EL6NnAYB/gdEnfAy4H+rpp4XJgT0n3APdRBigREQtEBhKd2wn4WYey84D/aDr+JXCepF0of8wb09YTgP0lvQzMAnZpXGB7rqSdgIslPd9FGxERMYLYvhBQ0/FEYGL9vGuH6o8C77dtSTsC76n1HgLW7nh9Pd666fPYprY+RkTEEJCdVTIjTVtbm9vb24e6GxExAkiaarttqPsR86vR/I6lDDyeo0Re+t8Fdf88RyKip7p6jmRGIiIiYgGzPZmyNy4iYsRK+NeIiIiIiOi1RWogIcmSTm86XkzSk5IuqccTepIQriaRu7ND2UGS9huAPr6u7YiIGJkkzar/fe1ve33WNJ4720g6oI9tv13SuQPX24iI3lnUlja9AKwtaSnbLwIfoWx4a5hA2Rw9ZQj61mMzHp3J2AMuHZJ7P5RoURERA8b2xcDFfbz2MUqEwV7LcyQiBsIiNSNRXUbJ7wAlMtOZUN4WAXsC+0qaVjfC9Ymk8ZJukjRd0gWS3tRN+YaS7qiZSf+tH98tIiJGEEm7Sjq2fp4o6RhJUyQ9IGn7Wi5Jh0q6U9IMSTvU8uZZjrUk3VKfX9MltcxuHRExkBbFgcRZwI6SlgTWBW6G10LuHQ8caXt83QjXldXqH+xpkqZRBiENpwHfsb0uMAP4QTflpwB72c7Gu4iIRdtKwGbA1sAhtezTwHjK5uwPA4dKWqnDdXsCR9seD7QBjyyIzkbEom2RG0jYng6MpcxGXNaPpv5cBxzj6x/u4wEkjQGWt93IXH0qsEUX5cvX8utq+W9a3UzSHpLaJbXPnd3XvEURETHMXWj7Vdt3A2+tZZsBZ9qea/sJ4Fpgow7X3Qh8V9J3gHfW5bvzyXMkIgbaIjeQqC4GDqMuaxoJbJ9gu81226ilxwx1dyIiYnDMafqsTmt1YPu3wDbAi8Blkj7Uok6eIxExoBbVgcTJwMG2Z3Qofx5Ytj8N254JPNu0x+ILwLVdlD8HPCdps1q+c3/uHxERC53JwA6SRklaEdgCuKW5gqR3Aw/YPga4iLJ0NyJiUC1qUZsAsP0IcEyLU78HzpW0LbAX8CagzfaBvbzFF4HjJS0NPADs1k35bsDJkgxc2V3j66w8hvZEvYiIWFRcAGwC3AEY+Lbtv9YgIQ2fBb4g6WXgr8BPumowz5GIGAiyPdR9iF5qa2tze3v7UHcjIkYASVNttw11P2J4yXMkInqqq+fIorq0KSIiIiIi+iEDiYiIiIiI6LUMJCIiIiIiotcykOiCpFn1v83ZQydIuqR+3kbSAX1s++2Szh243kZERERELDiLZNSmgWL7YkpOir5c+xiwfV+unfHoTMYecGlfLu23hxLlIyKGOUlHAn+xfVQ9vgJ42PaX6/HhwKO2j+hFmxOAf9ie0qF8LHA9sKrtV5vKpwFfBb4CHFETzPX0XnsCs22f1kWdNmAX23v3tN1meY5ExEDIjEQ/SNpV0rH180RJx0iaIukBSdvXckk6VNKdkmZI2qGWN89yrCXpFknTJE2XNG7ovlVExIh3A7ApgKQ3ACsAazWd3xSY0uK6rkxotNnM9kPA/wGNHEFIWgNY1vbNtr/cahAhaVRnN7J9fFeDiFqnva+DiIiIgZKBxMBaCdgM2Bo4pJZ9GhgPrAd8GDhU0kodrtsTONr2eKANeKRjw5L2kNQuqX3u7JmD0/uIiIXDFEreBSgDiDuB5yW9SdISwHuB2yRtKOlaSVMlXdH42yxpb0l31xc7Z9VZhz2BfesLn8073O9MYMem4x2Bs2pbk+rsAZJmSTpc0h3AJpK+JOn++iLpxKYXUwdJ2q/p+p/VOvc37t1hme3Gkm6UdHt9mfWegf11RkS0lqVNA+vCOrV9t6S31rLNgDNtzwWekHQtsBEwvem6G4HvSXoHcL7tP3Vs2PYJwAkAS6w0Lsk/IiI6YfsxSa9IWpUyi3AjsDJlcDETmEFJ7PYLYFvbT9bZ4h8DuwMHAO+yPUfS8rafk3Q8MMv2YS1u+TtgmqS9bL8C7AB8pkW9ZYCbbX9L0tuB04ENgOeBqykJ51pZzPbGkv4V+AHlpVSze4HNbb8i6cOUZHTbdWxE0h7AHgCjlluxk1tFRPRcBhIDa07TZ/X0Itu/lXQz8HHgMklftX31gPcuImLRMYUyiNgUOIIykNiUMpC4AXgPsDbwB0kAo4DH67XTgTMkXQhc2N2NbD9Rl6r+s6QngFds39mi6lzgvPp5Y+Ba288ASDoHWL2TW5xf/zsVGNvi/Bjg1Los1sDinfQzL6QiYkBlIDH4JgNflXQq8GZgC2B/YMlGBUnvBh6wfUx9g7Yu5e1US+usPIb2bFaLiOhKY5/EOpSlTQ8D3wL+DpxCedlzl+1NWlz7ccrf6k9QZovX6cH9GsubnqifW3mpzk73VuMl1VxaP7d/CFxj+1N1GdakPtwjIqLXMpAYfBdQptPvoLwp+rbtv9Y/9g2fBb4g6WXgr5Rp6YiI6LspwH6UlzRzgWckLU/ZM/EVyoBiRUmb2L5R0uKUGYF7gFVsXyPpesrgYDRl+dFyXdzvfOCnwGzgn3vQv1uBoyS9qba9HWXJVV+MAR6tn3ftyQV5IRURAyEDiS7YHl3/+xBlChzbk6hve2xPBCbWz7t2cq0pMxD7dzjf3OYhzNucHRER/TeDEq3ptx3KRtt+CqBG1ztG0hjK8/Ao4H7g9Fom4Ji6R+L3wLmStgX2sj25+Wa1zo3A22w/0F3nbD8q6SfALcAzlH0OfY2k8XPK0qbvA0MT0zUiFkkq/86NkaStrc3t7e1D3Y2IGAEkTbXdNtT9iNeTNNr2LEmLUWavT7Z9wYK4d54jEdFTXT1HEv41IiJiaBxUE9fdCTxIDzZ2R0QMJ8NyICHpSEn7NB1fIemkpuPDJX2zl21OkPS6ZEI1MdwjNWlRc/k0Se+TdJKkNXt5rz0l7dJNnTZJx/Sm3YiIWHjY3s/2eNtr2N7bWSIQESPMcN0jcQNlA/JRTVlJmze5bQrs28s2JwCz6JDN1PZDkhpZSa+F+bOSAje3akzSqM6ib9g+vrvO2G4H+jSvPOPRmYw9YGiWwT6UzXkRERERwTCdkSBZSZOVNCJiBJFkSYc3He8n6aBBvudDklYYzHtERHRlWM5IJCtp66ykERExbM0BPi3pp42oUMNZZrYjYiAM1xkJmD8r6Y31p3HcMSvpNOD7wDvqtY2spJ8HXunuRrafoMx6/LOk8fQyK6ntl4FzurhFT7KSnlMzox5JmYWZj6Q9JLVLap87u68RAiMiYpC8Qska/bplt5JWlHSepFvrzwckvaHOKCzfVO9Pkt4q6ROSbq6z1H+U9NZ6/i2SrpR0V903qKZrL6yz83dJ2mPwv25ExPAeSHTMSnoTZUZiU8ogo5GVdHz9Wcf2VvXajwPHUWYLbq2h9brTWN60I0OXlXRtSibVJTtWsH2C7TbbbaOWHtOHLkRExCA7Dti55qBodjRwpO2NKLPNJ9l+FbgI+BSApPcBf6kvtq4H3m97fcoy22/Xdn4AXG97LUq42FWb7rG77Q2BNmBvSW8ZlG8YEdFkOA8kpgBbA8/Ynmv7GWB5ymBiCnAfNSspgKTFJa1VN2evYvsa4DuUt/2NrKTLdnG/84F/pSxrOqsH/bsV+GDdt7EY/VuK1OuspBERMbzY/jtwGrB3h1MfBo6ts+cXA8tJGg2cTXnmQHmJdXb9/A7gCkkzKMlMG7PUW1CW1GL7UuDZpnvsXffv3QSsAozr2L/MbEfEQBuWeySqZCXtxDorj6E9a0wjIoajo4DbgFOayt5AmWF4qblifeb8k6QVgU8CP6qnfgEcYftiSROAg7q6Ya3zYWAT27MlTaKTmW3K8iuWWGlcQs1GRL8N24FEXUK0XIeyXTscT6O8oelosxbt3Q+s2809P9mibELT59EdTv/W9gmal5X0wlrvoE6uf4q6R8L2JGBS/XwjsHpTu9/vqp8RETE82X5G0u+ALwEn1+Irgb2AQwEkjbc9zbYlXQAcAdxj++lav3mW+otNzV8HfA74kaSPAW9qqv9sHUSsAbx/kL5eRMR8hu1AYoQ4qEZZWpLyoLhwaLsTERHDwOHAN5qO9waOkzSd8ty9jhKSHMpypluZf1nrQZQAHM9SIgK+q5YfDJwp6S7KEt//q+WXA3tKuoey7Pem7jqYme2IGAgZSPSD7f2Gug8RETH0mmes64bppZuOn2LeXoiO17XTFH2pll1E2Yjdse7TwFYdy6uP9b7XERH9M5w3W0dERERExDC1yM9ISLoGOMT2FU1l+wD/Dvza9iG9aOvtlM3d23dT7zLgc7af60ufk0goIiIiIobaIj+QYF7+iCuaynYEvmj7uo6VJS1Ws1+/ju3HgC4HEbXev/axrxERERERw0KWNsG5wMclvRFA0ljg7cBqko6tZRMlHS/pZuDnklaTdJOkGZJ+JGlW49qanRpJu0o6X9LlNVvpzxs3rNlMV6ifk400ImIBkvQ2SWdJ+nP9+3uZpNW7v3LQ+rOPpKWbji9rznjdi3bGSvrcgHYuIqILi/yMRA3Vdwtlo9pFlNmI3wEdY2y/A9jU9lxJlwBH2z5T0p50bjywPiWz9X2SfmH74Q51dq99WIqShfu8phCAr6mDjD0ARi23Yu+/aEREIEmUcN2n2t6xlq0HvJWSh2go7ENJNDcb+jVrPZYSHva33dQb0iWy3ckS2oiRIzMSRWN5E/W/Z7aoc07NbQElu/Y59XNXf7Cvsj2zJiG6G3hnizrdZiOFkkjIdpvttlFLj+n620RERGe2BF62fXyjwPYdwPWSDpV0Z51t3gFKsjdJkySdK+leSWfUwUhjdvlgSbfVa9ao5ctIOlnSLZJur4lQkTRK0mH1HtMl7SVpb8os+DV1z17HWetdat07JP2mlk2sCVmpx7Pqx0OAzSVNk7Tv4P4aIyIyI9FwEXCkpA2ApW1PlbROhzov9KHdOU2f59Lh993TbKQRETFg1gamtij/NGUWeT1gBcoMcWOf3PrAWsBjwA3AB4Dr67mnbG8g6evAfsCXge8BV9vevS5RukXSH4FdKLMG422/IunNdUb6m8CWNUzsayStRUlQuqntpyS9uZvvdgCwn+2te/i7iIjolwwkANuz6pugk2k9G9HRTcB2lERCO3ZTtyt9ykaaREIREQNuM+DMOvP8hKRrgY2AvwO32H4EQNI0ymCgMZA4v/53KmUwAiXXwzaSGrmGlgRWpbw4Or4RsMP2M9306UOU2fCneli/S1kiGxEDLUub5jmT8iaqJwOJfYBv1iyl/wTM7OM9LwcWq9lID6EH2UgjIqJf7gI27OU1Xc0uz2lRLmA72+Prz6q27+lTb1t7hfr8lvQG4I09uShLZCNioGUgUdm+0LZs31uPJ9r+Rv28q+1zm6o/Crzf9rrAbUB7rfeQ7bU7Xl+Pt7Y9qX4ea/sp23Nsf8z2e21/0vaERp2IiBgUVwNLNEfJk7Qu8BywQ93HsCKwBXBLH+9xBbBX016K9Wv5H4CvSlqsljeWKj0PLNtJXz8j6S0d6j/EvMHQNsDi3bQTETEosrSpbzYEjq0PieeA3Ye2OxER0RO2LelTwFGSvgO8RPmH+T7AaOAOStS+b9v+a2MDdS/9EDgKmF5nDB4EtgZOAlav5S8DJwLHAicAl0t6zPaWTX29S9KPgWslzQVuB3at111UA3Vczrw9fNOBubV8ou0jO+tglshGxECQ3THKaQx3bW1tbm9vH+puRMQIIGmq7bah7kcML3mORERPdfUcydKmASDpHZIuqonn/izpaElvVElKd2wn1/Qp4VBERERExHCQpU39VJc3nQ/8yva2kkZRpql/TNnU11I/Eg4lkVBEREREDLnMSPTfh4CXbJ8CUEMH7kvZN7E08HZJl9fZip83LuqQcOibNUHRnZL2WfBfISIiIiKidzIj0X9r0SG5ke2/S/o/yu93PCWZ0RzgPkm/sP1wo66kDYHdgPdRQgbeLOla27cvoP5HRERERPRaBhKD7yrbMwEk3Q28E3i46fxmwAW2X6h1zgc2p0TneE0SCUVELLwkvQM4DliTslrgEmB/4HNAW3M48aZrLgM+Z/u53t4vS2QjYiBkaVP/3U2H5EaSlqNkMX2FrhMZ9VgSCUVELJya9tpdaHscJUTsaMpeu07Z/te+DCIiIgZKBhL9dxWwtKRdAOpm68OBicDsHlw/GfikpKUlLQN8qpZFRMSiIXvtImJEytKmfmpKbvRLSf9JGZxdBnwX2KkH198maSLzMqie1N3+iCQSiohYqGSvXUSMSBlIDID6B/0TLU5NrD+Nels3fR7b9PkI4IhB62BERIxk2WsXEcNSljZFREQMrey1i4gRKQOJiIiIoZW9dhExIo24pU2S3gYcBWwEPAc8Aexj+/4h6s8+wAm2Z9fjPoXjkzQW2NT2bwe6jxERMXxlr11EjFSyPdR96LEaIm8KcKrt42vZesBytofk7Yukhygxvp/qZzsTgP2a91F0ZomVxnmlLx7Vn9sNmsT/jhheJE213TbU/Yjhpa2tze3t7UPdjYgYAbp6joy0pU1bAi83BhEAtu8Arpd0aA17N0PSDlD+cS5pkqRzJd0r6Yw6GGmEzTtY0m31mjVq+TKSTpZ0i6TbJW1by0dJOqzeY7qkvSTtDbwduEbSNU3tNsLx7VLr3iHpN7VsoqTtG/2XNKt+PATYXNI0SfsO7q8xIiIiIqJ/RtrSprXpECKv+jQlPN56wArArZKuq+fWp4TWewy4AfgAcH0995TtDSR9HdgP+DLwPeBq27tLWh64RdIfgV2AscB4269IerPtZyR9E9iy44yEpLWA71OWKz0l6c3dfLcD6OGMRERERETEUBtpMxKd2Qw40/Zc208A11L2UADcYvsR268C0yiDgYbz63+nNpVvBRwgaRowCViSEjnjw8Cvbb8CYPuZbvr0IeCcxgCjB/W7JGkPSe2S2ufOntmfpiIiIiIi+m2kDSTuokOIvB7oKmzenBblArazPb7+rGr7nj71trVXqL93SW8A3tiTixK2LyLi9SRZ0uFNx/tJOqiXbUyQtGnT8XxLULu47m2SzpL0Z0lTJV0mafVefYHW7Y6VdGf93CbpmP62GRExGEba0qargZ9I2sP2CQCS1qVEb9pB0qnAm4EtgP2BNfpwjyuAvSTtVSNprF+jX/wB+Kqka5qXNgHPA8sCHTdbXw1cIOkI20831X+IMhj6HbANsHit32gnIiJ6bg7waUk/7UvQC0mLAROAWZRgHj29TsAFlOAfO9ay9YC3At1GEazXq86Wd8p2OzDgu6JnPDqTsQdcOtDNLhAJ6hExfIyogURTiLyjJH0HeInyD/N9gNHAHYCBb9v+a2MDdS/9kBJednqdMXgQ2Bo4CVi9lr8MnAgcC5wAXC7pMdtbNvX1Lkk/Bq6VNJeSYXTXet1Fku4ALgdeqJdMB+bW8om2j+ysgwnbFxHxmlcof4f3pexxe00Nq30yZe/ck8Butv+vhkp9ibKH7lFgU8rf388De9XLt6h74N5Geaac2+G+nQX/QNJo4CLgTZSXRd+3fVHtzxXAzZQXSv8q6RvAxyjPrh/ZPrvDd5hA3T9XZ1pWBd5d/3uU7WNqvQuBVSjLcY9uvGyLiBhMIyr8axQJ2xcRPbWwh3+tke/eTnkZsx7wFWC07YMk/R441/apknYHtrH9yTqQWAHY1vbc+g/0WbYPq21OBJYBdqDMbF9s+5863Hdv4F22Xxdlr85yLG377zWK303AOOCdwAOUIBw3SdoO2BP4aO3PrcD7gCWAS2yv3WIgsRVlELMscB/wNtsvNwUAWaq280HbT3f2exvOYcS7kxmJiAVrYQr/GhERMR/bfwdOA/bucGoToJHk8zeUwBwN59ie20WzF9p+1fbdlOVKvSHKMtzpwB+BlZva+Ivtm+rnrgKFdOZS23PqMq6/NbW7d53RvokyMzHudZ1K0I6IGGAZSERExMLgKOBLlJmEnnihm/PNgTrU4nxXwT92BlYENrQ9HniCsuSoJ/ftzusCiNRZiw8Dm9hej7KUdsmOFyZoR0QMtAwkIiJixKvBLH5HGUw0TAF2rJ93BiZ3cnlfgl1cDSwhaY9GgaR1JW0OjAH+VpccbUlZ0tTKZEqgkFGSVqQECrmll/2g3u9Z27Pr3sD396GNiIheG1GbrQebpHcAxwFrUgZZlwD72/7HkHYsIiJ64nDgG03HewGnSNqfutm6k+t+D5wraVvmbbbuUjfBP84Afi9pBiXi0r2dNHMBZflVx0AhY3vShyaXA3tKuoeyb+KmbuonaEdEDIhstq5qKL6bgV/ZPkXSKEokkGds799Ub7FGUrqhkk1yEdFTC/tm6+ibBO2IiJ7KZuue+RDwku1TAOomvH2B3SV9XdLFkq4GrpK0jKSTJd0i6fb6FgtJS0v6naS7JV0g6WZJbfXcTpJmSLpT0s8aN5U0S9KPJd0h6SZJvd3UFxERERGxwGUgMc9awNTmghoJ5P8oS8A2ALa3/UFKrPKrbW9MCcN3qKRlgK9T1qmuCfwndSOepLcDP6MMVsYDG0n6ZL3NMsBNdYPcdZTQhRERERERw1oGEj33h7qZD0oc7wMkTQMmUaJjrEoJ5XcWgO07KXHNoYTzm2T7ybos6gzKpjqAf1D2YkAZyIxtdfOE7YuIiIiI4SSbree5G9i+uUDScpQBwivMH7JPwHa27+tQvy/3fdnzNqrMpZP/TWqW0hOg7JHoy40iIiIiIgZKBhLzXAUcImkX26fVzdaHAxOB2R3qXgHsJWmvGrljfdu3AzcAnwWukbQmsE6tfwtwTM1w+iywE/CLvnY00TYiIhYcSUdSEskdVY+vAB62/eV6fDjwqO0jBuHeJwFH1MR4A2bGozMZe8ClA9nkiJCAHxEDK0ubqjor8CngM5L+BNxPCef33RbVfwgsDkyXdFc9BvglsKKku4EfURIWzbT9OHAAcA0lzN9U2xcN5veJiIgBcwOwKYCkNwArUPbVNWxKyVkx4Gx/eaAHERERAyUzEk1sPwx8osWpifWnUe9F4Kst6r0EfN72S5JWA/4I/KVecyZwZot7jm76fC5wbt+/QUREDIIpwJH181rAncBKkt5EmbF+L3CEpK/bngYg6Xrg34CHgZOBd9e6e9ieLukg4F21fFVKlMD3Ax8DHgU+URPaTQL2s90uaRZwNLA18CKwre0n6vPmDErwjouAfZqfLRERgyUzEgNraeB6SXdQEg19PcnsIiJGNtuPAa9IWpUy+3AjJe/QJkAbMAP4NbArgKTVgSVt3wEcDNxue13KDPdpTU2vRonmtw1wOnCN7XUog4RWa3A6i/J3NHB0vfaRzr5HgnZExEDLQGIA2X7edpvt9Wyva/t/hrpPERExIKZQBhGNgcSNTcc3AOcAW0taHNidebPYmwG/AbB9NfCWGsgD4H9sv0wZiIyiZKimHo9t0YfOovxtUu8P8NvOvoDtE+ozqm3U0mN68p0jIro0pAMJSa6b1BrH+9Xp3t60MUHSpk3HEyVt39U1td7bJJ0l6c+Spkq6rL5F6hdJYyXdWT+3STqmv21GRMSQa+yTWIeytOkmyj/gNwWm2J4N/AHYlhJ044wetDkHwParzB/B71VaLz3uUZS/iIgFZaj/CM0BPi3pp7af6u3FkhYDJgCz6MVGN5U4rRcAp9resZatB7yVssm6J9er/vHvlO12oL2n/eqpkRxtIxEzImKEmgLsBzxgey7wjKTlKXsmGkuMTgJ+D0y2/WwtmwzsDPxQ0gTgKdt/72O48M7cBGwHnA3s2JMLEv0vIgbCUC9teoWSG2Hfjifqm/2rJU2XdFVdm9qYcThe0s3A74A9gX0lTZO0eb18C0lTJD3QyezElpQ3O8c3CmzfYXuypNH1frdJmiFp26b+3CfpNMrbqFUkHSrpzlpvhxbfYYKkS+rngySdLGlS7dfeTfUurLMid0nao2+/yoiIGEQzKNGabupQNrPxIsz2VODvwClNdQ4CNpQ0HTgE+OIg9G0f4Jv1Hv8EZANERCwQQz0jAXAcJYzqzzuU/4IyY3CqpN2BY4BP1nPvADa1PbcuhZpl+zAASV8CVqKsS10DuJjXR0Jam7K+tJWXgE/VN0YrADdJurieGwd80fZNkrYDxgPrUR4ut0q6rpvvugZlELMscJ+kX9X1sbvbfkbSUrWd82w/3U1bERGxgNRZiOU6lO3afCzp7ZQXdFc21XmGec+u5msP6nA8utU52xM6qdMc5e9R4P01r9GOwHt6+LUiIvplqGcksP13ShSLvTuc2oR5m8Z+QxkYNJxT/6h35kLbr9bY22/tZZcE/KS+2fkjsHJTG3+x3XgbtRlwpu25tp8ArgU26qbtS23PqW+v/tbU7t410tNNwCqUAcv8nUq0jYiIYUvSLpRITt/rbtnrINgQmFafW18HvrWA7x8Ri6jhMCMBcBRwG/NPB3flhW7Oz2n63Goh6l1AZxuydwZWBDasMbwfApbs4X2709yvucBidc3sh4FNbM+uMcOX7Hih7RMoy8BYYqVx7ng+IiKGju3TmD+064K892TK7HhExAI15DMS8NrU7++ALzUVT2HeprGdKRvWWnmeslSoN64GlmjejyBp3brHYgzwtzqI2BJ4ZydtTAZ2kDRK0orAFsAtvewH9X7P1kHEGpSERBERERERw9pwmZEAOBz4RtPxXsApkvYHngR26+S63wPn1k3Re/XkRnUd6aeAoyR9h7Iv4iHKhrUzgN9LmkGJuHRvJ81cQFl+dQdg4Nu2/yppbE/60ORyYE9J9wD3Mf9GvpYSbSMiYmSRZOAM25+vx4sBjwM32966i+vagF1sd1z+GxEx5DQvJHWMFG1tbW5vH/CoshGxEJI01XbbUPdjUSdpFvC/lGWsL0r6GPBT4JGuBhKDZYmVxnmlLx61oG877CVEecTrdfUcGRZLmyIiIhYBlwGNf6nuBJzZOCFpY0k3Srq9hi9/Ty3vaRjxz0u6pYZC/7WkUQvwe0XEIioDiYiIiAXjLGBHSUsC61KiPDXcC2xue33gQOAnnbSxBvAvwMbADyQtLum9wA7AB2yPpwTz2HlwvkJExDzDaY9ERETEQsv29LqPbifK7ESzMcCpksZR9t0t3kkzl9qeA8yR1Agj/s+UELC31ozZS1FCjM+nBhjZA2DUciv2+/tERAyrGQlJ36vZnafX6dn3dVN/YiNztaR9JC3dSb1JdcNaT/rw2jRyX0japSnb9e2S9qvl/yXpw931NSIiFmoXA4fRtKyp+iFwje21gU/QIgx49bow4pQw56faHl9/3tMx4R2UMOK222y3jVp6TH+/R0TE8JmRkLQJsDWwge05Nav0G3vRxD7A6cDsQehej9TNc/sAW9l+TNISwC4Atg9sqroP/ejrjEdnMvaAS/vX2SGSjWwRsYg7GXjO9oyaR6hhDCVDNcCuvWzzKuAiSUfa/pukNwPL2v5LfzsbEdGVYTOQAFYCnqpTttTszwBIOpDyhmYpSn6Jr7op3FTdcPZ24BpJT9nesrub1enl3wDL1KJv2J7Soc5GlCRw2wPLA0cAo4GngF1tP96h2f8A9rP9WP0Oc4ATa1sTgUtqP1/ra+3Durb3qfW+Aqxpe9/uvkNERIwsth8Bjmlx6ueUpU3fB3r1psj23fW6KyW9AXgZ+Deg04FEwohHxEAYTkubrgRWkXS/pF9K+mDTuWNtb1SnfJeizFy8xvYxwGPAlj0ZRFR/Az5iewPKJrX5/rBL2hQ4HtgW+D/gF8D2tjekvFH6cYs21wamdnXTFn39HfAJSY31sLvV9iMiYiFhe3SLskmN0K+2b7S9uu31bX/f9tgWdQ6yfVjT9Wvbfqh+Prsua1rX9oa2u81JFBHRX8NmRsL2LEkbApsDWwJnSzrA9kRgS0nfBpYG3gzcRUlE1x+LA8dKGk9ZZ7p607n3UmYiGkuU1qYMEv5QN7KNoiQS6rf6va8Gtq5J6Ra3PaNjvWySi4iIiIjhZNgMJABszwUmAZNqZukvSjoL+CXQZvthSQfR+Sa03tgXeAJYjzIz81LTucfrPdanzB4IuMv2Jt20eRclcsbVvezLScB3KeH/TmlVwfYJlMENS6w0LlkEIyIiImJIDZuBRE2+86rtP9Wi8ZT1nY1Bw1OSRlP2K5zboonngWUp+xd6Ygwlo+irkr5ImWVoeA74EmUG4gXKvowVJW1i+8a6DGl123d1aPOnwKGSPm77r5LeCOxi+6Su+mr7ZkmrABtQYot3KWtbIyIiImKoDac9EqMpG83uljQdWBM4yPZzlA3LdwJXALd2cv0JwOWSrunk/KWSHqk/51BmOb4o6Q5Kgp8XmivbfoKyF+M4yszE9sDPav1pwKYdb2D7MuBY4I+S7gJuA5brYV9/B9xg+9lO+h8REf0kyZIObzrer850D1T7Y+s9ftRUtoKklyUd28c2XwsfHhExnKgp+FEMoZq74kjbV3VXt62tze3t7QugVxEx0kmaartHeXQWBZJeoixf3cj2UzXXz+hWeRf62P5YyvLWmTVLNZK+BnwVuN72NwbiPv21xErjvNIXjxrqbgw7CVEe8XpdPUeG04zEIknS8pLuB17sySAiIiL65RXKrPDrQmxLWlHSeZJurT8fqOUz6t9qSXpa0i61/DRJH2lxj9nAPU2JUHegzDp3d5+Lmtr+qqQz6ufm5KsbSZoi6Q5Jt0haVtKSkk5pSoTa0+iFERH9Mmz2SCyq6tKt1burFxERA+Y4YLqkn3coP5oyM3y9pFUpy2nfC9wAfICyb+8BSnTB04BNgK91co+zgB0lPUGJDPgYJYdQV/fZA7hB0oPAt4D3NzdY992dDexg+1ZJywEvAv8O2PY6ktag5JNY3XZzEJGIiAGXgUQ/1WnsS2qOi0bZQcAsymbqKxsJ6rpoYxIlkV3WK0VEDDLbf5d0GrA35R/iDR8G1qxhvgGWq0E+JgNbUAYSvwL2kLQy8Kzt+fbXNbkc+CElOuDZHc61vI/tJ1QSsF4DfMr2Mx2uew/wuO1bG98DQNJmlFxH2L5X0l8oL6imN1+cMOIRMdAykBhcu1I2iXc5kOitGY/OZOwBvUp8OmJkfWpELCBHUQJiNIfcfgPw/o5v8iVdR8kUvSrwPeBTlAAckztr3PY/JE2lzCysCWzT3X2qdYCnmTd7MWASRjwiBlr2SAyuNuAMSdMkLSXpwLoe9k5JJ6jpdRTwmbre9X5Jmw9VhyMiFgX1bf/vKKG+G64E9moc1ISl2H4YWAEYZ/sB4HpgP+C6bm5zOPCdFjMLLe8jaWPgY5RIgftJeleH6+4DVpK0Ua2/rKTFKAOanWvZ6pQBz33d9C0iot8yIzG42mlasiTpWNv/VT//hhJetpGhezHbG0v6V+AHlKnviIgYPIcDzVGU9gaOqyHIF6MMFPas525mXr6hyZS8Qdd31XjNNdQx31DL+0j6d0qo891sPybpW8DJkj7U1N4/JO0A/ELSUpRlWR+mhDP/lUoi11eAXW3P6apvyUcUEQMhA4n+62x6uFX5lpK+DSwNvJnygGkMJM6v/50KjO14Yda2RkT0n+3RTZ+foPw9bhw/RYmw1Oq6LzR9nkInM/q2HwLWblE+EZjYzX3Wa6p/MXBxPdy1qfxWOmzCrnZr1Z+IiMGUpU399zTwpg5lb6ZDhm1JS1LeGm1vex3Km6clm6o03h7NpcUAz/YJtttst41aesxA9T0iIiIiok8ykOgn27OAxxvTz5LeDHyUMuX9PLBsrdoYNDxVo4Bsv6D7GhERERExULK0aWDsQlnvekQ9Ptj2nyVNBI6X9CIl3viJlChOfwVu7evNsrY1IiIiIoZaBhIDwPbdwOsyido+Dzivqej79adjvQlNn5+ixR6JiIiIiIjhJEubIiIiBoikd0i6SNKfJP1Z0tE1I3VExEInMxIREREDoOYGOh/4le1tJY2iJID7MbB/U73FbL8yRN0EFu7EpoMpSVMj5pcZiYiIiIHxIeAl26cA2J4L7AvsLunrki6WdDVwlaRlJJ1cE5HeLmlbAElLS/qdpLslXSDpZklt9dxOkmbUpKY/a9xU0ixJP5Z0h6SbJL11wX/1iFgULfQDCUlHStqn6fgKSSc1HR8u6ZuDdO+TJK05GG1HRMSwsxYlF9BrbP8d+D/KCoANKCHAPwh8D7ja9saUPXaHSloG+DrwrO01gf8ENgSQ9HbgZ5TBynhgI0mfrLdZBrjJ9nqUJHpfGcTvGBHxmkVhadMNwGeBoyS9AVgBWK7p/KaUN0YDzvaXB6PdRXVKOlPKETHC/cH2M/XzVsA2kvarx0sCqwKbAUcD2L6zZr8G2AiYZPtJAElnAFsAFwL/AC6p9aYCH2l18yQ2jYiBttDPSABTKKFXobwtuhN4XtKbJC0BvBc4QtL4xgWSrpe0nqQ3S7pQ0vQ6XbxuPX+QpFMlTZb0F0mflvTzOuV8uaTFa71JTVPSLaeeJa1Wj2dI+pGkWQvsNxMREQPpbuoMQoOk5SgDhFeAF5pPAdvZHl9/VrV9Tx/v+7Jt188tk5pCEptGxMBb6AcSth8DXpG0KmX24UbgZsrgog2YAfwa2BVA0urAkrbvAA4Gbre9LvBd4LSmplejTDFvA5wOXFMzVr8ItHp13tnU89HA0fXaRzr7HpL2kNQuqX3u7Jm9/j1ERMSguwpYWtIuAHWz9eHARGB2h7pXAHvVDdpIWr+WN2bRqUtj16nltwAflLRCbXcn4NrB+yoREd1bFJY2QZmV2LT+HAGsXD/PpPzRPgf4T0n7A7tT/uhDmWLeDsD21ZLeUt8uAfyP7ZclzQBGAZfX8hm0zgPR2dTzJsAn6+ffAoe1+gK2T6BE/2CJlca5VZ2IiBg6ti3pU8AvJf0n5WXdZZQXUTt1qP5D4Chgel12+yCwNfBL4FRJdwP3AncBM20/LukA4BrKbMalti/qa1+T2DQiBsKiMpC4gTJwWIeytOlh4FvA34FTbM+W9AdgW8qboA07a6jJHADbr0pqnlZ+lda/1x5NPUdExMhl+2HgEy1OTWTeSypsvwh8tUW9l4DP235J0mrAH4G/1GvOBM5scc/RTZ/PBc7t+zeIiOi5ReUfs1OA/YAHaji+ZyQtT9kz0VhidBLwe2Cy7Wdr2WRgZ+CHkiYAT9n+e52JHig3UWY9zgZ27MkFeZMUEbHQWhq4pu61E/B12/8Y4j5FRLS0qAwkZlCiNf22Q9lo208B2J4q6e/AKU11DgJOrlEzZgNfHIS+7QOcLul7lOVR2QAREbGIsv08Zf9eRMSwt0gMJOosxHIdynZtPq4xut8AXNlU5xnm7V9ovvagDsejW52zPaGTOs1Tz48C769ra3cE3tPDrxURERERMWQW+qhNPVEjbNwMfM/2qwv49hsC0+qsx9cpezciImKQDHSi0hoSfL9Ozk3pRz8nSNq0r9dHRAy2RWJGoju2T2P+0K4L8t6TgfWG4t4REYuoBZao1HZ/BgITgFmUfX4DalFNbNpfSYwaMb/MSPSAJEs6vel4MUlPSrqkm+vaJB0z+D2MiIhe6Emi0q0k3SrpTkknNOV72FvS3TVR6VlNba5Zk5A+IGnvRmEjyWidXZgk6VxJ90o6o6nNf61lUyUdI+kSSWOBPYF9JU2TtLmksZKurve+quZHQtLEet2Uev/tB/fXFxFRZEaiZ14A1pa0VA3Z9xHK3oYu2W4H2ge6M3mT1FreFEVET9h+TFLHRKUrUwYXMynBOI61/V8Akn5DyfHwe+AA4F2259Tofw1rAFsCywL3SfqV7Zc73Hp9ysDlMcqsyAcktVOSom5h+0FJZ9Y+PiTpeGCW7cNqP34PnGr7VEm7A8cwbx/fSpTcR2sAF5MQsBGxAGRGoucuY17G6p1oiuUtaWNJN0q6vb4Rek8tn9CYtahraE/u5I3V5yXdUt86/bpmLY2IiMHTnKj0xvrTOL4B2FLSzTXp6IcoAwCA6cAZkj4PvNLU3qW259RIgH8D3trinrfYfqTuxZtGSV66BiU0+YO1zuvyRDTZhHnRB39DGTg0XGj7Vdt3d3JvJO0hqV1S+9zZCRAYEf2XgUTPnQXsKGlJYF3K5uyGe4HNba8PHAj8pJM21gD+BdgY+IGkxSW9F9gB+IDt8ZRkdTsPzleIiIiqY6LSmyj/UN+UMsj4JbC97XWAE4El63UfB44DNgBuldSY2Z/T1HZnSUd7UqevmttumezI9gm222y3jVp6zADeOiIWVRlI9JDt6ZS3RztRZieajQHOkXQncCTz3lx11OqN1T9TIjfdKmlaPX53xwvzJikiYkBNoSxXesb23Brue3nKYKKxufkpSaOB7QHqxuxVbF8DfIfyt390x4Z76T7g3XVPBJQXSw3PU5ZKNfe5kbh0Z0rS1IiIIZM9Er1zMXAYJZLGW5rKfwhcY/tT9WEwqZPrW72NEmXN6390dWPbJwAnACyx0jj3oe8RETFPl4lKJZ1Iman4K3BrPT+KkkB0DOVv9zG2n6t7pvvE9ouSvg5cLumFpntB2ZNxrqRtgb3qzymS9geeBHbr633XWXkM7dlXFhH9lIFE75wMPGd7hqQJTeVjmLf5etdetnkVcJGkI23/TdKbgWVt/6W/nY2IiNa6S1Rq+/vA91tculnHghZJStdu+jy6/ncSTS+ZbH+j6ZJrbK9RozgdRw3SYft+ylLaZh9qcf9dOxz3d5YkIqJHMpDoBduPUKJkdPRz4FRJ3wd6FU7J9t31uivrtPnLwL8BnQ4k8iYpImKh8hVJXwTeCNxOieIUETHsyc4qmZGmra3N7e0DHlU2IhZCkqbabhvqfsTwkudIRPRUV8+RbLaOiIiIiIhey0AiIiIiIiJ6LXskIiJixJFk4Azbn6/HiwGPAzfb3noI+jMB2G+w7l2zaH/O9i/r8VhgU9u/7eq6zsx4dCZjD+jVlr7oxkPZuxiLoBE3kKgPjyNsf6se70cJ13fQALU/FngQ+HGN2oGkFSgPqF93iLTR0zb/C7jO9h8Hoo95ALSWP+IRi5QXgLUlLWX7ReAjzIuetzBaHvg6JVEelLxGn2P+8LUREQvUSFzaNAf4dP3H/WB5kJK9tOEzwF19bcz2gQM1iIiIiNdcxry/1TsBZzZOSNpY0o2Sbpc0RdJ7avlakm6RNE3SdEnjJC0j6VJJd0i6U9IOte6Bkm6tZSfU8KxI+idJf6z1b5O0Wr3taEnnSrpX0hlN9R9qPLMktUmaVD9/sPZjWu3nsrV8/3rf6ZIOrm0fAqxW6x5ajzevx/u2+l6D9luPiKhG4kDiFUpitn07npC0oqTz6h/gWyV9oJbPkLS8iqcl7VLLT5P0kRb3mA3cI6mxQ30H4Hc9uM9FTW1/VdIZ9fNESY3MqBvVh9od9Y/+spKWlHRK7eftkrYcqF9WRMRC7CxgR0lLUvIt3Nx07l5gc9vrAwcCP6nlewJH2x4PtAGPAB8FHrO9Xs0BcXmte6ztjWrZUpRM2ABnAMfZXg/YlDJjDbA+sA+wJvBu4APd9H8/4N9qXzYHXpS0FTAO2BgYD2woaQvgAODPtsfb3r8eT67HR3byveYjaQ9J7ZLa586e2U3XIiK6N+KWNlXHAdMl/bxD+dHAkbavl7QqcAXwXuAGyh/0vwAPUP5gnwZsAnytk3s0HlBPULJQPwa8vZv77AHcIOlB4FvA+5sblPRG4GxgB9u3SloOeBH4d8C215G0BiWnxOq2X2q6do/aPqOWW7EXv6qIiIWT7el1OepOlNmJZmMo+X3GAQYWr+U3At+T9A7gfNt/kjQDOFzSz4BLbE+udbeU9G1gaeDNwF11NmFl2xfUPrwEUCcfbqn5hpA0jbL86PouvsINwBH1pdP5th+pA4mtKPkkAEZTBhb/182v43Xfq2MF2ydQXsSxxErjEvs9IvptJM5IYPvvlIHA3h1OfRg4tv4BvxhYTtJoYDKwRf35FbCOpJWBZ22/0MltLqesud2R8o//bu9j+wnKm69rgG/ZfqbDde8BHrd9a+N72H6Fkin19Fp2L2XAs3qH73yC7TbbbaOWHtPl7yciYhFyMXAYTcuaqh9SMkavDXwCWBKgbk7ehvIS5zJJH6oZpDcAZgA/qkualqTsR9je9jrAiY02ujCn6fNc5r2se4V5z9vX2rB9CPBlymzHDfVFkoCf1pmG8bb/yfZ/d/dLaPW9ursmIqK/RuRAojoK+BKwTFPZG4D3N/0BXtn2LOA6yizE5sAk4Elge8oAoyXb/wCmUmYWzu1wurP7AKwDPM282YuIiBg8JwMH257RoXwM8zZf79oolPRu4AHbxwAXAetKejsw2/bpwKGUQUXjH/xP1RdS2wPYfh54RNIna3tLSFq6mz4+BGxYP2/X1JfVbM+w/TPgVmANygz37vWeSFpZ0v8DngeWbWpzvuNW36ubPkVE9NtIXdqE7Wck/Y4ymDi5Fl8J7EV5ECBpvO1pth+uG93eaPsBSddT1qZ2F4HpcODaeq/m8pb3kbQx8DHKOtlrJV1p+8Gm6+4DVpK0UV3atCzl7dFkYGfgakmrA6vWui2ts/IY2hOhKCKCupTomBanfk5Z2vR9oDnM3WeBL0h6GfgrZe/ERsChkl4FXga+Zvs5SScCd9Z6tza18QXg1yoR+V6mBOToysHAf0v6IeVlVsM+dU/cq5SAHv9je46k9wI31ufOLODztv8s6QZJdwL/A3wXmCvpDmAisESL79WpPEciYiDIHlnLJCXNst14U/NWSoSln9s+qA4WjqPsV1iMEnJ1z1r3N8Ao25+TtCll3eqKtp/u0P5YyhrZtTuU7wq02f5Gq/tQ9jncAuxm+zZJ21A2hH8IOKW2ea6kjYBfUKayX6Qsk3qFsuSqrX7+pu1rOvsdtLW1ub29vQ+/vYhY1Eiaarut+5qxKMlzJCJ6qqvnyIgbSEQeABHRcxlIRCt5jkRET3X1HBnJeyQiIiIiImKIZCARERERERG9NuIGEpKOlLRP0/EVkk5qOj5c0jd70d5Bkvbr5NyUfvRzQt2LERERCxFJlnR60/Fikp6UdEkf21te0tcHrocREQvGSIzadAMl6sZRkt4ArAAs13R+U1pkve4L2/0ZCEygRNvo82CkMzMencnYAy7tvmLM56FEKImIgfECsLakpWy/SMk59Gg313RleeDrlLwVC0SeIwtenkGxMBpxMxKUf5hvUj+vRQnN97ykN0laghJJaStJt0q6U9IJqjH0JO0t6W5J0yWd1dTmmpImSXpA0mtJ7iTNqv+dUM+fK+leSWc0tfmvtWyqpGMkXVIjP+0J7CtpmqTNJY2VdHW991UqGbGRNLFeN6Xef/vB/fVFRMQAuAxo/MtwJ5oS4kl6s6QL69/7myStW8sPknRyi+fNIcBq9XlxqKTR9Tlxm6QZkrat14+VdI+kEyXdJelKSUvVc1+pz707JJ2n7nNbRET024gbSNh+DHil/kN8U+BG4GbK4KKNkpn0WNsb1RCuSwFb18sPANa3vS7lH/oNawD/AmwM/EDS4i1uvT6wD7Am8G7gAyqZT38NfMz2hsCKtY8PAccDR9aEdZMpIV9Prfc+g/njnq9EyW69NeWBEhERw9tZwI71ObAu5TnUcDBwe/17/13gtKZzrZ43BwB/rs+L/YGXgE/Z3gDYEji88fIKGAccZ3st4DnmJbg7vz731gPuoeRYiogYVCNuIFFNoQwiGgOJG5uObwC2lHSzpBmUPA5r1eumA2dI+jwlX0PDpbbn2H4K+Bvw1hb3vMX2I7ZfBaYBYykPhAeaks6d2eK6hk2A39bPv6EMHBoutP2q7bs7uTeS9pDULql97uyZXdwmIiIGm+3plOfATpTZiWabUf7OY/tq4C2SGktwe/K8EfATSdOBPwIrN9V70Pa0+nlq7QOUpVaT63NvZ+Y99+Y1mudIRAywkTqQuIEyaFiHsrTpJso/1DelDDJ+CWxvex3gRGDJet3HKYnkNgBuldTYIzKnqe25tN470pM6fdXctlpVsH2C7TbbbaOWHjOAt46IiD66GDiMrl8iddSTZ8nOlBnuDW2PB55g3nOss+snAt+oz72Dm+q/Js+RiBhoI3UgMYWyDOgZ23NtP0PZrLYJ8zY3PyVpNLA9QN2YvUrNGP0dYAwwup/9uA94d90TAbBD07nngWU79HnH+nlnYHI/7x0REUPrZOBg2zM6lE+m/J1H0gTgKdt/76Kdjs+LMcDfbL8saUvgnT3oy7LA43Wp1M49635ERP+MxKhNUPZBrMC8pUKNstG2n5J0ImWm4q/ArfX8KOB0SWMob/2Psf3cvGWnvWf7xRqy73JJLzTdC+D3wLl1k9xe9ecUSfsDTwK79fW+66w8hvZEf4iIGFK2H2H+/W4NBwEn16VJs4EvdtPO05JukHQn8D/Az4Df12VK7cC9PejOf1L2aTxZ/7tsV5XzHImIgSDbQ92HEU3SaNuz6ka444A/2T5yMO/Z1tbm9vb2wbxFRCwkJE213TbU/YjhJc+RiOiprp4jI3Vp03DyFUnTgLso09G/HtruREREREQMvpG6tGnYqLMPgzoDEREREREx3AzJjIQkSzq96XgxSU9KumSI+jNhMO8tafm6l6JxPFbS5wbrfhERERERg22oZiReoMS8Xsr2i8BHgEeHqC8LwvLA1ylhaaHE/f4c828W77EZj85k7AGXDkjHongomw4jIiIiemUolzZdRsnrcC4loc+ZwOYAkjYGjqbEwX4R2M32fZLWAk4B3kiZTdkOeAz4HfAOSmSmH9o+W9KBwCcoma2nAF+1bUn/RMk6vSIlBvdnan9GSzoXWJuS5Ofztf5DQFuNBtUGHGZ7gqQP1j4CGNjC9vM1KtNngSWAC2z/gJKterW6l+IP9Xu+tx6fClzZ8XvZ/tNA/JIjImL4kDTL9utCj0uaCFxi+9wurp0E7Ge737uk80JqwcsLq1gYDeVm67OAHSUtCaxLCVfXcC+wue31gQOBn9TyPYGja4KeNuAR4KPAY7bXs702cHmte6ztjWrZUpS8EwBnAMfZXo+SwO7xWr4+sA+wJvBu4APd9H8/4N9qXzYHXpS0FTAO2BgYD2woaQvgAODPtsfb3r8eT67HR3byvSIiIiIihq0hG0jYnk5Z4rMTZXai2RjgnBpT+0hgrVp+I/BdSd8B3lmXRc0APiLpZ5I2tz2z1t1S0s01DveHgLUkLQusbPuC2oeXbM+u9W+x/YjtV4FptW9duQE4QtLewPK2XwG2qj+3A7cBa1AGFt1p9b3mI2kPSe2S2v9/e3ceZmdRp338exuQLRBEGY2IxmGRYTNA4wgDTHCZ0RkXUBB4UYmj4IYM+jrKOzIILiMKSgQVDA4GRAHZFFdUIAJh7UBIAAEVcBCQRSSACEK43z+qDnnSdHe6Oyd9Tnffn+vqq89Tz/Y7B3Kq66mqXy15dPEzrxAREWOGiq9IulnSL4C/aew7TNLVkq6XNFvLLni0p6SrJN0iqdWLv7qkb0paJOnauohdRMRK1+n0r+cBR1OGNTV9Grio9ia8kTLECdvfAd5EGe70Y0mvsn0LsC2lQfGZ+gW8OmU+wh62twJObF1jEI83Xi9h6bCvJ1n6OT19DdtHAu+h9HbMk7QZZaG7z9Wehum2N7b9P8v7EPp7X/0cM9t2j+2eSWtOWd4lIyKiu+0OvIzSC/5OSg95y0A96gCr2H4FpQf9k7Xsg4BrfbcPcHKtB5eRB1IR0W6dbkicBBxhe1Gf8iksnXw9s1Uo6W+BW20fC3wf2FrSC4FHbZ8KHEVpVLS+QO+XNBnYA8D2w8DvJe1Wr7eapDWXE+PtwHb19VsbsWxke5Htz1NWtN4MOB/4t3pPJG0g6W+Ah1l2ldFltvt7X8uJKSIixrZdgNNsL7F9F3BhY98zetQb+86pv+eztOd8J+BUANs3Ab8DNu17wzyQioh26+g6ErZ/Dxzbz64vUJ6oHAo0Z4O9DXiHpCeAP1DmTmwPHCXpKeAJ4P22H5R0InB9Pe7qxjXeAXxd0qfq8XsyuCOA/5H0aWBuo/zg2n38FGUxup/YflzS3wGX157oRyiTtn8raV4dqvUT4D+BJZKuA+ZQJmb3fV8D2mqDKfRm0lZExLjT6FHvsX2HpMNZtke91Xve7DmPiOgI2e50DDFMPT097u1d4aQdETEBSJpvu6fTcUTRytok6S3Ae4F/ocyPuBHYH/gFcDOlt2EScAVwlu3Dm1mbJD0P6LU9TdJHgC1sv1vSppTsgJvafrzv/VtSj0TEUA1Wj+RpRkRExOg7lzJs6UbgfylJN1hOj/pAvgYcX4dCPQnMHKwRERHRLmlIREREjJLWGhIuwwEOHOCYQ4FD+ymf0Xh9P3WOhO3HgHe1P9qIiMF1erJ1RERERESMQROuISHJkk5tbK8i6T5JPxzh9daV9IH2RRgRERER0f0m4tCmPwNbSlqjLvz2Wpammh2JdYEPUMaojopFdy5m2iE/Wv6B0Ta3J0tWRERExDImXI9E9WOg9ZfhPjQWxJO0nqTvSVoo6QpJW9fywyWdJGmupFvritYARwIbSVog6ShJkyVdIOmausrom+v50yT9StKJkm6Q9DNJa9R9+9dVTK+TdPYQ1raIiIhxoNYN1/cpO1zSRyXNrGslLe8acyUlM1dEjLqJ2CMBcDpwWB3OtDVlYbyd674jgGtt71ZXmD4FmF73bQbsSllM7mZJxwOHAFvang5lqBSwu+2Hanq+KySdV8/fBNjH9v6SvktZ4O5U4BzbJ9bzPwO8Gzhupb37iIgYC2ZSsjfd1e4Lp2e7+6TnO8aiCdkjYXshJdvFPpTeiaadgG/V4y4EnitpnbrvR7Yfr9ky7gWe38/lBfy3pIWUfOAbNI67zfaC+rq5KumWki6pqfv2ZdlVTMtFpQMk9UrqXfLo4mG+44iIGIN6gG/XHu81JB1We6+vlzRbdeXTak9JV0m6RdLOA10wIqKdJmRDojoPOJrGsKYhaOblHmhV0X2B9YHtai/FPSxdlXSg8+cAB9reitIj0lzFFADbs2332O6ZtOaUYYQcERFjVC+wr+3pdU7fV2xvb3tLYA3gDY1jV7H9CuBg4JOjH2pETEQTdWgTlOFMD9peJGlGo/wSSmPg07X8/jpMaaDrPEwZ6tQyBbjX9hOSdgVeMoRY1gbulrRqvfegk7+32mAKvekCjYgYDzyM8l0lfQxYE1gPuAH4Qd13Tv3d7O1ehqQDgAMAJq2z/gjDjYhYasI2JGz/Hji2n12HAyfVoUmPAvst5zp/lDSvTpb7CfB54Ad1mFIvcNMQwvkv4Ergvvp77cEPj4iIceKPwHP6lK0H3NYskLQ6JTtgj+07JB3Osr3XrR7vgXrLsT0bmA2w2tRNBmrAREQM2YRrSLRWFe1TNheYW18/AOzWzzGH99nesvH6//Q5fIcBbt885+jG6+OB45cTekREjDO2H5F0t6RX2b5Q0nrA64AvA3uw9MFSq9Fwv6TJdd9Zox9xRMRSE64hERER0WXeCXxV0pfq9hG2fytpDnCCpL9QHlCdSMni9Afg6hW5YYbIRkQ7pCERERHRQbZvpKQW71t+NnB2o+jQ+tP3uBmN1/czwByJiIh2m8hZm0ZE0iMDlM+RtMdyzs2iQRERERExLqRHYgzKQkKjLwsFRURERCwrPRIjpOIrkm6W9Avgbxr7hrVokKTVJX1T0iJJ19a0sRERERERXSsNiZHbHXgZsDllotyOjX3DXTTog4DrgnT7ACfXVH8RETHKJFnSqY3tVSTdJ+mHdftNkg7pXIQgaYakHZd/ZETEypOhTSO3C3Ca7SXAXZIubOwb7qJBOwHHAdi+SdLvgE2Bha0LZiGhiIhR82dgS0lr1BWlX0tjoVDb5wHndSq4agbwCHDZSE7OENmxJcNro1ulR6LNGosG7VF7GE5kmIsG9cf2bNs9tnsmrTmlbfFGRES/fgy0/nrbBzittUPSTElfqa/nSDpW0mWSbm0l3ag9BnMlnSXpJknfbg1zlbSdpF9Kmi/pfElTa/lBkm6UtFDS6bVsPUnfq2VXSNpa0jTgfcCHJS2QtLOkN0q6sg6P/YWk54/WBxURE1caEiN3MbCXpEm1EmjNa+hv0aDluQTYF0DSpsCLgZvbHG9ERAzd6cDe9eHQ1sCVgxw7ldKz/AbgyEb5NpRhrJsDfwv8g6RVKT3Qe9jeDjgJ+Gw9/hBgG9tbUxoKAEcA19ay/wROsX07cAJwjO3pti8BLgVeaXubGvvHVuC9R0QMSYY2jdy5wKuAG4H/BS4HsP2gpOEuGvQ14HhJi4AngZm2Hx/o4CwkFBGxctleWJ/870PpnRjM92w/BdzYpyfgKtu/B5C0gDKc9UFgS+DntYNiEnB3PX4h8G1J3wO+V8t2At5aY7pQ0nMlrdNPDC8CzqgPtp4N3Nb3gAyRjYh2S0NimGxPrr8NHDjAMcNaNMj2Y8C72h9tRESsgPOAoynzEZ47yHHNBz8aoLw1nFXADbZ36Oc6/0qZf/dG4BOSthpGrMcBX7J9nqQZwOF9D7A9G5gNsNrUTTyMa0dE9CtDmyIiIvp3EnCE7UVtvObNwPqSdgCQtKqkLSQ9C9jQ9kXAx4EpwGSWHfo6A7jf9kPAw8DajetOYemE8P3aGG9ExIDSIxEREdGPOizp2DZf8691QvaxkqZQ6uFZwC3AqbVMwLF1qOzhwEmSFgKPsrSR8APgLElvBj5E6YE4U9KfgAuBlw4WR4bIRkQ7qIzQibGkp6fHvb29nQ4jIsYASfNt93Q6juguqUciYqgGq0fG3dCmLCQUEREREbHyjcehTVlIKLpOFhOKiIiI8Wbc9UhUWUgoIiIiImIlGq8NiSwkFBERERGxEo3LhoTthZQ1Goa8kJDtG4FnLCRUFxlaUK/3MpYuJLSAslbEi+rxrYWE3k5ZVA5KA+VbNaYLgcEWEjq/Lkj3H8AWfQ+QdICkXkm9Sx5dvJy3FBERK4ukiyT9c5+ygyUd36mYGjGs2ckYImJiGY9zJFqykFBERKwMpwF7A+c3yvZmCL3JkibZXrKS4joYOJWSJnZQmWs3vmQeXnTKeG5InAQ8aHtR/eO8HZ5eSMj25XWo06bAr6gLCUm6lFKhNBcS+nRzISFJDwPNnolhLSSU/N8RER11FvAZSc+u60JMA14IrCHpcmA14LfAu2w/Iul24AxK8o8vSDqS0hh5PaUH+wDgc8DGwFG2T6jz8r5QjzHwGdtnNB423U/pIZ8PvJ2ylsQLgYsk3W9715X+KUTEhDcuhzZBWUjIdtsXEgL2AD4v6TrKkKcdgUmUhYQWAddSFxKifNlvVxcSOpJlFxLavTXZmqULCc2nVA4REdGlbD8AXEX5Ix/Kw6OfAZ8AXmN7W6AX+EjjtD/a3tb26XX7f21PpzxwmkOpW15JmVsH8BZgOvBy4DXAUa3kHvQzh6/Wd3cBu6YRERGjZdz1SNie3E/ZXGBufT2H8qWN7Zn9nds8vm4f2Hi9gDKEqa+d+rnvA8Bu/ZTfQpkE3vT9fq4ZERHdqTW86fv197mU7/t5Ncnfs4HLG8ef0ef8VhryRcBk2w8DD0t6XNK6lDrltDoM6h5JvwS2Bx6izuEDqPP1plGSdgxK0gGU3g8mrbP+sN5sRER/xl1DIiIiYhR8HzhG0rbAmsA1wM9t7zPA8X/us92ah/cUy87Je4rl1839zeFbrsy1i4h2G7dDmyIiIlYW248AF1Hm450GXEFJE74xgKS1JG26Are4BNhL0iRJ61N6wq9azjkPA2uvwD0jIoZlwvVISLoIONL2+Y2yg4GX2X5/B+M6GJhte7nZNiIioiucRhnStLft+yTNBE6TtFrdfyhwywivfS6wA3AdZbL1x2z/QdJmg5wzG/ippLuWN08iSTsioh1kT6zezTpGdAfb72qUXUH5kr54OeeutLR9NatHj+3lTrZebeomnrrfrJURRnRA0vbFyiRpvu2eTscR3aWnp8e9vb2dDiMixoDB6pGJOLTpLOBfJT0boG/aPknXSDpT0uS6/3ZJn5d0DbBn3f5czbjUK2lbSedL+q2k99VzJOkoSddLWiRpr1o+Q9JcSWdJuknSt+uxB7E0bd9FHfhMIiIiIiKGZcI1JJK2LyIiIiJixU24hkTVSttH/X0H5Q/7eTWV3n7ASxrHD5a270rbD9u+D3hG2j7b9wCttH1Q0/bZfoqyDsW0oQQs6YDaA9K75NHFQ36jERERERErw4SbbF0lbV9ERPRL0jHA72zPqtvnA3fYfk/d/iKwGPir7SM7EN8LKQuf7jHa946IaJqQDQnbj9S5CM20fV+VtLHt30haC9igLhw3EpcA75V0MrAeJW3ffwCDZdtope3LytYREZ01D3gbMEvSs4DnAes09u8IfNj2FZ0IzvZdlCG1I7bozsVMO+RHbYooul2SesTKMiEbElXS9kVERH8uA46pr7cArgemSnoO8Cjwd8DWkt5u+0BJewKfpPQyL7a9i6RJwOeB11F6q0+0fZykVwNHU+rfq4H32368Zu47GXgjsCqwp+2bJP0j8OUaiykPpp4L/ND2lrXuehOld30j4FzbH1tpn0xERMOEbUjY/h6gxvaFLJ3H0Dxu2kDbtudQJlv3d+x/1J/muXOBuY3tAxuvjwOOG8ZbiIiIlcD2XZKelPRiSu/D5cAGlAdEiynz4/7aOOUw4J9t31nnyQEcQJkDN932k5LWk7Q6pc54te1bJJ0CvB+YVc+53/a2kj4AfBR4T/39QdvzajbBx/oJeTolkcfjwM2SjrN9Rxs+ioiIQU3UydYRERGDuYzSiGg1JC5vbM/rc+w8YI6k/YFJtew1wNdtPwlPZwx8GXBbY9jsyZQehpZz6u/5LE3EMQ/4Uk0Tvm7ren1cYHux7ceAG1k2WcjTkrQjItotDYmIiIhnmkdpNGxFGdp0BaVHYkdKI+Nptt9HGQ67ITBf0nNHeM9WMo6nE3HUydzvAdagZBbsb4jskJJ42J5tu8d2z6Q1p4wwxIiIpdKQiIiIeKbLgDcAD9RU3g8A61IaE8s0JCRtZPtK24cB91EaFD+nJN1YpR6zHnAzME3SxvXUd1DSgw+oXnuR7c9T5lQMNtcuImJUjcocCUkGvmT7/9btjwKTbR/eputPo048a5QdDjxi++h23GMYsQx4X0mX2d5xNOOJiIgRWUTJ1vSdPmWTbd8vqXnsUZI2ocy7u4CSaON6YFNgoaQnKJOtvyLpXcCZtYFxNXDCcuI4WNKulAnbNwA/AaYOfsryJWlHRLSD7JW/JIGkx4C7ge3rF/CEbEgMcPwqA4x5HdBqUzfx1P1mjSC6GIuSti9WhKT5tns6HUd0l56eHvf29nY6jIgYAwarR0ZraNOTlPSmH+67Q9L6ks6WdHX9+YdavkjSuir+KOmdtfwUSa8dzs0l7V+vfV2915q1fI6k4yVdIelWSTMknSTpV5LmNM5/RNIxkm6QdIGk9Wv5QZJulLRQ0umNW24uaW695kHN69TfMyRdIuk84EZJkyQdVWNcKOm9w3l/ERERERGjbTTnSHwV2FdS3xleXwaOsb098FbgG7V8HvAPlBzetwI71/JnjE+tNpK0oPUDvK+x7xzb29t+OfAr4N2Nfc+p1/wwcB4ld/gWwFaSptdj1gJ6bW9BGc/6yVp+CLCN7a373G8z4J+BVwCflLRqP/FuC/y77U1rPIvrZ7A9sL+klzYPTraNiIiIiOgmo7aOhO2Has7sg4C/NHa9hvIEv7W9Ts2VfQklLd7vgOOBAyRtAPzJ9p/7ucVvbU9vbdQhRi1bSvoMZaLcZOD8xr4f2LakRcA9thfV82+gpN9bQBmbekY9/lSWpuhbCHxb0veA7zWu+SPbjwOPS7oXeD7w+z7xXmX7tvr6nyiLG7VWKp0CbAK09mN7NqVXh9WmbrLyx6NFRERERAxitBekmwVcA3yzUfYs4JU1//XTJF0MfBB4MfAJYHdgD0oDY7jmALvZvq6uAjqjsa+VNu8plk2h9xQDfz6tP+T/ldLYeSPwCUlb9bkmDJyKr9kYEvAh2+f3c1xERERERNcZ1YaE7QckfZcylOekWvwz4EPAUQCSptteYPsOSc8Dnm37VkmXUlb4PLC/ay/H2sDddYjRvsCdwzz/WZRGzOnA/wEulfQsYEPbF9XY9qb0dozE+cD7JV1o+wlJmwJ3DtDzkmwbETGhJPPf0/valvlv0Z2LmXbIj9pxqRgDkrQjVpZOrCPxRUpKvZaDgJ46yfhGlp1rcCXQWgH0EmAD4NIR3PO/6rXmATeN4Pw/A6+QdD3wKuBTlNVLT61Doq4FjrX94AiuDWVeyI3ANfUeX2f0e4siIrrV48Bb6sOlCau/RkRrnYqIiE4YlS8g25Mbr+8B1mxs3w/sNcB572i8vowBGj62bwe27FN2eOP18ZR5Fn3PmznQNZr76vZH+rn1Tv1c8/A+281rTq6/5wJzG+VPAf9ZfyIiYlnNzH+faO6oWfROoAyDBTjY9rz6kGdnYDFwP/Bh26fUuXrfsv3zod5c0v7AAcCzgd8A77D9aM3u9xdgG+BvgH8D3klJ4HFlqx6pGftOpMyH+wOwt+37ala/99X3d6PtvestN5c0t76nWbaPbV3H9mRJM4BPA38CNpP0d8CRlGG7qwFftf31ob6/iIiRysrWERExFiTz37KGlfkPkv0vItovXaJD0OxRiYiI0ZfMfyuW+Q+S/S8i2m9c9kjUxeMObmyfL+kbje0vSjpM0iEdiu+Fks7qxL0jIsawWZSn72s1ylqZ/6bXnw1sPwJcTOmF2JkylPQ+Vizz34G2twKOAFZv7FvRzH9fpfQuXN2Y7zDSzH+tz+Cltn82+FuKiFhx47VHYh7wNmBWza70PGCdxv4dKeNlr+hEcLbvolRoI5JsGxNLsm1EFMn8N6BhZf6LiGiX8dqQuIwyThXKWNXrgamSngM8CvwdpRv47bYPlLQnZczqEso4010kTQI+D7yO8mTpRNvHSXo1cDTls7saeL/txyXdDpxMWVNiVWBP2zdJ+kfKGF4oT6F2AZ5LTTVY17V4E2UC+kbAubY/ttI+mYiIse2LLNsYOAj4qqSFlO/li1k63+BKSoY9KD0Rn2PFMv/dV3+vPczzW5n/DgXupSQYaWX+m0LpUTjW9oONIVrD8Q3KMKprVC5wH7DbYCckjXhEtIPs8TlMUtJtwD8Cr6d8SW8AXE7J4HEk8D9AT21ILAJeZ/tOSevWL/P3A6+mZNd4UtJ6lEbIr4FX276ljte9xvas2pD4Ym1sfADY1vZ7JP0AOLJmEZkMPAa8iGUbEodRsn48DtwM7GT7joHe22pTN/HU/Wa19wOLrpUeiVgRkubb7ul0HBNZK9tSp+No6unpcW9vb6fDiIgxYLB6ZFzOkaguowxh2pHSgLi8sT2vz7HzgDk1xV/r6dVrgK/bfhJKlzrwMuA22621LU6m9DC0tCbQzac8HWpd+0s1zd+6rev1cYHtxXV17xuBl/Q9INk2IiIiIqKbjOeGxDxKo2ErytCmKygp+nakT+o/2+8DDgU2BOZLeu4I79maIPf05DjbRwLvAdYA5knabJDzljm3T4yzbffY7pm0Zt/shxER0a26rTciIqJdxnND4jLgDcADtpfUHoV16SeHuKSNbF9p+zDK2NINgZ8D721l0ahDm24GpknauJ76DkpO8AHVay+y/XnKnIr+GhIREREREWPKeJ1sDbCIkq3pO33KJtu+v8+EtqMkbUKZS3EBcB2lF2NTYKGkJyiTrb8i6V3AmbWBcTVlRdXBHCxpV8qE7RuAnwBTV+SNZZJcRMTgJC2hfOevQllEbj/bj3YolpnUOXltvOYLKRO0R5wBMCJiRY3bydbjWSbJRcRQTdTJ1s0JzpK+Dcy3/aUOxTKTNjckVlSSdkS7JCHI+DdRJ1tHRERASf26saS1JJ0k6SpJ10p6M5Q/9CWdI+mnkn4t6QutEyU9Iumzkq6TdIWk59fyPSVdX8svrmUXS5reOPdSSS9vbE+R9Lu6hgQ1njskrSppf0lX1+udLWnNeswcScdKukzSra3VqyVNk3R94/Ulkq6pPzuu7A80IgLSkIiIiHGsDkN9PWWY0yeAC22/AtiVMqy1tUr2dMr6DlsBe0nasJavBVxh++WUNSr2r+WHAf9cy99Uy/4HmFnvuymwuu3rWrHYXgwsoKQmhzKP73zbTwDn2N6+Xu9XlEX3WqYCO9Xjj+znbd4LvNb2tvU9HDvUzyciYkWkIREREePRGpIWAL3A/1L+yP8n4JBaPhdYHXhxPX6gNNx/BX5YX/dN7d03bfiZwBvqCtj/BszpJ64zKH/sQ1nN+oz6esvaq7CIsnr2Fo1zvmf7Kds3As/v55qrAifWc88ENu/vA0ka8Yhot66dbJ2JcgNbdOdiph3yo3aFEhNYxrbGOPYX29ObBXXV57favrlP+d8zcBruJ7x0MmEztff76nn/Skkbvp3tP0r6OfBm4G3Adv3EdR7w3zUT4HbAhbV8DrCb7etqnTOjcU4ztv6Wvv4wcA/wcsoDwsf6OQbbs4HZUOZI9HdMRMRwdHOPxF9sT7e9JeWJ0Ps6HVA72b4r2TYiIkbV+cCHaoMCSduM9EIDpA0H+AZlaNHVtv/U9zzbj1Ay/n0Z+KHtJXXX2sDdtTdj32GGMwW42/ZTlLTkk5ZzfEREW3Rtj0QflwBb17GsxwFbUrpyD7f9/fr05k3AmsBGwLm2PwZlohzlC/sNwF+AN9u+R9KewCcpT5gW296lTpg7yPaCeu6lwAdbQUiaAiwEXmr7qRrPTcDfUsbFHgA8G/gN8A7bj0qaAzwE9AAvAD5m+yxJ0yiVyJb19bcoY3EBDrS9zFoXERGxwj4NzKKk9X4WcBulbhiJ/tKGY3u+pIeAbw5y7hmUIUgzGmX/BVxJaZRcSWlYDNXXgLMlvRP4KfDn5Z2QNOIR0Q5dm/61lbqvTpQ7m/LluCFwo+1TJa0LXAVsA+xJmfi2DaUL+GZgJ9t3SDLwJts/qJk4HrL9mTqW9HW275S0ru0HJe0HbGP74DpR7ju2e5pDmyR9H5hl+yJJe1EmuL1H0nNt/7HG/hngHtvH1YbEWpQxsZsB59neuE9DYk3gKduP1YrptL5ptiQdQGmoMGmd9bd70fsHq6MihiZDm8a/iZr+tVPqsNW5wGa1h6ArJY14RAzVWE3/molyDbZn2+6x3TNpzSn9XCIiIjqp9ghcCXyimxsRERHt0s1DmzJRLiIixgzbpwCndDqOiIjR0s0Nif60Jsp9yLYlbWP72pFcqDVRDrhS0uspw6b+SJko9wPgkoEmykkaykS5O4cRzhTg93XexX4sZ6JcxrZGRERERKd189Cm/nyaMgxooaQb6vZIHSVpUV0Z9DIaE+Uok6OXN1Hu7Swd1gRLJ8rNo0zAHo6vAftJuo4yj2K5E+UiIiIiIjqpaydbd8pYmCiXSXIRMVSZbL3iJL2Aku1pe+BBylDUg23fMsDxrWQhy6wXJOk0yvy5b9o+ZgVj6gHeafugkZy/2tRNPHW/WSsSQkRbJOlH9xusHhlrQ5tWqjpR7rPAR7q1EREREaOnzs07FzjZ9t617OWUxBn9NiRabN8FtBoRLwC2t73xMO69iu0nB7h2LyUZSUREx4y1oU0rle1TbG9o+8xOxxIREV1hV0rSjhNaBbavA66VdIGka+ow2Tf3PVHStDp8FuBnwAaSFkjaWdJ0SVdIWijpXEnPqefMlTRLUi/w73X785KuknSLpJ3rcTMk/bC+foWkyyVdK+kySS9byZ9JRAQwARsSkl4g6XRJv5U0X9KP65oR/R3brATaHcfhkj66Mq4dERFtsyUldXhfjwG7296W0tj4YmvF7AG8Cfit7em2L6Fkd/q47a2BRZQFUlueXdN9f7Fur2L7FcDBfY5ruQnY2fY2lDWV/ru/ACQdIKlXUu+SRxcPEmpExNBMqKFNK9JFPcTrD9gN3U6L7lzMtEN+tLJvE7HCMvY1xjFRUoHvAjwFbECpS/6w3BOlKcC6tn9Zi06mrCHUckafU86pv5trITVNAU6uC5qakpTkGWzPBmZDmSOxvDgjIpZnovVIDNRFfamkoyRdX7uo9+p7oqTVJX2z7r9W0q61fKak8yRdCFwgafJA3d2SPlG7pi8FXtYo77eLOyIiOu4G+l9TaF9gfWC7uubRPZRFUtuhb+a+1lpEzTWSmj4NXGR7S+CNbYwjImJQE6pHgoG7qN8CTKcsCPc84GpJF/c55oOAbW8laTPgZ40hUdsCW9t+QNIqlO7uhyQ9D7hC0nn1mL3rfVYBrmnEcgrwIdu/lPQpStf1we14wxERsUIupPQ8HFCf6CNpa+AlwL22n6gPll4y1AvaXizpT5J2rsOc3gH8cnnnDWIKS9cumjmUE7IeUUS0w0TrkRjITsBptpfYvofyhb59P8ecCmD7JuB3QKsh8XPbD9TXre7uhcAvWNrdvTNwru1HbT9EWSF7oC7uXfoGmLGtERGjzyVH+u7Aa+rcuhuAzwE/BnokLQLeyfDXD9qPsp7RQsoDpk+tQJhfAD4n6Vom3gPCiOigifaFcwM1FV+bNbuhm93dT0i6nTZ0M2dsa0REZ9Q0rm/rZ9cOAxw/uf6+ndITvszrur0AeGU/584YaNv2/dQ5ErbnUtY8wvblLH2wBXDoIG8nIqJtJlqPxIXAapIOaBXULuoHgb0kTZK0PqVH4Ko+515CaSRQhzS9GLi5n3tMof/u7ouB3SStIWltyjhWbC8G/tRK6ceKd3FHRERERKx0E6pHwrYl7Q7MkvRxSvq+2ynzESYD11EyXnzM9h8kTWuc/jXg+NqN/SQw0/bj/WT7+zbwg3pcL7W72/Y1ks6o97gXuLpxzn7ACZLWBG4F3jXY+8jY1oiIiIjoNJXhnzGW9PT0uLc3C5pGxPJJmm+7p9NxRHdJPRIRQzVYPTLRhjZFRESMWBY1jYhYakINbYqIiBip8bKoKWRh0xgbsqhp9+uKHom6UNsNdUG2BZL+vpZ/Q9Lmo3D/mZKeqhOvW2XX95kj0e57zpD0w5V1/YiIaLssahoR0dDxHglJOwBvALatk5efBzwbwPZ7hnmtSbaXDLS9HL8HPgE8owLoNnmSFGNFnibFOJNFTSMiGrqhR2IqcL/tx6Hkya45u5E0V1JPff1Pki6vT2nOlDS5lt8u6fOSrgH27Gd7f0lXS7pO0tk1M1J/fghsIellfXf0d29Jr5N0ZuOYp3sYJB1fF4+7QdIRjWNeJ+mmGttbGuWvqNe/VtJl/cUQERFdq+sXNa3HZmHTiGirbmhI/AzYsHbXfk3SP/Y9oD6VORR4je1tKWlVP9I45I+2t7V9ej/b59je3vbLgV8B7x4gjqcoq4P+5xDv/Qvg7yWtVQ/dC2jd/xN1dvvWwD9K2lrS6sCJlPUjtgNe0LjNTcDOtrcBDgP+e+CPKyIiOuQGyvd3uw20qOl04B7asKgplIVNbffY7pm05pR2XDIiJriONyRsP0L5Yj4AuA84Q9LMPoe9EtgcmCdpAWXdhZc09p/R5/jm9paSLqnrOuwLbDFION8BXinppcu7d50Q91PgjbUr+l+B79dz3lZ7Ha6t99sc2Ay4zfavXXLuntq4xxTgzJrd45j+YsyTpIiIjsuiphERDR2fIwFQ5zHMBebWP/j3A+Y0DhGl63efAS7x50G25wC72b6uNlBmDBLHk5K+CHx8iPc+HTgQeADotf1wbYR8FNje9p8kzWH5T5M+DVxke/c6wXtuP7HNBmYDrDZ1kyz+ERExysbLoqaQhU0joj063pCo8wGesv3rWjSdMna06Qrgq5I2tv2bOpxoA9tDSbe3NnC3pFUpT4PuXM7xc4CP1fOWd+9fAicB+7N0WNM6lIbMYknPB15PaRjcBEyTtJHt3wLNhsmURlwzh/CeIiKiA+ocvrf1s+s/6k/z2NspE7Sx/Rj9/IFvew6NB2e27wd2GODenwU+20/5AkrveUTEqOp4Q4LyFOc4SetSntL8hjLM6Wm276u9CadJWq0WH8rQ8nb/F3AlZdjUlSxtIPTL9l8lHQt8eXn3tr2kTrCeSXkiRO35uJbScLgDmFfLH6vd4T+S9Cilm7sVyxeAkyUdCiw3HVOeJEVEREREp6kM14+xpKenx729vZ0OIyLGAEnza/KHiKelHomIoRqsHun4ZOuIiIiIiBh70pCIiIgYAkmWdGpjexVJ9zXWEHqTpEMGOX9azc7X375PSXpN+6OOiFh5umGORERExFjwZ0pK8TVs/wV4LY0EHrbPoy4UN1y2D2tPiEOz6M7FTDtkuVPyImIluH0czXOdMD0SeZIUERFt8GPKukFQsu+d1tohaaakr9TXz5d0rqTr6s+O9bBJkk6UdIOkn0laox4/R9Ie9fW/SLpJ0nxJxzbqqVdIulzStZIuq1kPW/c9R9JPJf1a0hdG56OIiIluIvVI5ElSRLTFeHqaFMN2OnBY/eN+a0oK8J37Oe5Y4Jd1faBJlAyFzwE2Afaxvb+k7wJvpbFAqaTVga8Du9i+TdJpjWveBOxc1zx6DfDf9XwoqdO3AR4HbpZ0nO072vauIyL6MWF6JKo8SYqIiBGzvRCYRqlDfjzIoa8Cjq/nLKkrUAPcVtd9AJhfr9W0GXCr7dvqdrMhMQU4s/aOHwNs0dh3ge3Fdb2KG1m6IvbTJB0gqVdS75JHF/fdHRExbBOtIXE6sHd94rM1ZV2J/rSeJL0c2Ba4oZZvAnzV9hbAgyx9EgQs8yTp9ba3A9Zv7G49SdoGOIzyJKllOrAXsBWwl6QNR/oGIyJipTsPOJpl/8gfqscbr5cwvJEBnwYusr0l8EZg9eFc1/Zs2z22eyatOWUYt42I6N+EakjkSVJERLTBScARthcNcswFwPsBJE2SNNS/3G8G/lbStLq9V2PfFJYOyZ055GgjIlaSiTRHoqX1JGkG8Nxhntv3ic8awzi39SRp91pBzB3kuv0+SQJmA6w2dZOsIhgR0SG2f0/puR7MvwOzJb2b8r3+fuDuIVz7L5I+APxU0p+Bqxu7vwCcLOlQYIUmym21wRR6M9cnIlbQRGxInAQ8aHuRpBkDHNN6kjSrMUluKJ5+kmT7dlbSk6RUABERo8/2M+oC23OpD4ZszwHm1Nf3AG/u5zJbNs49uvF6ZuOYi2xvJknAV4HeeszlwKaN4w7te9+6/YYhv6mIiBUwoYY2QXmSZHsoT5J2lbSIMoRp8yFe+y9A60nSfOBhoDUO6QvA5yRdy8RswEVExNDsL2kBZX7eFMrcu4iIriM7o2TaSdJk2480niT92vYx7bxHT0+Pe3t723nJiBinJM233dPpOKK7pB6JiKEarB6ZcD0SoyBPkiIiIiJi3MsQmzarvQ9t7YGIiIiIiOg2E6YhIcnAl2z/37r9UWCy7cPbcO3VgQXAnq10gJL+A9jY9nuHcP7hwCPNiXcREdFdxlM9sujOxUw7ZIUSP0XEGHR7m5P1TJiGBCXF6lskfc72/e28sO3HJB0MfE3SLsALgfcByx2XLGnY/w1SAURMXO2uBGJYxk09EhHRDhNpjsSTlHUYPtx3h6T1JZ0t6er68w+1fJGkdVX8UdI7a/kpkl7bvIbtn1JyhL+TMrTpcGCKpAslLZR0gaQX1/PnSDpB0pWUbE7NWPaX9BNJw1mjIiIiVr7UIxERDROpIQEli9K+/aww+mXgGNvbA28FvlHL5wH/QFmF+lZg51q+A3BZP9c/GPgssL7tbwHHASfb3hr4NssuYPQiYEfbH2kVSDoQeAOwW00lGxER3SX1SERENaG6Q20/JOkU4CCg+QX7GmDzkrEVgHUkTQYuAXYBfgccDxwgaQPgT7b/3M/175J0IfDDWrQD8Jb6+lss+9ToTNtLGtvvBO6gfPk/0ffakg4ADgCYtM76Q3/TERHRNqlHIiKWmmg9EgCzgHcDazXKngW80vb0+rOB7UeAiylPj3amrFx6H7AHpWIYyFP1Z3n6ViCLgGmUJ0zPYHu27R7bPZPW7PsgLCIiRtEsUo9EREy8hoTtB4DvUiqBlp8BH2ptSJpej70DeB6wie1bgUuBj1IqhqG4DNi7vt6XwSuOa4H3AudJeuEQrx8REaMs9UhERDGhhjY1fBE4sLF9EPBVSQspn8nFlGwZAFcCk+rrS4DPUSqCofgQ8M2awu8+4F2DHWz70ppO8EeSXjtQVpCtNphCbzK3RER0UuqRiJjwZLvTMcQw9fT0uLe3t9NhRMQYIGm+7eWmEI2JJfVIRAzVYPXIhBvaFBERERERKy49EmOQpIeBmzsdxwCeB7R1oaY26tbYujUuSGwj0W1xvcR2UvTEMlKPjFi3xtatcUH3xtatcUH3xTZgPTJR50iMdTd361AFSb2JbXi6NS5IbCPRrXFF9JF6ZAS6NbZujQu6N7ZujQu6O7a+MrQpIiIiIiKGLQ2JiIiIiIgYtjQkxqbZnQ5gEIlt+Lo1LkhsI9GtcUU0dfP/p4lt+Lo1Luje2Lo1Luju2JaRydYRERERETFs6ZGIiIiIiIhhS0NijJH0Okk3S/qNpEM6HU+TpNslLZK0QFJHVzqSdJKkeyVd3yhbT9LPJf26/n5Ol8R1uKQ76+e2QNK/dCCuDSVdJOlGSTdI+vda3g2f2UCxdcPntrqkqyRdV2M7opa/VNKV9d/pGZKePdqxRQykW+uR1CErFFs3fB+mHhlZbGO6HsnQpjFE0iTgFuC1wO+Bq4F9bN/Y0cAqSbcDPbY7nvtY0i7AI8AptresZV8AHrB9ZK08n2P7410Q1+HAI7aPHs1Y+sQ1FZhq+xpJawPzgd2AmXT+MxsotrfR+c9NwFq2H5G0KnAp8O/AR4BzbJ8u6QTgOtvHdyrOiJZurkdSh6xQbIfT+e/D1CMji21M1yPpkRhbXgH8xvattv8KnA68ucMxdSXbFwMP9Cl+M3ByfX0y5UtkVA0QV8fZvtv2NfX1w8CvgA3ojs9soNg6zsUjdXPV+mPgVcBZtbwjn1vEAFKPDEG31iGQeqTNsXXcWK9H0pAYWzYA7mhs/54u+YdQGfiZpPmSDuh0MP14vu276+s/AM/vZDB9HChpYe2y7kh3eYukacA2wJV02WfWJzbogs9N0iRJC4B7gZ8DvwUetP1kPaTb/p3GxNbN9UjqkBXT8e/DltQjw45pzNYjaUhEO+1ke1vg9cAHa/drV3IZ09ct4/qOBzYCpgN3A1/sVCCSJgNnAwfbfqi5r9OfWT+xdcXnZnuJ7enAiyhPezfrRBwR40DqkJHriu9DSD0yEmO5HklDYmy5E9iwsf2iWtYVbN9Zf98LnEv5x9BN7qnjJFvjJe/tcDwA2L6nfok8BZxIhz63OjbzbODbts+pxV3xmfUXW7d8bi22HwQuAnYA1pW0St3VVf9OY8Lr2nokdcjIdcv3YeqRFTMW65E0JMaWq4FN6kz+ZwN7A+d1OCYAJK1VJzAhaS3gn4DrBz9r1J0H7Fdf7wd8v4OxPK31BVvtTgc+tzrZ63+AX9n+UmNXxz+zgWLrks9tfUnr1tdrUCaw/opSEexRD+ua/9ci6NJ6JHXIiumS78PUIyOLbUzXI8naNMbU1GSzgEnASbY/29mICkl/S3mCBLAK8J1OxibpNGAG8DzgHuCTwPeA7wIvBn4HvM32qE5YGyCuGZRuVQO3A+9tjCcdrbh2Ai4BFgFP1eL/pIwh7fRnNlBs+9D5z21ryiS4SZQHM9+1/an67+F0YD3gWuDtth8fzdgiBtKN9UjqkBWObQad/z5MPTKy2MZ0PZKGREREREREDFuGNkVERERExLClIREREREREcOWhkRERERERAxbGhIRERERETFsaUhERERERMSwpSER0YUkvUDS6ZJ+K2m+pB9L2rSN158hacd2XS8iIrpL6pEYDWlIRHSZunDOucBc2xvZ3g74f8Dz23ibGUAqgIiIcSj1SIyWNCQius+uwBO2T2gV2L4OuFTSUZKul7RI0l7w9FOhH7aOlfQVSTPr69slHSHpmnrOZpKmAe8DPixpgaSdR/PNRUTESpd6JEbFKp0OICKeYUtgfj/lb6GswPlyyoqmV0u6eAjXu9/2tpI+AHzU9nsknQA8YvvodgUdERFdI/VIjIr0SESMHTsBp9leYvse4JfA9kM475z6ez4wbSXFFhER3S/1SLRVGhIR3ecGYLthHP8ky/5bXr3P/sfr7yWkFzIiYiJIPRKjIg2JiO5zIbCapANaBZK2Bh4E9pI0SdL6wC7AVcDvgM0lrSZpXeDVQ7jHw8Da7Q48IiK6QuqRGBVpVUZ0GduWtDswS9LHgceA24GDgcnAdYCBj9n+A4Ck7wLXA7cB1w7hNj8AzpL0ZuBDti9p9/uIiIjOSD0So0W2Ox1DRERERESMMRnaFBERERERw5aGREREREREDFsaEhERERERMWxpSERERERExLClIREREREREcOWhkRERERERAxbGhIRERERETFsaUhERERERMSw/X8KmLv7PBqi4QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 864x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "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(nrows=1, ncols=2, figsize=(12,8))\n",
     "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n",
-    "ski_data.Region.value_counts().plot(kind=___, 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"
@@ -771,14 +1622,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "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>Illinois</th>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>43.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Iowa</th>\n",
+       "      <td>35.666667</td>\n",
+       "      <td>41.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Tennessee</th>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>65.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Massachusetts</th>\n",
+       "      <td>40.900000</td>\n",
+       "      <td>57.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>North Carolina</th>\n",
+       "      <td>41.833333</td>\n",
+       "      <td>64.166667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                AdultWeekday  AdultWeekend\n",
+       "state                                     \n",
+       "Illinois           35.000000     43.333333\n",
+       "Iowa               35.666667     41.666667\n",
+       "Tennessee          36.000000     65.000000\n",
+       "Massachusetts      40.900000     57.200000\n",
+       "North Carolina     41.833333     64.166667"
+      ]
+     },
+     "execution_count": 17,
+     "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().sort_values(by='AdultWeekday')\n",
     "state_price_means.head()"
    ]
   },
@@ -789,7 +1715,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x720 with 1 Axes>"
       ]
@@ -815,14 +1741,19 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
     "The figure above represents a dataframe with two columns, one for the average prices of each kind of ticket. This tells you how the average ticket price varies from state to state. But can you get more insight into the difference in the distributions between states?"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The figure above represents a dataframe with two columns, one for the average prices of each kind of ticket. This tells you how the average ticket price varies from state to state. But can you get more insight into the difference in the distributions between states"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -839,7 +1770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -849,11 +1780,11 @@
     "#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')"
    ]
   },
   {
@@ -895,258 +1826,826 @@
        "      <td>65.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Alaska</td>\n",
-       "      <td>AdultWeekday</td>\n",
-       "      <td>47.0</td>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>89.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>74.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     state        Ticket  Price\n",
+       "0   Alaska  AdultWeekday   65.0\n",
+       "1   Alaska  AdultWeekday   47.0\n",
+       "2   Alaska  AdultWeekday   30.0\n",
+       "3  Arizona  AdultWeekday   89.0\n",
+       "4  Arizona  AdultWeekday   74.0"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ticket_prices.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is now in a format we can pass to [seaborn](https://seaborn.pydata.org/)'s [boxplot](https://seaborn.pydata.org/generated/seaborn.boxplot.html) function to create boxplots of the ticket price distributions for each ticket type for each state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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mTeO73/1u32N33XVXVq1axcc//vG+9JTzzjuP4447jrvvvpuTTz65L6DPgkaoRURERAbR1tZGR0cHnZ2dAEycOJHJkycza9asvuPXX399wWPVZtGiRcyZMweA97znPSxatIiOjg5mz54NwJQpU5gyZUqq73HMMcewfPlyenp6OProo3nlK1/J+eefz2233cZBBx3EmDFjWLZsGffcc0/fc5577jnWr1/P1VdfzR/+8Ie+APj555/vC3qvueYaVqxYwdVXX8348eP54x//yD333MOxxx4LwAsvvMDRRx/d1+bb3/52AI444gh+97vfAXDDDTf0/f8tb3kLL3rRi1L9rPkUUIuIiIiUkJ8KEedYtXjqqae45pprWL16NWZGT08PZsYrXvGKQZ9jZn3/f/7554f0fY499lja2tro6enhIx/5CDvuuCPPP/881113XV/+dG9vL7fccgtjxozp91x35/LLL+fAAw/sd//f/vY39ttvP+6//37++c9/Mm3aNNydGTNmsGjRooL92H777QFoaGigu7t7SH1PQykfIiIiIoOYNWsWc+fOZfLkyUyePJm5c+f2G4GeNWvWoMeqyWWXXcb73/9+HnjgAdauXctDDz3EPvvswxFHHMEll1wCwF133cWdd97Z95wJEyawZs0aent7ueKKKwq2u+OOO7Ju3bq+2wcffDCPPPIIN954Y1+wPnXqVC688MK+0eQ3vOENtLW19T3n9ttvB+CNb3wjbW1tfbnNt912W99j9t57by6//HJmzpzJ3XffzVFHHcVNN91ER0cHABs2bOCf//xn0dfgv/7rv/p+1j/96U88/fTTpV+4IVJALSIiIlLnFi1axMknn9zvvlNOOYV///vfrF+/noMPPpgvfelLHHHEEX3Hv/71r3PCCSdwzDHHsMceexRs9wMf+ABnnHEGU6dOZdOmTZgZr3rVq2hubmb06NEAHH300dx///19I9Tz5s1jxYoVTJkyhUMOOYQLL7wQgC9+8Yts2bKFKVOm8LKXvYwvfvGL/b7XQQcdxMUXX8w73/lOnnvuOebPn8+pp57KlClTOProowctBZhzzjnncMMNN/Cyl72M3/3ud+y1117xXsQiLMsVjsNt2rRpXqpWooiIiEhaudzjuXPnxjqWs2bNGg4++OC+25UqmydDM/D3BWBmK919WqHHK4daREREZJgp+K0vSvkQEREREUlBAbWIiIiISAoKqEVERESGQS2vWxtJkvyeFFCLiIiIlNmYMWPo6upSUF3l3J2urq5tamSXokWJIiIiImX20pe+lIcffpgnnnii0l2REsaMGcNLX/rSWM9RQC0iIiJSZqNHj2afffapdDekTJTyISIiIiKSggJqEREREZEUFFCLiIiIiKSggFpEREREJAUF1CIiIiIiKSigFhERERFJQQG1iIiIiEgKCqhFRERERFJQQC0iIiIikoIC6irR1dXF7Nmz6erqqnRXRERERCQGBdRVYsGCBaxevZqFCxdWuisiIiIiEoMC6irQ1dVFe3s77k57e3vJUepSo9ka7RYREREZPgqoq8CCBQvo7e0FoKenp+QodanRbI12i4iIiAwfBdRVYNmyZXR3dwPQ3d3N0qVLB31sqdHsuKPdIiIiIpKOAuoqMH36dBobGwFobGxkxowZgz621Gh23NFuEREREUlHAXUVaG1tZdSo8KtoaGhg5syZgz621Gh2nNFuEREREUlPAXUVaG5upqWlBTOjpaWF5ubmQR9bajQ7zmi3iIiIiKSngLpKtLa2cthhhxUdnc49rthodpzRbhERERFJTwF1lWhubmbevHlFR6dzjys2mh1ntFtERERE0musdAckvtbWVtauXTvo6HOp4yIiIiKSHQXUNSg3mp30uIiIiIhkRykfIiIiIiIpKKAWEREREUlBAbWIiIiISAoKqEVEREREUlBALSIiIiKSggJqEREREZEUFFCLiIiIiKSggFpEREREJAUF1CIiIiIiKSigFhERERFJoWwBtZn93MweN7O78u4718w6zez26N+b8479r5l1mNk/zOyN5eqXiIiIiEiWyjlCPR9oKXD/99x9avRvCYCZHQK8B3hZ9JwfmVlDGfsmIiIiIpKJsgXU7n4D8NQQH/424FJ33+zu/wY6gFeWq2+V0NXVxezZs+nq6qp0V0REREQkQ5XIof6kmd0ZpYS8KLpvIvBQ3mMeju6rGwsWLGD16tUsXLiw0l0RERERkQwNd0D9Y2A/YCrwKPCduA2Y2UfNbIWZrXjiiScy7l55dHV10d7ejrvT3t6uUWoRERGROjKsAbW7P+buPe7eC/yUrWkdncCeeQ99aXRfoTYucvdp7j5tt912K2+HM7JgwQJ6e3sB6Onp0Si1iIiISB0Z1oDazPbIu3kykKsA8gfgPWa2vZntA+wP3DqcfSunZcuW0d3dDUB3dzdLly6tcI9EREREJCvlLJu3CLgZONDMHjazDwHfNLPVZnYn8FrgLAB3vxv4DXAP0A58wt17ytW34TZ9+nQaGxsBaGxsZMaMGRXukYiIiIhkpbFcDbv7qQXu/lmRx38F+Eq5+lNJra2ttLe3A9DQ0MDMmTMr3CMRERERyYp2ShwGzc3NtLS0YGa0tLTQ3Nxc6S6JiIiISEbKNkIt/bW2trJ27VqNTouIiIjUGQXUw6S5uZl58+ZVuhsiIiIikjGlfIiIiIiIpKCAWkREREQkBQXUIiIiIiIpKKAWEREREUlBAbWIiIiISAoKqEVEREREUlBALSIiIiKSggJqEREREZEUFFCLiIiIiKSggFpEREREJAUF1CIiIiIiKSigFhERERFJQQG1iIiIiEgKCqhFRERERFJQQF2Durq6mD17Nl1dXYmOi4iIiEh2FFDXoAULFrB69WoWLlyY6LiIiIiIZEcBdY3p6uqivb0dd6e9vX2bUehSx0VEREQkWwqoa8yCBQvo7e0FoKenZ5tR6FLHRURERCRbCqhrzLJly+ju7gagu7ubpUuXxjouIiIiItlSQF1jpk+fTmNjIwCNjY3MmDEj1nERERERyZYC6hrT2trKqFHh19bQ0MDMmTNjHRcRERGRbCmgrjHNzc20tLRgZrS0tNDc3BzruIiIiIhkq7HSHZD4WltbWbt27aCjz6WOi4iIiEh2FFDXoObmZubNm5f4uIiIiIhkRykfIiIiIiIpKKAWEREREUlBAbWIiIiISArKoRYRqRJtbW10dHTQ2dkJwMSJE5k8eTKzZs0a0nEREakMBdQiIlVm06ZNqY6LiMjwUkAtIlIlciPNc+bMAWDu3LmxjouISGUoh1pEREREJAUF1CIiIiIiKSigFhERERFJQQG1iIiIiEgKCqhFRERERFJQQC0iIiIikoICahERERGRFBRQi4iIiIikoIBaRERERCQFBdQiIiIiIikooBYRERERSUEBtYiIiIhICgqoRURERERSUEAtIiIiIpKCAmoRERERkRQUUIuIiIiIpKCAWkREREQkBQXUIiIiIiIplC2gNrOfm9njZnZX3n3fMrN7zexOM7vCzHaO7p9kZpvM7Pbo34Xl6le16urqYvbs2XR1dVW6KyIiIiISQzlHqOcDLQPuWwoc6u5TgH8C/5t37F/uPjX6d0YZ+1WVFixYwOrVq1m4cGGluyIiIiIiMZQtoHb3G4CnBtx3tbt3RzdvAV5aru9fS7q6umhvb8fdaW9v1yi1iIiISA1prOD3/iDw67zb+5jZbcBzwNnu/tfKdCtbbW1tdHR00NnZCcDEiROZPHkys2bN6nvMggUL6O3tBaCnp4eFCxdy1llnVaS/IiIiIhJPRRYlmtkXgG7g4uiuR4G93P0VwKeAS8xs/CDP/aiZrTCzFU888cTwdDgDmzZtYtOmTQWPLVu2jO7uMHDf3d3N0qVLh7NrIiIiIpLCsI9Qm9kHgBOA17u7A7j7ZmBz9P+VZvYv4ABgxcDnu/tFwEUA06ZN82HqdmK5keg5c+YAMHfu3G0eM336dJYsWUJ3dzeNjY3MmDFjWPsoIiIiIskN6wi1mbUA/w840d035t2/m5k1RP/fF9gfuH84+1ZJra2tjBoVfhUNDQ3MnDmzwj0SERERkaEqZ9m8RcDNwIFm9rCZfQj4AbAjsHRAebz/Au40s9uBy4Az3P2pQu3Wo+bmZlpaWjAzWlpaaG5urnSXRERERGSIypby4e6nFrj7Z4M89nLg8nL1pRa0traydu1ajU6LiIiI1JhKVvmQPM3NzcybN6/S3RARERGRmLT1uIiIiIhICgqoRURERERSUEAtIiIiIpKCAmoRERERkRQUUIuIiIgMo46ODt7ylrfQ0dGR6LhUHwXUIiIiIsPoggsuYMOGDVxwwQWJjkv1UUAtIiIiMkw6OjpYu3YtAGvXrt1mFLrUcalOCqhFREREhsnAUee4t6U6KaAWERERGSa50eekt6U6KaAWERERGSaTJk1KdVuqkwJqERERkWFy9tlnp7ot1UkBtYiIiMgwmTx5ct+o86RJk5g8eXKs41KdFFCLiIiIDKOzzz6bHXbYYdDR51LHpfo0VroDIiIiIrWmra2tr6Rd7uucOXP6jk+ePJlZs2YVfO7kyZO56qqrBm271HGpPgqoRURERGLq6OjgvrtvY69xPWy3JUz4b35gBQAPrm+oZNekAhRQi4iIiCSw17gePn/4c9vc/9VV4yvQG6kkBdQiIiIiA+SndMC2aR2dnZ3sWpGeSTVSQC0iIiIyQEdHB7fftYaesbsAMOoFB2Dl/Y/RsPEpxo0ZDaMr2UOpJgqoRURERAroGbsLmw568zb3N927BHrXVaBHUq1UNk9EREREJAUF1CIiIiIiKSigFhERERFJQQG1iIiIiEgKCqhFRERERFJQQC0iIiIikoICahERERGRFBRQi4iIiIikoIBaRERERCQFBdQiIiIiIilo63ERERlWbW1tdHR00NnZCcDxxx/PrFmzCh6bOHEikydP7jsuIlKNFFCLiEhFbNq0KdExEZFqo4BaRESGVW60ec6cOf1uFzo2d+7cYe6diEh8yqEWEREREUlBAbWIiIiISApK+RARqXK5hXo5uf/n0iIALdwTEakgBdQiIlWuo6OD++6+jb3G9QCw3ZYwubj5gRUAPLi+oWJ9ExERBdQiIjVhr3E9fP7w5woe++qq8cPcGxERyaccahERERGRFBRQi4iIiIikoIBaRERERCQFBdQiIiIiIiloUaJIBeTKoHV2dgIwceLEfmXPSh0XERGR6hEroDazHYDn3b2nTP0RGVE2bdqU6riIiIhUXtGA2sxGAe8BTgOOBDYD25vZk8BVwE/cvaNIEyJSQG6kObcxx9y5c2MdFxERkepRKof6WmA/4H+BF7v7nu6+O3AccAvwDTN7X5n7KCIiIiJStUqlfEx39y0D73T3p4DLgcvNbHRZeiYiIiIiUgOKjlAXCqbNbLsol3rQx4iIiIiIjBQly+aZ2RwzOzD6//HAQ8B9Zvbf5e6ciIiIiEi1G0od6g8AuYWHZwMnAvsDHy9Tn0REREREakapKh/nABOAL5jZdsBU4I1AC7CjmX0JuM7dbyh3R0VEREREqlGpHOrzgNsBB3YALnf384EvA4+6+/nFgmkz+7mZPW5md+Xdt4uZLTWz+6KvL4ruNzObZ2YdZnanmR2ewc8nIiIiIlJWQ0n5+BAwDlgHfCa67wDg/4bw3PmE0ex8nwP+4u77A3+JbgO8iZBKsj/wUeDHQ2hfRERERKSiSu6U6O6PAp8dcN+9wL1DeO4NZjZpwN1vA14T/X8BcF3U/tuAhe7uwC1mtrOZ7RF9fxGRupXbaj4n9//cxj4dHR3sqQKlIiJVq+gItZmdbWa7FDn+OjM7Ieb3nJAXJP+HkKMNMJFQQSTn4eg+EZG61tHRwe13rWHl/Y+x8v7HWPeCs+4F77u9fsPGSndRqkBXVxezZ8+mq6sr0XERKZ9SI9SrgcVm9jywCngCGENIy5gKLAO+mvSbu7ubmcd5jpl9lJASwl577ZX0W4uIVJWesbuw6aA3Fzw2btUvgReGt0NSdRYsWMDq1atZuHAhZ511VuzjIlI+pRYlXunuxwJnAHcDDcBzwK+AV7r7We7+RMzv+ZiZ7QEQfX08ur8T2DPvcS+N7hvYp4vcfZq7T9ttt91ifmsRqTYaVRMprauri/b2dtyd9vb2bf5eSh0XkfIqmUMN4O73Afdl9D3/ALQCX4++Xpl3/yfN7FLgVcCzyp8WqX8aVauMXN52Z2cYt5g4cSKTJ09m1qxZFe6ZFLJgwQJ6e3sB6Onp2ebvpdRxkWJKnQ90vihtKFU+EjOzRcDNwIFm9rCZfYgQSM8ws/uA6dFtgCXA/YRNZH4KaCdGkTqnUbXK27RpE5s2bap0N6SEZcuW0d3dDUB3dzdLly6NdVxkKEqdD3S+GNyQRqiTcvdTBzn0+gKPdeAT5eyPiFQXjapVTm5kKVdJZO7cuZXsjpQwffp0lixZQnd3N42NjcyYMSPWcZFiSp0PdL4orawj1CIixWhUTWRoWltbGTUqfGQ3NDQwc+bMWMdFpLyGFFCb2QFm9pfcjodmNsXMzi5v10Sk3k2fPp3GxjBRplE1SaPeF7c2NzfT0tKCmdHS0kJzc3Os4yJSXkMdof4p8L/AFgB3vxN4T7k6JSIjg0bVJCv5i1vrVWtrK4cddtigfyeljotI+Qw1oB7r7rcOuK87686IyMiiUTXJwkhZ3Nrc3My8efMG/TspdVxEymeoixKfNLP9AAcws3cAKmknIqm1traydu1ajapJYoMtblWpLxEZLkMdof4E8BPgIDPrBM4EPl6uTonIyKFRNUmr1OJWlfoSkXIb6sYu9wPTzWwHYJS7rytvt0RERIZmsJJxKvUlIsNlqFU+vmpmO7v7BndfZ2YvMrMLyt05ERGRUrS4VUQqbagpH29y92dyN9z9aeDNZemRiIhIDFrcKiKVNtRFiQ1mtr27bwYwsyZg+/J1S0REZOi0uFVEKmmoAfXFwF/M7BfR7dOBBeXpkoiISDy5xa0iIpUwpJQPd/8G8BXg4Ojfl939m+XsmIiIiIhILeyEOtQcatz9T+7+P9G/P5ezUyIiIiIiUBs7oRYNqM3sxujrOjN7Lu/fOjN7bni6KCIiIiIjUa3shFo0h9rdj4u+7jg83RERERGRehV3B9PBdkJN0lY5lVyUaGYNwN3uftAw9EdERMoo9wGUk/t/bvMTQNtzi0jZDXX30kI7oeYC6rht5XR1dXHeeedxzjnnbFNms9ixYkoG1O7eY2b/MLO93P3BWD0WEZGq0tHRwX1338Ze43oA2G5LyPzb/MAKAB5c31CxvomMdANHXI8//vi6u7iNu4PpYDuhJmkrJz8ne2BwXuxYMUMtm/ci4G4zuxXYkLvT3U8c8ncSEZGqsNe4Hj5/eOFlMF9dNX6YeyMi+YH0pk2b+kZcr7/+ejo6Okb0rFFrayvt7e1ANjuhDszJnjlzZt9IdLFjpQy1yscXgROA84Hv5P0TERERkRRyM0e7dj/KnqOfYafGLezUuIVdux/lvrtv65emNdJkvRNqoZzsoRwrpegItZmNAc4AJgOrgZ+5e3fczouIiIjI4AabOdKsUbY7oRbLyR5KvvZgSo1QLwCmEYLpN6FRaREREREZRrmdUNOOTkPIyW5sDOPJA3Oyix0rpVQO9SHufhiAmf0MuDVet2tTqTIslSjTMjC/Kqepqanf4waudG1qaqpoGRkREREJBosfZPgUy8lOk69dKqDekvuPu3ebWYwu175SZVjilmlJo6Ojg9vvWoOPasR6+34trHvBsS2b2KGxh+1HOb09/X9HPVue475nHh+2foqIiEh/gy063LRpU999e46ucCdHiFxO9uLFi7fJyS52rJRSAfXL83ZENKApum2Au3tdJvaUKsOStExLWj1jd2HTQW/e5v5xq37JXuNe0Kp9ERHJRNJavFJYblCsZ+wuMGpHRjU4AM+O2pFnN0PDho2wc2X7WG7VVAO/WE520nztUjslqiCpiIjICJO0Fq8MbrBBMQgDY/DC8HZomFVTDfxcTnbcY8UMtQ61iIiIjABpavGKFFPPNfCHWodaRERERoA0tXhFRiqNUMdQTfk/IiIi5ZCmFm896ezspGHjszTdu2SbYw0bu9jY280DoxoKjqw+sK6BHaJKHjIyKKCOoZryf0RERMph+vTpLFmyhO7u7ti1eEVGKgXUMdVz/o+IiEiaWrz1ZOLEifxnc2PBhYRN9y5hXO869hz9zKC7G24/ceJwdFOqxIgJqIeyGUuplI6Ojg7ViRTJSDk2SFKpr61GygYSldhoq96lqcUrMlKNmIA6p9hmLP3qRAKjXgh1Ilfe/xgwMupEigy3LDdIUqmvbQ3nBlSVNFJ+zuGStBavyEg1YgLqoW7GMtLrRIoMl6w3SKrrUl+9PTywrvDiJyi8AGqw1zd/EXU9qNRGW/UuaS1ekZFqxATUlZCfQjJYRZDcMU1XihRXamq/UKkvjVKLiMhwUEAdR8xRovwUkm3SRzY+BWwNqjVdKTI0g/2t1HWpr1EN7L3j5qILorUASkSkchRQl9lgKSQ73H5pvwWQ+To6OpgzZ45GqkXylJraV6kvERGpFAXUcWQ4SmS9W/DNm/tqWKumtUg6KvUlIiKVoq3HKyhX0/rzhz/H3OOeYe5xz/Tdzm0eIyJDkyv1ZWYVKfXV1dXF7Nmz6erqSnRcRERql0aoRaRuVLLUV6mSfSrpJyK1ptT+HIDSUyMKqPN0dnbSsPFZmu5dUvgBPd08trEyg/pF+9azhY5nG2OV1BKpR5Uq9VWqZF81lfTr7OxkQ8wSfCIyMnV0dHDf3bf1zZorPXVwCqglE8VKmmkns6GrhtGAgb+v448/vu/76XdZWKmSfSrpJyK1KpeeWshgF+YjkQLqPBMnTuQ/mxuLbuyyuben7w2UG62eMDZ8UD64voH9K9C3cat+yeQdN1ZFSa1i5f9UGrC0ahoN0O9y6EqV7Kumkn4TJ05kc/ejVXG+EBGpFwqoY/BRo7HttmP7vUPt6Bei0cPc7f3ZWld6pClW0kw7mcVT6dGAgb+v/NFn/S4LK1Wyr5ZL+rW1tdHe3s7GjRtx9777zYyGhnCB19PT0+9Y7vjYsWNpaWkZ8TMYIqVTSgdP3VQaVm1QQB1D75jxTN53wjZb+CqoEBnZSpXsU0k/EZH6poBaRCSlXMm+xYsXFyzZV+p4NS+InjVrlkaYRVIaSkrpYKmb9ZKGVe8LohVQi9SpwabqQdPx5VCqZN9wlvTLX9w6cGFrR0cHe44uexdK9ivXFwh9y1/oOvBYjhbAiki1UkAtIpKBUiX7ih0fyujVhLEvDLkvHR0d3H7XGnrG7sKoF8LF1Mr7HwOgYcNG2HnITWUqv19Av741rOtih8YeNnc/Cqg8l0i9qfcF0QqoE8iNsuSPoGjkRKpNNUzVq8xe5fSM3WXQqkAw9OA8a8X6tde4F1SeS0RqkgLqARo2PtWXxzjq+XBi7x0zvu8YTOh7bFNT07D3T6QWqcyeiIjUMwXUeQaWvOvoWBfu3zcXRE/Q6JpIDCqzJyIiI4EC6jwDA2UFASIiMpIoTWtkKbU7byUXMdcaBdSV0tvDA3VcPkZEKqdoGb4KluCT2qE0rZGh2EJhqOwi5loz7AG1mR0I/Drvrn2BLxF+ZR8Bnoju/7y7D1KUVURERLKmNK3KeXD91kG23EXvhLG9PLi+gf3L+H0HWygMlV/EXEuGPaB2938AUwHMrAHoBK4ATge+5+7fHu4+VcSoBvbecXPdlo8RkcopVoYvbgk+ESk/HzUa2247tt87rOV6IUq92H7vyezPtmu8pPpUOuXj9cC/3P0BM6twV0RERESGX++Y8Uzed0LfjIBmCGpPpQPq9wCL8m5/0sxmAiuAT7v705XploiISH9tbW1cf/31gBbriUh/FVuZYmbbAScCv43u+jGwHyEd5FHgO4M876NmtsLMVjzxxBOFHiIiIlIWmzZt0oI9EdlGJUeo3wSscvfHAHJfAczsp8AfCz3J3S8CLgKYNm2al6Nj2glRRCR7xauPbKHj2caqrnw0a9asvs8FTcWLSL5K1k46lbx0DzPbI+/YycBdw96jAZqamrQbooiIiIgUVZERajPbAZgBfCzv7m+a2VTAgbUDjg0rjUSLiGSvVPWRyTtuVOUjqVsNG5/qm50Z9Xx4n/eOGd93DCZsM0Pe1tammKRGVCSgdvcNQPOA+95fib6IiIiIlNPAsncdHevC/ftOiO6Z0O8xmh2vPZWu8iEiIiJS1waOMqssXv1RQF1G2v5XRERERpJc2kpntIg4V2Ky3imgFhEREZFMjbTykgqoyyjr7X9zCxoGLmagp5sH1zf0lZvKjXxPGNsLwIPrG9g/6Q8hIiIiMkS59JaBaS1z5syp61hFAXWNyJ8uGbiYobOzG6BvBfwL0erg7fcOz9mfbRdEiIiIiGQpl+4B9NvLA0Ia7Nidd6/bWEUBdY3IX9BQajGDFjuIiEgc+YEQbBsMAdrgTErq6Ojg9rvW0DN2F0a9EPbeW3l/2LevYeNzTD304H4j1lA/sYoCakml1ElYJ2ARkXQGW+SVO7eWOj4UHR0d3Hf3bew1rgeA7baE6fjND6wAwnS8yFD0jN2lYKprwQINdUQBtaSSfzUK9LsiDYXqRURqW7FpbBi+gYNSi7zSLgLba1xP0Y11RGRwdR9QawS1/Ebq1aiIVK8sg+D80dtKjNwOtshrqMfzjdSSZtVi4E6Ic+bMURxSJ+o+oC42jaUprPLr7OzsO8krJ09EhkvWQfBgo7e1OnI70kqaVRvthFh/6j6ghvo7EdaSTZs2VXRkR0RGLp37t1WspJmU33AMILW1tdHe3s7GjRtx937HzIy3v/3tFR3IqtdR+hERUEtl6UNNRERE8tXbKL0CahEREZE6MWvWrKoe7a3mvqWhgFpEJIXhKGkmIiLVTQF1BdXzFpwiI025S5pJ5XR2drJhXUPBNLUH1jWwQ3SxJCIjlwLqCvFRo7HttuvbcrPetuCUwkqVcezo6GDP0RXpWlHaRW1wWZY0ExGR2qSAukJ6x4xn8r4T6nYLTims2EY4AA0bNsLOlerd4LSLmoxkEydOZHP3o4Murt5+4sQK9EpEqokCapFhNthGOADjVv0SeGF4OzRE2kVNRESksLoJqLX7U7a0kEpEpLJKpVrln58LHY9zzi6WJw71kyve1tbG7373u4L1mceOHUtLS4s+5ySRugmoc7TwJ1t6PUVEti4iH84F5CVTxNZ1sUNjD5u7HwW0E7CMHNW4rqduAmrt/pQtLaQSEQlys52dnZ082/0UAC9saaKpqYn9X1be2dBSKWJ7jXshk42ziuWJ59qrh1zxaq/RLENTjet66iagFhERKYf8muLXX389oDQ4kUqrtnU9CqhFRESGoJZHN2u1ZKfUls7OTho2PkvTvUu2OdawsYvOzu4K9Gp4KKAWkWGX/+FeDblvIvWuVkt2itQKBdQiMuzy89+qIfdNZCSo1ZKdUjsmTpzIfzY3FnyfNd27hIkTJ1SgV8NDAfUINrA03vHHHw9QdStnpT4Nlv+mmtYiIsOjWIoGAD1b6Hi2se7LKWZBAbX0K41XjStnRSRbuRJwwLCWgZPqpveFSHIKqEewgaXxZs2axZw5c6pu5ayIZMdHjca2247t9w6l3l6IZqFyt/cHbYo1Aul9MTIVS9GAkAo0eceNdV9OMQsKqEVERpDeMeOZvO+EbWr1q9b8yKb3hUg6oyrdARERERGRWqYR6hGoWD3Su+++m9HeoAUIUnO06YZIernPh/zPBf0tDa4SW9KPNIXO7dVIAfUIVKweaUN3N6O17lBqVP4CW6lvpTYq6ezsZNeK9Kw+NDU1VboLVa+pqYmJk+s/57waFqvWwrldAfUINVg90nGrfsneWoBQM9ra2mhvb2fjxo24e9/9ZsbYsWNpaWkZMSNLs2bN6guqlPdZ/4oODGx8itHWy4ZBZttqZaYtTUmzpD/jSDlfZGHixIkVzTkfWPq2HDNz1bBYtdC5Pb+U71Dkv1b5wXlTU1O/Ue80r6cCahGpadp1ceQabGCg6d4ljN70ZAV6JDL8yjl6Wy+LVXMX4D6qEevd0nf/uhecJ55eA2y9MEj6eiqgFqlhs2bNGvHBonZdlEK233579hy9adDNg2phpi1NSbNa+RkluYGlb2styB1uxS7AIf3rqYBaRGqedl0UqX3FFhYPR3qDSBoKqEVEqkDDxqf6RkpGPR8uDnrHRBcEPd1VsTBIpNxKTbfXwuI0GZkUUEsqxRbNNGzsYrM5jK5Ax0RqyMBFPR0d68L9+04AoLOzG6BvCr+eKwrIyFVsYbHSG6TaKaAWkZI6OzvZsE71yfMNpWwbhKnpQsfzp6sHTluXChoUVIiIZGPU88/R0bGu77yadHF7zQfUpT7UOjo62LOORkjzf96BBfcrUZC/2KKZpnuXMK53HaApOqk/xcq2ATSs62KHxh42dz8K0G/BpBZLiogUV6yCU5axnfVuwTdv7lvMnnRxe80H1CU/1DZshJ0r1bvyKFVwXwX5JWsTJ05kc/ejqk8+wGCrxiFUYNhr3AtaLCkikkB+fFfu2G6whe0w9PN1zQfUUPpDDV4Y3g6VUbEyaVrtLCIiIvWi2CZ01Rbb1UVAXc1yK/cHrtpv2PgUMKGCPRvcg+sbmHPjzjzfY/3uH9PgbO61ilcTGFg+6fjjj9fFRB0baWldIuVQatfFho1dfYtfh1uxqf1SaxFg5G3eVOqcCJV9TWox7smCAuoyyl91P3DVPkyoylX5uW1GG5qaGDWgPFFDUxP7523RORSbN2/mgefLs/2vyieNDCMxrUtkJCk6tV9kLQKMzM2bSp4TNz5Vsb7VYtyTFQXUZZR/dVgrq/IHbjNajQaWTxpJIxMj1UhK6xIph1K7Ljbdu4SJEys3elhsan+wtQgwctcjFDsnDjYLMRyGLe7p7eGBKqs8pYBayqoetv8VERERKUYBtYiISIV0dXVx3nnncc4559Dc3Fzp7ojUhlEN7L3j5qqqPFX/AXWRaYGRuBmFiIhUjwULFrB69WoWLlzIWWedVenuiEhC9R9Qi8iwG8ougrtWpGci1aOrq4v29nbcnfb2dmbOnKlRapEaVf8BdZFpAeXw1r+BJfYmRlVKBu4uOdjxYVeFCy2SKLYKvWHjU4wbMxpU6k5GuAULFtDb2wtAT0+PRqmlLlRi1+ZqUP8BtQilS+ypBF/2BluF3nTvEuhdV4EeiVSXZcuW0d0daj93d3ezdOnSERlQF62R3bOFjmcba36QYSQaabs2VyygNrO1wDqgB+h292lmtgvwa2ASsBZ4l7s/Xak+Su0bWGJvYPmeUseHXRUutJDaNFI3V6gl06dPZ8mSJXR3d9PY2MiMGTMq3SWpAdW8SQ+M3FK2lR6hfq27P5l3+3PAX9z962b2uej2ZyvTNRGR2jSSN1eoJa2trbS3twPQ0NDAzJkzK9yjyihWI3vcql8yeceNGmSQqlfpgHqgtwGvif6/ALgOBdRSBqUWzY2EfC+pX7W4qdRI1NzcTEtLC4sXL6alpUULEmVIqn2TnpGqkgG1A1ebmQM/cfeLgAnu/mh0/D9oXlLKpKOjg/vuvo29xvUA/bezHYlb2YpIZbS2trJ27dphGZ3OpQEBSgXKUP4AzUhZgCfbqmRAfZy7d5rZ7sBSM7s3/6C7exRs92NmHwU+CrDXXnsNT0+lLu01rmfQ6i8iIsOhubmZefPmlf37DEzzUSpQtmp5AV61XmgVX6zazWMbRw1/p4qoWEDt7p3R18fN7ArglcBjZraHuz9qZnsAjxd43kXARQDTpk3bJuAWkcordiJs2NjFxt5uHhhVuQ2X4pZTPP744zXilJFBP7x7unlw/db3RO7DcsLYUFbuwfUN7J/XTqn32GZzlWbMM/D9q1Sg7MyaNatmzw8j5UJrOCrJVCSgNrMdgFHuvi76/xuA84E/AK3A16OvV1aif1L7SuVI33333Yx27aA50g0sl5gfSG/atKnv+PXXX09HR4emclMq9uGdq0qQW2D2QvQ3u/3e4Tn7F3i+SLkNdgFYLyky1XyhVWqx6oSxL1SgV4Or1Aj1BOAKM8v14RJ3bzezvwO/MbMPAQ8A76pQ/6TGFdtYBKChu5vRSpUum2InwqZ7lzCudx17jn6mYhsuDVYucc6cOVtz60fDY1Fu/a7dj3Lf3dtMmElMcT68S32wD+U9BqovXysGK/MYd+YiS8VHb+tj5HakKBWcb2eb+24PfI+ZheeXUpGA2t3vB15e4P4u4PXD3yOpR4NtLALhD2jvQUoxjdQyTMVG9Ts6OthzBEyfd3Z24nmJZLkTKoA7fSkguccWqwVbbBpRsyAiWxUr81jJmYtqHr2V+AZNN+vtwcbu2PeeSvoeq7ayeSKDUqm78iq6XfiGjbBzBTtXTJHt2pMErpt7jAfWhemLLb0GwOhRzuYeY4f0vRWRAeKUeVRQK0kMZbYh7UZvNR9Qlx4lquxK0FJ72o/UPe+TUKm78htsVH/cql8C1ZWvVg7HH398vxzq3iiHeocX7cIB0cLFnFK1YIttSDFSZ0FEJBvDXZmj1mOV4ZhtqPmAulaUKqlTyyV3hlOWpe4Gy8srZ06elEGR7drjBq6q8iEi1a6SlTkUqwyu5gPqoYwSVXIlaKkPYX1IV4aPGo1tt13BnKmkOXmlSrHVukotDMpSqbQhUOpQPcm9Z2v1/ToU5RoY6Orq4rzzzuOcc87RDo5lkOb1rURut86JpdV8QC2SRO+Y8Uzed0K/6g6QzQlpYCm2elDsAgRqp6RZyeovG5+qWN8kW01NTUycXNvv11LKMTCQs2DBAlavXs3ChQs566yzsuiu5NHrW38UUItkJO2ChmpWzguQ4Vas+sugazGk5kycOLEu3q/FlOvvsquri/b2dtydP/3pT8ycOVOj1BnKf33b29v1+iZUbbOmCqhHoFI7jOXKFNWyUotVcz9nrS+0qGUjYTq+mum9v616T9saqgULFrBlyxYAtmzZMmyjqPkpWfW8gH/BggX09obzXU9Pj0apE6jGWVMF1CJoocVwizMdX+2VfGqd3vvbqse0rTiWLl2KRwXZ3Z2rr7562AK+kbCAf9myZXR3h4Gr7u5uli5dqoA6pmqcNVVAPQKV2mFs4sR45XaKbs06ZnRFRiJLLVbN/Zy1OLpRD7Kejl+3ZVRFRruLla4qtsNbNYzC672/raGkbZVaTDZw+/p8uWDwmWeeoaenpy9oBTAzxo4dS0tLS8V/NxMmTGDt2rX9bg+HWbNmDfqzV/o1ydL06dNZsmQJ3d3dNDY2MmPGjEp3STIwIgLqav5Qq3WliqVv2LCBHXaonikZqT2lLo52uP1Stt/O2H7vycP6HitVuqrYDm9679euUovJcgtffVQj1rul37F1LzjW/TwNtm27+cF1pT322GNFb0s6ra2ttLe3A9DQ0MDMmTMr3CPJQt0H1OVcBT0SlBqNiVO+pxqmZKT+5E/9Ded7LG7pKr3/a99QF5OVWvg6NXq/dnV1ceqpp/LCCy+w/fbbs3DhwqpYnDZjxgwWL16Mu2NmvOENb6h0l+pKc3MzLS0tLF68mJaWlqr4nUt6dR9QV2OeTS1RaZ/SVNu4dgx1saqMLEP9G96yZUvJxWRx3mPVujittbWVP/3pT2zZsoXRo0drBLUMWltbWbt2barXtp4WataDug+oJTmV9hmaYluiA9oWXaTKDbU++X333ZfpYrKhLE4b7i2mIYygvulNb2Lx4sW86U1v0nm/DJqbm5k3b14mbdXDQs16oIB6hCq6kDA6QVfr6Ek1GmxLdEi2LbqUx1AXq8rIM5T65ENZTBbnPVaqvUpuMZ3FCGq9qNaR4Cy/f7X+jFkb+HO2tbVlVppRAfUIVGohYe54rZf2qcTIjojUnqGmaXzpS1/KdDFZqcVpldhiOifLEdR6MRJGgkfCzwjFf86kr4EC6hFoqCfpWi7tU8mRHRGpT0NdTDbUi3ktTqsN9TZKW8hI+Bmh+M+Z9jVQQC2DquXSPpUc2RGR2hInTaNUKkTci3mlVojUBwXUMiiNnlTGYHXTc8dUO10ke3FGlYulQsS9mFdqhUh9UEAtRQ3n6MmHP/xhHn30UTZv3ty3GDJn1KhRIfXEGgsu8ntgXQM7dHaWtX/5O6BBGNWaPHkynZ2dbFjXMOjiwzh9K1Y3HSq/GU49LFxRbr0MlPubyu1uuKnneQCaeo2mpiYm7ntw3aSI1cPfsEg1UkAtRQ3n6MkzzzzD+g0bwQwG7BrW472DPGv4DdxOGGBzj/HAulAeb0tv2AZt9CjvO7bDENuulbrptbpwRbn1UsjAVf4DL5rrMdis1b9hkWqlgFqqRqk8xnGrfsnkHTcWLE/31VXj+7Z4ztrATR/y5e7fZdfdgBBs90YB96gxTTQ1NXFA9KFcD2o9sChHbn2pET+NCNaOai1DluV7TO87kfJQQC1SQulNH55j6qFhSnikjG7JtkqN+GlEcGTK8veu95hI9aqLgFo5kVJuQ9n0odYD52K1eLUl9+BK/d5r/X0hyWT5e9d7TKT61XxArZxIKSaLadehbvogIsNrKDu+iogMh5oPqFVveGQZrKRcqXJymgotrVgOu7bklmoz1B1fRUSGQ80H1DJyFCspN1g5uSymQuNs+iC1QQsFa58GU0SkmiiglppRKyXlpHZo5kJERLKggFpERhyNRIuISJYUUIuIiEhdaGtro729nY0bN+J5G4SZGWPHjqWlpUUX1FIWoyrdARGRLOTnRc+ZM4e2trZKd0lEREYIjVBLzRm4oKytrU0jDlWoEiXNlBMtMrLNmjVLnwdSEQqopWYpeKpelShppg9SERGplBETUKtMVv3Q76z6ZV3STLuhigyvrq4uzjvvPM455xyam5sr3R2RqjdiAuocjWqK1Bbthioy/BYsWMDq1atZuHAhZ511VqW7I1L1RkxArVFNSUMjpJWjDTxEhldXVxft7e24O+3t7cycOVOj1CIljJiAWiQpjZCKyEiyYMECent7Aejp6dEotVRMbjCrFgayFFBLVanGkeAsR0g7Oztp2Phs3884UMPGLjo7u+N3UkQkI8uWLaO7O5yHuru7Wbp0qQJqGXb5A1W1MJClgFoyUWzR51AXhGokWESk8qZPn86SJUvo7u6msbGRGTNmVLpLMgLlxwi1kOqngFoyVWzRZ6kFoSMhV3bixIn8Z3Mjmw56c8HjTfcuYeLE6prGEpGRpbW1lfb2dgAaGhqYOXNmhXskUv0UUEsmii361IJQEZHqkttRdO7cudvM/DU3N9PS0sLixYtpaWnRgkSRIdDW4yIiIiPMBRdcwIYNG7jgggsKHm9tbeWwww7T6LTIECmgFhERGUE6OjpYu3YtAGvXru1b35KvubmZefPmaXRaZIjqJuVDOyHGN/A1a2tr0+tVJfR+lrT0HtJrMJiBo9IXXHAB8+fPr0xnROpE3QTUOdoJMT69ZsMrTmlA/W4kLb2H9BoMlBudHuy2iMRXNwH1SB9xSEKvWTxZjHblFv90dnayadMmNvU8D0BTr9HU1MTEfQ/WCJpkQu8hvQaDmTRpUr8getKkSRXri0i9qJuAWmS4pBntyn3A54Lzzs5OIJTTUyAtIsPh7LPP5sMf/nC/2yKSjgJqkSHKMthV4CwilTJ58uS+UepJkyZpwyyRDKjKh4iIyAhz9tlns8MOO2h0WiQjGqEWEREZYSZPnsxVV11V6W6I1A2NUIuIiIiIpKCAWkREREQkhWEPqM1sTzO71szuMbO7zWxOdP+5ZtZpZrdH/9483H2rFV1dXcyePZuurq5Ex0VEREQkO5UYoe4GPu3uhwBHAZ8ws0OiY99z96nRvyUV6FtNWLBgAatXr2bhwoWJjouIiIhIdoY9oHb3R919VfT/dcAaYOJw96NWdXV10d7ejrvT3t6+zSh0qeMiIiIikq2KVvkws0nAK4C/AccCnzSzmcAKwij20xXsXlVasGABvb29APT09LBw4ULOOuusIR+vBVnsSCjJDLYt+sAt0UVERCqtmuKFii1KNLNxwOXAme7+HPBjYD9gKvAo8J1BnvdRM1thZiueeOKJ4epu1Vi2bBnd3d0AdHd3s3Tp0ljHa0lTU1OqXQklnsmTJzP10IM5Yt8JHLHvBHbczthxO+OIfScw9dCDtfmDiIhUpWqIFyoyQm1mownB9MXu/jsAd38s7/hPgT8Weq67XwRcBDBt2jQvf2+ry/Tp01myZAnd3d00NjYyY8aMWMdrgUaiK2Pg6z5nzhwA5s6dW4nuiIiIFFVN8UIlqnwY8DNgjbt/N+/+PfIedjJw13D3rRa0trYyalT4tTU0NDBz5sxYx0VEREQkW5VI+TgWeD/wugEl8r5pZqvN7E7gtUBtJf4Ok+bmZlpaWjAzWlpaaG5ujnVcRERERLI17Ckf7n4jYAUOqUzeELW2trJ27dpBR59LHRcRERGR7FS0yock09zczLx58xIfFxEREZHsaOtxEREREZEUFFCLiIiIiKSggFpEREREJAUF1CIiIiIiKSigFhERERFJQVU+RKSftrY2Ojo66Ojo6Ltdjt2oBn6fOXPmMHny5Kra+Wq4lXpN6uU1K/Yei/sz1strIiK1TQG1iBTU1NRUV9+nlpR6TerlNSv2c8T9GevlNRGR2qSAWkT6Ga6RPY0gbqvUa1Ivr1mxnyPuz1gvr4mI1DblUIuIiIiIpKCAWkTqRldXF7Nnz6arq6vSXRERkRFEAbWI1I0FCxawevVqFi5cWOmuiIjICKKAWkTqQldXF+3t7bg77e3tGqUWEZFho4BaRGpCW1sbc+bM6SuRNmfOHNra2vqOL1iwgN7eXgB6eno0Si0iUgfyS2MOPO9XEwXUIlJTmpqaCpZIW7ZsGd3d3QB0d3ezdOnS4e6aiIiUwWDn/WqisnkiUhNKlUebPn06S5Ysobu7m8bGRmbMmDFMPRMRkXKZNWtWTZTH1Ai1iNSF1tZWRo0Kp7SGhgZmzpxZ4R6JiMhIoYBaROpCc3MzLS0tmBktLS00NzdXuksiIjJCKOVDRMomfzEJwJw5c5g8eXLZpu9aW1tZu3atRqdFRGRYKaAWkbIbrsUkzc3NzJs3b1i+l4iISI4CahEpm1pYSCIiIpKWcqhFRERERFJQQC0iIiIikoICahERERGRFBRQi4iIiIikoIBaRERERCQFBdQiIiIiIikooBYRERERSUEBtYiIiIhICgqoRURERERSUEAtIiIiIpKCAmoRERERkRQUUIuIiIiIpKCAWkREREQkBQXUIiIiIiIpKKAWEREREUlBAbWIiIiISAoKqEVEREREUlBALSIiIiKSgrl7pfuQmJk9ATwwhIfuCjyZ0bdVW5VtT22prXK3p7bqo62s21Nbaqvc7amt6m9rb3ffrdCBmg6oh8rMVrj7NLU1/G1l3Z7aUlvlbk9t1UdbWbenttRWudtTW7XdllI+RERERERSUEAtIiIiIpLCSAmoL1JbFWsr6/bUltoqd3tqqz7ayro9taW2yt2e2qrhtkZEDrWIiIiISLmMlBFqEREREZGyUEAtIiJ1wcy2L3DfLpXoi4iMLEr5EBEZAcxsb2B/d19mZk1Ao7uvS9DOSuDnwCXu/nQG/doOOCC6+Q9335KirauAk3JtmNkewB/d/YiE7b0I2B8Yk7vP3W9I2j8ZOjM7FDiE/q/9woRtHUd47//CzHYDxrn7v7PpqUjQWOkOSHJmthNwLvDq6K7rgfPd/dkEbc1x97ml7ovZZiYflGa2H/Cwu282s9cAU4CF7v5MwvYy+5DMqi0zM+A0YF93P9/M9gJe7O63JulXVsrw2k8E9ibv3FPJAMXM3ufuvzKzTxU67u7fHe4+DZTFe8zMPgJ8FNgF2A94KXAh8PoEXXo3cDrwdzNbAfwCuNoTjM5E76kFwFrAgD3NrDXFe+L3wG/M7B3AnsAfgP9J0pCZfRiYQ3itbgeOAm4GXpewvZez9Vz9V3e/I0k71cbMVgOD/u7dfUqCNs8BXkMIqJcAbwJuBGIH1FFb04ADCe/V0cCvgGPjtpU1M/uLu7++1H0x2qu6C0Az2x/4GtteHO2bos2q+hzJqcuA2syOAtqAg4HtgAZgg7uPT9DWGOBDwMvo/2b4YMK+Zfnm+jlwF/Cu6Pb7CSeMtydoqxUYGDx/oMB9Q5LxB+XlwDQzm0xYiXslcAnw5gT9yuxDMuMP3B8BvdFzzwfWEX7uI2P0p43iH2yzE/Qry9f+G4Rg7B6gJ9ctIOnFzG7AZ9n2bynO679D9HXHJH0YpF+ZnTMyfI99Angl8LeoL/eZ2e5x+xM9twP4gpl9ETiBcB7qMbNfAHPd/akYzX0HeIO7/wPAzA4AFgGJRpTd/afRhfzvgUnAx9x9eZK2CK/7kcAt7v5aMzsI+GqShsxsDvAR4HfRXb8ys4vcvS1BW7tFbU2if0CR9DMp7eflCdHXT0Rffxl9PS1JfyLvAF4O3Obup5vZBEIQnMTJwCuAVQDu/oiZJf57N7NjCQNZuYDOQrND/wyPzhFjgV2jINiiQ+OBiQn7leVn29uBbwC7R33L/YyxYyhCTHIO8D3gtYSL8cTpxll+jpjZOrb9zHwWWAF82t3vj9NeXQbUwA+A9wC/JVyZzmTrSGlcvwTuBd5ICHROA9ak6FuWb6793P2UvNvnmdntcRows1OB9wL7mNkf8g7tCMT5YBwoyw/KXnfvNrOTgTZ3bzOz2xL2K7MPyYzbepW7H577udz96SgwiGNF9PVYQpD56+j2OwknnySyfO1PAg50980Jnz/QxYSf8S3AGYSLwifiNODuPzGzBuA5d/9eRv3K8pyR1Xtss7u/ECZCwMwaKXLxVYqZTSGcu95MuOi6GDgOuAaYGqOp0blzBIC7/9PMRifoT/4MgwF7EQUVZnZUwpmG5939eTPDzLZ393vN7MAE7UC4wHqVu2+I+vsNQrATO6AmXNT+FVjG1oAijVSfl+7+AICZzXD3V+Qd+pyZrQI+l6BPm9y918y6zWw88DhhxiGJF9zdzcyjfu5Q6gkl/Aw4C1hJ8tf/Y8CZwEuidnIB9XOE30cSWX4efRN4q7uniXVymtz9L2Zm0Xvl3Cht7EsJ2zuJ7D5Hvg88TBgkMsLfwX6Ei6+fE2ZJhqxeA2rcvcPMGty9B/hFFAT8b4KmJrv7O83sbe6+wMwuIZzMksryzbXJzI5z9xuh78p5U8w2lgOPEvax/07e/euAOxP0KSeTD8rIlijwbwXemms/YVtZfkhm2daWKLDLnfR3I4xYD5m7L4ie+3HgOHfvjm5fSPL3bJav/f3Rc7MKqJvd/WdRatL1wPVm9ve4jbh7T/QzZhVQZ3nOyOo9dr2ZfR5oMrMZwH8Di5N0KDpfPUMILD6X98H2t+gcFMcKM/s/to4+nsbWC8M4Bo44/m6Q++N42Mx2Jox2LzWzp4EHErZl9A++etgaRMU11t0/m/C5BWX0eWlmdqy73xTdOIbkg0Urotf+p4SAcz3hAiSJ35jZT4CdLaQ+fTBqN6ln3f1PKZ5PlEo518xmJZmlGESWn0ePZRRMA2w2s1HAfWb2SaATGJeivSw/R05095fn3b7IzG53989G58tY6jWg3hiN7t1uZt8kBIxJ/7Bzeb/PWFgk8R/CNEhSWb65Pg4ssJBLbYQR5Q/EaSAK6h8Ajk7Yh8Fk9UEJYSTsDOAr7v5vM9uHrdOKcWX5IZllW/OAK4DdzewrhCnPsxO29SLC1GFuhmFcdF8SWb72Gwl/k38h72SYMBUFtv5tPmpmbwEeIeQIJ3GTmf2AMOK9Ia9vq1L0K4tzRlbvsc8CHwZWE0bHlgD/l7BP7xxsKtTd46abfZyQKpB7D/yVkP4Ui7ufF12QfsPdE+VMF2jz5Oi/55rZtcBOQHvC5n5BuOC4Irp9EuGCJIk/mtmb3X1JwucPlNXn5YeAn+d9Hj1NCF5jc/f/jv57oZm1A+PdPdEAj7t/O7qIfI6QR/0ld18atx0zOzz677Vm9i3CRVv+eSz2uSKa8TuGbdN3kiy+zPLzaIWZ/TpqK/9n/N2gzxjcHEJ6y2zgy4QUlNaE/YJsP0c2mtm7gMui2+8Ans81GbexuqzyYWE1+2OEfLCzCCfCH7r7vxK09WHClOYUwklxHOEP8sKEfTuSMP27M+HNtRPwTXe/JUl7UZvjAdz9uRRt5OcSbUe4AkyUdx61tz3hg/K46K6/Aj/KcLo/NTM7nuhD0t1fqHRb0RTd6wkfRn9JOkJgZqcTcvyujdr6L+Dc3Ah2zLbmeEaLVc2s4Ek0Sb+i9k4gvK/2JEydjyf8nLFHXqOAqUDXYuVj59rK9JyR126i91gUaN7t7gel+f557U0gTCW/xN3fZGaHAEe7e+wAMZp+fz4aGc31dXt335iwbze7e2aDA1F/JtA/2HkwYVuHk3c+dPdEqVPRuXoH4AW2XrwlzW/NfV4+Tjjn5z4vf+QhVz5JeztFHYq9OD6vjawX6+VXuBkLNHjMCjeDnCNykp4rfklIMbidvHzgFIMMuXZTfR5ZWA8xkHvCPP0sZfk5Ymb7EtaJHU2If24h/A10AkfkZv+H3F6dBtRvGjglY2ZnpP1AqxY2SEWCnIT5gvntG/A24Ch3T5L/lgkz+427v8u2XUWeWyARe/V41G4mH5Jm9mXCQojludzINDL+8H4x8Kro5t/c/T8J21nl7ocPuO+2AbmSFZE/vVzsvlpkJWone7yFf5jZlcCspO+nAW39iXCh8AV3f7mFfOzb3P2wBG3dAkx39/XR7XGEiiHHJOzbjwmLun5L/5mG2CNrZjaLsN7lMbamX8U675jZeHd/brDfZ9zfYzWLBlFOYdvR1vNjtJFbrHctIX81f7Fee5KLQsurcOPu+1koDHBh0uA8S2a2BjjEUwRi1foeM7Pvu/uZZraYAqO97n5iBbpVVvWa8vFFM9vs7tcAmNlnCNMMsQPqLE4SUTtZvrlyeYEHEhYh5BYTvhVIXWYt+uP+vYVyQ4kCatt2JXSu7TjVTOZEX08o+qh4/Sr4IUkYTYzrfuBUYF40avRX4AZ3vzJlv3L5lUn7BWGl/hOE1/4AMzvAY1RYsTIsVrXsyye1AYcP4b6h9K3gGoa4f+dRW1mcM1YSfv+5BXZPR//fGXgQ2Cdmt14E3G1mt9I/0Ezyobaru//GzP43aqPbzJIuzhqTC6ajttZHI4hJjQG66F/ZwNmaUx3HHMLip64U/bmEcP7K/T5zcn/fid77ZnYiYeYJ4Dp3/2OCNgYbsAASlbu7klAhYSXJ81vzF+vlp1CkWayXWYWbnCjFbGAVn9jnCkKVrhcT0mySGvgey8/NT/QeM7OXEs6luTURfwXmuPvDMZrJpQZ+O+73H6RPmQ+wWcYVc+o1oD6RkGf2GaAFOIgw4ppEFicJyPDN5e7nAZjZDcDhuakrMzsXuCpJmxbK5OSMIqz2fn6Qhw9F6pXQ7v5o9PWBaJo5V0LuVnd/PGG/sviQzPXvF4QFPC8mlC78H8JISJKFUJn1y7aWFbqb/hcNccoKlWOxaiYVbszsaOAYYLcBszXjCRcSSeTPMIwhfEAlXZST+pzh7vsAmNlPgSs8ypc1szcR8m/j+mKSfgxig5k1s3UB7VGEnzdpW4d7lH9qZkcQf2F1H3c/PelzC3iI5D8XAO5+QvQ17gXQoMzs64Rz4cXRXXOimZm4iwizHrB4qbu3pGnAy7NYL+sKNxcSRtFfS1iH8A6SD2TtCtwTXejm5wMP+UK3HO8xwrn6EkKFKID3RffNiNGvldHX6zPqU+YDbGRcMacuUz4AoivQZYQPtQ8mnVIxs7vc/dBMO5cRM/sHMMWjvORoZOxOd4+9sndAzlQ3oX70T5MGrmb2N3d/VelHDqmtdwHfAq4jXI2+GviMu19W7HmDtHUtMMOjChgp+/V/hNHWxwh/lDcCq5K0nXG/+r0vUrb1DR9QUaDQfUNsa6W7H2Fmq3PpAbn7YrZzPGE6+Az6zzqtAxa7+31x+1bge2wP/NndX5PguZmdM/Jfq2L3DacoF7gNOJQwwrYb8A5PsGjMwpqSSwkLSo0wWvfu3IdxgvayGFnLtfUzwizgVfQPdhKl1FlGm1GY2Z3AVHfvjW43EFJuks5mZcLMLiKU1lydQVvbEf6++0bhgZ94gs3BLCy0fIZQDnAWocLNPe7+hYR9u9Pdp+R9HQf8yd1fXfLJ27Z1fKH7kwahGb7Hbnf3qaXuG2Jbqet2D2hvB7aWVTyAMGD6p4TvjUQ/02DqaoTati6sy02nbUeY7niHmbknW7Sx3MwOy+IkEfUxyzfXQuBW679yPNECr6xGdqwMK6GBLwBH5oL7aJpmGVtX5sZxP3CdhS2K035INhNGRJ8hpEE8mSIgzrJfWZYVmkGoEJHvTQXuG4pMKtz41hJ586PZi3HR/etLPDWOsYQNEpLI8pzxiJmdTf9qOY8M9clmdqO7H2fbbmCQeKMGd18VBQIHRu0k3gXV3f9uYTFubhAg1dbjZDCylufB6N920b/ELONNjQipP7nUq51S9i2rTTyOAz5gZv8mnHvSrHX5EeEclqv48n7gx4RKNXF9jlCBJIsKN7B1BmWjmb2EkGK0R5KG3P36rGZfM36PdZnZ+wh7R0BIbUw6e5pF3e58NwCvtrAhztXA3wk/d5KNhDKtmFO3I9RZMbN7gMlAFicJzOxeCry5kk71R1OkuZXjN3jyleMLCCM5z0S3XwR8J24ukZVnJXS/EbkoKLsjySidhbzwQh07L25beW0eTNjE4yzC6vHYgViW/TKzywm7jCUuK2ShlvV/E1ag56/23xG4yd3fl6BfmVa4sVCS7pdsLZX3JNDq7nclaCs/L6+BMOp6vrvHztvM8pxhYaHROWwdqbsBOM8ruJjNzN5JWCC2Lgr2DwcuiHOxbGavc/drBqSa9fFk5bkyHVnLUsazRqcCX6d/FZ/Pufuviz5x8PY6yGATDwuVNLbh0cYvMdu6w/vXBy54X4J2dyGkpiTeY8HCDqFthIpMPyScN/7P3WOnVWU8+5rle2xvws+Yq36xnLCo+aEEbWU2Wx21t8rDJmizCPt6fDPF6HmuYs5mQsWcNDtC1tcIdU40Cny7u2+IrrIOB77vyVa4vynb3qUvCp/P3Vea2UNEiyPMbK+EP+eUXDAdtfu0mcWu5ODur03wvUtpN7M/s/Vq+d2EUYbY0gTOA1ko2/ZqwgfazoRd4hJt4JFlvwiLVP9Q8lHFXQL8ibCIMH9h6rqkwZy75zZdWU/In07rIuBT7n4tgJm9JrovSYWI/Ly8bsLGBklnGzI7Z0Sv9ZySDxwCy66KzBfd/bdmdhwhqPg2YfQwzofm8YS/l7cWOJZ0ESFkMLJm5alOkNmskbsvMrPr2Dqq+VlPWMUnkskmHr51x8TdyVusl1CPme3nUalbC+XNEo1uRq/ViYT3/UrgcTNb7u5nJezbN6Og9XIz+yPhZ0263ijr2desZiZfOvB9HsVVsQNqsp2tjrpiRxNGpD8U3Zdo7Yy7p9n4aRt1OUId5Zi9nFAhYT5heudd7l4wX2kI7b2cEDhBqB96R4q+fZ3wy0/95rKw0vs7hBXRjxOqAdzr7i9L0NYdwGvc/eno9i7A9UlGgfPazGolNGZ2Cnl5ke5+RbHHF2lnN+D/FehXkpHzHxAC6L+6+5Cn4Qe0UfWlhaLAaX93/4WZ7Qrs6O7/TtBOoZ/xWcKGPz9x91gfSlmPYg34O78hzShW1F6/wCJJ8JrV+9UyKAGX19Zt7v4KM/sasNrdL7EKl1K0UC7vs4RqJgNH1mbHee3N7IhooCJ1fquZtUX9mEjKWaO8NgtVsXkWeCDJRaCZzSXkrv+eFJt4FPg82htYk/Dz6PWEVJ37CaOGewOn5y6eY7aVe79+GNjT3c+xKP85bltRe4VKiW5z3xDbynL2NfXMZF5bWf6MmdX4j9r7L0IBgJvc/RvRxdaZMWdgD/Kwk2TBnydpsF+XI9RAt7u7mb0N+IGH7Yk/VPJZBZjZHEJZldzJ5VdmdpEnX4GcG8WZlnef07/M01B9GTgKWBadMF5LyBlM4jvAzWb2W8IJ7B3AVxK2hWW7Ehp3v5ywWUZaFxN2wzuBsOillVBeLkmfPpnLf4v+MJPkv2VaWgjAMixPF6WiTCPkuP6CkEv6K7Ze3MRxPyGVIn+mYR1wAGEr4PfHbS+afs29hu+LvkdsBf7OL076dz5YYEEIiuPK6v2aWRUZoNPCVs4zgG9YWMCZaCdaCxtbfNKjTUCiqeafe/wawfcTRh/PSXsR6tlWJ8jtDnsPYVrfCTMgiSuZEPKKDydU2zHC4tC7gZ3M7OPufnXM9sYTdp97Q959SWYJsvw8uh/Yn7zcepKXEG00sz0IlZgSLUQEsFDNaSLQNCAQG0/4rEui0Oxr0hns1DOTVoYKSlnPWntYZHlD3u372brT6lB9ilCR6zsFjiWNx8Dd6+4fcD3wv8A/CVfeowgjKUnauhPYIe/2DoRKGknaagDOyvDnXBF9vQMYlft/ivYOAT4Z/TskZd/uHPB1HGEkN0lbbwfuI4zCPEcIwp5L2NbK/H5F//97wrbeSdjadQFhgei/CdUOyvbeHmK/biRMxd9JCObOJeQDJ2nrdsKH9m0Df7cJ2trmdc7dR9jJL257LyJs2b4q+vd94EUJ+5bl3/kdhAWrt0W3Xwv8LGFbmbxfCfm2jRm9v8ZGf5P7R7f3AN6QsK2PAfcCbyZc0PyTkM+bpK2JhIuPZYQL+Lfn/sVsZ3X0fhj4b3Xc9wRhCv6bhPz+ldH79ElC3uzohD/n74CX5d0+hJAisC8h1TH17zhhvzL7PIpeq4l5t/+L5J/h74x+fz+Kbu8LXJ6gndbo72gdIV0p9+/KuO+xAe2eAnw3+ndypX5/UV+OJ8xkPRp9zf37VO7vPUZb74u+fqrQvxR9PICQ2nd1/u+hkq9b7l+9jlC/m7ApxYfc/T9mthfhBJaE0T93K7fpRmzu3hMtKPlewr4M9IyFCgc3EEbUHqd/Pd2SrP8uS/8h5M7mju3iyRc/ZbYSmvCBlHrRTCRXQeDRKCXlEbYuaovrbDLKf8tyVJmwUOMvZmYe8hrPNbOVQMHNS0p4wd3dzDzq5w4J2sgZl5/jH/1d5qp8xN4e10N60uyorQZCQPxcwr5l9ncObHH3LjMbZWaj3P1aM/t+0rair4ner3mjTJlUkYle51Wet2Odh3rxiTamcPefmNndhEDlSeAVnjAf2N07o5/vK4Tc7Pwa7HFGW7Osc/tNwnt8H9+6X8B4wozUtwibmMR1gLvfnbvh7vdEU9j3m8V/y1rYnfBDbJtWFHdzi9SfR3nOIGwudgJwBOHc+OYkDbn7bwk7Z+Zu308IYuPaFfhj9A/C++oJ4EZPkAKX15/LzWwpUcZA3M9dy3CDHh9QQWmozxtE7rMi0zxlwu/yQsLMd6qqIdH57C1su7FLotKYdRlQRyfk7+bdfpAwgpjEL4C/Wf/SdD9P0b2botzbX9N/x7IkOTtvIwSuZxES9HcC4uYol2UnL0I5mp0JHxqroraSlirKZNFM5AIz2wn4NCHXcjzh9UtilPdP8egi4dQ3GW16EsmkPF3kN9H0/s4WtvD9ICE9I4lPAzea2b8I7699gP+OgvTY5R7N7BLCB28PoXTSeDOb6+5JLp7z/86N8Lf1swTtQLaBRdr3a+7DLJMScNGgwD8s+eLnfszs/YRNZ2YSpvSXmNnpHnOdipm9jLAw8hHglVGQn0h+IGHpS5qdQAiA+86t0QDGxwkj82cm6OLdUc74pdHtdxM2B9merRdgcfwy6ssbCZ8fp5FsU6O3ERbnpfk8AvrKKc4GlkZtTnf3RKl5FupQX0D4rGwnvM/OcvdfFX3itgqdQ/cGvmBm57r7pQWOl+rbx4DzCD9jL8k+d+dEX7O8EPxBbhAlT6z1Lu7+k+hrlgvuIaT0/jijthYTXvvVbL0AT6xeFyUeRfjwOZjw4dEArHf3nRK2dzhbS9P91ROWpovayiRBP7qyWublqaqRqehEP8ajPMkEz89k0UzWLKxcnkL//Lc7vYKbnkTPy7o83QxCfqURNjtZmqSdqK3tCYX4IdQcTrwbp0WlkszsNEJO6ecIKRJJFxtl8nceXSBsIlwQ5QKLXyWZ7TGz3ZIGEkXaHAWMSzqab2GH1lcQ1kSk2sbczH4PfDRvlueVwEUeswSWma0hlP2Mmz9crM3UJc3M7J/ufkDcYyXabCKUtMy9V28i5FU/D4z1mPXYbeuivdxGJaMJ7/+j4vYtLdt24fIhhNmPpyHxeyx3njiZEHR+irDoONHi5QLt70L4LE6yYO8+4Gh3fzKLvmQl+swduN7lOcLvZry7D3m9i5nNK3D3s4QUoSsT9O1cwtqUK+gfDyQ5vyZenFpIXY5QAz8A3kOYGphGGP2IfeICsLBbVpu7z8u771x3PzdJe1kFwNFIUa+Z7ZQ0UM1nZn/xAQuBCt03hHYK1pWNjiUNglMvmjGz/+ehXmVu1X0/nmAltLt/xvpXH7nIE1YfIcNRZc+wPF0UHF7j7kvN7EDgQDMb7TE237DBaw7vl+I9ATA6+vA/ibD4eEuBUZWh9nE/Qh73KguLqV5tZv/2vFKSMXwpuqjqJRp5t7DpQpLNcG4ys7WEGa3fRWkusWU8mp/ZNubuflLUv7HuvtHdb42C6rimegb1dwfIoqTZPWY20937zZBaKO13b5JOufsmM/sR8Ed3/8eAw0k2N8r9LT9jobb7fwibvAyJbbuhWt8h4tf0zWxxdp5cnPMW4Lfu/myS1JjBuPtTlrzBfxE+21KzbTdvgq2jyp+OUl2G6hh3PzLv9mIz+7u7HxmlaMUxhjCIkku7OYWw3ujlZvZadz8zZnut0dfP5N2XdDb9T2b2hqwuxOs1oMbdO8yswd17gF+Y2W2EhYpxvRGYZmbfyTspnkhY6BVbNH17Dls3ariesGAsSVC8Hlgd5V/ljxTFKR8zhrDIaFcLm7nkTgzjCYt84srVld2dsFr4muj2awklrGIHT57NLo65KcwVRR8Vk2dXfWQO4fcwmzCq/Dq2njiGpMDoTj9JRnfovytVO+H1i7sr1fGUp+bwT4C1hEVQN1ioEJE0h/pywt/5ZEJ+3h8I6VBJ8jYz213S3Q+IAsz3EKaW7wEuTTBdfUiUanAaoYrA5whpXrEDag+7u+1NWKS0zMzGkrACgIWqAj8jXDzuZaF04ccII7Bx+pR1MA3ZpHR9AvidmX2Q8HpDGORpAk5O0ikLVWS+RZh93cfMphI+Q5JWN7ko+vv+IuF9P44YF02eYS1fz6ayykB/tLCh2ibg49GFUeKZsYGiC/BEF7qEmGS5mf2NlKXuCIuyHyact4xwztiPkHL5c+A1MdrKcr3LFODYKBbLlbj8K2GGJfZusu6+T9znFHELcEU0mJV6Y5d6Tfm4AZhOyNn9D2HK6ANJpnjMbBUhGPwVIQdxDmGVfaKaqxZqRd7F1pzR9wMvd/dBR3aLtFUw4HL3IeejWigXdiahxFcnWwPq54CfeoKd4qJ2rybsWvdodHsPYL67vzFGG5mPKmdhkJEASPnHmJYNUjc3J8mHlWW4K9VwMLNGT1aLN/dz/j9gk7u3WczayrZ1d8l9CSNPOYl3lxzQ/q6EtSGnuXusADYaVZpK+LD9QRQUJ6rZbSGX/qPALu6+n4UFtRfGnc2K2voboSLHH3KvtZnd5e6Hxm0ra5ZtStfr2Fo28R53/0uKfq0kXHBfl/ea9atnHLO93MBTKtEsz8PuvtnCJktTgIVJZnmscNrmhsSBTkjLeDaa2d2BUEs/1uJXK7zobxdC3v5Md48942BmtxKqMvXL4Y3zGZ7XVqG6/Ll0l1h/62b2ZsLAQr/1LoT0p4+4+/djtPUPwrqGXGnMnQjrEQ6Me46Nnj8a+DhbByWvI+R1x14/YGb/JuT+r/YMguF6HaF+P+EP8JOEBRJ7kmxVL4SLjmeBt0a5O9cRciKT2s/d8/tynpndnqQhd19gZtuxNZ3lH3HfVO4+F5hrZrM8eW3tQvb0/guDHiNsPBNHZqPKWY7eZjkiY2ZF64bG7Nf1UZtvBa5y99SLLEJz2exKZSF/+hS2XVGddLOfCcBXgZe4+5vM7BDChh5JFhNusVCBZyZbR9JHx2wj890lLVSEOJmto01XAElSIrIczf9E1Ie/Abj7fRY2sUnE3R8aMGOeOLizsCHIcndPU+eZaAp/HmFBYi5XOXFKl7vnyntlYUuBtIU0wcC/zaydkFZ0TYrAIn+W5yJCObmkszxZpm2OJQSDexEuBF9CqG/9x2LPK2Dgoj8Hutw96YJjCKUTP1X6YUOy0ULefy4l6R1sHYmP9Tt19yXRhXKh9S7fj9mvbwK3W9ix0giB8FejC5tlMduCsPh4NGHdAIR478fAhxO09RBwVxbBNNRpQO1bV2lvIqygTaMv4HH3c6PRgaRVIQA2mdlx7n4jgIXtPBOd/KNRgAWED0oD9jSzVg+Fz+PqNbOdc6MJ0RTgqe7+o+JPG9RfbNuC9bH+eNx9cfQ19tV6AbncvLcTFjjmpsxPJQT7lXI04Y96ESFAySK5793A96PZkJ8nGTnJcyZhWvIKd7/bwq5UhRbWDsWVhJy+lWSzPe58QnWO3GYN/yQEBUkC6tMJOcZfcfd/m9k+bN0wZkiiC+9ngVOt/zbf48xsnCerinEHYTHu+e5+c4Ln5/o2jxAg5jwQTVUnsdndX8gFdGbWSPKA7iEzOwbwaORpDskqTOTMBH5sZk8RppVvIJQ1izUl7+5uZkuiUd+KLn4u4G4zey/QEAU9swnpdEkdRAgWPwH8PBp8uDT3GRVDr7t3W1j815ab5UnaKc8ubfMXhHPOMdHtTkKgHiug9vRl5Ar5k5l9lFBtItUCO8Kgx1xCoOmEdIb3WVjE+skE7R3B1sGPl1tY7xKrWlqUSrGG8NrnBgI+71t3Fv5MwScWd+SA0fZrLOz0nESunOifSFFONKeuUj4GmZLJ8STTm1mzkCO4kK2j3E8TUiPuTNDWSuC9Hi1MMbMDgEWerDLENtP4SaZjBjz/7fTfyjnW6E6Wo8p5ba5w92ml7hsuUeA1gxDYTwGuIvwO4y78GNju+KjN0wmv4S+idtel63GqPmU6lW9bF8n0vU+zSEeJLib3TPI3GT3/k4Q1Flls821RcDfW3RMvXjKzZsLajeMI74cbCUF67J0TLZQhe4YQvM4ijP7d4+6xd6GL0ljmElL0jLBZw5wk/RrQ7ksII3T/Q5jBiD14ZGYLCOkxfy/54GEUjbh+ga2LtP8MXOApKubktf0iwu8jSVrR3wijl18g7Bvw76R/85Zt2uYKd5824DyRKN0pa1HKwUDuMfcfiD5HvuHu/5NRv35JmBG7na0zRu7JtjFPFUcUaG8V8E53/1d0e1/gMk9WZeWcQvd7wlJ/9TZCXagOoxFSPmJd2ZrZje5+nG2bL5soT9bM5kTpFePc/eVRwIMn34gCwnRR3ypvd/9nNMqTREPuwzvqbwMp6tVG/fkd6UZ3yrHiewcz29ejFc/RSGSazUpSiUZf2glb0G5PCIKvM7PzPGH+etTuc2Z2GWHx05mEtIHPmNm8OKk9Fso8FspfT7I163IzO8zdYy9EGcSGKFDMvWePIowQxxZNR55IOCeuBB43s5sSTseeSXbbfB9lodJQv0V77h5r0R6hZvENbE19O40wmj89QZ8+R0j/WU1YQLiEhDXmPZQLi7PAtSgL1TNeDRxG2CjmB4SR6iReBZxmZg8QFn3nzv2ZldmKKzovX+WhWlTibbQLtHs8YWarhZBi964EzaSe5cnzfsIC0CzSNl+IRmlz54n9yGaGLJVo9PZz7v7rtG15yA0/rvQjh2waYSFzFiOuf7FQDet3GbX3GeBaM7uf8De5NwmrWeUCZwv7BuAxS04OVFcj1PnM7BWE3RLfSSjRcnmaACWD/uQWB6xKciU1SJs/J4yA5dIXTgMaPP4OV7kFOHsTci0hfFA+5O6fjtlOVS7Y6+uEWQshvy//j/Fj7v7nCvZpe0JJp1MJU2x/IKRqdCZs723AB4DJhNmQBe7+eDSydY+7T4rRVv5sxxjCh1q3u/+/GG3kZo4agf0Jr/1mUgYoFupGtwGHEhb67kbY+j3JbM9tHmrxfpgwOn2OJaxRGl2EzPAEiyMLtJXJor1Cz7EUC9nSssK1afskGQmL2n2SsJDqQuBad1+bpJ2orb0H6Vs5pv6HzMz+QtjqOnW51Ki9tcBtwG8I77M0OcGZsVCNA09Zh91CHf2zCTWtryaUOf2Au1+Xto9pZTk7aqF6xkRCOkt+1a/Yg1pm9ltgtqfYICmvrXWEQatuQk536lgg+sw8MLr5D09Y5cdCmchfsnX32ScJC0wTzRDXVUAdpTycGv17kjAC8z/uXvDEOIT2Ggi1aQ8q+eDSbS0iXPW9hP4VABIHFdGb6hPkbUYB/CjJmyu6Wv4YkFupvxT4P89g9XdSJVJ4Ym2pOqDd/M1F7k36x5gFM1tICAiXEPIW78qgzfmEgHybXHoze72nqDAQtXGruw95YdxggUlOmgDFQv7ugYS/o9iLcvPaWU2YQl8AfMHDTm1JA+qfRX1Ktc131Nbf3P1Vaaerzey7hI1YfhPd9Q7CyvshTxGX+HvcTDivfc2HsMuhmb1AuAj6DaFKQv8VdinWTVjYNfG/COfF/QnviyFvRFGgvd3pvyV36h0i0zCzKwkb6yQulzqgvfFpZkqzPE+bmRFSkz5JGKE2QiDW5gkXL0ftNgNHRe3d4lWykYqZfZ2tsUr+7zLJJiW/KHC3Jxxgu5ZQFehW+p/DkpZmzIyZfQK42DNY72Vmywnn+2uj268BvuruxxR73qDt1VlA3UsIKj/k7h3RfffHzUca0OaVwKwsTqJm9mJCvts2b8pKj3oARNNie/m2mwVURLkCMQuLoCbRv9JE0q3pU4nes7kTaRapRZnuoGmh3FTOKMJClXnufuAgTynW1lGEC9R10e3xwMHu/rcU/cvkd2lm7yTU3r3R3f87ysv7lvevyDPUtjLLy4vSdr5LSF14FWHR3jR3f0/MdnKjRLmc7lHkve+G8j4r8ffYSLgwPNeHkC8ZBTjvJKQZdBMCiss82UY6+e2OJ4xAHk9I/diVEEDFquketXUi8B3CIMjjhNmsNe7+sqJPLDML5RlzC0G7iRa1x70IsUHKkeYMNUDP8jxtZp8i1Gz/qLv/O7pvX0IVh3Z3/95Q2xrQ7kTC7y//PJFk8X6mLKMc6qzZICVYPUbpVTM7yN3vjWYSC7W1KmHfMlvvVWhwIsmARd9z6yygPolQaudYQl7qpYRR1sSFwK3wNrvu7m9L19vkyjFya3mbBbj7PpZ+s4CqZBkutqhWWU4JRyf83C5o3YT0qfM9fgUALKzSP9y9L09/FGH72UQpUNX+u8wiL8/KtGivXCzk/he8oCjynJcSztufAj7r7knzbjGzOwkLLm8kLIR+OEVbdxDqPS+L0oFeC7zP3T9U4qllEc3GfBX4IPAA4f2wF2HB8efjzs7YIPsY5KSZJUgqOkfMGDiCHKV/XJ0waPoG4cLtbvovEq6Lzzar3v0aLnL3j0aj3QO5J1uHk4t/puR9jjQQ6sPHvtA1sysIG9/kzjnvA45w95MT9a2eAuocC/UN30ZI/XgdIY/0Ck+wveSAKzUjjHq8J+4vz8x+4+7vKhAMx075KMfIrWW8WUAWrAz52Ga2huwWW1SlrKeEszLIyEKitIroual/l+X4MMo6Ly8rZjaFbUfzK1oSLhq9OpVQ6WYl8B13vyeDdlNVRYnayFWHuAN4hbv3phm9SsvMvkfYJOisAbM83wY2evwtnHPtZrlQOBUrsj6g2LESbf6DEIBVfCHiQBbWtXyKMDP8UQtlEA909yGX9LOwOG8mYc1MoXNYnI3eMivGYGZHehkq5FhG672itl5EKK2cq370V+A8j1lmM6feqnwA4GFRxSXAJdEL9k7Ctr+xA2oPO4oNXOB4YYJuzYm+FqpEErdPfQGzhc0tjoxu3ur9t8qNI+vNAlLzDDdQyXMXoQ516sUWVSxtdZV+MkyRud/MZhOmbyGUW7s/Rdey+F2WY0v6i4BPef+8vJ+ytQ5uSWb2pSKH3d2/HKdDFhYwT2HAKB0VqrFsZucTFuKuIcwk/q9ns4iz4FbmHr8qCsAz0SzDDcDFZvY4eReoFXACcED+BaSHaj4fB+4lVJdJ4kcW1pXMJ+SmZrLYMaFi21rH3fI6537CRiBVF1CTTY3seYTZ5T0IaxIWuXui+t/uflz0NYvP3ouiv59LgUvcPU19+XyfJWzQ8/Ho9lISVhkCXprlQFNdjlBnwTJe4FgOFnZF+hZh98bc6Pln3P2yYs8bpK2fAX8hlMQ6hbBZwGh3PyOzDlcBq+LFFtUoy7QKC4u75hFmQpzwfjsz6UVgtf4us8jLM7NCoy07EMrVNbv7uJh9usfdD4nznCJtpd6NMFo78G8gN4qc+yBKW/kls63Mo5nOTYR889MIewdcXKl0GzP7p7sX3C2w2LEhtr0/IZXknYS/p/kJZ3RTrcMxsx4KX7QYMMbdY5eFtbDB1csJ55v880TFU8MswxrZ0cz1e6J/TYRBxUXufl/CvuVvTgXEX5BrZgdG/Xk3sIWwgdmlnqL6zoD2dyEExUn3DPgrsD3hwuaStBeTCqgHYRkvcCxT+sIdhHyzx6PbuxHy/ZL8MeZvFmCExZNf9gw2C6gmlsFii2oXfTh+jVAmKr86Qez3bjWnyGTxu7QMt37PazPbvDyzHQkzXB8ijEB9J+5FSHTBnFU6xQLCDp+JdyMsR9pa1G7qqigWts6e4O43Dbj/OOBRjzaUGG5m9ntCLd+FA+5/H/CutBeSUQB1EuGi9znC58Dnh5oWZGZvJaSfVNU6nMFyxeOkQpSLhSoTrwducvfDLdTIXuQxqigN0u4rgJ8TUl1ibdATPX8WodpK6s2p8tp8OSG4fhfwH3c/NmE71zFgzwDCBf5ZCds7gFDHOncx+Qt3X5qkrbpM+cjI2wm//GvNLLfAMfG20GVKXxg14IO1izCaEluUb/gFMtwsoBrVU+BcxC8IJ8PvAa8lnCwSvS/IIK2iXItmMvpdlmPr9w8S8vJygchfo/tiiUZfPkUYHV1AWNCZKLePsI7kZjP7DylrgHtUMcO27kb4Q0IljCF/niQNmIcgi63Mv0/hjcCejY69NU0HU/gE8Dsz+yAhkIBQirWJsHFTIlFu/emEFJylhF0OV0W/35sZelrQuYTtpa8DcPfbLWzuUlHVEDgPZGY/JJxzziUUUNjTzC4mqpGdsM1GQoWU9xCC9Oui9pOYQ3abU+UWoO9OGPHegRAEJ7VTlOr0YWChR3sGJG3Mw4Z4ZxPS/uYBr7CQ+zrki8kcBdSDcPffA7+3rQsczwR2t1A8/Yok02H5LJvapu1m9mfCHyaEaZUlCftzAGGb3kn0n+JJtBK32pRjhqCKNbn7X8zMosDlXAuLTovl5fZjW7d93xG4x8zSpFWUI08ZC1vbf4NwojaS/S5fzNat399LBlu/R0FvqunkaOHN2wn52Id5yh28CHnF7yfsbthb4rGl+pblboRZO4NQFWUiIR/1akIgGscEL7BIz91Xm9mk1D1MyMNGT68ys9cBuUXxSzxlXXnC5kj/Rwgg+tJ43P2RKNAYqqpbhwNgZscSAstc2bzceaKSpen+yda856XAMsKs1hyPWSPbwsY1pwJvJoywXkooO5gm3/8hEu46O6Bvr476dhLh3HMpYVFtmrYbzWwPwkh3qgHADC8mQ3tVOJNbtWzrAsd3u/vrSz1+kDZS1zbNn5KMgorcxi7PEHL8Yk9JRukjFxJGPvo2c3H3lYM+SapSNI14HHAZcA0hsPi6x6gdPVg6RU41jPSbWQfhBJjJYhfbuvX7twgrvWPtrJpl+kiUcraZUKowi/rkN7v70XGeU6StzHYjrEZmdp+77z/IsQ53nzzcfRou0WfcnklyUqt1HY6Z3UvYvnzgZ1vFS08Okvd8qbv/M0Yb10TPuzzFDFaurU9F/30ZKTenMrOHCOUdLwV+EzdNrUi7qfcMMLMzgeWEGv8/IdTA3zTgMe/3mCU8FVAPM8ugtqmZ/ZGwKn71gPsPI+zyE3tK0sxWuvsRpR8p1c7MjiSMCu8MfJmwmOqb7n5Lgrb2IeSNPh/dbiJczK2N0UbmecpRuzclzcMb0E4mW7+b2RMUSR+p5EWImf2I8H5YTP8PyERVPiyj3QgtgwWOUTuZVUWxsKvtNe7+0wH3f5iwZuXdCbtZlQbJSb3J3T9V7HkF2qnKdTi5vPpK9mEo0uY9Z9SHojXkPcbmVGa2dxlTu1Ixs28TKqscDNwJ3EQIsJd7gl0q+9pVQD28LIPapmb2d3c/cpBjiWpHm9m5hBPpFfT/wE385pLaZ2YrgGPc/YXo9naED9uC779B2ihLoGlmcwkpG78nYZBoGW79Hi3qyk2/TiGD9JGsWLbbEme5G2HqBY5RO5lVRbFQivQKQpm2/Fzl7YCT3f0/cfpW7SxawBldMOyZy0lNkl+fYZ+ySOfKtfV1oIEwfZ9/nki0U1+WBsl7XuTuV1ayX/mi/OdxnmJ7+oz6UY49A7Yj/G0fQzgPHQ084wkrIimHevg9Y+lrm+5c5FhTwn7lPgw/k3efAxXdAlWGrkwjwY25YDpq44XoJBRH5nnKkfGEsmtvyLsvbm3l9xH+/uYAs/PyP2N/gLt7D2GBUXte+sh1FnYPjJU+kjV3Pz3D5m7M+/cDT7EboWewwDFq5zu5/9vWqiinE6abvzPY8wZp6zHgmGj2MFdu7yp3vyZOOzUkVU5q3nqLghKed75JdulcudHpaXn3OWGmuCLKlPecGTO7hLAeoQf4OzDezOa6+7cq2K3tzeyVwB2Ei90sFpA3ET5Hdor+PULI9U5EAfUwyeU9ExY4biLkdJ1GyKGeFbO5FWb2kUGmJBPlPHuK7dmlapSjYsUTZnaiu/8BwMzeRliINmTlCjSzCBLdPWn1k4IKpI/MI4x2VpSZjSGM1r6M/ouhY49Q50YuLZvdCDNb4GjZVkXBw8Y8hbZNrjfnE9IzbnT3v0c5qXFqF387+vp2wsXzr6LbpxLKriXxWFZrI9z9tVm0k7H/JeQ9fzpt3nOZHOKhksZpwJ8IefErCetLYjGzY33bEpTb3DcEOxGq7KRO0zCziwjnwnWEz8rlwHdT56Ar5WN4ZJn3XK4pSctuRzypgHKkHFioi3oxYdTQCAH7TI9qs8doJ5M85aitspThSyvL9JGsmdlvCbvpvZcQQJ1GWAw9p+gTC7fVtxuhu6fajTCrBY7WvyrKDz19VRSJKZfOWOq+Em28Pfrv8aRM58prcwLwVeAl7v4mMzsEONrdfxa3rZHCzO4mbJp1CWEW6vq4qal5ba1y98NL3RejvdRpGhZKIe9KKAu7nFDR4y5PGRAroB4mZcp7zp+SvDvNlKRluCOeVJ6lrFhRoL1xAEkClawDTTN7q7svtirbsMFCZY7clG3qyhxZysuTvdPdp1io0fxXdz8qQVuZ7UYYPTf1AkfLuCrKSGJhQ7CPsO1gSqzZCwubQL3F3e+Pbu9DKOt3cIw2CuX653UpUc7/nwi1+b/g7i+P8pZvS/KZO1KY2WzCFt93EAZC9gJ+5e6vjtHG0YSg90zCngg54wkDf7GD86jdnQhB9LHR152B1XFnLC3k970s6uMxhM+op4Cb3b3o4szBKOVj+Oxc5FiivOeMpySnUaU74snQlSPlwMzeQpQqkMsxdvfzYzSRWZ5y9L0XR1+rasOGrNNHMrYl+vqMmR0K/Iew4CsRd3/I+tcb7hnsscVECxz3IqS+TSJM68auk13lr321u5KQZrOMhL/HyFmEVK77CX/bewMfjdNALigaLE0gTltm1uju3cCu7v4bM/vf6Ht0W9jiXAbh7vMInx05D0QDeHFsB4wjxJn5G9s9R7ggjyXrNI0o1rnLzJ4h1Nx+FjiBsDmRAuoql3nec8ZS74gnlTVgJPi8LFIOzOxCYCzwWsLmD+8gLKIZsjLkKZelDF+du8hCjeEvEtJtxhFjo58BstiNMCezBY5Zy7LSRJUb6+6fTduIu7eb2f7AQdFd97r75mLPKaINGJgSUOi+Ym6NHr/BzJqJZi7M7Cgy2LSk3uUPpOTdPeSBFA/Vm643s/kelc9LWTFkL2B7Qn5/J/AwYe+N2KIR+NzI9BaiXGxC2cLEixKV8jFMypX3nBUzu5aQM5VmRzypoHKkHOSlCOS+jgP+FGfqL2tWxfWeRwIz25WwG+F0wmt/NWGHt8QbZWSxwDFrlvHGQdXKzC4gLOxKtMtuXjujgY8T0ncglID7ibtvGfRJ27aRWZpAXprT4YRg/FDCwNFuwDs8weY1I8VgAykeY7+MvLa2qRgCJKoYklWahpl9l2hRo7tnNoiogHqYZZn3nCUbZGc8BScjm0WbIpjZLYRFX12E923Fdosrx+LLemVbdz4ryGPsfFYOWS5wzJpltHFQtTOzdYSa3ZsJo3VJd+P8P2A0ocIKhK3ue9z9wzHaOB54DSEAuzDv0DpgsbsPufqImT1M2AkPYBRhdNMIP2dPpd/71SzLgRQzu93dp1qoGHI4UcUQT1Hn3MxeSsihPoaQptHs7jsnbS8rSvkYZhnnPWcmWsU7AcgtnLzVM9oqVGraH81sZ8LixlWEke//q2SHvIrrPVeh/NzFjxG22U3EMtyNMM/3gTcS0lBw9zvM7L+KPqPM8ipNrDCzX5NBpYlq5u47ln7UkBw5YAT5GgsbmMXpyzZpAik0EFKbBpYPHZuy3ZEgt7vlRgs14ruAPRK2NTqavTiJkNa1xcxij+SWK00jSwqoBQAzexchaLqOcAJqM7PPuPtlFe2YVFRekHS5hdKPY9y94vmH5Vh8WY88b6tgMzvJY2wdXEChTSf6diMkbHMfW1YLHDOUX8I07cZBNcHMJhIWEeZX+bghZjM9Zrafu/8ranNfkv8u5xcKutw9zmYsj8ZcPD3imdmZhCD1D9FAyjdJP5DyE2AtoWLIDWa2N2FhYlyTgN8CZ2WZppElpXwIANFIwozcqHRUSmlZ0tI2Uh/M7J1Au7uvM7OzCVN2X3b32yrYp6qt91zNLEXt1wJt5XYj/BDwG+A7SWa0zOwywrT8Dwg72s0Bprn7e7LoZxqDVZoYeF+tM7NvAO8G7qF/ydRY62fM7PWE8nT5VT5Oj2Zl4/bpiLybY4BTgG53/38x2rjNo7KOMjRm9m3CCHD+5ik3E3KNE6+RKPB9chVY6ooCagG2rYUdrca9Q7U6R7a8HLrjgAsIsxhfcvdXlXhqOftUtfWeq1kWAbVtuxvh3KRlq6L2Ml/gmJVCr1eWFyXVwsz+AUxJUZEjv63tgQOjm//Ios28tm9191fGePwuHnMHPQksg81T8toaMRvrKOVDctrN7M+EygkQRixSrfqWupAbsXoLcJG7XxVVBaiYrMvw1TMzW83Wi47JZparbJC7+BjywiDrvxvhYZ7BboTu/iQhOK8aeZUmdhuwqHM8IS+33txPWEyYRfB7BFs3iJlqZol2240u3HJGRe3uFKcNBdOpNBHe7ztF/x4heZ7yfKKNdaLb/wR+TViMXFcUUI9wZjYZmODun4kW4xwXHbqZsOW0jGydZvYTQlWNb0QjUApoa8cJGbb1aULQdTbwBUuxQU+ZFjhmJdMNKWrARuB2M/sL/Rdfxtol1wbZbReIHVATSss64b3VDfybkF4kZWQZb54SGTEb6yiglu8DuTf674gW3JjZYdGxtw72RBkR3gW0AN9292fMbA/gMxXukwxRBpUS8tvK8kKqLAscsxBVPLqRkAaRZhFnrfhD9C+tzHbbdfd9MuiPxJfZ5il5RszGOsqhHuHM7O/ufuQgx/rlVcvIY2b7AQ+7+2Yzew2h7vNCd3+mkv2S+pHVAsesmdnN7n50pfsxHMysCdjL3f+Roo3fArOzqMCQxSYxkkxWm6fktXcEoQpT3W+so4B6hDOz+9x9/0GOdVRyAw+pPDO7nTDyNImQU38l8DJ3f3MFuyV1IOsFjlkzsx8DEwmluvpG1OutDrWZvRX4NrCdu+9jZlOB84da5cPMFhNGH3cko912s9gkRtJJu3lKXgm+VdFdBxJSeP5RrxdGSvmQFWb2EXf/af6dZvZhtm6RLiNXb5Tz9nagzd3bzKxiJfMkmaik2XJ331TpvkB5FjiWwRjChhb5tY/rsQ71ucArCaPAuPvtUQ3pofp2GfqUepMYiS/jzVNeSkgbPSh67k1RW48QRrzrjkaoR7iopM0VwAtsDaCnERbmnOzu/6lU36TyzOxvhJPiF4C3uvu/zewudz+0sj2TOMxsAaH01VPAX4EbgBsrNSIclT7cTFhwptKHFWRmt7j7Ufl1m3PlMlO2Owo41d1jL243s1XAOwdsEnNZvZUsrDZm9l2iwDerzVOyLMFX7TRCPcK5+2PAMWb2WkKOE8BV7n5NBbsl1eN04AzgK1EwvQ/wywr3SWJy91aAaBvhdwA/BF5ChT4DaqH0YTTl3UaY9oZwITLH3R+uXK/K4m4zey/QYGb7A7MJI4lDYmbjgU8Q0mP+ACyNbv8PYXe8JNWiPgNca2b9NolJ0I7E4O6fKv2o2LIswVfVNEItIlLnzOx9wKuBw4AngRuBv7r7zRXtWBUzs6XAJWy9gHwfcJq7z6hcr7JnZmMJM1C5Ldb/DFzg7s8P8flXAk8TSq2+HtidEATPcffbU/SrbJvESPkVKMF3C3BLNa2TyJoCahEZVDRi9TXgEEJOKQDuHifHUirMzJ4E/gVcCFzr7msr26PqZ2a3u/vUUvfVKjMbQ5h9mkwYMfxZku2g86tBmVkD8CihYsiQAvIBbR0JPJRLNTSzmYRtxx8AztVmLbXDzNqBXQmVPZYTLrjuyqKsYrWq+mk3EamoXwA/JuS6vpawScOvKtojic3ddwU+SLgo+oqZ3RptxCGD6zKz95lZQ/TvfYRFivViASG3dTXwJpIvLuyr2ODuPYQym7GD6chPCOt5MLP/Ar5OOOc8S1jAKjXC3VuAI9n6vvo08Hczu9rM6rK+u0aoRWRQZrbS3Y8YMAq10t2PqHTfZOiiPNdjgeMJqR+7EqZfWyvasSpmZnsTcqiPJiycXE6os/xgRTuWkQF/043ArUkW/UW73uXKChohZ3YjyXbQvCNX3cPMfgg84e7nRrfrZnZgpElbgq9WaFGiiBSzOVqtf5+ZfZKwe9a4CvdJ4rsx798P6nBhXeaiXSZj11CuIfkjy915W8nH4u4NmfUoLIxsjFJPXg98NO+Y4pUaknEJvpqgN6iIFDMHGEtY+f9lQk1ejWrWmFwJNDMb6+4bK92famZmXypy2N29YtuiZ+zlZvZc9H8DmqLblSxduAi4Psr530SorIKZTaZOt6uuY5MImyKdlVUJvmqnlA8RkTpnZkcDPwPGufteZvZy4GPu/t8V7lrVMbNPF7h7B8LW6M3urhmaMjKzo4A9gKvdfUN03wGE9+6qok8WqSAF1CKyDTP7Q7HjSbYTlsqJNuh5B/CHvM07tEFPCWa2I2GW5kPAb4DvuPvjle2ViFQjpXyISCFHAw8RpmD/RpgGlhrm7g8NyJPtqVRfqp2Z7QJ8CjiNUA3j8Hqunysi6SmgFpFCXgzMAE4F3gtcBSxy97sr2itJ6iEzOwZwMxtNGHVdU+E+VSUz+xbwdkKZtsPcfX2FuyQiNUApHyJSVLRj2anAt4Dz3P0HFe6SxGRmuwJzgemE2YarCTvZ1VNd5UyYWS+wmVB7Pf8DspKL9USkyimgFpGCokD6LYRgehLwB+Dn7t5ZyX6JiIhUGwXUIrINM1sIHAosAS5197sq3CVJYASVgBMRqSgF1CKyjWjaO7f7maa9a5RKwImIDA8F1CIiI4BKwImIlI+qfIiI1DGVgBMRKT8F1CIidUol4EREhodSPkRE6pRKwImIDA8F1CIiIiIiKYyqdAdERERERGqZAmoRERERkRQUUIuIiIiIpKCAWkSkDpjZF8zsbjO708xuN7NXmdmZZjZ2CM8d0uNERKQwLUoUEalxZnY08F3gNe6+2cx2BbYDlgPT3P3JEs9fO5THiYhIYRqhFhGpfXsAT7r7ZoAoMH4H8BLgWjO7FsDMfmxmK6KR7POi+2YXeNwbzOxmM1tlZr81M21RLiJShEaoRURqXBTw3giMBZYBv3b36weOPJvZLu7+lJk1AH8BZrv7nfmPi0a3fwe8yd03mNlnge3d/fwK/GgiIjVBOyWKiNQ4d19vZkcArwZeC/zazD5X4KHvMrOPEs79ewCHAHcOeMxR0f03mRmE1JGby9V3EZF6oIBaRKQOuHsPcB1wnZmtBlrzj5vZPsD/AEe6+9NmNh8YU6ApA5a6+6nl7bGISP1QDrWISI0zswPNbP+8u6YCDwDrgB2j+8YDG4BnzWwC8Ka8x+c/7hbgWDObHLW9g5kdUMbui4jUPI1Qi4jUvnFAm5ntDHQDHcBHgVOBdjN7xN1fa2a3AfcCDwE35T3/ogGP+wCwyMy2j46fDfxzeH4UEZHao0WJIiIiIiIpKOVDRERERCQFBdQiIiIiIikooBYRERERSUEBtYiIiIhICgqoRURERERSUEAtIiIiIpKCAmoRERERkRQUUIuIiIiIpPD/ASifvJt/OkkuAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Code task 16#\n",
+    "#Create a seaborn boxplot of the ticket price dataframe we created above,\n",
+    "#with 'state' on the x-axis, 'Price' as the y-value, and a hue that indicates 'Ticket'\n",
+    "#This will use boxplot's x, y, hue, and data arguments.\n",
+    "plt.subplots(figsize=(12, 8))\n",
+    "sns.boxplot(x='state', y='Price', hue='Ticket', data=ticket_prices)\n",
+    "plt.xticks(rotation='vertical')\n",
+    "plt.ylabel('Price ($)')\n",
+    "plt.xlabel('State');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Aside from some relatively expensive ticket prices in California, Colorado, and Utah, most prices appear to lie in a broad band from around 25 to over 100 dollars. Some States show more variability than others. Montana and South Dakota, for example, both show fairly small variability as well as matching weekend and weekday ticket prices. Nevada and Utah, on the other hand, show the most range in prices. Some States, notably North Carolina and Virginia, have weekend prices far higher than weekday prices. You could be inspired from this exploration to consider a few potential groupings of resorts, those with low spread, those with lower averages, and those that charge a premium for weekend tickets. However, you're told that you are taking all resorts to be part of the same market share, you  could argue against further segment the resorts. Nevertheless, ways to consider using the State information in your modelling include:\n",
+    "\n",
+    "* disregard State completely\n",
+    "* retain all State information\n",
+    "* retain State in the form of Montana vs not Montana, as our target resort is in Montana\n",
+    "\n",
+    "You've also noted another effect above: some States show a marked difference between weekday and weekend ticket prices. It may make sense to allow a model to take into account not just State but also weekend vs weekday."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Thus we currently have two main questions you want to resolve:\n",
+    "\n",
+    "* What do you do about the two types of ticket price?\n",
+    "* What do you do about the state information?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.6.4 Numeric Features<a id='2.6.4_Numeric_Features'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Having decided to reserve judgement on how exactly you utilize the State, turn your attention to cleaning the numeric features."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.4.1 Numeric data summary<a id='2.6.4.1_Numeric_data_summary'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>4591.818182</td>\n",
+       "      <td>3735.535934</td>\n",
+       "      <td>315.0</td>\n",
+       "      <td>1403.75</td>\n",
+       "      <td>3127.5</td>\n",
+       "      <td>7806.00</td>\n",
+       "      <td>13487.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>1215.427273</td>\n",
+       "      <td>947.864557</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>461.25</td>\n",
+       "      <td>964.5</td>\n",
+       "      <td>1800.00</td>\n",
+       "      <td>4425.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>3374.000000</td>\n",
+       "      <td>3117.121621</td>\n",
+       "      <td>70.0</td>\n",
+       "      <td>869.00</td>\n",
+       "      <td>1561.5</td>\n",
+       "      <td>6325.25</td>\n",
+       "      <td>10800.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>0.172727</td>\n",
+       "      <td>0.559946</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>164.0</td>\n",
+       "      <td>0.006098</td>\n",
+       "      <td>0.078087</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>0.184848</td>\n",
+       "      <td>0.651685</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>1.018182</td>\n",
+       "      <td>2.198294</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>0.933333</td>\n",
+       "      <td>1.312245</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>1.500000</td>\n",
+       "      <td>1.619130</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>1.833333</td>\n",
+       "      <td>1.815028</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>2.621212</td>\n",
+       "      <td>2.059636</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>330.0</td>\n",
+       "      <td>8.266667</td>\n",
+       "      <td>5.798683</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.00</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>10.00</td>\n",
+       "      <td>41.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>326.0</td>\n",
+       "      <td>48.214724</td>\n",
+       "      <td>46.364077</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>19.00</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>60.00</td>\n",
+       "      <td>341.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>279.0</td>\n",
+       "      <td>2.820789</td>\n",
+       "      <td>2.008113</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>325.0</td>\n",
+       "      <td>1.433231</td>\n",
+       "      <td>1.156171</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>327.0</td>\n",
+       "      <td>739.801223</td>\n",
+       "      <td>1816.167441</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>85.00</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>690.00</td>\n",
+       "      <td>26819.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>284.0</td>\n",
+       "      <td>174.873239</td>\n",
+       "      <td>261.336125</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>50.00</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>200.50</td>\n",
+       "      <td>3379.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>279.0</td>\n",
+       "      <td>115.103943</td>\n",
+       "      <td>35.063251</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>97.00</td>\n",
+       "      <td>114.0</td>\n",
+       "      <td>135.00</td>\n",
+       "      <td>305.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>329.0</td>\n",
+       "      <td>63.656535</td>\n",
+       "      <td>109.429928</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>50.00</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>69.00</td>\n",
+       "      <td>2019.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>316.0</td>\n",
+       "      <td>185.316456</td>\n",
+       "      <td>136.356842</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>69.00</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>300.00</td>\n",
+       "      <td>669.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <td>276.0</td>\n",
+       "      <td>57.916957</td>\n",
+       "      <td>26.140126</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>40.00</td>\n",
+       "      <td>50.0</td>\n",
+       "      <td>71.00</td>\n",
+       "      <td>179.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Alaska</td>\n",
-       "      <td>AdultWeekday</td>\n",
-       "      <td>30.0</td>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>279.0</td>\n",
+       "      <td>64.166810</td>\n",
+       "      <td>24.554584</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>47.00</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>77.50</td>\n",
+       "      <td>179.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Arizona</td>\n",
-       "      <td>AdultWeekday</td>\n",
-       "      <td>89.0</td>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>283.0</td>\n",
+       "      <td>120.053004</td>\n",
+       "      <td>31.045963</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>100.00</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>139.50</td>\n",
+       "      <td>305.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Arizona</td>\n",
-       "      <td>AdultWeekday</td>\n",
-       "      <td>74.0</td>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>187.0</td>\n",
+       "      <td>100.395722</td>\n",
+       "      <td>105.169620</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>40.00</td>\n",
+       "      <td>72.0</td>\n",
+       "      <td>114.00</td>\n",
+       "      <td>650.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "     state        Ticket  Price\n",
-       "0   Alaska  AdultWeekday   65.0\n",
-       "1   Alaska  AdultWeekday   47.0\n",
-       "2   Alaska  AdultWeekday   30.0\n",
-       "3  Arizona  AdultWeekday   89.0\n",
-       "4  Arizona  AdultWeekday   74.0"
+       "                   count         mean          std    min      25%     50%  \\\n",
+       "summit_elev        330.0  4591.818182  3735.535934  315.0  1403.75  3127.5   \n",
+       "vertical_drop      330.0  1215.427273   947.864557   60.0   461.25   964.5   \n",
+       "base_elev          330.0  3374.000000  3117.121621   70.0   869.00  1561.5   \n",
+       "trams              330.0     0.172727     0.559946    0.0     0.00     0.0   \n",
+       "fastEight          164.0     0.006098     0.078087    0.0     0.00     0.0   \n",
+       "fastSixes          330.0     0.184848     0.651685    0.0     0.00     0.0   \n",
+       "fastQuads          330.0     1.018182     2.198294    0.0     0.00     0.0   \n",
+       "quad               330.0     0.933333     1.312245    0.0     0.00     0.0   \n",
+       "triple             330.0     1.500000     1.619130    0.0     0.00     1.0   \n",
+       "double             330.0     1.833333     1.815028    0.0     1.00     1.0   \n",
+       "surface            330.0     2.621212     2.059636    0.0     1.00     2.0   \n",
+       "total_chairs       330.0     8.266667     5.798683    0.0     5.00     7.0   \n",
+       "Runs               326.0    48.214724    46.364077    3.0    19.00    33.0   \n",
+       "TerrainParks       279.0     2.820789     2.008113    1.0     1.00     2.0   \n",
+       "LongestRun_mi      325.0     1.433231     1.156171    0.0     0.50     1.0   \n",
+       "SkiableTerrain_ac  327.0   739.801223  1816.167441    8.0    85.00   200.0   \n",
+       "Snow Making_ac     284.0   174.873239   261.336125    2.0    50.00   100.0   \n",
+       "daysOpenLastYear   279.0   115.103943    35.063251    3.0    97.00   114.0   \n",
+       "yearsOpen          329.0    63.656535   109.429928    6.0    50.00    58.0   \n",
+       "averageSnowfall    316.0   185.316456   136.356842   18.0    69.00   150.0   \n",
+       "AdultWeekday       276.0    57.916957    26.140126   15.0    40.00    50.0   \n",
+       "AdultWeekend       279.0    64.166810    24.554584   17.0    47.00    60.0   \n",
+       "projectedDaysOpen  283.0   120.053004    31.045963   30.0   100.00   120.0   \n",
+       "NightSkiing_ac     187.0   100.395722   105.169620    2.0    40.00    72.0   \n",
+       "\n",
+       "                       75%      max  \n",
+       "summit_elev        7806.00  13487.0  \n",
+       "vertical_drop      1800.00   4425.0  \n",
+       "base_elev          6325.25  10800.0  \n",
+       "trams                 0.00      4.0  \n",
+       "fastEight             0.00      1.0  \n",
+       "fastSixes             0.00      6.0  \n",
+       "fastQuads             1.00     15.0  \n",
+       "quad                  1.00      8.0  \n",
+       "triple                2.00      8.0  \n",
+       "double                3.00     14.0  \n",
+       "surface               3.00     15.0  \n",
+       "total_chairs         10.00     41.0  \n",
+       "Runs                 60.00    341.0  \n",
+       "TerrainParks          4.00     14.0  \n",
+       "LongestRun_mi         2.00      6.0  \n",
+       "SkiableTerrain_ac   690.00  26819.0  \n",
+       "Snow Making_ac      200.50   3379.0  \n",
+       "daysOpenLastYear    135.00    305.0  \n",
+       "yearsOpen            69.00   2019.0  \n",
+       "averageSnowfall     300.00    669.0  \n",
+       "AdultWeekday         71.00    179.0  \n",
+       "AdultWeekend         77.50    179.0  \n",
+       "projectedDaysOpen   139.50    305.0  \n",
+       "NightSkiing_ac      114.00    650.0  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "ticket_prices.head()"
+    "#Code task 17#\n",
+    "#Call ski_data's `describe` method for a statistical summary of the numerical columns\n",
+    "#Hint: there are fewer summary stat columns than features, so displaying the transpose\n",
+    "#will be useful again\n",
+    "ski_data.describe().T"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is now in a format we can pass to [seaborn](https://seaborn.pydata.org/)'s [boxplot](https://seaborn.pydata.org/generated/seaborn.boxplot.html) function to create boxplots of the ticket price distributions for each ticket type for each state."
+    "Recall you're missing the ticket prices for some 16% of resorts. This is a fundamental problem that means you simply lack the required data for those resorts and will have to drop those records. But you may have a weekend price and not a weekday price, or vice versa. You want to keep any price you have."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "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"
+    }
+   ],
    "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",
-    "plt.xticks(rotation='vertical')\n",
-    "plt.ylabel('Price ($)')\n",
-    "plt.xlabel('State');"
+    "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n",
+    "missing_price.value_counts()/len(missing_price) * 100"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Aside from some relatively expensive ticket prices in California, Colorado, and Utah, most prices appear to lie in a broad band from around 25 to over 100 dollars. Some States show more variability than others. Montana and South Dakota, for example, both show fairly small variability as well as matching weekend and weekday ticket prices. Nevada and Utah, on the other hand, show the most range in prices. Some States, notably North Carolina and Virginia, have weekend prices far higher than weekday prices. You could be inspired from this exploration to consider a few potential groupings of resorts, those with low spread, those with lower averages, and those that charge a premium for weekend tickets. However, you're told that you are taking all resorts to be part of the same market share, you  could argue against further segment the resorts. Nevertheless, ways to consider using the State information in your modelling include:\n",
-    "\n",
-    "* disregard State completely\n",
-    "* retain all State information\n",
-    "* retain State in the form of Montana vs not Montana, as our target resort is in Montana\n",
-    "\n",
-    "You've also noted another effect above: some States show a marked difference between weekday and weekend ticket prices. It may make sense to allow a model to take into account not just State but also weekend vs weekday."
+    "Just over 82% of resorts have no missing ticket price, 3% are missing one value, and 14% are missing both. You will definitely want to drop the records for which you have no price information, however you will not do so just yet. There may still be useful information about the distributions of other features in that 14% of the data."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Thus we currently have two main questions you want to resolve:\n",
-    "\n",
-    "* What do you do about the two types of ticket price?\n",
-    "* What do you do about the state information?"
+    "#### 2.6.4.2 Distributions Of Feature Values<a id='2.6.4.2_Distributions_Of_Feature_Values'></a>"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 2.6.4 Numeric Features<a id='2.6.4_Numeric_Features'></a>"
+    "Note that, although we are still in the 'data wrangling and cleaning' phase rather than exploratory data analysis, looking at distributions of features is immensely useful in getting a feel for whether the values look sensible and whether there are any obvious outliers to investigate. Some exploratory data analysis belongs here, and data wrangling will inevitably occur later on. It's more a matter of emphasis. Here, we're interesting in focusing on whether distributions look plausible or wrong. Later on, we're more interested in relationships and patterns."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "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"
+    }
+   ],
    "source": [
-    "Having decided to reserve judgement on how exactly you utilize the State, turn your attention to cleaning the numeric features."
+    "#Code task 18#\n",
+    "#Call ski_data's `hist` method to plot histograms of each of the numeric features\n",
+    "#Try passing it an argument figsize=(15,10)\n",
+    "#Try calling plt.subplots_adjust() with an argument hspace=0.5 to adjust the spacing\n",
+    "#It's important you create legible and easy-to-read plots\n",
+    "ski_data.hist(figsize=(15,10))\n",
+    "plt.subplots_adjust(hspace=0.5);\n",
+    "#Hint: notice how the terminating ';' \"swallows\" some messy output and leads to a tidier notebook"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 2.6.4.1 Numeric data summary<a id='2.6.4.1_Numeric_data_summary'></a>"
+    "What features do we have possible cause for concern about and why?\n",
+    "\n",
+    "* SkiableTerrain_ac because values are clustered down the low end,\n",
+    "* Snow Making_ac for the same reason,\n",
+    "* fastEight because all but one value is 0 so it has very little variance, and half the values are missing,\n",
+    "* fastSixes raises an amber flag; it has more variability, but still mostly 0,\n",
+    "* trams also may get an amber flag for the same reason,\n",
+    "* yearsOpen because most values are low but it has a maximum of 2019, which strongly suggests someone recorded calendar year rather than number of years."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.6.4.2.1 SkiableTerrain_ac<a id='2.6.4.2.1_SkiableTerrain_ac'></a>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "39    26819.0\n",
+       "Name: SkiableTerrain_ac, dtype: float64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#Code task 17#\n",
-    "#Call ski_data's `describe` method for a statistical summary of the numerical columns\n",
-    "#Hint: there are fewer summary stat columns than features, so displaying the transpose\n",
-    "#will be useful again\n",
-    "ski_data.___.___"
+    "#Code task 19#\n",
+    "#Filter the 'SkiableTerrain_ac' column to print the values greater than 10000\n",
+    "ski_data.SkiableTerrain_ac[ski_data.SkiableTerrain_ac > 10000]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Recall you're missing the ticket prices for some 16% of resorts. This is a fundamental problem that means you simply lack the required data for those resorts and will have to drop those records. But you may have a weekend price and not a weekday price, or vice versa. You want to keep any price you have."
+    "**Q: 2** One resort has an incredibly large skiable terrain area! Which is it?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>39</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Silverton Mountain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Colorado</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Colorado</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>13487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>3087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>10400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>26819.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>175.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>17.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>400.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <td>79.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>79.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>181.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "0    82.424242\n",
-       "2    14.242424\n",
-       "1     3.333333\n",
-       "dtype: float64"
+       "                                   39\n",
+       "Name               Silverton Mountain\n",
+       "Region                       Colorado\n",
+       "state                        Colorado\n",
+       "summit_elev                     13487\n",
+       "vertical_drop                    3087\n",
+       "base_elev                       10400\n",
+       "trams                               0\n",
+       "fastEight                         0.0\n",
+       "fastSixes                           0\n",
+       "fastQuads                           0\n",
+       "quad                                0\n",
+       "triple                              0\n",
+       "double                              1\n",
+       "surface                             0\n",
+       "total_chairs                        1\n",
+       "Runs                              NaN\n",
+       "TerrainParks                      NaN\n",
+       "LongestRun_mi                     1.5\n",
+       "SkiableTerrain_ac             26819.0\n",
+       "Snow Making_ac                    NaN\n",
+       "daysOpenLastYear                175.0\n",
+       "yearsOpen                        17.0\n",
+       "averageSnowfall                 400.0\n",
+       "AdultWeekday                     79.0\n",
+       "AdultWeekend                     79.0\n",
+       "projectedDaysOpen               181.0\n",
+       "NightSkiing_ac                    NaN"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "missing_price = ski_data[['AdultWeekend', 'AdultWeekday']].isnull().sum(axis=1)\n",
-    "missing_price.value_counts()/len(missing_price) * 100"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Just over 82% of resorts have no missing ticket price, 3% are missing one value, and 14% are missing both. You will definitely want to drop the records for which you have no price information, however you will not do so just yet. There may still be useful information about the distributions of other features in that 14% of the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.6.4.2 Distributions Of Feature Values<a id='2.6.4.2_Distributions_Of_Feature_Values'></a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that, although we are still in the 'data wrangling and cleaning' phase rather than exploratory data analysis, looking at distributions of features is immensely useful in getting a feel for whether the values look sensible and whether there are any obvious outliers to investigate. Some exploratory data analysis belongs here, and data wrangling will inevitably occur later on. It's more a matter of emphasis. Here, we're interesting in focusing on whether distributions look plausible or wrong. Later on, we're more interested in relationships and patterns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "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"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What features do we have possible cause for concern about and why?\n",
-    "\n",
-    "* SkiableTerrain_ac because values are clustered down the low end,\n",
-    "* Snow Making_ac for the same reason,\n",
-    "* fastEight because all but one value is 0 so it has very little variance, and half the values are missing,\n",
-    "* fastSixes raises an amber flag; it has more variability, but still mostly 0,\n",
-    "* trams also may get an amber flag for the same reason,\n",
-    "* yearsOpen because most values are low but it has a maximum of 2019, which strongly suggests someone recorded calendar year rather than number of years."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 2.6.4.2.1 SkiableTerrain_ac<a id='2.6.4.2.1_SkiableTerrain_ac'></a>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Code task 19#\n",
-    "#Filter the 'SkiableTerrain_ac' column to print the values greater than 10000\n",
-    "ski_data.___[ski_data.___ > ___]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q: 2** One resort has an incredibly large skiable terrain area! Which is it?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "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.___ > ___].___"
+    "ski_data[ski_data.SkiableTerrain_ac > 10000].T"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**A: 2** Your answer here"
+    "**A: 2** Your answer here: Silverton Mountain"
    ]
   },
   {
@@ -1179,35 +2678,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26819.0"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 21#\n",
     "#Use the .loc accessor to print the 'SkiableTerrain_ac' value only for this resort\n",
-    "ski_data.___[39, 'SkiableTerrain_ac']"
+    "ski_data.loc[39, 'SkiableTerrain_ac']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
     "#Code task 22#\n",
     "#Use the .loc accessor again to modify this value with the correct value of 1819\n",
-    "ski_data.___[39, 'SkiableTerrain_ac'] = ___"
+    "ski_data.loc[39, 'SkiableTerrain_ac'] = 1819"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1819.0"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 23#\n",
     "#Use the .loc accessor a final time to verify that the value has been modified\n",
-    "ski_data.___[39, 'SkiableTerrain_ac']"
+    "ski_data.loc[39, 'SkiableTerrain_ac']"
    ]
   },
   {
@@ -1231,7 +2752,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1345,7 +2866,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fastEight</th>\n",
-       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fastSixes</th>\n",
@@ -1377,11 +2898,11 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Runs</th>\n",
-       "      <td>97</td>\n",
+       "      <td>97.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TerrainParks</th>\n",
-       "      <td>3</td>\n",
+       "      <td>3.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LongestRun_mi</th>\n",
@@ -1389,23 +2910,23 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SkiableTerrain_ac</th>\n",
-       "      <td>4800</td>\n",
+       "      <td>4800.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Snow Making_ac</th>\n",
-       "      <td>3379</td>\n",
+       "      <td>3379.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>daysOpenLastYear</th>\n",
-       "      <td>155</td>\n",
+       "      <td>155.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>yearsOpen</th>\n",
-       "      <td>64</td>\n",
+       "      <td>64.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>averageSnowfall</th>\n",
-       "      <td>360</td>\n",
+       "      <td>360.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AdultWeekday</th>\n",
@@ -1417,7 +2938,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>projectedDaysOpen</th>\n",
-       "      <td>157</td>\n",
+       "      <td>157.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NightSkiing_ac</th>\n",
@@ -1436,7 +2957,7 @@
        "vertical_drop                          3500\n",
        "base_elev                              7170\n",
        "trams                                     2\n",
-       "fastEight                                 0\n",
+       "fastEight                               0.0\n",
        "fastSixes                                 2\n",
        "fastQuads                                 7\n",
        "quad                                      1\n",
@@ -1444,17 +2965,17 @@
        "double                                    3\n",
        "surface                                   8\n",
        "total_chairs                             28\n",
-       "Runs                                     97\n",
-       "TerrainParks                              3\n",
+       "Runs                                   97.0\n",
+       "TerrainParks                            3.0\n",
        "LongestRun_mi                           5.5\n",
-       "SkiableTerrain_ac                      4800\n",
-       "Snow Making_ac                         3379\n",
-       "daysOpenLastYear                        155\n",
-       "yearsOpen                                64\n",
-       "averageSnowfall                         360\n",
+       "SkiableTerrain_ac                    4800.0\n",
+       "Snow Making_ac                       3379.0\n",
+       "daysOpenLastYear                      155.0\n",
+       "yearsOpen                              64.0\n",
+       "averageSnowfall                       360.0\n",
        "AdultWeekday                            NaN\n",
        "AdultWeekend                            NaN\n",
-       "projectedDaysOpen                       157\n",
+       "projectedDaysOpen                     157.0\n",
        "NightSkiing_ac                          NaN"
       ]
      },
@@ -1553,13 +3074,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "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)"
    ]
   },
   {
@@ -1571,13 +3092,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "34      104.0\n",
+       "115    2019.0\n",
+       "Name: yearsOpen, dtype: float64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 25#\n",
     "#Filter the 'yearsOpen' column for values greater than 100\n",
-    "ski_data.___[ski_data.___ > ___]"
+    "ski_data.yearsOpen[ski_data.yearsOpen > 100]"
    ]
   },
   {
@@ -1596,14 +3130,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "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": [
     "#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 < 2000].hist(bins=30)\n",
     "plt.xlabel('Years open')\n",
     "plt.ylabel('Count')\n",
     "plt.title('Distribution of years open excluding 2019');"
@@ -1720,9 +3267,116 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "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>43682.0</td>\n",
+       "      <td>3258.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>428.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>5</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>256.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         state  resorts_per_state  state_total_skiable_area_ac  \\\n",
+       "0       Alaska                  3                       2280.0   \n",
+       "1      Arizona                  2                       1577.0   \n",
+       "2   California                 21                      25948.0   \n",
+       "3     Colorado                 22                      43682.0   \n",
+       "4  Connecticut                  5                        358.0   \n",
+       "\n",
+       "   state_total_days_open  state_total_terrain_parks  \\\n",
+       "0                  345.0                        4.0   \n",
+       "1                  237.0                        6.0   \n",
+       "2                 2738.0                       81.0   \n",
+       "3                 3258.0                       74.0   \n",
+       "4                  353.0                       10.0   \n",
+       "\n",
+       "   state_total_nightskiing_ac  \n",
+       "0                       580.0  \n",
+       "1                        80.0  \n",
+       "2                       587.0  \n",
+       "3                       428.0  \n",
+       "4                       256.0  "
+      ]
+     },
+     "execution_count": 40,
+     "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 +3387,10 @@
     "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()"
    ]
   },
@@ -1787,13 +3441,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
     "#Code task 28#\n",
     "#Use `missing_price` to remove rows from ski_data where both price values are missing\n",
-    "ski_data = ski_data[___ != 2]"
+    "ski_data = ski_data[missing_price != 2]"
    ]
   },
   {
@@ -1810,7 +3464,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1080x720 with 25 Axes>"
       ]
@@ -1849,14 +3503,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
     "#Code task 29#\n",
     "#Use pandas' `read_html` method to read the table from the URL below\n",
     "states_url = 'https://simple.wikipedia.org/w/index.php?title=List_of_U.S._states&oldid=7168473'\n",
-    "usa_states = pd.___(___)"
+    "usa_states = pd.read_html(states_url)"
    ]
   },
   {
@@ -1927,8 +3581,8 @@
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Name &amp;postal abbs. [1]</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Cities</th>\n",
-       "      <th>Established[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",
@@ -1940,8 +3594,8 @@
        "      <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",
@@ -2045,21 +3699,21 @@
        "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]         Numberof Reps.  \n",
+       "       km2           mi2     km2 Numberof 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,
@@ -2081,14 +3735,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "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]"
    ]
   },
   {
@@ -2149,37 +3803,112 @@
        "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,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "established"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Extract the state name, population, and total area (square miles) columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>state_population</th>\n",
+       "      <th>state_area_sq_miles</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>4903185</td>\n",
+       "      <td>52420</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>731545</td>\n",
+       "      <td>665384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>7278717</td>\n",
+       "      <td>113990</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arkansas</td>\n",
+       "      <td>3017804</td>\n",
+       "      <td>53179</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>California</td>\n",
+       "      <td>39512223</td>\n",
+       "      <td>163695</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        state  state_population  state_area_sq_miles\n",
+       "0     Alabama           4903185                52420\n",
+       "1      Alaska            731545               665384\n",
+       "2     Arizona           7278717               113990\n",
+       "3    Arkansas           3017804                53179\n",
+       "4  California          39512223               163695"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "established"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Extract the state name, population, and total area (square miles) columns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
    "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 +3921,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Massachusetts', 'Pennsylvania', 'Rhode Island', 'Virginia'}"
+      ]
+     },
+     "execution_count": 51,
+     "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"
    ]
   },
@@ -2225,11 +3965,11 @@
     {
      "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"
       ]
      },
@@ -2251,9 +3991,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20    Massachusetts\n",
+       "37     Pennsylvania\n",
+       "38     Rhode Island\n",
+       "45         Virginia\n",
+       "47    West Virginia\n",
+       "Name: state, dtype: object"
+      ]
+     },
+     "execution_count": 53,
+     "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 +4018,31 @@
     "#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": 54,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 54,
+     "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 +4055,133 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "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>43682.0</td>\n",
+       "      <td>3258.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>428.0</td>\n",
+       "      <td>5758736</td>\n",
+       "      <td>104094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>5</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>256.0</td>\n",
+       "      <td>3565278</td>\n",
+       "      <td>5543</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         state  resorts_per_state  state_total_skiable_area_ac  \\\n",
+       "0       Alaska                  3                       2280.0   \n",
+       "1      Arizona                  2                       1577.0   \n",
+       "2   California                 21                      25948.0   \n",
+       "3     Colorado                 22                      43682.0   \n",
+       "4  Connecticut                  5                        358.0   \n",
+       "\n",
+       "   state_total_days_open  state_total_terrain_parks  \\\n",
+       "0                  345.0                        4.0   \n",
+       "1                  237.0                        6.0   \n",
+       "2                 2738.0                       81.0   \n",
+       "3                 3258.0                       74.0   \n",
+       "4                  353.0                       10.0   \n",
+       "\n",
+       "   state_total_nightskiing_ac  state_population  state_area_sq_miles  \n",
+       "0                       580.0            731545               665384  \n",
+       "1                        80.0           7278717               113990  \n",
+       "2                       587.0          39512223               163695  \n",
+       "3                       428.0           5758736               104094  \n",
+       "4                       256.0           3565278                 5543  "
+      ]
+     },
+     "execution_count": 55,
+     "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 +4208,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "metadata": {},
-   "outputs": [],
+   "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": [
     "#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 +4240,118 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "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>141</th>\n",
+       "      <td>42.0</td>\n",
+       "      <td>42.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>63.0</td>\n",
+       "      <td>63.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>49.0</td>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>144</th>\n",
+       "      <td>48.0</td>\n",
+       "      <td>48.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>46.0</td>\n",
+       "      <td>46.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>67.0</td>\n",
+       "      <td>67.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>39.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>151</th>\n",
+       "      <td>81.0</td>\n",
+       "      <td>81.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     AdultWeekend  AdultWeekday\n",
+       "141          42.0          42.0\n",
+       "142          63.0          63.0\n",
+       "143          49.0          49.0\n",
+       "144          48.0          48.0\n",
+       "145          46.0          46.0\n",
+       "146          39.0          39.0\n",
+       "147          50.0          50.0\n",
+       "148          67.0          67.0\n",
+       "149          47.0          47.0\n",
+       "150          39.0          39.0\n",
+       "151          81.0          81.0"
+      ]
+     },
+     "execution_count": 57,
+     "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']]"
    ]
   },
   {
@@ -2654,7 +4658,7 @@
       " 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"
+      "memory usage: 53.0+ KB\n"
      ]
     }
    ],
@@ -2707,7 +4711,16 @@
    "cell_type": "code",
    "execution_count": 66,
    "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",
@@ -2718,7 +4731,15 @@
    "cell_type": "code",
    "execution_count": 67,
    "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",
@@ -2745,6 +4766,21 @@
    "source": [
     "**A: 3** Your answer here"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook, a dataset for multiple ski resorts accross the United States was taken from a github respository and cleaned/transformed to make it more suitable for analysis and possibly feeding it into a model. The dataset had 330 records with 27 features including the record for 'Big Mountain Resort' which is located in Montana and is the resort we are looking to develop a pricing model for. The dataset appeared to have quite a few missing values, but fortunatedly, the record for Big Mountain Resort had no missing values. With that said, some records were missing values for the features 'AdultWeekday' and 'AdultWeekend' which represent the prices for each resort and were decidedly the target variables for the study. The feature with the most missing values was 'fastEight' feature with about 50% of records missing a value for this feature. Also, all the records except one had a value of zero under fastEight. This feature would eventually be dropped along with the only resort to have been open for over 100 years.\n",
+    "\n",
+    "The silverton mountain skiable area was a huge outlier in the data. After researching online about the skiable area for that resort, it was found that the skiable area was much lower. In the data it was 26819, but online it was 1819. It is possible that there may have been an error in recording the data. A decision was made to replace the original value with 1819.\n",
+    "\n",
+    "The records mostly consisted of numerical variables with the only categorical variables being 'name', 'state', and 'region'. Sometimes, state and region can have the same name on the same record and regions can have the same names in different states. But there are no records that have the exact same name, state, and region meaning there are no duplicate records. \n",
+    "\n",
+    "The states Colorado, Michigan, and New York had the highest number of resorts which could be because of the high populations in those states. \n",
+    "\n",
+    "The Data will be explored some more in due course."
+   ]
   }
  ],
  "metadata": {
@@ -2763,7 +4799,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.5"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/Notebooks/03_exploratory_data_analysis.ipynb b/Notebooks/03_exploratory_data_analysis.ipynb
index c1746d2e4..d80c00198 100644
--- a/Notebooks/03_exploratory_data_analysis.ipynb
+++ b/Notebooks/03_exploratory_data_analysis.ipynb
@@ -3463,7 +3463,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**A: 1** Your answer here"
+    "**A: 1** Your answer here: Montana is the third largest state in the list of states although it doesn't have a high population relative to the other states, meaning it's less densely populated. The state with the highest population is California. The state hosting the most resorts is New York. Montana is in the top five for largest skiable areas and New York is not. Another interesting statistic is that New York tops the Night Skiing Area.\n",
+    "\n",
+    "It was decided that resort density (resorts per state/state population & resorts per state/state size) could be useful in predicting price of tickets in a state. Possibly, high competition could be repressing ticket price. Vermont ranks high in both of these new features.\n",
+    "\n",
+    "Since the dataset had a lot of dimensions making it very complicated to analyse, principle components analysis (PCA) was used to bring the data back down to a lower dimension and make it more suitable to fit into a model. To do this, the data was first scaled, then the PCA transformation was fitted using the scaled data, the transformation was applied to the data to get derived features, and those features were plotted on a scatterplot of the two most significant components with the points color coded based on the ticket price.\n",
+    "\n",
+    "The results of the plot showed no obvious patterns for the price. This tempts us to treat all states the same in our analysis. Two states stood out as outliers in the plot for both components: New Hampshire and Vermont.  Also, in analysis of the 2 components from the PCA, it was found that the second component was heavily influenced by resorts_per_100kcapita and resorts_per_100ksq_mile (0.662458 and 0.637691 respectively). These two states are both more than three standard deviations from the mean for the two features.The two outlier states mentioned above are both more than three standard deviations from the mean for the two features.\n",
+    "\n",
+    "Next, after analysing the data based on state, some more analysis was done based on resort data. This analysis was to incorporate the data that was taken from both resorts and state to get some information on how each resort in a state was able to share the state's resources such as skiable area  and population(market). \n",
+    "\n",
+    "From a heatmap, it was discovered that the ratio between resort night skiing and total night skiing for the state was the most correlated with ticket price. This suggests that a greater share of night skiing capacity can lead to a higher price for the tickets for that resort. Other features that correlated well with ticket price were Runs and total_chairs. Another feature with positive correlation to price is vertical drop.\n",
+    "\n",
+    "After observing the correlations with ticket price for a number of features, a scatterplots were made for each feature with ticket price on the y-axis. This was to get a more clear view of the relationships of these features with ticket price. From the scatterplots, it was clear that there was a strong correlation of ticket price with vertical drop. Other features once again included fastQuads, Runs, and total_chairs. \n",
+    "\n",
+    "Ticket price at a low Resorts_per_100kcapita value seemed have quite a lot of variance, but as the value rose, the ticket price rose as well. It could possibly be because the more resorts there are in an area, the more popular that area is for skiing. This is just speculation ofcourse. \n",
+    "\n",
+    "Finally, the final step of the analysis was to visualize the relationship between chairs to runs ration and ticket price for the resorts. The relationship seemed to be negative although it wasn't a very strong correlation. Basically, the less chairs there were, the higher the ticket price. It is important to note that this doesn't necessarily mean more revenue was generated from less chairs. This could mean chairs were more expensive due to a shortage of chairs and that less customers were able to occupy the chairs. It could've been useful to have data about the number of customers per year.\n",
+    "\n",
+    "\n",
+    "\n"
    ]
   },
   {
@@ -3932,7 +3951,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -3946,7 +3965,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.9.7"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/Notebooks/04_preprocessing_and_training.ipynb b/Notebooks/04_preprocessing_and_training.ipynb
index 94ff2aeba..4305f3eb5 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": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {
     "scrolled": true
    },
@@ -281,91 +281,91 @@
        "    </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 +380,8 @@
        "      <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 +395,7 @@
        "      <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 +419,13 @@
        "      <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 +433,7 @@
        "    </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 +441,18 @@
        "    </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",
@@ -475,17 +475,17 @@
        "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 +493,10 @@
        "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",
@@ -513,28 +513,28 @@
        "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",
@@ -551,31 +551,31 @@
        "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": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -601,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -610,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -696,11 +696,11 @@
        "    </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 +708,35 @@
        "    </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 +744,11 @@
        "    </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 +772,11 @@
        "    </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",
@@ -802,31 +802,31 @@
        "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": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -837,7 +837,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -846,7 +846,7 @@
        "(277, 36)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -857,7 +857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -866,7 +866,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -875,7 +875,7 @@
        "(276, 36)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -907,7 +907,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -916,7 +916,7 @@
        "(193.2, 82.8)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -927,7 +927,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -938,7 +938,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -947,7 +947,7 @@
        "((193, 35), (83, 35))"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -958,7 +958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -967,7 +967,7 @@
        "((193,), (83,))"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -978,41 +978,138 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((193, 32), (83, 32))"
+      ]
+     },
+     "execution_count": 14,
+     "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",
     "X_train.shape, X_test.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "summit_elev                             int64\n",
+       "vertical_drop                           int64\n",
+       "base_elev                               int64\n",
+       "trams                                   int64\n",
+       "fastSixes                               int64\n",
+       "fastQuads                               int64\n",
+       "quad                                    int64\n",
+       "triple                                  int64\n",
+       "double                                  int64\n",
+       "surface                                 int64\n",
+       "total_chairs                            int64\n",
+       "Runs                                  float64\n",
+       "TerrainParks                          float64\n",
+       "LongestRun_mi                         float64\n",
+       "SkiableTerrain_ac                     float64\n",
+       "Snow Making_ac                        float64\n",
+       "daysOpenLastYear                      float64\n",
+       "yearsOpen                             float64\n",
+       "averageSnowfall                       float64\n",
+       "projectedDaysOpen                     float64\n",
+       "NightSkiing_ac                        float64\n",
+       "resorts_per_state                       int64\n",
+       "resorts_per_100kcapita                float64\n",
+       "resorts_per_100ksq_mile               float64\n",
+       "resort_skiable_area_ac_state_ratio    float64\n",
+       "resort_days_open_state_ratio          float64\n",
+       "resort_terrain_park_state_ratio       float64\n",
+       "resort_night_skiing_state_ratio       float64\n",
+       "total_chairs_runs_ratio               float64\n",
+       "total_chairs_skiable_ratio            float64\n",
+       "fastQuads_runs_ratio                  float64\n",
+       "fastQuads_skiable_ratio               float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 2#\n",
     "#Check the `dtypes` attribute of `X_train` to verify all features are numeric\n",
-    "X_train.___"
+    "X_train.dtypes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "summit_elev                             int64\n",
+       "vertical_drop                           int64\n",
+       "base_elev                               int64\n",
+       "trams                                   int64\n",
+       "fastSixes                               int64\n",
+       "fastQuads                               int64\n",
+       "quad                                    int64\n",
+       "triple                                  int64\n",
+       "double                                  int64\n",
+       "surface                                 int64\n",
+       "total_chairs                            int64\n",
+       "Runs                                  float64\n",
+       "TerrainParks                          float64\n",
+       "LongestRun_mi                         float64\n",
+       "SkiableTerrain_ac                     float64\n",
+       "Snow Making_ac                        float64\n",
+       "daysOpenLastYear                      float64\n",
+       "yearsOpen                             float64\n",
+       "averageSnowfall                       float64\n",
+       "projectedDaysOpen                     float64\n",
+       "NightSkiing_ac                        float64\n",
+       "resorts_per_state                       int64\n",
+       "resorts_per_100kcapita                float64\n",
+       "resorts_per_100ksq_mile               float64\n",
+       "resort_skiable_area_ac_state_ratio    float64\n",
+       "resort_days_open_state_ratio          float64\n",
+       "resort_terrain_park_state_ratio       float64\n",
+       "resort_night_skiing_state_ratio       float64\n",
+       "total_chairs_runs_ratio               float64\n",
+       "total_chairs_skiable_ratio            float64\n",
+       "fastQuads_runs_ratio                  float64\n",
+       "fastQuads_skiable_ratio               float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 3#\n",
     "#Repeat this check for the test split in `X_test`\n",
-    "X_test.___"
+    "X_test.dtypes"
    ]
   },
   {
@@ -1038,13 +1135,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "63.811088082901556"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 4#\n",
     "#Calculate the mean of `y_train`\n",
-    "train_mean = y_train.___\n",
+    "train_mean = y_train.mean()\n",
     "train_mean"
    ]
   },
@@ -1057,17 +1165,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[63.81108808]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 5#\n",
     "#Fit the dummy regressor on the training data\n",
     "#Hint, call its `.fit()` method with `X_train` and `y_train` as arguments\n",
     "#Then print the object's `constant_` attribute and verify it's the same as the mean above\n",
     "dumb_reg = DummyRegressor(strategy='mean')\n",
-    "dumb_reg.___(___, ___)\n",
-    "dumb_reg.___"
+    "dumb_reg.fit(X_train, y_train)\n",
+    "dumb_reg.constant_"
    ]
   },
   {
@@ -1124,7 +1243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1140,9 +1259,9 @@
     "    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"
    ]
   },
@@ -1155,7 +1274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1164,7 +1283,7 @@
        "array([63.81108808, 63.81108808, 63.81108808, 63.81108808, 63.81108808])"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1183,7 +1302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1192,7 +1311,7 @@
        "array([63.81108808, 63.81108808, 63.81108808, 63.81108808, 63.81108808])"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1211,7 +1330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1220,7 +1339,7 @@
        "0.0"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1245,7 +1364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1254,7 +1373,7 @@
        "-0.0031235200417913944"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1296,7 +1415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1311,23 +1430,23 @@
     "    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": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "17.923463717146785"
+       "17.92346371714677"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1338,7 +1457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1347,7 +1466,7 @@
        "19.136142081278486"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1381,7 +1500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {
     "scrolled": true
    },
@@ -1398,23 +1517,23 @@
     "    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": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "614.1334096969057"
+       "614.1334096969046"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1425,16 +1544,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "581.4365441953481"
+       "581.4365441953483"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1452,7 +1571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1461,7 +1580,7 @@
        "array([24.78171523, 24.11299534])"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1493,7 +1612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1502,7 +1621,7 @@
        "(0.0, -0.0031235200417913944)"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1520,7 +1639,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1529,7 +1648,7 @@
        "(17.92346371714677, 19.136142081278486)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1547,7 +1666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1556,7 +1675,7 @@
        "(614.1334096969046, 581.4365441953483)"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1588,7 +1707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1597,7 +1716,7 @@
        "(0.0, -3.041041349306602e+30)"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1610,7 +1729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -1619,7 +1738,7 @@
        "(-0.0031235200417913944, 0.0)"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1632,7 +1751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1641,7 +1760,7 @@
        "(0.0, -3.041041349306602e+30)"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1654,15 +1773,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "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"
+      "<ipython-input-19-fc28c3263e5e>:15: RuntimeWarning: divide by zero encountered in double_scalars\n",
+      "  R2 = 1.0 - sum_sq_res / sum_sq_tot\n"
      ]
     },
     {
@@ -1671,7 +1790,7 @@
        "(-0.0031235200417913944, -inf)"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1737,7 +1856,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -1778,7 +1897,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1798,15 +1917,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "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 +1944,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1834,9 +1953,9 @@
     "#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 +1967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1864,15 +1983,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "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 +2003,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.8177988515690604, 0.7209725843435142)"
+       "(0.8177988515690603, 0.7209725843435146)"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1913,15 +2032,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8.547850301825427, 9.407020118581316)"
+      ]
+     },
+     "execution_count": 43,
+     "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 +2064,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(111.8958125365848, 161.7315645119226)"
+      ]
+     },
+     "execution_count": 44,
+     "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 +2109,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "summit_elev                           4074.554404\n",
+       "vertical_drop                         1043.196891\n",
+       "base_elev                             3020.512953\n",
+       "trams                                    0.103627\n",
+       "fastSixes                                0.072539\n",
+       "fastQuads                                0.673575\n",
+       "quad                                     1.010363\n",
+       "triple                                   1.440415\n",
+       "double                                   1.813472\n",
+       "surface                                  2.497409\n",
+       "total_chairs                             7.611399\n",
+       "Runs                                    41.188482\n",
+       "TerrainParks                             2.434783\n",
+       "LongestRun_mi                            1.293122\n",
+       "SkiableTerrain_ac                      448.785340\n",
+       "Snow Making_ac                         129.601190\n",
+       "daysOpenLastYear                       110.100629\n",
+       "yearsOpen                               56.559585\n",
+       "averageSnowfall                        162.310160\n",
+       "projectedDaysOpen                      115.920245\n",
+       "NightSkiing_ac                          86.384615\n",
+       "resorts_per_state                       16.264249\n",
+       "resorts_per_100kcapita                   0.424802\n",
+       "resorts_per_100ksq_mile                 40.957785\n",
+       "resort_skiable_area_ac_state_ratio       0.097205\n",
+       "resort_days_open_state_ratio             0.126014\n",
+       "resort_terrain_park_state_ratio          0.116022\n",
+       "resort_night_skiing_state_ratio          0.155024\n",
+       "total_chairs_runs_ratio                  0.271441\n",
+       "total_chairs_skiable_ratio               0.070483\n",
+       "fastQuads_runs_ratio                     0.010401\n",
+       "fastQuads_skiable_ratio                  0.001633\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 45,
+     "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 +2179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2012,7 +2196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2031,7 +2215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2047,7 +2231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2064,7 +2248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -2073,7 +2257,7 @@
        "(0.8170154093990025, 0.716381471695996)"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2084,16 +2268,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(8.536884040670973, 9.416375625789271)"
+       "(8.536884040670977, 9.416375625789273)"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2104,7 +2288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -2113,7 +2297,7 @@
        "(112.37695054778276, 164.3926930952436)"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2168,7 +2352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2181,7 +2365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -2190,7 +2374,7 @@
        "sklearn.pipeline.Pipeline"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2201,7 +2385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -2210,7 +2394,7 @@
        "(True, True)"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2235,13 +2419,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='median')),\n",
+       "                ('standardscaler', StandardScaler()),\n",
+       "                ('linearregression', LinearRegression())])"
+      ]
+     },
+     "execution_count": 56,
+     "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.___(___, ___)"
+    "pipe.fit(X_train, y_train)"
    ]
   },
   {
@@ -2253,7 +2450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2270,16 +2467,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.8177988515690604, 0.7209725843435142)"
+       "(0.8177988515690603, 0.7209725843435146)"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2297,16 +2494,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.8177988515690604, 0.7209725843435142)"
+       "(0.8177988515690603, 0.7209725843435146)"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2317,16 +2514,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(8.547850301825427, 9.40702011858132)"
+       "(8.547850301825427, 9.407020118581316)"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2336,26 +2533,24 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
     "Compare with your earlier result:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(8.547850301825427, 9.40702011858132)"
+       "(8.547850301825427, 9.407020118581316)"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2366,16 +2561,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(111.89581253658478, 161.73156451192284)"
+       "(111.8958125365848, 161.7315645119226)"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2393,16 +2588,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(111.89581253658478, 161.73156451192284)"
+       "(111.8958125365848, 161.7315645119226)"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2449,7 +2644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2459,7 +2654,7 @@
     "pipe = make_pipeline(\n",
     "    SimpleImputer(strategy='median'), \n",
     "    StandardScaler(),\n",
-    "    ___(___),\n",
+    "    SelectKBest(f_regression),\n",
     "    LinearRegression()\n",
     ")"
    ]
@@ -2473,7 +2668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -2482,11 +2677,11 @@
        "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 0x1CBF5340>)),\n",
        "                ('linearregression', LinearRegression())])"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2504,7 +2699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2514,7 +2709,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -2523,7 +2718,7 @@
        "(0.7674914326052744, 0.6259877354190833)"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2534,16 +2729,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(9.501495079727484, 11.201830190332057)"
+       "(9.501495079727484, 11.201830190332059)"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2568,7 +2763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2577,7 +2772,7 @@
     "pipe15 = make_pipeline(\n",
     "    SimpleImputer(strategy='median'), \n",
     "    StandardScaler(),\n",
-    "    ___(___, k=___),\n",
+    "    SelectKBest(f_regression, k=15),\n",
     "    LinearRegression()\n",
     ")"
    ]
@@ -2591,7 +2786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -2601,11 +2796,11 @@
        "                ('standardscaler', StandardScaler()),\n",
        "                ('selectkbest',\n",
        "                 SelectKBest(k=15,\n",
-       "                             score_func=<function f_regression at 0x7fc8cd9c8950>)),\n",
+       "                             score_func=<function f_regression at 0x1CBF5340>)),\n",
        "                ('linearregression', LinearRegression())])"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2623,7 +2818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2633,16 +2828,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.7924096060483825, 0.6376199973170795)"
+       "(0.7924096060483825, 0.6376199973170797)"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2653,16 +2848,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(9.211767769307116, 10.488246867294356)"
+       "(9.211767769307116, 10.488246867294354)"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2689,7 +2884,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2698,7 +2893,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
@@ -2707,7 +2902,7 @@
        "array([0.63760862, 0.72831381, 0.74443537, 0.5487915 , 0.50441472])"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2726,16 +2921,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.6327128053007867, 0.09502487849877672)"
+       "(0.6327128053007864, 0.09502487849877704)"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2751,9 +2946,16 @@
     "These results highlight that assessing model performance in inherently open to variability. You'll get different results depending on the quirks of which points are in which fold. An advantage of this is that you can also obtain an estimate of the variability, or uncertainty, in your performance estimate."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<span style=\"color:RED\">*****NOT SURE WHAT THE BELOW IS ABOUT*****</span>"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -2762,7 +2964,7 @@
        "array([0.44, 0.82])"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2797,14 +2999,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 76,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['memory', 'steps', 'verbose', 'simpleimputer', 'standardscaler', 'selectkbest', 'linearregression', 'simpleimputer__add_indicator', 'simpleimputer__copy', 'simpleimputer__fill_value', 'simpleimputer__missing_values', 'simpleimputer__strategy', 'simpleimputer__verbose', 'standardscaler__copy', 'standardscaler__with_mean', 'standardscaler__with_std', 'selectkbest__k', 'selectkbest__score_func', 'linearregression__copy_X', 'linearregression__fit_intercept', 'linearregression__n_jobs', 'linearregression__normalize', 'linearregression__positive'])"
+      ]
+     },
+     "execution_count": 76,
+     "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()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SimpleImputer(strategy='median')"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipe.get_params()['simpleimputer']"
    ]
   },
   {
@@ -2816,7 +3049,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2824,6 +3057,57 @@
     "grid_params = {'selectkbest__k': k}"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'selectkbest__k': [1,\n",
+       "  2,\n",
+       "  3,\n",
+       "  4,\n",
+       "  5,\n",
+       "  6,\n",
+       "  7,\n",
+       "  8,\n",
+       "  9,\n",
+       "  10,\n",
+       "  11,\n",
+       "  12,\n",
+       "  13,\n",
+       "  14,\n",
+       "  15,\n",
+       "  16,\n",
+       "  17,\n",
+       "  18,\n",
+       "  19,\n",
+       "  20,\n",
+       "  21,\n",
+       "  22,\n",
+       "  23,\n",
+       "  24,\n",
+       "  25,\n",
+       "  26,\n",
+       "  27,\n",
+       "  28,\n",
+       "  29,\n",
+       "  30,\n",
+       "  31,\n",
+       "  32]}"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grid_params"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2834,7 +3118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2843,7 +3127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {
@@ -2854,7 +3138,7 @@
        "                                        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 0x1CBF5340>)),\n",
        "                                       ('linearregression',\n",
        "                                        LinearRegression())]),\n",
        "             n_jobs=-1,\n",
@@ -2864,7 +3148,7 @@
        "                                            30, ...]})"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2875,7 +3159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2886,24 +3170,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 94,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time', 'param_selectkbest__k', 'params', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split3_test_score', 'split4_test_score', 'mean_test_score', 'std_test_score', 'rank_test_score'])"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lr_grid_cv.cv_results_.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'selectkbest__k': 8}"
+      ]
+     },
+     "execution_count": 90,
+     "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": 91,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#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 +3256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2944,9 +3272,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 97,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "vertical_drop        10.767857\n",
+       "Snow Making_ac        6.290074\n",
+       "total_chairs          5.794156\n",
+       "fastQuads             5.745626\n",
+       "Runs                  5.370555\n",
+       "LongestRun_mi         0.181814\n",
+       "trams                -4.142024\n",
+       "SkiableTerrain_ac    -5.249780\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 97,
+     "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 +3302,7 @@
     "#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)"
    ]
   },
   {
@@ -2990,7 +3337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3000,9 +3347,9 @@
     "#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 +3362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3023,12 +3370,12 @@
     "#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": 91,
    "metadata": {},
    "outputs": [
     {
@@ -3037,7 +3384,7 @@
        "array([0.69249204, 0.78061953, 0.77546915, 0.62190924, 0.61742339])"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3049,7 +3396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
@@ -3058,7 +3405,7 @@
        "(0.6975826707112506, 0.07090742940774528)"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3083,7 +3430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [
     {
@@ -3113,7 +3460,7 @@
        " 'simpleimputer__strategy': ['mean', 'median']}"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3130,37 +3477,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 95,
    "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": 96,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "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": 96,
+     "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": 97,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'randomforestregressor__n_estimators': 69,\n",
+       " 'simpleimputer__strategy': 'median',\n",
+       " 'standardscaler': None}"
+      ]
+     },
+     "execution_count": 97,
+     "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_"
    ]
   },
   {
@@ -3172,7 +3558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [
     {
@@ -3181,7 +3567,7 @@
        "array([0.6951357 , 0.79430697, 0.77170917, 0.62254707, 0.66499334])"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3194,7 +3580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [
     {
@@ -3203,7 +3589,7 @@
        "(0.7097384501425082, 0.06451341966873386)"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3221,9 +3607,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 102,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Code task 27#\n",
     "#Plot a barplot of the random forest's feature importances,\n",
@@ -3232,8 +3631,8 @@
     "#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 +3682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3294,16 +3693,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(10.499032338015297, 1.6220608976799646)"
+       "(10.499032338015292, 1.622060897679965)"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3316,7 +3715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [
     {
@@ -3325,7 +3724,7 @@
        "11.793465668669327"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3343,7 +3742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3353,7 +3752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [
     {
@@ -3362,7 +3761,7 @@
        "(9.644639167595688, 1.3528565172191818)"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3375,7 +3774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
@@ -3384,7 +3783,7 @@
        "9.537730050637332"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3423,7 +3822,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3437,12 +3836,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 110,
    "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",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -3477,7 +3876,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3492,19 +3891,28 @@
     "#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 = pd.__version__\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()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 114,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Directory ../models was created.\n",
+      "Writing file.  \"../models\\ski_resort_pricing_model.pkl\"\n"
+     ]
+    }
+   ],
    "source": [
     "# save the model\n",
     "\n",
@@ -3550,7 +3958,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.8.5"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/Notebooks/05_modeling.ipynb b/Notebooks/05_modeling.ipynb
index 4e4008174..d1a8aa621 100644
--- a/Notebooks/05_modeling.ipynb
+++ b/Notebooks/05_modeling.ipynb
@@ -89,7 +89,15 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Expected model version doesn't match version loaded\n"
+     ]
+    }
+   ],
    "source": [
     "# This isn't exactly production-grade, but a quick check for development\n",
     "# These checks can save some head-scratching in development when moving from\n",
@@ -222,11 +230,11 @@
        "    </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 +242,35 @@
        "    </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 +278,11 @@
        "    </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 +306,11 @@
        "    </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",
@@ -328,27 +336,27 @@
        "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"
       ]
      },
@@ -586,7 +594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -621,7 +629,7 @@
     "        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",
@@ -649,7 +657,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -671,7 +679,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -700,7 +708,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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hD32I888/H6iSqO22227WegcffDBnnnkmUD3p5pvf/CbrrrsuH/rQh7j//vuZOnUqp512GieddBLjx4/nuuuu4ze/+Q3rr78+66yzDltuuSVPPfUUAGeeeSYHlxsl77fffhxyyCFstNFGrLjiiv1KLHtijZck1XXIIdXrySe3Nw6pTV5//XXGjx/PG2+8wfTp07nqqqves86pp57K6NGjuffee7nnnnsYP358r+VtscUWfO5zn2PmzJlMmTKFSZMmceyxxwJVInbHHXdw55138uyzz/KRj3yEiRMn9hnjEksswW233capp57KiSeeyBlnnMFBBx3EiBEjOPzwwwF44YUX+OMf/0hEcMYZZ3DCCSfwgx/84D1lTZ8+neuvv57777+fHXbYYY6bU3ti4jWIxh152YDLmHr8toMQiaSm8P5JGkoOPPC987baCnbdtfqsdv2j0Gj77athxoz33gx40qQ+N9nY1HjjjTfymc98hnvuuedd61x//fUceuihAKy55pqstdZavZY3bNgwNtlkE6ZMmcLrr7/OuHHj3lXOnnvuybBhw1hyySX56Ec/yp/+9CcWXXTR2cbYVWv24Q9/mIsuuqjHdaZNm8buu+/O9OnT+fvf/97r7SB22mkn5ptvPlZfffVZtWIDZVOjJEmaYxtuuCHPPvsszzzzzIDK2WOPPTjkkEPYbbfdaq0///zz849//GPWdPfbOSy00EJAldS9/fbbPZbxpS99iYMPPpi7776b008/vddbQnSVBbzrIoCBsMZLkqRONLsaquHDZ7981KhaNVyzc//99zNz5kwWX3xxXnvttVnzN954Yy644AI+9rGPce+993L33XfPtpxNN92Uo446ij333PM9808//XT23Xdfnn/+ea699lq+//3v89Zbb3Hvvffy5ptv8vrrr3PllVeyySabzHYbI0eO5KWXXpo1/eKLL7LMMssAMHny5Dnd9QEx8ZIkSbV09fGCqgZo8uTJDBs27F3rfOELX2Dfffdl9dVXZ9VVV2WNNdZgscUW67XMiJjV96rRzjvvzI033sjaa69NRHDCCSfwz//8zwDsttturLnmmqywwgqss846fca9/fbbs8suu3DxxRfz4x//mGOOOYZdd92V0aNHs/nmm/PII4/MwVEYmBisqrNmWm+99bK3KyKGEvt4SXO5s8+uXvfZp71xaJ503333sdpqq7U7jD7NnDmTt956i+HDh/Pwww+z5ZZb8sADD7Dgggu2O7Sm6Om8RMStmbleT+tb4yVJdZlwSX167bXX+NjHPsZbb71FZnLqqafOtUlXf5h4SZKkQTNy5Mhe79slr2qUpPoOPLDnS/glqSYTL0mSOkQn9Muel/TnfJh4SZLUAYYPH85zzz1n8jVEZCbPPfccw4cPn6P32cdLkqQOMHbsWKZNmzbgG5Zq8AwfPpyxY8fO0XtMvCRJ6gALLLBAr4+2Uecw8ZKkurbaqt0RSOpwJl6SVNeuu7Y7Akkdzs71klTXG29UgyT1kzVeklTXIYdUrwN8uLCkeZc1XpIkSS1i4iVJktQiJl6SJEktYuIlSZLUInaul6S6tt++3RFI6nAmXpJUl4mXpAGyqVGS6poxoxokqZ+s8ZKkuo44onr1Pl6S+skaL0mSpBYx8ZIkSWoREy9JkqQWMfGSJElqETvXS1Jdu+zS7ggkdTgTL0mqa+ut2x2BpA5nU6Mk1fXUU9UgSf1kjZck1XX00dWr9/GS1E/WeEmSJLWIiZckSVKLmHhJkiS1SNMTr4gYFhG3R8SlZXqFiLgpIh6KiPMjYsFmxyBJkjQUtKLG61Dgvobp7wEnZeZKwAvA/i2IQZIGbu+9q0GS+qmpiVdEjAW2Bc4o0wFsDlxYVpkM7NTMGCRp0EycWA2S1E/NrvH6IXAE8I8yvTgwIzPfLtPTgGV6emNEHBgRt0TELc8880yTw5SkGh59tBokqZ+alnhFxHbA05l5a3/en5mTMnO9zFxvzJgxgxydJPXDccdVgyT1UzNvoLoxsENEfBIYDiwK/AgYFRHzl1qvscDjTYxBkiRpyGhajVdmHpWZYzNzHLAHcFVm7gVcDXQ9aXZf4OJmxSBJkjSUtOM+Xv8OfCUiHqLq8/WzNsQgSZLUci15VmNmXgNcU8b/CkxoxXYlSZKGEh+SLUl1HXBAuyOQ1OFMvCSprglW1ksaGJ/VKEl1/eUv1SBJ/WSNlyTVdeKJ1eukSe2NQ1LHssZLkiSpRUy8JEmSWsTES5IkqUVMvCRJklrEzvWSVNfBB7c7AkkdzsRLkupaa612RyCpw9nUKEl13XVXNUhSP1njJUl1nXJK9ep9vCT1kzVekiRJLWLiJUmS1CImXpIkSS1i4iVJktQidq6XpLoOP7zdEUjqcCZeklTXyiu3OwJJHc6mRkmq6+abq0GS+skaL0mq64wzqtcJE9obh6SOZY2XJElSi5h4SZIktYiJlyRJUouYeEmSJLWIneslqa6vf73dEUjqcCZeklTX8su3OwJJHc6mRkmq69prq0GS+skaL0mq65xzqteJE9sbh6SOZY2XJElSi5h4SZIktYiJlyRJUouYeEmSJLWIneslqa5jj213BJI6nImXJNW15JLtjkBSh7OpUZLq+t3vqkGS+skaL0mq68ILq9ett25vHJI6ljVekiRJLWLiJUmS1CImXpIkSS1i4iVJktQidq6XpLpOOKHdEUjqcCZeklTXqFHtjkBSh7OpUZLq+s1vqkGS+snES5LqMvGSNEA2Nc6Fxh152YDLmHr8toMQiSRJamSNlyRJUos0LfGKiOERcXNE3BkRf46Ib5X5K0TETRHxUEScHxELNisGSZKkoaSZNV5vAptn5trAeOATEbEB8D3gpMxcCXgB2L+JMUiSJA0ZTevjlZkJvFImFyhDApsDny7zJwPHAD9pVhySNGhOPrndEUjqcE3t4xURwyLiDuBp4ArgYWBGZr5dVpkGLNPMGCRp0AwfXg2S1E9NTbwyc2ZmjgfGAhOAVeu+NyIOjIhbIuKWZ555plkhSlJ9v/hFNUhSP7XkqsbMnAFcDWwIjIqIribOscDjvbxnUmaul5nrjRkzphVhStLsXXFFNUhSPzXzqsYxETGqjC8MbAXcR5WA7VJW2xe4uFkxSJIkDSXNvIHqUsDkiBhGleBdkJmXRsS9wJSI+A5wO/CzJsYgSZI0ZDTzqsa7gHV6mP9Xqv5ekiRJ8xTvXC9JktQifdZ4RcStwM+B/8rMF5ofkiQNUZMmtTsCSR2uTo3X7sDSwJ8iYkpEfDwioslxSZIkzXX6TLwy86HM/DqwMvBfVLVfj0bEtyLi/c0OUJKGjLPPrgZJ6qdafbwiYi3gB8D3gV8CuwIvAVc1LzRJGmKuu64aJKmf6vbxmkF124cjM/PNsuimiNi4ibFJkiTNVercTmLXcguI98jMTw1yPJIkSXOtOk2NB3TdgR4gIkaXm59KkiRpDtRJvLYpz1oEoNxS4pNNi0iShqrhw6tBkvqpTlPjsIhYqKtvV3nu4kLNDUuShqCTT253BJI6XJ3E61zgyoj4v2X6s8Dk5oUkSZI0d+oz8crM70XEXcAWZdaxmXl5c8OSpCHojDOq1wMOaG8ckjpWrYdkZ+Z/A//d5FgkaWi7+ebq1cRLUj/12bk+Ij4VEQ9GxIsR8VJEvBwRL7UiOEmSpLlJnRqvE4DtM/O+ZgcjSZI0N6tzO4mnTLokSZIGrk6N1y0RcT7wa6DrcUFk5kXNCkqShqRRo9odgaQOVyfxWhR4Ddi6YV4CJl5zsXFHXjbgMqYev+0gRCINISec0O4IJHW4OreT+GwrApEkSZrb1bmqceWIuDIi7inTa0XEN5ofmiQNMaecUg2S1E91Otf/FDgKeAsgM+8C9mhmUJI0JN11VzVIUj/VSbzel5k3d5v3djOCkSRJmpvV6Vz/bER8gKpDPRGxCzC9qVHNwwajU7skSRqa6iReXwQmAatGxOPAI8DeTY1KkiRpLlTnqsa/AltGxCLAfJn5cvPDkqQhaMkl2x2BpA7XZ+IVEf+72zQAmfntJsUkSUPTsce2OwJJHa5OU+OrDePDge0AHyEkSZI0h+o0Nf6gcToiTgQub1pEkjRU/aB8HR52WHvjkNSx6tR4dfc+YOxgByJJQ94DD7Q7Akkdrk4fr7spt5IAhgFjAPt3SZIkzaE6NV7bNYy/DTyVmd5AVZIkaQ7VSby63z5i0a4rGwEy8/lBjUiSJGkuVSfxug1YFngBCGAU8LeyLIEVmxKZJA01yy/f7ggkdbg6idcVwK8y87cAEbENsFNmfr6pkUnSUPP1r7c7Akkdrs5DsjfoSroAMvO/gY2aF5IkSdLcqU6N1xMR8Q3gnDK9F/BE80KSpCHquOOqV2u+JPVTnRqvPaluIfEr4KIyvmczg5KkIenRR6tBkvqpzp3rnwcOjYhFMvPVvtaXJElSz/qs8YqIjSLiXsrzGSNi7Yg4temRSZIkzWXq9PE6Cfg4cAlAZt4ZERObGpVUjDvysgGXMfX4bQchEkmSBq7Wsxoz87HGm6YCM5sTjiQNYaus0u4IJHW4OonXYxGxEZARsQBwKKXZUZLmKYcd1u4IJHW4Olc1HgR8EVgGeBwYX6YlSZI0B2Zb4xURw4AfZeZeLYpHkoauo4+uXo89tr1xSOpYs028MnNmRCwfEQtm5t9bFZQkDUlPPdXuCCR1uDp9vP4K3BARlwCz7uOVmf/ZtKgkSZLmQr328YqIs8voDsClZd2RDcNsRcSyEXF1RNwbEX+OiEPL/PdHxBUR8WB5HT3w3ZAkSRr6Zlfj9eGIWBr4G/DjfpT9NnBYZt4WESOBWyPiCmA/4MrMPD4ijgSOBP69H+VLkiR1lNklXqcBVwIrALc0zA8ggRVnV3BmTgeml/GXI+I+qisjdwQ2K6tNBq7BxEtSJ1hrrXZHIKnD9Zp4ZebJwMkR8ZPM/LeBbCQixgHrADcBS5akDOBJYMmBlC1JLXPwwe2OQFKH6/M+XoOQdI0Afgl8OTNf6lZ2UtWe9fS+AyPiloi45ZlnnhlICJIkSUNCnRuo9lu50/0vgXMz86Iy+6mIWKosXwp4uqf3ZuakzFwvM9cbM2ZMM8OUpHqOOKIaJKmfmpZ4RfVwx58B93W79cQlwL5lfF/g4mbFIEmDasaMapCkfqr1kOx+2hjYB7g7Iu4o874GHA9cEBH7A48CuzUxBkmSpCGjaYlXZl5PdQVkT7Zo1nYlSZKGqqb28ZIkSdI7mtnUKElzlwkT2h2BpA5n4iVJdR1wQLsjkNThbGqUJElqERMvSarrkEOqQZL6yaZGSarrjTfaHYGkDmeNlyRJUouYeEmSJLWIiZckSVKL2MdLkuradNN2RyCpw5l4SVJd++zT7ggkdTibGiVJklrExEuS6jrwwGqQpH4y8ZIkSWoREy9JkqQWMfGSJElqERMvSZKkFvF2EprrjTvysgG9f+rx2w5SJOp4W23V7ggkdTgTL0mqa9dd2x2BpA5nU6Mk1fXGG9UgSf1kjZck1XXIIdXrpEntjUNSx7LGS5IkqUWs8ZJaYKAd/MFO/pI0N7DGS5IkqUVMvCRJklrEpkZJqmv77dsdgaQOZ+IlSXWZeEkaIJsaJamuGTOqQZL6yRovSarriCOqV+/jJamfrPGSJElqERMvSZKkFjHxkiRJahETL0mSpBaxc73UIXzs0BCwyy7tjkBShzPxkqS6tt663RFI6nA2NUpSXU89VQ2S1E/WeElSXUcfXb16Hy9J/WSNlyRJUouYeEmSJLWIiZckSVKLmHhJkiS1iJ3rJamuvfdudwSSOpyJlyTVNXFiuyOQ1OFsapSkuh59tBokqZ+s8ZLmIT52aICOO6569T5ekvrJGi9JkqQWaVriFRE/j4inI+Kehnnvj4grIuLB8jq6WduXJEkaappZ43Um8Ilu844ErszMDwJXlmlJkqR5QtMSr8y8Fni+2+wdgcllfDKwU7O2L0mSNNS0unP9kpk5vYw/CSzZ4u1LUv8dcEC7I5DU4dp2VWNmZkRkb8sj4kDgQIDllluuZXFp8AzGFXTSkDJhQrsjkNThWn1V41MRsRRAeX26txUzc1JmrpeZ640ZM6ZlAUpSr/7yl2qQpH5qdeJ1CbBvGd8XuLjF25ek/jvxxGqQpH5q5u0kzgNuBFaJiGkRsT9wPLBVRDwIbFmmJUmS5glN6+OVmXv2smiLZm1TkiRpKPORQZJazkcXSZpX+cggSZKkFrHGS5LqOvjgdkcgqcOZeElSXWut1e4IJHU4mxolqa677qoGSeona7wkqa5TTqleJ01qbxySOpaJl6SO5JWRkjqRTY2SJEktYuIlSZLUIiZekiRJLWIfL0mq6/DD2x2BpA5n4iVJda28crsjkNThbGqUpLpuvrkaJKmfrPGSpLrOOKN6nTChvXFI6ljWeEmSJLWIiZckSVKLmHhJkiS1iH28isF4/Iikec9Q+e7w8UdSZzDxkqS6vv71dkcgqcOZeElSXcsv3+4IJHU4+3hJUl3XXlsNktRP1nhJUl3nnFO9TpzY3jgkdSwTL0nzrDntGP/dm/8GwNeGSIf6oWYwLjTwIgHN7WxqlCRJahETL0mSpBYx8ZIkSWoR+3hJUk3/uene7Q5BUocz8ZKkmp5dZHS7Q5DU4WxqlKSaNn3kNjZ95LZ2hyGpg1njJUk1bfPADQBct8K6bY5EUqeyxkuSJKlFTLwkSZJaxMRLkiSpRezjJfVhMB6DMjfxeAxNAz0vPqpHag0TL0mq6fjNPtvuECR1OBMvSarppeEj2h2CpA5nHy9JqmmLh25ii4duancYkjqYiZck1bTFQzezxUM3tzsMSR3MxEuSJKlF7OMlSZqrDMaVt17lOTTNDVfvWuMlSZLUIiZekiRJLWJToyTV9K0tP9/uECR1OBMvSarpzfkXbHcIkjqciZck1fTJ+68H4LerbtLmSAbfUHkU1NwUx1DoyK2hxz5eklTTJlNvZ5Opt7c7DEkdrC2JV0R8IiIeiIiHIuLIdsQgSZLUai1PvCJiGPB/gG2A1YE9I2L1VschSZLUau2o8ZoAPJSZf83MvwNTgB3bEIckSVJLtSPxWgZ4rGF6WpknSZI0VxuyVzVGxIHAgWXylYh4YBCKXQJ4dhDK0dDg+Zz7DOlzulfXyJ2XtzOMTjKkz2ezxffaHcGg6/jz2cJzsnxvC9qReD0OLNswPbbMe5fMnARMGswNR8QtmbneYJap9vF8zn08p3MXz+fcxfM5ONrR1Pgn4IMRsUJELAjsAVzShjgkSZJaquU1Xpn5dkQcDFwODAN+npl/bnUckiRJrdaWPl6Z+Vvgt23Y9KA2XartPJ9zH8/p3MXzOXfxfA6CyMx2xyBJkjRP8JFBkiRJLTLPJF4+pqgzRMTPI+LpiLinYd77I+KKiHiwvI4u8yMiTi7n9K6IWLfhPfuW9R+MiH3bsS+CiFg2Iq6OiHsj4s8RcWiZ7zntQBExPCJujog7y/n8Vpm/QkTcVM7b+eXCKSJioTL9UFk+rqGso8r8ByLi423aJVE9USYibo+IS8u057OJ5onEy8cUdZQzgU90m3ckcGVmfhC4skxDdT4/WIYDgZ9A9aMOfBNYn+pJCd/s+mFXy70NHJaZqwMbAF8sf3ue0870JrB5Zq4NjAc+EREbAN8DTsrMlYAXgP3L+vsDL5T5J5X1KJ+BPYA1qP7eTy3f02qPQ4H7GqY9n000TyRe+JiijpGZ1wLPd5u9IzC5jE8GdmqYf1ZW/giMioilgI8DV2Tm85n5AnAF703m1AKZOT0zbyvjL1N9uS+D57QjlfPySplcoAwJbA5cWOZ3P59d5/lCYIuIiDJ/Sma+mZmPAA9RfU+rxSJiLLAtcEaZDjyfTTWvJF4+pqizLZmZ08v4k8CSZby38+r5HoJKs8Q6wE14TjtWaZa6A3iaKgF+GJiRmW+XVRrPzazzVpa/CCyO53Mo+SFwBPCPMr04ns+mmlcSL80lsroM10txO0xEjAB+CXw5M19qXOY57SyZOTMzx1M9dWQCsGp7I1J/RcR2wNOZeWu7Y5mXzCuJV63HFGnIeqo0N1Feny7zezuvnu8hJCIWoEq6zs3Mi8psz2mHy8wZwNXAhlRNwl33hWw8N7POW1m+GPAcns+hYmNgh4iYStUFZ3PgR3g+m2peSbx8TFFnuwTouoptX+DihvmfKVfCbQC8WJqvLge2jojRpQP21mWeWqz0//gZcF9m/mfDIs9pB4qIMRExqowvDGxF1W/vamCXslr389l1nncBrio1nJcAe5Sr5Fagupji5pbshGbJzKMyc2xmjqP6XbwqM/fC89lUbblzfav5mKLOERHnAZsBS0TENKor2Y4HLoiI/YFHgd3K6r8FPknVkfM14LMAmfl8RBxLlXADfDszu3fYV2tsDOwD3F36BQF8Dc9pp1oKmFyuWJsPuCAzL42Ie4EpEfEd4HaqZJvyenZEPER10cweAJn554i4ALiX6srXL2bmzBbvi3r373g+m8Y710uSJLXIvNLUKEmS1HYmXpIkSS1i4iVJktQiJl6SJEktYuIlSZLUIiZekgYkIq6OiI93m/fliPjJHJSxX0Qs3TB9Rn8eZB8Rm0XEpTXWuyYi1pvT8mvG8MOImFjGN42IP0fEHeW+V3Na1tcaxheMiGsbbmwpqQOZeEkaqPMo9/NpsEeZ36dyT6j9gFmJV2YekJn3DlaAdZVYBvL+xYENysPeAfYC/iMzx2fm6/0oclbilZl/B64Edh9IjJLay8RL0kBdCGxbngrR9TDspYHrImLriLgxIm6LiF+UZzYSEVMj4nsRcRuwJ7AecG5XzVBjjVREfKK8/86IuLLMm1DKvT0i/hARq8wuwFLmlIi4LyJ+BSzcsOyViPhBRNwJbBgRX4mIe8rw5a59ioj7I+LcUsaFEfG+Hjb1L8D/lPccQHVj2GMj4twy76sR8aeIuCsivtUQw94RcXPZ/9OjehD18cDCZd65ZdVfUyVzkjqUiZekASl3kL8Z2KbM2gO4AFgc+AawZWauC9wCfKXhrc9l5rqZeU5Ztlf3mqGIGAP8FPiXzFwb2LUsuh/YNDPXAf438N0+wvw34LXMXI3qaQgfbli2CHBTKf91qrvlrw9sAHwuItYp660CnFrKeAn4Qg/b2Ri4tRyXM6gepfLVzNwrIramepTKBGA88OGImBgRq1HVYm1cHj49sxyLI4HXyzHpSrbuAT7Sx75KGsLsKyBpMHQ1N15cXvenSlxWB26oHtnIgsCNDe85v0a5GwDXZuYjMCvJg+rhvJMj4oNAAgv0Uc5E4ORSxl0RcVfDsplUD/EG2AT4VWa+ChARFwGbUiVQj2XmDWW9c4BDgBO7bWcp4JleYti6DLeX6RFUidhaVIngn8pxWph3Hhr+Lpk5MyL+HhEjM/PlPvZZ0hBk4iVpMFwMnBQR6wLvy8xbI2J74IrM3LOX97w6gO0dC1ydmTuXps1rBlDWGzWfK9f9+Wo9PW/tdWB4L+8Pqv5ep79rZsSXgMmZeVSNGAAWAt6oua6kIcamRkkDlpmvAFcDP+edTvV/BDaOiJUAImKRiFi5lyJeBkb2MP+PwMSIWKGU8f4yfzHg8TK+X40QrwU+XcpYk6qWqSfXATtFxPsiYhFg5zIPYLmI2LCMfxq4vof33wes1EvZlwP/2tDPbZmI+CeqDvO7lHEi4v0RsXx5z1sRMas2r3TefzYz3+pzjyUNSSZekgbLecDa5ZXMfIYqKTqvNO3dCKzay3vPBE7rftuFUsaBwEWl83tX8+QJwH9ExO3Uq7n/CTAiIu4Dvk3ph9VdZt5WYrkZuAk4IzO7mgYfAL5YyhhdyuzuMmCzXsr+HfBfwI0RcTfVRQkjy9Wb3wB+V47TFVRNlgCTgLsaOtd/rGxDUoeKzJ5qyyVJXUpz5qWZuWaNda8HtsvMGU2I4yLgyMz8y2CXLak1rPGSpMF1GLDcYBdabtfxa5MuqbNZ4yVJktQi1nhJkiS1iImXJElSi5h4SZIktYiJlyRJUouYeEmSJLWIiZckSVKL/H/n/NYYeQakYwAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -736,7 +744,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -772,7 +780,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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jjz3GYYcdxtixYzn88MN5/etfX/fpM0cccQTnnnsuABdeeCFvf/vbn3kvM/nIRz7CjjvuyGte8xrOO+88oEiiDjjggGeWmz59OmeddRZQPOnmk5/8JLvssguvec1rmDNnDnPnzuUb3/gGX/ziFxk3bhy/+c1vuOSSS3j961/PzjvvzF577cWCBQsAOOuss5he3oz6mGOO4dhjj2XXXXflJS95ySollgOxxkvqFsceW4y/8pX2xiGpaz3++OOMGzeOZcuWMX/+fC6//PLnLHPGGWew6aabcvPNN3Prrbcybty4utubNGkS73nPe1ixYgXnnnsuM2fO5NRTTwWKROzGG2/kpptuYtGiRbzuda9jjz32aBjjFltswQ033MAZZ5zB6aefzplnnsl73/teNtpoI4477jgAHn74YX7/+98TEZx55pmcdtppfP7zn3/OtubPn89VV13FnDlzOOigg4bcnDoQE68uNOaES1d7G3Nn7L8GIlGlvLeP1FumTXvuvL33hsmTi8+Dvn/Gah14YDEsWfLcGy7PnNlwl7VNjVdffTXvfOc7ufXWW1da5qqrruKDH/wgADvuuCNjx46tu71hw4ax2267cd555/H4448zZsyYlbZz5JFHMmzYMEaNGsWb3vQmrr32WjbZZJNBY+yrNXvta1/LhRdeOOAy8+bN4/DDD2f+/Pk8+eSTdW8Hccghh7DOOuvwqle96plasdVlU6MkSRqyf/qnf2LRokU8+OCDK82v7SzfjCOOOIIPfOADHHbYYU1tZ9111+Xpp59+Zrr/7Rye97znAUVS99RTTw24jQ984ANMnz6dW265hW9+85t1bwnRt63B4hkqa7wkSVobDVZDNXz44O+PGNFUDddg5syZw4oVK9h888157LHHnpm/2267cf7557Pnnnty++23c8sttwy6nd13350TTzyRI488cqX5e+yxB9/85jeZMmUKDz30EFdeeSWf+9znWL58ObfffjtPPPEEy5YtY/bs2ey2226D7mPjjTdm6dKlz0z//e9/Z5tttgFg1qxZQy36ajHxkiRJTenr4wVFDdCsWbMYNmzYSsu8//3vZ8qUKYwdO5add96ZsWPH8oIXvKDuNiPimb5Xtd72trdx9dVXs9NOOxERnHbaabzwhS8EeKbz/vbbb8/OO+/cMO4DDzyQQw89lIsuuoivfvWrnHzyyUyePJltttmGN7zhDdxzzz1DOQyrJdZU1VkrjR8/Puv9IkLPZR+vHnX22cX46KPbG4eklrjjjjt45Stf2e4wGlqxYgXLly9n+PDh/OUvf2HSpEnceeedrL/++u0OrSUGOi8RcX1mjh9oeWu8pG5hwiWpAzz22GPsueeeLF++nMzk61//etcmXavCxEuSJK0xG2+8cd37dqmFv2qMiB0i4saaYWlEfCgiNouIyyLirnK8aatikHrKtGkD/7xcktQxWpZ4ZeafMnNcZo4DXgs8BvwYOAGYnZnbA7PLaUmS1MDa0C+7l6zK+ajqPl6TgL9k5r3AwUDfbzdnAYdUFIMkSWut4cOHs3jxYpOvDpGZLF68mOHDhw9pvar6eB0B/KB8PSoz5wNk5vyI2LKiGCRJWmttu+22zJs37zk3LFX7DB8+nG233XZI67Q88YqI9YGDgBOHuN40YBrA6NGjWxCZJElrj/XWW6/uo2209qiixms/4IbM7HvI0YKI2Kqs7doKWDjQSpk5E5gJxX28KohTWrvtvXe7I5AkNVBF4nUkzzYzAlwMTAFmlOOLKohB6n6TJ7c7AklSAy3tXB8Rzwf2BmofDz4D2Dsi7irfm9HKGKSesWxZMUiSOlZLa7wy8zFg837zFlP8ylHSmnTsscV4NR98K0lqnapuJyFJktTzTLwkSZIqYuIlSZJUERMvSZKkilR153pJrXbgge2OQJLUgImX1C1MvCSp49nUKHWLJUuKQZLUsazxkrrF8ccXY+/jJUkdyxovSZKkiph4SZIkVcTES5IkqSImXpIkSRWxc73ULQ49tN0RSJIaMPGSusU++7Q7AklSAzY1St1iwYJikCR1LGu8pG7xiU8UY+/jJUkdyxovSZKkiph4SZIkVcTES5IkqSImXpIkSRWxc73ULY46qt0RSJIaMPGSusUee7Q7AklSAzY1St3i3nuLQZLUsazxkrrFZz5TjL2PlyR1LGu8JEmSKmLiJUmSVJGWJl4RMSIiLoiIORFxR0T8U0RsFhGXRcRd5XjTVsYgSZLUKVpd4/Vl4OeZ+QpgJ+AO4ARgdmZuD8wupyVJkrpeyzrXR8QmwB7AMQCZ+STwZEQcDEwsF5sFXAF8tFVxSD1j6tR2RyBJaqCVv2p8CfAg8N8RsRNwPfBBYFRmzgfIzPkRsWULY5B6x4QJ7Y5AktRAK5sa1wV2Ab6emTsD/2AIzYoRMS0irouI6x588MFWxSh1jzvvLAZJUsdqZeI1D5iXmX8opy+gSMQWRMRWAOV44UArZ+bMzByfmeNHjhzZwjClLnH66cUgSepYLUu8MvNvwP0RsUM5axJwO3AxMKWcNwW4qFUxSJIkdZJW37n+A8D3ImJ94G7gXRTJ3vkR8W7gPmByi2OQJEnqCC1NvDLzRmD8AG9NauV+JUmSOpF3rpckSaqID8mWusX06e2OQJLUgImX1C3Gjm13BJKkBmxqlLrFzTcXgySpY1njJXWLr32tGM+c2d44JEl1WeMlSZJUERMvSZKkiph4SZIkVcTES5IkqSJ2rpe6xXHHtTsCSVIDJl5St3j5y9sdgSSpAZsapW5xzTXFIEnqWNZ4Sd3izDOL8YQJ7Y1DklSXNV6SJEkVMfGSJEmqiImXJElSRUy8JEmSKmLneqlbnHRSuyOQJDVg4iV1i+22a3cEkqQGbGqUusWVVxaDJKljWeMldYtzzinGe+zR3jgkSXVZ4yVJklQREy9JkqSKmHhJkiRVxMRLkiSpInaul7rFqae2OwJJUgMmXlK3GDWq3RFIkhpoaeIVEXOBR4AVwFOZOT4iNgPOA8YAc4HDMvPhVsYh9YRf/rIY77NPe+OQJNVVRR+vPTNzXGaOL6dPAGZn5vbA7HJa0uq64IJikCR1rHZ0rj8YmFW+ngUc0oYYJEmSKtfqxCuBX0bE9RExrZw3KjPnA5TjLVscgyRJUkdodef6N2bmAxGxJXBZRMxpdsUyUZsGMHr06FbFJ0mSVJmW1nhl5gPleCHwY2ACsCAitgIoxwvrrDszM8dn5viRI0e2MkxJkqRKtKzGKyI2BNbJzEfK1/sAnwIuBqYAM8rxRa2KQeopp53W7ggkSQ20sqlxFPDjiOjbz/cz8+cRcS1wfkS8G7gPmNzCGKTeMWJEuyOQJDXQssQrM+8Gdhpg/mJgUqv2K/WsSy4pxgce2N44JEl1+axGqVtccsmzyZckqSOZeEmSJFXExEuSJKkiJl6SJEkVMfGSJEmqSKvvXC+pKl/5SrsjkCQ1YOIldYvhw9sdgSSpAZsapW7xwx8WgySpY5l4Sd3issuKQZLUsUy8JEmSKmLiJUmSVBETL0mSpIr4q0Z1tDEnXLra25g7Y/81EIkkSauvYeIVEdcB/w18PzMfbn1IklbJzJntjkCS1EAzTY1HAFsD10bEuRHxloiIFsclSZLUdRomXpn558w8CXg58H3gO8B9EXFKRGzW6gAlNenss4tBktSxmupcHxFjgc8DnwN+BBwKLAUub11okobkN78pBklSx2qmj9f1wBLg28AJmflE+dYfIuKNrQxOkiSpmzTzq8bJmXn3QG9k5tvXcDySJEldq5mmxqkRMaJvIiI2jYhPtzAmSZKkrtRM4rVfZi7pmyhvKfHW1oUkaZUMH14MkqSO1UxT47CIeF5f366I2AB4XmvDkjRkX/lKuyOQJDXQTOJ1DjA7Iv4bSOCfgVktjUqSJKkLNUy8MvO0iLgFmAQEcGpm/qLlkUkamjPPLMZTp7Y3DklSXU09qzEzfwb8rMWxSFod11xTjE28JKljNexcHxFvj4i7IuLvEbE0Ih6JiKVVBCdJktRNmvlV42nAQZn5gszcJDM3zsxNmt1BRAyLiD9GxE/K6c0i4rIymbssIjZd1eAlSZLWJs0kXgsy847V2McHgdr1TwBmZ+b2wOxyWpIkqes1k3hdFxHnRcSRZbPj2yOiqTvWR8S2wP7AmTWzD+bZX0XOAg4ZUsSSBjZiRDFIkjpWM53rNwEeA/apmZfAhU2s+yXgeGDjmnmjMnM+QGbOj4gtm4xV0mBOO63dEUiSGmjmdhLvWpUNR8QBwMLMvD4iJq7C+tOAaQCjR49elRAkSZI6SjO/anx5RMyOiFvL6bER8fEmtv1G4KCImAucC7w5Is4BFkTEVuW2tgIWDrRyZs7MzPGZOX7kyJFNFkfqYV/7WjFIkjpWM328vgWcCCwHyMybgSMarZSZJ2bmtpk5plz+8sw8CrgYmFIuNgW4aBXiltTfzTcXgySpYzWTeD0/M6/pN++p1djnDGDviLgL2LucliRJ6nrNdK5fFBEvpehQT0QcCswfyk4y8wrgivL1YorHD0mSJPWUZhKvfwVmAq+IiL8C9wBHtTQqSZKkLtTMrxrvBvaKiA2BdTLzkdaHJWnIRo1qdwSSpAYaJl4R8e/9pgHIzE+1KCZJq+LUU9sdgSSpgWaaGv9R83o4cAArPwJIkiRJTWimqfHztdMRcTrFLSEkdZLPl3+qH/5we+OQJNXVTI1Xf88HXrKmA5G0mv70p3ZHIElqoJk+XrdQ3koCGAaMBOzfJUmSNETN1HgdUPP6KWBBZq7ODVQlSZJ6UjOJV//bR2zS98tGgMx8aI1GJEmS1KWaSbxuAF4EPAwEMAK4r3wvsb+X1Bm2267dEUiSGmgm8fo5cHFm/hQgIvYD9spMfzoldZKTTmp3BJKkBpp5SPbr+pIugMz8GfCm1oUkSZLUnZp9SPbHgXMomhaPAha3NCpJQ/eZzxRja74kqWM1U+N1JMUtJH5cDiPLeZI6yb33FoMkqWM1c+f6h4APRsRGmfloBTFJkiR1pYY1XhGxa0TcDtxeTu8UEWe0PDJJkqQu00wfry8Cb6F8PmNm3hQRe7Q0KrXdmBMuXe1tzJ2x/xqIRJKk7tHUsxoz8/7am6YCK1oTjqRVtsMO7Y5AktRAM4nX/RGxK5ARsT5wLHBHa8OSNGQf9tZ6ktTpmvlV43uBfwW2AeYB48ppSZIkDcGgNV4RMQz4Uma+o6J4JK2qT3yiGJ96anvjkCTVNWjilZkrImJkRKyfmU9WFZSkVbBgQbsjkCQ10Ewfr7nAbyPiYuAffTMz8wutCkqSJKkb1e3jFRFnly8PB35SLrtxzSBJkqQhGKzG67URsR1wH/DViuKRJEnqWoMlXt8Afg68GLiuZn5QPCz7JS2MS9JQjR3b7ggkSQ3UTbwy8yvAVyLi65n5vgpjkrQqpk9vdwSSpAYa3sdrVZOuiBgeEddExE0RcVtEnFLO3ywiLouIu8rxpquyfUmSpLVNMzdQXVVPAG/OzJ0obrq6b0S8ATgBmJ2Z2wOzy2lJq+v444tBktSxWpZ4ZeHRcnK9ckjgYGBWOX8WcEirYpB6ypIlxSBJ6litrPEiIoZFxI3AQuCyzPwDMCoz5wOU4y3rrDstIq6LiOsefPDBVoYpSZJUiZYmXpm5IjPHAdsCEyJixyGsOzMzx2fm+JEjR7YuSEmSpIq0NPHqk5lLgCuAfYEFEbEVQDleWEUMkiRJ7dayxKt8xuOI8vUGwF7AHOBiYEq52BTgolbFIPWUCROKQZLUsZp5VuOq2gqYFRHDKBK88zPzJxFxNXB+RLyb4q74k1sYg9Q7pk5tdwSSpAZalnhl5s3AzgPMXwxMatV+JUmSOlUlfbwkVeDYY4tBktSxWtnUKKlKy5a1OwJJUgPWeEmSJFXExEuSJKkiJl6SJEkVsY+X1C12373dEUiSGjDxkrrF0Ue3OwJJUgM2NUqSJFXExEvqFtOmFYMkqWOZeEmSJFXExEuSJKkiJl6SJEkVMfGSJEmqiLeTkLrF3nu3OwJJUgMmXlK3mDy53RFIkhqwqVHqFsuWFYMkqWNZ46WWGXPCpe0Oobcce2wxnjmzvXFIkuqyxkuSJKkiJl6SJEkVMfGSJEmqiFOazWwAAA3fSURBVImXJElSRexcL3WLAw9sdwSSpAZMvKRuYeIlSR3PpkapWyxZUgySpI5ljZfULY4/vhh7Hy9J6ljWeEmSJFWkZTVeEfEi4LvAC4GngZmZ+eWI2Aw4DxgDzAUOy8yHWxXH2sa7vUuS1L1aWeP1FPDhzHwl8AbgXyPiVcAJwOzM3B6YXU5LkiR1vZYlXpk5PzNvKF8/AtwBbAMcDMwqF5sFHNKqGCRJkjpJJZ3rI2IMsDPwB2BUZs6HIjmLiC2riEHqeoce2u4IJEkNtDzxioiNgB8BH8rMpRHR7HrTgGkAo0ePbl2AUrfYZ592RyBJaqClv2qMiPUokq7vZeaF5ewFEbFV+f5WwMKB1s3MmZk5PjPHjxw5spVhSt1hwYJikCR1rJYlXlFUbX0buCMzv1Dz1sXAlPL1FOCiVsUg9ZRPfKIYJEkdq5VNjW8EjgZuiYgby3kfA2YA50fEu4H7gMktjEGSJKljtCzxysyrgHoduia1ar+SJEmdyjvXS5IkVcTES5IkqSI+JFtdb008hmnujP07P4ajjlrtfUiSWsvES+oWe+zR7ggkSQ3Y1Ch1i3vvLQZJUseyxkvqFp/5TDGeObO9cUiS6rLGS5IkqSImXpIkSRUx8ZIkSaqIiZckSVJF7FwvdYupU9sdgSSpARMvqVtMmNDuCCRJDdjUKHWLO+8sBklSx7LGS+oWp59ejL2PlyR1LGu8JEmSKmLiJUmSVBETL0mSpIqYeEmSJFXEzvVSt5g+vd0RSJIaMPGSusXYse2OQJLUgE2NUre4+eZikCR1LGu8pG7xta8VY+/jJUkdyxovSZKkiljjJTVhzAmXtjsESVIXsMZLkiSpIiZekiRJFbGpUeoWxx3X7ggkSQ20rMYrIr4TEQsj4taaeZtFxGURcVc53rRV+5d6zstfXgySpI7VyqbGs4B9+807AZidmdsDs8tpSWvCNdcUgySpY7WsqTEzr4yIMf1mHwxMLF/PAq4APtqqGKSecuaZxXjChPbGIUmqq+rO9aMycz5AOd6y3oIRMS0irouI6x588MHKApQkSWqVjv1VY2bOzMzxmTl+5MiR7Q5HkiRptVWdeC2IiK0AyvHCivcvSZLUNlUnXhcDU8rXU4CLKt6/JElS27Ssc31E/ICiI/0WETEP+CQwAzg/It4N3AdMbtX+pZ5z0kntjkCS1EArf9V4ZJ23JrVqn1JP2267dkcgSWqgYzvXSxqiK68sBklSx/KRQWvQmBMubXcI6mKNrq//+PlXAfjYvo/UXWbujP3XaEySpKGxxkuSJKkiJl6SJEkVMfGSJEmqiImXJElSRexcL3WJL+x+VLtDkCQ1YOIldYlFG27a7hAkSQ3Y1Ch1id3vuYHd77mh3WFIkgZhjZfUJfb7028B+M2Ld2lzJJKkeqzxkiRJqoiJlyRJUkVsapQ0JGvi0Vg+ukhSr7LGS5IkqSLWeEldYsbEd7U7BElSAyZeUpdYOnyjdocgSWrApkapS0z68x+Y9Oc/tDsMSdIgTLykLjHpz9cw6c/XtDsMSdIgTLwkSZIqYh+v0pr4ibwkSdJgrPGSJEmqiImXJElSRWxqlLrEKXv9S7tDkCQ1YOIldYkn1l2/3SH0pE55hFKnxCFpcDY1Sl3irXOu4q1zrmp3GJKkQZh4SV1it7l/ZLe5f2x3GJKkQbSlqTEi9gW+DAwDzszMGe2IQ+o13jZlZR6PNcvmTrVaN1xjldd4RcQw4L+A/YBXAUdGxKuqjkOSJKlq7WhqnAD8OTPvzswngXOBg9sQhyRJUqXakXhtA9xfMz2vnCdJktTVIjOr3WHEZOAtmTm1nD4amJCZH+i33DRgWjm5A/CnFoe2BbCoxfvoZL1c/l4uO/R2+S177+rl8vdy2aGa8m+XmSMHeqMdnevnAS+qmd4WeKD/Qpk5E5hZVVARcV1mjq9qf52ml8vfy2WH3i6/Ze/NskNvl7+Xyw7tL387mhqvBbaPiBdHxPrAEcDFbYhDkiSpUpXXeGXmUxExHfgFxe0kvpOZt1UdhyRJUtXach+vzPwp8NN27HsQlTVrdqheLn8vlx16u/yWvXf1cvl7uezQ5vJX3rlekiSpV/nIIEmSpIqYeFE8wigi/hQRf46IE9odT5UiYm5E3BIRN0bEde2Op9Ui4jsRsTAibq2Zt1lEXBYRd5XjTdsZY6vUKfvJEfHX8vzfGBFvbWeMrRIRL4qIX0XEHRFxW0R8sJzfK+e+Xvm7/vxHxPCIuCYibirLfko5v1fOfb3yd/257xMRwyLijxHxk3K6ree+55say0cY3QnsTXGri2uBIzPz9rYGVpGImAuMz8yeuKdLROwBPAp8NzN3LOedBjyUmTPKxHvTzPxoO+NshTplPxl4NDNPb2dsrRYRWwFbZeYNEbExcD1wCHAMvXHu65X/MLr8/EdEABtm5qMRsR5wFfBB4O30xrmvV/596fJz3yci/g0YD2ySmQe0+zPfGi8fYdRTMvNK4KF+sw8GZpWvZ1F8IXWdOmXvCZk5PzNvKF8/AtxB8cSMXjn39crf9bLwaDm5XjkkvXPu65W/J0TEtsD+wJk1s9t67k28fIRRAr+MiOvLpwX0olGZOR+KLyhgyzbHU7XpEXFz2RTZlc0ttSJiDLAz8Ad68Nz3Kz/0wPkvm5puBBYCl2VmT537OuWHHjj3wJeA44Gna+a19dybeEEMMK9n/hsA3piZuwD7Af9aNkepd3wdeCkwDpgPfL694bRWRGwE/Aj4UGYubXc8VRug/D1x/jNzRWaOo3hSyoSI2LHdMVWpTvm7/txHxAHAwsy8vt2x1DLxavIRRt0qMx8oxwuBH1M0vfaaBWUfmL6+MAvbHE9lMnNB+aH8NPAtuvj8l/1bfgR8LzMvLGf3zLkfqPy9dP4BMnMJcAVF/6aeOfd9asvfI+f+jcBBZV/mc4E3R8Q5tPncm3j18COMImLDsqMtEbEhsA9w6+BrdaWLgSnl6ynARW2MpVJ9Hz6lt9Gl57/sYPxt4I7M/ELNWz1x7uuVvxfOf0SMjIgR5esNgL2AOfTOuR+w/L1w7jPzxMzcNjPHUHy3X56ZR9Hmc9+WO9d3kh5/hNEo4MfFZzLrAt/PzJ+3N6TWiogfABOBLSJiHvBJYAZwfkS8G7gPmNy+CFunTtknRsQ4iub1ucC/tC3A1nojcDRwS9nXBeBj9Mi5p375j+yB878VMKv8Bfs6wPmZ+ZOIuJreOPf1yn92D5z7etr6d9/zt5OQJEmqik2NkiRJFTHxkiRJqoiJlyRJUkVMvCRJkipi4iVJklQREy9JdUXE5hFxYzn8LSL+WjO9fr9lPxQRz29im1dExPjWRf2c/Z0cEcdVsJ/dI+K28ths0MTyZ0XEoUPcx+9WPUJJncDES1Jdmbk4M8eVjxv5BvDFvunyofK1PgQ0TLzWJlFo9nPyHcDp5bF5vBXxZOau/eeV92eStJYw8ZI0JBExKSL+GBG3lA/XfV5EHAtsDfwqIn5VLvf1iLiurAU6pYntzo2IUyLihnLbryjnr1RjFRG3RsSYcpgTEWeW874XEXtFxG8j4q6IqH0Eyk4RcXk5/z012/pIRFxbPij4lHLemIi4IyLOAG5g5UeK1Sv/VOAw4N8j4nsDlO2d5T5uioiza97aIyJ+FxF399V+RcRGETG75jgcXLOdR8vxxIj4VUR8n+KmqBtGxKXl9m+NiMMbHW9J7dHzd66XNCTDgbOASZl5Z0R8F3hfZn4pIv4N2DMzF5XLnpSZD5U1MrMjYmxm3txg+4syc5eIeD9wHDC1wfIvo7jr9DSKx3/9X2A34CCKO7MfUi43FngDsCHwx4i4FNgR2J7iGXUBXBzFQ+LvA3YA3pWZ76/dWUQMVv7dgJ9k5gX91nk1cBLFA+kXRcRmNW9vVcb7CorHmFwALAPelplLI2IL4PcRcXE+927XE4AdM/OeiPg/wAOZuX+5zxc0OG6S2sQaL0lDMQy4JzPvLKdnAXvUWfawiLgB+CPwauBVTWy/7+HV1wNjmlj+nsy8pXzQ723A7DJBuaXf+hdl5uNlUvgriqRln3L4I0XN1isoEjGAezPz9wPsbweaL3+fNwMX9CWkmflQzXv/k5lPZ+btFI/wgiIJ/I+IuBn4X2CbmvdqXZOZ95SvbwH2ioj/jIjdM/PvDWKS1CbWeEkain80s1BEvJiixup1mflwRJxFUVvWyBPleAXPfj49xcr/JA4fYHmAp2umn2blz7f+tUVJkeB8NjO/2S/2MdQvZ9QPva4YYP99nui3HBR9xUYCr83M5RExl4GP3TMxlrVvrwXeCnw2In6ZmZ9ahVgltZg1XpKGYjgwJiJeVk4fDfy6fP0IsHH5ehOKxODvETEK2G819jkX2AUgInYBXrwK2zg4IoZHxOYUDwq/FvgF8M8RsVG57W0iYssG25lD/fLXM5ui9m/zcj+bNVj+BcDCMunaE9iuwfJExNbAY5l5DnA65fGS1Hms8ZI0FMuAdwE/jIh1KRKYb5TvzQR+FhHzM3PPiPgjRfPf3cBvV2OfPwLeGRE3lvu7s8HyA7kGuBQYDZyamQ8AD0TEK4GrIwLgUeAoitq2AWXmsoioV/5669wWEZ8Bfh0RKyiaNo8ZZJXvAZdExHXAjRTJXiOvAT4XEU8Dy4H3NbGOpDaI5/bXlCRJUivY1ChJklQREy9JkqSKmHhJkiRVxMRLkiSpIiZekiRJFTHxkiRJqoiJlyRJUkVMvCRJkiry/wFj897oLvYXwgAAAABJRU5ErkJggg==\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -808,7 +816,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -844,7 +852,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -880,7 +888,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -916,7 +924,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -952,7 +960,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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7sdtuuw0khhEDqyFL8vkktyS5oqfstCSXtsP1SS5ty2club9n2qcHFZckSRqce+65hzXXXBOA66+/ni222AKA++67j7333pvNN9+cPfbYg+c///l9n7azzz77cOqppwJwxhln8LrXvW7htKriH/7hH9hiiy14znOew2mnnQY0ydVrXvOahfMddNBBnHjiiUDzZJ8PfOADbL311jznOc/h6quv5vrrr+fTn/40H//4x5k9ezY//vGP+da3vsXzn/98nvvc57Lzzjtz8803A3DiiSdyUHtz7f3335+DDz6YF73oRTz1qU9dqoRzLIOsITsROA74wkhBVb1+5H2SY4C7e+a/tqpmDzAeabgdfHDzeuyx3cYhablz//33M3v2bB544AHmz5/PD3/4w0XmOf7441lzzTW58sorueKKK5g9e3bf9e2000687W1vY8GCBZx66qnMnTuXI488EmgStEsvvZTLLruM2267jec973nssMMOi41x7bXX5pJLLuH444/n6KOP5oQTTuAd73gHq6yyCu95z3sAuPPOO7ngggtIwgknnMBRRx3FMcccs8i65s+fz/nnn8/VV1/NbrvttsTNsmMZWEJWVeclmTXWtDS93fYGXjao7U+mWe/9r8e8jus/+reTEIn0GHiPImk4HHjgomUvfznstVfzd2Dkn7Neu+7aDHfdtegNpOfOXewme5ssf/azn/HmN7+ZK6644lHznH/++bz73e8GYIsttmDLLbfsu74VVliB7bbbjlNPPZX777+fWbNmPWo9++67LyussAIzZ87kJS95Cb/4xS9YbbXVxo1xpJZtm2224Ywzzhhznnnz5vH617+e+fPn85e//KXvbSt23313Hve4x7H55psvrEV7rLrq1L89cHNV/U9P2SZJfpnkR0m277dgkgOTXJTkoltvvXXwkUqSpAl74QtfyG233cZj/Y7eZ599OPjgg9l7770nNP+KK67II488snB89G0nVl55ZaBJ9h5++OEx1/Gud72Lgw46iF/96ld85jOf6XvripF1AY/68cFj0VWn/n2BU3rG5wMbVdXtSbYBvpHk2VV1z+gFq2ouMBdgzpw5k3MUJElaXoxXozVjxvjT11hjQjVi47n66qtZsGABa621Fvfdd9/C8he/+MV85Stf4aUvfSlXXnklv/rVr8Zdz/bbb89hhx3Gvvvuu0j5Zz7zGfbbbz/uuOMOzjvvPD72sY/x0EMPceWVV/Lggw9y//33c84557DddtuNu41VV12Ve+75a6px9913s/766wNw0kknLemuPyZTnpAlWRF4HbDNSFlVPQg82L6/OMm1wGbA2L39JEnStDHShwyaGqOTTjqJFVZY4VHz/N3f/R377bcfm2++Oc985jN59rOfzeqrr953nUkW9u3qtccee/Czn/2MrbbaiiQcddRRPOUpTwFg7733ZosttmCTTTbhuc997mLj3nXXXdlzzz355je/ySc/+UmOOOII9tprL9Zcc01e9rKX8bvf/W4JjsJjk8mqahtz5U0fsm9X1RY9Za8EDquql/SUrQPcUVULkjwV+DHwnKq6Y7z1z5kzp/r9QmMy2YdMy4UvfrF5fdObuo1D0qS66qqreNazntV1GIu1YMECHnroIWbMmMG1117LzjvvzDXXXMPjH//4rkMbiLHOS5KLq2rOWPMPrIYsySnAjsDaSeYBH6iqzwH78OjmSoAdgA8leQh4BHjH4pIxSUvIRExSh+677z5e+tKX8tBDD1FVHH/88cttMrY0Bvkry337lO8/RtnXgK8NKhZJktStVVddte99x+Sjk6ThceCBY/8cXpLUORMySZKWcYPsD64ltzTnw4RMkqRl2IwZM7j99ttNyqaJquL2229nxowZS7ScDxeXJGkZtsEGGzBv3rzHfCNWTZ4ZM2awwQYbLNEyJmSSJC3DVlpppb6P+NGyw4RMGhYvf3nXEUiS+jAhk4bFXnt1HYEkqQ879UvD4oEHmkGSNO1YQyYNi4MPbl4f44ODJUmTzxoySZKkjpmQSZIkdcyETJIkqWMmZJIkSR2zU780LHbdtesIJEl9mJBJw8KETJKmLZsspWFx113NIEmadqwhk4bFoYc2r96HTJKmHWvIJEmSOmZCJkmS1DETMkmSpI6ZkEmSJHXMTv3SsNhzz64jkCT1YUImDYtdduk6AklSHzZZSsPi5pubQZI07VhDJg2L97+/efU+ZJI07VhDJkmS1DETMkmSpI4NLCFL8vkktyS5oqfsiCQ3Jbm0HV7dM+2wJL9Nck2SVwwqLkmSpOlmkDVkJwKvHKP841U1ux2+A5Bkc2Af4NntMscnWWGAsUmSJE0bA+vUX1XnJZk1wdlfC5xaVQ8Cv0vyW2Bb4GeDik8aOm98Y9cRSJL66KIP2UFJLm+bNNdsy9YHbuyZZ15bJmmy7LBDM0iSpp2pTsg+BTwNmA3MB45Z0hUkOTDJRUkuuvXWWyc5PGk5dsMNzSBJmnamNCGrqpurakFVPQJ8lqZZEuAmYMOeWTdoy8Zax9yqmlNVc9ZZZ53BBiwtTz7ykWaQJE07U5qQJVm3Z3QPYOQXmGcC+yRZOckmwNOBC6cyNkmSpK4MrFN/klOAHYG1k8wDPgDsmGQ2UMD1wNsBqurXSb4CXAk8DLyzqhYMKjZJkqTpZJC/stx3jOLPjTP/RwDbUyRJ0tDxTv2SJEkd8+Hi0rA44ICuI5Ak9WFCJg2Lbbdd/DySpE7YZCkNi9/8phkkSdOONWTSsDj66OZ17txu45AkLcIaMkmSpI6ZkEmSJHXMhEySJKljJmSSJEkds1O/NCwOOqjrCCRJfZiQScNiyy27jkCS1IdNltKwuPzyZpAkTTvWkEnD4rjjmlfvQyZJ0441ZJIkSR0zIZMkSeqYCZkkSVLHTMgkSZI6Zqd+aVi85z1dRyBJ6sOETBoWm23WdQSSpD5sspSGxYUXNoMkadqxhkwaFiec0Lxuu223cUiSFmENmSRJUsdMyCRJkjpmQiZJktQxEzJJkqSO2alfGhaHH951BJKkPkzIpGGx8cZdRyBJ6sMmS2lYnHdeM0iSpp2BJWRJPp/kliRX9JR9LMnVSS5P8vUka7Tls5Lcn+TSdvj0oOKShtaXvtQMkqRpZ5A1ZCcCrxxVdjawRVVtCfwGOKxn2rVVNbsd3jHAuCRJkqaVgSVkVXUecMeosrOq6uF29AJgg0FtX5IkaVnRZR+y/w18t2d8kyS/TPKjJNt3FZQkSdJU6+RXlkkOBx4GTm6L5gMbVdXtSbYBvpHk2VV1zxjLHggcCLDRRhtNVciSJEkDM+UJWZL9gdcAO1VVAVTVg8CD7fuLk1wLbAZcNHr5qpoLzAWYM2dOTVHY0rLvyCO7jkCS1MeUJmRJXgkcCrykqu7rKV8HuKOqFiR5KvB04LqpjE1a7s2c2XUEkqQ+BpaQJTkF2BFYO8k84AM0v6pcGTg7CcAF7S8qdwA+lOQh4BHgHVV1x5grlrR0zjqred1ll27jkCQtYmAJWVXtO0bx5/rM+zXga4OKRRJw+unNqwmZJE073qlfkiSpYyZkkiRJHTMhkyRJ6pgJmSRJUsc6uTGspA4cdVTXEUiS+jAhk4bFGmt0HYEkqQ+bLKVh8a1vNYMkadoxIZOGhQmZJE1bJmSSJEkdMyGTJEnqmAmZJElSx0zIJEmSOuZtL6RhceyxXUcgSerDhEwaFjNmdB2BJKkPmyylYfHVrzaDJGnaMSGThsXZZzeDJGnaMSGTJEnqmAmZJElSx0zIJEmSOmZCJkmS1LHF3vYiycXA54EvV9Wdgw9J0kDMndt1BJKkPiZSQ/Z6YD3gF0lOTfKKJBlwXJIkSUNjsQlZVf22qg4HNgO+TFNbdkOSDyZ58qADlDRJvvjFZpAkTTsT6kOWZEvgGOBjwNeAvYB7gB8OLjRJk+rHP24GSdK0M9E+ZHcBnwPeW1UPtpN+nuTFA4xNkiRpKEzkWZZ7VdV1Y02oqtdNcjySJElDZyJNlgckWWNkJMmaST48uJAkSZKGy0QSsldV1V0jI+2tL149sIgkDcaMGc0gSZp2JpKQrZBk5ZGRJE8AVh5n/oWSfD7JLUmu6Cl7cpKzk/xP+7pmW54kxyb5bZLLk2y9pDsjaRzHHtsMkqRpZyIJ2cnAOUnemuStwNnASRNc/4nAK0eVvRc4p6qeDpzTjgO8Cnh6OxwIfGqC25AkSVqmLbZTf1X9a5LLgZ3aoiOr6vsTWXlVnZdk1qji1wI7tu9PAs4F/rEt/0JVFXBBkjWSrFtV8yeyLUmLccIJzesBB3QbhyRpERP5lSVV9V3gu5O0zZk9SdYfgZnt+/WBG3vmm9eWmZBJk+HCC5tXEzJJmnYW22SZ5HVtf6+7k9yT5N4k90zGxtvasFqSZZIcmOSiJBfdeuutkxGGJElSpybSh+woYLeqWr2qVquqVatqtcewzZuTrAvQvt7Slt8EbNgz3wZt2aNU1dyqmlNVc9ZZZ53HEIYkSdL0MJGE7OaqumoSt3kmsF/7fj/gmz3lb25/bfkC4G77j0mSpGEwkT5kFyU5DfgGMPLYJKrqjMUtmOQUmg78ayeZB3wA+CjwlfYXmzcAe7ezf4fm/ma/Be4D3jLhvZC0eGus0XUEkqQ+JpKQrUaTIO3SU1bAYhOyqtq3z6SdRhe0/cneOYF4JC2No47qOgJJUh8Tue2FNVWSJEkDNJFfWW6W5JyRu+0n2TLJPw0+NEmT6rjjmkGSNO1MpFP/Z4HDgIcAqupyYJ9BBiVpAC6/vBkkSdPORBKyJ1bVhaPKHh5EMJIkScNoIgnZbUmeRnsD1yR74t3zJUmSJs1EfmX5TmAu8MwkNwG/A9440KgkSZKGyER+ZXkdsHOSJwGPq6p7Bx+WpEk3c+bi55EkdWKxCVmSfx41DkBVfWhAMUkahCOP7DoCSVIfE2my/HPP+xnAa4DJfJSSJEnSUJtIk+UxveNJjga+P7CIJA3GMe2lfMgh3cYhSVrERGrIRnsisMFkByJpwK65pusIJEl9TKQP2a9ob3kBrACsA9h/TJIkaZJMpIbsNT3vHwZuripvDCtJkjRJJpKQjb7NxWojv7QEqKo7JjUiSZKkITORhOwSYEPgTiDAGsDv22kFPHUgkUmaXBtv3HUEkqQ+JpKQnQ18vaq+A5DkVcDuVfX2gUYmaXIdfnjXEUiS+pjIsyxfMJKMAVTVd4EXDS4kSZKk4TKRGrI/JPkn4Evt+BuAPwwuJEkD8ZGPNK/WlEnStDORGrJ9aW518XXgjPb9voMMStIA3HBDM0iSpp2J3Kn/DuDdSZ5UVX9e3PySJElaMoutIUvyoiRX0j6/MslWSY4feGSSJElDYiJNlh8HXgHcDlBVlwE7DDIoSZKkYTKhZ1lW1Y29N4MFFgwmHEkD84xndB2BJKmPiSRkNyZ5EVBJVgLeTdt8KWkZcsghXUcgSepjIk2W7wDeCawP3ATMbsclSZI0CcatIUuyAvCJqnrDFMUjaVDe//7m9cgju41DkrSIcROyqlqQZOMkj6+qv0xVUJIG4Oabu45AktTHRPqQXQf8JMmZwML7kFXVvw0sKkmSpCHStw9Zki+2b3cDvt3Ou2rPsFSSPCPJpT3DPUn+PskRSW7qKX/10m5DkiRpWTJeDdk2SdYDfg98crI2WFXX0PwwYKSP2k00j2V6C/Dxqjp6srYlSZK0LBgvIfs0cA6wCXBRT3mAAp46CdvfCbi2qm4YdZ8zSZNtyy27jkCS1EffhKyqjgWOTfKpqvo/A9r+PsApPeMHJXkzTQJ4SFXdOXqBJAcCBwJstNFGAwpLWg4ddFDXEUiS+ljsfcgGlYwleTxN/7SvtkWfAp5G05w5HzimTzxzq2pOVc1ZZ511BhGaJEnSlJrIjWEH5VXAJVV1M0BV3VxVC6rqEeCzwLYdxiYtfw49tBkkSdPOhJ5lOSD70tNcmWTdqprfju4BXNFJVNLy6q67uo5AktRHJwlZkicBLwfe3lN8VJLZND8YuH7UNEmSpOVWJwlZVf0ZWGtU2Zu6iEWSJKlrXfYhkyRJEt32IZM0lbb1dzKSNF2ZkEnD4oADuo5AktSHTZaSJEkdMyGThsXBBzeDJGnasclSGhYPPNB1BJKkPqwhkyRJ6pgJmSRJUsdMyCRJkjpmHzJpWGy/fdcRSJL6MCGThsWbfDqZJE1XNllKkiR1zIRMGhYHHtgMkqRpx4RMkiSpYyZkkiRJHTMhkyRJ6pgJmSRJUse87YU0LF7+8q4jkCT1YUImDYu99uo6AklSHzZZSsPigQeaQZI07VhDJg2Lgw9uXufO7TYOSdIirCGTJEnqmAmZJElSx0zIJEmSOmZCJkmS1DE79UvDYtddu45AktSHCZk0LEzIJGna6iwhS3I9cC+wAHi4quYkeTJwGjALuB7Yu6ru7CpGably113N6xprdBmFJGkMXfche2lVza6qOe34e4FzqurpwDntuKTJcOihzSBJmna6TshGey1wUvv+JGD37kKRJEmaGl0mZAWcleTiJAe2ZTOran77/o/AzG5CkyRJmjpddurfrqpuSvI3wNlJru6dWFWVpEYv1CZvBwJstNFGUxOpJEnSAHVWQ1ZVN7WvtwBfB7YFbk6yLkD7essYy82tqjlVNWedddaZypAlSZIGopOELMmTkqw68h7YBbgCOBPYr51tP+CbXcQnLZf23LMZJEnTTldNljOBrycZieHLVfW9JL8AvpLkrcANwN4dxSctf3bZpesIJEl9dJKQVdV1wFZjlN8O7DT1EUlD4Oabm9eZ/lZGkqYb79QvDYv3v795nTu32zgkSYuYbvchkyRJGjomZJIkSR0zIZMkSeqYCZkkSVLH7NQvDYs3vrHrCCRJfZiQScNihx26jkCS1IdNltKwuOGGZpAkTTvWkEnD4iMfaV69D5kkTTvWkEmSJHXMhEySJKljJmSSJEkdMyGTJEnqmJ36pWFxwAFdRyBJ6sOETBoW227bdQSSpD5sspSGxW9+0wySpGnHGjJpWBx9dPPqfcgkadqxhkySJKljJmSSJEkdMyGTJEnqmAmZJElSx+zULw2Lgw7qOgJJUh8mZNKw2HLLriOQJPVhk6U0LC6/vBkkSdOONWTSsDjuuObV+5BJ0rRjDZkkSVLHTMgkSZI6ZkImSZLUsSnvQ5ZkQ+ALwEyggLlV9YkkRwBvA25tZ31fVX1nquMblFnv/a/HvI7rP/q3kxCJJEmabrro1P8wcEhVXZJkVeDiJGe30z5eVUd3EJO0/HvPe7qOQJLUx5QnZFU1H5jfvr83yVXA+lMdhzR0Ntus6wgkSX102ocsySzgucDP26KDklye5PNJ1uwuMmk5dOGFzSBJmnY6S8iSrAJ8Dfj7qroH+BTwNGA2TQ3aMX2WOzDJRUkuuvXWW8eaRdJYTjihGSRJ004nCVmSlWiSsZOr6gyAqrq5qhZU1SPAZ4Ftx1q2quZW1ZyqmrPOOutMXdCSJEkDMuUJWZIAnwOuqqp/6ylft2e2PYArpjo2SZKkLnTxK8sXA28CfpXk0rbsfcC+SWbT3ArjeuDtHcQmSZI05br4leX5QMaYtNzcc0ySJGlJ+HBxaVgcfnjXEUiS+jAhk4bFxht3HYEkqQ+fZSkNi/POawZJ0rRjDZk0LL70peZ1hx26jUOStAgTsmXIY31AuQ8nlyRperLJUpIkqWMmZJIkSR0zIZMkSeqYfcikYXHkkV1HIEnqw4RMGhYzZ3YdgSSpD5sspWFx1lnNIEmadqwhk4bF6ac3r7vs0m0ckqRFWEMmSZLUMRMySZKkjpmQSZIkdcyETJIkqWN26h8ij/VZmODzMJdpRx3VdQSSpD5MyDTlTAw7ssYaXUcgSerDJktpWHzrW80gSZp2TMikYWFCJknTlgmZJElSx0zIJEmSOmanfi2RyeiQL0mSHs0aMkmSpI5ZQ6ZlkrfOWArHHtt1BJKkPkzIpGExY0bXEUiS+jAh09BanvrDTai276tfbV732muwwUiSlpgJmbQcmEhy+S/f+xwA77v4iWNOH7omXEmaRqZdp/4kr0xyTZLfJnlv1/FIkiQN2rRKyJKsAPwH8Cpgc2DfJJt3G5UkSdJgTbcmy22B31bVdQBJTgVeC1zZaVSSJmR5+fXr8rIfk8XjoeluefiMTqsaMmB94Mae8XltmSRJ0nIrVdV1DAsl2RN4ZVUd0I6/CXh+VR3UM8+BwIHt6DOAawYc1trAbQPehrrj+V2+eX6Xb57f5dvyeH43rqp1xpow3ZosbwI27BnfoC1bqKrmAnOnKqAkF1XVnKnanqaW53f55vldvnl+l2/Ddn6nW5PlL4CnJ9kkyeOBfYAzO45JkiRpoKZVDVlVPZzkIOD7wArA56vq1x2HJUmSNFDTKiEDqKrvAN/pOo4eU9Y8qk54fpdvnt/lm+d3+TZU53dadeqXJEkaRtOtD5kkSdLQMSEbh49xWvYk2TDJfye5Msmvk7y7LX9ykrOT/E/7umZbniTHtuf48iRb96xrv3b+/0myX1f7pEUlWSHJL5N8ux3fJMnP2/N4WvujIJKs3I7/tp0+q2cdh7Xl1yR5RUe7olGSrJHk9CRXJ7kqyQu9fpcfSf7f9m/zFUlOSTLD67dhQtaHj3FaZj0MHFJVmwMvAN7Znrf3AudU1dOBc9pxaM7v09vhQOBT0CRwwAeA59M8QeIDI18CmhbeDVzVM/6vwMeralPgTuCtbflbgTvb8o+389F+JvYBng28Eji+vebVvU8A36uqZwJb0Zxnr9/lQJL1gYOBOVW1Bc2P9/bB6xcwIRvPwsc4VdVfgJHHOGkaq6r5VXVJ+/5emj/m69Ocu5Pa2U4Cdm/fvxb4QjUuANZIsi7wCuDsqrqjqu4Ezqa58NWxJBsAfwuc0I4HeBlwejvL6PM7ct5PB3Zq538tcGpVPVhVvwN+S3PNq0NJVgd2AD4HUFV/qaq78PpdnqwIPCHJisATgfl4/QImZOPxMU7LuLZ6+7nAz4GZVTW/nfRHYGb7vt959vxPX/8OHAo80o6vBdxVVQ+3473nauF5bKff3c7v+Z2eNgFuBf6zbZI+IcmT8PpdLlTVTcDRwO9pErG7gYvx+gVMyLScSrIK8DXg76vqnt5p1fy02J8XL4OSvAa4paou7joWDcSKwNbAp6rqucCf+WvzJOD1uyxrm41fS5N4rwc8CWsuFzIh62+xj3HS9JRkJZpk7OSqOqMtvrltyqB9vaUt73eePf/T04uB3ZJcT9ON4GU0fY7WaJtA4NHnauF5bKevDtyO53e6mgfMq6qft+On0yRoXr/Lh52B31XVrVX1EHAGzTXt9YsJ2Xh8jNMyqO1f8Dngqqr6t55JZwIjv7TaD/hmT/mb219rvQC4u20a+T6wS5I12//qdmnL1KGqOqyqNqiqWTTX5A+r6g3AfwN7trONPr8j533Pdv5qy/dpf8W1CU2n8AunaDfUR1X9EbgxyTPaop2AK/H6XV78HnhBkie2f6tHzq/XL9PwTv3ThY9xWma9GHgT8Kskl7Zl7wM+CnwlyVuBG4C922nfAV5N0yn0PuAtAFV1R5IjaRJzgA9V1R1TsgdaGv8InJrkw8AvaTuFt69fTPJb4A6aJI6q+nWSr9B8GTwMvLOqFkx92BrDu4CT23+Er6O5Jh+H1+8yr6p+nuR04BKa6+6XNHfj/y+8fr1TvyRJUtdsspQkSeqYCZkkSVLHTMgkSZI6ZkImSZLUMRMySZKkjpmQSZIkdcyETBIASQ5P8usklye5NMnz2/Lrk6w9xvw/Xcz6ZiW5os+0c5PMWYLY9k+y3kTnX1JJdkvy3sXPOfWS/HuSHTrY7g/am6pKmgImZJJI8kLgNcDWVbUlzSNObhxvmap60VTE1tqf5tl3E9bzKJYxx3tV1ZlV9dGlC23cGFZ4jMuvBbygqs57jOtZmpuAfxH4u8eyXUkTZ0ImCWBd4LaqehCgqm6rqj/0zpDkCUm+m+Rt7fif2tdVkpyT5JIkv0ry2p7FVkxycpKrkpye5ImjN5xklyQ/a5f/avtg+N7pewJzaO7efmkbxzZJfpTk4iTf73nO4bltjdJFwLvHGN81yc+T/LKtAZrZLrd/kuPa9ycmOTbJT5Nc125/EUm+0W7/10kO7Cn/U5JjklwGvDDJG5Nc2Mb+mZEkLcmnklzULv/BPufl/wG+17Puf07yiyRXJJnbPn6GJJu2+3NZexyflmTHJD9OciZwZZIVknysXf7yJG9vl103yXltfFck2b7d3JnAvn3ikjTJTMgkAZwFbJjkN0mOT/KSUdNXAb4FnFJVnx017QFgj6raGngpcMxIogA8Azi+qp4F3MOoGpe2KfSfgJ3b5S8C/m/vPFV1elv+hqqaTfOolE8Ce1bVNsDngY/0LPL4qppTVceMMX4+TY3Tc2keTn5on+OxLrAdTa1hv5qz/91ufw5wcFubBfAk4OdVtRXNg5BfD7y4jX0B8IZ2vsOrag6wJfCSJFuOsY0XAxf3jB9XVc+rqi2AJ7TxAZwM/Ee7zRcB89vyrYF3V9VmwFtpnvX4POB5wNvSPAfwfwHfb+PbCrgUoKruBFbu2S9JA+SzLCVRVX9Ksg2wPU1SdVqS91bVie0s3wSOqqqTx1g8wL+0/ZweAdYHZrbTbqyqn7TvvwQcDBzds+wLgM2Bn7Q53OOBny0m3GcAWwBnt8uswF8TEIDTRs3fO75Bu2/rttv6XZ9tfKOqHqGpWZrZZ56Dk+zRvt+Q5gHHt9MkXV9ry3cCtgF+0cb6BOCWdtrebc3aijQJ4ObA5aO2sS5wa8/4S5McCjwReDLw6yTnAutX1dcBquoBgHZ7F1bVyD7uAmzZU+O3ehvzL4DPJ1mp3e9Le7Z3C01T8e19joGkSWJCJgmA9uG85wLnJvkVsB9wYjv5J8Ark3y5Fn0A7huAdYBtquqhJNcDM0ZWO3ozo8YDnF1VS9I0FuDXVfXCPtP/PM74J4F/q6ozk+wIHNFnHQ+O2t6jA2iW3Rl4YVXd1yZFI/v8QM+DjgOcVFWHjVp+E+A9wPOq6s4kJ/Ys3+v+kfIkM4DjgTlVdWOSI/os06t33wO8q6q+P8b+7AD8LXBikn+rqi+0k2a0MUgaMJssJZHkGUme3lM0G7ihZ/yfgTuB/xhj8dWBW9pk7KXAxj3TNkrzgwFomsbOH7XsBcCLk2zaxvGkJJuNsY17gVXb99cA64ysN8lKSZ69uH3sifWm9v1+E1ym33rubJOxZ9LU9I3lHGDPJH/TxvrkJBsDq9EkS3e3NXCv6rP8VcCm7fuR5Ou2tp/dngBVdS8wL8nu7TZWzhh99YDvA/+nrQkjyWbt8d4YuLltij6BppmTttn5KcD1iz0akh4zEzJJ0PQROynJlUkup2k+O2LUPO8GnpDkqFHlJwNz2lq1NwNX90y7BnhnkquANYFP9S5YVbfS/ILylHa7PwOeOUZ8JwKfTnIpTRPlnsC/th3nL6XpNzURRwBfTXIxcNsElxnL92h+sHAVTR+zC8aaqaqupOkjd1a7f2cD61bVZcAvaY7Vl2lqIMfyX8CO7bruAj4LXEGTXP2iZ7430TShXg78lCaRGu0E4ErgkjS3I/kMTSvJjsBlSX5J09/tE+382wAXVNXD4xwHSZMki7Y+SJKmiyTnA69pE7Kp3O4ngDOr6pyp3K40rKwhk6Tp7RBgow62e4XJmDR1rCGTJEnqmDVkkiRJHTMhkyRJ6pgJmSRJUsdMyCRJkjpmQiZJktSx/x+gnSc3O4XPIQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -1078,7 +1086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1103,7 +1111,7 @@
     "    \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()"
    ]
   },
@@ -1182,9 +1190,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "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",
@@ -1193,12 +1214,12 @@
     "#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 * i for i in runs_delta] #1\n",
     "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
     "fig.subplots_adjust(wspace=0.5)\n",
     "ax[0].plot(runs_closed, price_deltas, 'o-')\n",
     "ax[0].set(xlabel='Runs closed', ylabel='Change ($)', title='Ticket price')\n",
-    "revenue_deltas = [5 * expected_visitors * ___ for ___ in ___] #2\n",
+    "revenue_deltas = [5 * expected_visitors * i for i in price_deltas] #2\n",
     "ax[1].plot(runs_closed, revenue_deltas, 'o-')\n",
     "ax[1].set(xlabel='Runs closed', ylabel='Change ($)', title='Revenue');"
    ]
@@ -1226,14 +1247,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "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"
    ]
   },
@@ -1272,13 +1293,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
     "#Code task 5#\n",
     "#Repeat scenario 2 conditions, but add an increase of 2 to `Snow Making_ac`\n",
-    "ticket3_increase = predict_increase(['Runs', 'vertical_drop', 'total_chairs', ___], [1, 150, 1, ___])\n",
+    "ticket3_increase = predict_increase(['Runs', 'vertical_drop', 'total_chairs', 'Snow Making_ac'], [1, 150, 1, 2])\n",
     "revenue3_increase = 5 * expected_visitors * ticket3_increase"
    ]
   },
@@ -1324,13 +1345,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "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])"
    ]
   },
   {
@@ -1358,7 +1390,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**A: 1** Your answer here"
+    "**A: 1** Your answer here\n",
+    "\n",
+    "Now that the preprocessing for the data is complete and the random forest regressor model has been chosen, the task here has been to come up with a few scenarios of how the 8 key features could be manipulated to cause a change in the predicted ticket price. As expected, changing the most significant features had the biggest effect on the model's predicted price. It can also be noted that there needed to be a significant change made to the feature in order to see a real change in predicted price. For example, increasing the Snow Making_ac feature by just 4 acres alone (1% of a change) isn't enough to really affect ticket price. Vertical drop does seem to be quite sensitive to change with a change of 150 (6%) leading to a change of ticket price by approximately $1.99 (2.5% increase in price)."
    ]
   },
   {
@@ -1399,7 +1433,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.8.5"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/data/ski_data_cleaned.csv b/data/ski_data_cleaned.csv
new file mode 100644
index 000000000..4259e45d8
--- /dev/null
+++ b/data/ski_data_cleaned.csv
@@ -0,0 +1,278 @@
+Name,Region,state,summit_elev,vertical_drop,base_elev,trams,fastSixes,fastQuads,quad,triple,double,surface,total_chairs,Runs,TerrainParks,LongestRun_mi,SkiableTerrain_ac,Snow Making_ac,daysOpenLastYear,yearsOpen,averageSnowfall,AdultWeekend,projectedDaysOpen,NightSkiing_ac
+Alyeska Resort,Alaska,Alaska,3939,2500,250,1,0,2,2,0,0,2,7,76.0,2.0,1.0,1610.0,113.0,150.0,60.0,669.0,85.0,150.0,550.0
+Eaglecrest Ski Area,Alaska,Alaska,2600,1540,1200,0,0,0,0,0,4,0,4,36.0,1.0,2.0,640.0,60.0,45.0,44.0,350.0,53.0,90.0,
+Hilltop Ski Area,Alaska,Alaska,2090,294,1796,0,0,0,0,1,0,2,3,13.0,1.0,1.0,30.0,30.0,150.0,36.0,69.0,34.0,152.0,30.0
+Arizona Snowbowl,Arizona,Arizona,11500,2300,9200,0,1,0,2,2,1,2,8,55.0,4.0,2.0,777.0,104.0,122.0,81.0,260.0,89.0,122.0,
+Sunrise Park Resort,Arizona,Arizona,11100,1800,9200,0,0,1,2,3,1,0,7,65.0,2.0,1.2,800.0,80.0,115.0,49.0,250.0,78.0,104.0,80.0
+Yosemite Ski & Snowboard Area,Northern California,California,7800,600,7200,0,0,0,0,1,3,1,5,10.0,2.0,0.4,88.0,,110.0,84.0,300.0,47.0,107.0,
+Dodge Ridge,Sierra Nevada,California,8200,1600,6600,0,0,0,1,2,5,4,12,67.0,5.0,2.0,862.0,,,69.0,350.0,78.0,140.0,
+Donner Ski Ranch,Sierra Nevada,California,8012,750,7031,0,0,0,0,1,5,2,8,52.0,2.0,1.5,505.0,60.0,163.0,82.0,400.0,75.0,170.0,
+Mammoth Mountain Ski Area,Sierra Nevada,California,11053,3100,7953,3,2,9,1,6,4,0,25,154.0,7.0,3.0,3500.0,700.0,243.0,66.0,400.0,159.0,,
+Mt. Shasta Ski Park,Sierra Nevada,California,6890,1435,5500,0,0,0,0,3,0,1,4,32.0,2.0,1.1,425.0,225.0,140.0,34.0,300.0,59.0,130.0,
+Mountain High,Sierra Nevada,California,8200,1600,6600,0,0,2,2,2,5,3,14,59.0,1.0,1.6,290.0,275.0,118.0,95.0,108.0,84.0,150.0,73.0
+Mt. Baldy,Sierra Nevada,California,8600,2100,6500,0,0,0,0,0,4,0,4,26.0,,2.5,400.0,80.0,175.0,67.0,178.0,69.0,200.0,
+Ski China Peak,Sierra Nevada,California,8709,1679,7030,0,0,0,1,4,2,4,11,45.0,1.0,2.2,1400.0,150.0,140.0,62.0,300.0,83.0,144.0,
+Snow Valley,Sierra Nevada,California,7841,1041,6800,0,0,0,0,5,6,1,12,28.0,6.0,1.2,240.0,188.0,111.0,82.0,160.0,79.0,143.0,164.0
+Soda Springs,Sierra Nevada,California,7352,652,6700,0,0,0,0,1,1,2,4,18.0,,0.4,200.0,20.0,150.0,83.0,400.0,50.0,144.0,
+Sugar Bowl Resort,Sierra Nevada,California,8383,1500,6883,1,0,5,3,1,0,2,12,105.0,3.0,3.0,1650.0,375.0,151.0,80.0,500.0,125.0,150.0,
+Tahoe Donner,Sierra Nevada,California,7350,600,6750,0,0,0,1,1,0,3,5,14.0,2.0,1.0,120.0,,150.0,48.0,400.0,69.0,144.0,
+Arapahoe Basin Ski Area,Colorado,Colorado,13050,2530,10780,0,0,1,2,1,2,3,9,145.0,3.0,1.5,1428.0,125.0,230.0,73.0,350.0,85.0,233.0,
+Aspen / Snowmass,Colorado,Colorado,12510,4406,8104,3,1,15,4,3,5,9,40,336.0,10.0,5.3,5517.0,658.0,138.0,72.0,300.0,179.0,138.0,
+Copper Mountain Resort,Colorado,Colorado,12313,2738,9712,1,2,4,0,4,4,9,24,150.0,6.0,1.7,2527.0,364.0,164.0,47.0,300.0,158.0,164.0,
+Purgatory,Colorado,Colorado,10822,2029,8793,0,1,2,0,3,3,3,12,101.0,9.0,1.3,1605.0,250.0,130.0,54.0,260.0,89.0,130.0,
+Howelsen Hill,Colorado,Colorado,7136,440,6696,0,0,0,0,0,1,3,4,17.0,1.0,6.0,50.0,25.0,100.0,104.0,150.0,25.0,100.0,10.0
+Loveland,Colorado,Colorado,13010,2210,10800,0,0,1,3,3,2,1,10,94.0,1.0,2.0,1800.0,240.0,205.0,82.0,422.0,79.0,184.0,
+Monarch Mountain,Colorado,Colorado,11952,1162,10790,0,0,0,1,0,4,2,7,64.0,2.0,1.0,800.0,,143.0,80.0,350.0,89.0,136.0,
+Powderhorn,Colorado,Colorado,9850,1650,8200,0,0,1,0,0,2,2,5,42.0,2.0,1.5,1600.0,42.0,111.0,53.0,250.0,71.0,110.0,
+Silverton Mountain,Colorado,Colorado,13487,3087,10400,0,0,0,0,0,1,0,1,,,1.5,1819.0,,175.0,17.0,400.0,79.0,181.0,
+Cooper,Colorado,Colorado,11700,1200,10500,0,0,0,0,1,1,2,4,41.0,1.0,1.0,400.0,,130.0,74.0,260.0,56.0,130.0,
+Ski Granby Ranch,Colorado,Colorado,9202,1000,8202,0,0,2,0,1,1,1,5,40.0,1.0,0.6,406.0,170.0,116.0,36.0,220.0,84.0,92.0,100.0
+Sunlight Mountain Resort,Colorado,Colorado,9895,2010,7885,0,0,0,0,1,2,0,3,67.0,1.0,2.5,680.0,30.0,100.0,53.0,250.0,65.0,135.0,
+Telluride,Colorado,Colorado,13150,4425,8725,2,0,6,1,2,2,4,17,148.0,3.0,4.6,2000.0,220.0,131.0,47.0,280.0,139.0,137.0,
+Wolf Creek Ski Area,Colorado,Colorado,11904,1604,10300,0,0,3,1,2,1,3,10,120.0,,2.0,1600.0,5.0,130.0,80.0,430.0,72.0,150.0,
+Mohawk Mountain,Connecticut,Connecticut,1600,650,950,0,0,0,0,5,0,3,8,25.0,,1.5,107.0,100.0,,72.0,92.0,65.0,110.0,64.0
+Mount Southington Ski Area,Connecticut,Connecticut,525,425,100,0,0,0,0,2,2,3,7,14.0,2.0,0.3,51.0,51.0,63.0,55.0,80.0,60.0,95.0,51.0
+Powder Ridge Park,Connecticut,Connecticut,720,550,170,0,0,0,0,1,2,2,5,19.0,4.0,0.5,80.0,68.0,80.0,60.0,80.0,55.0,100.0,40.0
+Ski Sundown,Connecticut,Connecticut,1075,625,450,0,0,0,0,3,0,2,5,16.0,2.0,1.0,70.0,70.0,84.0,50.0,45.0,62.0,95.0,66.0
+Woodbury Ski Area,Connecticut,Connecticut,730,300,430,0,0,0,0,0,1,4,5,12.0,2.0,0.2,50.0,50.0,126.0,57.0,70.0,42.0,180.0,35.0
+Bogus Basin,Idaho,Idaho,7582,1800,5800,0,0,3,0,1,3,4,11,91.0,3.0,1.5,2600.0,,134.0,77.0,250.0,64.0,130.0,165.0
+Brundage Mountain Resort,Idaho,Idaho,7640,1800,5840,0,0,1,0,4,0,1,6,51.0,2.0,3.2,1920.0,2.0,126.0,58.0,320.0,70.0,,
+Kelly Canyon Ski Area,Idaho,Idaho,6600,1000,5600,0,0,0,0,0,4,2,6,51.0,1.0,1.3,740.0,,,62.0,200.0,42.0,,
+Lookout Pass Ski Area,Idaho,Idaho,5650,1150,4500,0,0,0,0,1,3,0,4,35.0,2.0,1.5,540.0,,113.0,84.0,400.0,47.0,140.0,
+Magic Mountain Ski Area,Idaho,Idaho,7200,700,6500,0,0,0,0,0,1,2,3,11.0,,1.5,280.0,,65.0,81.0,180.0,32.0,70.0,
+Pebble Creek Ski Area,Idaho,Idaho,8560,2200,6360,0,0,0,0,3,0,0,3,54.0,2.0,1.3,1100.0,30.0,85.0,70.0,250.0,47.0,91.0,30.0
+Schweitzer,Idaho,Idaho,6400,2400,4000,0,1,2,0,1,3,2,9,92.0,3.0,2.1,2900.0,47.0,136.0,56.0,300.0,81.0,,100.0
+Silver Mountain,Idaho,Idaho,6300,2200,4100,1,0,0,1,2,2,1,7,80.0,2.0,2.5,1600.0,225.0,130.0,29.0,300.0,62.0,193.0,20.0
+Soldier Mountain Ski Area,Idaho,Idaho,7200,1400,5800,0,0,0,0,0,2,1,3,36.0,,0.4,1142.0,,60.0,71.0,,43.0,,
+Tamarack Resort,Idaho,Idaho,7700,2800,4900,0,0,2,2,0,0,2,6,48.0,3.0,1.5,1020.0,200.0,,15.0,300.0,71.0,150.0,
+Chestnut Mountain Resort,Illinois,Illinois,1040,475,565,0,0,0,2,4,0,3,9,22.0,3.0,0.2,139.0,139.0,87.0,60.0,50.0,55.0,112.0,139.0
+Ski Snowstar Winter Sports Park,Illinois,Illinois,790,262,528,0,0,0,2,0,2,2,6,15.0,1.0,0.8,28.0,28.0,56.0,38.0,38.0,35.0,86.0,28.0
+Villa Olivia,Illinois,Illinois,500,180,320,0,0,0,1,0,0,6,7,7.0,1.0,0.1,15.0,15.0,,53.0,25.0,40.0,70.0,15.0
+Paoli Peaks,Indiana,Indiana,900,300,600,0,0,1,1,3,1,2,8,15.0,2.0,0.4,65.0,65.0,75.0,41.0,18.0,45.0,80.0,65.0
+Perfect North Slopes,Indiana,Indiana,800,400,400,0,0,0,2,3,0,6,11,23.0,2.0,1.0,100.0,100.0,82.0,39.0,24.0,52.0,90.0,100.0
+Mt. Crescent Ski Area,Iowa,Iowa,1500,300,1200,0,0,0,1,0,1,0,2,11.0,1.0,0.2,50.0,50.0,,58.0,30.0,39.0,,50.0
+Seven Oaks,Iowa,Iowa,975,275,800,0,0,0,0,2,0,2,4,11.0,2.0,1.0,35.0,35.0,100.0,22.0,40.0,40.0,100.0,35.0
+Sundown Mountain,Iowa,Iowa,1059,475,584,0,0,0,1,1,2,2,6,21.0,2.0,0.6,55.0,55.0,,46.0,45.0,46.0,,55.0
+Big Squaw Mountain Ski Resort,Maine,Maine,3200,660,1750,0,0,0,0,1,0,0,1,29.0,,0.8,,,67.0,6.0,,30.0,58.0,
+Camden Snow Bowl,Maine,Maine,1080,850,150,0,0,0,0,1,1,1,3,26.0,2.0,1.0,100.0,48.0,68.0,83.0,69.0,43.0,70.0,48.0
+Lost Valley,Maine,Maine,495,240,255,0,0,0,0,0,2,2,4,22.0,2.0,0.3,45.0,45.0,87.0,58.0,50.0,55.0,104.0,45.0
+Mt. Abram Ski Resort,Maine,Maine,2250,1150,1050,0,0,0,0,0,2,3,5,54.0,1.0,0.5,640.0,175.0,120.0,59.0,125.0,49.0,120.0,
+New Hermon Mountain,Maine,Maine,450,350,100,0,0,0,0,0,1,2,3,20.0,,1.9,70.0,70.0,102.0,55.0,90.0,32.0,117.0,45.0
+Shawnee Peak,Maine,Maine,1900,1350,600,0,0,0,1,2,1,2,6,43.0,3.0,0.8,239.0,234.0,97.0,81.0,110.0,75.0,103.0,110.0
+Sugarloaf,Maine,Maine,4237,2820,1417,0,0,2,3,1,5,2,13,162.0,4.0,3.5,1240.0,618.0,159.0,68.0,200.0,99.0,155.0,
+Sunday River,Maine,Maine,3140,2340,800,1,0,4,5,3,1,1,15,135.0,5.0,3.0,870.0,552.0,165.0,60.0,167.0,105.0,169.0,140.0
+Wisp,Maryland,Maryland,3115,700,2415,0,0,0,2,5,0,5,12,34.0,3.0,1.5,172.0,118.0,121.0,64.0,100.0,79.0,120.0,118.0
+Berkshire East,Massachusetts,Massachusetts,1720,1180,540,0,0,0,2,1,1,2,6,47.0,2.0,2.0,180.0,165.0,120.0,68.0,120.0,68.0,120.0,80.0
+Blandford Ski Area,Massachusetts,Massachusetts,1685,465,1035,0,0,0,0,0,3,2,5,29.0,2.0,0.5,132.0,70.0,,83.0,50.0,45.0,,70.0
+Blue Hills Ski Area,Massachusetts,Massachusetts,635,309,326,0,0,0,0,0,1,3,4,16.0,1.0,,60.0,60.0,,19.0,,45.0,,
+Bousquet Ski Area,Massachusetts,Massachusetts,1875,750,1125,0,0,0,0,0,3,2,5,23.0,1.0,1.0,200.0,98.0,,19.0,83.0,49.0,,100.0
+Bradford Ski Area,Massachusetts,Massachusetts,1548,248,1300,0,0,0,0,2,0,8,10,15.0,1.0,0.3,48.0,48.0,,71.0,,55.0,,
+Jiminy Peak,Massachusetts,Massachusetts,2380,1150,1230,0,1,0,2,3,1,2,9,45.0,3.0,2.0,167.0,163.0,121.0,71.0,90.0,81.0,120.0,104.0
+Nashoba Valley,Massachusetts,Massachusetts,440,240,200,0,0,0,0,3,1,7,11,17.0,2.0,0.5,52.0,52.0,112.0,55.0,55.0,58.0,126.0,52.0
+Otis Ridge Ski Area,Massachusetts,Massachusetts,1700,400,1300,0,0,0,0,0,1,3,4,11.0,1.0,1.0,60.0,55.0,106.0,73.0,70.0,40.0,106.0,35.0
+Ski Butternut,Massachusetts,Massachusetts,1800,1000,800,0,0,0,3,1,1,6,11,22.0,2.0,1.5,110.0,110.0,107.0,56.0,115.0,60.0,110.0,
+Wachusett Mountain Ski Area,Massachusetts,Massachusetts,2006,1000,1006,0,0,3,0,1,0,4,8,27.0,2.0,1.5,112.0,112.0,,57.0,100.0,71.0,120.0,104.0
+Alpine Valley Ski Area,Michigan,Michigan,500,240,126,0,0,0,1,2,5,6,14,25.0,3.0,0.2,100.0,100.0,,57.0,20.0,47.0,,100.0
+Apple Mountain,Michigan,Michigan,820,220,600,0,0,0,1,0,0,5,6,12.0,,,80.0,42.0,,58.0,52.0,35.0,,80.0
+Big Powderhorn Mountain,Michigan,Michigan,1800,600,1200,0,0,0,0,0,9,1,10,45.0,2.0,1.0,253.0,228.0,100.0,55.0,214.0,69.0,108.0,
+Bittersweet Ski Area,Michigan,Michigan,850,350,450,0,0,0,1,7,0,4,12,20.0,2.0,0.2,100.0,100.0,80.0,36.0,90.0,48.0,,100.0
+Big Snow Resort - Blackjack,Michigan,Michigan,850,465,385,0,0,0,0,0,4,2,6,26.0,2.0,1.0,170.0,86.0,95.0,42.0,210.0,65.0,115.0,
+Boyne Highlands,Michigan,Michigan,1290,552,745,0,0,1,3,4,0,2,10,55.0,4.0,1.2,435.0,400.0,97.0,56.0,140.0,98.0,120.0,150.0
+Caberfae Peaks,Michigan,Michigan,1569,485,1060,0,0,0,1,2,1,1,5,34.0,2.0,1.2,200.0,200.0,118.0,82.0,140.0,49.0,130.0,150.0
+Cannonsburg,Michigan,Michigan,1100,250,850,0,0,0,1,1,1,7,10,21.0,5.0,0.1,100.0,,100.0,54.0,100.0,37.0,100.0,
+Crystal Mountain,Michigan,Michigan,1132,375,757,0,0,1,3,2,0,2,8,58.0,3.0,0.3,102.0,96.0,120.0,63.0,132.0,64.0,135.0,56.0
+Big Snow Resort - Indianhead Mountain,Michigan,Michigan,1935,638,1297,0,0,0,1,1,5,2,9,32.0,2.0,1.0,240.0,150.0,120.0,60.0,204.0,49.0,120.0,
+Mont Ripley,Michigan,Michigan,1140,440,700,0,0,0,0,0,2,2,4,25.0,2.0,0.8,112.0,112.0,114.0,83.0,275.0,49.0,100.0,100.0
+Mount Bohemia,Michigan,Michigan,1500,900,600,0,0,0,0,1,1,0,2,,,2.3,585.0,,83.0,19.0,273.0,68.0,100.0,
+Mt. Brighton,Michigan,Michigan,1330,230,1100,0,0,0,2,3,0,8,13,25.0,5.0,0.1,130.0,130.0,111.0,59.0,60.0,59.0,100.0,130.0
+Mt. Holiday Ski Area,Michigan,Michigan,440,200,240,0,0,0,0,0,2,2,4,12.0,,0.1,45.0,45.0,100.0,70.0,120.0,34.0,90.0,45.0
+Mount Holly,Michigan,Michigan,1105,350,755,0,0,1,2,3,1,6,13,19.0,,0.1,100.0,100.0,,63.0,42.0,45.0,102.0,100.0
+Mulligan's Hollow Ski Bowl,Michigan,Michigan,700,130,570,0,0,0,0,0,0,5,5,6.0,,0.2,10.0,10.0,,19.0,60.0,20.0,,10.0
+Norway Mountain,Michigan,Michigan,1335,500,835,0,0,0,0,1,2,3,6,17.0,1.0,1.4,186.0,186.0,110.0,45.0,100.0,45.0,110.0,40.0
+Nubs Nob Ski Area,Michigan,Michigan,1338,427,911,0,0,0,3,4,2,1,10,53.0,3.0,0.9,248.0,248.0,133.0,61.0,135.0,85.0,130.0,160.0
+Pine Mountain,Michigan,Michigan,1650,500,1150,0,0,0,0,1,2,1,4,28.0,1.0,0.5,160.0,160.0,110.0,80.0,60.0,45.0,126.0,80.0
+Schuss Mountain at Shanty Creek,Michigan,Michigan,1125,450,675,0,0,0,5,0,0,3,8,42.0,3.0,1.0,70.0,70.0,94.0,57.0,160.0,78.0,111.0,70.0
+Ski Brule,Michigan,Michigan,1860,500,1360,0,0,0,0,0,5,7,12,17.0,3.0,1.0,150.0,150.0,164.0,62.0,150.0,49.0,165.0,40.0
+Snow Snake Mountain Ski Area,Michigan,Michigan,1230,210,1020,0,0,0,0,1,0,5,6,12.0,2.0,0.0,40.0,40.0,,72.0,,35.0,,40.0
+Swiss Valley,Michigan,Michigan,1200,225,975,0,0,0,2,1,0,4,7,11.0,2.0,0.1,60.0,60.0,89.0,51.0,60.0,42.0,80.0,60.0
+The Homestead,Michigan,Michigan,900,320,580,0,0,0,0,2,1,2,5,15.0,1.0,0.2,16.0,16.0,47.0,34.0,150.0,50.0,42.0,16.0
+Timber Ridge,Michigan,Michigan,850,250,600,0,0,0,1,1,2,4,8,16.0,2.0,0.3,50.0,50.0,80.0,58.0,,45.0,,50.0
+Afton Alps,Minnesota,Minnesota,1530,350,1180,0,0,0,1,3,14,4,22,48.0,5.0,0.5,250.0,250.0,135.0,56.0,60.0,60.0,135.0,250.0
+Andes Tower Hills Ski Area,Minnesota,Minnesota,1620,290,1330,0,0,0,1,2,0,3,6,15.0,2.0,0.2,35.0,35.0,100.0,38.0,55.0,45.0,110.0,35.0
+Buck Hill,Minnesota,Minnesota,1225,309,919,0,0,0,2,1,0,5,8,16.0,,0.2,45.0,45.0,115.0,65.0,60.0,47.0,112.0,45.0
+Buena Vista Ski Area,Minnesota,Minnesota,1510,230,1280,0,0,0,0,2,2,2,6,17.0,,0.3,30.0,30.0,57.0,70.0,78.0,44.0,60.0,30.0
+Coffee Mill Ski & Snowboard Resort,Minnesota,Minnesota,1150,425,725,0,0,0,0,0,2,1,3,14.0,1.0,1.0,40.0,35.0,57.0,39.0,48.0,37.0,56.0,35.0
+Elm Creek Winter Recreation Area,Minnesota,Minnesota,928,60,868,0,0,0,0,0,0,3,3,3.0,,1.0,15.0,20.0,105.0,13.0,45.0,17.0,102.0,15.0
+Giants Ridge Resort,Minnesota,Minnesota,1972,500,1472,0,0,1,1,1,2,2,7,35.0,2.0,0.8,202.0,202.0,120.0,35.0,85.0,58.0,125.0,121.0
+Hyland Ski & Snowboard Area,Minnesota,Minnesota,1075,175,900,0,0,0,2,1,0,5,8,14.0,1.0,1.0,35.0,35.0,110.0,61.0,55.0,35.34,115.0,35.0
+Lutsen Mountains,Minnesota,Minnesota,1688,825,800,1,1,0,0,1,4,1,8,62.0,2.0,2.0,393.0,231.0,135.0,71.0,120.0,84.0,127.0,
+Mount Kato Ski Area,Minnesota,Minnesota,540,240,300,0,0,0,5,0,3,2,10,19.0,4.0,1.0,55.0,55.0,115.0,43.0,50.0,46.0,120.0,50.0
+Powder Ridge Ski Area,Minnesota,Minnesota,790,300,500,0,0,0,1,0,2,3,6,15.0,4.0,,60.0,60.0,97.0,58.0,45.0,48.0,113.0,60.0
+Spirit Mountain,Minnesota,Minnesota,1320,700,620,0,0,1,1,2,1,2,7,22.0,3.0,1.0,175.0,175.0,100.0,45.0,100.0,59.0,125.0,144.0
+Welch Village,Minnesota,Minnesota,1060,360,700,0,0,0,3,1,4,2,10,50.0,1.0,0.8,125.0,125.0,114.0,54.0,45.0,60.0,122.0,100.0
+Wild Mountain Ski & Snowboard Area,Minnesota,Minnesota,1113,300,813,0,0,0,4,0,0,4,8,26.0,4.0,0.9,100.0,100.0,130.0,47.0,50.0,55.0,140.0,100.0
+Hidden Valley Ski Area,Missouri,Missouri,2566,310,2316,0,0,0,2,2,0,3,7,17.0,,0.1,30.0,30.0,,37.0,26.0,49.0,,17.0
+Snow Creek,Missouri,Missouri,1100,300,800,0,0,0,0,2,1,2,5,14.0,2.0,0.3,30.0,30.0,69.0,33.0,20.0,47.0,85.0,30.0
+Blacktail Mountain Ski Area,Montana,Montana,6676,1440,5236,0,0,0,0,1,2,1,4,27.0,,0.7,1000.0,,,21.0,250.0,42.0,,
+Bridger Bowl,Montana,Montana,8700,2600,6100,0,0,0,1,6,1,3,11,105.0,2.0,1.5,2000.0,100.0,122.0,64.0,350.0,63.0,133.0,
+Discovery Ski Area,Montana,Montana,8150,2380,5770,0,0,0,0,5,2,1,8,74.0,1.0,1.5,2400.0,25.0,116.0,46.0,225.0,49.0,116.0,
+Great Divide,Montana,Montana,7330,1580,5750,0,0,0,0,0,5,1,6,110.0,6.0,3.0,1600.0,150.0,94.0,78.0,180.0,48.0,100.0,100.0
+Lost Trail - Powder Mtn,Montana,Montana,8200,1800,6400,0,0,0,0,0,5,3,8,69.0,2.0,2.5,1800.0,,84.0,81.0,325.0,46.0,80.0,
+Maverick Mountain,Montana,Montana,8520,2020,6500,0,0,0,0,0,1,1,2,22.0,,1.3,255.0,,,83.0,160.0,39.0,,
+Montana Snowbowl,Montana,Montana,7600,2600,5000,0,0,0,0,0,2,2,4,37.0,,1.2,950.0,20.0,,58.0,300.0,50.0,,10.0
+Red Lodge Mountain,Montana,Montana,9416,2400,7016,0,0,2,0,1,3,1,7,70.0,2.0,2.5,1635.0,496.0,142.0,59.0,250.0,67.0,136.0,
+Showdown Montana,Montana,Montana,8200,1400,6800,0,0,0,0,1,2,1,4,36.0,1.0,1.8,640.0,,86.0,83.0,250.0,47.0,85.0,
+Teton Pass Ski Resort,Montana,Montana,7200,1010,6190,0,0,0,0,0,1,2,3,43.0,1.0,3.0,330.0,,40.0,54.0,250.0,39.0,150.0,
+Big Mountain Resort,Montana,Montana,6817,2353,4464,0,0,3,2,6,0,3,14,105.0,4.0,3.3,3000.0,600.0,123.0,72.0,333.0,81.0,123.0,600.0
+Diamond Peak,Sierra Nevada,Nevada,8540,1840,6700,0,0,1,2,0,3,1,7,30.0,3.0,2.5,655.0,492.0,100.0,53.0,300.0,99.0,122.0,
+Elko SnoBowl,Nevada,Nevada,7000,700,6300,0,0,0,0,0,1,1,2,10.0,,1.0,60.0,2.0,19.0,23.0,24.0,20.0,30.0,
+Lee Canyon,Nevada,Nevada,11289,860,8510,0,0,0,2,1,0,0,3,24.0,1.0,0.3,195.0,50.0,144.0,56.0,161.0,70.0,150.0,
+Mt. Rose - Ski Tahoe,Sierra Nevada,Nevada,9700,1800,8260,0,2,0,2,2,0,2,8,65.0,5.0,2.5,1200.0,330.0,152.0,55.0,350.0,135.0,150.0,
+Attitash,New Hampshire,New Hampshire,2350,1750,600,0,0,2,1,3,2,1,9,68.0,3.0,3.0,311.0,240.0,115.0,54.0,120.0,89.0,130.0,
+Black Mountain,New Hampshire,New Hampshire,2350,1100,1250,0,0,0,0,1,1,3,5,45.0,,1.6,143.0,120.0,110.0,84.0,125.0,59.0,107.0,
+Bretton Woods,New Hampshire,New Hampshire,3100,1500,1600,0,0,4,1,1,0,3,9,63.0,2.0,2.0,464.0,427.0,180.0,46.0,200.0,99.0,180.0,45.0
+Cannon Mountain,New Hampshire,New Hampshire,4080,2180,1900,1,0,1,2,3,1,3,11,97.0,3.0,2.3,285.0,192.0,124.0,81.0,160.0,79.0,143.0,
+Crotched Mountain,New Hampshire,New Hampshire,2066,1016,1050,0,0,1,1,1,1,1,5,25.0,3.0,1.2,100.0,100.0,105.0,16.0,105.0,69.0,100.0,100.0
+Dartmouth Skiway,New Hampshire,New Hampshire,1943,969,974,0,0,0,1,0,1,2,4,28.0,1.0,1.1,107.0,54.0,104.0,63.0,100.0,50.0,105.0,
+Gunstock,New Hampshire,New Hampshire,2300,1400,900,0,0,1,2,2,0,1,6,55.0,4.0,1.5,227.0,176.0,106.0,82.0,120.0,92.0,,60.0
+King Pine,New Hampshire,New Hampshire,850,350,500,0,0,0,0,3,0,3,6,17.0,2.0,0.3,48.0,45.0,105.0,57.0,120.0,58.0,107.0,23.0
+Mount Sunapee,New Hampshire,New Hampshire,2743,1510,1233,0,0,2,1,2,1,4,10,66.0,4.0,0.8,232.0,215.0,130.0,71.0,100.0,93.0,136.0,
+Pats Peak,New Hampshire,New Hampshire,1460,770,690,0,0,0,0,4,2,5,11,28.0,3.0,1.5,115.0,115.0,109.0,56.0,100.0,72.0,112.0,93.0
+Ragged Mountain Resort,New Hampshire,New Hampshire,2250,1250,1000,0,1,1,0,1,0,3,6,57.0,3.0,0.7,250.0,200.0,,54.0,100.0,84.0,140.0,
+Waterville Valley,New Hampshire,New Hampshire,4004,2020,1984,0,0,2,0,2,3,4,11,62.0,4.0,1.9,265.0,220.0,142.0,54.0,148.0,93.0,142.0,
+Whaleback Mountain,New Hampshire,New Hampshire,1800,700,1100,0,0,0,0,0,1,3,4,30.0,1.0,1.0,85.0,60.0,105.0,64.0,110.0,45.0,105.0,55.0
+Wildcat Mountain,New Hampshire,New Hampshire,4062,2112,1950,0,0,1,0,3,0,1,5,48.0,,2.8,225.0,200.0,156.0,61.0,200.0,89.0,150.0,
+Mountain Creek Resort,New Jersey,New Jersey,1480,1040,440,1,0,2,2,1,1,3,10,46.0,3.0,2.0,167.0,167.0,90.0,54.0,65.0,79.99,100.0,167.0
+Angel Fire Resort,New Mexico,New Mexico,10677,2077,8600,0,0,2,0,0,3,2,7,81.0,3.0,3.0,560.0,230.0,101.0,53.0,210.0,77.0,101.0,50.0
+Enchanted Forest Ski Area,New Mexico,New Mexico,10078,400,9820,0,0,0,0,0,0,0,0,33.0,,2.5,600.0,,130.0,34.0,240.0,20.0,140.0,
+Pajarito Mountain Ski Area,New Mexico,New Mexico,10441,1410,9031,0,0,0,1,1,3,1,6,45.0,2.0,0.6,750.0,35.0,89.0,62.0,163.0,49.0,117.0,
+Red River,New Mexico,New Mexico,10350,1600,8750,0,0,0,1,3,1,2,7,63.0,3.0,2.5,209.0,,110.0,60.0,214.0,79.0,,
+Sandia Peak,New Mexico,New Mexico,10378,1700,8678,0,0,0,0,0,4,1,5,39.0,1.0,2.0,200.0,30.0,32.0,82.0,100.0,55.0,38.0,
+Sipapu Ski Resort,New Mexico,New Mexico,9255,1055,8200,0,0,0,1,2,0,3,6,42.0,4.0,0.5,200.0,140.0,127.0,67.0,190.0,47.0,143.0,
+Ski Apache,New Mexico,New Mexico,11500,1900,9600,1,0,0,2,6,0,2,11,55.0,3.0,2.0,750.0,270.0,133.0,58.0,185.0,74.0,124.0,
+Ski Santa Fe,New Mexico,New Mexico,12075,1725,10350,0,0,0,1,2,2,2,7,83.0,1.0,3.0,660.0,275.0,107.0,73.0,225.0,80.0,130.0,
+Taos Ski Valley,New Mexico,New Mexico,12481,3281,9200,1,0,1,3,4,1,4,14,111.0,1.0,5.0,1294.0,647.0,137.0,64.0,300.0,110.0,136.0,
+Belleayre,New York,New York,3429,1404,2025,1,0,1,1,1,2,2,8,50.0,2.0,2.2,175.0,168.0,154.0,70.0,130.0,72.0,150.0,
+Brantling Ski Slopes,New York,New York,850,250,600,0,0,0,0,0,0,5,5,10.0,,0.1,20.0,16.0,,19.0,110.0,32.0,,
+Bristol Mountain,New York,New York,2200,1200,1000,0,0,2,1,1,1,1,6,34.0,3.0,2.0,160.0,148.0,129.0,55.0,60.0,76.0,129.0,154.0
+Buffalo Ski Club Ski Area,New York,New York,3429,500,2025,0,0,0,0,0,2,4,6,43.0,1.0,,225.0,150.0,,12.0,,50.0,,100.0
+Catamount,New York,New York,2000,1000,1000,0,0,0,1,1,2,3,7,36.0,5.0,2.0,133.0,130.0,100.0,80.0,108.0,69.0,90.0,55.0
+Dry Hill Ski Area,New York,New York,950,300,650,0,0,0,0,0,1,2,3,7.0,1.0,0.2,35.0,26.0,,55.0,125.0,35.0,,26.0
+Gore Mountain,New York,New York,3600,2537,998,1,0,2,2,3,2,4,14,110.0,7.0,4.5,439.0,338.0,142.0,55.0,150.0,88.0,,15.0
+Greek Peak,New York,New York,2100,952,1148,0,0,0,1,1,4,2,8,56.0,4.0,1.5,220.0,184.0,110.0,62.0,122.0,63.2,113.0,175.0
+Holiday Mountain,New York,New York,1550,400,1150,0,0,0,1,0,1,2,4,9.0,,0.4,37.0,37.0,75.0,60.0,50.0,42.0,85.0,37.0
+Holiday Valley,New York,New York,2250,750,1500,0,0,3,8,0,0,2,13,60.0,5.0,1.0,290.0,266.0,116.0,62.0,180.0,78.0,129.0,189.0
+Holimont Ski Area,New York,New York,2260,700,1560,0,0,1,1,2,3,1,8,53.0,3.0,1.5,135.0,135.0,110.0,57.0,180.0,75.0,119.0,
+Hunt Hollow Ski Club,New York,New York,2030,825,1000,0,0,0,0,1,1,1,3,19.0,1.0,1.0,400.0,400.0,,52.0,130.0,58.0,75.0,400.0
+Hunter Mountain,New York,New York,3200,1600,1600,0,2,1,2,2,2,4,13,67.0,4.0,2.0,320.0,320.0,148.0,59.0,120.0,89.0,155.0,
+Kissing Bridge,New York,New York,1700,550,1150,0,0,0,2,1,4,3,10,39.0,5.0,0.5,700.0,550.0,103.0,59.0,120.0,60.0,100.0,650.0
+Labrador Mt.,New York,New York,1825,700,1125,0,0,0,0,1,2,1,4,23.0,1.0,1.0,250.0,237.0,,62.0,125.0,59.0,100.0,180.0
+Maple Ski Ridge,New York,New York,1200,450,750,0,0,0,0,1,1,1,3,10.0,,0.3,25.0,25.0,,57.0,,38.0,,20.0
+McCauley Mountain Ski Center,New York,New York,2250,633,1563,0,0,0,0,0,1,4,5,23.0,1.0,0.3,70.0,55.0,105.0,61.0,200.0,30.0,105.0,
+Mount Peter Ski Area,New York,New York,1250,450,750,0,0,0,1,0,2,2,5,14.0,1.0,1.0,69.0,69.0,100.0,83.0,50.0,54.0,100.0,69.0
+Oak Mountain,New York,New York,2400,650,1750,0,0,0,1,0,0,3,4,22.0,1.0,1.2,46.0,18.0,,71.0,120.0,40.0,,12.0
+Peek'n Peak,New York,New York,1800,400,1400,0,0,0,0,8,0,2,10,27.0,4.0,2.4,110.0,110.0,110.0,55.0,225.0,63.0,,110.0
+Plattekill Mountain,New York,New York,3500,1100,2400,0,0,0,0,1,1,2,4,38.0,1.0,2.0,110.0,75.0,65.0,26.0,175.0,67.0,65.0,
+Royal Mountain Ski Area,New York,New York,1800,550,1250,0,0,0,0,0,3,0,3,14.0,,0.3,35.0,28.0,,63.0,90.0,45.0,,
+Snow Ridge,New York,New York,2000,650,1350,0,0,0,0,0,4,2,6,21.0,2.0,0.8,130.0,65.0,73.0,74.0,230.0,48.0,100.0,40.0
+Song Mountain,New York,New York,1940,700,1240,0,0,0,0,1,1,3,5,24.0,,0.4,93.0,70.0,90.0,55.0,125.0,59.0,122.0,70.0
+Swain,New York,New York,1970,650,1320,0,0,0,3,0,1,1,5,35.0,3.0,1.0,130.0,90.0,102.0,72.0,120.0,59.0,100.0,80.0
+Thunder Ridge,New York,New York,1270,500,770,0,0,0,0,1,2,3,6,30.0,,0.4,100.0,100.0,121.0,60.0,,57.0,121.0,100.0
+Titus Mountain,New York,New York,2025,1200,825,0,0,0,0,2,6,2,10,50.0,3.0,2.0,200.0,150.0,101.0,59.0,150.0,49.0,100.0,70.0
+Toggenburg Mountain,New York,New York,2000,700,1300,0,0,0,0,1,1,3,5,22.0,2.0,0.4,85.0,,,66.0,130.0,55.0,122.0,73.0
+West Mountain,New York,New York,1470,1010,460,0,0,0,0,1,2,2,5,29.0,1.0,0.6,124.0,105.0,,58.0,80.0,59.0,120.0,105.0
+Whiteface Mountain Resort,New York,New York,4650,3430,1220,1,0,1,1,2,5,2,12,86.0,5.0,2.1,288.0,220.0,122.0,61.0,168.0,96.0,141.0,
+Willard Mountain,New York,New York,1415,505,910,0,0,0,0,0,2,3,5,16.0,,0.4,50.0,35.0,85.0,19.0,80.0,46.0,120.0,35.0
+Windham Mountain,New York,New York,3100,1600,1500,0,1,2,0,3,1,5,12,54.0,6.0,2.0,285.0,280.0,123.0,59.0,105.0,95.0,130.0,56.0
+Woods Valley Ski Area,New York,New York,1400,500,900,0,0,0,0,0,2,4,6,21.0,,0.3,25.0,16.0,,55.0,180.0,39.0,,15.0
+Appalachian Ski Mountain,North Carolina,North Carolina,4000,365,3635,0,0,0,2,0,1,2,5,12.0,3.0,0.5,27.0,27.0,100.0,57.0,50.0,64.0,100.0,27.0
+Cataloochee Ski Area,North Carolina,North Carolina,5400,740,4660,0,0,0,1,1,1,2,5,18.0,2.0,1.0,50.0,50.0,141.0,58.0,50.0,70.0,108.0,50.0
+Sapphire Valley,North Carolina,North Carolina,3450,200,3200,0,0,0,1,0,0,2,3,,1.0,1.0,8.0,8.0,53.0,55.0,24.0,43.0,60.0,8.0
+Beech Mountain Resort,North Carolina,North Carolina,5506,830,4675,0,0,0,3,0,3,2,8,17.0,1.0,1.0,95.0,95.0,98.0,52.0,31.0,68.0,,95.0
+Sugar Mountain Resort,North Carolina,North Carolina,5300,1200,4100,0,1,0,0,1,4,2,8,21.0,1.0,1.5,125.0,125.0,114.0,50.0,77.0,75.0,120.0,95.0
+Wolf Ridge Ski Resort,North Carolina,North Carolina,4700,720,4000,0,0,0,1,0,1,2,4,15.0,1.0,0.6,65.0,65.0,,49.0,65.0,65.0,100.0,60.0
+Alpine Valley,Ohio,Ohio,1500,230,1260,0,0,0,1,2,1,1,5,11.0,1.0,0.2,72.0,72.0,105.0,53.0,120.0,43.0,,72.0
+Boston Mills,Ohio,Ohio,871,264,631,0,0,0,0,4,2,2,8,7.0,2.0,0.3,40.0,40.0,92.0,56.0,51.0,44.0,110.0,40.0
+Brandywine,Ohio,Ohio,871,240,631,0,0,0,2,7,2,5,16,11.0,2.0,0.3,85.0,85.0,92.0,56.0,51.0,44.0,110.0,85.0
+Mad River Mountain,Ohio,Ohio,1460,300,1160,0,0,0,1,2,3,6,12,20.0,4.0,0.5,144.0,144.0,99.0,57.0,36.0,44.0,90.0,144.0
+Snow Trails,Ohio,Ohio,1475,301,1174,0,0,0,0,4,2,3,9,17.0,3.0,0.2,80.0,80.0,101.0,58.0,50.0,52.0,70.0,80.0
+Anthony Lakes Mountain Resort,Oregon,Oregon,8000,900,7100,0,0,0,0,1,0,2,3,21.0,2.0,1.5,1100.0,,75.0,56.0,300.0,40.0,80.0,
+Cooper Spur,Mt. Hood,Oregon,4000,350,3500,0,0,0,0,0,1,1,2,10.0,,0.1,50.0,,78.0,66.0,100.0,39.0,90.0,
+Hoodoo Ski Area,Oregon,Oregon,5703,1035,4668,0,0,0,3,1,1,0,5,34.0,,0.4,806.0,,80.0,81.0,350.0,59.0,108.0,200.0
+Mt. Ashland,Oregon,Oregon,7533,1150,6383,0,0,0,0,2,2,1,5,23.0,2.0,1.0,220.0,,94.0,55.0,300.0,52.0,92.0,40.0
+Mt. Bachelor,Oregon,Oregon,9065,3365,5700,0,0,8,0,3,0,0,11,101.0,5.0,4.0,4318.0,20.0,185.0,61.0,462.0,99.0,185.0,
+Mt. Hood Skibowl,Mt. Hood,Oregon,5100,1500,3600,0,0,0,0,0,4,5,9,65.0,2.0,3.0,960.0,29.0,125.0,82.0,300.0,70.0,144.0,317.0
+Willamette Pass,Oregon,Oregon,6683,1563,5120,0,1,0,0,3,0,1,5,29.0,,2.1,555.0,60.0,3.0,78.0,430.0,60.0,100.0,
+Bear Creek Mountain Resort,Pennsylvania,Pennsylvania,1100,510,600,0,0,0,3,1,0,2,6,23.0,3.0,1.0,86.0,86.0,91.0,52.0,30.0,60.0,90.0,86.0
+Ski Big Bear,Pennsylvania,Pennsylvania,1250,650,600,0,0,0,0,0,4,2,6,18.0,1.0,1.5,26.0,26.0,75.0,43.0,69.0,62.0,75.0,26.0
+Big Boulder,Pennsylvania,Pennsylvania,2175,600,1700,0,0,0,0,2,5,1,8,16.0,8.0,,55.0,55.0,76.0,72.0,50.0,65.0,95.0,55.0
+Blue Knob,Pennsylvania,Pennsylvania,3146,1072,2074,0,0,0,0,2,2,2,6,34.0,1.0,2.0,100.0,84.0,87.0,56.0,120.0,68.0,105.0,42.0
+Blue Mountain Resort,Pennsylvania,Pennsylvania,1600,1082,460,0,1,1,1,1,3,9,16,39.0,5.0,1.2,164.0,164.0,122.0,42.0,33.0,65.0,112.0,164.0
+Camelback Mountain Resort,Pennsylvania,Pennsylvania,2100,800,1250,0,0,2,0,3,5,6,16,37.0,5.0,1.0,166.0,166.0,100.0,56.0,50.0,70.0,100.0,160.0
+Elk Mountain Ski Resort,Pennsylvania,Pennsylvania,2693,1000,1693,0,0,0,1,0,5,1,7,27.0,2.0,0.7,180.0,146.0,,60.0,60.0,69.0,100.0,90.0
+Jack Frost,Pennsylvania,Pennsylvania,2000,600,1400,0,0,0,1,2,5,1,9,20.0,1.0,1.0,100.0,100.0,96.0,47.0,50.0,65.0,105.0,
+Liberty,Pennsylvania,Pennsylvania,1190,620,570,0,0,0,5,0,0,3,8,16.0,3.0,1.0,100.0,100.0,107.0,54.0,31.0,77.0,97.0,100.0
+Mount Pleasant of Edinboro,Pennsylvania,Pennsylvania,1540,340,1200,0,0,0,0,1,0,1,2,10.0,,0.5,40.0,35.0,75.0,48.0,100.0,33.0,90.0,35.0
+Roundtop Mountain Resort,Pennsylvania,Pennsylvania,1400,600,800,0,0,0,3,2,0,3,8,20.0,2.0,0.4,103.0,103.0,,55.0,30.0,73.0,,100.0
+Seven Springs,Pennsylvania,Pennsylvania,2994,750,2240,0,2,0,3,5,0,4,14,33.0,7.0,1.2,285.0,285.0,99.0,87.0,135.0,87.0,115.0,200.0
+Shawnee Mountain Ski Area,Pennsylvania,Pennsylvania,1350,700,650,0,0,1,1,0,4,4,10,23.0,2.0,1.6,125.0,125.0,100.0,44.0,50.0,65.0,122.0,120.0
+Ski Sawmill,Pennsylvania,Pennsylvania,2215,515,1700,0,0,0,0,1,1,3,5,14.0,1.0,0.1,15.0,13.0,,50.0,24.0,44.0,90.0,15.0
+Tussey Mountain,Pennsylvania,Pennsylvania,1750,520,1230,0,0,0,1,0,1,3,5,8.0,1.0,0.3,38.0,30.0,100.0,39.0,41.0,45.0,100.0,30.0
+Whitetail Resort,Pennsylvania,Pennsylvania,1800,935,865,0,0,1,3,0,2,2,8,23.0,2.0,1.0,120.0,120.0,116.0,28.0,40.0,71.0,100.0,120.0
+Deer Mountain Ski Resort,South Dakota,South Dakota,6850,940,6040,0,0,0,0,1,1,2,4,63.0,2.0,1.6,500.0,50.0,69.0,51.0,200.0,45.0,81.0,
+Terry Peak Ski Area,South Dakota,South Dakota,7100,1100,5900,0,0,3,0,1,0,1,5,30.0,1.0,1.2,450.0,225.0,114.0,65.0,150.0,58.0,120.0,
+Ober Gatlinburg Ski Resort,Tennessee,Tennessee,3300,600,2700,0,0,0,2,0,1,1,4,10.0,1.0,1.0,,,83.0,44.0,35.0,65.0,94.0,
+Alta Ski Area,Salt Lake City,Utah,11068,2538,8530,0,0,3,0,1,2,0,6,116.0,,1.3,2614.0,140.0,150.0,81.0,545.0,116.0,140.0,
+Beaver Mountain,Utah,Utah,8600,1600,7232,0,0,0,0,1,3,1,5,48.0,2.0,0.8,464.0,,120.0,81.0,400.0,50.0,120.0,
+Brian Head Resort,Utah,Utah,10970,1548,9600,0,0,1,0,6,1,0,8,71.0,2.0,0.6,650.0,216.0,149.0,54.0,360.0,59.0,148.0,
+Brighton Resort,Salt Lake City,Utah,10500,1745,8755,0,0,3,1,1,0,2,7,66.0,4.0,1.2,1050.0,200.0,138.0,83.0,500.0,85.0,138.0,200.0
+Deer Valley Resort,Salt Lake City,Utah,9570,3000,6570,1,0,13,0,5,2,0,21,103.0,,2.8,2026.0,660.0,,39.0,300.0,169.0,,
+Eagle Point,Utah,Utah,10600,1500,9100,0,0,0,1,1,2,1,5,40.0,1.0,0.9,650.0,,,9.0,400.0,60.0,109.0,
+Powder Mountain,Utah,Utah,9422,2522,6900,0,0,1,4,1,0,3,9,167.0,2.0,3.5,8464.0,,120.0,47.0,500.0,88.0,146.0,300.0
+Snowbasin,Utah,Utah,9350,2900,6450,3,1,2,0,3,0,2,11,107.0,4.0,3.5,3000.0,625.0,143.0,79.0,300.0,115.0,138.0,
+Snowbird,Salt Lake City,Utah,11000,3240,7760,1,0,6,0,0,4,3,14,170.0,1.0,2.5,2500.0,,188.0,48.0,500.0,125.0,180.0,2.0
+Solitude Mountain Resort,Salt Lake City,Utah,10488,2494,7994,0,0,4,2,1,1,1,9,80.0,,3.0,1200.0,150.0,161.0,62.0,500.0,119.0,148.0,
+Sundance,Utah,Utah,8250,2150,6100,0,0,0,2,2,0,1,5,45.0,1.0,0.6,450.0,112.0,128.0,50.0,320.0,80.0,129.0,
+Nordic Valley Resort,Utah,Utah,6400,960,5440,0,0,0,0,0,2,2,4,23.0,1.0,0.4,140.0,84.0,105.0,51.0,300.0,50.0,105.0,140.0
+Bolton Valley,Vermont,Vermont,3150,1704,1446,0,0,0,2,0,3,1,6,71.0,3.0,0.6,300.0,90.0,133.0,53.0,300.0,79.0,132.0,50.0
+Bromley Mountain,Vermont,Vermont,3284,1334,1950,0,0,1,1,0,4,3,9,47.0,1.0,2.5,178.0,153.0,,83.0,168.0,91.0,152.0,
+Burke Mountain,Vermont,Vermont,3267,2011,1210,0,0,2,1,0,0,3,6,50.0,3.0,2.2,178.0,125.0,110.0,62.0,217.0,73.0,125.0,
+Jay Peak,Vermont,Vermont,3968,2153,1815,1,0,1,3,1,1,2,9,79.0,2.0,3.0,385.0,300.0,155.0,64.0,349.0,89.0,160.0,
+Killington Resort,Vermont,Vermont,4241,3050,1165,3,1,5,4,3,1,5,22,155.0,6.0,6.0,1515.0,600.0,192.0,61.0,250.0,119.0,,
+Magic Mountain,Vermont,Vermont,2850,1500,1350,0,0,0,0,0,3,3,6,50.0,1.0,1.6,205.0,95.0,76.0,59.0,150.0,74.0,80.0,
+Pico Mountain,Vermont,Vermont,3967,1967,2000,0,0,2,0,2,2,1,7,59.0,1.0,4.0,260.0,156.0,,82.0,250.0,81.0,,
+Smugglers' Notch Resort,Vermont,Vermont,3640,2610,1030,0,0,0,0,0,6,2,8,78.0,6.0,3.0,1000.0,192.0,136.0,63.0,280.0,79.0,135.0,
+Sugarbush,Vermont,Vermont,4083,2600,1483,0,0,5,5,2,1,3,16,111.0,4.0,3.0,581.0,406.0,150.0,61.0,250.0,119.0,156.0,
+Suicide Six,Vermont,Vermont,1200,650,550,0,0,0,0,0,2,1,3,24.0,,0.4,100.0,50.0,100.0,85.0,90.0,75.0,106.0,
+Bryce Resort,Virginia,Virginia,1750,500,1250,0,0,0,1,0,1,5,7,8.0,,0.4,25.0,25.0,100.0,54.0,30.0,68.0,95.0,20.0
+49 Degrees North,Washington,Washington,5774,1851,3932,0,0,0,1,0,5,1,7,89.0,1.0,2.0,2325.0,,101.0,48.0,301.0,62.0,135.0,250.0
+Bluewood,Washington,Washington,5670,1125,4545,0,0,0,0,2,0,1,3,24.0,2.0,2.0,355.0,,70.0,40.0,300.0,47.0,110.0,
+Crystal Mountain,Washington,Washington,7012,3100,4400,1,2,2,1,2,2,0,10,57.0,1.0,2.5,2600.0,10.0,,57.0,486.0,99.0,,
+Mt. Baker,Washington,Washington,5000,1500,3500,0,0,0,8,0,0,2,10,38.0,,0.7,1000.0,,143.0,66.0,663.0,60.01,165.0,
+Mt. Spokane Ski and Snowboard Park,Washington,Washington,5889,2000,4200,0,0,0,0,1,5,1,7,52.0,3.0,0.6,1704.0,,100.0,81.0,300.0,59.0,103.0,45.0
+The Summit at Snoqualmie,Washington,Washington,3865,1025,2840,0,0,3,3,4,10,7,27,112.0,5.0,0.8,1994.0,5.0,120.0,82.0,428.0,95.0,140.0,541.0
+White Pass,Washington,Washington,6550,2050,4500,0,0,2,1,1,2,2,8,45.0,2.0,2.5,1402.0,30.0,148.0,67.0,400.0,69.0,144.0,90.0
+Canaan Valley Resort,West Virginia,West Virginia,4280,850,3430,0,0,0,1,2,0,1,4,47.0,1.0,1.2,95.0,75.0,,48.0,160.0,68.0,93.0,
+Snowshoe Mountain Resort,West Virginia,West Virginia,4848,1500,3348,0,0,3,2,6,0,3,14,60.0,5.0,1.5,257.0,257.0,125.0,46.0,180.0,87.0,138.0,86.0
+Timberline Four Seasons,West Virginia,West Virginia,4265,1000,3268,0,0,0,0,2,1,1,4,40.0,1.0,2.0,100.0,100.0,97.0,37.0,150.0,92.0,115.0,27.0
+Winterplace Ski Resort,West Virginia,West Virginia,3600,603,2997,0,0,0,2,3,2,2,9,27.0,2.0,1.2,90.0,90.0,120.0,36.0,100.0,72.0,120.0,74.0
+Alpine Valley Resort,Wisconsin,Wisconsin,1040,388,820,0,0,3,0,3,1,5,12,21.0,3.0,0.2,90.0,90.0,100.0,55.0,80.0,65.0,120.0,90.0
+Bruce Mound,Wisconsin,Wisconsin,1375,375,1000,0,0,0,0,1,0,4,5,12.0,2.0,0.5,40.0,30.0,42.0,71.0,42.0,25.0,42.0,30.0
+Cascade Mountain,Wisconsin,Wisconsin,1280,460,820,0,0,2,2,3,2,3,12,47.0,4.0,1.1,175.0,175.0,120.0,57.0,56.0,64.0,120.0,
+Christie Mountain,Wisconsin,Wisconsin,1650,350,1300,0,0,0,0,0,1,5,6,30.0,4.0,0.8,45.0,41.0,92.0,43.0,48.0,38.0,120.0,35.0
+Devils Head,Wisconsin,Wisconsin,995,500,495,0,0,0,3,1,6,2,12,27.0,1.0,1.0,260.0,260.0,110.0,48.0,45.0,65.0,135.0,200.0
+Grand Geneva,Wisconsin,Wisconsin,1086,211,875,0,0,0,0,0,3,3,6,20.0,1.0,0.2,30.0,30.0,90.0,25.0,25.0,49.0,93.0,30.0
+Granite Peak Ski Area,Wisconsin,Wisconsin,1942,700,1242,0,1,2,0,2,0,2,7,75.0,4.0,0.6,220.0,160.0,136.0,82.0,75.0,92.0,135.0,200.0
+Mount La Crosse,Wisconsin,Wisconsin,1110,516,594,0,0,0,0,0,3,1,4,19.0,1.0,0.4,100.0,100.0,115.0,60.0,40.0,56.0,100.0,90.0
+Nordic Mountain,Wisconsin,Wisconsin,1137,265,872,0,0,0,0,1,1,5,7,18.0,4.0,1.0,60.0,60.0,68.0,43.0,80.0,47.0,90.0,60.0
+Sunburst,Wisconsin,Wisconsin,1100,214,866,0,0,0,0,0,3,6,9,13.0,4.0,0.5,37.0,37.0,99.0,59.0,50.0,44.0,115.0,37.0
+Trollhaugen,Wisconsin,Wisconsin,1200,260,920,0,0,0,2,0,1,5,8,24.0,4.0,0.5,86.0,86.0,130.0,69.0,50.0,54.0,120.0,86.0
+Tyrol Basin,Wisconsin,Wisconsin,1160,300,860,0,0,0,0,3,0,2,5,18.0,5.0,0.5,32.0,32.0,112.0,61.0,41.0,48.0,103.0,32.0
+Whitecap Mountain,Wisconsin,Wisconsin,1750,400,1295,0,0,0,1,0,4,0,5,43.0,1.0,1.0,400.0,300.0,105.0,57.0,200.0,60.0,118.0,
+Wilmot Mountain,Wisconsin,Wisconsin,1030,230,800,0,0,0,3,2,2,3,10,23.0,2.0,0.5,135.0,135.0,125.0,81.0,70.0,66.0,139.0,135.0
+Grand Targhee Resort,Wyoming,Wyoming,9920,2270,7851,0,0,2,2,0,0,1,5,95.0,1.0,2.7,2602.0,,152.0,50.0,500.0,90.0,152.0,
+Hogadon Basin,Wyoming,Wyoming,8000,640,7400,0,0,0,0,0,1,1,2,28.0,1.0,0.6,92.0,32.0,121.0,61.0,80.0,48.0,95.0,
+Sleeping Giant Ski Resort,Wyoming,Wyoming,7428,810,6619,0,0,0,0,1,1,1,3,48.0,1.0,1.0,184.0,18.0,61.0,81.0,310.0,42.0,77.0,
+Snow King Resort,Wyoming,Wyoming,7808,1571,6237,0,0,0,1,1,1,0,3,32.0,2.0,1.0,400.0,250.0,121.0,80.0,300.0,59.0,123.0,110.0
+Snowy Range Ski & Recreation Area,Wyoming,Wyoming,9663,990,8798,0,0,0,0,1,3,1,5,33.0,2.0,0.7,75.0,30.0,131.0,59.0,250.0,49.0,,
+White Pine Ski Area,Wyoming,Wyoming,9500,1100,8400,0,0,0,0,2,0,0,2,25.0,,0.4,370.0,,,81.0,150.0,49.0,,
diff --git a/data/ski_data_step3_features.csv b/data/ski_data_step3_features.csv
new file mode 100644
index 000000000..6895fd09a
--- /dev/null
+++ b/data/ski_data_step3_features.csv
@@ -0,0 +1,278 @@
+Name,Region,state,summit_elev,vertical_drop,base_elev,trams,fastSixes,fastQuads,quad,triple,double,surface,total_chairs,Runs,TerrainParks,LongestRun_mi,SkiableTerrain_ac,Snow Making_ac,daysOpenLastYear,yearsOpen,averageSnowfall,AdultWeekend,projectedDaysOpen,NightSkiing_ac,resorts_per_state,resorts_per_100kcapita,resorts_per_100ksq_mile,resort_skiable_area_ac_state_ratio,resort_days_open_state_ratio,resort_terrain_park_state_ratio,resort_night_skiing_state_ratio,total_chairs_runs_ratio,total_chairs_skiable_ratio,fastQuads_runs_ratio,fastQuads_skiable_ratio
+Alyeska Resort,Alaska,Alaska,3939,2500,250,1,0,2,2,0,0,2,7,76.0,2.0,1.0,1610.0,113.0,150.0,60.0,669.0,85.0,150.0,550.0,3,0.4100909718472548,0.45086746901037594,0.706140350877193,0.43478260869565216,0.5,0.9482758620689655,0.09210526315789473,0.004347826086956522,0.02631578947368421,0.0012422360248447205
+Eaglecrest Ski Area,Alaska,Alaska,2600,1540,1200,0,0,0,0,0,4,0,4,36.0,1.0,2.0,640.0,60.0,45.0,44.0,350.0,53.0,90.0,,3,0.4100909718472548,0.45086746901037594,0.2807017543859649,0.13043478260869565,0.25,,0.1111111111111111,0.00625,0.0,0.0
+Hilltop Ski Area,Alaska,Alaska,2090,294,1796,0,0,0,0,1,0,2,3,13.0,1.0,1.0,30.0,30.0,150.0,36.0,69.0,34.0,152.0,30.0,3,0.4100909718472548,0.45086746901037594,0.013157894736842105,0.43478260869565216,0.25,0.05172413793103448,0.23076923076923078,0.1,0.0,0.0
+Arizona Snowbowl,Arizona,Arizona,11500,2300,9200,0,1,0,2,2,1,2,8,55.0,4.0,2.0,777.0,104.0,122.0,81.0,260.0,89.0,122.0,,2,0.027477369981550318,1.7545398719185894,0.49270767279644895,0.5147679324894515,0.6666666666666666,,0.14545454545454545,0.010296010296010296,0.0,0.0
+Sunrise Park Resort,Arizona,Arizona,11100,1800,9200,0,0,1,2,3,1,0,7,65.0,2.0,1.2,800.0,80.0,115.0,49.0,250.0,78.0,104.0,80.0,2,0.027477369981550318,1.7545398719185894,0.507292327203551,0.48523206751054854,0.3333333333333333,1.0,0.1076923076923077,0.00875,0.015384615384615385,0.00125
+Yosemite Ski & Snowboard Area,Northern California,California,7800,600,7200,0,0,0,0,1,3,1,5,10.0,2.0,0.4,88.0,,110.0,84.0,300.0,47.0,107.0,,21,0.05314811064920341,12.828736369467608,0.0033913981809773393,0.04017531044558072,0.024691358024691357,,0.5,0.056818181818181816,0.0,0.0
+Dodge Ridge,Sierra Nevada,California,8200,1600,6600,0,0,0,1,2,5,4,12,67.0,5.0,2.0,862.0,,,69.0,350.0,78.0,140.0,,21,0.05314811064920341,12.828736369467608,0.033220286727300756,,0.06172839506172839,,0.1791044776119403,0.013921113689095127,0.0,0.0
+Donner Ski Ranch,Sierra Nevada,California,8012,750,7031,0,0,0,0,1,5,2,8,52.0,2.0,1.5,505.0,60.0,163.0,82.0,400.0,75.0,170.0,,21,0.05314811064920341,12.828736369467608,0.019462000924926778,0.059532505478451424,0.024691358024691357,,0.15384615384615385,0.015841584158415842,0.0,0.0
+Mammoth Mountain Ski Area,Sierra Nevada,California,11053,3100,7953,3,2,9,1,6,4,0,25,154.0,7.0,3.0,3500.0,700.0,243.0,66.0,400.0,159.0,,,21,0.05314811064920341,12.828736369467608,0.1348851549252351,0.0887509130752374,0.08641975308641975,,0.16233766233766234,0.007142857142857143,0.05844155844155844,0.0025714285714285713
+Mt. Shasta Ski Park,Sierra Nevada,California,6890,1435,5500,0,0,0,0,3,0,1,4,32.0,2.0,1.1,425.0,225.0,140.0,34.0,300.0,59.0,130.0,,21,0.05314811064920341,12.828736369467608,0.016378911669492832,0.05113221329437546,0.024691358024691357,,0.125,0.009411764705882352,0.0,0.0
+Mountain High,Sierra Nevada,California,8200,1600,6600,0,0,2,2,2,5,3,14,59.0,1.0,1.6,290.0,275.0,118.0,95.0,108.0,84.0,150.0,73.0,21,0.05314811064920341,12.828736369467608,0.01117619855094805,0.04309715120525932,0.012345679012345678,0.12436115843270869,0.23728813559322035,0.04827586206896552,0.03389830508474576,0.006896551724137931
+Mt. Baldy,Sierra Nevada,California,8600,2100,6500,0,0,0,0,0,4,0,4,26.0,,2.5,400.0,80.0,175.0,67.0,178.0,69.0,200.0,,21,0.05314811064920341,12.828736369467608,0.015415446277169724,0.06391526661796933,,,0.15384615384615385,0.01,0.0,0.0
+Ski China Peak,Sierra Nevada,California,8709,1679,7030,0,0,0,1,4,2,4,11,45.0,1.0,2.2,1400.0,150.0,140.0,62.0,300.0,83.0,144.0,,21,0.05314811064920341,12.828736369467608,0.05395406197009404,0.05113221329437546,0.012345679012345678,,0.24444444444444444,0.007857142857142858,0.0,0.0
+Snow Valley,Sierra Nevada,California,7841,1041,6800,0,0,0,0,5,6,1,12,28.0,6.0,1.2,240.0,188.0,111.0,82.0,160.0,79.0,143.0,164.0,21,0.05314811064920341,12.828736369467608,0.009249267766301835,0.04054054054054054,0.07407407407407407,0.27938671209540034,0.42857142857142855,0.05,0.0,0.0
+Soda Springs,Sierra Nevada,California,7352,652,6700,0,0,0,0,1,1,2,4,18.0,,0.4,200.0,20.0,150.0,83.0,400.0,50.0,144.0,,21,0.05314811064920341,12.828736369467608,0.007707723138584862,0.0547845142439737,,,0.2222222222222222,0.02,0.0,0.0
+Sugar Bowl Resort,Sierra Nevada,California,8383,1500,6883,1,0,5,3,1,0,2,12,105.0,3.0,3.0,1650.0,375.0,151.0,80.0,500.0,125.0,150.0,,21,0.05314811064920341,12.828736369467608,0.06358871589332511,0.05514974433893353,0.037037037037037035,,0.11428571428571428,0.007272727272727273,0.047619047619047616,0.0030303030303030303
+Tahoe Donner,Sierra Nevada,California,7350,600,6750,0,0,0,1,1,0,3,5,14.0,2.0,1.0,120.0,,150.0,48.0,400.0,69.0,144.0,,21,0.05314811064920341,12.828736369467608,0.004624633883150917,0.0547845142439737,0.024691358024691357,,0.35714285714285715,0.041666666666666664,0.0,0.0
+Arapahoe Basin Ski Area,Colorado,Colorado,13050,2530,10780,0,0,1,2,1,2,3,9,145.0,3.0,1.5,1428.0,125.0,230.0,73.0,350.0,85.0,233.0,,22,0.3820282784277661,21.13474359713336,0.032690810860308596,0.07059545733578883,0.04054054054054054,,0.06206896551724138,0.0063025210084033615,0.006896551724137931,0.0007002801120448179
+Aspen / Snowmass,Colorado,Colorado,12510,4406,8104,3,1,15,4,3,5,9,40,336.0,10.0,5.3,5517.0,658.0,138.0,72.0,300.0,179.0,138.0,,22,0.3820282784277661,21.13474359713336,0.12629916212627626,0.0423572744014733,0.13513513513513514,,0.11904761904761904,0.0072503172013775605,0.044642857142857144,0.0027188689505165853
+Copper Mountain Resort,Colorado,Colorado,12313,2738,9712,1,2,4,0,4,4,9,24,150.0,6.0,1.7,2527.0,364.0,164.0,47.0,300.0,158.0,164.0,,22,0.3820282784277661,21.13474359713336,0.05784991529691864,0.05033763044812769,0.08108108108108109,,0.16,0.009497427779976256,0.02666666666666667,0.0015829046299960427
+Purgatory,Colorado,Colorado,10822,2029,8793,0,1,2,0,3,3,3,12,101.0,9.0,1.3,1605.0,250.0,130.0,54.0,260.0,89.0,130.0,,22,0.3820282784277661,21.13474359713336,0.03674282313080903,0.03990178023327195,0.12162162162162163,,0.1188118811881188,0.007476635514018692,0.019801980198019802,0.0012461059190031153
+Howelsen Hill,Colorado,Colorado,7136,440,6696,0,0,0,0,0,1,3,4,17.0,1.0,6.0,50.0,25.0,100.0,104.0,150.0,25.0,100.0,10.0,22,0.3820282784277661,21.13474359713336,0.0011446362346046427,0.030693677102516883,0.013513513513513514,0.02336448598130841,0.23529411764705882,0.08,0.0,0.0
+Loveland,Colorado,Colorado,13010,2210,10800,0,0,1,3,3,2,1,10,94.0,1.0,2.0,1800.0,240.0,205.0,82.0,422.0,79.0,184.0,,22,0.3820282784277661,21.13474359713336,0.041206904445767134,0.06292203806015961,0.013513513513513514,,0.10638297872340426,0.005555555555555556,0.010638297872340425,0.0005555555555555556
+Monarch Mountain,Colorado,Colorado,11952,1162,10790,0,0,0,1,0,4,2,7,64.0,2.0,1.0,800.0,,143.0,80.0,350.0,89.0,136.0,,22,0.3820282784277661,21.13474359713336,0.018314179753674283,0.04389195825659914,0.02702702702702703,,0.109375,0.00875,0.0,0.0
+Powderhorn,Colorado,Colorado,9850,1650,8200,0,0,1,0,0,2,2,5,42.0,2.0,1.5,1600.0,42.0,111.0,53.0,250.0,71.0,110.0,,22,0.3820282784277661,21.13474359713336,0.036628359507348565,0.03406998158379374,0.02702702702702703,,0.11904761904761904,0.003125,0.023809523809523808,0.000625
+Silverton Mountain,Colorado,Colorado,13487,3087,10400,0,0,0,0,0,1,0,1,,,1.5,1819.0,,175.0,17.0,400.0,79.0,181.0,,22,0.3820282784277661,21.13474359713336,0.041641866214916896,0.053713934929404544,,,,0.0005497526113249038,,0.0
+Cooper,Colorado,Colorado,11700,1200,10500,0,0,0,0,1,1,2,4,41.0,1.0,1.0,400.0,,130.0,74.0,260.0,56.0,130.0,,22,0.3820282784277661,21.13474359713336,0.009157089876837141,0.03990178023327195,0.013513513513513514,,0.0975609756097561,0.01,0.0,0.0
+Ski Granby Ranch,Colorado,Colorado,9202,1000,8202,0,0,2,0,1,1,1,5,40.0,1.0,0.6,406.0,170.0,116.0,36.0,220.0,84.0,92.0,100.0,22,0.3820282784277661,21.13474359713336,0.009294446224989698,0.03560466543891958,0.013513513513513514,0.2336448598130841,0.125,0.012315270935960592,0.05,0.0049261083743842365
+Sunlight Mountain Resort,Colorado,Colorado,9895,2010,7885,0,0,0,0,1,2,0,3,67.0,1.0,2.5,680.0,30.0,100.0,53.0,250.0,65.0,135.0,,22,0.3820282784277661,21.13474359713336,0.01556705279062314,0.030693677102516883,0.013513513513513514,,0.04477611940298507,0.004411764705882353,0.0,0.0
+Telluride,Colorado,Colorado,13150,4425,8725,2,0,6,1,2,2,4,17,148.0,3.0,4.6,2000.0,220.0,131.0,47.0,280.0,139.0,137.0,,22,0.3820282784277661,21.13474359713336,0.0457854493841857,0.040208717004297116,0.04054054054054054,,0.11486486486486487,0.0085,0.04054054054054054,0.003
+Wolf Creek Ski Area,Colorado,Colorado,11904,1604,10300,0,0,3,1,2,1,3,10,120.0,,2.0,1600.0,5.0,130.0,80.0,430.0,72.0,150.0,,22,0.3820282784277661,21.13474359713336,0.036628359507348565,0.03990178023327195,,,0.08333333333333333,0.00625,0.025,0.001875
+Mohawk Mountain,Connecticut,Connecticut,1600,650,950,0,0,0,0,5,0,3,8,25.0,,1.5,107.0,100.0,,72.0,92.0,65.0,110.0,64.0,5,0.14024151833321272,90.20386072523904,0.2988826815642458,,,0.25,0.32,0.07476635514018691,0.0,0.0
+Mount Southington Ski Area,Connecticut,Connecticut,525,425,100,0,0,0,0,2,2,3,7,14.0,2.0,0.3,51.0,51.0,63.0,55.0,80.0,60.0,95.0,51.0,5,0.14024151833321272,90.20386072523904,0.1424581005586592,0.17847025495750707,0.2,0.19921875,0.5,0.13725490196078433,0.0,0.0
+Powder Ridge Park,Connecticut,Connecticut,720,550,170,0,0,0,0,1,2,2,5,19.0,4.0,0.5,80.0,68.0,80.0,60.0,80.0,55.0,100.0,40.0,5,0.14024151833321272,90.20386072523904,0.22346368715083798,0.22662889518413598,0.4,0.15625,0.2631578947368421,0.0625,0.0,0.0
+Ski Sundown,Connecticut,Connecticut,1075,625,450,0,0,0,0,3,0,2,5,16.0,2.0,1.0,70.0,70.0,84.0,50.0,45.0,62.0,95.0,66.0,5,0.14024151833321272,90.20386072523904,0.19553072625698323,0.23796033994334279,0.2,0.2578125,0.3125,0.07142857142857142,0.0,0.0
+Woodbury Ski Area,Connecticut,Connecticut,730,300,430,0,0,0,0,0,1,4,5,12.0,2.0,0.2,50.0,50.0,126.0,57.0,70.0,42.0,180.0,35.0,5,0.14024151833321272,90.20386072523904,0.13966480446927373,0.35694050991501414,0.2,0.13671875,0.4166666666666667,0.1,0.0,0.0
+Bogus Basin,Idaho,Idaho,7582,1800,5800,0,0,3,0,1,3,4,11,91.0,3.0,1.5,2600.0,,134.0,77.0,250.0,64.0,130.0,165.0,12,0.6714920833881253,14.359391640440833,0.15857526225908758,0.11795774647887323,0.1111111111111111,0.39759036144578314,0.12087912087912088,0.004230769230769231,0.03296703296703297,0.001153846153846154
+Brundage Mountain Resort,Idaho,Idaho,7640,1800,5840,0,0,1,0,4,0,1,6,51.0,2.0,3.2,1920.0,2.0,126.0,58.0,320.0,70.0,,,12,0.6714920833881253,14.359391640440833,0.11710173212978775,0.11091549295774648,0.07407407407407407,,0.11764705882352941,0.003125,0.0196078431372549,0.0005208333333333333
+Kelly Canyon Ski Area,Idaho,Idaho,6600,1000,5600,0,0,0,0,0,4,2,6,51.0,1.0,1.3,740.0,,,62.0,200.0,42.0,,,12,0.6714920833881253,14.359391640440833,0.0451329592583557,,0.037037037037037035,,0.11764705882352941,0.008108108108108109,0.0,0.0
+Lookout Pass Ski Area,Idaho,Idaho,5650,1150,4500,0,0,0,0,1,3,0,4,35.0,2.0,1.5,540.0,,113.0,84.0,400.0,47.0,140.0,,12,0.6714920833881253,14.359391640440833,0.032934862161502806,0.0994718309859155,0.07407407407407407,,0.11428571428571428,0.007407407407407408,0.0,0.0
+Magic Mountain Ski Area,Idaho,Idaho,7200,700,6500,0,0,0,0,0,1,2,3,11.0,,1.5,280.0,,65.0,81.0,180.0,32.0,70.0,,12,0.6714920833881253,14.359391640440833,0.017077335935594046,0.05721830985915493,,,0.2727272727272727,0.010714285714285714,0.0,0.0
+Pebble Creek Ski Area,Idaho,Idaho,8560,2200,6360,0,0,0,0,3,0,0,3,54.0,2.0,1.3,1100.0,30.0,85.0,70.0,250.0,47.0,91.0,30.0,12,0.6714920833881253,14.359391640440833,0.0670895340326909,0.07482394366197183,0.07407407407407407,0.07228915662650602,0.05555555555555555,0.0027272727272727275,0.0,0.0
+Schweitzer,Idaho,Idaho,6400,2400,4000,0,1,2,0,1,3,2,9,92.0,3.0,2.1,2900.0,47.0,136.0,56.0,300.0,81.0,,100.0,12,0.6714920833881253,14.359391640440833,0.17687240790436692,0.11971830985915492,0.1111111111111111,0.24096385542168675,0.09782608695652174,0.003103448275862069,0.021739130434782608,0.000689655172413793
+Silver Mountain,Idaho,Idaho,6300,2200,4100,1,0,0,1,2,2,1,7,80.0,2.0,2.5,1600.0,225.0,130.0,29.0,300.0,62.0,193.0,20.0,12,0.6714920833881253,14.359391640440833,0.09758477677482313,0.11443661971830986,0.07407407407407407,0.04819277108433735,0.0875,0.004375,0.0,0.0
+Soldier Mountain Ski Area,Idaho,Idaho,7200,1400,5800,0,0,0,0,0,2,1,3,36.0,,0.4,1142.0,,60.0,71.0,,43.0,,,12,0.6714920833881253,14.359391640440833,0.06965113442303,0.0528169014084507,,,0.08333333333333333,0.002626970227670753,0.0,0.0
+Tamarack Resort,Idaho,Idaho,7700,2800,4900,0,0,2,2,0,0,2,6,48.0,3.0,1.5,1020.0,200.0,,15.0,300.0,71.0,150.0,,12,0.6714920833881253,14.359391640440833,0.062210295193949744,,0.1111111111111111,,0.125,0.0058823529411764705,0.041666666666666664,0.00196078431372549
+Chestnut Mountain Resort,Illinois,Illinois,1040,475,565,0,0,0,2,4,0,3,9,22.0,3.0,0.2,139.0,139.0,87.0,60.0,50.0,55.0,112.0,139.0,4,0.03156610245678186,6.906792830749041,0.7277486910994765,0.3936651583710407,0.5,0.7277486910994765,0.4090909090909091,0.06474820143884892,0.0,0.0
+Ski Snowstar Winter Sports Park,Illinois,Illinois,790,262,528,0,0,0,2,0,2,2,6,15.0,1.0,0.8,28.0,28.0,56.0,38.0,38.0,35.0,86.0,28.0,4,0.03156610245678186,6.906792830749041,0.14659685863874344,0.25339366515837103,0.16666666666666666,0.14659685863874344,0.4,0.21428571428571427,0.0,0.0
+Villa Olivia,Illinois,Illinois,500,180,320,0,0,0,1,0,0,6,7,7.0,1.0,0.1,15.0,15.0,,53.0,25.0,40.0,70.0,15.0,4,0.03156610245678186,6.906792830749041,0.07853403141361257,,0.16666666666666666,0.07853403141361257,1.0,0.4666666666666667,0.0,0.0
+Paoli Peaks,Indiana,Indiana,900,300,600,0,0,1,1,3,1,2,8,15.0,2.0,0.4,65.0,65.0,75.0,41.0,18.0,45.0,80.0,65.0,2,0.02970788680522722,5.491488193300384,0.3939393939393939,0.47770700636942676,0.5,0.3939393939393939,0.5333333333333333,0.12307692307692308,0.06666666666666667,0.015384615384615385
+Perfect North Slopes,Indiana,Indiana,800,400,400,0,0,0,2,3,0,6,11,23.0,2.0,1.0,100.0,100.0,82.0,39.0,24.0,52.0,90.0,100.0,2,0.02970788680522722,5.491488193300384,0.6060606060606061,0.5222929936305732,0.5,0.6060606060606061,0.4782608695652174,0.11,0.0,0.0
+Mt. Crescent Ski Area,Iowa,Iowa,1500,300,1200,0,0,0,1,0,1,0,2,11.0,1.0,0.2,50.0,50.0,,58.0,30.0,39.0,,50.0,3,0.0950850535804277,5.3311534839088015,0.35714285714285715,,0.2,0.35714285714285715,0.18181818181818182,0.04,0.0,0.0
+Seven Oaks,Iowa,Iowa,975,275,800,0,0,0,0,2,0,2,4,11.0,2.0,1.0,35.0,35.0,100.0,22.0,40.0,40.0,100.0,35.0,3,0.0950850535804277,5.3311534839088015,0.25,1.0,0.4,0.25,0.36363636363636365,0.11428571428571428,0.0,0.0
+Sundown Mountain,Iowa,Iowa,1059,475,584,0,0,0,1,1,2,2,6,21.0,2.0,0.6,55.0,55.0,,46.0,45.0,46.0,,55.0,3,0.0950850535804277,5.3311534839088015,0.39285714285714285,,0.4,0.39285714285714285,0.2857142857142857,0.10909090909090909,0.0,0.0
+Big Squaw Mountain Ski Resort,Maine,Maine,3200,660,1750,0,0,0,0,1,0,0,1,29.0,,0.8,,,67.0,6.0,,30.0,58.0,,9,0.6695372456130432,25.438100621820237,,0.07745664739884393,,,0.034482758620689655,,0.0,
+Camden Snow Bowl,Maine,Maine,1080,850,150,0,0,0,0,1,1,1,3,26.0,2.0,1.0,100.0,48.0,68.0,83.0,69.0,43.0,70.0,48.0,9,0.6695372456130432,25.438100621820237,0.03109452736318408,0.07861271676300578,0.11764705882352941,0.12371134020618557,0.11538461538461539,0.03,0.0,0.0
+Lost Valley,Maine,Maine,495,240,255,0,0,0,0,0,2,2,4,22.0,2.0,0.3,45.0,45.0,87.0,58.0,50.0,55.0,104.0,45.0,9,0.6695372456130432,25.438100621820237,0.013992537313432836,0.10057803468208093,0.11764705882352941,0.11597938144329897,0.18181818181818182,0.08888888888888889,0.0,0.0
+Mt. Abram Ski Resort,Maine,Maine,2250,1150,1050,0,0,0,0,0,2,3,5,54.0,1.0,0.5,640.0,175.0,120.0,59.0,125.0,49.0,120.0,,9,0.6695372456130432,25.438100621820237,0.19900497512437812,0.13872832369942195,0.058823529411764705,,0.09259259259259259,0.0078125,0.0,0.0
+New Hermon Mountain,Maine,Maine,450,350,100,0,0,0,0,0,1,2,3,20.0,,1.9,70.0,70.0,102.0,55.0,90.0,32.0,117.0,45.0,9,0.6695372456130432,25.438100621820237,0.021766169154228857,0.11791907514450867,,0.11597938144329897,0.15,0.04285714285714286,0.0,0.0
+Shawnee Peak,Maine,Maine,1900,1350,600,0,0,0,1,2,1,2,6,43.0,3.0,0.8,239.0,234.0,97.0,81.0,110.0,75.0,103.0,110.0,9,0.6695372456130432,25.438100621820237,0.07431592039800995,0.11213872832369942,0.17647058823529413,0.28350515463917525,0.13953488372093023,0.02510460251046025,0.0,0.0
+Sugarloaf,Maine,Maine,4237,2820,1417,0,0,2,3,1,5,2,13,162.0,4.0,3.5,1240.0,618.0,159.0,68.0,200.0,99.0,155.0,,9,0.6695372456130432,25.438100621820237,0.3855721393034826,0.1838150289017341,0.23529411764705882,,0.08024691358024691,0.010483870967741936,0.012345679012345678,0.0016129032258064516
+Sunday River,Maine,Maine,3140,2340,800,1,0,4,5,3,1,1,15,135.0,5.0,3.0,870.0,552.0,165.0,60.0,167.0,105.0,169.0,140.0,9,0.6695372456130432,25.438100621820237,0.27052238805970147,0.1907514450867052,0.29411764705882354,0.36082474226804123,0.1111111111111111,0.017241379310344827,0.02962962962962963,0.004597701149425287
+Wisp,Maryland,Maryland,3115,700,2415,0,0,0,2,5,0,5,12,34.0,3.0,1.5,172.0,118.0,121.0,64.0,100.0,79.0,120.0,118.0,1,0.016540736525916026,8.060615831049493,1.0,1.0,1.0,1.0,0.35294117647058826,0.06976744186046512,0.0,0.0
+Berkshire East,Massachusetts,Massachusetts,1720,1180,540,0,0,0,2,1,1,2,6,47.0,2.0,2.0,180.0,165.0,120.0,68.0,120.0,68.0,120.0,80.0,11,0.1595936918707181,104.22588592003032,0.15437392795883362,0.17883755588673622,0.1111111111111111,0.137221269296741,0.1276595744680851,0.03333333333333333,0.0,0.0
+Blandford Ski Area,Massachusetts,Massachusetts,1685,465,1035,0,0,0,0,0,3,2,5,29.0,2.0,0.5,132.0,70.0,,83.0,50.0,45.0,,70.0,11,0.1595936918707181,104.22588592003032,0.11320754716981132,,0.1111111111111111,0.12006861063464837,0.1724137931034483,0.03787878787878788,0.0,0.0
+Blue Hills Ski Area,Massachusetts,Massachusetts,635,309,326,0,0,0,0,0,1,3,4,16.0,1.0,,60.0,60.0,,19.0,,45.0,,,11,0.1595936918707181,104.22588592003032,0.051457975986277875,,0.05555555555555555,,0.25,0.06666666666666667,0.0,0.0
+Bousquet Ski Area,Massachusetts,Massachusetts,1875,750,1125,0,0,0,0,0,3,2,5,23.0,1.0,1.0,200.0,98.0,,19.0,83.0,49.0,,100.0,11,0.1595936918707181,104.22588592003032,0.17152658662092624,,0.05555555555555555,0.17152658662092624,0.21739130434782608,0.025,0.0,0.0
+Bradford Ski Area,Massachusetts,Massachusetts,1548,248,1300,0,0,0,0,2,0,8,10,15.0,1.0,0.3,48.0,48.0,,71.0,,55.0,,,11,0.1595936918707181,104.22588592003032,0.0411663807890223,,0.05555555555555555,,0.6666666666666666,0.20833333333333334,0.0,0.0
+Jiminy Peak,Massachusetts,Massachusetts,2380,1150,1230,0,1,0,2,3,1,2,9,45.0,3.0,2.0,167.0,163.0,121.0,71.0,90.0,81.0,120.0,104.0,11,0.1595936918707181,104.22588592003032,0.1432246998284734,0.18032786885245902,0.16666666666666666,0.1783876500857633,0.2,0.05389221556886228,0.0,0.0
+Nashoba Valley,Massachusetts,Massachusetts,440,240,200,0,0,0,0,3,1,7,11,17.0,2.0,0.5,52.0,52.0,112.0,55.0,55.0,58.0,126.0,52.0,11,0.1595936918707181,104.22588592003032,0.044596912521440824,0.16691505216095381,0.1111111111111111,0.08919382504288165,0.6470588235294118,0.21153846153846154,0.0,0.0
+Otis Ridge Ski Area,Massachusetts,Massachusetts,1700,400,1300,0,0,0,0,0,1,3,4,11.0,1.0,1.0,60.0,55.0,106.0,73.0,70.0,40.0,106.0,35.0,11,0.1595936918707181,104.22588592003032,0.051457975986277875,0.15797317436661698,0.05555555555555555,0.060034305317324184,0.36363636363636365,0.06666666666666667,0.0,0.0
+Ski Butternut,Massachusetts,Massachusetts,1800,1000,800,0,0,0,3,1,1,6,11,22.0,2.0,1.5,110.0,110.0,107.0,56.0,115.0,60.0,110.0,,11,0.1595936918707181,104.22588592003032,0.09433962264150944,0.15946348733233978,0.1111111111111111,,0.5,0.1,0.0,0.0
+Wachusett Mountain Ski Area,Massachusetts,Massachusetts,2006,1000,1006,0,0,3,0,1,0,4,8,27.0,2.0,1.5,112.0,112.0,,57.0,100.0,71.0,120.0,104.0,11,0.1595936918707181,104.22588592003032,0.09605488850771869,,0.1111111111111111,0.1783876500857633,0.2962962962962963,0.07142857142857142,0.1111111111111111,0.026785714285714284
+Alpine Valley Ski Area,Michigan,Michigan,500,240,126,0,0,0,1,2,5,6,14,25.0,3.0,0.2,100.0,100.0,,57.0,20.0,47.0,,100.0,28,0.28036848830417815,28.951341067477305,0.022696323195642305,,0.047619047619047616,0.051387461459403906,0.56,0.14,0.0,0.0
+Apple Mountain,Michigan,Michigan,820,220,600,0,0,0,1,0,0,5,6,12.0,,,80.0,42.0,,58.0,52.0,35.0,,80.0,28,0.28036848830417815,28.951341067477305,0.018157058556513846,,,0.041109969167523124,0.5,0.075,0.0,0.0
+Big Powderhorn Mountain,Michigan,Michigan,1800,600,1200,0,0,0,0,0,9,1,10,45.0,2.0,1.0,253.0,228.0,100.0,55.0,214.0,69.0,108.0,,28,0.28036848830417815,28.951341067477305,0.057421697684975036,0.041858518208455424,0.031746031746031744,,0.2222222222222222,0.039525691699604744,0.0,0.0
+Bittersweet Ski Area,Michigan,Michigan,850,350,450,0,0,0,1,7,0,4,12,20.0,2.0,0.2,100.0,100.0,80.0,36.0,90.0,48.0,,100.0,28,0.28036848830417815,28.951341067477305,0.022696323195642305,0.033486814566764334,0.031746031746031744,0.051387461459403906,0.6,0.12,0.0,0.0
+Big Snow Resort - Blackjack,Michigan,Michigan,850,465,385,0,0,0,0,0,4,2,6,26.0,2.0,1.0,170.0,86.0,95.0,42.0,210.0,65.0,115.0,,28,0.28036848830417815,28.951341067477305,0.03858374943259192,0.03976559229803265,0.031746031746031744,,0.23076923076923078,0.03529411764705882,0.0,0.0
+Boyne Highlands,Michigan,Michigan,1290,552,745,0,0,1,3,4,0,2,10,55.0,4.0,1.2,435.0,400.0,97.0,56.0,140.0,98.0,120.0,150.0,28,0.28036848830417815,28.951341067477305,0.09872900590104403,0.04060276266220176,0.06349206349206349,0.07708119218910586,0.18181818181818182,0.022988505747126436,0.01818181818181818,0.0022988505747126436
+Caberfae Peaks,Michigan,Michigan,1569,485,1060,0,0,0,1,2,1,1,5,34.0,2.0,1.2,200.0,200.0,118.0,82.0,140.0,49.0,130.0,150.0,28,0.28036848830417815,28.951341067477305,0.04539264639128461,0.0493930514859774,0.031746031746031744,0.07708119218910586,0.14705882352941177,0.025,0.0,0.0
+Cannonsburg,Michigan,Michigan,1100,250,850,0,0,0,1,1,1,7,10,21.0,5.0,0.1,100.0,,100.0,54.0,100.0,37.0,100.0,,28,0.28036848830417815,28.951341067477305,0.022696323195642305,0.041858518208455424,0.07936507936507936,,0.47619047619047616,0.1,0.0,0.0
+Crystal Mountain,Michigan,Michigan,1132,375,757,0,0,1,3,2,0,2,8,58.0,3.0,0.3,102.0,96.0,120.0,63.0,132.0,64.0,135.0,56.0,28,0.28036848830417815,28.951341067477305,0.02315024965955515,0.05023022185014651,0.047619047619047616,0.02877697841726619,0.13793103448275862,0.0784313725490196,0.017241379310344827,0.00980392156862745
+Big Snow Resort - Indianhead Mountain,Michigan,Michigan,1935,638,1297,0,0,0,1,1,5,2,9,32.0,2.0,1.0,240.0,150.0,120.0,60.0,204.0,49.0,120.0,,28,0.28036848830417815,28.951341067477305,0.05447117566954154,0.05023022185014651,0.031746031746031744,,0.28125,0.0375,0.0,0.0
+Mont Ripley,Michigan,Michigan,1140,440,700,0,0,0,0,0,2,2,4,25.0,2.0,0.8,112.0,112.0,114.0,83.0,275.0,49.0,100.0,100.0,28,0.28036848830417815,28.951341067477305,0.02541988197911938,0.04771871075763918,0.031746031746031744,0.051387461459403906,0.16,0.03571428571428571,0.0,0.0
+Mount Bohemia,Michigan,Michigan,1500,900,600,0,0,0,0,1,1,0,2,,,2.3,585.0,,83.0,19.0,273.0,68.0,100.0,,28,0.28036848830417815,28.951341067477305,0.1327734906945075,0.034742570113018,,,,0.003418803418803419,,0.0
+Mt. Brighton,Michigan,Michigan,1330,230,1100,0,0,0,2,3,0,8,13,25.0,5.0,0.1,130.0,130.0,111.0,59.0,60.0,59.0,100.0,130.0,28,0.28036848830417815,28.951341067477305,0.029505220154335,0.046462955211385513,0.07936507936507936,0.06680369989722508,0.52,0.1,0.0,0.0
+Mt. Holiday Ski Area,Michigan,Michigan,440,200,240,0,0,0,0,0,2,2,4,12.0,,0.1,45.0,45.0,100.0,70.0,120.0,34.0,90.0,45.0,28,0.28036848830417815,28.951341067477305,0.010213345438039038,0.041858518208455424,,0.023124357656731757,0.3333333333333333,0.08888888888888889,0.0,0.0
+Mount Holly,Michigan,Michigan,1105,350,755,0,0,1,2,3,1,6,13,19.0,,0.1,100.0,100.0,,63.0,42.0,45.0,102.0,100.0,28,0.28036848830417815,28.951341067477305,0.022696323195642305,,,0.051387461459403906,0.6842105263157895,0.13,0.05263157894736842,0.01
+Mulligan's Hollow Ski Bowl,Michigan,Michigan,700,130,570,0,0,0,0,0,0,5,5,6.0,,0.2,10.0,10.0,,19.0,60.0,20.0,,10.0,28,0.28036848830417815,28.951341067477305,0.0022696323195642307,,,0.0051387461459403904,0.8333333333333334,0.5,0.0,0.0
+Norway Mountain,Michigan,Michigan,1335,500,835,0,0,0,0,1,2,3,6,17.0,1.0,1.4,186.0,186.0,110.0,45.0,100.0,45.0,110.0,40.0,28,0.28036848830417815,28.951341067477305,0.04221516114389469,0.046044370029300966,0.015873015873015872,0.020554984583761562,0.35294117647058826,0.03225806451612903,0.0,0.0
+Nubs Nob Ski Area,Michigan,Michigan,1338,427,911,0,0,0,3,4,2,1,10,53.0,3.0,0.9,248.0,248.0,133.0,61.0,135.0,85.0,130.0,160.0,28,0.28036848830417815,28.951341067477305,0.05628688152519292,0.05567182921724571,0.047619047619047616,0.08221993833504625,0.18867924528301888,0.04032258064516129,0.0,0.0
+Pine Mountain,Michigan,Michigan,1650,500,1150,0,0,0,0,1,2,1,4,28.0,1.0,0.5,160.0,160.0,110.0,80.0,60.0,45.0,126.0,80.0,28,0.28036848830417815,28.951341067477305,0.03631411711302769,0.046044370029300966,0.015873015873015872,0.041109969167523124,0.14285714285714285,0.025,0.0,0.0
+Schuss Mountain at Shanty Creek,Michigan,Michigan,1125,450,675,0,0,0,5,0,0,3,8,42.0,3.0,1.0,70.0,70.0,94.0,57.0,160.0,78.0,111.0,70.0,28,0.28036848830417815,28.951341067477305,0.015887426236949616,0.039347007115948095,0.047619047619047616,0.03597122302158273,0.19047619047619047,0.11428571428571428,0.0,0.0
+Ski Brule,Michigan,Michigan,1860,500,1360,0,0,0,0,0,5,7,12,17.0,3.0,1.0,150.0,150.0,164.0,62.0,150.0,49.0,165.0,40.0,28,0.28036848830417815,28.951341067477305,0.03404448479346346,0.06864796986186689,0.047619047619047616,0.020554984583761562,0.7058823529411765,0.08,0.0,0.0
+Snow Snake Mountain Ski Area,Michigan,Michigan,1230,210,1020,0,0,0,0,1,0,5,6,12.0,2.0,0.0,40.0,40.0,,72.0,,35.0,,40.0,28,0.28036848830417815,28.951341067477305,0.009078529278256923,,0.031746031746031744,0.020554984583761562,0.5,0.15,0.0,0.0
+Swiss Valley,Michigan,Michigan,1200,225,975,0,0,0,2,1,0,4,7,11.0,2.0,0.1,60.0,60.0,89.0,51.0,60.0,42.0,80.0,60.0,28,0.28036848830417815,28.951341067477305,0.013617793917385384,0.03725408120552533,0.031746031746031744,0.030832476875642344,0.6363636363636364,0.11666666666666667,0.0,0.0
+The Homestead,Michigan,Michigan,900,320,580,0,0,0,0,2,1,2,5,15.0,1.0,0.2,16.0,16.0,47.0,34.0,150.0,50.0,42.0,16.0,28,0.28036848830417815,28.951341067477305,0.003631411711302769,0.019673503557974047,0.015873015873015872,0.008221993833504625,0.3333333333333333,0.3125,0.0,0.0
+Timber Ridge,Michigan,Michigan,850,250,600,0,0,0,1,1,2,4,8,16.0,2.0,0.3,50.0,50.0,80.0,58.0,,45.0,,50.0,28,0.28036848830417815,28.951341067477305,0.011348161597821153,0.033486814566764334,0.031746031746031744,0.025693730729701953,0.5,0.16,0.0,0.0
+Afton Alps,Minnesota,Minnesota,1530,350,1180,0,0,0,1,3,14,4,22,48.0,5.0,0.5,250.0,250.0,135.0,56.0,60.0,60.0,135.0,250.0,14,0.24824314777985515,16.103800496917273,0.16025641025641027,0.09060402684563758,0.1724137931034483,0.24509803921568626,0.4583333333333333,0.088,0.0,0.0
+Andes Tower Hills Ski Area,Minnesota,Minnesota,1620,290,1330,0,0,0,1,2,0,3,6,15.0,2.0,0.2,35.0,35.0,100.0,38.0,55.0,45.0,110.0,35.0,14,0.24824314777985515,16.103800496917273,0.022435897435897436,0.06711409395973154,0.06896551724137931,0.03431372549019608,0.4,0.17142857142857143,0.0,0.0
+Buck Hill,Minnesota,Minnesota,1225,309,919,0,0,0,2,1,0,5,8,16.0,,0.2,45.0,45.0,115.0,65.0,60.0,47.0,112.0,45.0,14,0.24824314777985515,16.103800496917273,0.028846153846153848,0.07718120805369127,,0.04411764705882353,0.5,0.17777777777777778,0.0,0.0
+Buena Vista Ski Area,Minnesota,Minnesota,1510,230,1280,0,0,0,0,2,2,2,6,17.0,,0.3,30.0,30.0,57.0,70.0,78.0,44.0,60.0,30.0,14,0.24824314777985515,16.103800496917273,0.019230769230769232,0.03825503355704698,,0.029411764705882353,0.35294117647058826,0.2,0.0,0.0
+Coffee Mill Ski & Snowboard Resort,Minnesota,Minnesota,1150,425,725,0,0,0,0,0,2,1,3,14.0,1.0,1.0,40.0,35.0,57.0,39.0,48.0,37.0,56.0,35.0,14,0.24824314777985515,16.103800496917273,0.02564102564102564,0.03825503355704698,0.034482758620689655,0.03431372549019608,0.21428571428571427,0.075,0.0,0.0
+Elm Creek Winter Recreation Area,Minnesota,Minnesota,928,60,868,0,0,0,0,0,0,3,3,3.0,,1.0,15.0,20.0,105.0,13.0,45.0,17.0,102.0,15.0,14,0.24824314777985515,16.103800496917273,0.009615384615384616,0.07046979865771812,,0.014705882352941176,1.0,0.2,0.0,0.0
+Giants Ridge Resort,Minnesota,Minnesota,1972,500,1472,0,0,1,1,1,2,2,7,35.0,2.0,0.8,202.0,202.0,120.0,35.0,85.0,58.0,125.0,121.0,14,0.24824314777985515,16.103800496917273,0.1294871794871795,0.08053691275167785,0.06896551724137931,0.11862745098039215,0.2,0.034653465346534656,0.02857142857142857,0.0049504950495049506
+Hyland Ski & Snowboard Area,Minnesota,Minnesota,1075,175,900,0,0,0,2,1,0,5,8,14.0,1.0,1.0,35.0,35.0,110.0,61.0,55.0,35.34,115.0,35.0,14,0.24824314777985515,16.103800496917273,0.022435897435897436,0.0738255033557047,0.034482758620689655,0.03431372549019608,0.5714285714285714,0.22857142857142856,0.0,0.0
+Lutsen Mountains,Minnesota,Minnesota,1688,825,800,1,1,0,0,1,4,1,8,62.0,2.0,2.0,393.0,231.0,135.0,71.0,120.0,84.0,127.0,,14,0.24824314777985515,16.103800496917273,0.2519230769230769,0.09060402684563758,0.06896551724137931,,0.12903225806451613,0.020356234096692113,0.0,0.0
+Mount Kato Ski Area,Minnesota,Minnesota,540,240,300,0,0,0,5,0,3,2,10,19.0,4.0,1.0,55.0,55.0,115.0,43.0,50.0,46.0,120.0,50.0,14,0.24824314777985515,16.103800496917273,0.035256410256410256,0.07718120805369127,0.13793103448275862,0.049019607843137254,0.5263157894736842,0.18181818181818182,0.0,0.0
+Powder Ridge Ski Area,Minnesota,Minnesota,790,300,500,0,0,0,1,0,2,3,6,15.0,4.0,,60.0,60.0,97.0,58.0,45.0,48.0,113.0,60.0,14,0.24824314777985515,16.103800496917273,0.038461538461538464,0.06510067114093959,0.13793103448275862,0.058823529411764705,0.4,0.1,0.0,0.0
+Spirit Mountain,Minnesota,Minnesota,1320,700,620,0,0,1,1,2,1,2,7,22.0,3.0,1.0,175.0,175.0,100.0,45.0,100.0,59.0,125.0,144.0,14,0.24824314777985515,16.103800496917273,0.11217948717948718,0.06711409395973154,0.10344827586206896,0.1411764705882353,0.3181818181818182,0.04,0.045454545454545456,0.005714285714285714
+Welch Village,Minnesota,Minnesota,1060,360,700,0,0,0,3,1,4,2,10,50.0,1.0,0.8,125.0,125.0,114.0,54.0,45.0,60.0,122.0,100.0,14,0.24824314777985515,16.103800496917273,0.08012820512820513,0.07651006711409396,0.034482758620689655,0.09803921568627451,0.2,0.08,0.0,0.0
+Wild Mountain Ski & Snowboard Area,Minnesota,Minnesota,1113,300,813,0,0,0,4,0,0,4,8,26.0,4.0,0.9,100.0,100.0,130.0,47.0,50.0,55.0,140.0,100.0,14,0.24824314777985515,16.103800496917273,0.0641025641025641,0.087248322147651,0.13793103448275862,0.09803921568627451,0.3076923076923077,0.08,0.0,0.0
+Hidden Valley Ski Area,Missouri,Missouri,2566,310,2316,0,0,0,2,2,0,3,7,17.0,,0.1,30.0,30.0,,37.0,26.0,49.0,,17.0,2,0.03258694032744661,2.8691523089503206,0.5,,,0.3617021276595745,0.4117647058823529,0.23333333333333334,0.0,0.0
+Snow Creek,Missouri,Missouri,1100,300,800,0,0,0,0,2,1,2,5,14.0,2.0,0.3,30.0,30.0,69.0,33.0,20.0,47.0,85.0,30.0,2,0.03258694032744661,2.8691523089503206,0.5,1.0,1.0,0.6382978723404256,0.35714285714285715,0.16666666666666666,0.0,0.0
+Blacktail Mountain Ski Area,Montana,Montana,6676,1440,5236,0,0,0,0,1,2,1,4,27.0,,0.7,1000.0,,,21.0,250.0,42.0,,,12,1.1227776020838753,8.161044613710555,0.046707146193367584,,,,0.14814814814814814,0.004,0.0,0.0
+Bridger Bowl,Montana,Montana,8700,2600,6100,0,0,0,1,6,1,3,11,105.0,2.0,1.5,2000.0,100.0,122.0,64.0,350.0,63.0,133.0,,12,1.1227776020838753,8.161044613710555,0.09341429238673517,0.12828601472134596,0.07407407407407407,,0.10476190476190476,0.0055,0.0,0.0
+Discovery Ski Area,Montana,Montana,8150,2380,5770,0,0,0,0,5,2,1,8,74.0,1.0,1.5,2400.0,25.0,116.0,46.0,225.0,49.0,116.0,,12,1.1227776020838753,8.161044613710555,0.1120971508640822,0.12197686645636173,0.037037037037037035,,0.10810810810810811,0.0033333333333333335,0.0,0.0
+Great Divide,Montana,Montana,7330,1580,5750,0,0,0,0,0,5,1,6,110.0,6.0,3.0,1600.0,150.0,94.0,78.0,180.0,48.0,100.0,100.0,12,1.1227776020838753,8.161044613710555,0.07473143390938813,0.09884332281808622,0.2222222222222222,0.14084507042253522,0.05454545454545454,0.00375,0.0,0.0
+Lost Trail - Powder Mtn,Montana,Montana,8200,1800,6400,0,0,0,0,0,5,3,8,69.0,2.0,2.5,1800.0,,84.0,81.0,325.0,46.0,80.0,,12,1.1227776020838753,8.161044613710555,0.08407286314806166,0.08832807570977919,0.07407407407407407,,0.11594202898550725,0.0044444444444444444,0.0,0.0
+Maverick Mountain,Montana,Montana,8520,2020,6500,0,0,0,0,0,1,1,2,22.0,,1.3,255.0,,,83.0,160.0,39.0,,,12,1.1227776020838753,8.161044613710555,0.011910322279308735,,,,0.09090909090909091,0.00784313725490196,0.0,0.0
+Montana Snowbowl,Montana,Montana,7600,2600,5000,0,0,0,0,0,2,2,4,37.0,,1.2,950.0,20.0,,58.0,300.0,50.0,,10.0,12,1.1227776020838753,8.161044613710555,0.044371788883699206,,,0.014084507042253521,0.10810810810810811,0.004210526315789474,0.0,0.0
+Red Lodge Mountain,Montana,Montana,9416,2400,7016,0,0,2,0,1,3,1,7,70.0,2.0,2.5,1635.0,496.0,142.0,59.0,250.0,67.0,136.0,,12,1.1227776020838753,8.161044613710555,0.076366184026156,0.14931650893796003,0.07407407407407407,,0.1,0.004281345565749235,0.02857142857142857,0.0012232415902140672
+Showdown Montana,Montana,Montana,8200,1400,6800,0,0,0,0,1,2,1,4,36.0,1.0,1.8,640.0,,86.0,83.0,250.0,47.0,85.0,,12,1.1227776020838753,8.161044613710555,0.029892573563755253,0.0904311251314406,0.037037037037037035,,0.1111111111111111,0.00625,0.0,0.0
+Teton Pass Ski Resort,Montana,Montana,7200,1010,6190,0,0,0,0,0,1,2,3,43.0,1.0,3.0,330.0,,40.0,54.0,250.0,39.0,150.0,,12,1.1227776020838753,8.161044613710555,0.015413358243811303,0.04206098843322818,0.037037037037037035,,0.06976744186046512,0.00909090909090909,0.0,0.0
+Big Mountain Resort,Montana,Montana,6817,2353,4464,0,0,3,2,6,0,3,14,105.0,4.0,3.3,3000.0,600.0,123.0,72.0,333.0,81.0,123.0,600.0,12,1.1227776020838753,8.161044613710555,0.14012143858010276,0.12933753943217666,0.14814814814814814,0.8450704225352113,0.13333333333333333,0.004666666666666667,0.02857142857142857,0.001
+Diamond Peak,Sierra Nevada,Nevada,8540,1840,6700,0,0,1,2,0,3,1,7,30.0,3.0,2.5,655.0,492.0,100.0,53.0,300.0,99.0,122.0,,4,0.12986355236552954,3.61755236407047,0.3104265402843602,0.24096385542168675,0.3333333333333333,,0.23333333333333334,0.010687022900763359,0.03333333333333333,0.0015267175572519084
+Elko SnoBowl,Nevada,Nevada,7000,700,6300,0,0,0,0,0,1,1,2,10.0,,1.0,60.0,2.0,19.0,23.0,24.0,20.0,30.0,,4,0.12986355236552954,3.61755236407047,0.02843601895734597,0.04578313253012048,,,0.2,0.03333333333333333,0.0,0.0
+Lee Canyon,Nevada,Nevada,11289,860,8510,0,0,0,2,1,0,0,3,24.0,1.0,0.3,195.0,50.0,144.0,56.0,161.0,70.0,150.0,,4,0.12986355236552954,3.61755236407047,0.0924170616113744,0.3469879518072289,0.1111111111111111,,0.125,0.015384615384615385,0.0,0.0
+Mt. Rose - Ski Tahoe,Sierra Nevada,Nevada,9700,1800,8260,0,2,0,2,2,0,2,8,65.0,5.0,2.5,1200.0,330.0,152.0,55.0,350.0,135.0,150.0,,4,0.12986355236552954,3.61755236407047,0.5687203791469194,0.36626506024096384,0.5555555555555556,,0.12307692307692308,0.006666666666666667,0.0,0.0
+Attitash,New Hampshire,New Hampshire,2350,1750,600,0,0,2,1,3,2,1,9,68.0,3.0,3.0,311.0,240.0,115.0,54.0,120.0,89.0,130.0,,16,1.1767206413715856,171.14129853460264,0.09074992704989787,0.062263129399025445,0.06976744186046512,,0.1323529411764706,0.028938906752411574,0.029411764705882353,0.006430868167202572
+Black Mountain,New Hampshire,New Hampshire,2350,1100,1250,0,0,0,0,1,1,3,5,45.0,,1.6,143.0,120.0,110.0,84.0,125.0,59.0,107.0,,16,1.1767206413715856,171.14129853460264,0.04172745841844178,0.05955603681645912,,,0.1111111111111111,0.03496503496503497,0.0,0.0
+Bretton Woods,New Hampshire,New Hampshire,3100,1500,1600,0,0,4,1,1,0,3,9,63.0,2.0,2.0,464.0,427.0,180.0,46.0,200.0,99.0,180.0,45.0,16,1.1767206413715856,171.14129853460264,0.13539538955354538,0.09745533297238766,0.046511627906976744,0.1196808510638298,0.14285714285714285,0.01939655172413793,0.06349206349206349,0.008620689655172414
+Cannon Mountain,New Hampshire,New Hampshire,4080,2180,1900,1,0,1,2,3,1,3,11,97.0,3.0,2.3,285.0,192.0,124.0,81.0,160.0,79.0,143.0,,16,1.1767206413715856,171.14129853460264,0.083163116428363,0.06713589604764483,0.06976744186046512,,0.1134020618556701,0.03859649122807018,0.010309278350515464,0.0035087719298245615
+Crotched Mountain,New Hampshire,New Hampshire,2066,1016,1050,0,0,1,1,1,1,1,5,25.0,3.0,1.2,100.0,100.0,105.0,16.0,105.0,69.0,100.0,100.0,16,1.1767206413715856,171.14129853460264,0.029180040852057193,0.0568489442338928,0.06976744186046512,0.26595744680851063,0.2,0.05,0.04,0.01
+Dartmouth Skiway,New Hampshire,New Hampshire,1943,969,974,0,0,0,1,0,1,2,4,28.0,1.0,1.1,107.0,54.0,104.0,63.0,100.0,50.0,105.0,,16,1.1767206413715856,171.14129853460264,0.031222643711701196,0.056307525717379535,0.023255813953488372,,0.14285714285714285,0.037383177570093455,0.0,0.0
+Gunstock,New Hampshire,New Hampshire,2300,1400,900,0,0,1,2,2,0,1,6,55.0,4.0,1.5,227.0,176.0,106.0,82.0,120.0,92.0,,60.0,16,1.1767206413715856,171.14129853460264,0.06623869273416982,0.057390362750406064,0.09302325581395349,0.1595744680851064,0.10909090909090909,0.02643171806167401,0.01818181818181818,0.004405286343612335
+King Pine,New Hampshire,New Hampshire,850,350,500,0,0,0,0,3,0,3,6,17.0,2.0,0.3,48.0,45.0,105.0,57.0,120.0,58.0,107.0,23.0,16,1.1767206413715856,171.14129853460264,0.014006419608987453,0.0568489442338928,0.046511627906976744,0.061170212765957445,0.35294117647058826,0.125,0.0,0.0
+Mount Sunapee,New Hampshire,New Hampshire,2743,1510,1233,0,0,2,1,2,1,4,10,66.0,4.0,0.8,232.0,215.0,130.0,71.0,100.0,93.0,136.0,,16,1.1767206413715856,171.14129853460264,0.06769769477677269,0.07038440714672442,0.09302325581395349,,0.15151515151515152,0.04310344827586207,0.030303030303030304,0.008620689655172414
+Pats Peak,New Hampshire,New Hampshire,1460,770,690,0,0,0,0,4,2,5,11,28.0,3.0,1.5,115.0,115.0,109.0,56.0,100.0,72.0,112.0,93.0,16,1.1767206413715856,171.14129853460264,0.03355704697986577,0.05901461829994586,0.06976744186046512,0.2473404255319149,0.39285714285714285,0.09565217391304348,0.0,0.0
+Ragged Mountain Resort,New Hampshire,New Hampshire,2250,1250,1000,0,1,1,0,1,0,3,6,57.0,3.0,0.7,250.0,200.0,,54.0,100.0,84.0,140.0,,16,1.1767206413715856,171.14129853460264,0.07295010213014298,,0.06976744186046512,,0.10526315789473684,0.024,0.017543859649122806,0.004
+Waterville Valley,New Hampshire,New Hampshire,4004,2020,1984,0,0,2,0,2,3,4,11,62.0,4.0,1.9,265.0,220.0,142.0,54.0,148.0,93.0,142.0,,16,1.1767206413715856,171.14129853460264,0.07732710825795155,0.0768814293448836,0.09302325581395349,,0.1774193548387097,0.04150943396226415,0.03225806451612903,0.007547169811320755
+Whaleback Mountain,New Hampshire,New Hampshire,1800,700,1100,0,0,0,0,0,1,3,4,30.0,1.0,1.0,85.0,60.0,105.0,64.0,110.0,45.0,105.0,55.0,16,1.1767206413715856,171.14129853460264,0.024803034724248614,0.0568489442338928,0.023255813953488372,0.14627659574468085,0.13333333333333333,0.047058823529411764,0.0,0.0
+Wildcat Mountain,New Hampshire,New Hampshire,4062,2112,1950,0,0,1,0,3,0,1,5,48.0,,2.8,225.0,200.0,156.0,61.0,200.0,89.0,150.0,,16,1.1767206413715856,171.14129853460264,0.06565509191712868,0.0844612885760693,,,0.10416666666666667,0.022222222222222223,0.020833333333333332,0.0044444444444444444
+Mountain Creek Resort,New Jersey,New Jersey,1480,1040,440,1,0,2,2,1,1,3,10,46.0,3.0,2.0,167.0,167.0,90.0,54.0,65.0,79.99,100.0,167.0,2,0.022516969351027167,22.927891780350798,0.8789473684210526,0.5294117647058824,0.75,0.9226519337016574,0.21739130434782608,0.059880239520958084,0.043478260869565216,0.011976047904191617
+Angel Fire Resort,New Mexico,New Mexico,10677,2077,8600,0,0,2,0,0,3,2,7,81.0,3.0,3.0,560.0,230.0,101.0,53.0,210.0,77.0,101.0,50.0,9,0.4292195500920676,7.401924500370097,0.10721807390388666,0.10455486542443064,0.16666666666666666,1.0,0.08641975308641975,0.0125,0.024691358024691357,0.0035714285714285713
+Enchanted Forest Ski Area,New Mexico,New Mexico,10078,400,9820,0,0,0,0,0,0,0,0,33.0,,2.5,600.0,,130.0,34.0,240.0,20.0,140.0,,9,0.4292195500920676,7.401924500370097,0.11487650775416428,0.13457556935817805,,,0.0,0.0,0.0,0.0
+Pajarito Mountain Ski Area,New Mexico,New Mexico,10441,1410,9031,0,0,0,1,1,3,1,6,45.0,2.0,0.6,750.0,35.0,89.0,62.0,163.0,49.0,117.0,,9,0.4292195500920676,7.401924500370097,0.14359563469270534,0.09213250517598344,0.1111111111111111,,0.13333333333333333,0.008,0.0,0.0
+Red River,New Mexico,New Mexico,10350,1600,8750,0,0,0,1,3,1,2,7,63.0,3.0,2.5,209.0,,110.0,60.0,214.0,79.0,,,9,0.4292195500920676,7.401924500370097,0.04001531686770055,0.11387163561076605,0.16666666666666666,,0.1111111111111111,0.03349282296650718,0.0,0.0
+Sandia Peak,New Mexico,New Mexico,10378,1700,8678,0,0,0,0,0,4,1,5,39.0,1.0,2.0,200.0,30.0,32.0,82.0,100.0,55.0,38.0,,9,0.4292195500920676,7.401924500370097,0.03829216925138809,0.033126293995859216,0.05555555555555555,,0.1282051282051282,0.025,0.0,0.0
+Sipapu Ski Resort,New Mexico,New Mexico,9255,1055,8200,0,0,0,1,2,0,3,6,42.0,4.0,0.5,200.0,140.0,127.0,67.0,190.0,47.0,143.0,,9,0.4292195500920676,7.401924500370097,0.03829216925138809,0.13146997929606624,0.2222222222222222,,0.14285714285714285,0.03,0.0,0.0
+Ski Apache,New Mexico,New Mexico,11500,1900,9600,1,0,0,2,6,0,2,11,55.0,3.0,2.0,750.0,270.0,133.0,58.0,185.0,74.0,124.0,,9,0.4292195500920676,7.401924500370097,0.14359563469270534,0.13768115942028986,0.16666666666666666,,0.2,0.014666666666666666,0.0,0.0
+Ski Santa Fe,New Mexico,New Mexico,12075,1725,10350,0,0,0,1,2,2,2,7,83.0,1.0,3.0,660.0,275.0,107.0,73.0,225.0,80.0,130.0,,9,0.4292195500920676,7.401924500370097,0.1263641585295807,0.11076604554865424,0.05555555555555555,,0.08433734939759036,0.010606060606060607,0.0,0.0
+Taos Ski Valley,New Mexico,New Mexico,12481,3281,9200,1,0,1,3,4,1,4,14,111.0,1.0,5.0,1294.0,647.0,137.0,64.0,300.0,110.0,136.0,,9,0.4292195500920676,7.401924500370097,0.24775033505648095,0.14182194616977226,0.05555555555555555,,0.12612612612612611,0.010819165378670788,0.009009009009009009,0.0007727975270479134
+Belleayre,New York,New York,3429,1404,2025,1,0,1,1,1,2,2,8,50.0,2.0,2.2,175.0,168.0,154.0,70.0,130.0,72.0,150.0,,33,0.16963475221837276,60.48941435248832,0.03173739571998549,0.06459731543624161,0.027777777777777776,,0.16,0.045714285714285714,0.02,0.005714285714285714
+Brantling Ski Slopes,New York,New York,850,250,600,0,0,0,0,0,0,5,5,10.0,,0.1,20.0,16.0,,19.0,110.0,32.0,,,33,0.16963475221837276,60.48941435248832,0.003627130939426913,,,,0.5,0.25,0.0,0.0
+Bristol Mountain,New York,New York,2200,1200,1000,0,0,2,1,1,1,1,6,34.0,3.0,2.0,160.0,148.0,129.0,55.0,60.0,76.0,129.0,154.0,33,0.16963475221837276,60.48941435248832,0.029017047515415305,0.054110738255033555,0.041666666666666664,0.054301833568406205,0.17647058823529413,0.0375,0.058823529411764705,0.0125
+Buffalo Ski Club Ski Area,New York,New York,3429,500,2025,0,0,0,0,0,2,4,6,43.0,1.0,,225.0,150.0,,12.0,,50.0,,100.0,33,0.16963475221837276,60.48941435248832,0.040805223068552776,,0.013888888888888888,0.03526093088857546,0.13953488372093023,0.02666666666666667,0.0,0.0
+Catamount,New York,New York,2000,1000,1000,0,0,0,1,1,2,3,7,36.0,5.0,2.0,133.0,130.0,100.0,80.0,108.0,69.0,90.0,55.0,33,0.16963475221837276,60.48941435248832,0.024120420747188974,0.04194630872483222,0.06944444444444445,0.019393511988716503,0.19444444444444445,0.05263157894736842,0.0,0.0
+Dry Hill Ski Area,New York,New York,950,300,650,0,0,0,0,0,1,2,3,7.0,1.0,0.2,35.0,26.0,,55.0,125.0,35.0,,26.0,33,0.16963475221837276,60.48941435248832,0.006347479143997099,,0.013888888888888888,0.009167842031029619,0.42857142857142855,0.08571428571428572,0.0,0.0
+Gore Mountain,New York,New York,3600,2537,998,1,0,2,2,3,2,4,14,110.0,7.0,4.5,439.0,338.0,142.0,55.0,150.0,88.0,,15.0,33,0.16963475221837276,60.48941435248832,0.07961552412042075,0.05956375838926174,0.09722222222222222,0.005289139633286318,0.12727272727272726,0.03189066059225513,0.01818181818181818,0.004555808656036446
+Greek Peak,New York,New York,2100,952,1148,0,0,0,1,1,4,2,8,56.0,4.0,1.5,220.0,184.0,110.0,62.0,122.0,63.2,113.0,175.0,33,0.16963475221837276,60.48941435248832,0.03989844033369604,0.04614093959731544,0.05555555555555555,0.06170662905500705,0.14285714285714285,0.03636363636363636,0.0,0.0
+Holiday Mountain,New York,New York,1550,400,1150,0,0,0,1,0,1,2,4,9.0,,0.4,37.0,37.0,75.0,60.0,50.0,42.0,85.0,37.0,33,0.16963475221837276,60.48941435248832,0.00671019223793979,0.031459731543624164,,0.01304654442877292,0.4444444444444444,0.10810810810810811,0.0,0.0
+Holiday Valley,New York,New York,2250,750,1500,0,0,3,8,0,0,2,13,60.0,5.0,1.0,290.0,266.0,116.0,62.0,180.0,78.0,129.0,189.0,33,0.16963475221837276,60.48941435248832,0.05259339862169024,0.04865771812080537,0.06944444444444445,0.06664315937940761,0.21666666666666667,0.04482758620689655,0.05,0.010344827586206896
+Holimont Ski Area,New York,New York,2260,700,1560,0,0,1,1,2,3,1,8,53.0,3.0,1.5,135.0,135.0,110.0,57.0,180.0,75.0,119.0,,33,0.16963475221837276,60.48941435248832,0.024483133841131665,0.04614093959731544,0.041666666666666664,,0.1509433962264151,0.05925925925925926,0.018867924528301886,0.007407407407407408
+Hunt Hollow Ski Club,New York,New York,2030,825,1000,0,0,0,0,1,1,1,3,19.0,1.0,1.0,400.0,400.0,,52.0,130.0,58.0,75.0,400.0,33,0.16963475221837276,60.48941435248832,0.07254261878853827,,0.013888888888888888,0.14104372355430184,0.15789473684210525,0.0075,0.0,0.0
+Hunter Mountain,New York,New York,3200,1600,1600,0,2,1,2,2,2,4,13,67.0,4.0,2.0,320.0,320.0,148.0,59.0,120.0,89.0,155.0,,33,0.16963475221837276,60.48941435248832,0.05803409503083061,0.06208053691275168,0.05555555555555555,,0.19402985074626866,0.040625,0.014925373134328358,0.003125
+Kissing Bridge,New York,New York,1700,550,1150,0,0,0,2,1,4,3,10,39.0,5.0,0.5,700.0,550.0,103.0,59.0,120.0,60.0,100.0,650.0,33,0.16963475221837276,60.48941435248832,0.12694958287994196,0.04320469798657718,0.06944444444444445,0.22919605077574048,0.2564102564102564,0.014285714285714285,0.0,0.0
+Labrador Mt.,New York,New York,1825,700,1125,0,0,0,0,1,2,1,4,23.0,1.0,1.0,250.0,237.0,,62.0,125.0,59.0,100.0,180.0,33,0.16963475221837276,60.48941435248832,0.04533913674283642,,0.013888888888888888,0.06346967559943582,0.17391304347826086,0.016,0.0,0.0
+Maple Ski Ridge,New York,New York,1200,450,750,0,0,0,0,1,1,1,3,10.0,,0.3,25.0,25.0,,57.0,,38.0,,20.0,33,0.16963475221837276,60.48941435248832,0.004533913674283642,,,0.007052186177715092,0.3,0.12,0.0,0.0
+McCauley Mountain Ski Center,New York,New York,2250,633,1563,0,0,0,0,0,1,4,5,23.0,1.0,0.3,70.0,55.0,105.0,61.0,200.0,30.0,105.0,,33,0.16963475221837276,60.48941435248832,0.012694958287994197,0.044043624161073824,0.013888888888888888,,0.21739130434782608,0.07142857142857142,0.0,0.0
+Mount Peter Ski Area,New York,New York,1250,450,750,0,0,0,1,0,2,2,5,14.0,1.0,1.0,69.0,69.0,100.0,83.0,50.0,54.0,100.0,69.0,33,0.16963475221837276,60.48941435248832,0.012513601741022852,0.04194630872483222,0.013888888888888888,0.024330042313117067,0.35714285714285715,0.07246376811594203,0.0,0.0
+Oak Mountain,New York,New York,2400,650,1750,0,0,0,1,0,0,3,4,22.0,1.0,1.2,46.0,18.0,,71.0,120.0,40.0,,12.0,33,0.16963475221837276,60.48941435248832,0.008342401160681901,,0.013888888888888888,0.004231311706629055,0.18181818181818182,0.08695652173913043,0.0,0.0
+Peek'n Peak,New York,New York,1800,400,1400,0,0,0,0,8,0,2,10,27.0,4.0,2.4,110.0,110.0,110.0,55.0,225.0,63.0,,110.0,33,0.16963475221837276,60.48941435248832,0.01994922016684802,0.04614093959731544,0.05555555555555555,0.038787023977433006,0.37037037037037035,0.09090909090909091,0.0,0.0
+Plattekill Mountain,New York,New York,3500,1100,2400,0,0,0,0,1,1,2,4,38.0,1.0,2.0,110.0,75.0,65.0,26.0,175.0,67.0,65.0,,33,0.16963475221837276,60.48941435248832,0.01994922016684802,0.02726510067114094,0.013888888888888888,,0.10526315789473684,0.03636363636363636,0.0,0.0
+Royal Mountain Ski Area,New York,New York,1800,550,1250,0,0,0,0,0,3,0,3,14.0,,0.3,35.0,28.0,,63.0,90.0,45.0,,,33,0.16963475221837276,60.48941435248832,0.006347479143997099,,,,0.21428571428571427,0.08571428571428572,0.0,0.0
+Snow Ridge,New York,New York,2000,650,1350,0,0,0,0,0,4,2,6,21.0,2.0,0.8,130.0,65.0,73.0,74.0,230.0,48.0,100.0,40.0,33,0.16963475221837276,60.48941435248832,0.023576351106274936,0.030620805369127518,0.027777777777777776,0.014104372355430184,0.2857142857142857,0.046153846153846156,0.0,0.0
+Song Mountain,New York,New York,1940,700,1240,0,0,0,0,1,1,3,5,24.0,,0.4,93.0,70.0,90.0,55.0,125.0,59.0,122.0,70.0,33,0.16963475221837276,60.48941435248832,0.016866158868335146,0.037751677852348994,,0.02468265162200282,0.20833333333333334,0.053763440860215055,0.0,0.0
+Swain,New York,New York,1970,650,1320,0,0,0,3,0,1,1,5,35.0,3.0,1.0,130.0,90.0,102.0,72.0,120.0,59.0,100.0,80.0,33,0.16963475221837276,60.48941435248832,0.023576351106274936,0.04278523489932886,0.041666666666666664,0.028208744710860368,0.14285714285714285,0.038461538461538464,0.0,0.0
+Thunder Ridge,New York,New York,1270,500,770,0,0,0,0,1,2,3,6,30.0,,0.4,100.0,100.0,121.0,60.0,,57.0,121.0,100.0,33,0.16963475221837276,60.48941435248832,0.018135654697134566,0.05075503355704698,,0.03526093088857546,0.2,0.06,0.0,0.0
+Titus Mountain,New York,New York,2025,1200,825,0,0,0,0,2,6,2,10,50.0,3.0,2.0,200.0,150.0,101.0,59.0,150.0,49.0,100.0,70.0,33,0.16963475221837276,60.48941435248832,0.03627130939426913,0.04236577181208054,0.041666666666666664,0.02468265162200282,0.2,0.05,0.0,0.0
+Toggenburg Mountain,New York,New York,2000,700,1300,0,0,0,0,1,1,3,5,22.0,2.0,0.4,85.0,,,66.0,130.0,55.0,122.0,73.0,33,0.16963475221837276,60.48941435248832,0.015415306492564382,,0.027777777777777776,0.025740479548660086,0.22727272727272727,0.058823529411764705,0.0,0.0
+West Mountain,New York,New York,1470,1010,460,0,0,0,0,1,2,2,5,29.0,1.0,0.6,124.0,105.0,,58.0,80.0,59.0,120.0,105.0,33,0.16963475221837276,60.48941435248832,0.022488211824446862,,0.013888888888888888,0.03702397743300423,0.1724137931034483,0.04032258064516129,0.0,0.0
+Whiteface Mountain Resort,New York,New York,4650,3430,1220,1,0,1,1,2,5,2,12,86.0,5.0,2.1,288.0,220.0,122.0,61.0,168.0,96.0,141.0,,33,0.16963475221837276,60.48941435248832,0.05223068552774755,0.051174496644295304,0.06944444444444445,,0.13953488372093023,0.041666666666666664,0.011627906976744186,0.003472222222222222
+Willard Mountain,New York,New York,1415,505,910,0,0,0,0,0,2,3,5,16.0,,0.4,50.0,35.0,85.0,19.0,80.0,46.0,120.0,35.0,33,0.16963475221837276,60.48941435248832,0.009067827348567283,0.03565436241610738,,0.01234132581100141,0.3125,0.1,0.0,0.0
+Windham Mountain,New York,New York,3100,1600,1500,0,1,2,0,3,1,5,12,54.0,6.0,2.0,285.0,280.0,123.0,59.0,105.0,95.0,130.0,56.0,33,0.16963475221837276,60.48941435248832,0.051686615886833515,0.051593959731543626,0.08333333333333333,0.019746121297602257,0.2222222222222222,0.042105263157894736,0.037037037037037035,0.007017543859649123
+Woods Valley Ski Area,New York,New York,1400,500,900,0,0,0,0,0,2,4,6,21.0,,0.3,25.0,16.0,,55.0,180.0,39.0,,15.0,33,0.16963475221837276,60.48941435248832,0.004533913674283642,,,0.005289139633286318,0.2857142857142857,0.24,0.0,0.0
+Appalachian Ski Mountain,North Carolina,North Carolina,4000,365,3635,0,0,0,2,0,1,2,5,12.0,3.0,0.5,27.0,27.0,100.0,57.0,50.0,64.0,100.0,27.0,6,0.057207779800390615,11.148479161634366,0.07297297297297298,0.1976284584980237,0.3333333333333333,0.08059701492537313,0.4166666666666667,0.18518518518518517,0.0,0.0
+Cataloochee Ski Area,North Carolina,North Carolina,5400,740,4660,0,0,0,1,1,1,2,5,18.0,2.0,1.0,50.0,50.0,141.0,58.0,50.0,70.0,108.0,50.0,6,0.057207779800390615,11.148479161634366,0.13513513513513514,0.27865612648221344,0.2222222222222222,0.14925373134328357,0.2777777777777778,0.1,0.0,0.0
+Sapphire Valley,North Carolina,North Carolina,3450,200,3200,0,0,0,1,0,0,2,3,,1.0,1.0,8.0,8.0,53.0,55.0,24.0,43.0,60.0,8.0,6,0.057207779800390615,11.148479161634366,0.021621621621621623,0.10474308300395258,0.1111111111111111,0.023880597014925373,,0.375,,0.0
+Beech Mountain Resort,North Carolina,North Carolina,5506,830,4675,0,0,0,3,0,3,2,8,17.0,1.0,1.0,95.0,95.0,98.0,52.0,31.0,68.0,,95.0,6,0.057207779800390615,11.148479161634366,0.25675675675675674,0.19367588932806323,0.1111111111111111,0.2835820895522388,0.47058823529411764,0.08421052631578947,0.0,0.0
+Sugar Mountain Resort,North Carolina,North Carolina,5300,1200,4100,0,1,0,0,1,4,2,8,21.0,1.0,1.5,125.0,125.0,114.0,50.0,77.0,75.0,120.0,95.0,6,0.057207779800390615,11.148479161634366,0.33783783783783783,0.22529644268774704,0.1111111111111111,0.2835820895522388,0.38095238095238093,0.064,0.0,0.0
+Wolf Ridge Ski Resort,North Carolina,North Carolina,4700,720,4000,0,0,0,1,0,1,2,4,15.0,1.0,0.6,65.0,65.0,,49.0,65.0,65.0,100.0,60.0,6,0.057207779800390615,11.148479161634366,0.17567567567567569,,0.1111111111111111,0.1791044776119403,0.26666666666666666,0.06153846153846154,0.0,0.0
+Alpine Valley,Ohio,Ohio,1500,230,1260,0,0,0,1,2,1,1,5,11.0,1.0,0.2,72.0,72.0,105.0,53.0,120.0,43.0,,72.0,5,0.042774892848893416,11.154240842368269,0.171021377672209,0.2147239263803681,0.08333333333333333,0.171021377672209,0.45454545454545453,0.06944444444444445,0.0,0.0
+Boston Mills,Ohio,Ohio,871,264,631,0,0,0,0,4,2,2,8,7.0,2.0,0.3,40.0,40.0,92.0,56.0,51.0,44.0,110.0,40.0,5,0.042774892848893416,11.154240842368269,0.09501187648456057,0.18813905930470348,0.16666666666666666,0.09501187648456057,1.1428571428571428,0.2,0.0,0.0
+Brandywine,Ohio,Ohio,871,240,631,0,0,0,2,7,2,5,16,11.0,2.0,0.3,85.0,85.0,92.0,56.0,51.0,44.0,110.0,85.0,5,0.042774892848893416,11.154240842368269,0.20190023752969122,0.18813905930470348,0.16666666666666666,0.20190023752969122,1.4545454545454546,0.18823529411764706,0.0,0.0
+Mad River Mountain,Ohio,Ohio,1460,300,1160,0,0,0,1,2,3,6,12,20.0,4.0,0.5,144.0,144.0,99.0,57.0,36.0,44.0,90.0,144.0,5,0.042774892848893416,11.154240842368269,0.342042755344418,0.20245398773006135,0.3333333333333333,0.342042755344418,0.6,0.08333333333333333,0.0,0.0
+Snow Trails,Ohio,Ohio,1475,301,1174,0,0,0,0,4,2,3,9,17.0,3.0,0.2,80.0,80.0,101.0,58.0,50.0,52.0,70.0,80.0,5,0.042774892848893416,11.154240842368269,0.19002375296912113,0.2065439672801636,0.25,0.19002375296912113,0.5294117647058824,0.1125,0.0,0.0
+Anthony Lakes Mountain Resort,Oregon,Oregon,8000,900,7100,0,0,0,0,1,0,2,3,21.0,2.0,1.5,1100.0,,75.0,56.0,300.0,40.0,80.0,,10,0.23709396768930827,10.164770936886937,0.09342619330728724,0.0635593220338983,0.09090909090909091,,0.14285714285714285,0.0027272727272727275,0.0,0.0
+Cooper Spur,Mt. Hood,Oregon,4000,350,3500,0,0,0,0,0,1,1,2,10.0,,0.1,50.0,,78.0,66.0,100.0,39.0,90.0,,10,0.23709396768930827,10.164770936886937,0.004246645150331238,0.06610169491525424,,,0.2,0.04,0.0,0.0
+Hoodoo Ski Area,Oregon,Oregon,5703,1035,4668,0,0,0,3,1,1,0,5,34.0,,0.4,806.0,,80.0,81.0,350.0,59.0,108.0,200.0,10,0.23709396768930827,10.164770936886937,0.06845591982333957,0.06779661016949153,,0.1774622892635315,0.14705882352941177,0.00620347394540943,0.0,0.0
+Mt. Ashland,Oregon,Oregon,7533,1150,6383,0,0,0,0,2,2,1,5,23.0,2.0,1.0,220.0,,94.0,55.0,300.0,52.0,92.0,40.0,10,0.23709396768930827,10.164770936886937,0.018685238661457448,0.07966101694915254,0.09090909090909091,0.0354924578527063,0.21739130434782608,0.022727272727272728,0.0,0.0
+Mt. Bachelor,Oregon,Oregon,9065,3365,5700,0,0,8,0,3,0,0,11,101.0,5.0,4.0,4318.0,20.0,185.0,61.0,462.0,99.0,185.0,,10,0.23709396768930827,10.164770936886937,0.36674027518260577,0.15677966101694915,0.22727272727272727,,0.10891089108910891,0.0025474756831866605,0.07920792079207921,0.0018527095877721167
+Mt. Hood Skibowl,Mt. Hood,Oregon,5100,1500,3600,0,0,0,0,0,4,5,9,65.0,2.0,3.0,960.0,29.0,125.0,82.0,300.0,70.0,144.0,317.0,10,0.23709396768930827,10.164770936886937,0.08153558688635977,0.1059322033898305,0.09090909090909091,0.28127772848269744,0.13846153846153847,0.009375,0.0,0.0
+Willamette Pass,Oregon,Oregon,6683,1563,5120,0,1,0,0,3,0,1,5,29.0,,2.1,555.0,60.0,3.0,78.0,430.0,60.0,100.0,,10,0.23709396768930827,10.164770936886937,0.047137761168676746,0.002542372881355932,,,0.1724137931034483,0.009009009009009009,0.0,0.0
+Bear Creek Mountain Resort,Pennsylvania,Pennsylvania,1100,510,600,0,0,0,3,1,0,2,6,23.0,3.0,1.0,86.0,86.0,91.0,52.0,30.0,60.0,90.0,86.0,19,0.14841443778775315,41.255916967038694,0.045550847457627115,0.06481481481481481,0.06382978723404255,0.056282722513089,0.2608695652173913,0.06976744186046512,0.0,0.0
+Ski Big Bear,Pennsylvania,Pennsylvania,1250,650,600,0,0,0,0,0,4,2,6,18.0,1.0,1.5,26.0,26.0,75.0,43.0,69.0,62.0,75.0,26.0,19,0.14841443778775315,41.255916967038694,0.013771186440677966,0.053418803418803416,0.02127659574468085,0.017015706806282723,0.3333333333333333,0.23076923076923078,0.0,0.0
+Big Boulder,Pennsylvania,Pennsylvania,2175,600,1700,0,0,0,0,2,5,1,8,16.0,8.0,,55.0,55.0,76.0,72.0,50.0,65.0,95.0,55.0,19,0.14841443778775315,41.255916967038694,0.02913135593220339,0.05413105413105413,0.1702127659574468,0.03599476439790576,0.5,0.14545454545454545,0.0,0.0
+Blue Knob,Pennsylvania,Pennsylvania,3146,1072,2074,0,0,0,0,2,2,2,6,34.0,1.0,2.0,100.0,84.0,87.0,56.0,120.0,68.0,105.0,42.0,19,0.14841443778775315,41.255916967038694,0.05296610169491525,0.06196581196581197,0.02127659574468085,0.0274869109947644,0.17647058823529413,0.06,0.0,0.0
+Blue Mountain Resort,Pennsylvania,Pennsylvania,1600,1082,460,0,1,1,1,1,3,9,16,39.0,5.0,1.2,164.0,164.0,122.0,42.0,33.0,65.0,112.0,164.0,19,0.14841443778775315,41.255916967038694,0.08686440677966102,0.0868945868945869,0.10638297872340426,0.10732984293193717,0.41025641025641024,0.0975609756097561,0.02564102564102564,0.006097560975609756
+Camelback Mountain Resort,Pennsylvania,Pennsylvania,2100,800,1250,0,0,2,0,3,5,6,16,37.0,5.0,1.0,166.0,166.0,100.0,56.0,50.0,70.0,100.0,160.0,19,0.14841443778775315,41.255916967038694,0.08792372881355932,0.07122507122507123,0.10638297872340426,0.10471204188481675,0.43243243243243246,0.0963855421686747,0.05405405405405406,0.012048192771084338
+Elk Mountain Ski Resort,Pennsylvania,Pennsylvania,2693,1000,1693,0,0,0,1,0,5,1,7,27.0,2.0,0.7,180.0,146.0,,60.0,60.0,69.0,100.0,90.0,19,0.14841443778775315,41.255916967038694,0.09533898305084745,,0.0425531914893617,0.058900523560209424,0.25925925925925924,0.03888888888888889,0.0,0.0
+Jack Frost,Pennsylvania,Pennsylvania,2000,600,1400,0,0,0,1,2,5,1,9,20.0,1.0,1.0,100.0,100.0,96.0,47.0,50.0,65.0,105.0,,19,0.14841443778775315,41.255916967038694,0.05296610169491525,0.06837606837606838,0.02127659574468085,,0.45,0.09,0.0,0.0
+Liberty,Pennsylvania,Pennsylvania,1190,620,570,0,0,0,5,0,0,3,8,16.0,3.0,1.0,100.0,100.0,107.0,54.0,31.0,77.0,97.0,100.0,19,0.14841443778775315,41.255916967038694,0.05296610169491525,0.07621082621082621,0.06382978723404255,0.06544502617801047,0.5,0.08,0.0,0.0
+Mount Pleasant of Edinboro,Pennsylvania,Pennsylvania,1540,340,1200,0,0,0,0,1,0,1,2,10.0,,0.5,40.0,35.0,75.0,48.0,100.0,33.0,90.0,35.0,19,0.14841443778775315,41.255916967038694,0.0211864406779661,0.053418803418803416,,0.022905759162303665,0.2,0.05,0.0,0.0
+Roundtop Mountain Resort,Pennsylvania,Pennsylvania,1400,600,800,0,0,0,3,2,0,3,8,20.0,2.0,0.4,103.0,103.0,,55.0,30.0,73.0,,100.0,19,0.14841443778775315,41.255916967038694,0.05455508474576271,,0.0425531914893617,0.06544502617801047,0.4,0.07766990291262135,0.0,0.0
+Seven Springs,Pennsylvania,Pennsylvania,2994,750,2240,0,2,0,3,5,0,4,14,33.0,7.0,1.2,285.0,285.0,99.0,87.0,135.0,87.0,115.0,200.0,19,0.14841443778775315,41.255916967038694,0.15095338983050846,0.07051282051282051,0.14893617021276595,0.13089005235602094,0.42424242424242425,0.04912280701754386,0.0,0.0
+Shawnee Mountain Ski Area,Pennsylvania,Pennsylvania,1350,700,650,0,0,1,1,0,4,4,10,23.0,2.0,1.6,125.0,125.0,100.0,44.0,50.0,65.0,122.0,120.0,19,0.14841443778775315,41.255916967038694,0.06620762711864407,0.07122507122507123,0.0425531914893617,0.07853403141361257,0.43478260869565216,0.08,0.043478260869565216,0.008
+Ski Sawmill,Pennsylvania,Pennsylvania,2215,515,1700,0,0,0,0,1,1,3,5,14.0,1.0,0.1,15.0,13.0,,50.0,24.0,44.0,90.0,15.0,19,0.14841443778775315,41.255916967038694,0.007944915254237288,,0.02127659574468085,0.00981675392670157,0.35714285714285715,0.3333333333333333,0.0,0.0
+Tussey Mountain,Pennsylvania,Pennsylvania,1750,520,1230,0,0,0,1,0,1,3,5,8.0,1.0,0.3,38.0,30.0,100.0,39.0,41.0,45.0,100.0,30.0,19,0.14841443778775315,41.255916967038694,0.020127118644067795,0.07122507122507123,0.02127659574468085,0.01963350785340314,0.625,0.13157894736842105,0.0,0.0
+Whitetail Resort,Pennsylvania,Pennsylvania,1800,935,865,0,0,1,3,0,2,2,8,23.0,2.0,1.0,120.0,120.0,116.0,28.0,40.0,71.0,100.0,120.0,19,0.14841443778775315,41.255916967038694,0.0635593220338983,0.08262108262108261,0.0425531914893617,0.07853403141361257,0.34782608695652173,0.06666666666666667,0.043478260869565216,0.008333333333333333
+Deer Mountain Ski Resort,South Dakota,South Dakota,6850,940,6040,0,0,0,0,1,1,2,4,63.0,2.0,1.6,500.0,50.0,69.0,51.0,200.0,45.0,81.0,,2,0.22607581000136776,2.593495513252762,0.5263157894736842,0.3770491803278688,0.6666666666666666,,0.06349206349206349,0.008,0.0,0.0
+Terry Peak Ski Area,South Dakota,South Dakota,7100,1100,5900,0,0,3,0,1,0,1,5,30.0,1.0,1.2,450.0,225.0,114.0,65.0,150.0,58.0,120.0,,2,0.22607581000136776,2.593495513252762,0.47368421052631576,0.6229508196721312,0.3333333333333333,,0.16666666666666666,0.011111111111111112,0.1,0.006666666666666667
+Ober Gatlinburg Ski Resort,Tennessee,Tennessee,3300,600,2700,0,0,0,2,0,1,1,4,10.0,1.0,1.0,,,83.0,44.0,35.0,65.0,94.0,,1,0.014643059321669063,2.3728170083523157,,1.0,1.0,,0.4,,0.0,
+Alta Ski Area,Salt Lake City,Utah,11068,2538,8530,0,0,3,0,1,2,0,6,116.0,,1.3,2614.0,140.0,150.0,81.0,545.0,116.0,140.0,,13,0.4054950189615709,15.312673003757494,0.08568244394912809,0.09715025906735751,,,0.05172413793103448,0.0022953328232593728,0.02586206896551724,0.0011476664116296864
+Beaver Mountain,Utah,Utah,8600,1600,7232,0,0,0,0,1,3,1,5,48.0,2.0,0.8,464.0,,120.0,81.0,400.0,50.0,120.0,,13,0.4054950189615709,15.312673003757494,0.015209125475285171,0.07772020725388601,0.07692307692307693,,0.10416666666666667,0.010775862068965518,0.0,0.0
+Brian Head Resort,Utah,Utah,10970,1548,9600,0,0,1,0,6,1,0,8,71.0,2.0,0.6,650.0,216.0,149.0,54.0,360.0,59.0,148.0,,13,0.4054950189615709,15.312673003757494,0.02130588698046414,0.09650259067357513,0.07692307692307693,,0.11267605633802817,0.012307692307692308,0.014084507042253521,0.0015384615384615385
+Brighton Resort,Salt Lake City,Utah,10500,1745,8755,0,0,3,1,1,0,2,7,66.0,4.0,1.2,1050.0,200.0,138.0,83.0,500.0,85.0,138.0,200.0,13,0.4054950189615709,15.312673003757494,0.03441720204536515,0.08937823834196891,0.15384615384615385,0.3115264797507788,0.10606060606060606,0.006666666666666667,0.045454545454545456,0.002857142857142857
+Deer Valley Resort,Salt Lake City,Utah,9570,3000,6570,1,0,13,0,5,2,0,21,103.0,,2.8,2026.0,660.0,,39.0,300.0,169.0,,,13,0.4054950189615709,15.312673003757494,0.06640881080372361,,,,0.20388349514563106,0.010365251727541954,0.1262135922330097,0.006416584402764067
+Eagle Point,Utah,Utah,10600,1500,9100,0,0,0,1,1,2,1,5,40.0,1.0,0.9,650.0,,,9.0,400.0,60.0,109.0,,13,0.4054950189615709,15.312673003757494,0.02130588698046414,,0.038461538461538464,,0.125,0.007692307692307693,0.0,0.0
+Powder Mountain,Utah,Utah,9422,2522,6900,0,0,1,4,1,0,3,9,167.0,2.0,3.5,8464.0,,120.0,47.0,500.0,88.0,146.0,300.0,13,0.4054950189615709,15.312673003757494,0.27743542677330535,0.07772020725388601,0.07692307692307693,0.4672897196261682,0.05389221556886228,0.0010633270321361058,0.005988023952095809,0.00011814744801512288
+Snowbasin,Utah,Utah,9350,2900,6450,3,1,2,0,3,0,2,11,107.0,4.0,3.5,3000.0,625.0,143.0,79.0,300.0,115.0,138.0,,13,0.4054950189615709,15.312673003757494,0.09833486298675757,0.09261658031088082,0.15384615384615385,,0.102803738317757,0.0036666666666666666,0.018691588785046728,0.0006666666666666666
+Snowbird,Salt Lake City,Utah,11000,3240,7760,1,0,6,0,0,4,3,14,170.0,1.0,2.5,2500.0,,188.0,48.0,500.0,125.0,180.0,2.0,13,0.4054950189615709,15.312673003757494,0.08194571915563131,0.12176165803108809,0.038461538461538464,0.003115264797507788,0.08235294117647059,0.0056,0.03529411764705882,0.0024
+Solitude Mountain Resort,Salt Lake City,Utah,10488,2494,7994,0,0,4,2,1,1,1,9,80.0,,3.0,1200.0,150.0,161.0,62.0,500.0,119.0,148.0,,13,0.4054950189615709,15.312673003757494,0.03933394519470303,0.10427461139896373,,,0.1125,0.0075,0.05,0.0033333333333333335
+Sundance,Utah,Utah,8250,2150,6100,0,0,0,2,2,0,1,5,45.0,1.0,0.6,450.0,112.0,128.0,50.0,320.0,80.0,129.0,,13,0.4054950189615709,15.312673003757494,0.014750229448013635,0.08290155440414508,0.038461538461538464,,0.1111111111111111,0.011111111111111112,0.0,0.0
+Nordic Valley Resort,Utah,Utah,6400,960,5440,0,0,0,0,0,2,2,4,23.0,1.0,0.4,140.0,84.0,105.0,51.0,300.0,50.0,105.0,140.0,13,0.4054950189615709,15.312673003757494,0.004588960272715353,0.06800518134715026,0.038461538461538464,0.21806853582554517,0.17391304347826086,0.02857142857142857,0.0,0.0
+Bolton Valley,Vermont,Vermont,3150,1704,1446,0,0,0,2,0,3,1,6,71.0,3.0,0.6,300.0,90.0,133.0,53.0,300.0,79.0,132.0,50.0,15,2.4038885300862676,155.99001663893512,0.04144218814753419,0.07484524479459764,0.06,1.0,0.08450704225352113,0.02,0.0,0.0
+Bromley Mountain,Vermont,Vermont,3284,1334,1950,0,0,1,1,0,4,3,9,47.0,1.0,2.5,178.0,153.0,,83.0,168.0,91.0,152.0,,15,2.4038885300862676,155.99001663893512,0.02458903163420362,,0.02,,0.19148936170212766,0.05056179775280899,0.02127659574468085,0.0056179775280898875
+Burke Mountain,Vermont,Vermont,3267,2011,1210,0,0,2,1,0,0,3,6,50.0,3.0,2.2,178.0,125.0,110.0,62.0,217.0,73.0,125.0,,15,2.4038885300862676,155.99001663893512,0.02458903163420362,0.061902082160945414,0.06,,0.12,0.033707865168539325,0.04,0.011235955056179775
+Jay Peak,Vermont,Vermont,3968,2153,1815,1,0,1,3,1,1,2,9,79.0,2.0,3.0,385.0,300.0,155.0,64.0,349.0,89.0,160.0,,15,2.4038885300862676,155.99001663893512,0.05318414145600221,0.08722566122678672,0.04,,0.11392405063291139,0.023376623376623377,0.012658227848101266,0.0025974025974025974
+Killington Resort,Vermont,Vermont,4241,3050,1165,3,1,5,4,3,1,5,22,155.0,6.0,6.0,1515.0,600.0,192.0,61.0,250.0,119.0,,,15,2.4038885300862676,155.99001663893512,0.20928305014504767,0.1080472706809229,0.12,,0.14193548387096774,0.014521452145214522,0.03225806451612903,0.0033003300330033004
+Magic Mountain,Vermont,Vermont,2850,1500,1350,0,0,0,0,0,3,3,6,50.0,1.0,1.6,205.0,95.0,76.0,59.0,150.0,74.0,80.0,,15,2.4038885300862676,155.99001663893512,0.028318828567481698,0.04276871131119865,0.02,,0.12,0.02926829268292683,0.0,0.0
+Pico Mountain,Vermont,Vermont,3967,1967,2000,0,0,2,0,2,2,1,7,59.0,1.0,4.0,260.0,156.0,,82.0,250.0,81.0,,,15,2.4038885300862676,155.99001663893512,0.0359165630611963,,0.02,,0.11864406779661017,0.026923076923076925,0.03389830508474576,0.007692307692307693
+Smugglers' Notch Resort,Vermont,Vermont,3640,2610,1030,0,0,0,0,0,6,2,8,78.0,6.0,3.0,1000.0,192.0,136.0,63.0,280.0,79.0,135.0,,15,2.4038885300862676,155.99001663893512,0.1381406271584473,0.07653348339898705,0.12,,0.10256410256410256,0.008,0.0,0.0
+Sugarbush,Vermont,Vermont,4083,2600,1483,0,0,5,5,2,1,3,16,111.0,4.0,3.0,581.0,406.0,150.0,61.0,250.0,119.0,156.0,,15,2.4038885300862676,155.99001663893512,0.08025970437905788,0.08441193021947102,0.08,,0.14414414414414414,0.027538726333907058,0.04504504504504504,0.008605851979345954
+Suicide Six,Vermont,Vermont,1200,650,550,0,0,0,0,0,2,1,3,24.0,,0.4,100.0,50.0,100.0,85.0,90.0,75.0,106.0,,15,2.4038885300862676,155.99001663893512,0.01381406271584473,0.056274620146314014,,,0.125,0.03,0.0,0.0
+Bryce Resort,Virginia,Virginia,1750,500,1250,0,0,0,1,0,1,5,7,8.0,,0.4,25.0,25.0,100.0,54.0,30.0,68.0,95.0,20.0,4,0.04686299684881493,9.35125657510228,0.09293680297397769,0.273224043715847,,0.14814814814814814,0.875,0.28,0.0,0.0
+49 Degrees North,Washington,Washington,5774,1851,3932,0,0,0,1,0,5,1,7,89.0,1.0,2.0,2325.0,,101.0,48.0,301.0,62.0,135.0,250.0,10,0.13132160885254723,14.02563886785043,0.15166340508806261,0.09882583170254403,0.047619047619047616,0.12518778167250877,0.07865168539325842,0.003010752688172043,0.0,0.0
+Bluewood,Washington,Washington,5670,1125,4545,0,0,0,0,2,0,1,3,24.0,2.0,2.0,355.0,,70.0,40.0,300.0,47.0,110.0,,10,0.13132160885254723,14.02563886785043,0.023157208088714937,0.0684931506849315,0.09523809523809523,,0.125,0.008450704225352112,0.0,0.0
+Crystal Mountain,Washington,Washington,7012,3100,4400,1,2,2,1,2,2,0,10,57.0,1.0,2.5,2600.0,10.0,,57.0,486.0,99.0,,,10,0.13132160885254723,14.02563886785043,0.16960208741030658,,0.047619047619047616,,0.17543859649122806,0.0038461538461538464,0.03508771929824561,0.0007692307692307692
+Mt. Baker,Washington,Washington,5000,1500,3500,0,0,0,8,0,0,2,10,38.0,,0.7,1000.0,,143.0,66.0,663.0,60.01,165.0,,10,0.13132160885254723,14.02563886785043,0.06523157208088715,0.13992172211350293,,,0.2631578947368421,0.01,0.0,0.0
+Mt. Spokane Ski and Snowboard Park,Washington,Washington,5889,2000,4200,0,0,0,0,1,5,1,7,52.0,3.0,0.6,1704.0,,100.0,81.0,300.0,59.0,103.0,45.0,10,0.13132160885254723,14.02563886785043,0.1111545988258317,0.09784735812133072,0.14285714285714285,0.022533800701051578,0.1346153846153846,0.004107981220657277,0.0,0.0
+The Summit at Snoqualmie,Washington,Washington,3865,1025,2840,0,0,3,3,4,10,7,27,112.0,5.0,0.8,1994.0,5.0,120.0,82.0,428.0,95.0,140.0,541.0,10,0.13132160885254723,14.02563886785043,0.13007175472928897,0.11741682974559686,0.23809523809523808,0.27090635953930897,0.24107142857142858,0.01354062186559679,0.026785714285714284,0.0015045135406218655
+White Pass,Washington,Washington,6550,2050,4500,0,0,2,1,1,2,2,8,45.0,2.0,2.5,1402.0,30.0,148.0,67.0,400.0,69.0,144.0,90.0,10,0.13132160885254723,14.02563886785043,0.09145466405740378,0.14481409001956946,0.09523809523809523,0.045067601402103155,0.17777777777777778,0.005706134094151213,0.044444444444444446,0.0014265335235378032
+Canaan Valley Resort,West Virginia,West Virginia,4280,850,3430,0,0,0,1,2,0,1,4,47.0,1.0,1.2,95.0,75.0,,48.0,160.0,68.0,93.0,,4,0.22319597666932456,16.50846058605035,0.1752767527675277,,0.1111111111111111,,0.0851063829787234,0.042105263157894736,0.0,0.0
+Snowshoe Mountain Resort,West Virginia,West Virginia,4848,1500,3348,0,0,3,2,6,0,3,14,60.0,5.0,1.5,257.0,257.0,125.0,46.0,180.0,87.0,138.0,86.0,4,0.22319597666932456,16.50846058605035,0.474169741697417,0.3654970760233918,0.5555555555555556,0.45989304812834225,0.23333333333333334,0.054474708171206226,0.05,0.011673151750972763
+Timberline Four Seasons,West Virginia,West Virginia,4265,1000,3268,0,0,0,0,2,1,1,4,40.0,1.0,2.0,100.0,100.0,97.0,37.0,150.0,92.0,115.0,27.0,4,0.22319597666932456,16.50846058605035,0.18450184501845018,0.28362573099415206,0.1111111111111111,0.1443850267379679,0.1,0.04,0.0,0.0
+Winterplace Ski Resort,West Virginia,West Virginia,3600,603,2997,0,0,0,2,3,2,2,9,27.0,2.0,1.2,90.0,90.0,120.0,36.0,100.0,72.0,120.0,74.0,4,0.22319597666932456,16.50846058605035,0.16605166051660517,0.3508771929824561,0.2222222222222222,0.39572192513368987,0.3333333333333333,0.1,0.0,0.0
+Alpine Valley Resort,Wisconsin,Wisconsin,1040,388,820,0,0,3,0,3,1,5,12,21.0,3.0,0.2,90.0,90.0,100.0,55.0,80.0,65.0,120.0,90.0,15,0.2576242169511926,22.90216196408941,0.05142857142857143,0.06583278472679395,0.075,0.08450704225352113,0.5714285714285714,0.13333333333333333,0.14285714285714285,0.03333333333333333
+Bruce Mound,Wisconsin,Wisconsin,1375,375,1000,0,0,0,0,1,0,4,5,12.0,2.0,0.5,40.0,30.0,42.0,71.0,42.0,25.0,42.0,30.0,15,0.2576242169511926,22.90216196408941,0.022857142857142857,0.027649769585253458,0.05,0.028169014084507043,0.4166666666666667,0.125,0.0,0.0
+Cascade Mountain,Wisconsin,Wisconsin,1280,460,820,0,0,2,2,3,2,3,12,47.0,4.0,1.1,175.0,175.0,120.0,57.0,56.0,64.0,120.0,,15,0.2576242169511926,22.90216196408941,0.1,0.07899934167215274,0.1,,0.2553191489361702,0.06857142857142857,0.0425531914893617,0.011428571428571429
+Christie Mountain,Wisconsin,Wisconsin,1650,350,1300,0,0,0,0,0,1,5,6,30.0,4.0,0.8,45.0,41.0,92.0,43.0,48.0,38.0,120.0,35.0,15,0.2576242169511926,22.90216196408941,0.025714285714285714,0.06056616194865043,0.1,0.03286384976525822,0.2,0.13333333333333333,0.0,0.0
+Devils Head,Wisconsin,Wisconsin,995,500,495,0,0,0,3,1,6,2,12,27.0,1.0,1.0,260.0,260.0,110.0,48.0,45.0,65.0,135.0,200.0,15,0.2576242169511926,22.90216196408941,0.14857142857142858,0.07241606319947334,0.025,0.18779342723004694,0.4444444444444444,0.046153846153846156,0.0,0.0
+Grand Geneva,Wisconsin,Wisconsin,1086,211,875,0,0,0,0,0,3,3,6,20.0,1.0,0.2,30.0,30.0,90.0,25.0,25.0,49.0,93.0,30.0,15,0.2576242169511926,22.90216196408941,0.017142857142857144,0.05924950625411455,0.025,0.028169014084507043,0.3,0.2,0.0,0.0
+Granite Peak Ski Area,Wisconsin,Wisconsin,1942,700,1242,0,1,2,0,2,0,2,7,75.0,4.0,0.6,220.0,160.0,136.0,82.0,75.0,92.0,135.0,200.0,15,0.2576242169511926,22.90216196408941,0.12571428571428572,0.08953258722843976,0.1,0.18779342723004694,0.09333333333333334,0.031818181818181815,0.02666666666666667,0.00909090909090909
+Mount La Crosse,Wisconsin,Wisconsin,1110,516,594,0,0,0,0,0,3,1,4,19.0,1.0,0.4,100.0,100.0,115.0,60.0,40.0,56.0,100.0,90.0,15,0.2576242169511926,22.90216196408941,0.05714285714285714,0.07570770243581304,0.025,0.08450704225352113,0.21052631578947367,0.04,0.0,0.0
+Nordic Mountain,Wisconsin,Wisconsin,1137,265,872,0,0,0,0,1,1,5,7,18.0,4.0,1.0,60.0,60.0,68.0,43.0,80.0,47.0,90.0,60.0,15,0.2576242169511926,22.90216196408941,0.03428571428571429,0.04476629361421988,0.1,0.056338028169014086,0.3888888888888889,0.11666666666666667,0.0,0.0
+Sunburst,Wisconsin,Wisconsin,1100,214,866,0,0,0,0,0,3,6,9,13.0,4.0,0.5,37.0,37.0,99.0,59.0,50.0,44.0,115.0,37.0,15,0.2576242169511926,22.90216196408941,0.021142857142857144,0.065174456879526,0.1,0.03474178403755868,0.6923076923076923,0.24324324324324326,0.0,0.0
+Trollhaugen,Wisconsin,Wisconsin,1200,260,920,0,0,0,2,0,1,5,8,24.0,4.0,0.5,86.0,86.0,130.0,69.0,50.0,54.0,120.0,86.0,15,0.2576242169511926,22.90216196408941,0.04914285714285714,0.08558262014483213,0.1,0.08075117370892018,0.3333333333333333,0.09302325581395349,0.0,0.0
+Tyrol Basin,Wisconsin,Wisconsin,1160,300,860,0,0,0,0,3,0,2,5,18.0,5.0,0.5,32.0,32.0,112.0,61.0,41.0,48.0,103.0,32.0,15,0.2576242169511926,22.90216196408941,0.018285714285714287,0.07373271889400922,0.125,0.03004694835680751,0.2777777777777778,0.15625,0.0,0.0
+Whitecap Mountain,Wisconsin,Wisconsin,1750,400,1295,0,0,0,1,0,4,0,5,43.0,1.0,1.0,400.0,300.0,105.0,57.0,200.0,60.0,118.0,,15,0.2576242169511926,22.90216196408941,0.22857142857142856,0.06912442396313365,0.025,,0.11627906976744186,0.0125,0.0,0.0
+Wilmot Mountain,Wisconsin,Wisconsin,1030,230,800,0,0,0,3,2,2,3,10,23.0,2.0,0.5,135.0,135.0,125.0,81.0,70.0,66.0,139.0,135.0,15,0.2576242169511926,22.90216196408941,0.07714285714285714,0.08229098090849243,0.05,0.1267605633802817,0.43478260869565216,0.07407407407407407,0.0,0.0
+Grand Targhee Resort,Wyoming,Wyoming,9920,2270,7851,0,0,2,2,0,0,1,5,95.0,1.0,2.7,2602.0,,152.0,50.0,500.0,90.0,152.0,,8,1.3822679215355613,8.17887192908918,0.3988962133987429,0.2122905027932961,0.07142857142857142,,0.05263157894736842,0.001921598770176787,0.021052631578947368,0.0007686395080707148
+Hogadon Basin,Wyoming,Wyoming,8000,640,7400,0,0,0,0,0,1,1,2,28.0,1.0,0.6,92.0,32.0,121.0,61.0,80.0,48.0,95.0,,8,1.3822679215355613,8.17887192908918,0.01410393990495171,0.16899441340782123,0.07142857142857142,,0.07142857142857142,0.021739130434782608,0.0,0.0
+Sleeping Giant Ski Resort,Wyoming,Wyoming,7428,810,6619,0,0,0,0,1,1,1,3,48.0,1.0,1.0,184.0,18.0,61.0,81.0,310.0,42.0,77.0,,8,1.3822679215355613,8.17887192908918,0.02820787980990342,0.08519553072625698,0.07142857142857142,,0.0625,0.016304347826086956,0.0,0.0
+Snow King Resort,Wyoming,Wyoming,7808,1571,6237,0,0,0,1,1,1,0,3,32.0,2.0,1.0,400.0,250.0,121.0,80.0,300.0,59.0,123.0,110.0,8,1.3822679215355613,8.17887192908918,0.06132147784761613,0.16899441340782123,0.14285714285714285,1.0,0.09375,0.0075,0.0,0.0
+Snowy Range Ski & Recreation Area,Wyoming,Wyoming,9663,990,8798,0,0,0,0,1,3,1,5,33.0,2.0,0.7,75.0,30.0,131.0,59.0,250.0,49.0,,,8,1.3822679215355613,8.17887192908918,0.011497777096428024,0.1829608938547486,0.14285714285714285,,0.15151515151515152,0.06666666666666667,0.0,0.0
+White Pine Ski Area,Wyoming,Wyoming,9500,1100,8400,0,0,0,0,2,0,0,2,25.0,,0.4,370.0,,,81.0,150.0,49.0,,,8,1.3822679215355613,8.17887192908918,0.05672236700904492,,,,0.08,0.005405405405405406,0.0,0.0
diff --git a/data/state_summary.csv b/data/state_summary.csv
new file mode 100644
index 000000000..53979a710
--- /dev/null
+++ b/data/state_summary.csv
@@ -0,0 +1,36 @@
+state,resorts_per_state,state_total_skiable_area_ac,state_total_days_open,state_total_terrain_parks,state_total_nightskiing_ac,state_population,state_area_sq_miles
+Alaska,3,2280.0,345.0,4.0,580.0,731545,665384
+Arizona,2,1577.0,237.0,6.0,80.0,7278717,113990
+California,21,25948.0,2738.0,81.0,587.0,39512223,163695
+Colorado,22,43682.0,3258.0,74.0,428.0,5758736,104094
+Connecticut,5,358.0,353.0,10.0,256.0,3565278,5543
+Idaho,12,16396.0,1136.0,27.0,415.0,1787065,83569
+Illinois,4,191.0,221.0,6.0,191.0,12671821,57914
+Indiana,2,165.0,157.0,4.0,165.0,6732219,36420
+Iowa,3,140.0,100.0,5.0,140.0,3155070,56273
+Maine,9,3216.0,865.0,17.0,388.0,1344212,35380
+Maryland,1,172.0,121.0,3.0,118.0,6045680,12406
+Massachusetts,11,1166.0,671.0,18.0,583.0,6892503,10554
+Michigan,28,4406.0,2389.0,63.0,1946.0,9986857,96714
+Minnesota,14,1560.0,1490.0,29.0,1020.0,5639632,86936
+Missouri,2,60.0,69.0,2.0,47.0,6137428,69707
+Montana,12,21410.0,951.0,27.0,710.0,1068778,147040
+Nevada,4,2110.0,415.0,9.0,0.0,3080156,110572
+New Hampshire,16,3427.0,1847.0,43.0,376.0,1359711,9349
+New Jersey,2,190.0,170.0,4.0,181.0,8882190,8723
+New Mexico,9,5223.0,966.0,18.0,50.0,2096829,121590
+New York,33,5514.0,2384.0,72.0,2836.0,19453561,54555
+North Carolina,6,370.0,506.0,9.0,335.0,10488084,53819
+Ohio,5,421.0,489.0,12.0,421.0,11689100,44826
+Oregon,10,11774.0,1180.0,22.0,1127.0,4217737,98379
+Pennsylvania,19,1888.0,1404.0,47.0,1528.0,12801989,46054
+Rhode Island,1,30.0,100.0,1.0,30.0,1059361,1545
+South Dakota,2,950.0,183.0,3.0,0.0,884659,77116
+Tennessee,1,0.0,83.0,1.0,0.0,6829174,42144
+Utah,13,30508.0,1544.0,26.0,642.0,3205958,84897
+Vermont,15,7239.0,1777.0,50.0,50.0,623989,9616
+Virginia,4,269.0,366.0,4.0,135.0,8535519,42775
+Washington,10,15330.0,1022.0,21.0,1997.0,7614893,71298
+West Virginia,4,542.0,342.0,9.0,187.0,1792147,24230
+Wisconsin,15,1750.0,1519.0,40.0,1065.0,5822434,65496
+Wyoming,8,6523.0,716.0,14.0,110.0,578759,97813
diff --git a/models/ski_resort_pricing_model.pkl b/models/ski_resort_pricing_model.pkl
new file mode 100644
index 000000000..4712143f5
Binary files /dev/null and b/models/ski_resort_pricing_model.pkl differ