diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 000000000..60d714cad
Binary files /dev/null and b/.DS_Store differ
diff --git a/Notebooks/.DS_Store b/Notebooks/.DS_Store
new file mode 100644
index 000000000..985d9d6fc
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diff --git a/Notebooks/02_data_wrangling.ipynb b/Notebooks/02_data_wrangling.ipynb
index a52eb6c24..766a4ade2 100644
--- a/Notebooks/02_data_wrangling.ipynb
+++ b/Notebooks/02_data_wrangling.ipynb
@@ -120,15 +120,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "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"
@@ -162,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -179,13 +179,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 330 entries, 0 to 329\n",
+      "Data columns (total 27 columns):\n",
+      " #   Column             Non-Null Count  Dtype  \n",
+      "---  ------             --------------  -----  \n",
+      " 0   Name               330 non-null    object \n",
+      " 1   Region             330 non-null    object \n",
+      " 2   state              330 non-null    object \n",
+      " 3   summit_elev        330 non-null    int64  \n",
+      " 4   vertical_drop      330 non-null    int64  \n",
+      " 5   base_elev          330 non-null    int64  \n",
+      " 6   trams              330 non-null    int64  \n",
+      " 7   fastEight          164 non-null    float64\n",
+      " 8   fastSixes          330 non-null    int64  \n",
+      " 9   fastQuads          330 non-null    int64  \n",
+      " 10  quad               330 non-null    int64  \n",
+      " 11  triple             330 non-null    int64  \n",
+      " 12  double             330 non-null    int64  \n",
+      " 13  surface            330 non-null    int64  \n",
+      " 14  total_chairs       330 non-null    int64  \n",
+      " 15  Runs               326 non-null    float64\n",
+      " 16  TerrainParks       279 non-null    float64\n",
+      " 17  LongestRun_mi      325 non-null    float64\n",
+      " 18  SkiableTerrain_ac  327 non-null    float64\n",
+      " 19  Snow Making_ac     284 non-null    float64\n",
+      " 20  daysOpenLastYear   279 non-null    float64\n",
+      " 21  yearsOpen          329 non-null    float64\n",
+      " 22  averageSnowfall    316 non-null    float64\n",
+      " 23  AdultWeekday       276 non-null    float64\n",
+      " 24  AdultWeekend       279 non-null    float64\n",
+      " 25  projectedDaysOpen  283 non-null    float64\n",
+      " 26  NightSkiing_ac     187 non-null    float64\n",
+      "dtypes: float64(13), int64(11), object(3)\n",
+      "memory usage: 69.7+ KB\n"
+     ]
+    }
+   ],
    "source": [
     "#Code task 2#\n",
     "#Call the info method on ski_data to see a summary of the data\n",
-    "ski_data.___"
+    "ski_data.info()"
    ]
   },
   {
@@ -204,211 +245,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "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": {
@@ -456,47 +296,1002 @@
        "  </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>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",
-       "      <td>57.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hilltop Ski Area</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>2090</td>\n",
+       "      <td>294</td>\n",
+       "      <td>1796</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arizona Snowbowl</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>11500</td>\n",
+       "      <td>2300</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>777.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sunrise Park Resort</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>11100</td>\n",
+       "      <td>1800</td>\n",
+       "      <td>9200</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>800.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>115.0</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>250.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>78.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Name   Region    state  summit_elev  vertical_drop  \\\n",
+       "0       Alyeska Resort   Alaska   Alaska         3939           2500   \n",
+       "1  Eaglecrest Ski Area   Alaska   Alaska         2600           1540   \n",
+       "2     Hilltop Ski Area   Alaska   Alaska         2090            294   \n",
+       "3     Arizona Snowbowl  Arizona  Arizona        11500           2300   \n",
+       "4  Sunrise Park Resort  Arizona  Arizona        11100           1800   \n",
+       "\n",
+       "   base_elev  trams  fastEight  fastSixes  fastQuads  ...  LongestRun_mi  \\\n",
+       "0        250      1        0.0          0          2  ...            1.0   \n",
+       "1       1200      0        0.0          0          0  ...            2.0   \n",
+       "2       1796      0        0.0          0          0  ...            1.0   \n",
+       "3       9200      0        0.0          1          0  ...            2.0   \n",
+       "4       9200      0        NaN          0          1  ...            1.2   \n",
+       "\n",
+       "   SkiableTerrain_ac  Snow Making_ac  daysOpenLastYear  yearsOpen  \\\n",
+       "0             1610.0           113.0             150.0       60.0   \n",
+       "1              640.0            60.0              45.0       44.0   \n",
+       "2               30.0            30.0             150.0       36.0   \n",
+       "3              777.0           104.0             122.0       81.0   \n",
+       "4              800.0            80.0             115.0       49.0   \n",
+       "\n",
+       "   averageSnowfall  AdultWeekday  AdultWeekend  projectedDaysOpen  \\\n",
+       "0            669.0          65.0          85.0              150.0   \n",
+       "1            350.0          47.0          53.0               90.0   \n",
+       "2             69.0          30.0          34.0              152.0   \n",
+       "3            260.0          89.0          89.0              122.0   \n",
+       "4            250.0          74.0          78.0              104.0   \n",
+       "\n",
+       "   NightSkiing_ac  \n",
+       "0           550.0  \n",
+       "1             NaN  \n",
+       "2            30.0  \n",
+       "3             NaN  \n",
+       "4            80.0  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "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": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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": 24,
+     "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": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>166</td>\n",
+       "      <td>50.303030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>143</td>\n",
+       "      <td>43.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <td>54</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>51</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>51</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>51</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>47</td>\n",
+       "      <td>14.242424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>46</td>\n",
+       "      <td>13.939394</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>14</td>\n",
+       "      <td>4.242424</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1.515152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1.212121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.909091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.303030</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   count          %\n",
+       "fastEight            166  50.303030\n",
+       "NightSkiing_ac       143  43.333333\n",
+       "AdultWeekday          54  16.363636\n",
+       "AdultWeekend          51  15.454545\n",
+       "daysOpenLastYear      51  15.454545\n",
+       "TerrainParks          51  15.454545\n",
+       "projectedDaysOpen     47  14.242424\n",
+       "Snow Making_ac        46  13.939394\n",
+       "averageSnowfall       14   4.242424\n",
+       "LongestRun_mi          5   1.515152\n",
+       "Runs                   4   1.212121\n",
+       "SkiableTerrain_ac      3   0.909091\n",
+       "yearsOpen              1   0.303030\n",
+       "total_chairs           0   0.000000\n",
+       "Name                   0   0.000000\n",
+       "Region                 0   0.000000\n",
+       "double                 0   0.000000\n",
+       "triple                 0   0.000000\n",
+       "quad                   0   0.000000\n",
+       "fastQuads              0   0.000000\n",
+       "fastSixes              0   0.000000\n",
+       "trams                  0   0.000000\n",
+       "base_elev              0   0.000000\n",
+       "vertical_drop          0   0.000000\n",
+       "summit_elev            0   0.000000\n",
+       "state                  0   0.000000\n",
+       "surface                0   0.000000"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 5#\n",
+    "#Count (using `.sum()`) the number of missing values (`.isnull()`) in each column of \n",
+    "#ski_data as well as the percentages (using `.mean()` instead of `.sum()`).\n",
+    "#Order them (increasing or decreasing) using sort_values\n",
+    "#Call `pd.concat` to present these in a single table (DataFrame) with the helpful column names 'count' and '%'\n",
+    "missing = pd.concat([ski_data.isnull().sum(), 100 * ski_data.isnull().mean()], axis=1)\n",
+    "missing.columns=['count','%']\n",
+    "missing.sort_values(by='count', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`fastEight` has the most missing values, at just over 50%. Unfortunately, you see you're also missing quite a few of your desired target quantity, the ticket price, which is missing 15-16% of values. `AdultWeekday` is missing in a few more records than `AdultWeekend`. What overlap is there in these missing values? This is a question you'll want to investigate. You should also point out that `isnull()` is not the only indicator of missing data. Sometimes 'missingness' can be encoded, perhaps by a -1 or 999. Such values are typically chosen because they are \"obviously\" not genuine values. If you were capturing data on people's heights and weights but missing someone's height, you could certainly encode that as a 0 because no one has a height of zero (in any units). Yet such entries would not be revealed by `isnull()`. Here, you need a data dictionary and/or to spot such values as part of looking for outliers. Someone with a height of zero should definitely show up as an outlier!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.6.3 Categorical Features<a id='2.6.3_Categorical_Features'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So far you've examined only the numeric features. Now you inspect categorical ones such as resort name and state. These are discrete entities. 'Alaska' is a name. Although names can be sorted alphabetically, it makes no sense to take the average of 'Alaska' and 'Arizona'. Similarly, 'Alaska' is before 'Arizona' only lexicographically; it is neither 'less than' nor 'greater than' 'Arizona'. As such, they tend to require different handling than strictly numeric quantities. Note, a feature _can_ be numeric but also categorical. For example, instead of giving the number of `fastEight` lifts, a feature might be `has_fastEights` and have the value 0 or 1 to denote absence or presence of such a lift. In such a case it would not make sense to take an average of this or perform other mathematical calculations on it. Although you digress a little to make a point, month numbers are also, strictly speaking, categorical features. Yes, when a month is represented by its number (1 for January, 2 for Februrary etc.) it provides a convenient way to graph trends over a year. And, arguably, there is some logical interpretation of the average of 1 and 3 (January and March) being 2 (February). However, clearly December of one years precedes January of the next and yet 12 as a number is not less than 1. The numeric quantities in the section above are truly numeric; they are the number of feet in the drop, or acres or years open or the amount of snowfall etc."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 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>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": 26,
+     "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": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Crystal Mountain           2\n",
+       "Elk Mountain Ski Resort    1\n",
+       "Seven Oaks                 1\n",
+       "Sunrise Park Resort        1\n",
+       "Tahoe Donner               1\n",
+       "Name: Name, dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "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": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Alpental, Washington              1\n",
+       "Lutsen Mountains, Minnesota       1\n",
+       "Sierra-at-Tahoe, Sierra Nevada    1\n",
+       "Ski Big Bear, Pennsylvania        1\n",
+       "Mountain High, Sierra Nevada      1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 28,
+     "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": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Alpental, Washington              1\n",
+       "Teton Pass Ski Resort, Montana    1\n",
+       "Tahoe Donner, California          1\n",
+       "Boston Mills, Ohio                1\n",
+       "Powderhorn, Colorado              1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 29,
+     "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** No, after concatenating, dataframe doesn't contain duplicate rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "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",
@@ -532,7 +1327,7 @@
        "[2 rows x 27 columns]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -571,13 +1366,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False    297\n",
+       "True      33\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 31,
+     "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).value_counts()"
    ]
   },
   {
@@ -589,7 +1397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -597,11 +1405,11 @@
       "text/plain": [
        "New York               33\n",
        "Michigan               29\n",
-       "Sierra Nevada          22\n",
        "Colorado               22\n",
+       "Sierra Nevada          22\n",
        "Pennsylvania           19\n",
-       "Wisconsin              16\n",
        "New Hampshire          16\n",
+       "Wisconsin              16\n",
        "Vermont                15\n",
        "Minnesota              14\n",
        "Montana                12\n",
@@ -617,26 +1425,26 @@
        "North Carolina          6\n",
        "Connecticut             5\n",
        "Ohio                    5\n",
-       "West Virginia           4\n",
-       "Virginia                4\n",
-       "Mt. Hood                4\n",
        "Illinois                4\n",
-       "Alaska                  3\n",
+       "Mt. Hood                4\n",
+       "Virginia                4\n",
+       "West Virginia           4\n",
        "Iowa                    3\n",
-       "Missouri                2\n",
+       "Alaska                  3\n",
+       "South Dakota            2\n",
        "Arizona                 2\n",
        "Indiana                 2\n",
-       "South Dakota            2\n",
+       "Missouri                2\n",
        "New Jersey              2\n",
        "Nevada                  2\n",
        "Rhode Island            1\n",
        "Maryland                1\n",
-       "Tennessee               1\n",
        "Northern California     1\n",
+       "Tennessee               1\n",
        "Name: Region, dtype: int64"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -654,15 +1462,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "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": 33,
+     "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.state != ski_data.Region]\n",
+    " .groupby('state')['Region']\n",
     " .value_counts())"
    ]
   },
@@ -682,14 +1507,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Region    38\n",
+       "state     35\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 34,
+     "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()"
    ]
   },
   {
@@ -714,84 +1552,335 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\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(1, 2, figsize=(12,8))\n",
+    "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n",
+    "ski_data.Region.value_counts().plot(kind='barh', ax=ax[0])\n",
+    "#Give the plot a helpful title of 'Region'\n",
+    "ax[0].set_title('Region')\n",
+    "#Label the xaxis 'Count'\n",
+    "ax[0].set_xlabel('Count')\n",
+    "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n",
+    "ski_data.state.value_counts().plot(kind='barh', ax=ax[1])\n",
+    "#Give the plot a helpful title of 'state'\n",
+    "ax[1].set_title('state')\n",
+    "#Label the xaxis 'Count'\n",
+    "ax[1].set_xlabel('Count')\n",
+    "#Give the subplots a little \"breathing room\" with a wspace of 0.5\n",
+    "plt.subplots_adjust(wspace=0.5);\n",
+    "#You're encouraged to explore a few different figure sizes, orientations, and spacing here\n",
+    "# as the importance of easy-to-read and informative figures is frequently understated\n",
+    "# and you will find the ability to tweak figures invaluable later on"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How's your geography? Looking at the distribution of States, you see New York accounting for the majority of resorts. Our target resort is in Montana, which comes in at 13th place. You should think carefully about how, or whether, you use this information. Does New York command a premium because of its proximity to population? Even if a resort's State were a useful predictor of ticket price, your main interest lies in Montana. Would you want a model that is skewed for accuracy by New York? Should you just filter for Montana and create a Montana-specific model? This would slash your available data volume. Your problem task includes the contextual insight that the data are for resorts all belonging to the same market share. This suggests one might expect prices to be similar amongst them. You can look into this. A boxplot grouped by State is an ideal way to quickly compare prices. Another side note worth bringing up here is that, in reality, the best approach here definitely would include consulting with the client or other domain expert. They might know of good reasons for treating states equivalently or differently. The data scientist is rarely the final arbiter of such a decision. But here, you'll see if we can find any supporting evidence for treating states the same or differently."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.6.3.5 Distribution Of Ticket Price By State<a id='2.6.3.5_Distribution_Of_Ticket_Price_By_State'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our primary focus is our Big Mountain resort, in Montana. Does the state give you any clues to help decide what your primary target response feature should be (weekend or weekday ticket prices)?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.6.3.5.1 Average weekend and weekday price by state<a id='2.6.3.5.1_Average_weekend_and_weekday_price_by_state'></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Alaska</th>\n",
+       "      <td>47.333333</td>\n",
+       "      <td>57.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>81.500000</td>\n",
+       "      <td>83.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>78.214286</td>\n",
+       "      <td>81.416667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>90.714286</td>\n",
+       "      <td>90.714286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>47.800000</td>\n",
+       "      <td>56.800000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             AdultWeekday  AdultWeekend\n",
+       "state                                  \n",
+       "Alaska          47.333333     57.333333\n",
+       "Arizona         81.500000     83.500000\n",
+       "California      78.214286     81.416667\n",
+       "Colorado        90.714286     90.714286\n",
+       "Connecticut     47.800000     56.800000"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Code task 14#\n",
+    "# Calculate average weekday and weekend price by state and sort by the average of the two\n",
+    "# Hint: use the pattern dataframe.groupby(<grouping variable>)[<list of columns>].mean()\n",
+    "state_price_means = ski_data.groupby('state')[['AdultWeekday', 'AdultWeekend']].mean()\n",
+    "state_price_means.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# The next bit simply reorders the index by increasing average of weekday and weekend prices\n",
+    "# Compare the index order you get from\n",
+    "# state_price_means.index\n",
+    "# with\n",
+    "# state_price_means.mean(axis=1).sort_values(ascending=False).index\n",
+    "# See how this expression simply sits within the reindex()\n",
+    "(state_price_means.reindex(index=state_price_means.mean(axis=1)\n",
+    "    .sort_values(ascending=False)\n",
+    "    .index)\n",
+    "    .plot(kind='barh', figsize=(10, 10), title='Average ticket price by State'))\n",
+    "plt.xlabel('Price ($)');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "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",
-    "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n",
-    "ski_data.Region.value_counts().plot(kind=___, ax=ax[0])\n",
-    "#Give the plot a helpful title of 'Region'\n",
-    "ax[0].set_title(___)\n",
-    "#Label the xaxis 'Count'\n",
-    "ax[0].set_xlabel(___)\n",
-    "#Specify a horizontal barplot ('barh') as kind of plot (kind=)\n",
-    "ski_data.state.value_counts().plot(kind=___, ax=ax[1])\n",
-    "#Give the plot a helpful title of 'state'\n",
-    "ax[1].set_title(___)\n",
-    "#Label the xaxis 'Count'\n",
-    "ax[1].set_xlabel(___)\n",
-    "#Give the subplots a little \"breathing room\" with a wspace of 0.5\n",
-    "plt.subplots_adjust(wspace=___);\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"
+    "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": [
-    "How's your geography? Looking at the distribution of States, you see New York accounting for the majority of resorts. Our target resort is in Montana, which comes in at 13th place. You should think carefully about how, or whether, you use this information. Does New York command a premium because of its proximity to population? Even if a resort's State were a useful predictor of ticket price, your main interest lies in Montana. Would you want a model that is skewed for accuracy by New York? Should you just filter for Montana and create a Montana-specific model? This would slash your available data volume. Your problem task includes the contextual insight that the data are for resorts all belonging to the same market share. This suggests one might expect prices to be similar amongst them. You can look into this. A boxplot grouped by State is an ideal way to quickly compare prices. Another side note worth bringing up here is that, in reality, the best approach here definitely would include consulting with the client or other domain expert. They might know of good reasons for treating states equivalently or differently. The data scientist is rarely the final arbiter of such a decision. But here, you'll see if we can find any supporting evidence for treating states the same or differently."
+    "##### 2.6.3.5.2 Distribution of weekday and weekend price by state<a id='2.6.3.5.2_Distribution_of_weekday_and_weekend_price_by_state'></a>"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### 2.6.3.5 Distribution Of Ticket Price By State<a id='2.6.3.5_Distribution_Of_Ticket_Price_By_State'></a>"
+    "Next, you can transform the data into a single column for price with a new categorical column that represents the ticket type."
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 38,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Our primary focus is our Big Mountain resort, in Montana. Does the state give you any clues to help decide what your primary target response feature should be (weekend or weekday ticket prices)?"
+    "#Code task 15#\n",
+    "#Use the pd.melt function, pass in the ski_data columns 'state', 'AdultWeekday', and 'Adultweekend' only,\n",
+    "#specify 'state' for `id_vars`\n",
+    "#gather the ticket prices from the 'Adultweekday' and 'AdultWeekend' columns using the `value_vars` argument,\n",
+    "#call the resultant price column 'Price' via the `value_name` argument,\n",
+    "#name the weekday/weekend indicator column 'Ticket' via the `var_name` argument\n",
+    "ticket_prices = pd.melt(ski_data[['state','AdultWeekend','AdultWeekday']], \n",
+    "                        id_vars='state', \n",
+    "                        var_name='Ticket', \n",
+    "                        value_vars=['AdultWeekday', 'AdultWeekend'], \n",
+    "                        value_name='Price')"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 39,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>65.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>89.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>AdultWeekday</td>\n",
+       "      <td>74.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     state        Ticket  Price\n",
+       "0   Alaska  AdultWeekday   65.0\n",
+       "1   Alaska  AdultWeekday   47.0\n",
+       "2   Alaska  AdultWeekday   30.0\n",
+       "3  Arizona  AdultWeekday   89.0\n",
+       "4  Arizona  AdultWeekday   74.0"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "##### 2.6.3.5.1 Average weekend and weekday price by state<a id='2.6.3.5.1_Average_weekend_and_weekday_price_by_state'></a>"
+    "ticket_prices.head()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "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.head()"
+    "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": 18,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
+       "<Figure size 864x576 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -801,64 +1890,64 @@
     }
    ],
    "source": [
-    "# The next bit simply reorders the index by increasing average of weekday and weekend prices\n",
-    "# Compare the index order you get from\n",
-    "# state_price_means.index\n",
-    "# with\n",
-    "# state_price_means.mean(axis=1).sort_values(ascending=False).index\n",
-    "# See how this expression simply sits within the reindex()\n",
-    "(state_price_means.reindex(index=state_price_means.mean(axis=1)\n",
-    "    .sort_values(ascending=False)\n",
-    "    .index)\n",
-    "    .plot(kind='barh', figsize=(10, 10), title='Average ticket price by State'))\n",
-    "plt.xlabel('Price ($)');"
+    "#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": "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?"
+    "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": [
-    "##### 2.6.3.5.2 Distribution of weekday and weekend price by state<a id='2.6.3.5.2_Distribution_of_weekday_and_weekend_price_by_state'></a>"
+    "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": [
-    "Next, you can transform the data into a single column for price with a new categorical column that represents the ticket type."
+    "### 2.6.4 Numeric Features<a id='2.6.4_Numeric_Features'></a>"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "#Code task 15#\n",
-    "#Use the pd.melt function, pass in the ski_data columns 'state', 'AdultWeekday', and 'Adultweekend' only,\n",
-    "#specify 'state' for `id_vars`\n",
-    "#gather the ticket prices from the 'Adultweekday' and 'AdultWeekend' columns using the `value_vars` argument,\n",
-    "#call the resultant price column 'Price' via the `value_name` argument,\n",
-    "#name the weekday/weekend indicator column 'Ticket' via the `var_name` argument\n",
-    "ticket_prices = pd.melt(ski_data[[___, ___, ___]], \n",
-    "                        id_vars=___, \n",
-    "                        var_name=___, \n",
-    "                        value_vars=[___, ___], \n",
-    "                        value_name=___)"
+    "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": 20,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -882,145 +1971,350 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>state</th>\n",
-       "      <th>Ticket</th>\n",
-       "      <th>Price</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>0</th>\n",
-       "      <td>Alaska</td>\n",
-       "      <td>AdultWeekday</td>\n",
-       "      <td>65.0</td>\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>1</th>\n",
-       "      <td>Alaska</td>\n",
-       "      <td>AdultWeekday</td>\n",
-       "      <td>47.0</td>\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": 41,
      "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": null,
-   "metadata": {},
-   "outputs": [],
-   "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');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Aside from some relatively expensive ticket prices in California, Colorado, and Utah, most prices appear to lie in a broad band from around 25 to over 100 dollars. Some States show more variability than others. Montana and South Dakota, for example, both show fairly small variability as well as matching weekend and weekday ticket prices. Nevada and Utah, on the other hand, show the most range in prices. Some States, notably North Carolina and Virginia, have weekend prices far higher than weekday prices. You could be inspired from this exploration to consider a few potential groupings of resorts, those with low spread, those with lower averages, and those that charge a premium for weekend tickets. However, you're told that you are taking all resorts to be part of the same market share, you  could argue against further segment the resorts. Nevertheless, ways to consider using the State information in your modelling include:\n",
-    "\n",
-    "* disregard State completely\n",
-    "* retain all State information\n",
-    "* retain State in the form of Montana vs not Montana, as our target resort is in Montana\n",
-    "\n",
-    "You've also noted another effect above: some States show a marked difference between weekday and weekend ticket prices. It may make sense to allow a model to take into account not just State but also weekend vs weekday."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Thus we currently have two main questions you want to resolve:\n",
-    "\n",
-    "* What do you do about the two types of ticket price?\n",
-    "* What do you do about the state information?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2.6.4 Numeric Features<a id='2.6.4_Numeric_Features'></a>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Having decided to reserve judgement on how exactly you utilize the State, turn your attention to cleaning the numeric features."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.6.4.1 Numeric data summary<a id='2.6.4.1_Numeric_data_summary'></a>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
    "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.___.___"
+    "ski_data.describe().T"
    ]
   },
   {
@@ -1032,7 +2326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -1044,7 +2338,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1077,17 +2371,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZM2c+O29oaIiZM2e2ffQBYN7eM0ddpltqcVdRlWM3MzMzM5sIWrnmrZH7JG0AkP8uztMXANMKy21EuiO6mZmZmZmZjcNYG2/nAPvm5/sCZxem7ylpZUmbADOAK8cXopmZmZmZmY3abVLSaaTBSSZLWgB8FpgNzJG0P3A3sAdARNwoaQ5wE7AUODAinu5S7GZmZmZmZhNGK6NN7tVk1vZNlj8SOHI8QZmZmZmZmdlwY+02aWZmZmZmZj3kxpuZWQlJOkHSYkk3FKatJ+lCSbflv+sW5h0uaa6kWyXt2J+ozczMrJvceDMzK6cTgZ3qps0CLoqIGcBF+TWSNgP2BDbP7/m2pEm9C9XMzMx6wY03M7MSiohLgYfqJu8GnJSfnwTsXph+ekQ8FRF3AnOBrXsRp5mZmfVOp2/SbWZm3TMlIhYBRMQiSevn6VOBywvLLcjTliPpAOAAgClTpjA0NLT8RlaFQ7dY2lZgjdZTZkuWLKlczO2aCPtoZjbRuPFmZlZ9ajAtGi0YEccBxwFstdVWMXPmzOWWOeaUsznq+vZ+Hubtvfx6ymxoaIhG+z5IJsI+mplNNG68mZXE9Fnntv2eebN37UIkVmL3Sdogn3XbAFicpy8AphWW2whY2PPozMzMrKt8zZuZWXWcA+ybn+8LnF2YvqeklSVtAswAruxDfGZmZtZFPvNmZlZCkk4DZgKTJS0APgvMBuZI2h+4G9gDICJulDQHuAlYChwYEU/3JXAzMzPrGjfezMxKKCL2ajJr+ybLHwkc2b2IzMzMrN/cbdLMzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKsCNNzMzM+sYSdMkXSLpZkk3Svponr6epAsl3Zb/rlt4z+GS5kq6VdKO/YvezKzc3HgzMzOzTloKHBoRLwO2AQ6UtBkwC7goImYAF+XX5Hl7ApsDOwHfljSpL5GbmZWcG29mZmbWMRGxKCKuyc8fB24GpgK7ASflxU4Cds/PdwNOj4inIuJOYC6wdU+DNjOrCN8qwMzMzLpC0nTgVcAVwJSIWASpgSdp/bzYVODywtsW5GmN1ncAcADAlClTGBoaWm6ZKavCoVssbTnGRuuomiVLlgzEfrRrou63TWxuvJmZmVnHSVoDOBM4JCIek9R00QbTotGCEXEccBzAVlttFTNnzlxumWNOOZujrm+9ejNv7+XXUTVDQ0M0+iwG3UTdb5vY3G3SzMzMOkrSiqSG2ykR8bM8+T5JG+T5GwCL8/QFwLTC2zcCFvYqVrMiSSdIWizphsI0D7ZjpeHGm5mZmXWM0im244GbI+JrhVnnAPvm5/sCZxem7ylpZUmbADOAK3sVr1mdE0kD5xR5sB0rDTfezMzMrJNeD+wDbCfp2vzYBZgN7CDpNmCH/JqIuBGYA9wEnAccGBFP9yd0m+gi4lLgobrJHmzHSsPXvJmZmVnHRMRlNL6ODWD7Ju85Ejiya0GZjU8pB9sBD7hTVePZZzfebMKSNA94HHgaWBoRW0laDzgDmA7MA94dEQ/3K0YzMzMrrb4OtgMecKeqxrPPbrzZRPeWiHig8LrWr322pFn59WH9Cc2sOqbPOret5efN3rVLkZiZddx9kjbIZ9082I711cA13lyBsHHaDZiZn58EDOHGm5mZ2URWG2xnNssPtnOqpK8BG+LBdqwHBq7xZtaGAC6QFMD3cpeGZv3ahxmp73qtH3O7/dbHol99xAe9f/qg75+ZmTUm6TTSQdzJkhYAnyU12uZI2h+4G9gD0mA7kmqD7SzFg+1YD7jxZhPZ6yNiYW6gXSjpllbfOFLf9Vo/5v3aPAs8Fv3q6z7o/dMHff/MzKyxiNirySwPtmOlMK7Gmwd8sCqLiIX572JJZ5GG923Wr72U3E3YzMzMbOLoxH3e3hIRW0bEVvl1wxsZmpWJpNUlrVl7DrwNuIHmN5E1MzMzM+urbnSb9IAPVgVTgLMkQfoenBoR50m6igb92s3MzMzM+m28jTcP+FBQ5UEOqhz7WETEHcArG0x/kCb92s3MzMzM+mm8jTcP+FBQ5UEOqhy7tc7XyJmZmZlV17iueSsO+AAMG/ABoAoDPpiZmZmZmVXBmBtvHvDBzKw/JM2TdL2kayX9MU9bT9KFkm7Lf9ftd5xmZmbWWeM58zYFuEzSdaS7yZ8bEeeRbmS4g6TbgB3yazMz6yyP9GtmZjbBjPmaNw/4YGZWKh7p18zMbMB141YBZmbWXV0Z6bdmyqp0fbTffo9uOxFG2J0I+2hmNtG48WZmVj1dGem35phTzuao67v789DJkX7HYiKMsDsR9tHMbKKZ8I03D51uZlVTHOlX0rCRfvNZN4/0a2ZmNoDGdasAMzPrLY/0a2ZmNnFN+DNvZmYVMwU4SxKkMvzUiDhP0lXAHEn7A3cDe/QxRjMzM+sCN97MzCrEI/2amZlNXO42aWZmZmZmVgFuvJmZmZmZmVWAu02aWVPNRmM9dIul7NdgnkdjNTMzM+seN95KyLcvMDMzMzOzeu42aWZmZmZmVgFuvJmZmZmZmVWAu02aWce02+UX3O3XzMzMrFVuvA0AXyNnZtZ7LnvNzKzX3HgzMzNjbGeOzczMesmNtwmoUQWl2dDvNT5ibGZmZmbWXx6wxMzMzMzMrALceDMzMzMzM6sAd5vsgUG4jsIX5puZmZmZ9Zcbb2ZmZj3QiwN5PnBmZjbY3G3SzMzMzMysAnzmzcz6yl1yrVtGyq3RRtg1MzMrIzfe2uTKgFl/ubFnZmZmE5Ubb2Zm1nODMJCTmZlZr7nxZl3hsyNmZmZmZp3lxpuZmZlZG3yA0sz6xY03MzOzAVFsVLRyHbYbFWZm1eLGm5lZwfRZ57Y9+JArwGZmZtYLXbvPm6SdJN0qaa6kWd3ajlmnOXetypy/VlXOXasy56/1SlfOvEmaBHwL2AFYAFwl6ZyIuKkb2zPrFOfu4JlIoxo6f62qnLvjc/09j7Z9qyL3GOgc56/1Ure6TW4NzI2IOwAknQ7sBjiJreycu1Zlzl+rKuduyXmQlhE5fye4dr8fJ+60+pi31a3G21RgfuH1AuB1xQUkHQAckF8ukXRrYfZk4IEuxdY1B1c0buh/7PpK01kb9zAMaCF3YTDztx39zpdua3f/qpS/o+RuzUD/f2Hwcxha28cq5S50J39H+Aw6pgfbaDufux1TLz5Xmu936fK3W2Vvjz7nbhv48rjeW74y9tztVuNNDabFsBcRxwHHNXyz9MeI2KobgXVTVeOGasfeYaPmLgxm/rZj0Pexwvs3rrL32ZVUd/9b5n0snXGXvc+uqFr73RETcZ+hVPvtsnccJuJ+j2efuzVgyQJgWuH1RsDCLm3LrJOcu1Zlzl+rKueuVZnz13qmW423q4AZkjaRtBKwJ3BOl7Zl1knOXasy569VlXPXqsz5az3TlW6TEbFU0kHA+cAk4ISIuLGNVYx4WrnEqho3VDv2julA7sLE+CwHfR8ruX8dyl+o6P63yftYIh3MXajQfnfQRNxnKMl+u+wdt4m432PeZ0Us16XczMzMzMzMSqZrN+k2MzMzMzOzznHjzczMzMzMrAJK1XiTtJOkWyXNlTSr3/EASJon6XpJ10r6Y562nqQLJd2W/65bWP7wHP+tknYsTH9NXs9cSd+U1GhY2fHGeoKkxZJuKEzrWKySVpZ0Rp5+haTpnd6HqitjDrdC0jRJl0i6WdKNkj6ap5cy18dC0iRJf5L0i/x6YPatU6qavzVVKq9b5XK9dVXP37FoVnZPBPVlepWMlqtKvpnn/1nSq/sRZye1sM8zJT2ay+9rJX2mH3F2UqPyu27+2P7PEVGKB+kCz9uBFwIrAdcBm5UgrnnA5LppXwVm5eezgK/k55vluFcGNsn7MynPuxLYlnQvkF8BO3ch1jcBrwZu6EaswEeA7+bnewJn9Pv/U6ZHWXO4xdg3AF6dn68J/CXnSClzfYz7+N/AqcAv8uuB2bcOfT6Vzd/CPlSmvG5jn1yut/Y5VT5/x7jfDcvufsfVo30fVqZX5dFKrgK75O+pgG2AK/oddw/2eWbV/pct7Pdy5Xcn/s9lOvO2NTA3Iu6IiL8DpwO79TmmZnYDTsrPTwJ2L0w/PSKeiog7gbnA1pI2ANaKiD9E+m/9qPCejomIS4GHuhhrcV0/BbYftDMP41SlHB4mIhZFxDX5+ePAzcBUSprr7ZK0EbAr8IPC5IHYtw6qbP6OotL/Z5frLRvU/B3RCGX3QGtSpldFK7m6G/CjSC4H1snf46qaqN/PRuV30Zj+z2VqvE0F5hdeL6AcBVAAF0i6WtIBedqUiFgEqeAE1s/Tm+3D1Py8fnovdDLWZ98TEUuBR4Hndi3y6ilrDrcld5t6FXAF1cr1kRwNfAJ4pjBtUPatUwYhf6teXrfK5fryBiF/x6Wu7B50R7N8mV4VreTqoOVzq/uzraTrJP1K0ua9Ca2vxvR/7sp93sao0ZG+MtzH4PURsVDS+sCFkm4ZYdlm+1DGfRtLrGXcjzKp/OcjaQ3gTOCQiHhshAPwlcl1SW8HFkfE1ZJmtvKWBtNKuW8dNgj7N6jldasmcrk+KPsxJvVld7/j6aYxlOll00quDlo+t7I/1wAbR8QSSbsAPwdmdDuwPhvT/7lMZ94WANMKrzcCFvYplmdFxML8dzFwFunU732105r57+K8eLN9WJCf10/vhU7G+ux7JK0ArM3Ip4MnmlLmcKskrUj68T8lIn6WJ1cp15t5PfAOSfNIXTW2k3Qyg7FvnVTp/IWBKK9b5XJ9eZXP37FqUnYPsmZlelW0kquDls+j7k9EPBYRS/LzXwIrSprcuxD7Ykz/5zI13q4CZkjaRNJKpAunz+lnQJJWl7Rm7TnwNuCGHNe+ebF9gbPz83OAPfPoXZuQjhhcmbu1PC5pm3wtwfsL7+m2TsZaXNe7gIvz9ROWlC6HW5X/18cDN0fE1wqzqpTrDUXE4RGxUURMJ/1PLo6I9zEA+9Zhlc1fGJjyulUu15dX6fwdqxHK7oE1QpleFa3k6jnA+/NohNsAj9a6SlfUqPss6fm1620lbU1qozzY80h7a2z/51ZGNenVgzTqyl9II9J8qgTxvJA0Is51wI21mEjXA1wE3Jb/rld4z6dy/LdSGKEM2IpUkbgdOBZQF+I9DVgE/IPUmt+/k7ECqwA/IV0EfyXwwn7/j8r2KFsOtxH3G0in6v8MXJsfu5Q118exnzNZNtrkQO1bhz6fSuZvjr1S5XUb++VyvfXPqrL5O459blh29zuuHu7/s2V6lR6NchX4MPDh/FzAt/L864Gt+h1zD/b5oFx2XwdcDvxTv2PuwD43Kr/H/X+uFd5mZmZmZmZWYmXqNmlmZmZmZmZNuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb22QtKmkP0l6XNLBPdrmJyX9oBfbssHSj3ztBkkzJS3odxxm9STNk/TWfsdhE4ekF0haImlSC8tOlxSSVuhFbDaxSTpR0hfHuY79JF02wvwhSR8czzYGgRtv7fkEMBQRa0bEN8eygkaJJ2k3SddKekzSA5IukjQdICK+FBETPlFtTLqVr5L0cUm3SXpS0t2SviRppY5EbWY2QY12QCAi7o6INSLi6V7GZWbl4cZbezYGbuzkCiW9GPgRcCiwNrAJ8G3gmU5uxyakjudr9k3gAOD9wJrAzsBbgdO7sC0zMwN8Bs3MwI23lkm6GHgLcGzusvDR3CXtMUnzJR1RWHYVSSdLelDSI5KukjRF0pHAGwvrOBbYErgzIi6K5PGIODMi7s7rOkLSyfn5eyTdIWmt/HpnSfdKel5+/W+Sbpb0sKTzJW2cp0vS1yUtlvSopD9LennvPj3rtW7lq6QZwEeAvSPiDxGxNCJuBP4V2FXSm/M6h52xq+8KIekbOY7HJF0t6Y2Feavm7hcPS7oJeG3dvh0m6Z7cHfRWSdt34zO06pL0KknX5Bw5Q9Lpkr7YqEtO7lb24vx812bfkzx/H0l35e/Kp3q4SzYBSPox8ALg/3KZ+4mcn/tLuhu4WHVdIXNZ+2VJV+bf97Mlrddk/WtLOl7SolyGflEtdL80a6S+nAVWKcz7d0lzJT0k6RxJG+bpy3Xlra8vpEk6JufzLSP9xjer9w46N95aFBHbAb8FDoqINYDrSGce1gF2Bf5D0u558X1JZ9GmAc8FPgw8GRGfKq4jIg4CrgFemhtXb5G0xggxnAH8AfimpOcCxwMfjIj787Y/CbwTeF7ezmn5rW8D3gS8JMf7HuDB8X4mVl5dzNftgQURcWXd9uYDl5NyrRVXkQ5crAecCvxEUq3g/yzwovzYMccHpOv4gIOA10bEmnn+vBa3aROAUvfdnwM/JuXXT0gHF1rxBE2+J5I2A74D7ANsSPqubNS5yG2ii4h9gLuBf87l9pw8683Ay0jlXSPvB/6NlJdLSb0jGjkpz38x8CpSee3LMqxtI5WzkrYDvgy8G9gAuIv2eua8DrgDmEyqD/ys0QGJUeq9A82NtzGKiKGIuD4inomIP5MS5s159j9IP+wvjoinI+LqiHisyXruAGYCU0kF9QP5rEOzRtyBwHbAEPB/EfGLPP1DwJcj4uaIWAp8CdgyH4X4B6l720sB5WUWjesDsErpVL6SCtNmubOIVIC2Es/JEfFgPnN3FLAysGme/W7gyIh4KDcKixWRp/Oym0laMSLmRcTtrWzTJoxtgBWBoyPiHxHxU9LBglGN8j15F/CLiLg0Ip4C/gd3b7feOCIinoiIJ5vM/3FE3BART5Dy8t31Z9QkTSF1cT8kr2sx8HVgz65GboNqpHJ2b+CEiLgml5WHA9sqj+XQgsWF9Z4B3Eo6mFZvpHrvQHPjbYwkvU7SJZLul/Qo6WzF5Dz7x8D5wOmSFkr6qqQVm60rIi6PiHdHxPNI3dTeBDTskhMRj5COcLwcOKowa2PgG0rd3h4BHgIETI2Ii4FjgW8B90k6TrnrpU0MHczXB0hH0hrZALi/xXgOzV0dHs35unYhng2B+YXF76o9iYi5wCHAEcDi3B1uw1a2aRPGhsA9ERGFaXc1W7holO/JsLzMFWX3YLBemN/G/LtIlerJdctsnKcvKtQTvges36kgbUIZqZzdkOG/20tIZeXUFtfdaL2Nfueb1ntb3E5lufE2dqcC5wDTImJt4LukpCEfLfhcRGwG/BPwdlK3BoBotLKaiLgK+BmpcbYcSVuSukecxvAzEvOBD0XEOoXHqhHx+7zeb0bEa4DNSd0nPz6Gfbbq6lS+XgxMk7R1caKkaaQjcb/Jk54AViss8vzCsm8EDiOdYVs3ItYBHq3FQzqDN63w3hcUtxURp0bEG0gFdwBfaWH/beJYBEyVpMK0Wg4Ny0tJz2e4pt8T6vJS0mqkM9ZmndSojjBivYHly8t/kA60Fc0HngImF+oIa0XE5mMP1SawkcrZhaTfZwAkrU4qK+8hlcHQpH6QNVrvwgYxjFjvHWRuvI3dmsBDEfG3XJF9b21GvnZti9xt4TFSQVob1vc+4IWFZd+QL+xcP79+KfAO0vVDw+Rrgk4m9fH9ACnBP5Jnfxc4XNLmedm1Je2Rn782H1FekfTF+VshHpsYOpKvEfEXUq6dImkbSZNyzp0J/B74dV70WuCdklZTGgxi/7pYlpLO0q0g6TNA8UzwHFIurytpI+A/C7FuKmk7SSuT8vhJnMs23B9I+XWwpBUkvROoHWy4Dthc0pa5PD2i7r1NvyfAT4G35zJ7JeDz+DfUOm9Ymdui90naLB9Q+Dzw0/pbCeRLJS4AjpK0lqTnSHqR8iBTZm0aqZw9FfhALmdXJnVnvCJf5nA/qRH3vlx/+DfS9e1F6+f1rpjrsS8Dftkghqb13kHnH56x+wjweUmPA59h2YXFkI4i/JRUEb6ZdDbi5DzvG8C7lEbG+SbwCKmxdr2kJcB5wFnAVxts88ukwSK+k/sRvw/4oqQZEXEW6QzE6ZIeA24g9W+HVDH+PvAw6fTzg8D/jv8jsArpVL5CGjDkB3mZv5Jy7S5g94ioXQP0deDvpIrIScAphe2dD/wK+Et+398Y3u3nc3n6naTKxo8L81YGZpOOKt9LKuQ/2d5HYYMsIv5OuoB9P1KZ9x5Sb4bawYfPkw4y3AbU3wy26fckj6p6IKlisiiv2zePt077MvDp3A3sXS2+58fAiaQycRXg4CbLvR9YCbiJlL8/pXk3eLOmRilnLyJde3kmqax8EcOvrfx3Uu+vB0m9werPlF0BzCD9zh8JvCsiluuiPkq9d6BpeLdSM7P2SPo8sDvwpnxNplmpSDqRdODr0/2OxayTJA0BJ0fED/odi5n1hm/4aGbjEhGfkbSYdM3bef2Ox8zMzGxQufFmZuMWEcf2OwYzMzOzQeduk2ZmZmZmZhXgAUvMzMzMzMwqoBTdJidPnhzTp09fbvoTTzzB6quv3vuA2uAYO6NZjFdfffUD+eblpeX87a4qx1j2/K1i7pY1trLGBWOLrey5C9XL37LGBeWNbaxxlT1/q5a7zTjezmspdyOi74/XvOY10cgll1zScHqZOMbOaBYj8McoQY6O9HD+dleVYyx7/lYxd8saW1njihhbbGXP3ahg/pY1rojyxjbWuMqev1XL3WYcb+e1krvuNmlmZmZmZlYBpeg22cz19zzKfrPObes982bv2qVozNrTbv46d60sXPZalbnstapy2Wut8Jk3MzMzMzOzCnDjzczMzDpK0gmSFku6oTBtPUkXSrot/123MO9wSXMl3Sppx/5EbWZWfm68mZmZWaedCOxUN20WcFFEzAAuyq+RtBmwJ7B5fs+3JU3qXahmZtXhxpuZmZl1VERcCjxUN3k34KT8/CRg98L00yPiqYi4E5gLbN2LOM3MqqbUA5aYmZnZwJgSEYsAImKRpPXz9KnA5YXlFuRpy5F0AHAAwJQpUxgaGlp+I6vCoVssbTmoY045u+Vla7aYunbb71myZEnDeMugrLGVNS6zfnLjzczMzPpJDaZFowUj4jjgOICtttoqZs6cudwyx5xyNkdd393qzby9l9/uaIaGhmgUbxmUNbayxmXWT+42aWZmZr1wn6QNAPLfxXn6AmBaYbmNgIU9js3MrBLceDMzM7NeOAfYNz/fFzi7MH1PSStL2gSYAVzZh/jMzErP3SbNzMysoySdBswEJktaAHwWmA3MkbQ/cDewB0BE3ChpDnATsBQ4MCKe7kvgZmYl58abmZmZdVRE7NVk1vZNlj8SOLJ7EZmZDQZ3mzQzMzMzM6sAN97MzMzMzMwqwI03MzMzMzOzCnDjzQaWpGmSLpF0s6QbJX00T19P0oWSbst/1y2853BJcyXdKmnH/kVvZmZmZjacG282yJYCh0bEy4BtgAMlbQbMAi6KiBnARfk1ed6ewObATsC3JU3qS+RmZmZmZnXceLOBFRGLIuKa/Pxx4GZgKrAbcFJe7CRg9/x8N+D0iHgqIu4E5gJb9zRoMzMzM7MmfKsAmxAkTQdeBVwBTImIRZAaeJLWz4tNBS4vvG1BntZofQcABwBMmTKFoaGh5ZaZsiocusXSlmNstI5uW7JkSV+22w7HaGZmZpa48WYDT9IawJnAIRHxmKSmizaYFo0WjIjjgOMAttpqq5g5c+ZyyxxzytkcdX3rX7F5ey+/jm4bGhqiUexl4hjNzMzMEnebtIEmaUVSw+2UiPhZnnyfpA3y/A2AxXn6AmBa4e0bAQt7FauZmZmZ2UjceLOBpXSK7Xjg5oj4WmHWOcC++fm+wNmF6XtKWlnSJsAM4MpexWtmZmZmNpJRG28ebt0q7PXAPsB2kq7Nj12A2cAOkm4DdsiviYgbgTnATcB5wIER8XR/QjczMzMzG66VC3Jqw61fI2lN4GpJFwL7kYZbny1pFmm49cPqhlvfEPi1pJe4Emy9FhGX0fg6NoDtm7znSODIrgVlZmZmZjZGo55583DrZmZmZmZm/dfWaJOdHG69G0OtQ++HW6/CEOGO0czMzMys+lpuvHV6uPVuDLUOvR9uvQpDhDtGMzMzM7Pqa2m0SQ+3bmZmZmZm1l+tjDbp4dbNzMzMzMz6rJUzbx5u3cysxySdIGmxpBsK03yLFjOzLnLZa2XXymiTl0WEIuIVEbFlfvwyIh6MiO0jYkb++1DhPUdGxIsiYtOI+FV3d8HMbCCdCOxUN20W6RYtM4CL8mvqbtGyE/BtSZN6F6qZ2cA4EZe9VmItXfNmZma9FRGXAg/VTfYtWszMushlr5Vde0M5mplZP43rFi1Q/du0lPW2ImWNC8odm1lFTPiyt5GqlS1Vi7cZN97MzKqvpVu0QPVv01LW24qUNS4od2xmFTdhyt5Gqla2VC3eZtxt0sysOnyLFjOz3nPZa6XhxpuZWXX4Fi1mZr3nstdKw90mzcxKSNJpwExgsqQFwGdJt2SZI2l/4G5gD0i3aJFUu0XLUnyLFjOzMXHZa2XnxpuZWQlFxF5NZm3fZPkjgSO7F5FZZ0iaBzwOPA0sjYitJK0HnAFMB+YB746Ih/sVo01cLnut7Nx4MzMzs157S0Q8UHhdu4/WbEmz8uvD+hPa6KbPOret5efN3rVLkZjZRONr3szMzKzfmt1Hy8zMCnzmzczMzHopgAskBfC9PIR6s/toDdOte2V129DQUKnvMVXW2Moal1k/ufFmZmZmvfT6iFiYG2gXSrql1Td2615Z3TZv75mlvsdUWWMra1xm/VSu0s3MzCrJ1wBZqyJiYf67WNJZwNbk+2jls27F+2iZmVmBr3kzMzOznpC0uqQ1a8+BtwE30Pw+WmZmVuDGmw00SSdIWizphsK09SRdKOm2/HfdwrzDJc2VdKukHfsTtZnZwJoCXCbpOtLNjM+NiPNI99HaQdJtwA75tZmZ1XG3SRt0JwLHAj8qTGs4JLWkzYA9gc2BDYFfS3qJb7hpZtYZEXEH8MoG0x+kyX20zKw5d1mfeHzmzQZaRFwKPFQ3udmQ1LsBp0fEUxFxJzCXdC2GmZmZmVnf+cybTUTNhqSeClxeWG5BnracbgxX3Y/hkKswDLNjNDMzM0vceDNbRg2mRaMFuzFc9by9l19Ht1VhGGbHaGZmZpa426RNRPfloaipG5J6ATCtsNxGwMIex2ZmZmZm1pDPvNlEVBuSejbDh6Q+BzhV0tdIA5bMII2GZmZmNmbTZ53LoVssZb8WB5fwoBLWLR7gpPrceLOBJuk0YCYwWdIC4LOkRtscSfsDdwN7AETEjZLmADcBS4EDPdKkmZmZmZWFG2820CJiryazGg5JHRFHAkd2LyIzMzMzs7HxNW9mZmZmZmYV4MabmZmZmZlZBbjxZmZmZmZmVgFuvJmZmZmZmVWABywxM7Oe83DVZmZm7fOZNzMzMzMzswpw483MzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKsCNNzMzMzMzswpw483MzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKsA36TYzs9Kr3dT70C2Wsl+LN/j2jb1tomj3pvfg74e1ZqTcalYeO7e6y403s5Lwj6+ZmZmZjcTdJs3MzMzMzCrAZ97MKqzds3U+U2dmZjD896OV7sj+/TArh4FrvLkya2ZmZtZZrl+ZlYO7TZqZmZmZmVVA1868SdoJ+AYwCfhBRMzu1rbMOsm5a1Xm/B07n1noL+euVZnz13qlK403SZOAbwE7AAuAqySdExE3dWN7vVT/4+5+4oNlkHN3LFyZrRbnb2/5mqHOce4Onon0++H8HW4i/e/7oVtn3rYG5kbEHQCSTgd2AyZkElulDHTujuXgQ7eNVsjXx+hCfkQDnb82ukbfp5G+5yX6Pjl3rcqcv9YziojOr1R6F7BTRHwwv94HeF1EHFRY5gDggPxyU+DWBquaDDzQ8QA7yzF2RrMYN46I5/UqiFZyN093/vZOlWMsXf4OQO6WNbayxgVji610uZunVzl/yxoXlDe2scZVuvyteO4243g7b9Tc7daZNzWYNqyVGBHHAceNuBLpjxGxVScD6zTH2BklinHU3AXnby85xrYMfNlb1tjKGheUO7aCgS97yxoXlDe2ssbVwMCXvY043v7o1miTC4BphdcbAQu7tC2zTnLuWpU5f62qnLtWZc5f65luNd6uAmZI2kTSSsCewDld2pZZJzl3rcqcv1ZVzl2rMuev9UxXuk1GxFJJBwHnk4ZMPSEibhzDqkY8vVwSjrEzShFjB3MXSrJPo3CMnVGKGCdI2VvW2MoaF5Q7NmDClL1ljQvKG1tZ4xpmgpS9jTjePujKgCVmZmZmZmbWWd3qNmlmZmZmZmYd5MabmZmZmZlZBZS28SZpJ0m3SporaVa/46knaZqkSyTdLOlGSR/td0zNSJok6U+SftHvWBqRtI6kn0q6JX+e2/Y7pvEoe+4CSJon6XpJ10r6Y7/jqZF0gqTFkm4oTFtP0oWSbst/1y1ZfEdIuid/ltdK2qVf8bVrtFxV8s08/8+SXt2juEYtXyXNlPRo4XP/TI9iG/G704/PTNKmhc/hWkmPSTqkbpm+fF7dVMb8de6OKa4Jmb/1qlB3KCprPaKm7PWJcYmI0j1IF3veDrwQWAm4Dtis33HVxbgB8Or8fE3gL2WLsRDrfwOnAr/odyxN4jsJ+GB+vhKwTr9jGse+lD53c5zzgMn9jqNBXG8CXg3cUJj2VWBWfj4L+ErJ4jsC+Fi/P7sx7MuouQrsAvyKdA+jbYArehTbqOUrMLMfZdpo351+fWZ1/9d7STd67fvn1eX9LF3+Onc78n8d+Pxtst+lrzvUxVzKekQhvlLXJ8bzKOuZt62BuRFxR0T8HTgd2K3PMQ0TEYsi4pr8/HHgZmBqf6NanqSNgF2BH/Q7lkYkrUX6gh0PEBF/j4hH+hrU+JQ+d8ssIi4FHqqbvBupgU/+u3svYypqEl9VtZKruwE/iuRyYB1JG3Q7sKqUr0305TMr2B64PSLu6uE2+6GU+evcHbeJkr/1XHfosLLXJ8ajrI23qcD8wusFlLjwkzQdeBVwRZ9DaeRo4BPAM32Oo5kXAvcDP1Tq2vkDSav3O6hxqEruBnCBpKslHdDvYEYxJSIWQaoYAev3OZ5GDsrdjE6oUDeMVnK17/k8Svm6raTrJP1K0uY9Cmm0706/P7M9gdOazOvH59Utpc9f5+6YTJT8rVeGz75dVapH1FShPjGqsjbe1GBaKe9pIGkN4EzgkIh4rN/xFEl6O7A4Iq7udywjWIF0Wvs7EfEq4AnSqeyqqkruvj4iXg3sDBwo6U39DqjCvgO8CNgSWAQc1ddoWtdKrvY1n0cpX68hda16JXAM8PMehTXad6dvn5nSzYHfAfykwex+fV7dUur8de62b4Llb72q1B2KXI/ok7I23hYA0wqvNwIW9imWpiStSCqcT4mIn/U7ngZeD7xD0jzSKfjtJJ3c35CWswBYEBG1I5M/JTXmqqoSuRsRC/PfxcBZpC4bZXVfretO/ru4z/EMExH3RcTTEfEM8H3K/VkWtZKrfcvn0crXiHgsIpbk578EVpQ0udtxtfDd6WcZsDNwTUTcVz+jX59XF5U2f527YzaR8rdevz/7tlWsHlFT6vpEq8raeLsKmCFpk3wkZk/gnD7HNIwkka7TujkivtbveBqJiMMjYqOImE76DC+OiPf1OaxhIuJeYL6kTfOk7YGb+hjSeFUhd1eXtGbtOfA24IaR39VX5wD75uf7Amf3MZbl1F0T8i+U+7MsaiVXzwHen0eh2wZ4tNblpJtaKV8lPT8vh6StSb9nD3Y5rla+O335zLK9aNLlrB+fV5eVMn+du+MykfK3XunrDkUVrEfUlLo+0aoV+h1AIxGxVNJBwPmkEXhOiIgb+xxWvdcD+wDXS7o2T/tkPiJk7flP4JRcYN0BfKDP8YxZRXJ3CnBW/h1cATg1Is7rb0iJpNNIo4pNlrQA+CwwG5gjaX/gbmCPksU3U9KWpC4u84AP9Su+djTLVUkfzvO/C/ySNALdXOCv9O672bB8BV5QiO1dwH9IWgo8CewZEd3uZtTwu1OGz0zSasAOFPKvLq5+fF5dU+L8de6OwUTL33oVqTsUlbYeUVP2+sR4aIBy38zMzMzMbGCVtdukmZmZmZmZFbjxZmZmZmZmVgFuvJmZmZmZmVWAG29mZmZmZmYV4MabmZmZmZlZBbjxZmZmZmZmVgFuvJmZmZmZmVWAG28lIWlTSX+S9Likg/sdj5mZmZWPpBslzezDdockfbDX2zUrC0m/krRvv+Nw4608PgEMRcSaEfHNfgdjViTpRElfHMf7x/yjL+m7kv5nrNu2iUfSPElPSloi6d6cv2v0Oy4bHDm3ao9nCvm2RNLe3dx2RGweEUMtxln8Ltwn6Yf+Lgy2/D9/a7/jGImkmZIW1E07QtI/cq4+Iun3krbtV4yNRMTOEXFSv+Nw463PJK2Qn24M3NjPWKza2imwq1C410TEhyPiC/2OwyrnnyNiDWBL4FXA4f0NxwZJRKxRewB3k/MtP05pZR2F3/8Rp3VA7bvwauC1wKfbebMS1xetF87IuToZuAT4SZ/jKSV/GcdJ0mGS7sndHW+VtH39WYr6Iwy54nyYpD8DT0i6GHgLcGw+4vASSbvmbpSPSZov6Yi67b4hH5V4JM/fL09fWdL/Sro7H2X7rqRVe/JhmPVYlyo6NkAi4l7gfGDLJkd7nz2QkY/8zpH0o1ym3yhpq8Kyy5X3vd0bKztJz5E0S9Ltkh7M+bRenjddUkjaX9LdwMWS9pP0O0lfl/QQcISkF0m6OL//AUmnSFqnsI2Wc7YoIu4BfgW8XNK6kn4h6X5JD+fnGxW2MSTpSEm/A/4KvLBuPzeQ9GdJH8uv95N0R47hzm6ffbT25Lrh0ZIW5sfRklbO82ZKWiDpUEmLJS2S9IHCe58r6f9yffQqSV+UdFlh/kslXSjpoVwuvrswbxdJN+W8uEfSxyStTsrDDbXsbPWGxXgjYilwCjBV0vPyuoYddM65f3J+Xvtu7Zvrvw9I+lQLn8sRkn4i6eQc4/W5Dn54/izmS3pbYflSdB12420cJG0KHAS8NiLWBHYE5rX49r2AXYF1ImI74LfAQfmo3V+AJ4D3A+vk5f5D0u55uy8gJf4xwPNIR5avzev9CvCSPO3FwFTgM2PeSasEST8GXgD8Xy4IPyHpHfmH/JFc4Lys2bJ5+k+Uupg9KulSSZuPIY7dJF2bC/nbJe1UmL1xrqQ8LukCSZML72u6bRUOhhR+ZA6TdC/wQ0mTc8Xjkfzj8Vv5KLFluUK6MzC3xbe8AzidVPaeAxyb1zOe8t4mjoOB3YE3AxsCDwPfqlvmzcDLSDkE8DrgDmB94EhAwJfz+18GTAOOGGGbDXO2nqRpwC7An0j1vx+Sev28AHiywfv2AQ4A1gTuKqxnOvAb4NiI+N9cGf8msHP+bvwTy+okVg6fArYh1Q1fCWzN8DOwzwfWJtUZ9we+JWndPO9bpDrp84F98wOA/L+/EDiVlL97Ad8u/IYfD3wo58XLgYsj4glSmbywcLZ6YTFYSSuR6sAPkr5DrXoDsCmwPfCZWr1nFP8M/BhYl/TdOJ/0/ZgKfB74Xhvb7wlXcMbnaWBlYDNJK0bEvIi4vcX3fjMi5kfEk41mRsRQRFwfEc9ExJ+B00gFPsDewK8j4rSI+EdEPBgR10oS8O/Af0XEQxHxOPAlYM9x7aWVXkTsQ6HrDvBzUs4cQmrg/5LUWFupftmI+Gpeza+AGaQC+BrSUa+WSdoa+BHwcVIl4k0Mr9y+F/hAXv9KwMcK89rZ9vOB9UiVjgOAQ4EFeT+nAJ8Eop3YbSD9XNLjwHxgMfDZFt93WUT8MiKeJv2gvzJPH095bxPHh4BPRcSCiHiK1Oh6l4b3EjgiIp4o/P4vjIhjImJpRDwZEXMj4sKIeCoi7ge+xrLf/0aa5WzNzyU9AlxGanR9KdcbzoyIv+a6wpENtnFiRNyY4/pHnrYZMAR8NiKOKyz7DOmM3qoRsSgifBlIuewNfD4iFuec+hypcV7zjzz/HxHxS2AJsKmkScC/kv7ff42Im4DiNV9vB+ZFxA9znlwDnAm8q7DezSStFREP5/kjeXfO1SdJ9dl35bNwrfpc/g5dB1zH8t+FRn4bEefn7fyEVJeYnXP+dGC6Cme+y8CNt3GIiLmkyvERwGJJp9ef+h3B/JFmSnqdpEuUujQ8CnyY1AcY0lG4RpWG5wGrAVfnsxCPAOfl6TaxvAc4N1cA/gH8L7Aq6YhoQxFxQkQ8XqhwvFLS2m1sc3/ghLzNZyLinoi4pTD/hxHxl1xhmUM6AjiWbT9D+iF5Kq/rH8AGwMb5h+e3EeHGm+2ej/bOBF7KsvJzNPcWnv8VWEXSCuMs723i2Bg4q/AbfDOp4T+lsEz97/+w15LWz/l1j6THgJMZOX8b5mxh2u4RsU5EbBwRH4mIJyWtJul7ku7K27gUWCdX1pvFCakRcA/w09qEfCblPaR6yiJJ50p66QjxWu9tSOHsaX5eLL8erGsk/RVYg1R/XIHhuVB8vjHwulq+55zfm3SQFVLDbxfgLkm/0egDkMyJiHVI35cbgNe0sG9F9d+FVgbnua/w/EnggXwgpPaaFtfTM268jVNEnBoRbyAlcJC6LT5BakTVPL/RW0dZ9amk7g/TImJt4LukrhSQvjgvavCeB0iJtnkuqNeJiLXzmRibWIYV1BHxDClvpjZaWNIkSbNzV8fHWHbGrNUKLzQ/qFDTsFAdw7bvj4i/FV7/P1KXuAuUrrmY1UbMNuAi4jfAiaQDGMPK5lxRbfngVpPy3qxoPqn74DqFxyqRrjerqf/9r3/95TztFRGxFvA+lv3+d8qhpO5lr8vbeFOeXtxOo3rKEaS6xqnFhl4+c7ED6UDaLcD3Oxyvjc9CUrlV84I8bTT3A0uBjQrTphWezwd+U5fva0TEfwBExFURsRupV83PSQduYZQ6cEQ8QDqLfYSkDfLkVurWE4Ibb+OgdG+27ZQu+vwbqeH0NKmv9y6S1pP0fNLR2natCTwUEX/L3dHeW5h3CvBWSe+WtILSxaRb5gr694GvS1o/xzhV0o7Lr94GULEwHFZQ5y6100hHTOuXhZRfuwFvJfV7n157axvbb3ZQYTTtbntY7PmM3aER8UJS3/X/lgeSsOGOBnYg/fCvojQg1Iqkaz5WbmUFI5T3ZkXfBY6UtDGApOdJ2q3NdaxJ6rb2iKSppK7onbYmKYcfURpQpdVuxf8A9gBWB36sNEDLFKVrrFcHnsqx+7vRXytKWqX2IF1G8emcj5NJYyGcPNpK8hmon5EaUavlM6rvLyzyC+AlkvaRtGJ+vFbSyyStJGlvSWvnHkCPsSwv7gOeO1Lvntxz53zSrbQg1a33zNvYimVdMyccN97GZ2VgNuko1L2kIwufJPU5v450BuEC4IwxrPsjwOfzNRufYdnRCiLibtJp6EOBh0gJXevXexjpLMTl+SzGr0lH12zw3ceyEcHmALsqjX66IilXngJ+32BZSD/kT5EuDl6NdK1ku44HPpC3+Zx84KCVrjPj2rakt0t6cW6g1n4cXHGwZ+VrPH5EGkziI8APSAcyniBdL9mKZuW9WdE3SL1mLsi/35eTBiRpx+dIw/o/CpxLqjx32tGkrvQPkGI8r9U3RsTfgXeSvgMnkLrVHUo6aPgQ6dq5j3Q2XGvTL0mN89pjFeCPwJ+B60nXlrd679aDSAdW7yXVb08j/WaTr5d8G2lshYV5ma+w7KDYPsC8XB/9MOkscq1hdhpwR+5u2awL+v8DDsgnJP6HdID4YdJ35NQW4x848qUhZoMhH909BliLVCjfTroIfSqpgf+R2kXkDZb9LumM7nakH9//IV2UPCMi5ko6EVgQESPeH0jSv5AK1U1IDcQDI+J8SUPAyRHxg7zcfsAHI+INSjeMbWnbkmbm9RSHtP4v4KOk7m8PA98L3xfOzMys4yR9BXh+ROw76sLWFW68mZmZmZnZcnIPmpVIZ+xeSzqr98GI+Hk/45rI3G3SzMzMzMwaWZPUdfcJ0iUZRwFn9zWiNkj6lZbdDLz4qGy3d595M7OW5cKuUYH324jYudfxmJmZmU0kbryZmZmZmZlVwAqjLSDpBNId1BdHxMvztPVIIyhOJ42o+O6IeDjPO5x0s96ngYMj4vzRtjF58uSYPn36ctOfeOIJVl999RZ3pT8cY2c0i/Hqq69+ICJKfZPxKudvJ3l/l1f2/G2Uu2X8P5YtpokQT9lzF5qXvWVVtrxpR9ViL3v+Vrne4Bg7Y1z13ogY8UG6ceOrgRsK074KzMrPZwFfyc83Iw2RvzJptLnbgUmjbeM1r3lNNHLJJZc0nF4mjrEzmsUI/DFGyZ9+P6qcv53k/V1e2fO3Ue6W8f9YtpgmQjxlz90Yoewtq7LlTTuqFnvZ87fK9QbH2BnjqfeOOmBJRFxKGr67aDfSUN7kv7sXpp8eEU9FxJ2k+41tPdo2zMzMzMzMbGSjdptsYkpELAKIiEX55nmQ7id1eWG5BXnaciQdABwAMGXKFIaGhpZbZsmSJQ2nl4lj7IwqxGhmZmZm1k9jbbw1owbTGo6IEhHHAccBbLXVVjFz5szlljnmlLM56rIn2gpg3uxd21p+vIaGhmgUe5k4xmqYPuvctt/T63y3icG5aFXWbv46d63KnO8Tz1gbb/dJ2iCfddsAWJynLwCmFZbbCFg4ngDb5SQ2MzMzM7NBNNabdJ8D7Juf78uym/WdA+wpaWVJmwAzgCvHF6KZmZmZmZm1cquA04CZwGRJC4DPArOBOZL2B+4G9gCIiBslzQFuApYCB0bE012K3czMzMzMbMIYtfEWEXs1mbV9k+WPBI4cT1BmZmZmZmY23Fi7TZqZmZmZmVkPdXq0STMzMzMza9P19zzKfmMY7dcmFp95MzMzMzMzqwA33szMzMzMzCrAjTczMzMzM7MKcOPNzMzMzMysAtx4MzMzMzMzq4AJP9rk9DZH9Zk3e9cuRWJmZmZmZtacz7yZmZmZmZlVgBtvZmZmZmZmFeDGm5mZmZmZWQW48WZmZmZmZlYBbryZmZmZmZlVgBtvZmZmZmZmFeDGm5mZmZmZWQW48WYDTdIJkhZLuqEwbT1JF0q6Lf9dtzDvcElzJd0qacf+RG1mZmZmtjw33mzQnQjsVDdtFnBRRMwALsqvkbQZsCeweX7PtyVN6l2oZmZmZmbNufFmAy0iLgUeqpu8G3BSfn4SsHth+ukR8VRE3AnMBbbuRZxmZmZmZqNZod8BmPXBlIhYBBARiyStn6dPBS4vLLcgT1uOpAOAAwCmTJnC0NDQcsssWbKk4fRmDt1iacvL1rSz/m5rd3+rbqLtr5mZmfWfG29my6jBtGi0YEQcBxwHsNVWW8XMmTOXW2ZoaIhG05vZb9a5LS9bM2/v1tffbe3ub9VNtP01a4ekE4C3A4sj4uV52nrAGcB0YB7w7oh4OM87HNgfeBo4OCLO70PYZmal526TNhHdJ2kDgPx3cZ6+AJhWWG4jYGGPYzMzGwQn4uuNzcw6zo03m4jOAfbNz/cFzi5M31PSypI2AWYAV/YhPjOzSvP1xmZm3eFukzbQJJ0GzAQmS1oAfBaYDcyRtD9wN7AHQETcKGkOcBOwFDgwIp7uS+BmZoOnJ9cbt3v9cK+uXa3ydbJVjt1s0LjxZgMtIvZqMmv7JssfCRzZvYjMzKxOR683bvf64V5dO1zl62SrHLsNN73d78fsXbsUiY2Vu02amZWQbzBvA8jXG1ulSZon6XpJ10r6Y57WtFw26wY33szMyulEPOCDDRZfb2yD4C0RsWVEbJVfNyyXzbrFjTczsxLygA9WZfl64z8Am0pakK8xng3sIOk2YIf8moi4Eahdb3wevt7YqqVZuWzWFb7mzcysOro+4MOSJUs4dIv2683dHMygbIMlOJ7R+XpjG1ABXCApgO/lazCblcvDtDLYzpRV2x9wp9sa/UaUrbypN+gxjqvxJmke8DjppppLI2KrkW7CaWZmXdGxAR+GhoY46rIn2g6gm4M+lG2wBMdjNmG9PiIW5gbahZJuafWNrQy2c8wpZ3PU9eU6r1JftlehvBn0GDvRbdJ9f83MesMDPpiZ9UlELMx/FwNnkbqnNyuXzbqiG9e8ue+vmVl3eMAHM7M+kLS6pDVrz4G3ATfQvFw264rxnpt1398B71fbK1WI0ayXfIN5M7NSmQKcJQlS/fnUiDhP0lU0KJfNumW8jTf3/R3wfrW9UoUY23X9PY+2fbNYs5qqDfjgG7+a2SCLiDuAVzaY/iBNymWzbhhXt0n3/TUzMzMzM+uNMZ/Wyv19nxMRjxf6/n6eZX1/Z+O+v2Zd5bMdZmZmZhPHePokuu+vmZmZmZlZj4y58ea+v2ZmZmZmZr3TjVsFmJmZmZmZWYe58WZmZmZmZlYBbryZmZmZmZlVQLluomZmZmZmZqVQP6r1oVssHfUeth7Zurt85s3MzMzMzKwC3HgzMzMzMzOrADfezMzMzMzMKsCNNzMzMzMzswpw483MzMzMzKwCPNqkmZmZmZn1Rf2IlqOZ6KNZuvFmZmZmE1a7FUdw5dHM+sfdJs3MzMzMzCrAjTczMzMzM7MKcOPNzMzMzMysAnzNm5mZmZmZdcRYriO11rnx1qb6hDx0i6XsN0qS+sJmMzMzMzMbL3ebNDMzMzMzqwCfeTMzMzNrg+9LZWb94sabmZmZWRdNn3VuS5dZ1LixZ2bNuPHWAz5CZ2ZmZmZm4+XGm9kE4gMJZmZmZtXlxpuZmfVcOwcSDt1iKTO7F4qZmVlleLRJMzMzMzOzCnDjzczMzMzMrALcbdLMzEqv3es1wddsmpnZ4PGZNzMzMzMzswpw483MzMzMzKwC3G3SzMwGkm+NYWZmg8aNtwEwWgXl0C2Wsl9hGVdQrFUezt3MzMzKZKLXe7vWeJO0E/ANYBLwg4iY3a1tDZqxXJhvnePcHTsPKtF/zl+rKufuMr04a+yDc53l/C2vMtarT9xp9TG/tyuNN0mTgG8BOwALgKsknRMRN3Vje2ad4tztPXdt6xznr1WVc3d8ylg5nUicv9ZL3TrztjUwNyLuAJB0OrAb4CSuqG7/MIznCESHOXdLriyVlGK3jBI1KJ2/49BqbtX+9yX6vz+rwgdDnLsTXMV7bjh/rWcUEZ1fqfQuYKeI+GB+vQ/wuog4qLDMAcAB+eWmwK0NVjUZeKDjAXaWY+yMZjFuHBHP61UQreRunj4o+dtJ3t/llS5/W8jdMv4fyxbTRIindLmbp7dS9pZV2fKmHVWLvXT5O0D1BsfYGWOu93brzJsaTBvWSoyI44DjRlyJ9MeI2KqTgXWaY+yMEsU4au7C4ORvJ3l/S2HcZW8Z96tsMTmeruhY2VtWVf4/VTn2HnG9t0QGPcZu3edtATCt8HojYGGXtmXWSc5dqzLnr1WVc9eqzPlrPdOtxttVwAxJm0haCdgTOKdL2zLrJOeuVZnz16rKuWtV5vy1nulKt8mIWCrpIOB80pCpJ0TEjWNYVRW6RjjGzihFjB3MXSjJPvWQ97fPOpS/pdsvyheT4+mwDpe9ZVXl/1OVY+8613tLZ6Bj7MqAJWZmZmZmZtZZ3eo2aWZmZmZmZh3kxpuZmZmZmVkFlLbxJmknSbdKmitpVr/jaUTSPEnXS7pW0h/7HQ+ApBMkLZZ0Q2HaepIulHRb/rtuCWM8QtI9+bO8VtIu/YxxPKqQu+MhaZqkSyTdLOlGSR/N00uVZ50maZKkP0n6RX49cPvbj9xtt8ySdHiO71ZJO3Yhnrbzu5sxSVpF0pWSrsvxfK6f8Vj7ylhXGEkV6hGDqOx1h2ZlYxnV/16XjaR1JP1U0i3589y2nfeXsvEmaRLwLWBnYDNgL0mb9Teqpt4SEVuW6H4SJwI71U2bBVwUETOAi/LrfjqR5WME+Hr+LLeMiF/2OKaOqFjujtVS4NCIeBmwDXBg3sey5VmnfRS4ufB6oPa3j7l7Ii2WWTmePYHN83u+nePupLbyuwcxPQVsFxGvBLYEdpK0TR/jsbEpW11hJCdS/nrEQKlI3aFZ2VhG9b/XZfMN4LyIeCnwStqMtZSNN2BrYG5E3BERfwdOB3brc0yVEBGXAg/VTd4NOCk/PwnYvZcx1WsS46AY+NyNiEURcU1+/jip0JlKyfKskyRtBOwK/KAwedD2ty+522aZtRtwekQ8FRF3AnNz3J2Mp9387mpMkSzJL1fMj+hXPDb4qlCPGEClrzuMUDaWSpPf69KQtBbwJuB4gIj4e0Q80s46ytp4mwrML7xeQAkThPQDeoGkqyUd0O9gRjAlIhZB+vIB6/c5nmYOkvTn3GWjql0yqpK7HSFpOvAq4Aqqk2djcTTwCeCZwrRB298y5W6zz7anMbaY312PKXcBuhZYDFwYEX2Nx9pWlbrCSAatvCubSn1v68rGsjma5X+vy+SFwP3AD3PXzh9IWr2dFZS18aYG08p4T4PXR8SrSae5D5T0pn4HVGHfAV5E6ha0CDiqr9GMXVVyd9wkrQGcCRwSEY/1O55ukfR2YHFEXN3vWLqsCrnbsxjbyO+uxxQRT0fElsBGwNaSXt7PeKxtrivYaCrzvS3zb39Ffq9XAF4NfCciXgU8QZvdkMvaeFsATCu83ghY2KdYmoqIhfnvYuAsyts15T5JGwDkv4v7HM9yIuK+XEF5Bvg+5f0sR1OJ3B0vSSuSCu9TIuJneXLp82yMXg+8Q9I8UleW7SSdzODtb5lyt9ln25MY28zvnn1uuWvNEOl6pL7HY62pUF1hJINW3pVNJb63TcrGMmn2e10mC4AFuQcFwE9JjbmWlbXxdhUwQ9ImklYiXXx9Tp9jGkbS6pLWrD0H3gbcMPK7+uYcYN/8fF/g7D7G0lDtRyH7F8r7WY6m9Lk7XpJE6qt9c0R8rTCr9Hk2FhFxeERsFBHTSf/PiyPifQze/pYpd5t9tucAe0paWdImwAzgyk5ueAz53dWYJD1P0jr5+arAW4Fb+hWPtadidYWRDFp5VzZlKn8bGqFsLI0Rfq9LIyLuBeZL2jRP2h64qZ11rNDxqDogIpZKOgg4H5gEnBARN/Y5rHpTgLNSLrMCcGpEnNffkEDSacBMYLKkBcBngdnAHEn7A3cDe/QvwqYxzpS0JambwDzgQ/2Kbzwqkrvj9XpgH+D6fB0OwCcpWZ71wEDtb79yt50yKyJulDSH9EO3FDgwIp7ucEht5XcPYtoAOCmPRvccYE5E/ELSH/oUj7WnlHWFkVShHjFoKlJ3aFg2VnV08D77T+CU3FC/A/hAO29WRCm71JqZmZmZmVlBWbtNmpmZmZmZWYEbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZl0nKSS9uMm8JZJe2OuYrHMknSjpi/2Ow8zMrF2S/kXS/FwfedUoy+4n6bLC66b1m24Z+MZb/YdcN29vSRe0uJ4jJJ08wvx5kt461ji7TdILclJO6ncs1pykN0j6vaRHJT0k6XeSXtvHeIZywfTKuuk/z9NnjncbEbFGRNwx3vXY4JK0jqTvSLpX0l8lXS/pAz3a9kxJCzq8ziFJH8zPJelSSZ+pW2ZfSbdLWq2T27bBJmkzSefk35DHJV0i6Z/6HZdZM5JWknSUpAW5nnqnpK/3OIz/BQ7K9ZE/9XjbbRuYxttYKr0RcUpEvK2HMb4xJ+YSSU/kyu+SwuMF3dp2RNydk/Lpbm3DxkfSWsAvgGOA9YCpwOeAp/oZF/AX4P21F5KeC2wD3N+3iGzCkLQS8GtgY2BbYG3g48BsSf/dz9g6ISIC2B/4b0mbA0h6Hqky8cGI+GsntiNphU6sx/pntP+hpBcBvwOuBzYBNgTOAi6QtG33I7RB1IOy43BgK2BrYE3gLUCvG1AbAzf2eJtjNhCNtxJXeoeJiN/mBtQawOZ58jq1aRFxdyvrafRF8hm1gfASgIg4LSKejognI+KCiPgzLDuLLOl/JT2cj07tXHuzpA3zEdeHJM2V9O95+iqSnpQ0Ob/+tKSl+XuDpC9KOnqEuE4B3lPIsb1IFYK/F7a9taQ/SHpE0iJJx+ZK93LygZb5kt6SXz/b5SB3v/uWpHPzUeMrcoWk9t63Sbo1H6T5tqTf1M5gNCPpRZIulvSgpAcknSJpncL8aZJ+Jun+vMyxI63PQNKrJF2T/0dnAKvk6etK+kX+LB/OzzfK8/aQdHXdeg6V9PP8fBdJN+V13iPpY3mxfYAXAHtExJ0R8Y+IOA84GPh8IY/nSTo8r+NhST+UtEphW2+XdG3O0d9LekVh3jxJH5P055xbZxTfO8LnsKukP0l6LOf0EYV5q0g6OefUI5KukjRF0pHAG4Fj80G7YyPiNuBI4HhJzwG+CZwZEZeMEvcspbNzj+f9/pfCvP2UDmJ+XdJDwLOxWedJ+rikM+umHSPpaElrSzo+l4335DJ3Ul5mtPJpnqTDJP0ZeELSCvn1Pfn/fquk7fPiRwB/iIhPRcRDEfF4RHwT+DHwlby+6bnMPUDSwhzToYXtPaeQVw9KmiNpvbr37ivp7hzvp7r4sVoLGpUDklbOZcbLC8s9T6kusH5+PVqZWJ93I5U3k5TOnj2gVDc5KOfKCnl+0+8A8FrgrIhYGMm8iPhRXSxNy2dJ/65U53lIqQ60YZ7+OUnH5OcrKp00+Wp+vaqkv+UyeQkwCbhO0u3NPtMO/9vGJyIq/yC12B9pMm8/4LLC6/8HXEY6els/7xvAfOAx4GrgjYV5RwA/Bc4AHgeuAV5ZmD8PeGt+/hxgFnA78CAwB1ivLq7pQAAr5NdrA8cDi4B7gC8Ckwr78Dvg68BDed6JwHeAXwJPAG8FdiUdrXgs78cRI2xvCPhCXu/jwAXA5BY+658A9wKPApcCmxfmrQocBdyV518GrNrv/KjKA1gr58tJwM7Aug1y+R/Av5MKmv8AFgLK838DfJtUkd6SdGZs+zzvUuBf8/MLcm7uXJj3L01iGgI+mN9TW/5K0hmQBcDMPO01pLNxK+Rcuxk4pLCeAF4M7Jhzc+v6efn5iTnHt87rOgU4Pc+bnHP7nXneR/Pn8cFRPtcXAzsAKwPPy/t7dJ43Cbguf7dWz5/dG/qdC2V+ACvl7/h/ASsC78r/hy8CzwX+FViNdAT1J8DP8/tWzv/blxXW9adCXi4il7nAusCr8/PTgZMaxLECsBTYMb+eB9wATCMdxPsd8MU879XAYuB1+X++b15+5cJ7rySdqVgv5++H87yZwIImn8VMYAtSmf8K4D5g9zzvQ8D/5c9iUv6OrFX8XtWtaxJwBfAz4O78+Y0W9x455ucA7yH9FmxQKC+WAv+ZPyuXxd39XmyQP/91Cvm5OP/ffw58L5cx6+dc+1Bermn5VMjNa3NerwpsSipDN8zzpwMvys/vBT7QILa3AE/nXJxOKnNPy/FsQfqtqNVfDgEuBzbKMX0POK2wrQC+n2N5Jekg+cs6/Xn60VbuNSwHgBOAIwvLHQicl5+3UiY+m3cjbSfP+zBwU86bdUm9JYp1zpG+A58mlXkfyfmouv2bR/PyeTvggbw/K5NO4lxamHd9fv5PpHrPFYV51xW28Ww9pIV93Y/hbYdh7+3J/7zfSdehxG1a6a19yPkf8H3gfGC1Jv+A95EqHysAh5IKwlXyvCNIFZR3kSosHwPuBFYsJNeohV9hW9PbSOz9qPsRJlVyHwVen/dtFUauSNRvbygn8kvy+oaA2S181v9GqlSsDBwNXFuY9628nqmkwuCfyAWBHy3n8svy/3ZB/p+fA0wp5MHcwrKr5f/p80kF7NPAmoX5XwZOzM+/QDqav0LO648Cs3PePEmThjvLGm/vI/3Ybwr8Jc97tvHW4H2HkI6k1V4HqWvEXcAWdcvWN95+UJi3C3BLfv5+0lHl2jyRKjEjNt4axLY78Kf8fFtSxWWFfv/vq/IA3kThoEGe9ntyQ6lu2S2Bhwuvv0OuTJB6HzzMssrC3aQGz1p16/h1s7Ip5/Le+fk88g96IXduL2z3C3XvvRV4c+G97yvM+yrw3fx8Jk0abw3iORr4en7+b/lzeUWD5YYa5W3+TALYrZW4G7z/2sJ79wPu7ne+TKQH8Cvg3/Pzt5Mqs1NIDZxVC8vtBVzSZB3Plk/59Tzg3wqvX0yqdL+VXP8ozFsK7NRgnS/NeTWVZXWBlxbmfxU4Pj+/mXzQL7/egFT3WaHw3o0K868E9uz3Z+/HsP/3tcBuOUfuKEz/HfD+/LyVMvHfWtlOfn4xuc6aX78158oKo30HSPXFA3N8T5F+X/YtLDuP5uXz8cBXC/PWyPk6nVS3/RupXj8L+CSp3rIGqXfeNwvvG7EBxvJla18bbwPRbTIiHgPewLIjQvfnU6dT8iIrkiqe6wH/HE2uIYiIkyPiwYhYGhFHkRoomxYWuToifhoR/wC+Rqr4btNgVR8CPhURCyLiKVLD711q0m84x7kz6UzFExGxmHQmYM/CYgsj4pgc25N52tkR8buIeCYi/hYRQxFxfX7957zPbx7ho/thRPwlr28OqaI1oog4IVJXjNp+vTKfDn8OqbLy0Yi4J1K3v9/n5axFEXFzROwXERsBLycd+Tm6sMi9hWVrebxGXu6hiHi8sOxdpB9rSGflZpKOTl0PXEjKjW1IDcIHRgntZ6QjVf9J6oIzjKSXKHWRu1fSY8CXSGfKig4B5kTE9aNs697C87/m/YO0j/NrMyKVmqMOJCFpfUmn564ajwEnF2KbBtwVEUtHW489a0Pgnvz519wFIGk1Sd+TdFf+rC8F1il0jzkJeK8kkbpDzimUEf9KanDdpdQdtnaNzgOkCuQwuTydnOfXzC88vyvHCul6hkNz96BHJD1C+t9vWFi+Wd41Jel1SgNC3C/pUdLR51pu/Zh0sPD03D3tq5JWHGl9EVG75qL2d8S4Jb2/0O3pEVKZUfzeFT8P676TSAe6yH9/TPofrggsKvyfvkc6SDta+VRTLPfmksrSI4DF+b21PG74XcnTniEdLFlunSz/XTmrEOvNpAODUwrLt/1dse4ZoRy4GFg1l1Mbk+p4Z+W3tVImDis/RilvNqxbvvh8xO9Ari9+KyJeD6xD6kJ+gqSXFdYxUr3grtqMiFhCOpkzNddt/0iq67yJVA/6Pemkx5vz64ZaKFv7aiAabzBqpffFpKMQn4uIvzdZRe36i5tzn9pHSF0ZG/4QRsQzpIrjhiyvlcKvfvmmiV2/7WbTRqlINNJWAazUp3l27gf8GOloCHkbk0mN2dtHWoe1LiJuIZ2Jevkoi0I6UrWepDUL015A6oILqcDaFPgX4DcRcVOevysjFGCFWP5KOqr8HzRovJGO4t0CzIiItUhHuFS3zB7A7pIOaWF/GllEOpsNpFH6iq9H8GXSgZ1X5NjeV4htPvCCZgdWrKFFwNT8+dfUBls6lJRnr8uf9ZvydAFExOWkayXfCLyXQi5FxFURsRup3Ps56YASpDNvO0tavS6OfyUdpb28MG1aXUwL8/P5pDN+6xQeq0XEaW3t+fJOJZ0dnxYRawPfLezrPyLicxGxGakXwttZNvBPNFpZA03jzpWx7wMHAc+NiHVI3UaL/5dWt2Od8XPgFUrXGb2d1O17PilPJxf+h2tFRO2695HKp5ph/8eIODUi3kCqOwT5ejbSd2WPBnG9m9RroXjgeqTvys51ObdKRNyDlc5I5UCup84hneV6L/CLwgHeVsrEaGU7eZFhv88Mz6/RvgPLNpiu9f8W6UDDZi18BAtJ34NanKuTzrTV8vU3pAPPrwKuyq93JF2acWmjFbZYtvbVwDTeihpUem8GPgD8StKmjd4j6Y3AYaRCbt38z3qU4f+saYXln0NK1IUsr93Cr5XEbvQjXD+taUWiQ97LslPxa5NOS5O38QDp9PSLGr7TRiXppfkAQm2Ah2mkQvfykd8JETGf1ED7stJACa8gjWB3Sp7/V9J1nAeyrLH2e9JZ4lEbb9knSV0q5jWYtybperQlkl5KauTVWwhsDxws6SMtbrPoXGALSbvnxtaBpC6jo1kTWAI8ImkqaaTCmitJPzqzJa2eP7vXjyG2ieQPpO5ZBytdxP5O0g8hpM/6SdJnvR7w2Qbv/xFwLLA0Ii6DZ4eK3lvS2rlnw2OkA16QGngLgJ8oDZiwoqQdSd2Aj4iIRwvrPlDSRnnbnyRdowzph/jD+QCX8v9617qDHSPKuVF8KO/vQxHxN0lbk8rI2vJvkbSF0lnHx0hdeWr7dB/Qyr0NR4p7ddJvwP15ex+gtQM91iUR8TfStfGnAldGGuV5Eema4aMkraU0IMiLJNV6xYxUPi1H0qaStpO0Muk390mW5dXngH+SdKSk9SStKek/SQcNDqtb1f8onSnfnFQ/qn1XvgscmSuwtUEudhvzh2LdNlo5cCrpmq298/OadsvE0bYzB/iopKlKA+48m2+jfQckHaJ0S5ZV82/KvqTvxZ9a2P9TgQ9I2jJ/J75Euq5tXp7/G1L+35RP3gyRLgW5MyKajZhd+rJ1IBpvrVR689GETwK/VmH0uoI1SRWS+4EVlO65s1bdMq+R9M5ccTyE5Y/61rRV+LVQuLeqaUWiQ9Yk7fODpOutvlSbkY/wnAB8TWnUw0mSts1fJmvN46SLh6+Q9AQpt24gnc1oxV6kBvVCUteIz0bEhYX5vyGd4b2y8HpNmhx9qhdpJKiG90wkXQP63rwP32dZRaB+HXeTGnCHaZRRIhu89wHSUeWvknJwM1KXiNG65n6O1F30UVID8GeFdT4N/DPp7PzdpEbCe9qJa6LJP4DvJPX7f5j0edU+06NJ1xk8QMrf8xqs4sekH8L6M7j7APOUzup/mNz9LHerfCvpINcVpIbQ10hd0/9f3TpOJZWld+THF/M6/kga6OfYHPPcHH+rppIqycXHi0gX2H9e0uPAZ1h2thDSgYWf5nhvJn3favcK/QapK/3Dkr7ZbKMjxZ3Pnh9FakzfR7re+Xdt7JN1x0mk/0Uxv99PGujnJtL/8acs697YtHxqYmXS9coPkHrPrE+q2xBp1NI3kAYSmUc6MPWvpEF96nPjN6R8ugj434io3fP2G6SDwBfkvL6c9LtkJTRaORARV5AG29iQ1HumNr2tMrGF8ub7pLL3z6RG1y9JderagYWRvgNP5nXfS8rrA0kDWY16/9eIuAj4H+BMUr6/iOGXHP2e9JtUq+fcRDro0bTeU4myNUpwceV4H6Qf1jmk06RP5L/fIzW+9mP4hYX/TuofO704j3TB5PGkH9pFwCcYPgjJEQwfbfJP5NHQYtkFlcXRJv+bdPHn46SuhF+qi3k6y482+R1S5fHRvP49o8HFkXnaidQNEEAaTOWuvM1fkL6UJzfZ3hCFC+YbbaPB57wGcHZe/12kL+OzF2qSviBH58+/NhqlRzjzoyuP/D1bCLyl37H40db/bdVchszo8HqfLYP98KNfD1IXxL9SN/BOWR71dQE//OjGgzSOw139jmNQH7Uhxs3MSi93l7uCdKTu46QjdC+MZYP4WMkp3Vj77RGxXYfXO490QOrXnVyvWavy5RRfIzXc/q3f8TQiaTrLRsr2QE3WEZJWJd2S4gLS+A5nApdHxCH9jGtQ+SJ9M6uSbUld42rdL3aPiCclfZdlo7wVnRwRH+5lgNZcbmCJNBy62cBQGijhPlKvlJ36HI5Zr4nUBfgM0sHVc0ldya0LfObNhpG0N6nLab27osHIQGZmZmZm1htuvJmZmZmZmVVAKbpNTp48OaZPn77c9CeeeILVV6+/tU/5Oe7Oufrqqx+IiOf1O46RVCV/yxYPlC+mTsdT9vxtlLtl+5804hjHb7T4yp67UJ2ytxe8z8OVPX+LuVvW/10Z4ypjTNDZuFrK3X6PmBIRvOY1r4lGLrnkkobTy85xdw7wxyhBjo70qEr+li2eiPLF1Ol4yp6/jXK3bP+TRhzj+I0WX9lzNypU9vaC93m4sudvMXfL+r8rY1xljCmis3G1krsDcZ83MzMzMzOzQVeKbpPNXH/Po+w369y23jNv9q5disasPe3mr3PXrLnphe/SoVssbem75e/UxOSy16pqept1XnD+TkQ+82ZmZmZmZlYBbryZmZmZmZlVgBtvZmZmZmZmFeDGm5mZmZmZWQW48WZmZmZmZlYBbryZmZmZmZlVgBtvZmZmZmZmFeDGm5mZmZmZWQW48WZmVkKSTpC0WNINhWnrSbpQ0m3577qFeYdLmivpVkk79idqMzMz6yY33szMyulEYKe6abOAiyJiBnBRfo2kzYA9gc3ze74taVLvQjUzM7NecOPNzKyEIuJS4KG6ybsBJ+XnJwG7F6afHhFPRcSdwFxg617EaWZmZr2zQr8DMOsWSdOAHwHPB54BjouIb0haDzgDmA7MA94dEQ/n9xwO7A88DRwcEef3IXSzZqZExCKAiFgkaf08fSpweWG5BXnaciQdABwAMGXKFIaGhobNX7JkyXLTyuDQLZY++3zKqsNfN9PP/Sjr51hT9vjMzKwxN95skC0FDo2IayStCVwt6UJgP1LXs9mSZpG6nh1W1/VsQ+DXkl4SEU/3KX6zVqnBtGi0YEQcBxwHsNVWW8XMmTOHzR8aGqJ+WhnsN+vcZ58fusVSjrp+9J+veXvP7GJEIyvr51hT9vjMzKwxd5u0gRURiyLimvz8ceBm0tkIdz2zqrpP0gYA+e/iPH0BMK2w3EbAwh7HZmZmZl3mM282IUiaDrwKuIIedD2D1rt21XS7C1MZu0mVLaayxdPAOcC+wOz89+zC9FMlfY101ngGcGVfIjQzM7OucePNBp6kNYAzgUMi4jGpUQ+ztGiDaWPqegZwzClnt9S1q6bbXbzK2E2qbDGVKR5JpwEzgcmSFgCfJTXa5kjaH7gb2AMgIm6UNAe4idRd+EB39zUzMxs8brzZQJO0IqnhdkpE/CxPvk/SBvmsm7ueWSlFxF5NZm3fZPkjgSO7F5GZmZn1m695s4GldIrteODmiPhaYVat6xks3/VsT0krS9oEdz0zMzMzsxLxmTcbZK8H9gGul3RtnvZJ3PXMzMzMzCrIjTcbWBFxGY2vYwN3PTMzMzOzinG3STMzMzMzswpw483MzMzMzKwC3HgzMzMzMzOrgFEbb5KmSbpE0s2SbpT00Tx9PUkXSrot/1238J7DJc2VdKukHbu5A2ZmZmZmZhNBK2felgKHRsTLgG2AAyVtBswCLoqIGcBF+TV53p7A5sBOwLclTepG8GZmZmZmZhPFqI23iFgUEdfk548DNwNTgd2Ak/JiJwG75+e7AadHxFMRcScwF9i6w3GbmZlZCbnHjplZ97R1qwBJ04FXAVcAUyJiEaQGnqT182JTgcsLb1uQp9Wv6wDgAIApU6YwNDS03PamrAqHbrG0nRAbrqfXlixZUoo42lXVuM3MrFRqPXaukbQmcLWkC4H9SD12ZkuaReqxc1hdj50NgV9Leonvs2lmtryWG2+S1gDOBA6JiMekZrfPanhfrVhuQsRxwHEAW221VcycOXO5Nx1zytkcdX17t6Kbt/fy6+m1oaEhGu1P2VU1bjMzK498YLd2cPdxScUeOzPzYicBQ8BhFHrsAHdKqvXY+UNvIzernumzzm1r+Xmzd+1SJNYrLbWMJK1IaridEhE/y5Pvk7RBPuu2AbA4T18ATCu8fSNgYacCNjMzs2roZI+dvL6O99oZhB4nE7HnzETcZzNoofGmdIrteODmiPhaYdY5wL7A7Pz37ML0UyV9jdT9YQZwZSeDNjMzs3LrdI8d6E6vnTL02BmvidhzZiLusxm0dubt9cA+wPWSrs3TPklqtM2RtD9wN7AHQETcKGkOcBOp3/uB7rduZma95u5E/eMeO2Zm3TFq4y0iLqPxUTGA7Zu850jgyHHEZWZmZhXkHjs2qCTNAx4HngaWRsRWktYDzgCmA/OAd0fEw/2K0QZfK/d5MzMzM2tVrcfOdpKuzY9dSI22HSTdBuyQXxMRNwK1Hjvn4R47Vm5viYgtI2Kr/LrhfY/NuqW9oRzNzMzMRuAeOzbBNBtF1awr3HgzMzMzMxtdABdICuB7eQCdZqOoDtNspNTiqJnt3tt4LFodobOMo3mWMSbofVxuvJmZmZmZje71EbEwN9AulHRLq29sNlJqcdTM/docZGksWh1dtYyjeZYxJuh9XL7mzczMzMxsFBGxMP9dDJxFupn8fXn0VOpGUTXrCp95MzMzMzMbgaTVgedExOP5+duAz9N8FNVSavUWKodusZT9Zp3rW6iUkBtvZmZmZmYjmwKclW82vwJwakScJ+kqGtz32Kxb3HgzM6sY32vIzKy3IuIO4JUNpj9Ik1FUzbrB17yZmVWT7zVkZmY2wbjxZgNN0gmSFku6oTBtPUkXSrot/123MO9wSXMl3Sppx/5EbTYmu5HuMUT+u3v/QjEzM7NucLdJG3QnAscCPypMq52hmC1pVn59mKTNgD2BzYENgV9LeklEPN3jmM1G0/F7DdWU9T46xfsfTVm1O/dD6uR+l/VzrCl7fGZm1pgbbzbQIuJSSdPrJu8GzMzPTwKGgMPy9NMj4ingTklzScMA/6EnwZq1ruP3Gqop6310ivc/OnSLpRx1fed/vlq9/1Eryvo51pQ9PjMza8yNN5uImp2hmApcXlhuQZ62nNHOXkD7Zwe6fRS8jEfayxZT2eJppnivIUnD7jWUc9r3GjIzMxtAbryZLaMG06LRgqOdvQA45pSz2zo70Mmj/o2U8Uh72WIqWzyNTLR7DZmZmdkybrzZRNTsDMUCYFphuY2AhT2PzmxkvteQmZnZBOXGm01Ezc5QnAOcKulrpAFLZgBX9iVCsyZ8ryEzMyuzdntWzJu9a5ciGUxuvNlAk3QaaXCSyZIWAJ8lNdqWO0MRETdKmgPcBCwFDvRIk2ZmZjZRuYt7+bjxZgMtIvZqMqvhGYqIOBI4snsRmZmZmZmNjW/SbWZmZmZmVgFuvJmZmZmZmVWAu02amZmZmVlftHpd3aFbLGW/WedO+AFOfObNzMzMzMysAtx4MzMzMzMzqwA33szMzMzMzCrAjTczMzMzM7MKcOPNzMzMzMysAtx4MzMzMzMzqwA33szMzMzMzCrAjTczMzMzM7MK8E26zczMaP1GsTUT/UaxZmbWez7zZmZmZmZmVgFuvJmZmZmZmVWAG29mZmZmZmYV4GvezEqi3ettwNfcWHmMJX/NzMysPW68mZmZmZlZJUz0waXceDMzMzMzs4E0aD2bfM2bmZmZmZlZBbjxZmZmZmZmVgFd6zYpaSfgG8Ak4AcRMbtb2yqa6P1gbfz6lbtmneD87Z2Rfm8O3WIp+9XN9+/NyJy7VmXOX+uVrjTeJE0CvgXsACwArpJ0TkTc1I3tmXWKc9eqrFP565Ejrddc9lqVOX+tl7p15m1rYG5E3AEg6XRgN8BJbGVXqdxtp5J96BZLmdm9UKwcKpW/E82gXTTfYc5dqzLn74Bpt35V39NiNOMp27vVeJsKzC+8XgC8rriApAOAA/LLJZJubbCeycADXYmwFsdXurLarsfdJWWMe+Meb2/U3IXy5G87DobJB7+vPPFkpfqM6Hw8pcvfFnK3bP+T5Rw8gWLs0m8UjB5f6XIXulP2dvEz7qXSfye6YKR9Ll3+jpC7pfzflbGcLWNMMLa4Rih3Rs3dbjXe1GBaDHsRcRxw3Igrkf4YEVt1MrBecNyVNmruQjXzt2zxQPliKls8YzDusrcKn4FjHL8SxjewZW8veJ/7bsxlb8n241lljKuMMUHv4+rWaJMLgGmF1xsBC7u0LbNOcu5alTl/raqcu1Zlzl/rmW413q4CZkjaRNJKwJ7AOV3allknOXetypy/VlXOXasy56/1TFe6TUbEUkkHAeeThkw9ISJuHMOqRuwaUWKOu6I6mLtQvs+zbPFA+WIqWzxt6VD+VuEzcIzjV6r4Brzs7QXvcx+NM39Lsx91yhhXGWOCHseliOW6lJuZmZmZmVnJdKvbpJmZmZmZmXWQG29mZmZmZmYVUNrGm6SdJN0qaa6kWX3Y/gmSFku6oTBtPUkXSrot/123MO/wHOutknYsTH+NpOvzvG9KUp6+sqQz8vQrJE3vUNzTJF0i6WZJN0r6aFViHxS9zN1u5+kY4ul6/rUZzyqSrpR0XY7nc/3+jMqs3+VujqEjOd3lGDuW512Kr2N5XyVlyN9OKVvZ3gtl+/3ohX7lbJnLMEmTJP1J0i9KFNM6kn4q6Zb8mW3b17gionQP0sWetwMvBFYCrgM263EMbwJeDdxQmPZVYFZ+Pgv4Sn6+WY5xZWCTHPukPO9KYFvSPUB+Beycp38E+G5+vidwRofi3gB4dX6+JvCXHF/pYx+ER69zt9t5Wsb8azMeAWvk5ysCVwDb9PMzKuuj17nb7Zzucowdy/MuxdexvK/Koyz528H9KVXZ3qN9LtXvRw/2t285W+YyDPhv4FTgF/l1GWI6Cfhgfr4SsE4/4+p78jb5kLYFzi+8Phw4vA9xTK8rOG8FNsjPNwBubRQfabShbfMytxSm7wV8r7hMfr4C6c7s6sI+nA3sUMXYq/joR+52M0/LmH/jiGU14BrgdWWIp2yPspS7edvjyuk+xDumPO9RbGPO+37l4hj3szT528F9Km3Z3qP9L83vR5f2rzQ5W5YyjHR/vIuA7VjWeOt3TGsBd1JXz+1nXGXtNjkVmF94vSBP67cpEbEIIP9dP09vFu/U/Lx++rD3RMRS4FHguZ0MVqk746tIR10rFXuFlSF3O/m/HrMu5l+7cUySdC2wGLgwIvoaT4mVIXebaff/1TPjzPNuxtWJvK+SQdiH0UyYcqssvx9dVoqcLVkZdjTwCeCZwrR+x/RC4H7gh7k75w8krd7PuMraeGvUNzl6HkXrmsU70n50dR8lrQGcCRwSEY+NtGiTOPoWe8WV+bMZy/96bBvqbv61JSKejogtSUf0tpb08n7GU2JV3Me+xtyBPO+aDuV9lQzCPozVQJVbZfr96LK+x1mmMkzS24HFEXF1q29pMK0bn98KpC7M34mIVwFPkLpJ9i2usjbeFgDTCq83Ahb2KZai+yRtAJD/Ls7Tm8W7ID+vnz7sPZJWANYGHupEkJJWJH0ZT4mIn1Up9gFQhtzt5P+6bT3IvzGJiEeAIWCnMsRTQmXI3Wba/X91XYfyvOvGmfdVMgj7MJqBL7fK+vvRJX3N2RKWYa8H3iFpHnA6sJ2kk/scU207C3LvBYCfkhpzfYurrI23q4AZkjaRtBJpUIxz+hwTpBj2zc/3JfURrk3fU2kUxk2AGcCV+TTq45K2ySMdvb/uPbV1vQu4OHLn2PHI2zkeuDkivlal2AdEGXK3k//rtvQo/9qJ53mS1snPVwXeCtzSr3hKrgy520xb/69uB9OpPO9ifB3J+27F1yVlzt9OGehyq2y/Hz3Qt5wtYxkWEYdHxEYRMZ30WVwcEe/rZ0w5rnuB+ZI2zZO2B27qa1ydvrCvUw9gF9LoN7cDn+rD9k8DFgH/ILWi9ydd13URcFv+u15h+U/lWG+lMKoRsBVwQ553LPmCR2AV4CfA3PxPfWGH4n4D6fTsn4Fr82OXKsQ+KI9e5m6387SM+ddmPK8A/pTjuQH4TJ7et8+ozI9e5m63c7rLMXYsz7sUX8fyvkqPMuRvB/elVGV7j/a5VL8fPdrnvuRsBcqwmSwbsKTvMQFbAn/Mn9fPgXX7GVetMm5mZmZmZmYlVtZuk2ZmZmZmZlbgxpuZmZmZmVkFuPFmZmZmZmZWAW68mZmZmZmZVYAbb2ZmZmZmZhXgxpuZmZmZmVkFuPFmZmZmZmZWAW68NSHpRElf7PSy3dRmzPtJuqzbMVnvDXrujnM7Q5I+2O3tWGOS3ijp1hLEUYq8t2qTtETSC1tcNiS9uEPbnZ7Xt0KDeS/IcU3Kr38lad9ObNfMymFCNt5yBe5hSSt3Yd0zJS0ovN4gF7JTCtM+1WTaeZ2OxwaLc9eqLCJ+GxGbjnc9Ha4Iz5T0TK7wLpG0QNIcSa/txPpH2bYkfVzSbZKelHS3pNnd+H5b+yTNk3SfpNUL0z4oaQggItaIiDs6sJ3lDqZK2kjSmZIekPSopOsl7TfauiLi7hzX0/n1zhFx0nhjNLPymHCNN0nTgTcCAbyj29uLiEXAXOBNhclvAm5pMO3Sbsdj1eXctbJrdCagIhZGxBrAmsA2pBz/raTtu7zdbwIHAO/P294Z2A6Y0+XtWutWAD7ah+3+GJgPbAw8l5Qj9/UhDjMrmQnXeCMVgJcDJwLPdiWQ9CpJ10h6XNIZwCqFeY2Oii135DcfnfsVsGHhKO6GpIrtm/Iyk4BXAd+om7ZtXg5J/ybp5nyG5XxJGxe28VJJF0p6SNKtkt7daCclrSnpEknfzEd3nyvpHEmPSboSeFHd8t+QND/Pv1rSG/P050v6q6TnFpZ9jaT7Ja3YwudtnTNRc7fp+5S6v31L0rl5/6+Q9KLC/B0k3ZKPXB8LqOVP256Vz0AcLumm/L/9oaRVlM/WSjpM0r3ADyWtLOloSQvz42jlM0la/uzuhvnswv2S7pR0cGHeJEmflHR7/t9eLWmapNqBgutynr4nL/92SddKekTS7yW9orCupt+RokgWRMRngB8AXymsY0xlpKQXS/pNzsEH8vaRNAP4CLB3RPwhIpZGxI3AvwI7SdouL3eipO/m78DjeV0tfa9G+35YS/4f8DFJ69TPKJalSr+x/5fz4ypJX9Tylya8Veks68P5/yJJLwO+C2yb8/mRvOxrgRMj4omcG3+KiF81ClDSv+bv6MtV16VSha7iyr8Hkv43x3CnpJ0L69lE0qU5V36dYzx5tA9I0k8k3Ztz/FJJmxfmrSrpKEl35fmXSVp1tHWaWXMTtfF2Sn7sKGmKpJWAn5OOdK0H/IT0A9qWiHiCdOR0Ye62sEZELKRQASZVfm8BLqqbtiJwpaTdgU8C7wSeB/wWOA2erWBfCJwKrA/sBXy7WFDm5Z6b1/+7iDg4IgL4FvA3YAPg3/Kj6Cpgy7z/pwI/kbRKRNwLDAHFivb7gNMj4h/tfkY2LhMud4HVWnjfXsDngHVJZwqPzOuaDJwJfBqYDNwOvL7dz8aetTewI+nAz0tInyvA80m5tzHpLNKnSGevtgReCWxdWPZZkp4D/B9wHTAV2B44RNKOeZH/Jv1vdwHWIpVZf42IWu69MufpGZJeDZwAfIh0luJ7wDlKDcmxfkd+Brxay7rMjbWM/AJwASk/NwKOyctsDyyIiCuLG42I+aSDNDsUJu+d1zMZuJZUBrT6vWr4/bCW/ZH0//3YKMt9C3iC9H3Yl8IBtoK3kxplryTly44RcTPwYeAPOZ/XycteDnxL0p6SXtBso5I+QDrI8NaIuKGF/XkdcCspl74KHC+pdlDrVOBK0nfoCGCfFtYH6cDfDFIOXkPOz+x/gdcA/0T67nwCeKbF9ZpZIxExYR7AG4B/AJPz61uA/yJVRBcCKiz7e+CL+fl+wGV16wrgxfn5iYVlZ5J+kIvLTgeeJv14/hdwZJ5+T2HaJXnar4D9C+99DvBXUsXoPcBv69b9PeCzhThOAG4APl5YZlLe75cWpn2pfp/q1vswqXJE3u7vCuu6F9i63//PifSYwLnbyvt+UJi3C3BLfv5+4PLCPAELgA/2+/9ZtQcwD/hw3ed8e86ZvwOrFObdDuxSeL0jMK8+x0iVyLvrtnM48MP8/FZgtybxPJvD+fV3gC/ULXMr8OYWviPL5X2e/tK8nalNYmipjAR+BBwHbFT3/k8X87Nu3unA92NZjp9emLdG/k5OG8/3w4+W8/6twMuBR0kHpT4IDBXzkGW/sZsW3vtFCmVvXvYNhddzgFn5+X4sX06vC8wGbsz/72uB1+Z50/P6PgbcVMytwrwV8ushcpmXtzO3sOxqednnAy8AlgKrFeafDJzc5me2Tl7n2qTfgCdr3xM//PCjM4+JduZtX+CCiHggvz41T9sQuCciorDsXZ3aaETMI1Ua30CqSPw2z/pDYVqtK9DGwDeUuv48AjxEqnROzfNeV5uX5+9NKnhrdgVWJXXDqHkeqd/+/MK0Yfsn6VCl7m6P5vWuTToyB3A2sJnSqFo7AI9G3dFi67qJmrutvO/ewvO/kiq3kD6bZ3M+f0bF74C1p7782DA/vz8i/laYtyHDc7C4bNHGpG66xf/tJ4HaYDjTSA3BVmwMHFq3rml5u2P9jkwlVUIfgXGVkZ8gfQ+ulHSjpFqvhwdIPSEa2SDPrynm8RLSd2tDxvf9sBZFOqP1C2BWk0Ua/cY2Kmta/l9ExMMRMSsiNid9J64Ffl44SwbwceBbEbGg0TqaeDaGiPhrfroGKZ8eKkxrtg/DKHVvnq3UvfkxUoMX0ndjMqmLcqvfYzNrQVUvLm9b7mP9bmCS0rUZACuTjhItAqZKUuEH/gUsK3CeIB2hqq2r+MNYL5pM/y2porsty7pT1Ka9ATg2T5tPOrtxSv0KlK5z+E1E7FA/r+D7pCN2v5S0U6TucPeTjqhNI52xqe1fbb1vBA4jdeO5MSKekfQw+fqgiPibpDmkSsFLSd2PrEcmeO7Ob+F9zSwi5XwtBhVfW9uKn90LSGezYPm8WUhqVNzYYNmi+cCdETGjyfbmk7pottIVrJZ7y3UJlPRmRv6ONPMvwDUR8cR4yshI3Sr/PcfyBuDXStftXUzq4rh18WCYpGmkbqdfKMRSzOM1SN3PFjK+74e157OkLoFHNZhX+43dCPhLntZOWdOs7E0zIx6Q9L+k8ne9wqy3AedJujcizmxje40sAtaTtFqhAdfKPrwX2I10hnIe6aBG7bvxAOlyjReRukebWQdMpDNvu5O6HmxGum5hS+BlpEro7qSC92BJK0h6J+k6jZrrgM0lbSlpFVJf8GbuA54rae266ZeSunEtjIjH8rTL8rS1SWcyIJ11OLx2zYKktSXtkef9AniJpH2ULoRfUdJrlS54LjqI1GXoF5JWjTRk8M+AIyStJmkzhvfHXzPv//3ACpI+Q7rGpOhHpC4X7yB1pbDe2Z0JmrttvK+Rc/O+v1Pp4v2DGX5GwtpzoNLw5euRzpCd0WS504BPS3qe0nWHn6FxmXEl8JjSYCer5iP4L9eyIfp/AHxB0gwlr9CyQUHuA4r31/o+8GFJr8vLri5pV0lrkvJzpO/Is/J7p0r6LKl73CfzrDGXkZL2kLRRfvkwqaL+dET8hfSdOUXSNnn/Nyddp/nriPh1Yd27SHqD0vV7XwCuiHRt3Hi+H9aGiJhLyvmDG8yr/419Kal8bNV9wEb5/wuApK/k78MKOY//g9Tl8cHC+24EdiJdGzeuEYgj4i7S9X1HSFpJ0rbAP7fw1jWBp4AHSQcKv1RY5zOk7vBfUxqcaJKkbeVbYZiNy0RqvO1Lupbi7oi4t/YgnTXYizTIwn6kH9f3kApiAPKP7OeBXwO3kSquDUXELaTKyx1K3Vhq3YV+Q7qYt/jea0ndxK6uHemKiLNIFx+frtQF4QbSQBJExOOkI217ko663puXHVYQ5qPLB5COyp6dK+0HkbpG3Eu6DuKHhbecT7pe6S+k7kR/o667RET8jnSR8TW5K531zoTNXdJ1JKO+r8n+PADsQbpu5EHSBfW/G+191tSppIE37siPZje5/iKpEvhn4HrS2Yrlls0V3n8mHYy4k3SU/gekAwIAXyNdF3QB8BhwPCnnIB2EOCnn6bsj4o+ks1vHkr4Hc0nfCSLi74zwHck2lLQEWEIamGQLYGZEXJDnj6eMfC1wRV7/OcBHI+LOPO+gvM8n522fR7pGqX5AlVNJZ34eIg3+sHfeZkvfK+uYzwOrN5l3ECl37yWdeT2N1KhpxcWkhti9kmrdZVcDziJ1272DdDZ7uQZaRFxHGgjl+yqMHDlGe5N6WDxI+s6ewej78CPSd+Ie0vV3l9fN/xipHLiKlL9fYWLVPc06TsMvAzBrTtLFwKkR8YN+x2JmvSNpHmnQg1+Ptuwo69mONIDGC0dduIK6UUZKOpE0oMpyI3ZaeUn6CvD8iNh31IVLSv+/vfsPlew+6zj+frJNatlGknTTdekGb1pCoTQ1DcuqRMqt1brNSreClUCVBFIWxEDFiN5S0Opfq1BRRIqx1i7aNko1zdKANqRe/M9m12yymyYx23hrN7tmjT9qsn9Yt33845zb3Cz3x/w653y/d94vGO7Mydydz8w8Ocxzv88503ytxdOZ+ZtDZ5H0Cv/6oZG0o0y3svGolCRt5e00q2zbjvvI+RbN9+29ox293Q/cTbNyVo125PYtEXFFRBygOZbtiwPHknSZuTlhiSYXEUdpjq36SDumI0ljiYg/oBn7qnYlYiPuI0Vz7Nfnac7aeIHmxCYPDppofD9AM1L8BpqzDP9iZj4WER+i+QqKy32jPRumpB45NilJkiRJFXBsUpIkSZIqUMTY5K5du3JhYWHQDBcvXmTnzo1OIjUMM8GJEydezMzre3vACQxZvyXWCJhrVen1W8K+dz2l1s9GassLW2cuvXZh4/ot8f0oMRNs31w11K80qSKat4WFBY4fPz5ohuXlZRYXFwfNcDkzQUR8o7cHm9CQ9VtijYC5VpVevyXse9dTav1spLa8sHXm0msXNq7fEt+PEjPB9s1VQ/1Kk3JsUpIkSZIqYPMmSZIkSRUoYmxylhaWHhrr/itHDnaURBrPuLUL8JkD5R2rIHVhkv8/3L/Pp1PPf4u7xqgX60RSTbZd8yZJKt9mzdi9N18a68O3JEnzwrFJSZIkSaqAzZskSZIkVWDuxyZXR3dGHdNxNl6SJEnSEFx5kyRJkqQK2LxJkiRJUgVs3iRJkiSpAjZvkiRJklSBuT9hiVQzv4xWkiRpfrjyJkmSJEkVsHmTJEmSpArYvEmSJElSBWzeJEmSJKkCNm+SJEmSVAGbN0mSJEmqgM2bJEmSJFXA5k2SJEmSKmDzJkmSJEkVsHmTJEmSpAq8ZugAkvqzsPTQWPdfOXKwoySSJEkalytvkiRJklQBmzdJkiRJqoDNmyRJkiRVYKpj3iJiBXgJ+A5wKTP3RcR1wF8CC8AK8HOZ+V/TxZQkSZq9cY8FBo8HljScWay8vTszb8nMfe3tJeCRzLwJeKS9LUmSJEmaQhdjk4eAo+31o8AHOngMaWoRsRIRpyLiZEQcb7ddFxEPR8Sz7c9rh84pSZIkwfRfFZDAlyMigT/OzPuA3Zl5HiAzz0fEG9f7xYg4DBwG2L17N8vLy1NGadx786WJfm/360b73VnlHMXLL7/c6+ONosRMU3p3Zr645vbqyvGRiFhqb//6MNEkTcOvxpAkbTfTNm+3Zea5tkF7OCKeHvUX20bvPoB9+/bl4uLilFEad00wuw5N4/aJUyO8HKcujv1vT/qBYHl5mVm9LrNSYqYZOwQsttePAsvYvEnSzHi8vCRNbqrmLTPPtT8vRMQDwH7ghYjY06667QEuzCCn1IWiVo4nWTUedcV4UpM+r1JXaEvNJc0hpx4kaQITN28RsRO4IjNfaq+/F/ht4BhwJ3Ck/fngLIJKHShq5XiSVeORV4wntPKhxYl+r9QV2lJzSXLqQZJGMc2nvt3AAxGx+u98LjP/NiIeBf4qIu4G/hX44PQxpdlz5Vi1cuxMlet06qHriQQYfyqh1FV/c0n1mbh5y8zngB9aZ/t/AO+ZJpTUNVeOtQ04dqZadTr18IeffbDTiQQYfyqh1FV/c0n16XbvJpXLlWNtN46dqQpOPUjS5GzeNJdcOVblijrZziQ2G2vrY+xtPdvtBD2bGSqzUw+SNB2bN0mqT1En25nEZifo6fpEPBvZbifo2cyAmZ16kKQp2LxJUmUcO1OtnHqQpOlcMXQASdLoImJnRFy9ep1m7Ow0r4ydgWNnkiRtS668SVJdHDuTJGlO2bxJUkUcO5MkaX45NilJkiRJFbB5kyRJkqQK2LxJkiRJUgVs3iRJkiSpAjZvkiRJklQBmzdJkiRJqkDRXxWwsPTQ0BEkSZIkqQhFN2+ShjXuH1BWjhzsKIkkSZIcm5QkSZKkCti8SZIkSVIFbN4kSZIkqQIe89YDjxuSJEmSNC1X3iRJkiSpAjZvkiRJklQBmzdJkiRJqoDNmyRJkiRVwBOWSJIkjWHcE5F95sDOjpJImjeuvEmSJElSBWzeJEmSJKkCjk1KkqY27hiZJEkanytvkiRJklQBmzdJkiRJqoDNmyRJkiRVwGPepI54DJAkSZJmyZU3SZIkSaqAzZskSZIkVcCxSUmSpA6dev5b3DXGKP3KkYMdppFUM5u3Aq0eK3XvzZdG2tm7k1cpxq1dsH5VjnGPU7V2JUl9s3nbBvzAoZpZv5IkSaOxeZMkvYpnSpUkqUw2b5IkSQVxIkHSRjo722REHIiIZyLiTEQsdfU40qxZu6qZ9ataWbuStLVOVt4iYgfwR8BPAmeBRyPiWGZ+rYvH03imGYka50QU4yjlr4bWbvms341Zv6qVtdu/haWHOj25VB/76lL2vVKfuhqb3A+cycznACLifuAQ4E5YpbN2VTPrt0eTnF11XF19YF7NXNCHX2u3cB4LK5UhMnP2/2jEzwIHMvPD7e1fAH44M+9Zc5/DwOH25luBZ2YeZDy7gBcHznA5M8EPZub1fT3YKLXbbi+lfkusETDXquLqt6Da3Uyp9bOR2vLC1pmLq912+yj1W+L7UWIm2L65eq1fqU9drbzFOtte1SVm5n3AfR09/tgi4nhm7hs6x1pmGsSWtQvl1G+p74e5BlPdvnc9tb1PteWFIjPPbN9b4HMrMhOYS6pRVycsOQvcsOb2XuBcR48lzZK1q5pZv6qVtStJI+iqeXsUuCkiboyIq4A7gGMdPZY0S9auamb9qlbWriSNoJOxycy8FBH3AH8H7AA+nZlPdvFYM1TiGJGZelZh7Zb6fphrABXW70Zqe59qywuFZZ5x7Rb13FolZgJzSdXp5IQlkiRJkqTZ6uxLuiVJkiRJs2PzJkmSJEkVmMvmLSJWIuJURJyMiOPttusi4uGIeLb9eW0POT4dERci4vSabRvmiIiPRsSZiHgmIn6qx0wfj4jn29frZETc3mcmNazbiXJZu4UptX62yHxDRPx9RDwVEU9GxEdKzh0R3xcRX42Ix9u8v1Vy3lmJiANt/jMRsdTzYxdbIxGxIyIei4gvFZTpmoj4QkQ83b5mP1pCLqkKmTl3F2AF2HXZtt8FltrrS8Dv9JDjXcCtwOmtcgBvAx4HXgvcCHwd2NFTpo8Dv7rOfXvJ5OV7r7d1O34ua7ewS6n1s0XmPcCt7fWrgX9usxWZm+Y7017fXr8S+EfgR0rNO6PnvKPN/Wbgqvb5vM0aSYBfAT4HfKm9XUKmo8CH2+tXAdeUkMuLlxouc7nytoFDNDsT2p8f6PoBM/MfgP8cMcch4P7M/N/M/BfgDLC/p0wb6SWTNmXdbp5rI9buQEqtn81k5vnM/Kf2+kvAU8CbKDR3Nl5ub17ZXrLUvDOyHziTmc9l5reB+2meVy9KrZGI2AscBD61ZvPQmb6f5o84fwqQmd/OzP8eOpdUi3lt3hL4ckSciIjD7bbdmXkemp0w8MaBsm2U403AN9fc72y7rS/3RMQT7cjT6ijD0JnmjXU7GWu3fCXXz6tExALwTprVrGJzt6NyJ4ELwMOZWXTeGSjmORRWI78P/Brw3TXbhs70ZuDfgT9rxzk/FRE7C8glVWFem7fbMvNW4H3AL0XEu4YONIJYZ1tf3/PwSeAtwC3AeeATBWSaR9bt+KzduhX1PkXE64G/Bn45M/9ns7uus63X3Jn5ncy8BdgL7I+It29y98HzzkARz6GkGomInwYuZOaJUX9lnW1dvIavoRmd/mRmvhO4SDMmOXQuqQpz2bxl5rn25wXgAZrl9xciYg9A+/PCQPE2ynEWuGHN/fYC5/oIlJkvtB8Evgv8Ca+MKwyWaR5Zt+OzdqtRZP2sFRFX0nwo/2xm/k27ufjc7TjaMnCACvJOYfDnUGCN3Aa8PyJWaMZIfzwi/mLgTKuPc7ZdDQb4Ak0zN3QuqQpz17xFxM6IuHr1OvBe4DRwDLizvdudwIPDJNwwxzHgjoh4bUTcCNwEfLWPQKs709bP0Lxeg2aaN9btZKzdahRZP6siImiOz3kqM39vzX8qMndEXB8R17TXXwf8BPB0qXln5FHgpoi4MSKuAu6geV69KLFGMvOjmbk3MxdoXo+vZObPD5mpzfVvwDcj4q3tpvcAXxs6l1SNoc+Y0veFZtb68fbyJPCxdvsbgEeAZ9uf1/WQ5fM0o1z/R/OXpbs3ywF8jOYsS88A7+sx058Dp4AnaHaie/rM5MW6nSKXtVvYpdT62SLzj9GMaT0BnGwvt5eaG3gH8Fib9zTwG+32IvPO8HnfTnOWx6+v7iOtke891iKvnG1y8Ew0o+zH29fri8C1JeTy4qWGS2Q6NixJkiRJpZu7sUlJkiRJqpHNmyRJkiRVwOZNkiRJkipg8yZJkiRJFbB5kyRJkqQK2LxJkiRJUgVs3iRJkiSpAv8P2vUGp/NbpuEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 25 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Code task 18#\n",
     "#Call ski_data's `hist` method to plot histograms of each of the numeric features\n",
     "#Try passing it an argument figsize=(15,10)\n",
     "#Try calling plt.subplots_adjust() with an argument hspace=0.5 to adjust the spacing\n",
     "#It's important you create legible and easy-to-read plots\n",
-    "ski_data.___(___)\n",
-    "#plt.subplots_adjust(hspace=___);\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"
    ]
   },
@@ -1114,13 +2421,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "39    26819.0\n",
+       "Name: SkiableTerrain_ac, dtype: float64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 19#\n",
     "#Filter the 'SkiableTerrain_ac' column to print the values greater than 10000\n",
-    "ski_data.___[ski_data.___ > ___]"
+    "ski_data.SkiableTerrain_ac[ski_data.SkiableTerrain_ac > 10000]"
    ]
   },
   {
@@ -1132,21 +2451,194 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "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>39</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Name</th>\n",
+       "      <td>Silverton Mountain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Region</th>\n",
+       "      <td>Colorado</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <td>Colorado</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>summit_elev</th>\n",
+       "      <td>13487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vertical_drop</th>\n",
+       "      <td>3087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>base_elev</th>\n",
+       "      <td>10400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>trams</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastEight</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastSixes</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>fastQuads</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quad</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>triple</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>double</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>surface</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_chairs</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Runs</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TerrainParks</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LongestRun_mi</th>\n",
+       "      <td>1.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SkiableTerrain_ac</th>\n",
+       "      <td>26819.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Snow Making_ac</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>daysOpenLastYear</th>\n",
+       "      <td>175.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yearsOpen</th>\n",
+       "      <td>17.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>averageSnowfall</th>\n",
+       "      <td>400.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekday</th>\n",
+       "      <td>79.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AdultWeekend</th>\n",
+       "      <td>79.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>projectedDaysOpen</th>\n",
+       "      <td>181.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>NightSkiing_ac</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   39\n",
+       "Name               Silverton Mountain\n",
+       "Region                       Colorado\n",
+       "state                        Colorado\n",
+       "summit_elev                     13487\n",
+       "vertical_drop                    3087\n",
+       "base_elev                       10400\n",
+       "trams                               0\n",
+       "fastEight                         0.0\n",
+       "fastSixes                           0\n",
+       "fastQuads                           0\n",
+       "quad                                0\n",
+       "triple                              0\n",
+       "double                              1\n",
+       "surface                             0\n",
+       "total_chairs                        1\n",
+       "Runs                              NaN\n",
+       "TerrainParks                      NaN\n",
+       "LongestRun_mi                     1.5\n",
+       "SkiableTerrain_ac             26819.0\n",
+       "Snow Making_ac                    NaN\n",
+       "daysOpenLastYear                175.0\n",
+       "yearsOpen                        17.0\n",
+       "averageSnowfall                 400.0\n",
+       "AdultWeekday                     79.0\n",
+       "AdultWeekend                     79.0\n",
+       "projectedDaysOpen               181.0\n",
+       "NightSkiing_ac                    NaN"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 20#\n",
     "#Now you know there's only one, print the whole row to investigate all values, including seeing the resort name\n",
     "#Hint: don't forget the transpose will be helpful here\n",
-    "ski_data[ski_data.___ > ___].___"
+    "ski_data[ski_data.SkiableTerrain_ac > 10000].T"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**A: 2** Your answer here"
+    "**A: 2** Silverton Mountain has 26819 acres."
    ]
   },
   {
@@ -1163,13 +2655,6 @@
     "You can see if your data are correct. Search for \"silverton mountain skiable area\". If you do this, you get some [useful information](https://www.google.com/search?q=silverton+mountain+skiable+area)."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![Silverton Mountain information](images/silverton_mountain_info.png)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1179,35 +2664,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26819.0"
+      ]
+     },
+     "execution_count": 46,
+     "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": 47,
    "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": 48,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1819.0"
+      ]
+     },
+     "execution_count": 48,
+     "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']"
    ]
   },
   {
@@ -1226,12 +2733,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1265,7 +2772,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -1276,7 +2783,7 @@
        "Name: Snow Making_ac, dtype: float64"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1287,7 +2794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -1345,7 +2852,7 @@
        "    </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 +2884,11 @@
        "    </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 +2896,23 @@
        "    </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 +2924,7 @@
        "    </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 +2943,7 @@
        "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",
@@ -1444,21 +2951,21 @@
        "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"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1483,7 +2990,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -1492,7 +2999,7 @@
        "2880.0"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1524,7 +3031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -1535,7 +3042,7 @@
        "Name: fastEight, dtype: int64"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1553,13 +3060,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "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 +3078,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "34      104.0\n",
+       "115    2019.0\n",
+       "Name: yearsOpen, dtype: float64"
+      ]
+     },
+     "execution_count": 55,
+     "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 +3116,27 @@
   },
   {
    "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 26#\n",
     "#Call the hist method on 'yearsOpen' after filtering for values under 1000\n",
     "#Pass the argument bins=30 to hist(), but feel free to explore other values\n",
-    "ski_data.___[ski_data.___ < ___].hist(___)\n",
+    "ski_data.yearsOpen[ski_data.yearsOpen < 1000].hist(bins=30)\n",
     "plt.xlabel('Years open')\n",
     "plt.ylabel('Count')\n",
     "plt.title('Distribution of years open excluding 2019');"
@@ -1625,7 +3158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -1642,7 +3175,7 @@
        "Name: yearsOpen, dtype: float64"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1660,7 +3193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1720,9 +3253,116 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>state</th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>580.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>80.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>California</td>\n",
+       "      <td>21</td>\n",
+       "      <td>25948.0</td>\n",
+       "      <td>2738.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>587.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>22</td>\n",
+       "      <td>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": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 27#\n",
     "#Add named aggregations for the sum of 'daysOpenLastYear', 'TerrainParks', and 'NightSkiing_ac'\n",
@@ -1733,10 +3373,10 @@
     "state_summary = ski_data.groupby('state').agg(\n",
     "    resorts_per_state=pd.NamedAgg(column='Name', aggfunc='size'), #could pick any column here\n",
     "    state_total_skiable_area_ac=pd.NamedAgg(column='SkiableTerrain_ac', aggfunc='sum'),\n",
-    "    state_total_days_open=pd.NamedAgg(column=__, aggfunc='sum'),\n",
-    "    ___=pd.NamedAgg(column=___, aggfunc=___),\n",
-    "    ___=pd.NamedAgg(column=___, aggfunc=___)\n",
-    ").___\n",
+    "    state_total_days_open=pd.NamedAgg(column='daysOpenLastYear', aggfunc='sum'),\n",
+    "    state_total_terrain_parks=pd.NamedAgg(column='TerrainParks', aggfunc='sum'),\n",
+    "    state_total_nightskiing_ac=pd.NamedAgg(column='NightSkiing_ac', aggfunc='sum')\n",
+    ").reset_index()\n",
     "state_summary.head()"
    ]
   },
@@ -1756,7 +3396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -1768,7 +3408,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1778,22 +3418,15 @@
     "missing_price.value_counts()/len(missing_price) * 100"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "About 14% of the rows have no price data. As the price is your target, these rows are of no use. Time to lose them."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "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]"
    ]
   },
   {
@@ -1805,12 +3438,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1080x720 with 25 Axes>"
       ]
@@ -1849,19 +3482,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 63,
    "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)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -1870,7 +3503,7 @@
        "list"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1881,7 +3514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -1890,7 +3523,7 @@
        "1"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1901,7 +3534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -1927,8 +3560,8 @@
        "      <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 +3573,8 @@
        "      <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",
@@ -2045,24 +3678,24 @@
        "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,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2081,19 +3714,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
     "#Code task 30#\n",
     "#Use the iloc accessor to get the pandas Series for column number 4 from `usa_states`\n",
     "#It should be a column of dates\n",
-    "established = usa_sates.___[:, 4]"
+    "established = usa_states.iloc[:, 4]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -2149,37 +3782,112 @@
        "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": 68,
+     "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": 69,
+   "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": 69,
      "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 +3900,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 70,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Massachusetts', 'Pennsylvania', 'Rhode Island', 'Virginia'}"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 32#\n",
     "#Find the states in `state_summary` that are not in `usa_states_sub`\n",
     "#Hint: set(list1) - set(list2) is an easy way to get items in list1 that are not in list2\n",
-    "missing_states = ___(state_summary.state) - ___(usa_states_sub.state)\n",
+    "missing_states = set(state_summary.state) - set(usa_states_sub.state)\n",
     "missing_states"
    ]
   },
@@ -2219,21 +3938,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "20    Massachusetts[upper-alpha 3]\n",
-       "37     Pennsylvania[upper-alpha 3]\n",
-       "38     Rhode Island[upper-alpha 4]\n",
-       "45         Virginia[upper-alpha 3]\n",
-       "47                   West Virginia\n",
+       "20    Massachusetts[C]\n",
+       "37     Pennsylvania[C]\n",
+       "38     Rhode Island[D]\n",
+       "45         Virginia[C]\n",
+       "47       West Virginia\n",
        "Name: state, dtype: object"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2251,9 +3970,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 72,
    "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": 72,
+     "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 +3997,31 @@
     "#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": 73,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 73,
+     "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 +4034,133 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 74,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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": 74,
+     "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 +4187,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 75,
    "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 +4219,118 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 76,
    "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": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 37#\n",
     "#Use the loc accessor on ski_data to print the 'AdultWeekend' and 'AdultWeekday' columns for Montana only\n",
-    "ski_data.___[ski_data.state == ___, [___, ___]]"
+    "ski_data.loc[ski_data.state == 'Montana', ['AdultWeekend','AdultWeekday']]"
    ]
   },
   {
@@ -2359,7 +4342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -2370,7 +4353,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2388,7 +4371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2398,7 +4381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -2407,7 +4390,7 @@
        "(277, 25)"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2439,7 +4422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -2536,7 +4519,7 @@
        "39       4  16.0"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2556,7 +4539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -2565,7 +4548,7 @@
        "array([ 0.,  4.,  8., 12., 16., 20.])"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2583,7 +4566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
@@ -2598,7 +4581,7 @@
        "Name: %, dtype: int64"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2616,7 +4599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
@@ -2678,7 +4661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
@@ -2687,7 +4670,7 @@
        "(277, 25)"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2705,9 +4688,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A file already exists with this name.\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# save the data to a new csv file\n",
     "datapath = '../data'\n",
@@ -2716,7 +4708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2743,7 +4735,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**A: 3** Your answer here"
+    "**A: 3** \n",
+    "\n",
+    "**Data wrangling in summary**\n",
+    "\n",
+    "- The raw data has 330 rows and 27 columns.\n",
+    "- 13 columns contain missing values. For the fastEight column which describes the number of fast eight person chairs, about half of the entries are missing. Since this column has no valuable information, the entire column is discarded.\n",
+    "- There are also 15-16% missing values in the target feature, the ticket price. 272 rows have both weekend and weekday prices while 58 rows are missing some or all information about prices. Compared to prices, weekend prices have the least missing values, 51, therefore keep rows that have the weekend price and drop the weekday price column.\n",
+    "- There are 3 object data type columns, Name, Region, and state. All these categorical variables taken together, resort names are all unique.\n",
+    "- The histograms of each of the numeric features show that two columns, SkiableTerrain_ac, and yearsOpen are left_skewed which implies the presence of outliers. Drop two rows with unreasonable values.\n",
+    "- Finally, ski_data has 277 rows and 25 columns and save it as ‘ski_data_cleaned.csv’\n",
+    "\n",
+    "- The total number of the resort, total skiable area, total days open, the total number of terrain parks, and total skiable area for nightskiing are aggregated by states in state_summary. From the Wikipedia, import population and area data for the US state and extract the state name and population, and total area columns. Some states contain extra strings after the state name. Remove it and merge the dataframe with state_summary. The final dataframe has 35 rows and 8 columns and is saved as ‘state_summary.csv'."
    ]
   }
  ],
@@ -2763,7 +4766,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.8.8"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/Notebooks/03_exploratory_data_analysis.ipynb b/Notebooks/03_exploratory_data_analysis.ipynb
index c1746d2e4..5e3f38f55 100644
--- a/Notebooks/03_exploratory_data_analysis.ipynb
+++ b/Notebooks/03_exploratory_data_analysis.ipynb
@@ -70,13 +70,7 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "execution": {
-     "iopub.execute_input": "2020-10-07T07:04:19.124917Z",
-     "iopub.status.busy": "2020-10-07T07:04:19.124711Z",
-     "iopub.status.idle": "2020-10-07T07:04:19.128523Z",
-     "shell.execute_reply": "2020-10-07T07:04:19.128112Z",
-     "shell.execute_reply.started": "2020-10-07T07:04:19.124888Z"
-    }
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -107,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -164,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -369,7 +363,7 @@
        "[5 rows x 25 columns]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -378,6 +372,27 @@
     "ski_data.head()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AdultWeekend    51.909091\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ski_data[ski_data['state']== 'Montana'][['Name','state','AdultWeekend']].mean()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -387,7 +402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -396,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -427,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "scrolled": true
    },
@@ -546,7 +561,7 @@
        "4                       256.0           3565278                 5543  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -578,7 +593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -594,7 +609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -609,7 +624,7 @@
        "Name: state_area_sq_miles, dtype: int64"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -634,7 +649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -649,7 +664,7 @@
        "Name: state_population, dtype: int64"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -674,7 +689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -689,7 +704,7 @@
        "Name: resorts_per_state, dtype: int64"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -714,7 +729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -729,7 +744,7 @@
        "Name: state_total_skiable_area_ac, dtype: float64"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -754,7 +769,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -769,7 +784,7 @@
        "Name: state_total_nightskiing_ac, dtype: float64"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -794,7 +809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -809,7 +824,7 @@
        "Name: state_total_days_open, dtype: float64"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -843,7 +858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -960,7 +975,7 @@
        "4                       256.0                0.140242                90.203861  "
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -989,12 +1004,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1013,12 +1028,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1051,7 +1066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1066,7 +1081,7 @@
        "Name: resorts_per_100kcapita, dtype: float64"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1077,7 +1092,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1092,7 +1107,7 @@
        "Name: resorts_per_100ksq_mile, dtype: float64"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1152,17 +1167,151 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Alaska</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>0.410091</td>\n",
+       "      <td>0.450867</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>0.027477</td>\n",
+       "      <td>1.754540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>21</td>\n",
+       "      <td>25948.0</td>\n",
+       "      <td>2738.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>587.0</td>\n",
+       "      <td>0.053148</td>\n",
+       "      <td>12.828736</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>22</td>\n",
+       "      <td>43682.0</td>\n",
+       "      <td>3258.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>428.0</td>\n",
+       "      <td>0.382028</td>\n",
+       "      <td>21.134744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>5</td>\n",
+       "      <td>358.0</td>\n",
+       "      <td>353.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>256.0</td>\n",
+       "      <td>0.140242</td>\n",
+       "      <td>90.203861</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             resorts_per_state  state_total_skiable_area_ac  \\\n",
+       "state                                                         \n",
+       "Alaska                       3                       2280.0   \n",
+       "Arizona                      2                       1577.0   \n",
+       "California                  21                      25948.0   \n",
+       "Colorado                    22                      43682.0   \n",
+       "Connecticut                  5                        358.0   \n",
+       "\n",
+       "             state_total_days_open  state_total_terrain_parks  \\\n",
+       "state                                                           \n",
+       "Alaska                       345.0                        4.0   \n",
+       "Arizona                      237.0                        6.0   \n",
+       "California                  2738.0                       81.0   \n",
+       "Colorado                    3258.0                       74.0   \n",
+       "Connecticut                  353.0                       10.0   \n",
+       "\n",
+       "             state_total_nightskiing_ac  resorts_per_100kcapita  \\\n",
+       "state                                                             \n",
+       "Alaska                            580.0                0.410091   \n",
+       "Arizona                            80.0                0.027477   \n",
+       "California                        587.0                0.053148   \n",
+       "Colorado                          428.0                0.382028   \n",
+       "Connecticut                       256.0                0.140242   \n",
+       "\n",
+       "             resorts_per_100ksq_mile  \n",
+       "state                                 \n",
+       "Alaska                      0.450867  \n",
+       "Arizona                     1.754540  \n",
+       "California                 12.828736  \n",
+       "Colorado                   21.134744  \n",
+       "Connecticut                90.203861  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 1#\n",
     "#Create a new dataframe, `state_summary_scale` from `state_summary` whilst setting the index to 'state'\n",
-    "state_summary_scale = state_summary.set_index(___)\n",
+    "state_summary_scale = state_summary.set_index('state')\n",
     "#Save the state labels (using the index attribute of `state_summary_scale`) into the variable 'state_summary_index'\n",
-    "state_summary_index = state_summary_scale.___\n",
+    "state_summary_index = state_summary_scale.index\n",
     "#Save the column names (using the `columns` attribute) of `state_summary_scale` into the variable 'state_summary_columns'\n",
-    "state_summary_columns = state_summary_scale.___\n",
+    "state_summary_columns = state_summary_scale.columns\n",
     "state_summary_scale.head()"
    ]
   },
@@ -1177,7 +1326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1193,13 +1342,126 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>resorts_per_state</th>\n",
+       "      <th>state_total_skiable_area_ac</th>\n",
+       "      <th>state_total_days_open</th>\n",
+       "      <th>state_total_terrain_parks</th>\n",
+       "      <th>state_total_nightskiing_ac</th>\n",
+       "      <th>resorts_per_100kcapita</th>\n",
+       "      <th>resorts_per_100ksq_mile</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.806912</td>\n",
+       "      <td>-0.392012</td>\n",
+       "      <td>-0.689059</td>\n",
+       "      <td>-0.816118</td>\n",
+       "      <td>0.069410</td>\n",
+       "      <td>0.139593</td>\n",
+       "      <td>-0.689999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.933558</td>\n",
+       "      <td>-0.462424</td>\n",
+       "      <td>-0.819038</td>\n",
+       "      <td>-0.726994</td>\n",
+       "      <td>-0.701326</td>\n",
+       "      <td>-0.644706</td>\n",
+       "      <td>-0.658125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.472706</td>\n",
+       "      <td>1.978574</td>\n",
+       "      <td>2.190933</td>\n",
+       "      <td>2.615141</td>\n",
+       "      <td>0.080201</td>\n",
+       "      <td>-0.592085</td>\n",
+       "      <td>-0.387368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.599351</td>\n",
+       "      <td>3.754811</td>\n",
+       "      <td>2.816757</td>\n",
+       "      <td>2.303209</td>\n",
+       "      <td>-0.164893</td>\n",
+       "      <td>0.082069</td>\n",
+       "      <td>-0.184291</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.553622</td>\n",
+       "      <td>-0.584519</td>\n",
+       "      <td>-0.679431</td>\n",
+       "      <td>-0.548747</td>\n",
+       "      <td>-0.430027</td>\n",
+       "      <td>-0.413557</td>\n",
+       "      <td>1.504408</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   resorts_per_state  state_total_skiable_area_ac  state_total_days_open  \\\n",
+       "0          -0.806912                    -0.392012              -0.689059   \n",
+       "1          -0.933558                    -0.462424              -0.819038   \n",
+       "2           1.472706                     1.978574               2.190933   \n",
+       "3           1.599351                     3.754811               2.816757   \n",
+       "4          -0.553622                    -0.584519              -0.679431   \n",
+       "\n",
+       "   state_total_terrain_parks  state_total_nightskiing_ac  \\\n",
+       "0                  -0.816118                    0.069410   \n",
+       "1                  -0.726994                   -0.701326   \n",
+       "2                   2.615141                    0.080201   \n",
+       "3                   2.303209                   -0.164893   \n",
+       "4                  -0.548747                   -0.430027   \n",
+       "\n",
+       "   resorts_per_100kcapita  resorts_per_100ksq_mile  \n",
+       "0                0.139593                -0.689999  \n",
+       "1               -0.644706                -0.658125  \n",
+       "2               -0.592085                -0.387368  \n",
+       "3                0.082069                -0.184291  \n",
+       "4               -0.413557                 1.504408  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 2#\n",
     "#Create a new dataframe from `state_summary_scale` using the column names we saved in `state_summary_columns`\n",
-    "state_summary_scaled_df = pd.DataFrame(___, columns=___)\n",
+    "state_summary_scaled_df = pd.DataFrame(state_summary_scale, columns=state_summary_columns)\n",
     "state_summary_scaled_df.head()"
    ]
   },
@@ -1226,13 +1488,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "resorts_per_state             -6.344132e-17\n",
+       "state_total_skiable_area_ac   -5.432163e-17\n",
+       "state_total_days_open          9.754102e-17\n",
+       "state_total_terrain_parks      4.282289e-17\n",
+       "state_total_nightskiing_ac     6.344132e-17\n",
+       "resorts_per_100kcapita         5.075305e-17\n",
+       "resorts_per_100ksq_mile        5.075305e-17\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 3#\n",
     "#Call `state_summary_scaled_df`'s `mean()` method\n",
-    "state_summary_scaled_df.___"
+    "state_summary_scaled_df.mean()"
    ]
   },
   {
@@ -1251,13 +1531,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "resorts_per_state              1.014599\n",
+       "state_total_skiable_area_ac    1.014599\n",
+       "state_total_days_open          1.014599\n",
+       "state_total_terrain_parks      1.014599\n",
+       "state_total_nightskiing_ac     1.014599\n",
+       "resorts_per_100kcapita         1.014599\n",
+       "resorts_per_100ksq_mile        1.014599\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 4#\n",
     "#Call `state_summary_scaled_df`'s `std()` method\n",
-    "state_summary_scaled_df.___"
+    "state_summary_scaled_df.std()"
    ]
   },
   {
@@ -1271,13 +1569,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "resorts_per_state              1.0\n",
+       "state_total_skiable_area_ac    1.0\n",
+       "state_total_days_open          1.0\n",
+       "state_total_terrain_parks      1.0\n",
+       "state_total_nightskiing_ac     1.0\n",
+       "resorts_per_100kcapita         1.0\n",
+       "resorts_per_100ksq_mile        1.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 5#\n",
     "#Repeat the previous call to `std()` but pass in ddof=0 \n",
-    "state_summary_scaled_df.___(___)"
+    "state_summary_scaled_df.std(ddof=0)"
    ]
   },
   {
@@ -1303,7 +1619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1319,9 +1635,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Code task 6#\n",
     "#Call the `cumsum()` method on the 'explained_variance_ratio_' attribute of `state_pca` and\n",
@@ -1330,10 +1659,10 @@
     "#title to 'Cumulative variance ratio explained by PCA components for state/resort summary statistics'\n",
     "#Hint: remember the handy ';' at the end of the last plot call to suppress that untidy output\n",
     "plt.subplots(figsize=(10, 6))\n",
-    "plt.plot(state_pca.explained_variance_ratio_.___)\n",
-    "plt.xlabel(___)\n",
-    "plt.ylabel(___)\n",
-    "plt.title(___);"
+    "plt.plot(state_pca.explained_variance_ratio_.cumsum())\n",
+    "plt.xlabel('Component #')\n",
+    "plt.ylabel('Cumulative ratio variance')\n",
+    "plt.title('Cumulative variance ratio explained by PCA components for state/resort summary statistics');"
    ]
   },
   {
@@ -1359,18 +1688,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
     "#Code task 7#\n",
     "#Call `state_pca`'s `transform()` method, passing in `state_summary_scale` as its argument\n",
-    "state_pca_x = state_pca.___(___)"
+    "state_pca_x = state_pca.transform(state_summary_scale)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -1379,7 +1708,7 @@
        "(35, 7)"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1406,12 +1735,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x576 with 1 Axes>"
       ]
@@ -1452,24 +1781,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "state\n",
+       "Alaska         57.333333\n",
+       "Arizona        83.500000\n",
+       "California     81.416667\n",
+       "Colorado       90.714286\n",
+       "Connecticut    56.800000\n",
+       "Name: AdultWeekend, dtype: float64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 8#\n",
     "#Calculate the average 'AdultWeekend' ticket price by state\n",
-    "state_avg_price = ski_data.groupby(___)[___].___\n",
+    "state_avg_price = ski_data.groupby('state')['AdultWeekend'].mean()\n",
     "state_avg_price.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1503,16 +1849,91 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PC1</th>\n",
+       "      <th>PC2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Alaska</th>\n",
+       "      <td>-1.336533</td>\n",
+       "      <td>-0.182208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>-1.839049</td>\n",
+       "      <td>-0.387959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>3.537857</td>\n",
+       "      <td>-1.282509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>4.402210</td>\n",
+       "      <td>-0.898855</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>-0.988027</td>\n",
+       "      <td>1.020218</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  PC1       PC2\n",
+       "state                          \n",
+       "Alaska      -1.336533 -0.182208\n",
+       "Arizona     -1.839049 -0.387959\n",
+       "California   3.537857 -1.282509\n",
+       "Colorado     4.402210 -0.898855\n",
+       "Connecticut -0.988027  1.020218"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 9#\n",
     "#Create a dataframe containing the values of the first two PCA components\n",
     "#Remember the first component was given by state_pca_x[:, 0],\n",
     "#and the second by state_pca_x[:, 1]\n",
     "#Call these 'PC1' and 'PC2', respectively and set the dataframe index to `state_summary_index`\n",
-    "pca_df = pd.DataFrame({'PC1': ___, 'PC2': ___}, index=__)\n",
+    "pca_df = pd.DataFrame({'PC1': state_pca_x[:, 0], 'PC2': state_pca_x[:, 1]}, index=state_summary_index)\n",
     "pca_df.head()"
    ]
   },
@@ -1525,7 +1946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -1540,7 +1961,7 @@
        "Name: AdultWeekend, dtype: float64"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1552,7 +1973,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -1618,7 +2039,7 @@
        "Connecticut     56.800000"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1637,14 +2058,96 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PC1</th>\n",
+       "      <th>PC2</th>\n",
+       "      <th>AdultWeekend</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Alaska</th>\n",
+       "      <td>-1.336533</td>\n",
+       "      <td>-0.182208</td>\n",
+       "      <td>57.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>-1.839049</td>\n",
+       "      <td>-0.387959</td>\n",
+       "      <td>83.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>3.537857</td>\n",
+       "      <td>-1.282509</td>\n",
+       "      <td>81.416667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Colorado</th>\n",
+       "      <td>4.402210</td>\n",
+       "      <td>-0.898855</td>\n",
+       "      <td>90.714286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Connecticut</th>\n",
+       "      <td>-0.988027</td>\n",
+       "      <td>1.020218</td>\n",
+       "      <td>56.800000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  PC1       PC2  AdultWeekend\n",
+       "state                                        \n",
+       "Alaska      -1.336533 -0.182208     57.333333\n",
+       "Arizona     -1.839049 -0.387959     83.500000\n",
+       "California   3.537857 -1.282509     81.416667\n",
+       "Colorado     4.402210 -0.898855     90.714286\n",
+       "Connecticut -0.988027  1.020218     56.800000"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 10#\n",
     "#Use pd.concat to concatenate `pca_df` and `state_avg_price` along axis 1\n",
     "# remember, pd.concat will align on index\n",
-    "pca_df = ___([___, ___], axis=___)\n",
+    "pca_df = pd.concat([pca_df, state_avg_price], axis=1)\n",
     "pca_df.head()"
    ]
   },
@@ -1657,7 +2160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1686,6 +2189,13 @@
        "      <th>AdultWeekend</th>\n",
        "      <th>Quartile</th>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
@@ -1729,6 +2239,7 @@
       ],
       "text/plain": [
        "                  PC1       PC2  AdultWeekend      Quartile\n",
+       "state                                                      \n",
        "Alaska      -1.336533 -0.182208     57.333333  (53.1, 60.4]\n",
        "Arizona     -1.839049 -0.387959     83.500000  (78.4, 93.0]\n",
        "California   3.537857 -1.282509     81.416667  (78.4, 93.0]\n",
@@ -1736,7 +2247,7 @@
        "Connecticut -0.988027  1.020218     56.800000  (53.1, 60.4]"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1748,7 +2259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -1761,7 +2272,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1781,7 +2292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -1810,6 +2321,13 @@
        "      <th>AdultWeekend</th>\n",
        "      <th>Quartile</th>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
@@ -1825,10 +2343,11 @@
       ],
       "text/plain": [
        "                   PC1       PC2  AdultWeekend Quartile\n",
+       "state                                                  \n",
        "Rhode Island -1.843646  0.761339           NaN      NaN"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1853,20 +2372,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "PC1             -1.84365\n",
-       "PC2             0.761339\n",
-       "AdultWeekend     64.1244\n",
-       "Quartile              NA\n",
+       "PC1             -1.843646\n",
+       "PC2              0.761339\n",
+       "AdultWeekend    64.124388\n",
+       "Quartile               NA\n",
        "Name: Rhode Island, dtype: object"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1896,12 +2415,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x576 with 1 Axes>"
       ]
@@ -1949,9 +2468,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Code task 11#\n",
     "#Create a seaborn scatterplot by calling `sns.scatterplot`\n",
@@ -1966,8 +2498,8 @@
     "plt.subplots(figsize=(12, 10))\n",
     "# Note the argument below to make sure we get the colours in the ascending\n",
     "# order we intuitively expect!\n",
-    "sns.___(x=___, y=___, size=___, hue=___, \n",
-    "                hue_order=___, data=pca_df)\n",
+    "sns.scatterplot(x=x, y=y, size='AdultWeekend', hue='Quartile', \n",
+    "                hue_order=pca_df.Quartile.cat.categories, data=pca_df)\n",
     "#and we can still annotate with the state labels\n",
     "for s, x, y in zip(state, x, y):\n",
     "    plt.annotate(s, (x, y))   \n",
@@ -2005,7 +2537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -2142,7 +2674,7 @@
        "6               -0.007887                -0.005631  "
       ]
      },
-     "execution_count": 43,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2167,7 +2699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -2208,51 +2740,51 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>state_total_skiable_area_ac</th>\n",
-       "      <td>3427</td>\n",
-       "      <td>7239</td>\n",
+       "      <td>3427.0</td>\n",
+       "      <td>7239.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>state_total_days_open</th>\n",
-       "      <td>1847</td>\n",
-       "      <td>1777</td>\n",
+       "      <td>1847.0</td>\n",
+       "      <td>1777.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>state_total_terrain_parks</th>\n",
-       "      <td>43</td>\n",
-       "      <td>50</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>50.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>state_total_nightskiing_ac</th>\n",
-       "      <td>376</td>\n",
-       "      <td>50</td>\n",
+       "      <td>376.0</td>\n",
+       "      <td>50.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_100kcapita</th>\n",
-       "      <td>1.17672</td>\n",
-       "      <td>2.40389</td>\n",
+       "      <td>1.176721</td>\n",
+       "      <td>2.403889</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_100ksq_mile</th>\n",
-       "      <td>171.141</td>\n",
-       "      <td>155.99</td>\n",
+       "      <td>171.141299</td>\n",
+       "      <td>155.990017</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                        17       29\n",
-       "state                        New Hampshire  Vermont\n",
-       "resorts_per_state                       16       15\n",
-       "state_total_skiable_area_ac           3427     7239\n",
-       "state_total_days_open                 1847     1777\n",
-       "state_total_terrain_parks               43       50\n",
-       "state_total_nightskiing_ac             376       50\n",
-       "resorts_per_100kcapita             1.17672  2.40389\n",
-       "resorts_per_100ksq_mile            171.141   155.99"
+       "                                        17          29\n",
+       "state                        New Hampshire     Vermont\n",
+       "resorts_per_state                       16          15\n",
+       "state_total_skiable_area_ac         3427.0      7239.0\n",
+       "state_total_days_open               1847.0      1777.0\n",
+       "state_total_terrain_parks             43.0        50.0\n",
+       "state_total_nightskiing_ac           376.0        50.0\n",
+       "resorts_per_100kcapita            1.176721    2.403889\n",
+       "resorts_per_100ksq_mile         171.141299  155.990017"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2263,7 +2795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -2342,7 +2874,7 @@
        "resorts_per_100ksq_mile      3.483281  3.112841"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2395,7 +2927,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -2541,91 +3073,91 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Runs</th>\n",
-       "      <td>76</td>\n",
-       "      <td>36</td>\n",
-       "      <td>13</td>\n",
-       "      <td>55</td>\n",
-       "      <td>65</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>65.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TerrainParks</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LongestRun_mi</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>1.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SkiableTerrain_ac</th>\n",
-       "      <td>1610</td>\n",
-       "      <td>640</td>\n",
-       "      <td>30</td>\n",
-       "      <td>777</td>\n",
-       "      <td>800</td>\n",
+       "      <td>1610.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>777.0</td>\n",
+       "      <td>800.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Snow Making_ac</th>\n",
-       "      <td>113</td>\n",
-       "      <td>60</td>\n",
-       "      <td>30</td>\n",
-       "      <td>104</td>\n",
-       "      <td>80</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>80.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>daysOpenLastYear</th>\n",
-       "      <td>150</td>\n",
-       "      <td>45</td>\n",
-       "      <td>150</td>\n",
-       "      <td>122</td>\n",
-       "      <td>115</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>115.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>yearsOpen</th>\n",
-       "      <td>60</td>\n",
-       "      <td>44</td>\n",
-       "      <td>36</td>\n",
-       "      <td>81</td>\n",
-       "      <td>49</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>49.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>averageSnowfall</th>\n",
-       "      <td>669</td>\n",
-       "      <td>350</td>\n",
-       "      <td>69</td>\n",
-       "      <td>260</td>\n",
-       "      <td>250</td>\n",
+       "      <td>669.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>250.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AdultWeekend</th>\n",
-       "      <td>85</td>\n",
-       "      <td>53</td>\n",
-       "      <td>34</td>\n",
-       "      <td>89</td>\n",
-       "      <td>78</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>78.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>projectedDaysOpen</th>\n",
-       "      <td>150</td>\n",
-       "      <td>90</td>\n",
-       "      <td>152</td>\n",
-       "      <td>122</td>\n",
-       "      <td>104</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>104.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NightSkiing_ac</th>\n",
-       "      <td>550</td>\n",
+       "      <td>550.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>30</td>\n",
+       "      <td>30.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>80</td>\n",
+       "      <td>80.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2647,17 +3179,17 @@
        "double                          0                    4                 0   \n",
        "surface                         2                    0                 2   \n",
        "total_chairs                    7                    4                 3   \n",
-       "Runs                           76                   36                13   \n",
-       "TerrainParks                    2                    1                 1   \n",
-       "LongestRun_mi                   1                    2                 1   \n",
-       "SkiableTerrain_ac            1610                  640                30   \n",
-       "Snow Making_ac                113                   60                30   \n",
-       "daysOpenLastYear              150                   45               150   \n",
-       "yearsOpen                      60                   44                36   \n",
-       "averageSnowfall               669                  350                69   \n",
-       "AdultWeekend                   85                   53                34   \n",
-       "projectedDaysOpen             150                   90               152   \n",
-       "NightSkiing_ac                550                  NaN                30   \n",
+       "Runs                         76.0                 36.0              13.0   \n",
+       "TerrainParks                  2.0                  1.0               1.0   \n",
+       "LongestRun_mi                 1.0                  2.0               1.0   \n",
+       "SkiableTerrain_ac          1610.0                640.0              30.0   \n",
+       "Snow Making_ac              113.0                 60.0              30.0   \n",
+       "daysOpenLastYear            150.0                 45.0             150.0   \n",
+       "yearsOpen                    60.0                 44.0              36.0   \n",
+       "averageSnowfall             669.0                350.0              69.0   \n",
+       "AdultWeekend                 85.0                 53.0              34.0   \n",
+       "projectedDaysOpen           150.0                 90.0             152.0   \n",
+       "NightSkiing_ac              550.0                  NaN              30.0   \n",
        "\n",
        "                                  3                    4  \n",
        "Name               Arizona Snowbowl  Sunrise Park Resort  \n",
@@ -2674,20 +3206,20 @@
        "double                            1                    1  \n",
        "surface                           2                    0  \n",
        "total_chairs                      8                    7  \n",
-       "Runs                             55                   65  \n",
-       "TerrainParks                      4                    2  \n",
-       "LongestRun_mi                     2                  1.2  \n",
-       "SkiableTerrain_ac               777                  800  \n",
-       "Snow Making_ac                  104                   80  \n",
-       "daysOpenLastYear                122                  115  \n",
-       "yearsOpen                        81                   49  \n",
-       "averageSnowfall                 260                  250  \n",
-       "AdultWeekend                     89                   78  \n",
-       "projectedDaysOpen               122                  104  \n",
-       "NightSkiing_ac                  NaN                   80  "
+       "Runs                           55.0                 65.0  \n",
+       "TerrainParks                    4.0                  2.0  \n",
+       "LongestRun_mi                   2.0                  1.2  \n",
+       "SkiableTerrain_ac             777.0                800.0  \n",
+       "Snow Making_ac                104.0                 80.0  \n",
+       "daysOpenLastYear              122.0                115.0  \n",
+       "yearsOpen                      81.0                 49.0  \n",
+       "averageSnowfall               260.0                250.0  \n",
+       "AdultWeekend                   89.0                 78.0  \n",
+       "projectedDaysOpen             122.0                104.0  \n",
+       "NightSkiing_ac                  NaN                 80.0  "
       ]
      },
-     "execution_count": 46,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2712,7 +3244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -2829,7 +3361,7 @@
        "4                       256.0                0.140242                90.203861  "
       ]
      },
-     "execution_count": 47,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2840,7 +3372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -2986,91 +3518,91 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Runs</th>\n",
-       "      <td>76</td>\n",
-       "      <td>36</td>\n",
-       "      <td>13</td>\n",
-       "      <td>55</td>\n",
-       "      <td>65</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>65.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TerrainParks</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LongestRun_mi</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>1.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SkiableTerrain_ac</th>\n",
-       "      <td>1610</td>\n",
-       "      <td>640</td>\n",
-       "      <td>30</td>\n",
-       "      <td>777</td>\n",
-       "      <td>800</td>\n",
+       "      <td>1610.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>777.0</td>\n",
+       "      <td>800.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Snow Making_ac</th>\n",
-       "      <td>113</td>\n",
-       "      <td>60</td>\n",
-       "      <td>30</td>\n",
-       "      <td>104</td>\n",
-       "      <td>80</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>80.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>daysOpenLastYear</th>\n",
-       "      <td>150</td>\n",
-       "      <td>45</td>\n",
-       "      <td>150</td>\n",
-       "      <td>122</td>\n",
-       "      <td>115</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>115.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>yearsOpen</th>\n",
-       "      <td>60</td>\n",
-       "      <td>44</td>\n",
-       "      <td>36</td>\n",
-       "      <td>81</td>\n",
-       "      <td>49</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>49.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>averageSnowfall</th>\n",
-       "      <td>669</td>\n",
-       "      <td>350</td>\n",
-       "      <td>69</td>\n",
-       "      <td>260</td>\n",
-       "      <td>250</td>\n",
+       "      <td>669.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>250.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AdultWeekend</th>\n",
-       "      <td>85</td>\n",
-       "      <td>53</td>\n",
-       "      <td>34</td>\n",
-       "      <td>89</td>\n",
-       "      <td>78</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>78.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>projectedDaysOpen</th>\n",
-       "      <td>150</td>\n",
-       "      <td>90</td>\n",
-       "      <td>152</td>\n",
-       "      <td>122</td>\n",
-       "      <td>104</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>104.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NightSkiing_ac</th>\n",
-       "      <td>550</td>\n",
+       "      <td>550.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>30</td>\n",
+       "      <td>30.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>80</td>\n",
+       "      <td>80.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_state</th>\n",
@@ -3082,43 +3614,43 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>state_total_skiable_area_ac</th>\n",
-       "      <td>2280</td>\n",
-       "      <td>2280</td>\n",
-       "      <td>2280</td>\n",
-       "      <td>1577</td>\n",
-       "      <td>1577</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>2280.0</td>\n",
+       "      <td>1577.0</td>\n",
+       "      <td>1577.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>state_total_days_open</th>\n",
-       "      <td>345</td>\n",
-       "      <td>345</td>\n",
-       "      <td>345</td>\n",
-       "      <td>237</td>\n",
-       "      <td>237</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>345.0</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>237.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>state_total_terrain_parks</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>6</td>\n",
-       "      <td>6</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>state_total_nightskiing_ac</th>\n",
-       "      <td>580</td>\n",
-       "      <td>580</td>\n",
-       "      <td>580</td>\n",
-       "      <td>80</td>\n",
-       "      <td>80</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>580.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>80.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_100kcapita</th>\n",
        "      <td>0.410091</td>\n",
        "      <td>0.410091</td>\n",
        "      <td>0.410091</td>\n",
-       "      <td>0.0274774</td>\n",
-       "      <td>0.0274774</td>\n",
+       "      <td>0.027477</td>\n",
+       "      <td>0.027477</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_100ksq_mile</th>\n",
@@ -3148,22 +3680,22 @@
        "double                                    0                    4   \n",
        "surface                                   2                    0   \n",
        "total_chairs                              7                    4   \n",
-       "Runs                                     76                   36   \n",
-       "TerrainParks                              2                    1   \n",
-       "LongestRun_mi                             1                    2   \n",
-       "SkiableTerrain_ac                      1610                  640   \n",
-       "Snow Making_ac                          113                   60   \n",
-       "daysOpenLastYear                        150                   45   \n",
-       "yearsOpen                                60                   44   \n",
-       "averageSnowfall                         669                  350   \n",
-       "AdultWeekend                             85                   53   \n",
-       "projectedDaysOpen                       150                   90   \n",
-       "NightSkiing_ac                          550                  NaN   \n",
+       "Runs                                   76.0                 36.0   \n",
+       "TerrainParks                            2.0                  1.0   \n",
+       "LongestRun_mi                           1.0                  2.0   \n",
+       "SkiableTerrain_ac                    1610.0                640.0   \n",
+       "Snow Making_ac                        113.0                 60.0   \n",
+       "daysOpenLastYear                      150.0                 45.0   \n",
+       "yearsOpen                              60.0                 44.0   \n",
+       "averageSnowfall                       669.0                350.0   \n",
+       "AdultWeekend                           85.0                 53.0   \n",
+       "projectedDaysOpen                     150.0                 90.0   \n",
+       "NightSkiing_ac                        550.0                  NaN   \n",
        "resorts_per_state                         3                    3   \n",
-       "state_total_skiable_area_ac            2280                 2280   \n",
-       "state_total_days_open                   345                  345   \n",
-       "state_total_terrain_parks                 4                    4   \n",
-       "state_total_nightskiing_ac              580                  580   \n",
+       "state_total_skiable_area_ac          2280.0               2280.0   \n",
+       "state_total_days_open                 345.0                345.0   \n",
+       "state_total_terrain_parks               4.0                  4.0   \n",
+       "state_total_nightskiing_ac            580.0                580.0   \n",
        "resorts_per_100kcapita             0.410091             0.410091   \n",
        "resorts_per_100ksq_mile            0.450867             0.450867   \n",
        "\n",
@@ -3182,23 +3714,23 @@
        "double                                      0                 1   \n",
        "surface                                     2                 2   \n",
        "total_chairs                                3                 8   \n",
-       "Runs                                       13                55   \n",
-       "TerrainParks                                1                 4   \n",
-       "LongestRun_mi                               1                 2   \n",
-       "SkiableTerrain_ac                          30               777   \n",
-       "Snow Making_ac                             30               104   \n",
-       "daysOpenLastYear                          150               122   \n",
-       "yearsOpen                                  36                81   \n",
-       "averageSnowfall                            69               260   \n",
-       "AdultWeekend                               34                89   \n",
-       "projectedDaysOpen                         152               122   \n",
-       "NightSkiing_ac                             30               NaN   \n",
+       "Runs                                     13.0              55.0   \n",
+       "TerrainParks                              1.0               4.0   \n",
+       "LongestRun_mi                             1.0               2.0   \n",
+       "SkiableTerrain_ac                        30.0             777.0   \n",
+       "Snow Making_ac                           30.0             104.0   \n",
+       "daysOpenLastYear                        150.0             122.0   \n",
+       "yearsOpen                                36.0              81.0   \n",
+       "averageSnowfall                          69.0             260.0   \n",
+       "AdultWeekend                             34.0              89.0   \n",
+       "projectedDaysOpen                       152.0             122.0   \n",
+       "NightSkiing_ac                           30.0               NaN   \n",
        "resorts_per_state                           3                 2   \n",
-       "state_total_skiable_area_ac              2280              1577   \n",
-       "state_total_days_open                     345               237   \n",
-       "state_total_terrain_parks                   4                 6   \n",
-       "state_total_nightskiing_ac                580                80   \n",
-       "resorts_per_100kcapita               0.410091         0.0274774   \n",
+       "state_total_skiable_area_ac            2280.0            1577.0   \n",
+       "state_total_days_open                   345.0             237.0   \n",
+       "state_total_terrain_parks                 4.0               6.0   \n",
+       "state_total_nightskiing_ac              580.0              80.0   \n",
+       "resorts_per_100kcapita               0.410091          0.027477   \n",
        "resorts_per_100ksq_mile              0.450867           1.75454   \n",
        "\n",
        "                                               4  \n",
@@ -3216,27 +3748,27 @@
        "double                                         1  \n",
        "surface                                        0  \n",
        "total_chairs                                   7  \n",
-       "Runs                                          65  \n",
-       "TerrainParks                                   2  \n",
+       "Runs                                        65.0  \n",
+       "TerrainParks                                 2.0  \n",
        "LongestRun_mi                                1.2  \n",
-       "SkiableTerrain_ac                            800  \n",
-       "Snow Making_ac                                80  \n",
-       "daysOpenLastYear                             115  \n",
-       "yearsOpen                                     49  \n",
-       "averageSnowfall                              250  \n",
-       "AdultWeekend                                  78  \n",
-       "projectedDaysOpen                            104  \n",
-       "NightSkiing_ac                                80  \n",
+       "SkiableTerrain_ac                          800.0  \n",
+       "Snow Making_ac                              80.0  \n",
+       "daysOpenLastYear                           115.0  \n",
+       "yearsOpen                                   49.0  \n",
+       "averageSnowfall                            250.0  \n",
+       "AdultWeekend                                78.0  \n",
+       "projectedDaysOpen                          104.0  \n",
+       "NightSkiing_ac                              80.0  \n",
        "resorts_per_state                              2  \n",
-       "state_total_skiable_area_ac                 1577  \n",
-       "state_total_days_open                        237  \n",
-       "state_total_terrain_parks                      6  \n",
-       "state_total_nightskiing_ac                    80  \n",
-       "resorts_per_100kcapita                 0.0274774  \n",
+       "state_total_skiable_area_ac               1577.0  \n",
+       "state_total_days_open                      237.0  \n",
+       "state_total_terrain_parks                    6.0  \n",
+       "state_total_nightskiing_ac                  80.0  \n",
+       "resorts_per_100kcapita                  0.027477  \n",
        "resorts_per_100ksq_mile                  1.75454  "
       ]
      },
-     "execution_count": 48,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3264,7 +3796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3293,15 +3825,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "#Code task 12#\n",
     "#Show a seaborn heatmap of correlations in ski_data\n",
     "#Hint: call pandas' `corr()` method on `ski_data` and pass that into `sns.heatmap`\n",
     "plt.subplots(figsize=(12,10))\n",
-    "sns.___(ski_data.___);"
+    "sns.heatmap(ski_data.corr());"
    ]
   },
   {
@@ -3333,7 +3878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3355,24 +3900,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
     "#Code task 13#\n",
     "#Use a list comprehension to build a list of features from the columns of `ski_data` that\n",
     "#are _not_ any of 'Name', 'Region', 'state', or 'AdultWeekend'\n",
-    "features = [___ for ___ in ski_data.columns if ___ not in [___, ___, ___, ___]]"
+    "features = [x for x in ski_data.columns if x not in ['Name', 'Region', 'state', 'AdultWeekend']]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1080x1080 with 28 Axes>"
       ]
@@ -3403,7 +3948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3415,12 +3960,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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Y3z0z1QmndtXTmGNdV20t7thabU0ppdTttuVyvZ3ZZyfKeH7I61fmODpc4I1CCYCYYxF37AWvmV99fKmq5ZrRSqm71UomiZ8Fvi4ivygivwA8A3x6fYe18W5m6Uk7nLZn47iWRbHm8cK5Gc5NljtVT3vSMcr1qNJarekzV/fpy8S12ppSSqnbbcvl+tHhAkEQcnK8TDpu49oWQWj47plpzk2W6UnH6MvEl6w+vlTVcs1opdTd6obVTY0xHxORHwDvBAT4N8aYr677yDbY6GyNwXxiwbGllp7Mn1BmEg6nJipMl5uMlRr87nsf4MBgnn39mQXVTQ/t69HqpkoppW67rZjro7M1rpTqxB2LhOsSd2ymK03m6n4ni4Elq5LfqGq5UkrdbVbSAgNjzFeAr6zzWDaVoa4kxZpHPul2ji219GT+hLI/m6A/myA0hrFifUEA/f77Hro9g1dKKaWWsdVyfagrycsXZulJR5mdjjvYlrCzO8nuntSCLF7KgcG8TgqVUqplyeWmIvLt1vc5ESnN+5oTkdLtG+LGuJmlJ0vtZYjbclOFb5RSSqn1spVz/fDBARxLKNV9jDHUvYCGH5KK2VyYrmoOK6XUTVpykmiM+bHW96wxJjfvK2uMyd2+IW6M9tKTfNJlrFgnn3SX7Je02ITy4nSVy8X6igvfKKWUUutpK+f6gcE8v/bOewGYqjSJOxbbc3FOT1TYno1rDiul1E1adrmpiFjAq8aYg7dpPJvKSpeeLLaXoZmLE3PsznLV9vejwwVdzqKUUmpDbOVcf89DQ+zrz3Sy+MJ0lUd3dbG3PwNoDiul1M1YdpJojAlF5BUR2W2MuXC7BnUnunZC+eTTr9Cb0Z5LSimlNo+tnuvzs/jJp19ZcQE6pZRSC62kcM0g8JqIPA9U2geNMe9dt1FtATdT+EYppZS6je6KXNccVkqpW7eSSeLvrvsotpCRsSJHhwu8drnIpZka9w9k2N2bZq7uU6x5vP+tOzd6iEoppe5ud0WuHz44wJFnzwLRFcS5us/F6SrNXJwnn36Foa4khw8O6NJTpZRaxJKFa9qMMd8CTgB5IAecaB1T1xgZK3Lk2bMUax4HBnPs35bhxJUyr18pLVv4RimllLpd7pZcv7YAnecHhMYQc2wtZKOUUjdwwyuJIvLPgP8N+AZR091PiMi/NsZ8cr0Hd6c5Olwgn3Q7S1v29mfoycTJJ10+8q79Gzw6pZRS6u7K9fl7FJ965iSuFpRTSqkVWcly098AHjXGTAGISC/wHWDLhclqjc7WdJO8Ukqpze6uzHXNaKWUWrkbLjcFLgFz8+7PARdv9CIR+aSIjIvI8LxjPSLyjIi80frePe+x3xKRUyJyQkTefTM/xGYx1JVkru4vOKab5JVSSm0yt5TrcGdnu2a0Ukqt3EomiaPAcyLyOyLyUeC7wCkR+XUR+fVlXvcp4PA1x/4X4OvGmDcBX2/dR0QeAD4APNh6zb8XEfumfpJN4PDBAYo1j2LNIzSmc/vwwYGNHppSSinVdqu5DndwtmtGK6XUyq1kuenp1lfbX7e+Z5d7kTHmWRG555rDPwW8vXX708A3gX/ROv5nxpgGcFZETgE/DBxbwfhuu3YF09HZ2nXV0VKuxfNnpzEYHt3V1SlWs9xrlnvsZj9fKaWUuoFbynXY3Nk+MlbkM8fO89LFWQTh0V15dvcm+frrkxRKdQZyCd755j6qTcNrl4uU6j75pMPR4QKA5qhSSs1zw0miMWYtS2UPGGPGWu87JiLbWseHiM5ktl1qHdt02hVM80l3QXW0nzjQz9dGJsgnXd55YFun5cVyr3ni8b0AncdcG755YpwvvjTKj93Xy4cO7bkutJZ7Lw04pZRSN7LGuQ6bINtHxop87OgJLkxVycRtDPDMSIFizWMwl2BbNk6p5vHZ717kgz+yi2zCZWd3iobv3zB3lVLqbrSS5aa3gyxyzCz6RJEnROQFEXlhYmJinYd1vfkVTC2Rzu1PH7uw6PGjw4UlXzP/MS8IeOlCVIa7O+nw2uXSoqW5l3svpZRSahO5bdl+dLjAdKVJJuGQiDkkYw7lRrT/0AsNlmWRT7qk404nr1eau0opdTdayXLTtVQQkcHWmcZBYLx1/BKwa97zdgKXF3sDY8wR4AjAY489tmjYrKelqqMVSnXetrfnuuPtqmmOBcfHSpTrPpmEw76+VCfABvMJnj9bIu5YJFwbYwzlRtCZ/M1flvq3x6+AgWzSpTflMlX1mKt5IOiyU6WUUhthw7N9dLZG0w/JJq7+s8YPDJZAww+pNHxmqk0aXkix5lH3fM5OVm+Yu/N9+dVRPn3sQmfp6ocP7eY9D23KRU9KKbVqN7ySKCL/9UqOrdCXgA+3bn+Yq/sgvgR8QETiIrIXeBPw/C1+xrpaqjraQC6xZNW0mC187+wMDS8gE7dpeAHfOztDzJbO+5XqHnEn+s/R8EMyCWfBJLO9zDRmW7i2MFNp8F9OTzFbaeLaQsy29AyoUkqpG1rjXIdNkO1DXUlijkXDDzvHHFswgGAYK9bxg5DQGFwnyuRCqbZs7s735VdH+YOvnKBU89iWiVGqefzBV07w5VdH1+PHUUqpDbeS5aafWOGxBUTkc0Sb0+8XkUsi8ovAHwDvEpE3gHe17mOMeQ34c+A4cBT4FWNMsLIf4fZaqjrahw/tXrJqmnB1fU2l6XOlWKdQqvODS0X2D6Qp1jyCwHBussLJwhyXZmr0ptwFpbnby0wPDuVoBoZSzSdmC8WaRzMwHBzK6bJTpZRSK3FLuQ6bN9sPHxygJx2jXPepN31qTZ9MPLqq2PQNtgV+CH5oeMvuLgxQa4bUvYC6F9DwQ+7rTy/ZEuPTxy7g2MJ0ucHw5RLnpyqUah5//M0z6/HjKKXUhltyuamIHAJ+FOi/piR2DrhhCWtjzM8v8dA7l3j+7wG/d6P33WgHBvM88fjeTnXRmC00PJ9/93dnKNaaNPwAx7LY3Zvmw4d2c2AwTyMwvG1fNz+4VOLidI2ka7OnN0nNC/nayARv3p7m5QszVL2QVMwin3A4WShT90N+8/D9wNVlrpa4/NCeLr76WoHQGDCGH9rTRV8mQWiMNgVWSim1qNXmOmzebD8wmOc3D9+/oLrpuw4MsLs3yZ/8/TnqXkAm4fK2vd28aSDHrp4az52ZZrbm051y2dOT5LXLJWaqHgcHs/z2F16lEZhOBfEz43OUaj6+iZawOlY0yTw1UWZkrKhbPZRSW85yexJjQKb1nPllsUvAz6znoDa7A4P5TluLj3/1JGcnKzgWVBo+oYFMymJ7Ns7XRibY159hqCtJseaRjjvs7UuTcG3qXkA6YZNPunz99Un+wf3b8IKAU+MVCqUatWbA+alK58pg+z3ySZe+TIJ9/RlK8+6DNgVWSim1rC2d6wcG8/z++x667ni1aTr52RZ3HP7bh4c4fHCAzxw7z7dPTdGdcnlgMMOJQhkDvG1fN8Wax8e/epJS3cMPwZKoGk97Vasr8Jlj5+nPJrQtlVJqS1lykmiM+RbwLRH5lDHmvIikjTGV2zi2TWG5noRHhwtMlhtkEw6T5QZxxwYMNS/kylyDBwZzHB0ucPjgAEeePctkuUFPyu0sbXlwR25B0RtLXIwhCrOE0AjC61psQFQQZ3s2zthsjfsHMoTGdFpuvP+tOzfwt6WUUmqz2uq5vlRetzMYovycn5cHBvP0ZxO8483byCddjp2ZItMqfnNmosqP7OvlpXID17bwgxDTmiQaE20jSTgW3z41xTvevE3bUimltpSVVDfdISJfITr7uFtEHgZ+yRjzP67v0DbejXoSjs7WaPgBuYRLpeHT9EPqXkgITJQbnLhSIhOzmZyrM1ePqpBOzDWIORbbcwlEFha9ySddTk1UCEPDlUqTwBhGxkpsz8U5WagsWOa6tz/Duw8OcLJQ6QRiO/CUUkqpZWy5XL82r89Nlvnnnx2l6Qc0A0PMFnrScQa7EjwwmO+cUP2XX3iVL/9gDNe26Eq6TJab2BbEHZuE2y5qE+DaFp4V4oUQEl1RTLg2jm3TnXI7Vynb35eqkKqUUneKlUwS/y/g3URVyjDGvCIij6/rqDaJo8MFwjBkZKxEqe6RS7hsz8U7f/yHupK8UZijWPOoeQFN33QK1IQGSjWfUs3nC98fZag7gTGG0Bi6Uy6WwLHT0+zti/Yutq8STszVmSk3QYSd3QnqXsDrV+aoegEfedf+60LnPbf5d6KUUuqOt+Vy/bPHznNmokwzCHFEmKw0oj2EYUg65lANDJZ49GfjHD44AMDHjp7gwlTUBqPhBZyfaiIIcdei2gwIQ8M3RgrUmgFBaIi7NuKHWJYQhAZbQAQe2JFdMJalKqQqpdSdZEV9Eo0xF0UW9MTdlJVH19rxsSIXpqokXJts3FkwYYOomtrwaJEXz89gQnNdh+Co9HYUIpeLdWJOdMax0gxwbZtswmEgF+c9Dw2xrz/D0eECc3Ufx7YY7EqQikX/eRp+SLF2tb3GcktgN4PNPj6llLrbbaVcHxkr8venpuhKOmTjDuenqszWPDAhIhbJmI0fGJpBwHSl2dnrP11pkkk4pOI2p8bLWJZFEIZUmgGOJaRjNmPFOjXPJx1z6E7HmC43KLV6HG/Lxrl/e5bjl+d4OShiiyASFbTpycS0oI1S6o62khYYF0XkRwEjIjEReRIYWedxbQrFmo+IkHDtBd/bE7YDg3mefPd+utMuYglWK28trv5iLQFDdNYxZgtBCF3JGO96YIDH9/fTDEznvT7yrv08squLvkwcSwRjDHUvAAO51h6J9pKaYs1bsAR2s/RH3OzjU0optbVy/ehwge6Ui4ggIgTGdIrLOHYUzHbr6l/TDxmdrTE6W6Pph8Qdi1TMIe5YuJYQhmBLtGzUti0Q6E7F6c/Gyadi9OeSPLyrm/e9ZYi9/RlqXki57uP7AZdmapybrOIHAYO5uGafUuqOtpJJ4i8DvwIMAZeAR4A7dt/CzcglHDBQ9wIqDY9zk2Uuz1aZKNUX/OHvz0ZX/WK2dJafzL+qaInBtS0wUPOCzqb4xaqRDmTjBCYKsTOTFUJj2D+Q4cEdV4vl5JPR/gdLpHN7s/RH3OzjU0optbVyfXS2xoHBLA0/ZLbapNb0qfshgYFyI2C81KBc97AtIeZYDHUlGepKEnMsGq0ypem4Q9y1cR2hP5voVCa/tz/Djq4EpbrX6nkcTUBrzYBS3WdPb5q37euh4oWIQCpmk0m43NOX0exTSt3RVrLc9K3GmH8y/4CI/DLwx+szpM3jwR15Uq7N2akKF2dqJBybgWycVNxdUHF0ezbOxakKs9Xrl5wGBirNENeChheQcB329aUo1rzrqpGOjBW5XKwThjCUT2CIAq7SDDp7KNr9EifLdU6NVyjVPbJxh3zKZTNoj28+3Z+hlFKbypbK9XaLqH19KZ47O0N4TRB7ocFrBNS8gFTMZv9AGoC/fqnJeLkRPckYvMAQcyzSMatThfzgUI7R6SrFqk867pGN2xRrHpdna3SlXI5fLjLXKlw31NomMtdajqrZp5S6k63kSuK/EpF3tO+IyG8CP7V+Q9o8Dh8cwLYtLBH29KTYnk9g2zYP7siRT7p8+tgF8kmXvf0Z0nEH127tQWx9tcXtaAmMAWI2TJab5JPudSWyjw4XOmcl4zEHP6Szb7H9vKGuJBemKrx4fpa6F2AJnJmo8NyZaX77C69u+NKWoa4kc3V/wTHt36iUUpvKlsr1wwcHKNY8RmfrDHVFlcNdS3Cu+ReOJcLDO7v4ixdH+cL3L7OnN4VjWQShITDQl4mTSzjMNaLtmY/uzuPaNlfmmjy6O08u6VJphuSSLvf2pbg8W6dU98nGHWwRLs3UKdaiIneg2aeUurOt5Erie4H/LCK/ARwG3tw6tuUdGMzzxON7+fU/fwUMZJMOD+7I0Z9NEBrT6W8IMFvz6ErGcGyLZhDtc5go1QmBbNJt7XuwScUcHtwR7T+8VvsqnCUu/dnoalxoDGPFeuc5hw8O8JHPXwHAD0JGZ+sgMJCNMXy5tOH9mZbrR6WUUmpT2FK5fm1WByGkYhbNwCCEhAZijuCHkEk4nJuqAhB3bfb2pUm4NnUvIOHaHBjM0fQD+rMJRtybeYQAACAASURBVGdrbMu67OxOcmAwhzWv0M+x05MkXRuIisv1ZWJcnKlRKDV4y56uRVcLKaXUneSGk0RjzKSIvBf4GvAi8DPGmGtXVW5ZBwbz/MMHtkcN7pNXl3TO72/Y9AMqDZ/QGBzLwrWFSsPHb/2WBEPDDzEGvNam+cW0l8zkk25nOelkuUFvJt6pknZgMM/O7iSlmseZyQpxx6I/GycVs5lr+J09EBs1SWyH9fzqptq/USmlNo+tmOvtrD47UebcVIViPWjtIQRHAISYDafGKzT9EEO0vDQTjyZ6fhByerZGqeaBwB/+3MOd3HrqmZPX/RtgqtJkR1eC+7ZlODVRwQsMfRmXqYrHd05PM5BL8OFDuzX7lFJ3rCUniSIyx9UuDgaIAfuAnxERY4zJ3Z4hbrzFro4Nj84yW/X4/PcuRPsfwqjJbmORKuLRchSbpg9VL6pyutznzFQavH5lDhHBtSy2Z+N87OgJduQTNAJDqe6zPZeg3AjIxKOKq3UvIJdw12wPxGraWLSf1359e+O+hqVSSm2crZzrI2NF3rhS4usnxvFaZ2jbs17fgO+FOAKvXymxLRsHoNRsMlYMcCyoeyGpmI3b2h7y8a+eZCAXpxkYqg2P45dLOLZNT9pley6Ba1tsz13df19r+szUfAayCd59cDvnJyt84uun+auXL/PAYL6TodoiSil1p1hykmiMyS712N3m2qtjlbrHpZkaXakY6ZjNbM2jVSBtUb5vqFohMVuiSd0iz2kHx+WZKq+NlQgNdKVcHh7Kk0k4fP/8DMcvl+hJxwiN4fxkmVzSpeEBIp0N9muxB6LdxiKfdBe0sVjpMtbVvl4ppdTa26q53s6c0WKdhGPh+Yu3fPRN1Nqq1vRJxhx6UjHqBJTqAWEY0pOO0QwM+/qSvH6lzGS5wQM7srx+pYwfQjouTFWajJfq7OxJ8vy5aepeSH/GpdL0CQKDF4ScHp/jzGQVBIpVr5OB7WJ3tysbdUKqlFqNGy43FZGfBr5hjCm27ncBbzfG/NV6D24zaS/1BPi5/3CMrlQsWlbavHH/4YBoj8SuviQHd+ZoBAtX9bQDLghCijUfS6JlMdm4w9mpKicLc5SbASLRlcyGH+LaNmFomKn5dKfczgb7tdgDMb+NBdD5vtJlrKt9vQabUkqtn62W6+3M8QJDMzAkXIu6H15X5bTDCGFoyMQdEKj5ITHbwgsNP7ani1PjFTJxGy8wnJmokm21rYq7Ng/3p3nuzDRz9YC+dIwrpQaFchMB9vQmsS2LVy4V6cvEiTtWZxsIwKePXeCBwdyi2dj+vla5pydrlVKrtZLqph9tBwmAMWYW+OitfqCI3C8iL8/7KonI/ywivyMio/OO/+StfsZ6K5Tq2GK4PFuPmt7f4PkCPLyri3ccGCDuONdd6fvMsfOcmSjznTNTzFSbxBxBxKLc8Ik7FpeLdWwr6r8kIiRcm960S8y1+Q8fegtvv38bXsCiFVNvxehsrROKbTezjHU1r28HW7HmLQi2ja7aqpRSW8ia5jpsbLa3MyeTcDCtxbRL5bIAfmv7ZSru8N6Hh/ivhvLsa/U17Mtc7YmYSTiU6h5+EDIx1+DElTm+eXICy4JmEBIA+/rT7O1Nk4472JZF3Lma3Q0/7FQ6zSYcCqX6otn42uW1zz3tWayUWq2VVDddbCK5ktctyhhzgqhxLyJiA6PAF4H/HnjKGPPxW33v22Ugl+DMeBnHEtJxm8YSS1vmmyrX+ZsfXO6Uy/5Pr4wSd23u6Unx0sVZBrJxjDEYAS8AY0JCE+1fDEKDHxh6umKd94tyUBZc4Vwr8wvotLWXsa7kKt9yr7+R1V6FVEopdUNrmuuwsdnezpz7+tO8Plai4QUsVYbHAMZAzQsYn4sqh/elXb5TmEOArx2/wnixTjMw7OlN0fQCpqoejgipmEWtGVCt++zsSZGOO9S9gLhjkWhNCht+SDrmMF6qM9cIyCcdvjZSwPNDKg2fZ09OdKqkQ5SNpbrPzu7Umuae9ixWSq3WSq4kviAifygi94rIPhF5iqga2lp4J3DaGHN+jd7vtvjwod1UvYAgDLFFSFzbjOkaMRsuz9a5UqwTBAGFUoPpchM/CDl2dppy3afmhcRdG0FIRKXY8IKQ16+UsS0h7kb9Go0x1L2AciPg0V3rM2lq95wq1jxCYzq39w+kV3S2c6nXHz44cMPPXu1VTKWUUje0nrkOtznb25kTc2x+9N4eXDvK5MVLxEWCECZLDU5eKfL6lTIZ16Y75XB2qsJcw8MLAq4U6xTm6jSaAUFo6EnHiNkWgYne+75taRp+SKnu05eJs39bBgz0pl2mqx65pEPCsbg4XeXybI37B9LM1X2eOzNNoVTrZGM+6ax57mnPYqXUaq1kkvhrQBP4PPA0UAd+ZY0+/wPA5+bd/1UReVVEPiki3Wv0GWvuPQ8N8Q/e1IdtWVS9kFwqxiM7c2TjNtcWLk04gm1brdYYFrVWBbW4a1NpRiW6Y47FVKVJJubgh4ZaM6DWDOjPxtnRleBt93RjiVBrBpTqHgB7+9J88NCedfn52oV68kmXsWK9s4z1ZKFy3fKVIAj56JeO8+TTr/DUMyc7rToWe/1KzohqsCml1Lpbz1yH25zt8zMnk4jxU4/s4CcfHGBbLh5l8LxctgDXAtsCRDh2ZoZswuHx+/tJxVxitk02ESPp2lQ9n3IjJMTQk3YJDfRn4/RmYlS9gJ50nPsHMgDkki57+zM89YGHeceB7fw39/ezLZtgvNwk6VoMdSfxQ+HQvT1kEg6vXCp2srE/E+fZkxM8c7zAsTNTTMzVV517qzlZq5RSALJRrZFEJAZcBh40xhREZACYJFoN8m+AQWPMLyzyuieAJwB27979Q+fPb8xFyC+/OsonvnEaLwjpTccYzEctKQZycY6dmWJ0pkYmbpNwHaYrzdYSlxARoTcdxwtCyg0f09pA0ZuJsbM7xfhcnUKpjmNFS0nv25amL5Pg7ESZK3MNdvek1qWYy0qWkT759CsM5hOdhsJvFEo8d3aGph9wYDAXPWZZq9oXOX+zfbvdSLHm6WZ7pdaQiLxojHlso8ehtp7Nku0jY0U+e+w83zk9xVS1Sa3hY1mCIIhEJyTTMZtz01X+6aF7sET40+fOY7UGWqx5dKdilOpNQiPs7Uvzlt1d9GejPD47WSEwYDA8uquLDx3a08mo+Vn5zPFCpxfjXMPnHz6wndAYxop1Pv6zDzMyVuTjXz3J2clKp/p5uRGwuzfFbx6+/7rcm5/VMVsQoBGYRXNbi8ApdfdYj1xfrk/iHxljflVE/hOL7AE3xrx3lZ/9j4DvG2MKrffr7KYWkT8B/vNiLzLGHAGOADz22GMbMsMdGSvytZEJ9m/LcKVUZ7riUar5/No77+U9Dw3xjo//HY4llBs+c42AsFVizRhIxqKehpVmgG1B0nUoN3wqjYB9/SkODOb4xuvj/Oi9PQzkrp5F3NOXJubafPxnH17VuBcLjGuroJ2bLPORz19hZ3eSB3dc7e80f6/hZLnOc2dnCEJDNuHS8ENOFMrcP5BZ1T6Ka9uNDHUlef9bd2qwKaXUKt2GXIcNzvaRsSL/99fe4L+cnsK2hKGuBLt78nz37AwxW8gmXXpSMdJxh1rTx7Ut5upXK5AiUG0EuLaFYwtJ16HSjFa3nBovE3OiVUB92Th7etOdk5nzK4fOz8pMwqHhBVSbPtVmwDPHCwRhiGNbPPn0K1yYrjKYi3Po3h5OjVco1T0yCYcd+cSiE8R2VjsWPHdmGgO8bV/3otVL16NmgVLq7rHcRvV/CvwqsF6bzX+eectRRGTQGDPWuvvTwPA6fe6qzS+usrc/WmpSrHmcLFTYN1akUKrT9EOifr4Ls84LQmpeiBhIp1xCov0LuWSMVy6WeNcDA/zYfb3EHHvB625l6cn8SWHcFi4X6+zpTV9XDnv+zzNZrnOiUAagVPMWPO/wwQGOPHsWgDcKZfzWnsyedIyEG413rFjHbY39Vs9iarAppdS6WO9chw3M9pGxIv/qr4b5wWgJYwyC4eQVn5gbFZZp+gFBYLhSrGNbgmMLP3xPNMECGMjGuThTo+kH5JMufhASAru6k+QSDoW5BoeSLs1cnJhjL1loZn5W7utL8e2Tk8zWPXbkE/hBwOXZOl2pGI4F0+UmxWqTx+7p5kf29QJ0rjRe6+hwgSAIOT5W4sxEuXMC+pnj49zbn2F7Lq5F3pRSa2a5SeJpAGPMt9b6Q0UkBbwL+KV5hz8mIo8QzarOXfPYprJc1bDPHDuPIARLnAd1LQtjotLZpbpHV8rl0d3d5JIur1wqMjpbo9rwePnCLDU/xBLoScfY3ZPmrW/ZwVPPnFxy0rXcpPDZkxOU635rCYy7INTm/zynxivEnaiM92S5wchYiclyg49+6Ti/+94HOpPKwlyDdMwhE3dIx1s9pFp7Kw/d26c9mpRSavNZt1yHjc/2zxw7z6nxCkEQAnSWgzYbAbZE9626R9y1ILQQgfc8NMi+/mgFzGBXkkrDZ86KMtxG6ErY7B/Icnq8QrHa5E+fO0+t6XNPb5pHdncBUW4WijXKzYDjY0UeGMzzEwf6OVmoUG74dKWjK5eWLRRrHju6kiRjNmcmq/RkYpRqHqfGK/RlrlY8Xeyk8GuXi1yarhF3LfwgpNoMorZYjkXdC3j9yhxV78bV1pVSaiWWmyT2i8ivL/WgMeYPb/VDjTFVoPeaYx+61fe73Ya6kp09guW6TybhsD0bZ29/hr89foXBfJw3JqrXvS4Xt7EsIRFzCEJDKmbTn43z0oUZKo0A27awgfFyg7m6T8KxCI0wUW4Ssy3+4sXRRa8ELrZk9NpJYTMIycRtTk1UOqW32xPb+UtjSnWPbNxpbXL3ySYCelIu0+Vm5/M+8q79AJydKHNyvNwpAV6qR0t3Dh8cWFErC90vsfb0d6qUWsa65Xrr9Rua7S9dnKXh+a3J4UKhgZgFIdCVitGXTbA9G+dkocJ7HhpakEufPXaeb5yYIAwNDT/g79+YpBmEiDGIFbW6OHFljiulGrlEDMcWZms+jiVcmKqScm1evTTLjtbJ19DAW+7pYls2yd8ev0K2dWK1XPd5ZFee75+fZbLcIDSGubrPxekqzVycJ59+ZcHf8VLdB4GEa+OHBtuS1vsbEq5Nww8p1nzU5qUZre4ky1U3tYEMkF3i6661fyDNSxdnKdU80jGLiVKdb56Y4BsjVzg3WaFQanSeO7/YacOPlpr2pmNYEvVp8oOQqYpH3Q8Z6kowUW5SrPnEbItU3GH/9ix7elJMlptMV5pLNsa9tnHu/EkhQC7hYohCqa19tnJ+FbRs3KFU95ksN+lJuyRcm2Zg6MnEFnze4YMD2LbF/m0Z4o7FdMUDA7/2jns5MJi/YSuL9qR2LZsH3+30d6qUuoEtnev1ZkDdN9dvtmzJJaNM688mOLSvlz196evaTBwYzPPBQ3t48/Ys2/MJal5IzQto+IZmCMYYXEsIDcxUfWarTUqtidn2fIKEa3N6osKFqSrDl0sM5hO4tvDcmRkmy3VyrT38DT8kk3DozybYP5ChNxNnrFjH84OoR7JjX/d3PJ90Om2wbBFCYwhCgyVQ9wIwkEusqt2lWkea0epOs9xfkzFjzL++bSO5g5wsVHjL7i6ulBqMz9Up1X0yCZvLxQZx26Lc9BHaDe8jVue+oSsVXVUrN30Kcw0sEeKOkI67XCk1AIMfRiEC0TLOuh/QbN1vmz/punYJbC7hUmv6nUnhfdvSHDs9TTbhdM5WFmtepyhMZ29iyqVU80m6Fl1Jl7oX0PBDHtyRW/B5818Tc20O3bvwjFj76mTTDzg1UaFc93Ft4cEdOYAVXWlUN0d/p0qpG9jSuX5tC6r5XFsAIelanVZSSy3rPDpc6KzaOTdVwRbBxxAa8EODa1u4Ap5vqHrRKqChrgTpeDSJOzNZYSifoBmEWBLl3nNnphkeLfHAjizPnZlBgO25ON88Mc5M1ePH7+vlg4f2cHS4gLvEfscHBvOkXJsrpQauY2ELiCWICHHXZk9PqlMnQW0+mtHqTrPcJHG5PrRb3nJLAkZna+zuTXNPX4bvnpkilwgYn6sTGsOu3hTnJitUGtG+gPZE0baFuGNhCZTqUSnut+/v5+WLRSypd37ZcceiKoIXGLqc6EJvww9JODax1v2JuTqnJipMl5v0ZGKMjBU7kzIvCDg1XmFiLqq6ui0bJzQG17bZ25dmIBedrWxXDQUW7HP89dZS0o9+6Xjn/R/ckaM/G531mh+o7d/H0eECx8eKHDszRS7h8OCOPPsH0vzFi6O8USjTDAKqjYBmEPBGYY7vnJ4E4Ef29V6tKMfVSe9qlmPczUs5ltsrq5RSbPFcD4j6H15zPhUBMnGns8Lm6pYKr5OD87X/lv7d69N4QYgfmgXv5QchtiVYVlS1POZYNFv7INsndw1gi/DdM1OU6h6J1uTUC+DQvh6mK01eujRL2Np68r1zM3zn9BQ1L2B3T4r7tmU6W0Pqns9zZ6fY2Z3k4lSN7bkY27Nxzk1VcUR4254uBrtSq+6DuFx+3s3ZulY0o9WdZrlJ4jtv2yg2mRsVXVlsD1+57mOIKpK19wYYE515TMVsMIaGF4LAWLHGIzvz9GbiuLbgWhax1sbz7pTLbLWJMZByLepNn3IjYE9vinTcifYBFsogURgO5uIcefYsb96e5ksvjzNRbpKKWeQTUVGZph8wMlbiwR153v/u/dcVulnq5/zd9z6woF9h+3N39SZ56pmTnSA68uxZwjDkwlQVEWG8VOf8VIUvvmQwYdipGueFIcZE4Vqq+YTAt09O8vj9/Z0gnKv7xGy55YI3d3uxnPn/X7attiGzUmpL2dK5LoC1xDR4MJ+gLxOjUGqST0VXc5Zqr9SuO3Bxpop9zWNBGO1rNBgcW4jbFnHbolBs4PkhcdemNx1jqhLVErAt6WzjMKEhbguNwHBppoYtwkA+QbnucX66ShCEuI7FRLlBqe7zllZhnG+/MUUzCDDGUPMCvne+Sncqxj19KRp+yMiVMr2Z+Jr1KL42P4F1z9a7YRKqGa3uNEtOEo0x07dzIJvJjZYE7B9I84lvnKbS8JiuNAmMoekbUq5FzLaoNn2MMcScKDyagaHuG5KOxWBXgmoz4Px0DdexeXBHjkKpQTpmt3ouBmzLJhjKJ5iu+TRDw9v29fChQ3uA6AqfF4b0ZeLcty1NXyZq7PvFl8ZwbYuUa+EFhumqzw/f081APkk+6XaKzbSNjBUXXC3sTblMVT2my83rKpm+drnIpZka92/PsLs33QmIlGuRT7qMjJVIuDahMVwpesQdi+25OCcK5ahSqmsRNgxYVhRyfsju7iSFUoPXLpd4fH+8s/y1/Z63shzjbl/KMb/sert311JnypVSd5+tnuvRfvxFjgODXQv7/i7n8MEBPvJnV8BAMzTYFgSt9w2JJqPpmENPOsaBwSxTFQ8vDGmGhjf1pujPxDl2eoq4axN3okI39aaPH8Lw5WjJ6RvjZYIwpNLwqXkBlgiWIwShYarcpDcT49R4mWLNY7baZEdXklzCZabiYYnQnXZ5x5ujk7Xticdqcm65/GzfX69s3YgTvBsxKdWMVnca3eG8iOWWBIyMFfnayATbszF+MFdHRGh6IY5AMzBUmz51LyDh2Fi2hWWD74W4tpCI2XSn4yRjUdo8uCPPR961v/PHarG9fdfa3ZPibXt7sOTq6dIrpTpeEBJzLO7pSyMi1L2AqarHfQPZ65YytP8gT5Ub9KRcZioNXh8rsT2XoDvlMFVuLKhk+tQzJ9nZnbouIJ4/O807D2zrXE0dna0Rs4XAGOKujQEcW5hr7Yu0rVbFOWPIJ6OKq15gFix//X+/fY6ezNIFb271v9vdYP4+0XbwLXWmXCmltpqZSmPR47Z1NW9X4sBgnl29SSbLdWoeuLZFPmnjB4ZyIyDuWty/PcubBjL0ZRK8CQhND2PFOh//2YcB+Gef/h6lmke5EZBJOGQSLrbAXN3jpQtRoRLbFiqNqBpr3In2FtoWbM8lmGt4FOYaVBpRlfLudAyAwBgSrizoo7gWOXej/FzPbL3dJ3g3atWRZrS60+gkcRHLLQlo/zG7PFtjZ3eKhGtz/HKxs/cwCE1UtEYMTT+g6YMfhlEj3+BqIZpS3Vv2D+xSZ7kWG9t0xSPhWIzPNThVKGMw2CIkY1dbc8zX/hn6MnHqXkC1GRBrFdxJxmz6MvFOJdN2pdLFAsIQFcDJJa4WuLEE4nZ05jSbsPGDqDy3LdEynSCMbr8xXiZmW/zovl5+730PLfjdv3JxhpPjZSoNn3TcYf+2DA/v6l7Vf7e7xYHBvAaOUuquVPWijLWIrvi1NUP4d3/3Bp/+zjm60y6H9vXyoUN7lv1b+cBgnrMTVQZyCWZrHg0/OhG7vytJCDywI79s1jy4I78gj545XsCS6HmWBIQmJAii9hiWQMMLkdY2knLDx7aEH7u3l68eLzBRblD1AnrSsWj/oxeAwGS5zqnxCpPlBr2ZOCNjxVv++z8/P6993zf1p5mr++uWrbf7BO9GrjrSjFZ3kuVaYNy15reECI3p3D58cKDT2qFUj5ZVVhrRXsSmH5JNuAgGLzSUG1G7i7oX4odQbkb9i753bpqXLswwXmoQsyVa9vnXr/G558/zt6+N8bnnz/MbT7/Cx46eoFjzcCz45olxfukz3+e3v/Aq+wfS143NDwMmytEZxyCM9kE2A0Ol4fPcuWn2D6QX/Hztn6E37XJppsbEXIOa5zNX82n4IfdtSy/4Az3UlexcDWybq/s8uquLYs1jey7eKb/d9KNN+A0/5K17esjEbNIxB8uKmv/6QbQM1xhwLeFysb6g/HMqJjx/bppqIyDlWlQbAcfOTPHiuSmefPoVnnrm5JLlopf776aUUmpr6/QNXOQxP4TZmsf5ySp/+eIl/vlnXuTLr44u+V6HDw7ghwGFUtQPuVhtMlFucHK8zNhsjeOXiwuy5uJ0lYm5eienrs1q1xamKk2qzQAw5JOxThX09piFqDJ5tRkwU/E4UZijO+UCQsMLGZ2p4VpC3Q9JuzbffmOS0+NlJuYazFQafOzoiVtup9DOz3OTZV44N0Ox5uFaFtuzcS4X61ycrq5bti71b4z1OsF7oxZdSqmIXklcxHJLAtpn23IJl+lKg+mKhy0WlgTMVhtUPbNgo/tiDX2NgVLd45nXxvjiS6PUvZCYI2TjNkEIJ67MMVVuMDHX4MJMlaQT7dMbvlyi6oX8xIF+ThYqnbE9sD3Hd85OY4jKfAfGEAKWZehKuFGz4HljaG/KPzNZpS8To+H7NLwQx4Z9fSn6MgnOTZYZKzV48ulXiNkS9X7sSS1YR9/e0H50uEDVi65GjpcbpGIOBwazJFyHuh+yI5+gMNfglYuzNPxoY/5ALsHDO/PEHHvB2buvvz7JtkwcLzTRc22h6Qsnx8s8uqdn2WUha7GU427YPK+UUlvR9mycM80q4VKNEokmY15gmKl6fOIbp9nXn1nyb3w65nBppragWqptDF5gODU+R18mRrnhErel09uwNxNVTv3ayMSCrH5wR45jp6dIujamdfUwk3AJw5ByI8C1oSsZww8NtiXkEzG8wPDWe3r4zqlJal5IEBjqXsibt2eZrXoUSg1SMZtduSSObXHh/2fvzqPjvO/73r+/sw8wwIAASRCkuIqiTImRLYteaMeq4yWR46Z2UjduGjtu2sZdTt0bt47jm5xrZ7nNddL0+jbOSRMlzvWWmyZuNjWOaUtOHHmhJGsXTUoURYqUSBDEQgwwwGzPPL/7xzPzcABiGZAAZgB8XufgYDDr75nB/L7P97eOTvOFY+f4tYbROfOZK9Z98O69fOK+E1R96M3E2b+lM1zZvOxVyabjKzJMcrnm6jUbvzXqSKQ5ShLnMd+QgHpltq07yenLk1R9iEUj9HTEw1ao+rLYzl2bJEKQyPm+Y6JYpVR1tSW1HZOlKt0pwzkYmiwzUfSCIZrVYDPf+jDQU0NTM+ZWfORLT9GTijFUqRKJGIlIhGQsEgyBjXBN61g4Kd+CYRa+CxaR6cskGMmXiUfzPH5+nDt39jCQTTFZ9PCdo+JVGcx51wSI2Sum1ivpbDrOR++5Nbz9I196ioFsasZ8St+5GeUbmiiytStJJBJ0cr98ZRqzWG0oqy06LORGhnJs9NVRRUTWsgPburiQK1KozNWXeJUPFD2fStWfN5Z88dg5ChU/WCxmunJ1O6uIkUnGqPqOkakKf/q+w3zq/lPX7G04li/xuWPn2dXbESYslydLXMoVeOlKgVQsyo6eJNGI8fzlPDdv7qRK0JO4f2snj527Qtnz2dKV4g37N3N6eIrJQgUMfvXdh/iPf/oU+zZ3kEo0nMY5xxMvjS/6Pi0U6+Za96ArFWMw5zU9p3OplquBt9n4rQVkRJqjJHGJGiuz7754BYcjYkbFD0JIzByRiOF8B7V5eFeDS/AciahRcC7csSoaASNIHIsVH98FQczzHbGIUfV9ChUoecEQl+6G1i8IWsWe70jUAqMjFo3gVYP0NBmLXtM6Vp+Un5uuMFny6O1McsvWDCP5MkOTJTDjzp094VzGbDrO7r7OOVdJnev9ma9ib6b1rr87xUShQjZ9dY9I33d0Jq/+q67UsJCNvjqqiMha1pGM8+YDm/nK9y4vel+v6tPXmZg3ljzxUo5MMspE0YjY1UbfkueYKntECBo14do5dcOTRU4N5an4Pq/be3UETH9Xkps2dfDKncF+xhPFCjEz9m/JcNee3hmxMRm7OiZpS1cq7NGrr2Jq2DWN0MG5xuJbYS4U61rVy3ajc/WWEr+1gIxIc5QkXofGyuzFkXy41cNEoUKxXKVSdcQiUJumBwS/fR9S8UiQUNaGm8RqwQdzVH0oVKrhfIqIGZ7v4wVTGHDOMTxZ4oXhKf7V574b23d6tAAAIABJREFULud9z6F+nn55nNHJErmiR6Vawas60olg+e3Z8wZODubIFTzGpir0ZhLcXBtSsrW7wpHacJKlTiJvZphHM613Hziyi09+5bngPslgWE7Z83n9vt7wPisVsOY67pLncf+JMQ0/FRFpc0GCEyMZM0re/GNODTALVuzc0ZOeM345HIYFo3JqCWL9Gat+sPZAptZ4OTuxOj08RbHiUfEdX376IpPFYNXzSMTo7Uhwx01ZXru3N4yBbzu4hf/52AXGpsqUawvkJGMROpOxYOpHrsjoVJl4NMKH3nIzAHfuzHLszBhmFm6zkS9VOdIQK+ezUIz/l9+/Z831sp0czHH/iSF855NNJ8LtwRY6b9ECMiKL08I1N+CeQ/08N5QHghVL+zoTQYUdtTlb+XwgWpupbg5ikQgD2RRmQQLpIJxLkYrWAlFtb6ZEzIBg4nt3OsZEbeJ4vTL/6D23csdNWaq1Hs1MKsZANj2jBw6uDskY6E4SjcBEocLj58Z58vwYD70wyonBHOfHpjk3MsXwZJFjZ0a5/8QQD54aJhGdu4Wy/py5QmXGMI/ZE+jrrXfZdJzBXJFsOn7NUJB33rGDj73jVrrTcS7ny2zLprhtezdbu9MrvhjN7MnzI/kiD5+5QjxqCx6XiIi0Xn3xlf6uFJFFOtR29KSJRCIc6O+cM37t6e0gX6rSkYhe3b4Jait1ByOGdvemZ7xufWGXi1cKjE2ViUWMkXypNqcvGBVT9nyefjnHd18c48TgBJPFCt85PcpUKYg9rvZKXak4r9+3iecu5cMVzH3f8etHT/GLf/40b9jfx97NwaJ0E8UKAHs3d/K+2p7Kix37fAvFNBOn20n9/CMeNZLRCMVKlcfOjTOSL2qeocgNUk/iDTg4kOWmTelwL6T+bJpNHfGgFbFUJRWLsDmTIBY1pkrBVhOec2zOJIJtISJRejvjmMGF8SJRYFNnnFQsSr5UJRqxYClsCyba15OVbDpOvlSdMZziw28/wC3butmaTc8YJpIrVGYMt2gckpFJxTh9eYoLV6YZvlji+/f3sauvk/OjUzx0doy4GX2ZBLFIEECGJkpzLrG91GEeiwWbd96xg3fesSP8e3Yr70oNC5nd03n8wgQG3L69u6n5kCIi0jr1BOeL8Qh//cwgucLMRKgjEbSL+77jjfs38/4ju+eNXx3xCLv6OhibKgdDP70qVR8ikSAWHN7dQ0cyPuN163HKx9HXlaTs+VT9YJ9F3wVjhDZ3JZkue4xPV3j9vj66UjEePDVMvujxun29bOkKevhyhQpff3aE19/cR9mr8vj5cdKJKKl4JFzE7sdevX3GInbNjnRZbFTPWuplq39+t2/v5vHz4yRjRiJqHL8wwb4tmbbuARVpd0oSb1DjXkgj+SKPnRvnpk0djORLbM+mKFcdd+3uYXMmhe9cuNnuycEcXzh2jideGieTSrCrL8Lr9vbS351meLLId14YZbrs4ZyjIxknEYswXfYYzZe5Ml1may2QNA6naGaYaON9NmdSbM6kOPaCY3SqzJ7NwRzEPZszPDs4yXihQtl3dKfifN9NWeLR6JwJ0o3ucbTYUNV6wKrf7zPfenFFhn7ODvTlqs9r9m4Kg/ZSj0tERFbXwYEs7zuym4u5IicuTlCu+phzVB30ZRJ0pYKEor4C6H/92qkZm97v39JJXybJYM7jo/fcytHjQ5SrPolohEM7utmcuZrENTbINiZWP/P573J+dJrx6Qq+77DaYnbR2qJyL18p09ORCB9frvpkklFOD0+F8aYrFWNoosjr9vby8NkJkrEIqXgU5xyTJW/ORezqGs8vDOPOncF70rjY3HqZk1c//4hYnFfv6mlY4Me1dQ+oyFrQkiTRzF4EJoEq4DnnDptZL/AnwB7gReDHnXNXWlG+pWhskXu+NvQUYFt3qjZXwDh9eYrNmdSMoQ8HB7Izlqn+1P2nyBWCISNbulK84eY+vndxgs5EDEcwyT4RjVB1jnIV8sUKw5NFEg0L0zQz4Xyu+4xNVejrTMw4LotAb2eCH7xtW3jd7JVIF3rOZod5NLsi2WqtPNoY6Bs/k6Uel4jIRtMusf3o8SF293UykE3VepciOOcwM/ZtyfD+2pDMk4M5Xr4SxLTuVIxSJeixO7A1w97a1hgHB7JhnI9Ho/jOLTpP77aBLB3xKJMlj0K5SgSjIxElXdtD2HfMiLndqTiFske+YQjoZNGjvzs4b8gXPTLJYCGbkhesuDpfg+XJwRy/cfQ5zo9Ok0lGccCxM2NcmijxkR86MCNRXA8JVOP5x1wL/IjI9WvlnMQfcM69yjl3uPb3x4CvO+duAb5e+7vt1Vvkyl6V713McW50mku5AtPlIIjgHLlCmafOj/G/nrrInz76Ej/+e8dmbOJ7cjDHyGSRv332Mt947jJDEwUSsSj7tmT4nfe9mjfc3EdfJlnrTaxSrfpMV6o8du7KjPl5zWwmP9d9YpEggD10ZpSvnbjEQ2dGcT4kYjP/PeZLkG5kE/vGoT71IZ3ZdJyjx4eu637L6UaOS0Rkg2ppbD85mONrJy7x8JlRTg9P0dcRZzhf4tzYNBfGC7zt4JZwZMon7jtBvlRheLLE0ESRC1emOTOc56snhnh+aIKTg7lwBMtkscKJwQmevTSx6Dy9ew71E4lEeM3uTfR3J4ma4btgCOtk0aOn1thZt39rZ22vRJsRaz5wZFewqX3UKFWqFCtVSp7P/q2d88bjo8eHGJsqk0nFSCVipBMxulIxRvKlFY2XrbLR4/TJwRyfuv8UH/nSU3zq/lNaM0GWVTsNN30X8Oba5c8B3wB+vlWFWUjj8Mhk1BibKvPES+NUqg6cT8EZF8YLZBLB0JDRfJkXLk+RikXwqlWePH+Fx89d4ZN/8yzbe9Lkih639md44/5eTlyc5DsvjHH7QBe9nQk+860XOXFxgk0dUV6+4hOLRHA4CuUqpUqR//DW/fMOIUlEjY54JByeeaC/k1NDU+RLFS6MF+hOxbh9e5YfffUAX3zoJTqTMbqSUXKFCuPFCrs74uQKlUVXOKu/7heOneOBk6Ph8JZmNDtU9UaHtF6PZobkNLt5r4jIBrVqsf3kYI7f/OopLo0XmCpVqfjBMjAxC+JFb0eCB04OA/DAyWHG8mUGulOcHclzbqwcPk8qBs9eyvNzX3qKfNGj6ly4yf1EIc67X7U9rOfniwH12DFdqTI4XmQ0X+RKwaM7FePgQBcTtZjalYoRj0bZnEkwVa5y31MX6e9O8YEju3jnHTvYtyXDF46d41unR9nUEefOXcHUj/ni8YXxAmXPpyt19fQuGYswUays+akSi73X1zN0di3HcO3tLCutVUmiA75mZg74PefcvUC/c24QwDk3aGZbW1S2GRorkETUGJ8qc3xwkk0dcbZnkzw+NMXYdBm/6uNcsC9iImKYOXIFj6Lnk4pFiUaMqbKH7yAWCe53YbzIRMljUzrOc0N57trdw5tv3Rpsq3EpT382TW8mxjMvj/PI2Qk6ai2CVd9R8ny2diU5NTTFOxvK2zh/7zeOPseFKwXGpsuUKj7Vqs9r9/XyfTf1hEnfPYf6OXp8iDt39nBpskS+6NGdjnNga4ZMKka2tiXGfBVv/f353sVg2M6t/Rl29QWtnM1UVs0OVb3RvZuuNxAsNCRHFbSIyAwtje1fPHaOp1++Qr6WINZ5DiYKHumEh+/7fO7YeW4b6CYZj3BqKE+uYZhn1KBcdRQqHhfGy6TjQfwONs5wxCNVPv31FwD49unRMHm7bXvXNTGgMZGsx4p6o+tLY9OUvSqDOY9k1OhMxrhteza8/YGTw+yrDXn9tR+7Y0YM29oVDxPET91/akZc29GT5vmhSUqeTyp+dYhqMhYlGbVr7r8csWo1Eq3F4u31vN5aj+Ha21lWWquSxDc65y7WgsX9ZvZssw80sw8CHwTYtWvXSpUPuNoqOZIvkZsuM170qFb9sEfrkRfHg20vgKlylWQsAjiKnh8+R7XsU/Iczjlq2yNSrgaByAHFSpVyIkY2FgnnLg7mini+C7/w8WgEMCq+T7hbk0E8avO2DH7h2DmeH8qTK5Sp+rUyAI++OMaOTUFidWY4z3/806cAeOVNWY5s6Qsf7zvHycGJGYu2zH5vvnjsHN+sBUivGhzzc0N5MqlYOLl/scpqrlXWTlzMMV2u8pdPXghbVO851M9vfvUUT+RLlLwqyViUzZkk7/2hA2F55gtSKxUIVEGLiMzQ0tj+96eGGclXrtl+CmrxtuwxmCsyNFli3+YOpkoe+dLMFVB9F+yheClXql1TpScdrFLuVR1FL9gL+dN/+wLJWIRN6eA06onzOe7a3YPv+3zivhPs6u0IY9GcsaK3g2w6zofffoBP3X+KeCy6YCyZnQjVG4Lreys+PzTJ0y+P8/p9m8hNlxmdKpNORMmm4kyXqzjg689epr87xcGBaxPauTST/C0UX+vHMF9crjcwTxQ9sukYt9Xmf85VnpWIt2s9hrdihJVsLC1JEp1zF2u/L5vZXwCvBYbMbKDW0jgAXJ7nsfcC9wIcPnx4/t1yl8EXj53j7MgUXakYU+VqMCfA83lpbJr9/V34zpEveaTiEXIF8HxHxZ/5HI7g+tmqtR5FgzBITZWD34O5AgPZqz1kU2WPaMRRrDjG8ehMRNmRTVHy3LwbAT/x0jjTZY9y1RGLRDDzwTmmyz4PnxkjFo0E+x66YO7hd89e4XX7LEwKn3npCs9cnOTsyBR9nQkqXpV7H5wOK/57HzzLmeE8qZhxKVdgbKpCNh2lL5MKk91mKqvZQ0WmixVOD+eJYDjnmCp6/Mpfn+RfvHE3vnMUK1VGp8p4VUduusyZ4XxYnvmSwPkCwReOnWNLV2pJrZ+N7/WJixO8cmc3cLV3UxW0iGxUrYztJwdzXJoozpkgQrBP8XjR4+J4ge2bOjg5OBmMlLFgHmD9cY5gH+P63yXPMV2qgBme7/BdsOI3JcdI3sc5RyoeJZOM8fRLOcrVYNuL1+3tDWNRvlRhcybBQ2cmmChW6E7F2belgwvjQcyf62S/WPF4+OzovPHpC8fOBYvTpIIRRiXP5/mhPGeH87xyZw+nL+e5MF7kYq5ATzpOJhknFQ/WGXj4zBidyRiFSpVP3HeCDxzZxbdPj85YDfUN+/t44OTwoo2rjfF1JF/k9OUpRvIlfu5LT9PfnWJnbwcD2RQvjuT58J9c4qZNabZ2JRmaKNGZiPLyWAEMctNlOuLR8DxjuVdRn8taT7JudISVyGJWPUk0s04g4pybrF3+QeBXgPuADwCfrP3+q9Uu22xPvJQjk4xS9R0TRY+oOaIWBI2L40ViEaNQqbIlk2QsWqHk+Ys/6SyJ2uavETM64sE8g9y0x82bg8p8JF9kouARj0YBR0c8iu9gquSRisfCjYBnV+SlSpWpkkc0YkQMIhYkpsGejAUO9HcB0JWOccvWTo69MMb3Lk5w94Ek50en+O65K+DgYtnjwpUCzw1NcudN2XDiezYdZ7JYqS18EyEeM6bKPi5folLrVWy2smpsIf2Hv/VNPM+RTkSIRiJUfUehVOUPvvkib7i5j0EzdvSka3MsPD799Re4a3fPgq2BF8YLxCJwYnAiWCUuFWNTOsbJS3ne8oqtTfcu1ltMq1WfSxNFBnMFLo4XeMPNvdzS372kY5brs5bnjywXvQfSjlod248eH8IWuY/vYLLk8YEju/j1o6foSceCHsJZDbn1SB6PQMWHybJPMgpWG3Y6UayQ8iKk4jGcgVd1jORLVH3Htmya3kx8xt66zw9N8sLloMG5KxmjWKny8JkrHNnXC8w82R+eLPL0y8EieB2JKLEIc8anJ14aJ5OMhkNKU/Eo5WqVqZJjz+ZMuKXVN54LcvJyNRh+WqhUuTJdYbpcZVdvmotXCvzK/zpBPBqlrzMerob67RdGuWNHdtFetnqiVd8CLBmL0FvbL3q6XGVbNsnYVJXnaqu/TxSChYImix6dySjJeLC1R7FS5dJEif6u5DU9sQcHsuzoSfPUS1c4dTnPVMmjMxnjwNYMr9y5acn/K3ULJVlroZ5dbL/L+ayFY5P20IrVTfuBb5nZU8AjwJedc0cJAsjbzex54O21v1vK4TDgynQ56PUzIxaM/KTq+8HeSsUKl/Mlbu3vXDRAzVb1g6DT1xknGjHSiSjd6WBi+qXJMrlCheeH8nSnY5gZ/d3BCqeVqs9kqcqH3nozp4am5lz1syMRC1tHHUGSGMyHNKp+0KtY8nz2b+lkcybF6/ZtolIN9nE8PTyF5wcT9a2WYBbKVZ54Ocf3Lua4MF6gKxWrDas1YlELk9eqD4VKdd4VxhZbieulK9Ok4sFzmkEsaqTixnihwmCuGO4VZWZ0p2J4vuOJl3IzJunDzNbARNT47tkrlCpVMskopUqVh89eIRWLLGm11KPHh6hWfU5dzlPyfLZnU/i+4ztnxhiaKGy4VdVWWz1JzxUqMxL7jbSam94DaWMtje0XxgthwrSQrmSMd96xgzft76vFdMMIEsLZzIJFbwA8Hwxja1eCYAyQsaUrSdAm6nAuiH042L+l8+rrpWLkS5Xw/GCq5DGYKzA8WeTJl4PVU+srdJ4dzvP4uXEu5YpEDHo64jz5Uo6yV70mPhl2Ta9p2fOJRu2a60pele5UnJLnMzZVJhG12nZaDh+H50OlWp2xGmquUOHSRHHmezdHL9uOnjSTRY/Tl6fC+FyuBov8ZJJRTl+eCm/rTsWC+aJVRyYZDWM6BIvrDE0UODWUZzRfuqZ+60gYj7w4xnSpSkc8wnSpyiMvjtGRWOqZ11XzrYxab3xv93q2PhIrm44zmCsuuuourF4M0aqr68Oq9yQ6584Ar5zj+lHgratdnoXcubOHh8+MMV2uEjXIl4P2xYgFFb3D2N3bQSRijBeqZNMxpkseFZ85h7xE6715BMGnft3O3k72b82EQz3r8wGz6ThDk0HL2sFtXYxOVZgoVrhpU5psR5x33rGDv/vSU3MOl9iWTTE2VWIkX8b3HdGokU4YEYxYNAJmvHpXNnzNZCzG22/r58NvP8A7/tuDxCLBEJyoGVhQ3kK5ykTR4/btWXKFCslYhFLFx6s6IhGjMxGlWttD6uGzo9y5sycsU31z34Um+UMw/7LqXMMAzvrQXGN0qjxjb6mS59PbGWey5DFZ9OYdchEsNzCT57tguG3NSL7I80N5hiaDeShztaxdGC9waeJqoko8ys4+uDhe5KmXc/zgbdvW7IbEa8Fanz+yHPQeSLtqdWyv1+f1ODtbPRGM1RZvuZwvUar4ZNNxtnYlGJwo4ZV9HJCIBhvfp+NRrkxXgsfFomzrSrKlO0XVD6aaRCPGtu4kI/lg7n86HuVAf2bGXP7JokdHIs4dN3Vz/MIEL48XSMWi7NyUpliphjHwg3fv5RP3nQjWHjDY2ZumMxmnWKlyeniK1+3tnZGg3bkzy9+fGqYyUcLzg5XPq74jm45y7MxoOGqmWuslnS55nBubpuT5dMQjJGLBno2pWIRyxZ/Rm5qMRYhYsIdyo7lGytR7s0byJXo74uE2Hf3dKQyYKAbP0ZUMhsRmag26pdr0mvoCOyXPp1J1pOKwOZOc0RN79PgQx86MsjWTpFJbuK8zGaUnEuPrz47wr//BLUv/h2H+FczXUj271EV7VuPY1vqCQHJVO22B0Xbef2Q3g7kiV6ZKFCuOq9sGBsFo56Y0//CVO4BgOMjlXIG/OzVM2b922KkB0YhhviMei5CMRdnSlSAaibCtO3VNULl9e5YPv/1A+NzZdJx6Ndg4PGK+4RK3b8/yo3du57989TlyBY+oGZs649y0qYP33LWDB04Ok4jNvTGwYUTNqNbmX1htjyeAbDoWBoXuVJyYGblihZLn2JKJUyj7bO5KcveBLeEKp287uIUHTg5zZjh/zST/eutoveJ47e5NPHh6BMOIR41KNQgId+3sYWS6wkQxWEK85PmUPJ/dvR3sr7V6wtxDLkpVx+v2beLM8HQ4H2RPX5pCbQJpfZgMQH9Xct4KbUdPmifPj9PbefW9jkUi3LI1WNG1/nnJyljr80eWg94DkbkZkIpFmK715s2+DYJhpBXPJ1eo8Ipt3XTEo3zr9CgQ5dCOHkYni7w8XgSCqSXxqJGIRUjFItw60M2RfcHibt+oVOlMeiTjUSpVx+7NnWzrStKVijFd8a/ZOurOnVnisSgdyRh7+jrD4ZVd6WgYAz/89gPs6u3gdXt7eeTsGMVKFQgStnzRuyZBe8P+Ph549nKQBDqo+o6IwWSxEvQGJqNMFCqM5IuYRdjSlWTnphQvjEwxWfLY1Zng1bt6OD08Ra6QJxa52mha8nw2dyYxs0W3waonWp+47wRj+TK9mQS3bw+mXzx8ZoxMKhZODwFm3LapI06pEsRy5xwRC9Yi2L91Zk/shfECQxNFtnYliUSudvn6vs/QrN7OpZoryfrMt15ct/XsasSQtZRky8KUJC7g4ECWj95zKz/3pad5cWSKzmSMLV0JLowXcQ46ElffvmBISZzejjijU2UqVUfVBeN5I5FgqIpX60Z0DtJx47V7e8kXPZ67lKc3k6RY8Tg5OMmV6Qpv2t8XDkNZaMz5QrcfHMiyb0tmzrHns69v7AG7c2eWi+MFkhYMSSlVfCq+IwJ87+IEZ4bzfPDuveHqpgPZNAcHujg5OIlzHrdv757RClhfbrw+xMRq3ainL0/x2lmtox962y2MTJU5NzrNdKVKKhbltoFufvFHbuPMcJ5Pf/0FxqYq9HbG2d3bQTQa4f1HdgPMezz1RPr1+66u3np2OM+py/lwSG/d/q2ZeSu0ew7189Xjl65JVPf0dWy4eYitmNOgSfp6D0TmU6o6utMxJkseOHdNb2J3KpiC0Zm8evJan7f33KU8tw1081ChzNauBEMTJSKRYF781q4Eo1MVtnUlw0bV3s4EmzNJdvZ2zIi775snFgH8xtHneH5okggQi0ZIJ6Ic2tE94wS9/v3ev7WTx86NU6xUuTJdpuw5HjozyofecnN4PKeGpnjdnt5w66pMKsaVqRJGEHsnihXitTmUpUqVyWIw+ueWrV2M5ct0peL0ZZLkix7nRvLEo1GKZQ8H5EtV9m7u5MdevZ1TQ1OL7j94cCDLL/+j267Z5mNXXwfbsyku50tMFDwO9GfoyyRn3DY0WQpXN80VPAa6k+Hq6HC1fjvfnWKiUCGbvpokTpaq9HfPvQL7jVjP9exqHNtGaMzcKPM6lSQu4uBAllcMdPN9N3Vz/OUJBieKFCvVoMWy7DE8Gczhq7egxWMRbu3vIlVLIKdKHhevTJMrecQiRrIWHFLxGM7Brr5OpitVyl6Vb5waxvcdHYkoj7w4xndeGGV/f4atmWS4n9Lsivp6N5JdaIjC+47s5vnLQRLle8EwlJhBZzJKImJ88ivP8bF33Mp/nrV3U7nq85q9m2b0inalYgxNFHnd3l4yqRilSpVUPBpu7ju7cjo4kOWX33X7nF++hZLe+mPnMlciHY1G+NBbgjmd9SG9jUN+56rQDg5k+dBbb+bTf/tCOPR1T18HkUhkQ81DbNVQkuudpL+e6D0QmVsialyeLNOdjofTEzzPBYvQGFSdIx2PEpk197Aeg4P56RF6O5Mc2p5ldLpCvugRjxqHtmfxqj5//Mh5KlWfnZs6+JFXbmO67OaMu3NtExExIxGNUPKqxLjau9kYA+vf72w6zt6+NMfOXKHq++zq7eDmLZkZeydeGC+we3Mne7dkwtf52vcu4XC8fl9fOELGr62+ujmTpOT53HFTFt85nnppgsFckb1bMvzQof4Zq5se2dfL+47s5uBAdsY+zAuZ61zko/fces2WF3Pd1vg+1efLza7fDvR38smvPAdAVzLKZClYnK8xcV4OJwdzDE8WZ0yNScZi66aeXY0Ysp6TbNhYw2nNuRXdRWJFHT582D366KMr/jqfuv9UsMH9UJ5kLELV9zk3GiQQfZ1xUokYzjlesa2Lp17O4Xk+fV3JYM6e53NudJqtXUk2dSaCBLM21CQZD3rJsuk4I5NFjp0ZoysVw6v6nBubplzx6UhG2d3XSW9nYs5KdSGzN/A9PzrFc0N5btqU5vbt8+9HVH/sF46d4y+euIDvglbY/u4UncmgsuxOx/nTf33kmvdpdsWQK1Q4MTjBbQPdlL0qj58PVj9ztWGs+7ZkVuWLtVCrz3zlru9htZTn2giW+n4tp43+3sPyvAdm9phz7vAKFVHkhlxPbP/FP3+aLz9ziWjESMYijEwWKfvBaJ50IkI6HsSu7T0p3n3n1RPixrprrk3vg6GpnXzxoZfoTMZmJCgfe8etvPOOHYuWrV5nNhMD69/v+08MEY8at2/vDhsvG8s6Vz1cX8n0zbdu5aEzoxQrVQZzBQxjz+ZOxqZKTJerpOMxejMJfvkf3XbN3outrl8XKsOXn77A546dZ2iiGO6h3Mz7v5TXrn/+s0d21ZPm9WClP+f5vkfrJYlq5TnQQlYirqsnsQn3HOrnw39yCaCW+MHmTILx6TLjBY9bs2n2b+0Mh0g89XIwv22y6AXzGeIR7trTQ8QsnPuWiBpj+XLYgvOf/vTpcDnrM+PTVKo+kSgUK1WKlSpPvDTOv/3iY7zrVTc1/YWevX9R4xLUi7V8HBzI8ms/dgfffmGUrZnEjHkAXcnonPMA5muh+sCRXeF+S6/ameXk4CTjBY837e/jDfv7OHp8iM9868UVDUoL9ZwutWVtqRPF15tWDiXZ6O896D0QmUup6jiybxOPvDhOoVINt7EwC6aGRGormeYK3rzz7OYbmfOJ+07QmYw1zLEK4uHnjp1vKkmp15kRi4fzACcLFYqeRzoeuSb+1XsKg8dcnSvYWM/ec6if3zj6HGNTZcqeTyIWIR41sulEbZXOMsloJJgW4xxjUyXGalNhMskYA93JGecA7dI7slD99s47diwJPNZrAAAgAElEQVRrUjhb4zlTNh2nv/tqj9h6qnNXOoZc7wi3tWIjDKetU5LYhIMDWW7alA62vChVyaRiHNnezePnr2DYjLlu9aErt9Uq+R09aUYmi8RjwQT1u3b3hJvN9mWSYQUcbLcRBIN8qRoscgN4zjGSLwerqxarS6q4G/+R60tQJ2MR8qVq0xOJ+5cwD2ChiqE+TDRf8njzrVvD4ZnLHZSup4VsvVdoy229DyURkbUnqJdi3HMoyenLUzx+/grJGMFiKASrmu7b0sHoVFB3zVfXz3UCPTRRZGsmMeO6+RpL5y9b8LpbuoKF6urz4hOxKH2Z2DXxr5l6tp5AutpKPdl0IpxHGLFgFfM33FxbbOfUcJAgpmIc3rOJzZkg5tbPATbSYiPznSdspJP/lbaeGzM30jmQksQm1bd9aPynSMau3ZNpsuhx20B2RpdzvYUOoLczycGB6DVd7/XtNoK9MQzfD/YgjNZaP50LVmZbSsXd+I88UaxcswR1M5XfB47sWnAewFyVbX3Yzuwewtnd8J+6/9ScQenTDzzPyHRlyUNKbqQldD1XaMtN8+JEpN00zud77d5ezo7kKVaC+XydySDm5QqV61qJeimNpQuVDa7WmaeG8mzLJjk5OBGuur2tOxnG9gP9nXz6b1+gUvXp60wEvYqRSFjPHj0+xM7eDg7tuBq3coUKp4am+PDbD4SvmYhF6UrF6IjH6EpeTRDrZamfAywlQWqHYanzWaxsC50nbKSTf7l+G+kcaI7tY2Uuc226ujmTpLczcc1GrLMXMWlmw9P3H9nNrr4OIBiK6rury29DsOnttlpAKnke958YWnST0sYydyVjTBQ9Sp4fbvTbWPnNt/HpO+/YwcfecSvd6TiX88GiAPV5GPNtyvrlpy80tVnrhfECXamZ7RSXctN84/kRJgoVtmYSTBQqfPIrz/Hlpy8s+hk1toTWV1edvQGx3Ljr2cBXRGQlza6XDu/eRDoewfMdvh9sSzFV8vjAkV1Lfu4PHNnFVCk4Ebye55qrzuzpDC4XK1W6kjGKlSrPXprkxGCOk4M5Hjg5zIGtGdLxCM9fzvN3zw1TrO0tCEH8LHkeD50Z5WsnLvHQmVFKnhcmdbNfszeT4BXbuuZcORSCRuXJojej3HMlSKu1Gfv1aKZsC50nzHWeN9c5nWxsG+kcSD2JTZpzSOIPBa2RzQxTXKynqr7dxtHjQ3yvK8eJizlKFZ/JUpDYbeqIc8dNWUbyRR4+c4WuVGzR3rLGMmc74jOWoK5Xfu99zU2L9sDNNw9gvuEp9S0vFhu2Mler3RPnc3TEo9c190NDRVaPel5FpN3MrpdmL3TyobfcfF1z2uqPmeu5mu1Vm122H/+9Y5gZqXgwIqm+oXyu4IWxtVKt4vmwq7cDnONirhjG5mTUwsXu6knmw2eucGRf75yvudDKodB870g7D0ttpmwLnSdo6ok0a6OcAylJXIL5/imWuuLoQls41CeQ1yekTxQrXJmqEMHwnePkxUkMrtmLcHYFPft1/uPb509o5xv2uVilP19lW9/yYvb1s5O1uYJSvuSxtSvBy1emKXk+yViEbO05F6OhIiIiUvfOO3bM2Dbp1NAU+wZz13VyV28srcfWv3tuhG+fHmVoosTO3o4lT3HoTsWYmK4EK53XVkKntpJ4PbY+cnaCZCxCKh7FOReuJ3D0+BCOq9to1Bkw33r1iyVAzSZI7dwY20zZFjtPmO88r52H2IqsFCWJs6xkRdDsnLmjx4fY3dfJHTf1ADCSL/Lds1d44ORlKlWfmzbNTHpmV4ILvc5cczGut9Kfr7Lt704xWfQWTdbmCkrbupMM58uk4xESUcOr+rw8XmRnb8eCZYGNNU5cREQWttwrds5+vgdPDTNZ9NiWTRKxeNMNrCcHc0wUPabKHhMlRzIaYUt3it29HeG+h7lCJVxLAAjXE2iMza/Zu4kzI9Pkix6ZVIyDA12Uq/Nva9bMiKalrHVQ1y6Nsc2U7XrOE9pl5VeR1aY5iQ1Weqx9s3Pm5pqrZwY96QS39ndR9eHx8+MMTwa9a7MrwaXOzWt2LsJs843f/8CRXU2P6z9YW+TnN//JK/nw2w9w67YufN/h1dYv93zwfcfu3sUD0EYaJy4iIgtb7nnqs58v2E4iyunLU+F9FmtgrZ9nDHQn6UhE6U7FScaibOtKEo1GuOdQfxhbE9FIuA1WfT2Bemze0ZMmFY9xZF8fb7+tnyP7+kjFYyuerLXzvL1mynY95wla70A2KvUkNljpsfbN9tjNbg07fXkKM6M3E+eWrZ3hXouna8tnz24FW2rP4PX2wDWz5cVSx/V3JOO86ZY+nrowwVTJozMZ47V7euhIxhd9bL1MSgpFRGS5h0bOfr5MKkap7DFRrITXLdbA2niekUnFwi2xLk2WZmxu/8G79/LFY+f45ulRNnUEewzPjvetGDnTzvP2mi3bUs8T2nmIrchKUpLYYKUrgmaHacxO2kbyJeKRCPu3dLI5k+Ku3T08P5RnaLLEkXT8mkpwqcNBbqTSX2ie5vUEjfpeV/9k28xlvRuPRUREZDHLPTRy9vPt39LJw2fGyKRi+M41lag1nmdszqTYnEnhO8dgrnjNfo3/+cfumDEFJjsr3rcqWWvnxtiVKFs7D7EVWUmrniSa2U7g88A2gq3/7nXO/Tcz+yXgZ4Dh2l1/wTn3N6tZtpWuCJrtsZudtPVlkmzrSrKl62pgiUejHEnH55xjeD09g+1S6WteoYjI2tOOsX2548ns50vEouzq62B7NsVgrthUonY9jbjzPV+7xO31TuclslGZc/NPcl6RFzQbAAacc4+bWRfwGPBu4MeBvHPuN5t9rsOHD7tHH3102crWODm5sSJYznlt17Mwzlzlemlsmv7uJOWqm3fD2FasxLUcr6tVxERWlpk95pw73OpyyPrRrrF9uePJjT7ffOcZbzu4hVNDU6sW9xRnl0bvl7S7lYjrq54kXlMAs78Cfht4Iy1OEqF9K4LGciWjxsVckd19nSuWzF5vGVc6yRaRG6ckUVZau8X2djL7PONAfycPnBxetdipWC2y/qxEXG/pnEQz2wPcCTxMEEj+vZn9FPAo8J+cc1dWu0ztOnyjsVyfuv8U8Vh0xRbYuV7tvMmuiIisjnaM7e1k9nnG9e5VfL0Uq0WkGS3bAsPMMsCfAT/rnJsA/jtwM/AqYBD4r/M87oNm9qiZPTo8PDzXXda9ubbIaIeVttq1XCIisjoU25dutWOnYrWINKMlSaKZxQmCyB855/4cwDk35JyrOud84PeB1871WOfcvc65w865w1u2bFm9QreR693XcKW1a7lERGTlKbZfn9WOnYrVItKMVqxuasBngJPOuf+74foB59xg7c8fBY6vVBnadd5ho4XK2K4rbbVrudaLtfB/KyIbU6tje7stULMUqx07FatF2ku7nt+1YnXT7we+CTxDsEw2wC8AP0EwHMUBLwL/uiGwzOl6JrevhQnbzZSxXf+h2rVca91a+L+VtUML18hya2VsX+76sRX17WrHTsVqkfawXPXNuli4xjn3LcDmuGlV9k1aCxO2mynjWlhgR5bPWvi/FZGNq5Wxfbnrx1bUt6sdOxWrRdpDO5/ftXR101a4MF5gIJuacV27TdheC2WU1dVu/xNqhRaRdrHc9WO71bcSUNyR9aid65uWrW7aKmthwvZaKKOsrnb6n6gPjcgVKgxkU+QKFe598CwnB3OrXhYRkeWuH9upvpWA4o6sV+1c32y4JPGeQ/3kChVyhQq+c+Hlew71t7poobVQRlld7fQ/0Tg0ImIWXj56fGjVyyIistz1YzvVtxJQ3JH1qp3rmw2XJB4cyPLBu/eSTccZzBXJpuNtt/jHWiijrK52+p/QHlsi0k6Wu35sp/pWAoo7sl61c32z4eYkwtqYsL0Wyiirq13+J3b0pMkVKuHkamifoREisjEtd/3YLvWtBBR3ZD1r1/pmw/UkisiNaeehESIisv4o7oisPiWJIrIk7Tw0QkRE1h/FHZHVtyGHm4rIjWnXoREiIrI+Ke6IrC71JIqIiIiIiEhISaKIiIiIiIiEzDnX6jJcNzMbBs7dwFNsBkaWqTgrZS2UEVTO5aZyLq+1UM7VLONu59yWVXotkSW5wdi+Fr7ry22jHfNGO17QMW8UN3LMyx7X13SSeKPM7FHn3OFWl2Mha6GMoHIuN5Vzea2Fcq6FMoq0u434Pdpox7zRjhd0zBtFux2zhpuKiIiIiIhISEmiiIiIiIiIhDZ6knhvqwvQhLVQRlA5l5vKubzWQjnXQhlF2t1G/B5ttGPeaMcLOuaNoq2OeUPPSRQREREREZGZNnpPooiIiIiIiDRY90mimd1jZs+Z2Wkz+9gct5uZ/Vbt9qfN7NVtWs6frJXvaTP7jpm9sh3L2XC/15hZ1czes5rla3j9RctpZm82syfN7Htm9verXcZaGRb73LNm9r/M7KlaOX+6BWX8QzO7bGbH57m9Xb5Di5WzXb5DC5az4X4t/Q6JtKu1EteXUxPH/AozO2ZmJTP7SCvKuNzWynnRcmrimN9VO94nzexRM/v+VpRzOa2V88rl1MTn/GYzy9U+5yfN7OOtKCfOuXX7A0SBF4B9QAJ4Crht1n1+GPgKYMDrgYfbtJxvADbVLr+jXcvZcL+/Bf4GeE87lhPoAU4Au2p/b23Tcv4C8Ou1y1uAMSCxyuW8G3g1cHye21v+HWqynC3/DjVTzob/jZZ9h/Sjn3b9WStxvQXHvBV4DfCfgY+0usyrdMxtUaev8jFnuDpV7A7g2VaXe6WPueF+6yImNvk5vxn461aXdb33JL4WOO2cO+OcKwP/A3jXrPu8C/i8CzwE9JjZQLuV0zn3HefcldqfDwE3rXIZobn3E+BDwJ8Bl1ezcA2aKec/A/7cOXcewDnXirI2U04HdJmZEQSHMcBbzUI65x6sve582uE7tGg52+Q71Mz7Ca3/Dom0q7US15dTM+cIl51z3wUqrSjgClgr50XLqZljzrtaFgF0EpwjrGVr5bxyOTV7zC233pPEHcBLDX+/XLtuqfdZaUstw78kaCVdbYuW08x2AD8K/O4qlmu2Zt7PA8AmM/uGmT1mZj+1aqW7qply/jZwELgIPAP8b845f3WK17R2+A4tVau+Q4tqk++QSLtaK3F9Oa2342nGWjkvWk5NHbOZ/aiZPQt8GfgXq1S2lbJWziuXU7P/20dqU42+Yma3r07RZoq14kVXkc1x3exWl2bus9KaLoOZ/QBBZdiKcejNlPP/AX7eOVcNOr9aoplyxoC7gLcCaeCYmT3knDu10oVr0Ew5fwh4EngLcDNwv5l90zk3sdKFW4J2+A41rcXfoWa0w3dIpF2tlbi+nNbb8TRjrZwXLaemjtk59xfAX5jZ3cCvAm9b6YKtoLVyXrmcmjnmx4Hdzrm8mf0w8JfALSteslnWe5L4MrCz4e+bCHpklnqfldZUGczsDuAPgHc450ZXqWyNminnYeB/1L7Im4EfNjPPOfeXq1NEoPnPfcQ5NwVMmdmDwCuB1UwSmynnTwOfrA0vOW1mZ4FXAI+sThGb0g7foaa0wXeoGe3wHRJpV2slri+n9XY8zVgr50XLaUmfs3PuQTO72cw2O+dGVrx0K2OtnFcup0WPubEjwDn3N2b2O634nNf7cNPvAreY2V4zSwD/FLhv1n3uA36qthra64Gcc26w3cppZruAPwfev8q9XY0WLadzbq9zbo9zbg/wP4F/14IvcjOf+18BbzKzmJl1AK8DTrZhOc8T9HZiZv3ArcCZVS3l4trhO7SoNvkOLapNvkMi7WqtxPXl1Mwxrzdr5bxoOTVzzPtraxRgwaq9CWAtJ8dr5bxyOTXzOW9r+JxfS5CvrfrnvK57Ep1znpn9e+CrBKsJ/aFz7ntm9m9qt/8uwUpJPwycBqYJem7asZwfB/qA36n933jOucNtWM6Wa6aczrmTZnYUeBrwgT9wzi24JUErykkwlOSzZvYMwRCFn1/tliQz+2OClbY2m9nLwCeAeEMZW/4darKcLf8ONVlOEZnHWonry6mZYzazbcCjQDfgm9nPEqyY2E5TE5q2Vs6LllOTx/yPCRpAKkABeG/DQjZrzlo5r1xOTR7ze4B/a2Yewef8T1vxOdsa/t8SERERERGRZbbeh5uKiIiIiIjIEihJFBERERERkZCSRBEREREREQkpSRQREREREZGQkkQREREREREJKUkUERERERGRkJJEaQkz6zGzf7fIffaY2T9r4rn2mNmS9zg0sxfNbPMS7n/YzH5rqa+zFpjZPzez7Q1//4GZ3dbKMomISPtR/G6uLGb2nUUeN++xm9k3zGzF9nw0s3c3xngz+xUze9tKvZ6sTUoSpVV6gAWDDLAHWDTIrBbn3KPOuf8w+3ozi13P81lg1b6DZhZd4OZ/DoRJonPuXznnTqx4oUREZK3Z8PG7ydd8w0o9dzMWifnvBsIk0Tn3cefcAytfKllLlCRKq3wSuNnMnjSz/1L7OW5mz5jZexvu86bafT5ca3X7ppk9XvtpqgI2s6iZ/WbtuZ82sw813Pyh2nM9Y2avqN3/tWb2HTN7ovb71tr1bzazv65d/iUzu9fMvgZ83sxuN7NHamV92sxumacse8zspJn9DvA4sNPM8g23v8fMPlu7/Fkz+61aGc6Y2Xtq1w+Y2YO11zpuZm9a4NjztRbCh4EjZvZxM/tu7XH31hLV9wCHgT+qPWe6sRXTzH6i9v4cN7Nfb+Y9FxGRdWujxu9OM/uymT1VO973zro9bWZHzexnan/na78zZvb1hrK+q+FhMTP7XO11/6eZdczxuj9oZsdqj/+SmWUWeL9erMX5bwH/xMx+phbznzKzPzOzjtp7/4+A/1I75ptr5xv1c4y31t6/Z8zsD80sudjnJOuUc04/+ln1H4JWxuO1y/8YuB+IAv3AeWAAeDPw1w2P6QBStcu3AI/Ofq55XuvfAn8GxGp/99Z+vwh8qHb53wF/ULvc3XDftwF/Vrsclgf4JeAxIF37+9PAT9YuJ+rXz3PcPvD6huvyDZffA3y2dvmzwJcIGnNuA07Xrv9PwC/WLkeBrgWO3QE/3vB3b8PlLwA/Urv8DeBww23fIEgct9c+jy1ADPhb4N2t/v/Rj370ox/9tOZnA8fvfwz8fsPf2Yay7AEeAH6q4fZ87XcM6K5d3gycBqz2GAe8sXbbHwIfqV2ux+DNwINAZ+36nwc+vsD79SLw0Ya/+xou/58N79lngfc03PZZgvOPFPAScKB2/eeBn231/5x+WvOjnkRpB98P/LFzruqcGwL+HnjNHPeLA79vZs8QJE/Nzpl7G/C7zjkPwDk31nDbn9d+P0ZQYQNkgS9ZMFfgU8Dt8zzvfc65Qu3yMeAXzOzngd0N18/lnHPuoSbL/pfOOd8FQz/7a9d9F/hpM/sl4Pucc5MLPL5KEGDrfsDMHq69h29h/mOrew3wDefccO39+yPg7ibLLiIi69tGit/PAG8zs183szc553INt/0V8P865z4/x+MM+DUze5ogkdzB1Xj+knPu27XLXyR4Pxu9nuC9+raZPQl8ANg9T/nq/qTh8qFaD+4zwE+yeMy/FTjrnDtV+/tzKOZvWEoSpR1Yk/f7MDAEvJKghS2xhOd389xWqv2uErT2Afwq8HfOuUPAjxC0rM1lqn7BOff/EQzfKABfNbO3LFCeqVl/N5Zt9muVGi5b7bUeJKi0LwBfMLOfWuC1is65KoCZpYDfIWg9/D7g9+d4vdma/WxERGTj2TDxu5Y43UWQLP5fZvbxhpu/DbzDzOZ6P36SYDTOXc65VxG8D/VyzT622X8bcL9z7lW1n9ucc/9ynmO65tgIegj/fS3m/zKK+bIEShKlVSaBrtrlB4H31uYebCFIgB6ZdR8IWggHnXM+8H6C4S3N+Brwb6w2Qd3Mehe5f5YgAYNgQZdFmdk+4Ixz7reA+4A7miwbwJCZHbRgEZsfbeK1dgOXnXO/D3wGeHWTr1MPDiO1OQ3vabht9ntd9zDwD8xsswWT4H+CoKVYREQ2pg0Zvy1YAXzaOfdF4DeZGXs/DowSNMTOVabLzrmKmf0AM3sCd5nZkdrlnwC+NeuxDwFvNLP9tTJ0mNmBZo6rpgsYNLM4QbJaN1/MfxbYU389gs9KMX+DUpIoLeGcGyUYPnEcOAI8DTxFMOfto865S7XrvNqE6w8TVL4fMLOHgANc2yM3nz8gmCfxtJk9xeIrrv0GQSvht2k+kL0XOF4bDvIKgnH8zfoY8NcExz7YxP3fDDxpZk8QzJH4b828iHNunKD38BngLwmGrdZ9Fvjd2iT2dMNjBoH/Hfg7gs/ncefcXzXzeiIisv5s4Pj9fcAjtfv9IsEcv0Y/C6TM7DdmXf9HwGEze5QgUXu24baTBO/L00Av8N8bH+icGyZIdv+4dp+HamVs1v9B0Nh7/6zX/R/Az9UWqLm54fWKwE8TDNl9hmANhd9dwuvJOmLOzdeLLyIiIiIiIhuNehJFREREREQktGKbiIqsNjP7IWD2Pn5nnXOLzvNbgbL0AV+f46a31obqLPfrPQzM3svo/c65Z5b7tURERJbTRo7f18PM/gLYO+vqn3fOfbUV5ZH1ScNNRUREREREJKThpiIiIiIiIhJSkigiIiIiIiIhJYkiIiIiIiISUpIoIiIiIiIiISWJIiIiIiIiElKSKCIiIiIiIiEliSIiIiIiIhJSkigiIiIiIiIhJYkiIiIiIiISUpIoIiIiIiIiISWJIiIiIiIiElKSKCIiIiIiIiEliSIiIiIiIhJSkigiIiIiIiIhJYkiIiIiIiISUpIoIiIiIiIiISWJIiIiIiIiElKSKCIiIiIiIiEliSIiIiIiIhJSkigiIiIiIiIhJYkiIiIiIiISUpIoIiIiIiIiISWJIiIiIiIiElKSKCIiIiIiIiEliSIiIiIiIhJSkigiIiIiIiIhJYkiIiIiIiISUpIoIiIiIiIiISWJIiIiIiIiElKSKCIiIiIiIiEliSIiIiIiIhJSkigiIiIiIiIhJYkiIiIiIiISUpIoIiIiIiIiISWJIiIiIiIiElKSKCIiIiIiIiEliSIiIiIiIhJSkigiIiIiIiIhJYkiIiIiIiISUpIoIiIiIiIiISWJIiIiIiIiElKSKCIiIiIiIiEliSIiIiIiIhJSkigiIiIiIiIhJYkiIiIiIiISUpIoIiIiIiIiISWJIiIiIiIiElKSKCIiIiIiIiEliSIiIiIiIhJSkigiIiIiIiIhJYkiIiIiIiISUpIoIiIiIiIiISWJIiIiIiIiElKSKCIiIiIiIiEliSIiIiIiIhJSkigiIiIiIiIhJYkiIiIiIiISUpIoIiIiIiIiISWJIiIiIiIiElKSKCIiIiIiIiEliSIiIiIiIhJSkigiIiIiIiIhJYkiIiIiIiISUpIoIiIiIiIiISWJIiIiIiIiElKSKCIiIiIiIiEliSIiIiIiIhKKtboAN2Lz5s1uz549rS6GiMia8thjj40457a0uhwic1FsFxFZmpWI62s6SdyzZw+PPvpoq4shIrKmmNm5VpdBZD6K7SIiS7MScV3DTUVERERERCSkJFFERERERERCa3q4qaw9JwdzHD0+xIXxAjt60txzqJ+DA9lWF0tERGRBil8ispGoJ1FWzcnBHPc+eJZcocJANkWuUOHeB89ycjDX6qKJiIjMS/FLRDYaJYmyao4eHyKbjpNNx4mYhZePHh9qddFERETmpfglIhvNiiWJZvaHZnbZzI43XPcnZvZk7edFM3uydv0eMys03Pa7K1UuaZ0L4wW6UjNHOHelYlwYL7SoRCIishQbNbYrfonIRrOScxI/C/w28Pn6Fc6599Yvm9l/BRrHabzgnHvVCpZHWmxHT5pcoUI2HQ+vmyx67OhJt7BUIiKyBJ9lA8Z2xS8R2WhWrCfROfcgMDbXbWZmwI8Df7xSry/t555D/eQKFXKFCr5z4eV7DvW3umgiItKEjRrbFb9EZKNp1ZzENwFDzrnnG67ba2ZPmNnfm9mbWlQuWUEHB7J88O69ZNNxBnNFsuk4H7x7r1aHExFZH9ZtbFf8EpGNplVbYPwEM1saB4FdzrlRM7sL+Eszu905NzH7gWb2QeCDALt27VqVwsryOTiQVVAVEVmf1nVsV/wSkY1k1XsSzSwG/BjwJ/XrnHMl59xo7fJjwAvAgbke75y71zl32Dl3eMuWLatRZBEREVmAYruIyPrSiuGmbwOedc69XL/CzLaYWbR2eR9wC3CmBWUTERGRpVNsFxFZR1ZsuKmZ/THwZmCzmb0MfMI59xngn3LtpPa7gV8xMw+oAv/GOTfnxHgRkdV0cjDH0eNDXBgvsKMnzT2H+jXkTDYsxfbWUn0kIqvFnHOtLsN1O3z4sHv00UdbXQwRWadODua498GzZNNxulIxJoseuUJlzS9YYWaPOecOt7ocInNRbJ/beq2PROTGrURcb9XqpiIibe/o8SGy6TjZdJyIWXj56PGhVhdNRDYY1UcispqUJIqIzOPCeIGu1MxR+V2pGBfGCy0qkYhsVKqPRGQ1KUkUEZnHjp40k0VvxnWTRY8dPekWlUhENirVRyKympQkiojM455D/eQKFXKFCr5z4eV7DvW3umgissGoPhKR1aQkUURkHgcHsnzw7r1k03EGc0Wy6bgWiRCRllB9JCKracW2wBARWQ8ODmR1EiYibUH1kYisFvUkioiIiIiISEg9ibLuafNhERG5XoohIrIRKUlchxTQrmrcfHggmyJXqHDvg2c1j0NERBaNl4ohIrJRabjpOlMPaLlCZUZAOzmYa3XRWkKbD4uIyFyaiZeKISKyUSlJXGcU0GbS5sMiIjKXZuKlYoiIbFRKEtcZBbSZtPmwiIjMpZl4qRgiIhuVksR1RgFtpv+/vTuPk+yu6/3/+tTS1URhb7AAACAASURBVHvP1tPpTDKZmSQTZjKGJEwIEYiRAEbwsqgIXq+Gn/iLXFkEjRr03hvQhxoByf0pP9EoXKIiCgoaQQJhkQiGZQJJmGTIkGSSyUx6enqWXqerupbP/aNOdap7qqqru/aq9/PxqEdXnTrLp05Xnc/5nvNdNPiwiIgUUk6+VA4RkU6lQmKbUUJbSoMPi4hIIeXkS+UQEelU6t20zeQSWn5vba+/6ryOTmgafFhERJYrN18qh4hIJ6pZIdHMPgL8BHDc3fcE094N/L/ARDDbb7v7vwXvvQt4E5AG3u7un69VbO1OCU1ERGqh3XK78qWISGG1rG76UeCGAtNvd/fLg0cuiewG3gBcGizzZ2YWrmFsIiIisnofRbldRKTt1ayQ6O73AqfKnP3VwN+7e8LdDwGPAc+vVWwiIiKyesrtIiKdoREd17zVzB4ys4+Y2fpg2hbg6bx5jgTTzmJmN5nZPjPbNzExUWgWERERqS/ldhGRNlLvQuKHgAuBy4Ex4I+D6VZgXi+0Ane/w933uvve4eHh2kQpIiIi5VJuFxFpM3UtJLr7uLun3T0D/CXPVjs5ApyfN+t5wDP1jE1ERERWT7ldRKT91LWQaGajeS9fC+wPnt8FvMHMYma2HbgY+FY9YxMREZHVU24XEWk/tRwC4+PAdcAmMzsC3ApcZ2aXk61u8iTwywDu/rCZfQJ4BEgBb3H3dK1iExERkdVTbhcR6QzmXrB5QEvYu3ev79u3r9FhiIi0FDO73933NjoOkUKU20VEVqcWeb0RvZuKiIiIiIhIk1IhUURERERERBbVrE2idJ4DY1PcvX+co5PzbFnXww17Rtg1OtTosERERKpGuU5EOoHuJEpVHBib4o57DzE1n2R0qJup+SR33HuIA2NTjQ5NRESkKpTrRKRTqJAoVXH3/nGGeqIM9UQJmS0+v3v/eKNDExERqQrlOhHpFCokSlUcnZxnoHtp7eWB7ghHJ+cbFJGIiEh1KdeJSKfoyDaJak9QfVvW9TA1n2SoJ7o4bSaeYsu6ngZGJSIinaBeeV25TkQ6RcfdSVR7gtq4Yc8IU/NJpuaTZNwXn9+wZ6TRoYmISBurZ15XrhORTtFxhUS1J6iNXaND3HTtdoZ6ooxNxRnqiXLTtdt1h1ZERGqqnnlduU5EOkXHVTc9OjnP6FD3kmlqT1Adu0aHlChFRKSu6p3XletEpBN03J3ELet6mImnlkxTewIREZHWpLwuIlJ9HVdIVHsCERGR9qG8LiJSfR1XSFR7AhERkfahvC4iUn0d1yYR1J5ARESknSivi4hUV83uJJrZR8zsuJntz5v2PjP7vpk9ZGafNrN1wfRtZjZvZg8Ejz+vVVwiIiKyNsrtIiKdoZZ3Ej8KfBD467xp9wDvcveUmf0R8C7gt4L3Hnf3y2sYj6xBvQYortd2RESkIh+lg3J7odwEKF+JSNtb8U6ime00sy/lrhqa2WVm9j9WWs7d7wVOLZv2BXfPdUH2DeC8NcQsdVKvAYrrORCyiEinW2teh87K7YVy0/s/f5D33v2o8pWItL1yqpv+JdmrgkkAd38IeEMVtv2LwOfyXm83s++a2VfN7MVVWL9UqF4DFNdzIGQREalZXoc2yu2FctOJ2QSn5haUr0Sk7ZVT3bTX3b9lZvnTUsVmLoeZ/U6wjo8Fk8aAre5+0syeB/yzmV3q7tMFlr0JuAlg69atlYQhK6jXAMX1HghZRKTDVT2vQ/vl9kK5KZFKYyzZb8pXItKWyrmTeMLMLgQcwMx+muyBf03M7EbgJ4Cfc3cHcPeEu58Mnt8PPA7sLLS8u9/h7nvdfe/w8PBaw5Ay1GuAYg2ELCJSV1XN68E62i63F8pNsUiYrsjSUyflKxFpR+UUEt8C/AXwHDM7CrwD+O9r2ZiZ3UC2Mfur3P1M3vRhMwsHz3cAFwNPrGUbUj31GqBYAyGLiNRV1fI6tG9uL5SbNvXH2NDXpXwlIm3Pggt+K89o1geE3H2mzPk/DlwHbALGgVvJtoGIASeD2b7h7m82s58CfpdsNZU0cKu7/+tK29i7d6/v27evrPiluFI9i9ai19Fye4srNE09yIlUzszud/e9jY5DGmu1eT1YpqNy+0r5ai6e5MjpeaYTKUYGu7nxmq288rItDY66fOpZXKQ91CKvr1hINLM/AN7r7pPB6/XAr7t7WT2h1VIzJZJWleu9bagnykB3hJl4iqn5JDddu71mQ12Us716xyXSSVRI7GzNnNehdXL7Zx86ym2fe5S+WISBWJiZRJq5RIpbfvySligoKs+KtI9a5PVyqpv+eC6RALj7aeAV1QxCqufA2BS333OQmz/5ILffc3DFbrnr3bNoudtTj6ciIjXTsXl9tTmylDvvO0xfLJLNU6EQQz1R+mIR7rzvcBUjrh3lWREppZzeTcNmFnP3BICZ9ZCtViI1UEnVj/yrgvnjN5W6KljvnkXL3Z56PBURqZmOyev5OTUWNp6ZinPBxr6yc2Qp49NxNvd3LZk2EAszPh2vVvg1pTwrIqWUcyfxb4EvmdmbzOwXgXuAO2sbVmeqdFD5tVwVrHfPouVuTz2eiojUTEfk9eU5df8z0xw+eYaFVLoqd85GBruZSaSXTJtJpBkZ7C6yRHNRnhWRUlYsJLr7e4HfB3YBlwK/F0yTKqu06sfRyXkGupfeHF7pqmC9exYtd3vq8VREpDY6Ja8vz6kL6Qz9sTCPTcwtzlPJnbMbr9nKXCLbji+TyTA1n2QukeLGa5pjnMeVKM+KSCnlVDfF3T8HfK7GsXS8Sqt+bFnXw9R8kqGe6OK0la4K7hod4qZrty+p4vr6q86rWaP1crdX77hERDpJJ+T15Tl1sDvK/EKK2by7Z5XcOct1TnPnfYcZn44zMtjN215yYUt0WgPKsyJSWtFCopl9zd1fZGYzBAPu5t4C3N0Hax5dh1lLIS/fDXtGuOPeQwBLeip7/VXnlVxu1+hQXZNCudurd1wiIu2s0/L68px60eY+7nv8FAPdETLuZefIUl552ZaWKRQWojwrIsUULSS6+4uCvwP1C6f1VHOMobUW8nJ0VVBERIrptLyey6mn5xKMTcU5ObdAxp0tQ92MTcWVI0VESihZ3dTMQsBD7r6nTvG0lLX0JlpKNQp5uiooIiLFdFJe3zU6xEt3DfOnX3qcVMbZ2NfFOYPdhMMh3vSibcqVIiIllCwkunvGzB40s63u3hoD/9RRfqN4YPHv3fvH15x8VMgTEZFa6bS8fnB8jhdcuHFJM46p+WRFeVpEpBOU03HNKPCwmX0LWOwSzN1fVbOoWoTGGBIRkRbUMXldeVpEZG3KKSS+p+ZRtKhKO5oRERFpgI7J68rTIiJrs2Ih0d2/ambnAM8n2xvat939WM0jawGVdjTTCNXsaEdERFpPJ+X1ZsvTysEi0ipCK81gZr8EfAv4SeCngW+Y2S/WOrBWkOtoZqgnythUnKGe6Jo7ramHXEc7U/PJJR3tHBibanRoIiJSJ52U15spTysHi0grKae66W8AV7j7SQAz2wj8J/CRWgbWKlqpo5ladLQjIiItp6PyerPkaeVgEWklK95JBI4AM3mvZ4CnaxOO1NLRyXkGupdeF1ADfhGRjqO83gDKwSLSSsopJB4Fvmlm7zazW4FvAI+Z2a+Z2a8VW8jMPmJmx81sf960DWZ2j5n9IPi7Pu+9d5nZY2b2qJn9WCUfSgrbsq6HmXhqyTQ14BcR6Thryuug3F4J5WARaSXlVDd9PHjk/Evwd2CF5T4KfBD467xptwBfcvfbzOyW4PVvmdlu4A3ApcC5wBfNbKe7p8uIr2k1WwP1chrw58fcFTYMSKS9KeKvh2b4nzVDDCLS1taa16EFc3upY+pnHzrKnfcdZnw6Tn8swgUbeuiNRWty7G22TnRA+UZEijN3r93KzbYBn3H3PcHrR4Hr3H3MzEaBf3f3S8zsXQDu/ofBfJ8H3u3u95Va/969e33fvn01i78SuQbqQz3RJcmgVIP5ehysS20jP+Z4MsW3D53Ggat3rCcWiawYf6tby/+sHWOQ9mdm97v73kbHIa2plXJ7qWPqExOz3Pa5R+mLRQjhHJmMk8k4L754IyNDvTx96gwjgzEWqnihtJkKZco3Iu2jFnm9nDuJ1TTi7mMAQTLZHEzfQra6S86RYNpZzOwm4CaArVu31jDUyqy2gXr+wTq/17NqH6xLNeDPj/mRsWn6g7YTT0yc4QU7NpaMvx00Q6cCzRCDiMgqNW1uL3VMve+Jk/TFIgz1RDly+gw90RCpDDx4dJof7Y9x6MQcJ2YTXLtzuGo5uVk60QHlGxEprd6FxGKswLSCtzjd/Q7gDshebaxlUGt1YGyKLzxyDBwGeqJcNNzH8EB3yQbqzXCwPjo5z+hQNwCz8RT9sTAA0/Ek0P4N7PM/f069P3MzxCAiUiUNz+2ljqnj03E293cBkEhl6Aob4RDMJVI8dnyO/liYZNoJmZXMyc10d3A1lG9EpJRyxkl8YTnTyjQeVEUh+Hs8mH4EOD9vvvOAZ9a4jYbK3RHsCoeIho1EMs13Dk8yMRMv2UC92r2eHRib4vZ7DnLzJx/k9nsOljUOU36j+v7uCIlUhkQqw2B3NjkePjnH4VNnVrXOVtIMnQo0Qwwi0t6qnNehiXN7qWNqfyzCEyfO8PjELIlkmvhCmkTK6YtFmI4nMVisUQOFc3KzjH1Yac7PUb4RkZxyejf90zKnleMu4Mbg+Y0821j+LuANZhYzs+3AxWQH+m05uTuCe7YMspDOXgyNhY2Hn5lmaj7JDXtGCi5XzYP1WpPWDXtGmJpPMjWfZMemXmbjKWbiKXYM9/LkiVm+c3iScwZibTsIcP7nz7gvPi/2P2vXGESk7VUzr0MT5/Zix9SdI330dYVJJNNkMk4kZMwk0pxJJHnulkG6wiFmE2kuGu5bXFehnJxfCyh3x3GoJ8rd+8fr9hmrkfOVb0RkuaLVTc3sGuCHgeFlXWIPAuGVVmxmHweuAzaZ2RHgVuA24BNm9ibgMPA6AHd/2Mw+ATwCpIC3tGrPprnqGyGL8rwL1vHY8Tmm5hcIWahkW4Zq9nq2UtXV5VVjdo70cXB8jqOT8/RGQyyk0qQycPWODYu9m45NJ7ji/HVsH+4vuM52sGt0iJuu3b5k37z+qvPq+vmaIQYRaU+V5vVgHS2V24sdU+/eP87uc4fY1N/FA0emmEukGOiOsKGvi77uLvb0xXhmKk5XJEzGvWhOboYqm8Vy/t/e9xSbBrqLVoPdNTrES3cNL/buOjLYzY3XbFW+ERGgdJvELqA/mCe/W+xp4KdXWrG7/2yRt64vMv/vA7+/0nqroZbtB7as62FqPslQT5RN/d1s6u9efF1qG9U8WD/8zBTT80lmE2n6uyNcNNzHxv4YRyfnz+og59DELJ/6zhGu3LqOrRv7ivZudvMnH2x4IqyHZuhUoBliEJG2VFFeh+bN7aXyeqFj6oe/9iSjQ90M9Qxy8cggABl3xqbivP91zy24zkIX7PJzfk6hO461PO8oVFBNpFJ8/bFTvOQ5m4t2hndgbIovHphg9+ggV2/fwEw8xRcPTAAsXjhupTaWIlJdRQuJ7v5V4Ktm9lF3f8rM+tx9ro6x1UStexFd6x3BYgfrHcP9Be/+FTtoHxib4sjpbMFtsDuy2CZy5+Z+tg/3n3XF8dhMgr5YhGPTCbZt6i96h7DcRCgiIs1Jef1Z5eS0ci7YlTv+8GriW22BstBneeSZGdb3lu4Mr9AdyFOzCf70y4/zgh0ba9rTuog0v3LaJJ5rZo8ABwDM7Llm9me1Dat2at1+IFe1ZagnythUnKGeaFkH11Jxffaho7zz7x/ksw+NcfjkHIcmZou2N7h7/ziXjGSrhCZSGWKR7L/44PgsN+wZOauDnNl4ioFYeLEHUyh8h1BtF0RE2kZH5fVCnbpUK6eVk/NXc96xlvaFhT7L6TNJdo0OLJlveW4v1GHesek4yXSmoW0sRaQ5lDMExv8GfoxsA3Tc/UEzu7amUdXQ0cl55uILfOGRaeYSKfpiEZ67ZZDZRFfVtpF/9TF3RfDDX3vyrPZ/+VcIi1UXueuB4xyfTZDOZIiFQ8zEF5iYSfBDW4YKtgc8OjnP1o199HdHeOz4HNPxJIPdEYZ6gx5KT53hu4dPs6k/xkWbs/NNl3GHUG3lRETaRsfk9UJ38d7/+YOMDMaYiSc5OjnPUE+E3aNDRXNaoTt7QNl3+45OzhMJwSNj09nhpboj2c7hEqmz5l1tnwK57S7Pzy+6aCNdkaXNTJfn9kJ3IE/NJdnYt/R8qB2blojIysoaJ9HdnzZbMtxRS3YqA3AmkeQ/fnCSWDQc9GyW4T9+cJJrd24qusxa2xIsT05Pnsi2/7vi/HVcsKlvSTWOLet6ODQxy7GZBLPxFOEQnJpdYD6ZJplMEwqFWEg7kbBz+kySJ07M0hU9u5+B3EE/1x4SYGo+STKV5o57D3HOQIzpM9krjvuePM3oYIyxRIpLRvpLNs4HtZUTEWkX7ZjXwyEj7c7J2QRfPHCcq3dsOKvQlUynOXRijhOzCa7dObyY80o14ShUyMy4c8HGvrKqZHaFjW8+cYpQCOYSacam5jl4bIZrdmw4a95SHeGsVG01f9u5eXPLF8rtharKRkJ21vbVtESkM5VT3fRpM/thwM2sy8xuJqii0oqeOjWP48STKU7NLRBPpnCcp04VvkpWyRhIy6uYHJsO2v/NJM6qxrFzpI/vPj3J9HySvq4QY5NxpuJJkukMkXCIcAhCZiwEA/4enYwXPGgXq0LjZK9Ibh/u58oL1jHYEyWdgTMp55Yfv4Rtm/pXVT22HtYy7pOIiKyo7fJ62jPMJlKcSaRJpx13eOjINI+MTS2pUvnY8Tn6Y2GSaS+rOmWhqqInZhOcmlsou0qmAfFkmonZBVKZDNGQkc443z82c1ZeKzUc1mqqrZZTDbbQPG+7/kJCoVDNmpYor4u0jnLuJL4Z+P+ALWQHxv0C8Cu1DKqWTs4mwJ2FtJNxSGecrrBlpxeQf1A+MRvnsePZK5C33vUI73nV7pKFqeVXBKfjSQZiYWbzEkB+NY4rt67j2HQi2z7QYHSomyOn5+mJhplPpjFzUplsD2zpjBc8aBfrJfUrj55gY3/23z080M3wQPdiT26vvGwLr1zT3lybcu7M1rqDIRGRDtZ2eT2ZyuZ0gIxDyCCZznBsMs7x6QQL6QyD3VEmZuJEQsbcQpovPHKMwe4oO4Z7OTp5dtVPODuPn5iNc+T0GVIZ574nTnLRcB/DA90lq2Qm0s76viipmQxph1gkxObBGPPJzFnNRkp1hJPrkTVfqe2WU/un0Dw7gk7uqt20RHldpLWUU0i8yt1/Ln+Cmb0Z+PPahFRb6YwTTzkhg1xNm3jK6c1ll2VyCeLEbJz7n5okFgmxoTfKqdmFVfeeNtgdZWo+yWCB9n+PjE0xdSbJTCLFYHeUsBkhg65IiAzQEw0TT6bJuJNyeOGFG4tWjSnUS2pvNMRMPFVW76S17Kq73CRx9/5xMpkMB8amg3aVUc4ZjLXVuIwiIg3Sdnk9vSyFpx1CmQzHZxP0d0Xoj4WZX0gxMZMglXHOW9/DQCxCPJnmm0+cLlj1E5bm8dx5gGHEIrbYe/iVW9fRFQkvyaf5efTwqTNMzae4YGMfuSq+8WSangjc88jK7QtzhbRiPbLGwsbt9xysWs6uVdOSldpbikhzKae66f80s5fkXpjZbwKvrl1ItWWWTR6pDKQzwV9/tsC4XK7qx2PH54hFQnRHwyyknQ39XSv2+LW86uc5gzHmEinOGYgtqcaxc6SPp0/OMx1PLSatuUSKk3MLjAx0s647gpkRCYfYtrGPK85fx9tfenHBbRarjuJQVk9ulVSvLUe51WUeGZvi+8dmiCfTi/vk+8dmeERVU0REKtV2ed159pEJ/qYzcM5gN1fv2ECsK0IqA9FwiHDI6O169hq5BfMXkp/HfzA+C2Tv3vUGfQLEwsbDz0wvyafL8+g5AzGm5pNMzCZwd+LJNNPzSU7PJ4mG7axcu2t0iHe+bCfvf91zeefLdi4WoAo1J3n61BmemYrXLGdXU6HeVNUpjkjzKqeQ+CrgD8zsxWb2+8DVwbSWtJDKELZsUoDs37BlpxeSOyifmE3QFTbiyTSJVIaLhvtWPLgtr++/bVM/t/z4JWwfXtr+7+D4HDuXDVsRi4bpCofYu209F40McOHmfq7esYEb9ozymzdcUrIXtUIH4YW0lzU0R62HCCk3SUzNpzAzuqPhJX+n5gtXCRIRkbK1XV4vdJ0347BrdIDhgW6u2bGRl+0eYfNgjE19XcSiYWYTaWLRMFdtX8/C8luRgfw8Pj6TYLA7wjUXbuSFF2+iOxomkc6QDPJrofEHQ2ZsH+7nivOHmJ7PXvyNRUJEwyG6I2EuPXew7FxbqA3hyGCMCzb2tcSQFaXaW4pI81mxuqm7nzCzVwFfBO4Hftrdi110a3pOtq2CG3hwB7HUVcTcQfnWux7h1OwCG/q7uPTcQYYHslfsVjq4Faq2sbz934e/9iQXbAqGrZiYYzaeYrA7wuD6Hv7gJy9bnG/5cBqFqpQUqo7y1Ik5js0kFpd704u2VaWr7rUoZwBjgMHuCNNnksSTaWKREIlUBjw7XURE1q4d83oklL1zmPsQBoRCcCaR5r4nTjIbT2EGx6cTOLB5CC4/f2gxl+fnpOXy83j+vJv6n102P6cW6qH0svPXE42EufTc7JBXM4kprrxgHcMDz85Xzl215ecUN3/ywcX+Bgqtp5bNR1arVHtLEWk+Re8kmtmMmU2b2QzwGLATeB0wbWbT9Qqw2vq7IiTzqpmmMpDMZKcXs2t0iPe8ajc/dN4Qu0cH2dgfq2qPX7mra/lXO3efO8Sl557dnfVKVUqWV0c5NDHLd5+eZHQwVlZVlK6w8e1Dp0kk0/THwiSSab596DRd4SL1ccuQ35vZxEycp0+dWbHa66XnDrFzpH/J1d6dI/1L9omIiJSv3fN6rppprsppfyyy2Gs4nuHpU2eYT6aJRYzp+STfeWqSQxOzZefyYr2HL1+22B2zS899thrpy3efQ3c0ctY8q72rVuruXK2bj6zW8juhyVSanmiID3/tSfV0KtKEihYS3X3A3Qfz/na7e3/udT2DrK5iF0tLX0Qtpzvpzz50lJ/5i/v4kfd9hZ/5i/v47ENHy4qonMRTbjXQ5XEem0lw5dZ1bNvUX3ZX3cv3hEPBqjzlWJ6kuiJhMu4kU+mS1V5v2DNCOBxi9+gg1+/azO7RQcLhUNW64RYR6TSdltcTyRS90TAnZhMcOnmGSMjYuqGX4f5uhnqiJDMZjs0kyu5ds9R5wGovhpZb4FxJqfXUuvnIWuTaW77pRds4k8zQFQk3RQFWRM62Yt09M3st8GV3nwperwOuc/d/rnVwtXBsqvBQF8Wm5yvV49dnHzrKbZ97lL5YhM39XUzPJ7ntc48C8MrLtiyZt1D1j2K9meWUGmC3VJw3f/LBVXWZnUg7V+9YzxMTZxZ7Fd197gCJIu01VlKoN7Nc+4l3vmxn0eWKDeWhHtBERCrTbnl9rEj+nk9BLBpix6Y+fnA82+lMNGzMLaTojUWIhm3JkFTlKHQesLzX7pl4Ku9iaKpgTi/Vi+lq4ym2ntUOmbEWa63Oqp5ORZpfOQ28bnX3T+deuPukmd0KtGQySa9yernuvO8wfbFI3gEvtDg9v5BYagiIUoWmUm35Sh2ky20DuHw7L9ixcXHa1HySzQPF22uUsprCbb5iQ3nsGO5XAhERqUxb5fXC3c5lmRlmRm9XmEQyw9hkHMwY6E4TC4fArOKx+u7eP046nVnSlv+cgRibBrpXvBhajXxWbD2rzf+rVcm4h2s9NxCR+imnd9NC86y59xAzu8TMHsh7TJvZO8zs3WZ2NG/6K9a6jUYYn44zEAsvmTYQCzM+HV8yba3VP4pVKdk50leyzcFqq7RUqwpMzlp7M6t2NZn8qkBq+yAiHa6qeR2aN7cnUhniyTTreqKkMs5MIsWG3myhKZF2Lj13sOIqmA8/M8XB8dklbfkPjs/y8DONzTPVzufLVZKni50bdAVjPipXizReOYXEfWb2ATO70Mx2mNntZHtDWxN3f9TdL3f3y4HnAWeA3BXN23Pvufu/rXUbjTAy2M1MYun9yJlEmpHBpVfKyhkColCBplhbiIPjcyUP0uW0pcy32vlXstYkVc3xlJqt8b6ISINVNa9D8+b2K7euIxYN4xjDA130dIWJhLPDTF25Ndu7aKV3sKbjKTCWDNmEBdMbIHcO8eGvPUlPNLRiHwBrVUmeLnRu8NTJOcanE8rVIk2inCuHbwP+J/APZPsv+QLwlipt/3rgcXd/yoqNZt8ibrxm62IbxIFYmJlEmrlEire95MIl861U/WOl6hvLD+7ltDlYbZWWalWBya1rLe0uqllNRm0fRESWqGVehybK7V2R8GKThan5JD3REF2RcFWrYA71RJg6s7BkyCZ3Z6in/kM2FWofOTWfrGrhMKeSPF3o3ODcoW6ief8b5WqRxipnnMQ54JYabf8NwMfzXr/VzH4B2Af8urufrvYGi42JWGkay7U7zO9o5W0vufCsTmtWGidotQWaWrc5KFepdpFrKXRWczwltX0QEXlWjfM61Dm3lxrreKgnuuQCJVD1sfp2jw7RGw1zbDqx2OHbto29bNvUv+Z1rlU9L4qWk6dXc25w8ycfZEOJMR9FpL6KFhLN7IPu/lYz+1cKHH/d/VWVbNjMuoBXAe8KJn0I+L1gW78H/DHwiwWWuwm4CWDr1q2r3u7aBsAozysv23JWoXC5le6s5RdoTszGeez4HFPzC4QsVLDXsNxB+vRcgrGpOCfnFoiGQ2fdwaylShqvF1Otnt+geQrSIiKNVOu8Hmyj7rm9VP4u1HFMsdyy1p46s3n4g4dOmQAAHj1JREFUDLtGB5cUlipp+7fWWOp5UXSlPL3acwPlapHmYu6FD69mNu3ug2b2I4Xed/evVrRhs1cDb3H3lxd4bxvwGXffU2ode/fu9X379q1qu9tu+WzR95687ZWrWlct3H7PQabmkyTTae5/apJYJATuYMaO4f4lB9dcEvnG4yd4bGKO3q7seEPnDHYTDodqUr2kVMy5A/uJ2Tj7j06zkM7w8t3nsHOkj4Pjc6tOdtWSn6jyE3i99o9UZq0nS1Kcmd3v7nsbHYfUV63zerCNuuf2Unn9V6+/uKxjRy5PpNMZjk3HOTWXJBIy3nb92TWCii1freNUJTlreT4GFl/nF5jrcVwtN5b8mJSrBZT316IWeb1UddPHoTpJo4ifJa86ipmNuvtY8PK1wP4abbep5e4MPnJ0itNnFkhmnJAZV29fv9ghTe6KZ+5g6sDwQAyAi0f62dSfvWJXr3r8RyfniYbhG09MMzETZ2o+23tcJBzi0MQsn/rOEa7cuo6erjD//uhxPv3do7z4oo38t2suqDi+cg4k1bwrKfVVi7vUIh2s1nkdmiy3f/ahMTb0RVlIprnj3jMFL7QenZzn8Kkz9ESM8ZkFYpEQG/qiTMdT/OmXHy9r6KVqtuWvpMpouVVAKzmulnsCX85dzeXreumu4SUXlZWrO4/yfvMoVUgcNrNfK/amu39grRs1s17gZcAv501+r5ldTrbmyJPL3usYuUHkv3RgnHDI6ImG6YuFeeLEGYZ6oxydzPaWlp9EZhNpBrsjJFIZHjs+x6b+yntrW41Y2LjviVMMdEeIJ7MjVk3MLnDe+h6OzSToi0V4fGKOVMaJRUKs64mw/5npin/0qzmQVDOBS/2o0yGRqqpZXofmzO0b+qIkUhkOHp9l5+b+ghdaR4e6+e7h00yeSbKhL0p3NHucGeyOcHJuoe7Hm0qqjJZzUbSS4+pq8u5aOur74oEJFQY6nPJ+8yhVSAwD/VTep8tZ3P0MsHHZtJ+v9nZa1cHxObZv6gOyXWoDxJNpHnlmhusu2QwsTSL93RESQa9q0/EkcPaBuJa37Z1nvySJVJpwyEh7dvpsPMVALMyhk2fYsq6H7mgY9+xYVfl3RtdCB5L2p06HRKqqZnkdmjO3Lw5JARybjtMVDXNgbIpb73qEk7MJNvXHuGhzH5v6Y0zMJJhLpNmQTb8kUhk29nXV/XhTadu8lS6KVnJcXU3erXZHfdIZlPebR6lC4pi7/27dIpFFRyfn2TU6wANPZ8cGyrVLPD2fWmwIn59ELhru4zuHJ0mkMgx2RxbHHnr9VefV5bb9Qtq5avt6njhxBjPDMLYMxXDPFmCn55PPfg4I4oxW/KPXgaT9qSMDkarq2Lwei4Q4NZfkos3GHfce4tTsAht6o8ST2fb/Ozb1EgkZcwsp3J1EKkMilWHbxt66H2+q2bt3IZUcV1eTd1fTUd9K65LOobzfPEoVElt74MIWlvuBXLl1HY9NzDEbTxENGy+6aOPiwTU/iWzsj7Fzcz8PHZkkkUrzrUOnuOL87Hz1uFKXi/eaHRu5eHMf9z81SdphsDvMOQMxnjo5h2ecg+MzxCJhervC7NkyWPGPXgeS9lfrk6VWpAb9UoGOy+vxxVo2KSIhw8jmwQ39XSSS6cW7jCfnklx23hDfOzrNybkFNvZ1sW1jL6FQqKJeStdieeGqK2z0RkN8+GtPVvSbzx07Hn5miiOn57lkpJ+tG/tWdVxdbd4tdVdTOVwK6cS836x5vVQh8fq6RdHhln85do708cUDEwz1RJcMAvzz11ywuMzyJNLfHeHCzQOcv6F38Ud1x72HmIkn2TU6uGR7xa7UVdb9d/YHvaEvxiUj/Tw6PosBT5yYI51xHFhIZYiEQmTcmY2nCIUyFf3oO/FA0mnU6dBSatAvFeq4vB6LhJ4dGur6C/nnB55h6kyS4zNxZuJpNvV3Mdgd4cRsgsvOW8fvvnp3Q3vjzskVrvJ/8xv6I2v+zeevZ9foIL3RMI8em+VMMs3u0aGyjqsHxqaYmInztcdOsr43yu5zB4hFImXl3ULnF8rhUkin5f1mzutFC4nufqqegXSqYg23y+nhK/8K3e33HKQrEj7rjuHRyXlm4qkVr9RV8iVd/oPetqmfl186whcPTPDExCznDnVjZkzNJ+mLRUgkM4xNJ3jPq3ZX9APotANJp1KnQ89SGx6pRCfm9a0b+7jmwk2LdwOfPjkPBpsHuomGFzgxu8CZhTSj63oW813jB8N6VrV+88vXs324nw39saLDUSyXf47wwxdu4MDYDF9/7BQvvmjjiucJpc4vlMOlkE7K+82c10vdSZQ6KPblODg+V9aBO6dY3f6hnuxVvtzrYlfqKv2SLv9B337PQYZ6oiykMwzEIphlaznFomFe8pwNjE3Fq/Ll76QDiYja8Iiszvtf99zF57ffc5CdI/0cPD5LIpVhfW8X4VC2rXylFy1rpVq/+UrXk3+OMNQTZWTw2aqiK+23UucX73zZzqbc7yL10sx5PdToADrd0cl5BrqXltXX8uXYsq6HmXhqybSZeIrdwd22oZ4oY1NxhnqiBa/6VSuO5esb7M52Pw7Zaj+z8ZTaHIisUbHfuX5PIis7OjnPBZv6uHLrOmLR8OLwUeet72nagkq1fvOVrqeSc4Rqn1+ItJNmzusqJDZYtb4cN+wZWezVNOO++DzXnuKdL9vJ+1/33KJX7ar9Jc2t76LNfSRSGeLJNIlkmmjYFuMSkdUp9TsXkdJyeWl4oJtrdmzkZbtH2H3uEJee25wFRKjeb77S9VRyjtDMJ8EijdbMeV2FxDo6MDbF7fcc5OZPPsjt9xzkwNhU1b4cu8q8Y1hMtb+kufVFw2Gu2JqN4fR8ikvPHeSlu4a5e//4kv0gIiur9Hcu0skK5bmnTs5xYibedPkod77w4a89SU80RDKVrug338hzhGY+CRZptGbO6+bujY5hzfbu3ev79u1b1TLbbvls0feevK12zdXzG27ntw286drtADXt+rbcXkur3QVvofUBRfdDM/wgZG2atftmKczM7nf3vY2OQ6SQ1eb21eT1/GNVV9gYn04s6RW8nvmo2HGz1PlCflz1Pu5Wsj3lCJHaqkVeV8c1ddKohtur6bW02p3AFFpfrkObZuzFSdammbtvFhHJV06v4PXIR6WOm+V0JNeI424l5wjqZE6k9ai6aZ00quF2frIJmS0+v3v/eE23W4wasLefZvuOiYiUo5H5qNRxs5y4dNwVkVpTIbFOGtVwu9kKZWrA3n6a7TsmIlKORuajUsfNcuLScVdEak2FxDppVMPtZiuUqQF7+2m275iISDkamY9KHTfLiUvHXRGpNRUS66RRvRc1W6GsmXtxkrVptu+YiEg5GpmPVhq2aqW4dNwVkVpTxzV11IiG27lkk9+r2OuvOq+hhTI1YG8vzfgdExEpR6Py0UrHzZXi0nFXRGqtIYVEM3sSmAHSQMrd95rZBuAfgG3Ak8DPuPvpRsTXblQok1rTd0xElNtXp9Ljpo67IlJLjaxu+qPufnnemB63AF9y94uBLwWvRUREpHUot4uItIFmapP4auDO4PmdwGsaGIuIiIhUTrldRKQFNaqQ6MAXzOx+M7spmDbi7mMAwd/NhRY0s5vMbJ+Z7ZuYmKhTuCIiIrIC5XYRkTbRqI5rXujuz5jZZuAeM/t+uQu6+x3AHQB79+71WgUoIiIiq6LcLiLSJhpSSHT3Z4K/x83s08DzgXEzG3X3MTMbBY43IjZpfgfGppb06JbrMrxdtysi0gqU2ztDO+TCdvgMIrVW9+qmZtZnZgO558DLgf3AXcCNwWw3Av9S79ik+R0Ym+KOew8xNZ9kdKibqfkkd9x7iANjU225XRGRVqDc3hnaIRe2w2cQqYdG3EkcAT5tZrnt/527321m3wY+YWZvAg4Dr2tAbNLk7t4/zlBPlKGeKMDi37v3j9f0KmCjtisi0iKU2ztAO+TCdvgMIvVQ90Kiuz8BPLfA9JPA9fWOR1rL0cl5Roe6l0wb6I5wdHK+LbcrItIKlNs7Qzvkwnb4DCL10ExDYIisaMu6HmbiqSXTZuIptqzracvtioiINIt2yIXt8BlE6kGFRGkpN+wZYWo+ydR8koz74vMb9oy05XZFRESaRTvkwnb4DCL1oEKitJRdo0PcdO12hnqijE3FGeqJctO122vejqBR2xUREWkW7ZAL2+EziNRDo8ZJFFmzXaNDDTmYN2q7IiIizaIdcmE7fAaRWtOdRBEREREREVmkQqKIiIiIiIgsUiFRREREREREFqmQKCIiIiIiIotUSBQREREREZFFKiSKiIiIiIjIIhUSRUREREREZFHHFRJtldNFRESkeSmvi4hUX8cVEot94I7bESIiIm2gO1K4OFhsuoiIrKzjykaDPREge4Ux98ifLiIiIq3jopEBDAgZRELZvxZMFxGRtal7IdHMzjezr5jZATN72Mx+NZj+bjM7amYPBI9X1GL7F58zyHB/lGjYMINo2Bjuj3LxOYO12JyIiEjba2Ruf8lzRnjeBUP0doUxM3q7wjzvgiFe8pyRam9KRKRjNOJOYgr4dXffBbwAeIuZ7Q7eu93dLw8e/1aLjd94zVbSGSccMiIhIxwy0hnnxmu21mJzIiIinaBhuf2GPSOELERfV4TerjB9XRFCFuKGPSokioisVd0Lie4+5u7fCZ7PAAeALXUNwnKVTH3ZaxEREVmtRub2JyZmeerUGdIZJ2LZC79PnTrDExOz9di8iEhbamibRDPbBlwBfDOY9FYze8jMPmJm62uxzTvvO8zmgW4uO28dV2zdwGXnrWPzQDd33ne4FpsTERHpKPXO7Xfed5j1vV1cuLmfi0YGuHBzP+t7u5TXRUQq0LBCopn1A/8EvMPdp4EPARcClwNjwB8XWe4mM9tnZvsmJiZWvd3x6TgDsfCSaQOxMOPT8VWvS0RERJ7ViNyuvC4iUn0N6dLTzKJkk8jH3P1TAO4+nvf+XwKfKbSsu98B3AGwd+9eX+22Rwa7OTQxw+R8arFt4rqeCNuH1QuaiIjIWjUqtyuvi4hUXyN6NzXgw8ABd/9A3vTRvNleC+yvxfZHB6NMzCZJpZ0QkEo7E7NJRgejtdiciIhI22tkbldeFxGpvkbcSXwh8PPA98zsgWDabwM/a2aXk+1N5kngl2ux8W8/NUVPxEhnIO1ONGSEQ9npIiIisiYNy+3K6yIi1Vf3QqK7f41nx7DPV5MhL5abmk/SGwuTSPlitZRYxJiaT9Zj8yIiIm2nkbldeV1EpPoa2rtpI/RGw0zPp3CHcMhwh+n5FL3R8MoLi4iISFNRXhcRqb6OKyResrmfDJDKZPCMk8pkyATTRUREpLUor4uIVF/HFRLPWd/L1dvW0xUOsZBxusIhrt62nnPW9zY6NBEREVkl5XURkepryBAYjbRlXQ/9sQhXbd+0OG1qPslQj3pBExERaTXK6yIi1ddxdxJv2DPC1HySqfkkGffF5zfsGWl0aCIiIrJKyusiItXXcYXEXaND3HTtdoZ6ooxNxRnqiXLTtdvZNTrU6NBERERklZTXRUSqr+Oqm0I2oSh5iIiItAfldRGR6uq4O4kiIiIiIiJSnAqJIiIiIiIiskiFRBEREREREVlk7t7oGNbMzCaApypYxSbgRJXCqaVWiRNaJ9ZWiRMUay20SpxQm1gvcPfhKq9TpCoqzO2t9NvOacWYQXHXUyvGDIq7ni5x94FqrrClO66p9CTHzPa5+95qxVMrrRIntE6srRInKNZaaJU4obViFamGSnJ7K/5eWjFmUNz11Ioxg+KuJzPbV+11qrqpiIiIiIiILFIhUURERERERBZ1eiHxjkYHUKZWiRNaJ9ZWiRMUay20SpzQWrGKNFor/l5aMWZQ3PXUijGD4q6nqsfc0h3XiIiIiIiISHV1+p1EERERERERydOWhUQzu8HMHjWzx8zslgLvm5n9SfD+Q2Z2ZbnLNkusZna+mX3FzA6Y2cNm9qvNGGfe+2Ez+66ZfaaWcVYaq5mtM7N/NLPvB/v2miaN853B/32/mX3czLprFWeZsT7HzO4zs4SZ3byaZZsl1ib8TRXdp8H7dftNiTRCLXK5mW0ws3vM7AfB3/UtEve7zeyomT0QPF7RRDF/xMyOm9n+Zcs0+74uFndN93UlcZfKU7Xe3zWKuZn3dbeZfcvMHgzifk/eMk373V4h7tXtb3dvqwcQBh4HdgBdwIPA7mXzvAL4HGDAC4BvlrtsE8U6ClwZPB8ADtYq1krizHv/14C/Az7TrP//4L07gV8KnncB65otTmALcAjoCV5/Anhjg/fpZuAq4PeBm1ezbBPF2my/qYJx5r1fl9+UHno04lHhMbLossB7gVuC57cAf9Qicb+70HGg0TEH710LXAnsX7ZM0+7rFeKu2b6uwnekaJ6q5f6uYczNvK8N6A+eR4FvAi9o9u/2CnGvan+3453E5wOPufsT7r4A/D3w6mXzvBr4a8/6BrDOzEbLXLYpYnX3MXf/DoC7zwAHyBYemipOADM7D3gl8Fc1iq8qsZrZINmk8WEAd19w98lmizN4LwL0mFkE6AWeqVGcZcXq7sfd/dtAcrXLNkuszfabKrFP6/2bEmmEWuXyV5O9GEjw9zUtEnctVZSP3P1e4FSB9Tbzvi4Vd63V6tyvlvu7Vc5Xqxm3u/tsME80eHjeMk353V4h7lVpx0LiFuDpvNdHOPvLWGyecpatpkpiXWRm24AryF4tqIVK4/zfwG8CmRrFV24cK82zA5gA/o9lq/H9lZn1NVuc7n4UeD9wGBgDptz9CzWKs9xYa7HsWlRle03ymyqlnr8pkUaoVS4fcfcxyF4YInvHvppqeQ7y1qBa2UeqXL2tKuciBTTzvl5JrfZ1uTGt5dyvlvu7luerTbuvLdus4wHgOHCPu9djX9cybljF/m7HQqIVmLa8BF1snnKWraZKYs2+adYP/BPwDnefrmJsZcdQah4z+wnguLvfX/2wCqpkn0bIVj35kLtfAcyRrUZQC5Xs0/VkryBtB84F+szsv1U5vhXjqMOya1Hx9proN1V4wfr/pkQaoZVyeb5axf0h4ELgcrIXB/94rQEWUPG5SIPUKu5a7utyY2qGc7+y4ylnniIxN/W+dve0u18OnAc838z2VDm+YmoV96r2dzsWEo8A5+e9Po+zq+IVm6ecZaupklgxsyjZH9zH3P1TTRrnC4FXmdmTZG+Xv8TM/rZ2oVb8/z+Sd8XlH8kWGpstzpcCh9x9wt2TwKeAH65RnOXGWotl16Ki7TXZb6qYev+mRBqhVrl8PK8pxCjZK+3VVJO43X08OPHLAH9JtjpaM8RcSjPv66JqvK/LjWkt53613N81ibnZ93VenJPAvwM3BJNa4ru9PO7V7u92LCR+G7jYzLabWRfwBuCuZfPcBfxC0DPQC8hW1xsrc9mmiNXMjGzbuQPu/oEaxlhRnO7+Lnc/z923Bct92d1rederkliPAU+b2SXBfNcDjzRbnGSrmb7AzHqD78H1ZOv410olv4tm/E0V1IS/qYIa8JsSaYRa5fK7gBuD5zcC/9IKcduz7dEBXgvsp3oqibmUZt7XRdV4X0Ptzv1qub9rEnOT7+thM1sXxNlD9gL99/OWacrvdqm4V72/vUY9CjXyQbbHn4Nkewb6nWDam4E3+7M9//z/wfvfA/aWWrYZYwVeRPa28kPAA8HjFc0W57J1XEcdemKs8P9/ObAv2K//DKxv0jjfQ/ZHvx/4GyDW4H16DtmrWtPAZPB8sNiyzRhrE/6miu7TvHXU5Telhx6NeFR4jCx43AE2Al8CfhD83dAicf9NMO9DZE8OR5so5o+TrbqWDI5Tb2qRfV0s7pru60ripkSeqvX+rlHMzbyvLwO+G8S2H/hfeets2u/2CnGvan9bsJCIiIiIiIhIW1Y3FRERERERkTVSIVFEREREREQWqZAoIiIiIiIii1RIFBERERERkUUqJIqIiIiIiMgiFRJFRERERERkkQqJ0jTM7O1mdsDMPraKZdaZ2a8sm3apmX3ZzA6a2eNm9h4zq8p33czeaGYfrMa6Gs3MfnvZ6/9sVCwiItK6Oil/m9k2MztrEHIz22tmf7LWGMxsttLYVtj2O8ysN+/1v+UGXRcpRIVEaSa/QnaA1Z9bxTLrguUAMLMesgOE3ubuO4EfAp4P/Go1A10rM4vUcVvhFWZZUkh09x+uYTgiItK+2j5/r8Td97n72xu1fcsqdV7/DmCxkOjur3D3ydpHJq1KhURpCmb258AO4C4z+y0z+08z+27w95JgnkvN7Ftm9oCZPWRmFwO3ARcG094H/Ffg6+7+BQB3PwO8FfiNYB3vNrOb87a738y2Bc//2czuN7OHzeymvHn+n+Cq5leBF+ZNf12w/INmdm+Jz/ZGM/ukmf0r8AUzu87MPpP3/gfN7I3B8yeDK6ffMbPvmdlzguk/EnzGB4L9MlBkW9eZ2VfM7O+A7xX7XGZ2G9ATrO9jwbTZ4K+Z2fuCz/Y9M3v9Cv8+ERHpUG2evwvFnf/+juCzXpWf283s+YX2Q+B8M7vbzB41s1uLbPc3zOzbwTbfUyK+bZa9g/tnwHeCdX/IzPYF++I9wXxvB84FvmJmXwmmPWlmm4Lnvxbsj/1m9o5i25MO4+566NEUD+BJYBMwCESCaS8F/il4/qfAzwXPu4AeYBuwP28dHwB+tcC6T5O9avlu4Oa86fuBbcHzDcHfnmD6RmAUOAwMB9v8OvDBYL7vAVuC5+tKfK43Akfy1n8d8Jm89z8IvDFvH7wteP4rwF8Fz/8VeGHwvD+3fwps6zpgDtieN+2szxW8nl227Gzw96eAe4AwMBJ8/tFGfz/00EMPPfRozkcb5++icQOXAN8FLg/eX8ztJfbDG4GxIL5crHuD93I5+OXAHYCRvZnzGeDaIvFtAzLAC/Km5fZFGPh34LL8/1GB/9nzgv3RR/b84mHgikZ/p/Ro/KNuVd9EVmEIuDO4YudANJh+H/A7ZnYe8Cl3/4GZLV/WgmUKTV/J283stcHz84GLgXOAf3f3CQAz+wdgZzDP14GPmtkngE+tsO573P1UGTGQt677gZ/M29YHgrt+n3L3IyWW/5a7H8p7XehznSyx/IuAj7t7GhgPrsBeRbYakIiISDHtlr+LxT0M/AvwU+7+cIHliu0HyJ4PnAxi+hTZnLsv7/2XB4/vBq/7g89T7I7nU+7+jbzXPxPcTY2QLSjvBh4q8RlfBHza3efyYnpx3valQ6m6qTSj3wO+4u57gP8CdAO4+98BrwLmgc+b2UsKLPswsDd/gpntAE54tu59iqXf++5gnuvIXu27xt2fS/bg2B3MUyhp4e5vBv4H2YT0gJltLPGZ5vKeF4whTyL4myZ7kMfdbwN+ieyVx2/kqqGutK0VPlcx5SRkERGR5doqf5eIewp4mrwqrMsU3A9FYlr+2oA/dPfLg8dF7v7hItuBpTl/O3AzcL27XwZ8FuV8WSMVEqUZDQFHg+dvzE0MksUT7v4nZO9qXQbMAPnt8z4GvMjMXhos0wP8CZCr9/8kcGXw3pXA9rxtnnb3M0EB7AXB9G8C15nZRjOLAq/Li+dCd/+mu/8v4ATZZFOOp4DdZhYzsyHg+pUWCLb1PXf/I7JXHEsVEvMV+1wAyeAzLXcv8HozC5vZMHAt8K0ytyciIp2rrfJ3kbgBFoDXAL9gZv+13P0QeJmZbQg+32vI3tXM93ngF82sP4hhi5ltLhRfAYNkC41TZjYC/Hjee8v3d869wGvMrNfM+oDXAv9R5vakjamQKM3ovcAfmtnXydapz3k9sN/MHiBbSPrroMrG14PG1u9z93myV/1+x8wOkj34f93dc91y/xOwIVjHfwcOBtPvBiJm9hDZK4DfAHD3MbLtIO4Dvki2YXjO+yzbsct+sgfZB8v5cO7+NPAJstU/PkZ5VTrekWtkT/aK5ufK2RZFPlfgDuAhO7vL8k8HsT0IfBn4TXc/Vub2RESkc7Vb/j4r7twbQfXMnwDeaWavLnM/AHwN+BvgAbJtFfOrmuLZjnv+DrjPzL4H/COFC3dncfcHyZ5TPAx8hKUF0DuAz+U6rslb5jvAR8leDP4m2b4QVNVUMPeCd+JF2oKZvYZsY/gfdfenGh2PiIiIrEz5W6SxVEgUERERERGRRerdVKRKzOzHgD9aNvmQu7+20PwVbuuHyFZXyZdw96urvS0REZF2Vs/8vRZBxzpfKvDW9bmeUkWqTXcSRUREREREZJE6rhEREREREZFFKiSKiIiIiIjIIhUSRUREREREZJEKiSIiIiIiIrJIhUQRERERERFZ9H8B+qspegvvN1IAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 1080x576 with 4 Axes>"
       ]
@@ -3463,12 +4008,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**A: 1** Your answer here"
+    "**A: 1** \n",
+    "\n",
+    "**EDA in summary**\n",
+    "\n",
+    "1. Final Dataframe, ski_data has 36 columns and 277 rows. Name, State, and Region are the only categorical features. Other than that, the rest of the features are numerical. Previously, there are two separate Datafame, state_summary and ski_data. Here, two Dataframe are merged. The absolute size and population of a state have replaced the ratio of resorts serving a given population or a given area. In case of `state_total_skiable_area_ac`, `state_total_days_open`, `state_total_terrain_parks`, and `state_total_nightskiing_ac` features, the state total value is replaced with the ratio of the resort value to the state total, which explains state resort competition. 4 additional features, `total_chairs_runs_ratio`, `total_chairs_skiable_ratio`, `fastQuads_runs_ratio`, and `fastQuads_skiable_ratio` are added to show the mobility of visitors in the resort.\n",
+    "    \n",
+    "2. The state-wide summary statistics indicates that the population and the area of the state are not necessarily linked to the total number of resorts or the total skiing area in terms of the absolute value. However, when plotting the ratio of resorts serving a given population or a given area, we can check that most of the states are distributed on the left side of the histogram. Few states fall far away from all the other states in both plots ‘Number of resorts per 100k square miles’ and ‘Number of resorts per 100k population’.  These long skewed tails are caused by the states such as New Hampshire and Vermont. The fact that these states ranked 6 and 7 in the total number of resorts but their population and area are at the lower end leads to relatively high ratios. \n",
+    "\n",
+    "3. From principle components analysis (PCA) for analyzing the relationship between the ticket price and the state-related features, no obvious pattern is shown. PCA plot shows that most states are clustered together except Vermont and New Hampshire(for the same reason stated in the above answer #2). This supports that all states can be considered equally to build a pricing model. \n",
+    "\n",
+    "\n",
+    "4. Feature correlation heatmap tells us `resort_night_skiing_state_ratio`, `fastQuads`, `runs`, `total_chairs`, `vertical_drop` and `Snow Runs Making_ac` are highly correlated with our target feature `AdultWeekend`. \n",
+    "\n",
+    "5. From scatterplots of numeric features against the ticket price, we can identify the pattern between the ticket price and the correlated feature.  `fastQuads`, `runs`, `total_chairs`, and `vertical_drop` show a strong positive correlation with the ticket price.\n",
+    "\n",
+    "6. The new features to measure how easily a resort can transport people around are generated. The scatterplot of the ratio of the number of chairs to the number of runs or the skiable area against the ticket price presents that the easily people move around a resort, the lower the ticket price stays. If there are no fast quads, it may limit the ticket price."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -3614,91 +4174,91 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Runs</th>\n",
-       "      <td>76</td>\n",
-       "      <td>36</td>\n",
-       "      <td>13</td>\n",
-       "      <td>55</td>\n",
-       "      <td>65</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>65.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TerrainParks</th>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LongestRun_mi</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>1.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SkiableTerrain_ac</th>\n",
-       "      <td>1610</td>\n",
-       "      <td>640</td>\n",
-       "      <td>30</td>\n",
-       "      <td>777</td>\n",
-       "      <td>800</td>\n",
+       "      <td>1610.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>777.0</td>\n",
+       "      <td>800.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Snow Making_ac</th>\n",
-       "      <td>113</td>\n",
-       "      <td>60</td>\n",
-       "      <td>30</td>\n",
-       "      <td>104</td>\n",
-       "      <td>80</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>104.0</td>\n",
+       "      <td>80.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>daysOpenLastYear</th>\n",
-       "      <td>150</td>\n",
-       "      <td>45</td>\n",
-       "      <td>150</td>\n",
-       "      <td>122</td>\n",
-       "      <td>115</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>115.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>yearsOpen</th>\n",
-       "      <td>60</td>\n",
-       "      <td>44</td>\n",
-       "      <td>36</td>\n",
-       "      <td>81</td>\n",
-       "      <td>49</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>81.0</td>\n",
+       "      <td>49.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>averageSnowfall</th>\n",
-       "      <td>669</td>\n",
-       "      <td>350</td>\n",
-       "      <td>69</td>\n",
-       "      <td>260</td>\n",
-       "      <td>250</td>\n",
+       "      <td>669.0</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>260.0</td>\n",
+       "      <td>250.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AdultWeekend</th>\n",
-       "      <td>85</td>\n",
-       "      <td>53</td>\n",
-       "      <td>34</td>\n",
-       "      <td>89</td>\n",
-       "      <td>78</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>78.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>projectedDaysOpen</th>\n",
-       "      <td>150</td>\n",
-       "      <td>90</td>\n",
-       "      <td>152</td>\n",
-       "      <td>122</td>\n",
-       "      <td>104</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>152.0</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>104.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NightSkiing_ac</th>\n",
-       "      <td>550</td>\n",
+       "      <td>550.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>30</td>\n",
+       "      <td>30.0</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>80</td>\n",
+       "      <td>80.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_state</th>\n",
@@ -3713,8 +4273,8 @@
        "      <td>0.410091</td>\n",
        "      <td>0.410091</td>\n",
        "      <td>0.410091</td>\n",
-       "      <td>0.0274774</td>\n",
-       "      <td>0.0274774</td>\n",
+       "      <td>0.027477</td>\n",
+       "      <td>0.027477</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>resorts_per_100ksq_mile</th>\n",
@@ -3728,7 +4288,7 @@
        "      <th>resort_skiable_area_ac_state_ratio</th>\n",
        "      <td>0.70614</td>\n",
        "      <td>0.280702</td>\n",
-       "      <td>0.0131579</td>\n",
+       "      <td>0.013158</td>\n",
        "      <td>0.492708</td>\n",
        "      <td>0.507292</td>\n",
        "    </tr>\n",
@@ -3752,13 +4312,13 @@
        "      <th>resort_night_skiing_state_ratio</th>\n",
        "      <td>0.948276</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>0.0517241</td>\n",
+       "      <td>0.051724</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>total_chairs_runs_ratio</th>\n",
-       "      <td>0.0921053</td>\n",
+       "      <td>0.092105</td>\n",
        "      <td>0.111111</td>\n",
        "      <td>0.230769</td>\n",
        "      <td>0.145455</td>\n",
@@ -3766,7 +4326,7 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>total_chairs_skiable_ratio</th>\n",
-       "      <td>0.00434783</td>\n",
+       "      <td>0.004348</td>\n",
        "      <td>0.00625</td>\n",
        "      <td>0.1</td>\n",
        "      <td>0.010296</td>\n",
@@ -3774,18 +4334,18 @@
        "    </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",
@@ -3808,17 +4368,17 @@
        "double                                           0                    4   \n",
        "surface                                          2                    0   \n",
        "total_chairs                                     7                    4   \n",
-       "Runs                                            76                   36   \n",
-       "TerrainParks                                     2                    1   \n",
-       "LongestRun_mi                                    1                    2   \n",
-       "SkiableTerrain_ac                             1610                  640   \n",
-       "Snow Making_ac                                 113                   60   \n",
-       "daysOpenLastYear                               150                   45   \n",
-       "yearsOpen                                       60                   44   \n",
-       "averageSnowfall                                669                  350   \n",
-       "AdultWeekend                                    85                   53   \n",
-       "projectedDaysOpen                              150                   90   \n",
-       "NightSkiing_ac                                 550                  NaN   \n",
+       "Runs                                          76.0                 36.0   \n",
+       "TerrainParks                                   2.0                  1.0   \n",
+       "LongestRun_mi                                  1.0                  2.0   \n",
+       "SkiableTerrain_ac                           1610.0                640.0   \n",
+       "Snow Making_ac                               113.0                 60.0   \n",
+       "daysOpenLastYear                             150.0                 45.0   \n",
+       "yearsOpen                                     60.0                 44.0   \n",
+       "averageSnowfall                              669.0                350.0   \n",
+       "AdultWeekend                                  85.0                 53.0   \n",
+       "projectedDaysOpen                            150.0                 90.0   \n",
+       "NightSkiing_ac                               550.0                  NaN   \n",
        "resorts_per_state                                3                    3   \n",
        "resorts_per_100kcapita                    0.410091             0.410091   \n",
        "resorts_per_100ksq_mile                   0.450867             0.450867   \n",
@@ -3826,10 +4386,10 @@
        "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",
@@ -3846,28 +4406,28 @@
        "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",
@@ -3884,31 +4444,31 @@
        "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": 56,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3919,9 +4479,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing file.  \"../data/ski_data_step3_features.csv\"\n"
+     ]
+    }
+   ],
    "source": [
     "# Save the data \n",
     "\n",
@@ -3946,7 +4514,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.8.8"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/Notebooks/04_preprocessing_and_training.ipynb b/Notebooks/04_preprocessing_and_training.ipynb
index 94ff2aeba..cf4fda4a9 100644
--- a/Notebooks/04_preprocessing_and_training.ipynb
+++ b/Notebooks/04_preprocessing_and_training.ipynb
@@ -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",
@@ -551,27 +551,27 @@
        "double                                                1  \n",
        "surface                                               0  \n",
        "total_chairs                                          7  \n",
-       "Runs                                                 65  \n",
-       "TerrainParks                                          2  \n",
+       "Runs                                               65.0  \n",
+       "TerrainParks                                        2.0  \n",
        "LongestRun_mi                                       1.2  \n",
-       "SkiableTerrain_ac                                   800  \n",
-       "Snow Making_ac                                       80  \n",
-       "daysOpenLastYear                                    115  \n",
-       "yearsOpen                                            49  \n",
-       "averageSnowfall                                     250  \n",
-       "AdultWeekend                                         78  \n",
-       "projectedDaysOpen                                   104  \n",
-       "NightSkiing_ac                                       80  \n",
+       "SkiableTerrain_ac                                 800.0  \n",
+       "Snow Making_ac                                     80.0  \n",
+       "daysOpenLastYear                                  115.0  \n",
+       "yearsOpen                                          49.0  \n",
+       "averageSnowfall                                   250.0  \n",
+       "AdultWeekend                                       78.0  \n",
+       "projectedDaysOpen                                 104.0  \n",
+       "NightSkiing_ac                                     80.0  \n",
        "resorts_per_state                                     2  \n",
-       "resorts_per_100kcapita                        0.0274774  \n",
+       "resorts_per_100kcapita                         0.027477  \n",
        "resorts_per_100ksq_mile                         1.75454  \n",
        "resort_skiable_area_ac_state_ratio             0.507292  \n",
        "resort_days_open_state_ratio                   0.485232  \n",
        "resort_terrain_park_state_ratio                0.333333  \n",
-       "resort_night_skiing_state_ratio                       1  \n",
+       "resort_night_skiing_state_ratio                     1.0  \n",
        "total_chairs_runs_ratio                        0.107692  \n",
        "total_chairs_skiable_ratio                      0.00875  \n",
-       "fastQuads_runs_ratio                          0.0153846  \n",
+       "fastQuads_runs_ratio                           0.015385  \n",
        "fastQuads_skiable_ratio                         0.00125  "
       ]
      },
@@ -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",
@@ -802,27 +802,27 @@
        "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"
       ]
      },
@@ -978,41 +978,138 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((193, 32), (83, 32))"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 1#\n",
     "#Save the 'Name', 'state', and 'Region' columns from the train/test data into names_train and names_test\n",
     "#Then drop those columns from `X_train` and `X_test`. Use 'inplace=True'\n",
     "names_list = ['Name', 'state', 'Region']\n",
-    "names_train = X_train[___]\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": 14,
    "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": 14,
+     "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": 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 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": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "63.811088082901556"
+      ]
+     },
+     "execution_count": 16,
+     "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": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[63.81108808]])"
+      ]
+     },
+     "execution_count": 17,
+     "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": 18,
    "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": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1164,7 +1283,7 @@
        "array([63.81108808, 63.81108808, 63.81108808, 63.81108808, 63.81108808])"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1183,7 +1302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1192,7 +1311,7 @@
        "array([63.81108808, 63.81108808, 63.81108808, 63.81108808, 63.81108808])"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1211,7 +1330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1220,7 +1339,7 @@
        "0.0"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1245,7 +1364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1254,7 +1373,7 @@
        "-0.0031235200417913944"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1296,7 +1415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1311,14 +1430,14 @@
     "    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": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1327,7 +1446,7 @@
        "17.923463717146785"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1338,7 +1457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1347,7 +1466,7 @@
        "19.136142081278486"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1381,7 +1500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {
     "scrolled": true
    },
@@ -1398,14 +1517,14 @@
     "    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": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1414,7 +1533,7 @@
        "614.1334096969057"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1425,7 +1544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1434,7 +1553,7 @@
        "581.4365441953481"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1452,7 +1571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1461,7 +1580,7 @@
        "array([24.78171523, 24.11299534])"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 29,
      "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-18-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(value = X_defaults_median)\n",
+    "X_te = X_test.fillna(value = 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)"
+       "(0.8177988515690604, 0.7209725843435145)"
       ]
      },
-     "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, 9.40702011858132)"
+      ]
+     },
+     "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": [
+       "(111.89581253658478, 161.7315645119227)"
+      ]
+     },
+     "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": [
@@ -2064,16 +2248,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.8170154093990025, 0.716381471695996)"
+       "(0.8170154093990025, 0.7163814716959958)"
       ]
      },
-     "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)"
+       "(8.536884040670973, 9.416375625789277)"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2104,16 +2288,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(112.37695054778276, 164.3926930952436)"
+       "(112.37695054778276, 164.3926930952438)"
       ]
      },
-     "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)"
+       "(0.8177988515690604, 0.7209725843435145)"
       ]
      },
-     "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)"
+       "(0.8177988515690604, 0.7209725843435145)"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2317,7 +2514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -2326,7 +2523,7 @@
        "(8.547850301825427, 9.40702011858132)"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2346,7 +2543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -2355,7 +2552,7 @@
        "(8.547850301825427, 9.40702011858132)"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2366,16 +2563,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(111.89581253658478, 161.73156451192284)"
+       "(111.89581253658478, 161.7315645119227)"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2393,16 +2590,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(111.89581253658478, 161.73156451192284)"
+       "(111.89581253658478, 161.7315645119227)"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2449,7 +2646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2459,7 +2656,7 @@
     "pipe = make_pipeline(\n",
     "    SimpleImputer(strategy='median'), \n",
     "    StandardScaler(),\n",
-    "    ___(___),\n",
+    "    SelectKBest(f_regression),\n",
     "    LinearRegression()\n",
     ")"
    ]
@@ -2473,7 +2670,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -2482,11 +2679,11 @@
        "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 0x7febe8fed820>)),\n",
        "                ('linearregression', LinearRegression())])"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2504,7 +2701,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2514,16 +2711,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.7674914326052744, 0.6259877354190833)"
+       "(0.7674914326052744, 0.6259877354190837)"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2534,16 +2731,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(9.501495079727484, 11.201830190332057)"
+       "(9.501495079727484, 11.201830190332052)"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2568,7 +2765,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2577,7 +2774,7 @@
     "pipe15 = make_pipeline(\n",
     "    SimpleImputer(strategy='median'), \n",
     "    StandardScaler(),\n",
-    "    ___(___, k=___),\n",
+    "    SelectKBest(f_regression, k=15),\n",
     "    LinearRegression()\n",
     ")"
    ]
@@ -2591,7 +2788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -2601,11 +2798,11 @@
        "                ('standardscaler', StandardScaler()),\n",
        "                ('selectkbest',\n",
        "                 SelectKBest(k=15,\n",
-       "                             score_func=<function f_regression at 0x7fc8cd9c8950>)),\n",
+       "                             score_func=<function f_regression at 0x7febe8fed820>)),\n",
        "                ('linearregression', LinearRegression())])"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2623,7 +2820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2633,16 +2830,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.7924096060483825, 0.6376199973170795)"
+       "(0.7924096060483825, 0.6376199973170793)"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2653,16 +2850,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(9.211767769307116, 10.488246867294356)"
+       "(9.211767769307114, 10.48824686729436)"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2689,7 +2886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2698,7 +2895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -2707,7 +2904,7 @@
        "array([0.63760862, 0.72831381, 0.74443537, 0.5487915 , 0.50441472])"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2726,16 +2923,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(0.6327128053007867, 0.09502487849877672)"
+       "(0.6327128053007862, 0.09502487849877704)"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2753,7 +2950,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -2762,7 +2959,7 @@
        "array([0.44, 0.82])"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2797,14 +2994,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 80,
    "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": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Code task 18#\n",
     "#Call `pipe`'s `get_params()` method to get a dict of available parameters and print their names\n",
     "#using dict's `keys()` method\n",
-    "pipe.___.keys()"
+    "pipe.get_params().keys()"
    ]
   },
   {
@@ -2816,7 +3024,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2834,7 +3042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2843,7 +3051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
@@ -2854,7 +3062,7 @@
        "                                        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 0x7febe8fed820>)),\n",
        "                                       ('linearregression',\n",
        "                                        LinearRegression())]),\n",
        "             n_jobs=-1,\n",
@@ -2864,7 +3072,7 @@
        "                                            30, ...]})"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2875,7 +3083,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2886,24 +3094,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 85,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'selectkbest__k': 8}"
+      ]
+     },
+     "execution_count": 85,
+     "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": 86,
    "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 +3160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2944,9 +3176,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 89,
    "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": 89,
+     "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 +3206,7 @@
     "#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 +3241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3000,9 +3251,9 @@
     "#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 +3266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3023,12 +3274,12 @@
     "#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=None)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
@@ -3037,7 +3288,7 @@
        "array([0.69249204, 0.78061953, 0.77546915, 0.62190924, 0.61742339])"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3049,7 +3300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [
     {
@@ -3058,7 +3309,7 @@
        "(0.6975826707112506, 0.07090742940774528)"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3083,7 +3334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
@@ -3113,7 +3364,7 @@
        " 'simpleimputer__strategy': ['mean', 'median']}"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3130,37 +3381,75 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 98,
    "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=None, n_jobs=-1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 99,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(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": 99,
+     "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": 100,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'randomforestregressor__n_estimators': 69,\n",
+       " 'simpleimputer__strategy': 'median',\n",
+       " 'standardscaler': None}"
+      ]
+     },
+     "execution_count": 100,
+     "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 +3461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [
     {
@@ -3181,7 +3470,7 @@
        "array([0.6951357 , 0.79430697, 0.77170917, 0.62254707, 0.66499334])"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3194,7 +3483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [
     {
@@ -3203,7 +3492,7 @@
        "(0.7097384501425082, 0.06451341966873386)"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3221,9 +3510,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 105,
    "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 +3534,8 @@
     "#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 +3585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3294,16 +3596,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(10.499032338015297, 1.6220608976799646)"
+       "(10.499032338015297, 1.622060897679967)"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3316,16 +3618,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "11.793465668669327"
+       "11.793465668669326"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3343,7 +3645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3353,7 +3655,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [
     {
@@ -3362,7 +3664,7 @@
        "(9.644639167595688, 1.3528565172191818)"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3375,7 +3677,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
@@ -3384,7 +3686,7 @@
        "9.537730050637332"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3423,7 +3725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3437,12 +3739,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -3477,7 +3779,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3492,19 +3794,28 @@
     "#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 = '1.0'\n",
+    "best_model.pandas_version = pd.__version__\n",
+    "best_model.numpy_version = np.__version__\n",
+    "best_model.sklearn_version = sklearn_version\n",
     "best_model.X_columns = [col for col in X_train.columns]\n",
-    "best_model.build_datetime = ___"
+    "best_model.build_datetime = datetime.datetime.now()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 115,
    "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",
@@ -3530,8 +3841,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**A: 1** Your answer here"
+    "**A: 1** \n",
+    "\n",
+    "**Pre-Processing and Training Data in summary**\n",
+    "\n",
+    "- A simple prediction such as the average or median ticket price provides the baseline performance which helps us to compare against any other machine learning algorithm. Baselines help us to judge how much better a more advanced model is in terms of accuracy.\n",
+    "The average ticket price of the training set is $63.8,  Mean absolute error(MAE)  and Mean Squared Error(MSE) on the test set is 17.9 and 581.4 respectively. \n",
+    "\n",
+    "\n",
+    "- A linear model predicts the ticket price as a linear combination of features based on the learning process on training data. Missing values are replaced with the median for each feature. Using GridSearchCV, the best hyperparameters that maximized the performance of the model on the test set are 8. The most useful 8 features are `vertical_drop` `Snow Making_ac` `total_chairs` `fastQuads` `Runs` `LongestRun_mi` `trams` and `SkiableTerrain_ac`. The performance of the model on the test set is consistent with the cross-validation result. However,  The cross-validation method gives a more accurate estimate of the model’s performance than the train test split.\n",
+    "\n",
+    "- According to results using RandomForestRegressor, the top 4 important features are `fastQuads`, `Runs`,  `Snow Making_ac`, and `vertical_drop`.\n",
+    "\n",
+    "- Comparing two different model performances, MAE of the linear regression model on the test set is 11.9 and one of the random forest regression is 9.5. Thus, the random forest model shows a better performance than the linear regression model since it has a lower MAE, and also less variability.\n",
+    "\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -3550,7 +3881,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.8.8"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/Notebooks/05_modeling.ipynb b/Notebooks/05_modeling.ipynb
index 4e4008174..4cb777554 100644
--- a/Notebooks/05_modeling.ipynb
+++ b/Notebooks/05_modeling.ipynb
@@ -222,11 +222,11 @@
        "    </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 +234,35 @@
        "    </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 +270,11 @@
        "    </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 +298,11 @@
        "    </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 +328,27 @@
        "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 +586,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -621,7 +621,7 @@
     "        ski_x = ski_data.loc[ski_data.state == state, feat_name]\n",
     "    ski_x = ski_x[np.isfinite(ski_x)]\n",
     "    plt.hist(ski_x, bins=30)\n",
-    "    plt.___(x=big_mountain[feat_name].___, c=___, ls=___, alpha=0.8, label=___)\n",
+    "    plt.axvline(x=big_mountain[feat_name].values, c='r', ls='--', alpha=0.8, label='Big Mountain')\n",
     "    plt.xlabel(description)\n",
     "    plt.ylabel('frequency')\n",
     "    plt.title(description + ' distribution for resorts in market share')\n",
@@ -644,12 +644,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -666,12 +666,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -695,12 +695,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -731,12 +731,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -767,12 +767,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -803,12 +803,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -839,12 +839,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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PPf/884wdO5Zly5axcOFCfv3rX79mm/PPP58NNtiAO+64g7lz5zJ27Nguy5s4cSIf/vCHWbFiBRdffDHTp0/nzDPPBIrkbM6cOdx+++08/vjjvO1tb2PChAndxrjxxhtz2223cf7553POOecwY8YMPvKRjzB8+HBOOukkAJ588kl+//vfExHMmDGDs88+m6985SuvKWvhwoXccMMNzJs3j0MPPbTHXbGNMhnrQ6OnXtXrMuZPO6gPIlFb8x5BkvrClCmvXbfffjBpUvE90/Efv1qHHFJMS5e+9ubT06d3e8jabsqbbrqJD3zgA8ydO3elbW644QY+8YlPALDTTjsxZsyYLssbMmQIe+65J5dccgnPP/88o0ePXqmcY445hiFDhjBy5Eje+c53csstt7DeeuutMsaO1rW3vvWtXH755XW3WbBgAUcddRQLFy7kxRdf7PLWFIcffjive93r2HHHHV9pPWsGuyklSVKPvf3tb+fxxx/nscceW2l97YD8Rhx99NF8/OMf58gjj2yonDXWWIOXX375leXOt5ZYa621gCLRe+mll+qW8fGPf5wTTjiBO++8k+985ztd3p6io6xVxdMXbBmTJKk/WlVL1rBhq35//fUbaglblXnz5rFixQo22mgjnnvuuVfW77nnnlx66aXss88+3H333dx5552rLGevvfbi1FNP5Zhjjllp/YQJE/jOd77D5MmTeeKJJ7j++uv58pe/zPLly7n77rt54YUXWLZsGbNmzWLPPfdc5THWXXddnnrqqVeW//a3v7H55psDMHPmzJ6eep8zGZMkSQ3pGDMGRUvRzJkzGTJkyErbfPSjH2Xy5MmMGTOGXXbZhTFjxvCGN7yhyzIj4pWxXLXe8573cNNNN7HzzjsTEZx99tm88Y1vBHjlBwLbbbcdu+yyS7dxH3LIIRxxxBFcccUVfOMb3+D0009n0qRJbL755uy+++488MADPamGPhfNbHbrK+PGjcuufonRThwzpoZceGExf//7q41DUr9yzz33sMMOO1QdRrdWrFjB8uXLGTZsGH/+85+ZOHEi9913H2uuuWbVoTVFvb9LRNyameMaLcOWManVTMIkDWDPPfcc++yzD8uXLycz+da3vjVgE7G+YjImSZL6zLrrrtvlfcVUn7+mlFptypT6P0mXJA1KJmOSJPUT/WGc92DSV38PkzFJkvqBYcOGsWTJEhOyNpGZLFmyhGHDhvW6rKaNGYuI7YFLalZtA3wO+O9y/WhgPnBkZj7ZrDgkSRoItthiCxYsWPCam6yqOsOGDWOLLbbodTlNS8Yy815gLEBEDAEeAX4KTAVmZea0iJhaLn+6WXFIkjQQDB06tMvH9qh/a9WvKScCf87MByPiMGDvcv1M4DpMxjSY7Ldf1RFIktpIq5Kxo4Efla9HZuZCgMxcGBGbtCgGqT1MmlR1BJKkNtL0AfwRsSZwKPDjHu43JSJmR8Rs+8c1oCxbVkySJNGaX1MeCNyWmYvK5UURsSlAOV9cb6fMnJ6Z4zJz3IgRI1oQptQiJ55YTJIk0Zpk7Bhe7aIEuBKYXL6eDFzRghgkSZLaUlOTsYh4PbAfcHnN6mnAfhHxp/K9ac2MQZIkqZ01dQB/Zj4HbNRp3RKKX1dKkiQNet6BX5IkqUKturWFpA6HHFJ1BJKkNmIyJrWayZgkqYbdlFKrLV1aTJIkYcuY1HqnnFLMp0+vNg5JUluwZUySJKlCJmOSJEkVMhmTJEmqkMmYJElShRzAL7XaEUdUHYEkqY2YjEmttv/+VUcgSWojdlNKrbZoUTFJkoQtY1Lrffazxdz7jEmSsGVMkiSpUiZjkiRJFTIZkyRJqpDJmCRJUoUcwC+12rHHVh2BJKmNmIxJrTZhQtURSJLaiN2UUqs9+GAxSZKELWNS6511VjH3PmOSJGwZkyRJqpTJmCRJUoVMxiRJkipkMiZJklQhB/BLrXb88VVHIElqI01NxiJifWAGsBOQwD8D9wKXAKOB+cCRmflkM+OQ2sr48VVHIElqI83upvxP4FeZ+WZgZ+AeYCowKzO3A2aVy9Lgcd99xSRJEk1MxiJiPWAC8D2AzHwxM5cChwEzy81mAoc3KwapLZ1zTjFJkkRzW8a2AR4Dvh8Rf4yIGRGxDjAyMxcClPNNmhiDJElSW2tmMrYGsCvwrczcBXiWHnRJRsSUiJgdEbMfe+yxZsUoSZJUqWYmYwuABZn5h3L5MorkbFFEbApQzhfX2zkzp2fmuMwcN2LEiCaGKUmSVJ2mJWOZ+Vfg4YjYvlw1EbgbuBKYXK6bDFzRrBgkSZLaXbPvM/Zx4AcRsSbwF+BDFAngpRFxHPAQMKnJMUjt5YQTqo5AktRGmpqMZeYcYFydtyY287hSWxszpuoIJEltxMchSa12xx3FJEkSPg5Jar3zzivm06dXG4ckqS3YMiZJklQhkzFJkqQKmYxJkiRVyGRMkiSpQg7gl1rtpJOqjkCS1EZMxqRWe9Obqo5AktRG7KaUWu3mm4tJkiRsGZNab8aMYj5+fLVxSJLagi1jkiRJFTIZkyRJqpDJmCRJUoVMxiRJkirkAH6p1U47reoIJEltxGRMarWttqo6AklSG7GbUmq1668vJkmSsGVMar2LLirmEyZUG4ckqS3YMiZJklQhkzFJkqQKmYxJkiRVyGRMkiSpQg7gl1rtzDOrjkCS1EZMxqRWGzmy6ggkSW3Ebkqp1a65ppgkScKWMan1LrusmO+/f7VxSJLaQlOTsYiYDzwNrABeysxxEbEhcAkwGpgPHJmZTzYzDkmSpHbVim7KfTJzbGaOK5enArMycztgVrksSZI0KFUxZuwwYGb5eiZweAUxSJIktYVmJ2MJXBMRt0bElHLdyMxcCFDON2lyDJIkSW2r2QP435GZj0bEJsC1ETGv0R3L5G0KwKhRo5oVn9R6Z59ddQSSpDbS1JaxzHy0nC8GfgqMBxZFxKYA5XxxF/tOz8xxmTluxIgRzQxTaq311y8mSZJoYjIWEetExLodr4H9gbnAlcDkcrPJwBXNikFqSz/7WTFJkkRzuylHAj+NiI7j/DAzfxURtwCXRsRxwEPApCbGILWfjkTskEOqjUOS1Baaloxl5l+AneusXwJMbNZxJUmS+hMfhyRJklQhkzFJkqQKmYxJkiRVyAeFS6127rlVRyBJaiMmY1KrDRtWdQSSpDZiN6XUaj/+cTFJkoTJmNR6115bTJIkYTImSZJUKZMxSZKkCpmMSZIkVchkTJIkqULd3toiImYD36d40PeTzQ9JGuCmT686AklSG2mkZexoYDPgloi4OCLeFRHR5LgkSZIGhW6Tscy8PzNPA94E/BD4L+ChiDgjIjZsdoDSgHPhhcUkSRINjhmLiDHAV4AvAz8BjgCeAn7dvNCkAeq3vy0mSZJobMzYrcBS4HvA1Mx8oXzrDxHxjmYGJ0mSNNA18mzKSZn5l3pvZOZ7+zgeSZKkQaWRbsrjI2L9joWI2CAivtjEmCRJkgaNRpKxAzNzacdCeXuLdzcvJGmAGzasmCRJorFuyiERsVbHWLGIWBtYq7lhSQPYuedWHYEkqY00koxdBMyKiO8DCfwzMLOpUUmSJA0S3SZjmXl2RNwJTAQCODMzr256ZNJANWNGMT/++GrjkCS1hUZaxsjMXwK/bHIs0uBw883F3GRMkkRj9xl7L/AlYBOKlrEAMjPXa3Jsg9LoqVf1uoz50w7qg0gkSVIrNNIydjZwSGbe0+xgJEmSBptGbm2xyERMkiSpORppGZsdEZcA/wN0PAqJzLy8kQNExBBgNvBIZh5cPlz8EmA0MB84srx3mTQ4rL9+99tIkgaNRpKx9YDngP1r1iXQUDIGfAK4pywHYCowKzOnRcTUcvnTDZYl9X9nn111BJKkNtLIrS0+tLqFR8QWwEHAWcD/LVcfBuxdvp4JXIfJmCRJGqS6HTMWEW+KiFkRMbdcHhMR/9Zg+V8HTgFerlk3MjMXApTzTXoYs9S/nXdeMUmSRGMD+L8LnAosB8jMO4Cju9spIg4GFmfmrasTWERMiYjZETH7scceW50ipPZ0xx3FJEkSjSVjr8/Mmzute6mB/d4BHBoR84GLgX+IiIuARRGxKUA5X1xv58ycnpnjMnPciBEjGjicJElS/9NIMvZ4RPwdxaB9IuIIYGF3O2XmqZm5RWaOpmhJ+3VmHgtcCUwuN5sMXLE6gUuSJA0Ejfya8mPAdODNEfEI8ABwbC+OOQ24NCKOAx4CJvWiLEmSpH6tkV9T/gXYNyLWAV6XmU/39CCZeR3FrybJzCUUDx2XBqeRI6uOQJLURhp5NuXnOi0DkJlfaFJM0sB25plVRyBJaiONdFM+W/N6GHAwxU1cJUmS1EuNdFN+pXY5Is6hGIQ/oIyeelXVIWiw+Er5kfrUp6qNQ5LUFhppGevs9cA2fR2INGjce2/VEUiS2kgjY8bupLytBTAEGAE4XkySJKkPNNIydnDN65eARZnZyE1fJUmS1I1GkrHOt7JYr+MXlQCZ+USfRiRJkjSINJKM3QZsCTwJBLA+xc1aoei+dPyY1BNbbVV1BJKkNtJIMvYr4MrM/AVARBwI7JuZ/hRMWh2nnVZ1BJKkNtLIsynf1pGIAWTmL4F3Ni8kSZKkwaORlrHHI+LfgIsouiWPBZY0NSppIDvrrGJuC5kkicZaxo6huJ3FT8tpRLlO0up48MFikiSJxu7A/wTwiYgYnpnPtCAmSZKkQaPblrGI2CMi7gbuLpd3jojzmx6ZJEnSINBIN+XXgHdRjhPLzNuBCc0MSpIkabBo6NmUmflw7Y1egRXNCUcaBLbfvuoIJEltpJFk7OGI2APIiFgTOBG4p7lhSQPYp7xFnyTpVY10U34E+BiwObAAGFsuS5IkqZdW2TIWEUOAr2fm+1oUjzTwffazxfzMM6uNQ5LUFlaZjGXmiogYERFrZuaLrQpKGtAWLao6AklSG2lkzNh84MaIuBJ4tmNlZn61WUFJkiQNFl2OGYuIC8uXRwE/L7ddt2aSJElSL62qZeytEbEV8BDwjRbFI0mSNKisKhn7NvArYGtgds36oHhg+DZNjEsauMaMqToCSVIb6TIZy8xzgXMj4luZ+a8tjEka2E44oeoIJEltpNv7jJmISZIkNU8jN31dLRExLCJujojbI+KuiDijXL9hRFwbEX8q5xs0KwapLZ1ySjFJkkQTkzHgBeAfMnNnirv2HxARuwNTgVmZuR0wq1yWBo+lS4tJkiSamIxl4ZlycWg5JXAYMLNcPxM4vFkxSJIktbtmtowREUMiYg6wGLg2M/8AjMzMhQDlfJNmxiBJktTOGrkD/2rLzBXA2IhYH/hpROzU6L4RMQWYAjBq1KgmRTgwjZ56Va/LmD/toD6IRJIkdaepLWMdMnMpcB1wALAoIjYFKOeLu9hnemaOy8xxI0aMaEWYUmuMH19MkiTR3F9TjihbxIiItYF9gXnAlcDkcrPJwBXNikFqS8cfX0ySJNHcbspNgZkRMYQi6bs0M38eETcBl0bEcRSPWprUxBgkSZLaWtOSscy8A9ilzvolwMRmHVdqeyeeWMzPPbfaOCRJbaGpA/gl1bFsWdURSJLaSEsG8EuSJKk+kzFJkqQKmYxJkiRVyDFjUqvttVfVEUiS2ojJmOryLv5N9P73Vx2BJKmN2E0pSZJUIZMxqdWmTCkmSZIwGZMkSaqUyZgkSVKFTMYkSZIqZDImSZJUIW9tIbXafvtVHYEkqY2YjEmtNmlS1RFIktqI3ZRSqy1bVkySJGHLmNR6J55YzKdPrzYOSVJbsGVMkiSpQiZjkiRJFTIZkyRJqpDJmCRJUoUcwC+12iGHVB2BJKmNmIxJrWYyJkmqYTel1GpLlxaTJEnYMia13imnFHPvMyZJwpYxSZKkSpmMSZIkVahpyVhEbBkRv4mIeyLiroj4RLl+w4i4NiL+VM43aFYMkiRJ7a6ZLWMvAZ/KzB2A3YGPRcSOwFRgVmZuB8wqlyVJkgalpg3gz8yFwMLy9dMRcQ+wOXAYsHe52UzgOuDTzYpDajtHHFF1BJKkNtKSX1NGxGhgF+APwMgyUSMzF0bEJq2IQWob++9fdQSSpDbS9GQsIoYDPwE+mZlPReZlifsAAAx7SURBVESj+00BpgCMGjWqeQGqaUZPvarXZcyfdlAfRNJmFi0q5iNHVhuHJKktNPXXlBExlCIR+0FmXl6uXhQRm5bvbwosrrdvZk7PzHGZOW7EiBHNDFNqrc9+tpgkSaK5v6YM4HvAPZn51Zq3rgQml68nA1c0KwZJkqR218xuyncA7wfujIg55brPANOASyPiOOAhYFITY5AkSWprzfw15Q1AVwPEJjbruJIkSf2Jd+CXJEmqkA8Kl1rt2GOrjkCS1EZMxqRWmzCh6ggkSW3Ebkqp1R58sJgkScKWMan1zjqrmE+fXm0ckqS2YMuYJElShUzGJEmSKmQyJkmSVCGTMUmSpAo5gF9qteOPrzoCSVIbMRmTWm38+KojkCS1EbsppVa7775ikiQJW8ak1jvnnGLufcYkSdgyJkmSVCmTMUmSpAqZjEmSJFXIZEySJKlCDuCXWu2EE6qOQJLURkzGpFYbM6bqCCRJbcRuSqnV7rijmCRJwpYxqfXOO6+Ye58xSRK2jEmSJFXKZEySJKlCJmOSJEkVMhmTJEmqkAP4pVY76aSqI5AktZGmtYxFxH9FxOKImFuzbsOIuDYi/lTON2jW8aW29aY3FZMkSTS3m/IC4IBO66YCszJzO2BWuSwNLjffXEySJNHEbsrMvD4iRndafRiwd/l6JnAd8OlmxSC1pRkzivn48dXGIUlqC60ewD8yMxcClPNNWnx8SZKkttK2A/gjYgowBWDUqFEVR6OqjJ56Va/LmD/toD6IpHdqz+Pfb34IgM/08Nza4TwkSX2v1S1jiyJiU4ByvrirDTNzemaOy8xxI0aMaFmAkiRJrdTqZOxKYHL5ejJwRYuPL0mS1Faa1k0ZET+iGKy/cUQsAD4PTAMujYjjgIeASc06vtSuvvn2o6oOQZLURpr5a8pjunhrYrOOKfUHj7zB361Ikl7l45CkFhv/8FzGPzy3+w0lSYNC2/6aUhqoDr/rNwDcvOVOFUciSWoHtoxJkiRVyGRMkiSpQiZjkiRJFTIZkyRJqpAD+KUW++pex1YdgiSpjZiMSS32+DobVB2CJKmN2E0ptdheD9zGXg/cVnUYkqQ2YcuY1IDRU6/qs7IOvPdGAH679a59VqYkqf+yZUySJKlCJmOSJEkVsptSA15fdjFKktTXbBmTJEmqkC1jUotN2/tDVYcgSWojJmNSiz01bHjVIUiS2ojJmNRiE+//AwCztt2t4kiq0xfj+OZPO6gPIpGk6jlmTGqxifffzMT7b646DElSmzAZkyRJqpDJmCRJUoVMxiRJkipkMiZJklQhf00ptdgZ+/5L1SFIktqIyZjUYi+sseZq7eftIFZmfUgaKOymlFrs3fNu4N3zbqg6DElSmzAZk1psz/l/ZM/5f6w6DElSm6ikmzIiDgD+ExgCzMjMaVXEIQ02fdG1N5DY1Sn1LwP1M9vylrGIGAJ8EzgQ2BE4JiJ2bHUckiRJ7aCKbsrxwP2Z+ZfMfBG4GDisgjgkSZIqV0UytjnwcM3ygnKdJEnSoFPFmLGosy5fs1HEFGBKufhMRNzbx3FsDDzex2UOZNZXz3RZX+/reHH71S0Lph/ot9dXfKmSw/bb+qqQddYzA7a+mvSZ7VxfW/Vk5yqSsQXAljXLWwCPdt4oM6cD05sVRETMzsxxzSp/oLG+esb66hnrq2esr56zznrG+uqZ3tZXFd2UtwDbRcTWEbEmcDRwZQVxSJIkVa7lLWOZ+VJEnABcTXFri//KzLtaHYckSVI7qOQ+Y5n5C+AXVRy7RtO6QAco66tnrK+esb56xvrqOeusZ6yvnulVfUXma8bOS5IkqUV8HJIkSVKFBl0yFhEHRMS9EXF/REytOp52FBHzI+LOiJgTEbPLdRtGxLUR8adyvkHVcVYpIv4rIhZHxNyadV3WUUScWl5z90bEu6qJujpd1NfpEfFIeZ3NiYh317w32Otry4j4TUTcExF3RcQnyvVeY3Wsor68xuqIiGERcXNE3F7W1xnleq+vOlZRX313fWXmoJkofjDwZ2AbYE3gdmDHquNqtwmYD2zcad3ZwNTy9VTgS1XHWXEdTQB2BeZ2V0cUj/26HVgL2Lq8BodUfQ5tUF+nAyfV2db6gk2BXcvX6wL3lfXiNdaz+vIaq19fAQwvXw8F/gDs7vXV4/rqs+trsLWM+Sim1XcYMLN8PRM4vMJYKpeZ1wNPdFrdVR0dBlycmS9k5gPA/RTX4qDRRX11xfrKXJiZt5WvnwbuoXhSiddYHauor64M9vrKzHymXBxaTonXV12rqK+u9Li+Blsy5qOYGpPANRFxa/kkBICRmbkQii8+YJPKomtfXdWR113XToiIO8puzI4uEeurRkSMBnah+N+411g3OtUXeI3VFRFDImIOsBi4NjO9vlahi/qCPrq+Blsy1tCjmMQ7MnNX4EDgYxExoeqA+jmvu/q+BfwdMBZYCHylXG99lSJiOPAT4JOZ+dSqNq2zbtDVWZ368hrrQmauyMyxFE/BGR8RO61ic+urfn312fU12JKxhh7FNNhl5qPlfDHwU4rm1UURsSlAOV9cXYRtq6s68rqrIzMXlV9wLwPf5dVmfOsLiIihFInFDzLz8nK111gX6tWX11j3MnMpcB1wAF5f3aqtr768vgZbMuajmLoREetExLodr4H9gbkU9TS53GwycEU1Eba1ruroSuDoiFgrIrYGtgNuriC+ttLxpV96D8V1BtYXERHA94B7MvOrNW95jdXRVX15jdUXESMiYv3y9drAvsA8vL7q6qq++vL6quQO/FVJH8XUiJHAT4vvNtYAfpiZv4qIW4BLI+I44CFgUoUxVi4ifgTsDWwcEQuAzwPTqFNHmXlXRFwK3A28BHwsM1dUEnhFuqivvSNiLEXz/XzgX8D6Kr0DeD9wZzlOBeAzeI11pav6OsZrrK5NgZkRMYSiUebSzPx5RNyE11c9XdXXhX11fXkHfkmSpAoNtm5KSZKktmIyJkmSVCGTMUmSpAqZjEmSJFXIZEySJKlCJmOSGhYRG0XEnHL6a0Q8UrO8ZqdtPxkRr2+gzOsiYlzzon7N8U6PiJNacJy9IuKusm7WbvbxJPVfJmOSGpaZSzJzbPlYkG8DX+tYzswXO23+SaDbZKw/iUKj35vvA84p6+b5PihP0gDll4CkXomIiRHxx4i4s3xY7loRcSKwGfCbiPhNud23ImJ22Vp0RgPlzo+IMyLitrLsN5frV2rZioi5ETG6nOZFxIxy3Q8iYt+IuDEi/hQR42uK3zkifl2u/3BNWSdHxC1RPPj3jHLd6Ii4JyLOB25j5cecdHX+xwNHAp+LiB902v415UXEMzXvHxERF5SvL4iIcyPidxHxl4g4oly/aURcX7a6zY2Ivbr/S0lqVyZjknpjGHABcFRmvoXiqQ3/mpnnUjyLbZ/M3Kfc9rTMHAeMAd4ZEWMaKP/x8qH13wIa6VrcFvjP8hhvBv4J2LPc9zM1240BDgLeTpEwbRYR+1M8tmQ8xYN/3xoRE8rttwf+OzN3ycwHOwqJiK7OfwbFI1FOzsz31Ymzbnld2LQ8h4Mp7sBPeV5Xly2UOwNzuthXUj9gMiapN4YAD2TmfeXyTGBCF9seGRG3AX8E/h7YsYHyOx6QfSswuoHtH8jMO8sH994FzMriMSN3dtr/isx8PjMfB35DkYDtX05/pGixejNFcgbwYGb+vs7xtqfx86/VVXn1/E9mvpyZd1M8rgyK5+x+KCJOB96SmU83WJakNmQyJqk3nm1ko/JhuScBEzNzDHAVRatad14o5yt49Vm6L7Hyd9ewOtsDvFyz/DIrP4u383PgEgjgP2rGwG2bmd8r3+/qPKP7U6irc3m18XSul9pzCoDMvJ4i6XsEuDAiPrCacUhqAyZjknpjGDA6IrYtl98P/G/5+mlg3fL1ehQJyN8iYiRwYC+OOR/YFSAidgW2Xo0yDouIYRGxEcUDzG8Brgb+OSKGl2VvHhGbdFPOPLo+/55YFBE7lIP539PdxhGxFbA4M78LfI+yPiT1T2t0v4kkdWkZ8CHgxxGxBkVS8+3yvenALyNiYWbuExF/pOg6/AtwYy+O+RPgAxExpzzefd1sX8/NFK1zo4AzM/NR4NGI2AG4KSIAngGOpWiVqyszl0VEV+ffE1OBnwMPA3OB4d1svzdwckQsL+O0ZUzqx6IYTiFJkqQq2E0pSZJUIZMxSZKkCpmMSZIkVchkTJIkqUImY5IkSRUyGZMkSaqQyZgkSVKFTMYkSZIq9P8BjE0SPCbVyaoAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -875,12 +875,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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8UFeMklSfqVOrQRolxowZw+TJk9l0003ZfPPN+d3vfgfAfffdxx577DGobW277bast956tD4zerfddmPFFVcc1pgBLrvsshdjXZTzzjuP6dOnD/v+W9XZx2s2MDszryrTZ1ElXnMjYq3MnBMRawH397VyZs4AZgBMmTLFJ3lLGnkOPLDpCKRhtdxyy3HDDTcA8Mtf/pIjjzySX//616y99tqcddZZg97e+PHj+e1vf8vWW2/NvHnzmDNnzjBHXLnssstYccUV2XLLLRe53K677squu+5aSww9aqvxysy/A/dGxEZl1jTgNuA84IAy7wDg3LpikCRJ9XjsscdYddVVAZg1axabbLIJAE899RR77bUXkyZNYu+99+ZNb3pTv0+f2WeffTj99NMBOPvss9l9991ffC8z+fSnP80mm2zCG97wBs444wygSqJ23nnnF5c7+OCDOeWUU4DqSTdf+MIX2HzzzXnDG97AHXfcwaxZs/j2t7/N17/+dSZPnsxvfvMbzj//fN70pjex2Wabsf322zN37lwATjnlFA4uNz5+3/vex6GHHsqWW27Jq171qiElln2p+6rGQ4Aflisa7wLeT5XsnRkRHwTuAfasOQZJasahh1bj449vNg5pmDz99NNMnjyZ+fPnM2fOHC699NKXLHPCCSew6qqrctNNN3HLLbcwefLkfrc3bdo0PvShD/H8889z+umnM2PGDI4++migSsRuuOEGbrzxRh588EHe+MY3ss022wwY4+qrr87111/PCSecwHHHHcfJJ5/Mhz/8YVZccUUOO+wwAB555BF+//vfExGcfPLJHHvssXz1q199ybbmzJnDFVdcwR133MGuu+466ObUvtSaeGXmDcCUPt6aVud+1R0mHvGzxd7GrOk7DUMkUkO835LqdNBBL523ww6w557Vd68n8W+1yy7VMG/eS2/uO2PGgLtsbWq88soree9738stt9yy0DJXXHEFH//4xwHYZJNNmDRpUr/bGzNmDFtvvTVnnHEGTz/9NBMnTlxoO/vuuy9jxoxhwoQJvO1tb+Oaa65h5ZVXXmSMPbVmW2yxBWeffXafy8yePZu9996bOXPm8Oyzz/Z7O4jddtuNpZZaio033vjFWrHF5Z3rJUnSoL3lLW/hwQcfpPcFcK2d5duxzz77cMghh7DXXnu1tZ2ll16aF1544cXp3rdzWHbZZYEqqXvuuef63MYhhxzCwQcfzM0338xJJ53U7y0hera1qHgGy4dkS5I0Ei2qhmrcuEW/P358WzVci3LHHXfw/PPPs9pqq/HUU0+9OH/rrbfmzDPPZLvttuO2227j5ptvXuR23vrWt3LkkUey7777LjR/m2224aSTTuKAAw7g4Ycf5vLLL+crX/kKCxYs4LbbbuOZZ55h/vz5XHLJJWy99daL3MdKK63EY4899uL0o48+yjrrrAPAzJkz+1utFiZekiSpLT19vKCqAZo5cyZjxoxZaJmPfvSjHHDAAUyaNInNNtuMSZMmscoqq/S7zYh4se9Vq3/+53/myiuvZNNNNyUiOPbYY3n5y18O8GLn/Q022IDNNttswLh32WUX9thjD84991y++c1vctRRR7Hnnnuyzjrr8OY3v5m//vWvgyiFxRPDVXVWpylTpmR/V0Soe9nHS0u8U0+txvvv32wcGhVuv/12Xve61zUdxoCef/55FixYwLhx4/jLX/7CtGnTuPPOO1lmmWWaDq0WfX0uEXFdZvbVx90aL0mqjQmXlkBPPfUU2223HQsWLCAzOfHEE0dt0jUUJl6SJGnYrLTSSv3et0te1ShJ9TnooL4v+Ze0xDLxkiRphBgJ/bKXJEP5PEy8JEkaAcaNG8dDDz1k8tUlMpOHHnqIcePGDWo9+3hJkjQCrLvuusyePfslNyxVc8aNG8e66647qHVMvCRJGgHGjh3b76NtNHKYeElSXXbYoekIJHUZEy9JqsueezYdgaQuY+d6SarL/PnVIEmFNV6SVJdDD63Gi/kwYkmjhzVekiRJHWLiJUmS1CEmXpIkSR1i4iVJktQhdq6XpLrsskvTEUjqMiZeklQXEy9JvdjUKEl1mTevGiSpsMZLkupy+OHV2Pt4SSqs8ZIkSeoQEy9JkqQOMfGSJEnqEBMvSZKkDrFzvSTVZY89mo5AUpcx8ZKkuuy4Y9MRSOoyNjVKUl3mzq0GSSqs8ZKkunz+89XY+3hJKqzxkiRJ6hATL0mSpA4x8ZIkSeoQEy9JkqQOqbVzfUTMAh4Hngeey8wpEfEy4AxgIjAL2CszH6kzDklqxH77NR2BpC7TiRqv7TJzcmZOKdNHAJdk5gbAJWVakkafbbapBkkqmmhqfDcws7yeCezWQAySVL+7764GSSrqTrwSuCgirouIg8q8CZk5B6CM1+xrxYg4KCKujYhrH3jggZrDlKQaHHNMNUhSUfcNVLfKzPsiYk3g4oi4o90VM3MGMANgypQpWVeAkiRJnVJrjVdm3lfG9wPnAFOBuRGxFkAZ319nDJIkSd2itsQrIlaIiJV6XgM7ArcA5wEHlMUOAM6tKwZJkqRuUmdT4wTgnIjo2c+PMvMXEXENcGZEfBC4B9izxhgkSZK6Rm2JV2beBWzax/yHgGl17VeSusaBBzYdgaQuU3fneklack2d2nQEkrqMjwySpLrceWc1SFJhjZck1eW446rxjBnNxiGpa1jjJUmS1CEmXpIkSR1i4iVJktQhJl6SJEkdYud6SarLwQc3HYGkLmPiJUl1mTSp6QgkdRmbGiWpLjfdVA2SVFjjJUl1+da3qrH38ZJUWOMlSZLUISZekiRJHWLiJUmS1CEmXpIkSR1i53pJqsthhzUdgaQuY+IlSXXZcMOmI5DUZWxqlKS6XH11NUhSYY2XJNXl5JOr8dSpzcYhqWtY4yVJktQhJl6SJEkdYuIlSZLUISZekiRJHWLnekmqy2c/23QEkrqMiZck1WX99ZuOQFKXMfHqMhOP+Nlib2PW9J2GIRJJi+3yy6vxNts0G4ekrmHiJUl1Oe20amziJamwc70kSVKHmHhJkiR1iImXJElSh5h4SZIkdYid6yWpLkcf3XQEkrqMiZck1WXChKYjkNRlbGqUpLpcdFE1SFJhjZck1eWss6rxjjs2G4ekrlF7jVdEjImIP0TEBWX6ZRFxcUT8qYxXrTsGSZKkbtCJpsaPA7e3TB8BXJKZGwCXlGlJkqRRr9bEKyLWBXYCTm6Z/W5gZnk9E9itzhgkSZK6Rd01Xt8ADgdeaJk3ITPnAJTxmn2tGBEHRcS1EXHtAw88UHOYkiRJ9autc31E7Azcn5nXRcS2g10/M2cAMwCmTJmSwxudJHXAscc2HYGkLlPnVY1bAbtGxLuAccDKEXEaMDci1srMORGxFnB/jTFIUnPGj286Akldpramxsw8MjPXzcyJwD7ApZm5H3AecEBZ7ADg3LpikKRGnX9+NUhS0cQNVKcDO0TEn4AdyrQkjT4mXpJ66cgNVDPzMuCy8vohYFon9itJktRNfGSQJElSh5h4SZIkdYiJlyRJUof4kGxJqsvxxzcdgaQuY+IlSXUZN67pCCR1GZsaJakuP/lJNUhSYeIlSXW5+OJqkKTCxEuSJKlDTLwkSZI6xMRLkiSpQ0y8JEmSOmTA20lExLXA94EfZeYj9YckSaPEjBlNRyCpy7RT47UPsDZwTUScHhFvj4ioOS5JkqRRZ8DEKzP/nJmfBTYEfgR8D7gnIr4YES+rO0BJGrFOPbUaJKloq49XREwCvgp8BfgpsAfwGHBpfaFJ0gj3m99UgyQV7fTxug6YB3wXOCIznylvXRURW9UYmyRJ0qjSzrMa98zMu/p6IzN3H+Z4JEmSRq12mhoPjIjxPRMRsWpEfKm+kCRJkkandhKvd2bmvJ6JckuJd9UWkSSNFuPGVYMkFe00NY6JiGV7+nZFxHLAsvWGJUmjwPHHNx2BpC7TTuJ1GnBJRHwfSOADwMxao5IkSRqFBky8MvPYiLgZmAYEcHRm/rL2yCRppDv55Gp84IHNxiGpa7RT40Vm/hz4ec2xSNLocvXV1djES1IxYOf6iNg9Iv4UEY9GxGMR8XhEPNaJ4CRJkkaTdmq8jgV2yczb6w5GkiRpNGvndhJzTbokSZIWXzs1XtdGxBnA/wI9jwsiM8+uKyhJGhXGj286Akldpp3Ea2XgKWDHlnkJmHhJ0qIce2zTEUjqMu3cTuL9nQhEkiRptGvnqsYNI+KSiLilTE+KiM/VH5okjXDf+lY1SFLRTuf67wBHAgsAMvMmYJ86g5KkUeGmm6pBkop2Eq/lM/PqXvOeqyMYSZKk0aydxOvBiHg1VYd6ImIPYE6tUUmSJI1C7VzV+DFgBvDaiPgb8Fdgv1qjkiRJGoXauarxLmD7iFgBWCozH29nwxExDrgcWLbs56zM/EJEvAw4A5gIzAL2ysxHhha+JHWxCROajkBSlxkw8YqIf+81DUBm/scAqz4D/FNmPhERY4ErIuLnwO7AJZk5PSKOAI4APjOU4CWpqx19dNMRSOoy7fTxerJleB54J1Vt1SJl5YkyObYMCbwbmFnmzwR2G1TEkiRJI1Q7TY1fbZ2OiOOA89rZeESMAa4DXgP8T2ZeFRETMnNO2faciFhz8GFL0gjw1XL6/NSnmo1DUtdop3N9b8sDr2pnwcx8HpgcEeOBcyJik3Z3EhEHAQcBrLfeekMIs/MmHvGzpkOQ1E3++MemI5DUZdrp43Uz5VYSwBhgDWCg/l0Lycx5EXEZ8A5gbkSsVWq71gLu72edGVRXUzJlypTsaxlJkqSRpJ0ar51bXj8HzM3MAW+gGhFrAAtK0rUcsD3wZapmygOA6WV87qCjliRJGoHaSbx63z5i5Z4rGwEy8+F+1lsLmFn6eS0FnJmZF0TElcCZEfFB4B5gz8GHLUmSNPK0k3hdD7wCeAQIYDxVwgRVE2Sf/b3KMx0362P+Q8C0IcQqSSPL+us3HYGkLtNO4vUL4LzMvBAgIt4JbJ+ZXqYjSYvy2c82HYGkLtPOfbze2JN0AWTmz4G31ReSJEnS6NROjdeDEfE54DSqpsX9gIdqjUqSRoNjjqnG1nxJKtqp8dqX6hYS55RhjTJPkrQod99dDZJUtHPn+oeBj0fEii2PAJIkSdIgDVjjFRFbRsRtwG1letOIOKH2yCRJkkaZdpoavw68ndKvKzNvBLapMyhJkqTRqK1nNWbmva03TQWeryccSRpFNtqo6QgkdZl2Eq97I2JLICNiGeBQ4PZ6w5KkUeBT3u5Q0sLaaWr8MPAxYB1gNjC5TEuSJGkQFlnjVZ6z+I3MfE+H4pGk0ePzn6/GRx/dbBySusYiE6/MfD4i1oiIZTLz2U4FJUmjwty5TUcgqcu008drFvDbiDgPeLJnZmZ+ra6gJEmSRqN++3hFxKnl5d7ABWXZlVoGSZIkDcKiary2iIj1gXuAb3YoHkmSpFFrUYnXt4FfAK8Erm2ZH1QPy35VjXFJ0sg3aVLTEUjqMv0mXpl5PHB8RJyYmR/pYEySNDocfHDTEUjqMu08JNukS+oCE4/42WJvY9b0nYYhEknSULVzA1VJ0lAcfng1SFLR1rMaJUlDMG9e0xFI6jLWeEmSJHWIiZckSVKHmHhJkiR1iH28JKkuU6c2HYGkLmPiJUl1OfDApiOQ1GVsapQkSeoQEy9Jqsuhh1aDJBU2NUpSXebPbzoCSV3GGi9JkqQOMfGSJEnqEJsaNer5cGlJUrcw8ZKkurz1rU1HIKnLmHhJUl3237/pCCR1Gft4SZIkdYiJlyTV5aCDqkGSitoSr4h4RUT8KiJuj4hbI+LjZf7LIuLiiPhTGa9aVwySJEndpM4ar+eAT2Xm64A3Ax+LiI2BI4BLMnMD4JIyLUmSNOrVlnhl5pzMvL68fhy4HVgHeDcwsyw2E9itrhgkSZK6SUf6eEXERGAz4CpgQmbOgSo5A9bsRAySJElNq/12EhGxIvBT4BOZ+VhEtLveQcBBAOutt159AUpSXXbYoekIJHWZWhOviBhLlXT9MDPPLrPnRsRamTknItYC7u9r3cycAcwAmDJlStYZpyTVYs89m45AUpep86rGAL4L3J6ZX2t56zzggPL6AODcumKQpEbNn18NklTUWeO1FbA/cHNE3FDm/RswHTgzIj4I3AP4L6Gk0enQQ6vxjBnNxiGpa9SWeGXmFUB/Hbqm1bVfSZKkbuWd6yVJkjrExEuSJKlDTLwkSZI6pPb7eEnSEmuXXZqOQFKXMfGSpLqYeEnqxaZGSarLvHnVIEmFNV6SVJfDD6/G3sdLUmGNlyRJUoeYeEmSJHWIiZckSVKHmHhJkiR1iJ3rJakue+zRdASSuoyJlyTVZccdm45AUpexqVGS6jJ3bjVIUmGNlyTV5fOfr8bex0tSYY2XJElSh5h4SZIkdYiJlyRJUoeYeEmSJHWIneslqS777dd0BJK6jImXJNVlm22ajkBSlzHxUp8mHvGzpkOQRr67767G66/fbBySuoaJlyTV5ZhjqrH38ZJU2LlekiSpQ0y8JEmSOsTES5IkqUPs4yVpUIbjwotZ03cahkgW32g6Fkkjg4mXJNXlwAObjkBSlzHxkqS6TJ3adASSuox9vCSpLnfeWQ2SVFjjJUl1Oe64aux9vCQV1nhJkiR1iImXJElSh5h4SZIkdYiJlyRJUofU1rk+Ir4H7Azcn5mblHkvA84AJgKzgL0y85G6YhiM4biRoiQt5OCDm45AUpeps8brFOAdveYdAVySmRsAl5RpSRqdJk2qBkkqaku8MvNy4OFes98NzCyvZwK71bV/SWrcTTdVgyQVnb6P14TMnAOQmXMiYs0O71+SOudb36rG3sdLUtG1nesj4qCIuDYirn3ggQeaDkeSJGmxdTrxmhsRawGU8f39LZiZMzJzSmZOWWONNToWoCRJUl06nXidBxxQXh8AnNvh/UuSJDWmtsQrIn4MXAlsFBGzI+KDwHRgh4j4E7BDmZYkSVoi1Na5PjP37eetaXXtU5K6ymGHNR2BpC7T6asaJWnJseGGTUcgqct07VWNkjTiXX11NUhSYY2XJNXl5JOr8dSpzcYhqWtY4yVJktQhJl6SJEkdYuIlSZLUISZekiRJHWLnekmqy2c/23QEkrqMiZck1WX99ZuOQFKXsalRkupy+eXVIEmFNV6SVJfTTqvG22zTbBySuoY1XpIkSR1ijZe62sQjftZ0CMDixzFr+k7DFMni6ZbylKQllTVekiRJHWLiJUmS1CE2NUpSXY4+uukIJHUZEy9JqsuECU1HIKnLmHiNQnaglrrERRdV4x13bDYOSV3DxEuS6nLWWdXYxEtSYed6SZKkDjHxkiRJ6hATL0mSpA4x8ZIkSeoQO9dLUl2OPbbpCCR1GRMvSarL+PFNRyCpy9jUKEl1Of/8apCkwhovqQO8qe3ChqM8Zk3faRgiqVlP0rXLLotcbIkpD0nWeEmSJHWKiZckSVKHmHhJkiR1iH28JGkxLKp/1n9efQ8A/7aE9PHrlr5q3RKHht9o+GxNvCSpJl/c/l+bDkFSlzHxkqSaPLP0Mk2HIKnL2MdLkmryrjuu4F13XNF0GJK6iDVeklSTrWf9AYALX7t1w5FopOqWewA23S9qNGmkxisi3hERf4yIP0fEEU3EIEmS1GkdT7wiYgzwP8A7gY2BfSNi407HIUmS1GlN1HhNBf6cmXdl5rPA6cC7G4hDkiSpo5pIvNYB7m2Znl3mSZIkjWqRmZ3dYcSewNsz88AyvT8wNTMP6bXcQcBBZXIj4I81h7Y68GDN+1jSWKbDy/Icfpbp8LI8h59lOvw6UabrZ+Yafb3RxFWNs4FXtEyvC9zXe6HMnAHM6FRQEXFtZk7p1P6WBJbp8LI8h59lOrwsz+FnmQ6/psu0iabGa4ANIuKVEbEMsA9wXgNxSJIkdVTHa7wy87mIOBj4JTAG+F5m3trpOCRJkjqtkRuoZuaFwIVN7HsROtasuQSxTIeX5Tn8LNPhZXkOP8t0+DVaph3vXC9JkrSk8lmNkiRJHWLihY8wGm4R8b2IuD8ibmk6ltEgIl4REb+KiNsj4taI+HjTMY1kETEuIq6OiBtLeX6x6ZhGi4gYExF/iIgLmo5lNIiIWRFxc0TcEBHXNh3PSBcR4yPirIi4o5xP39JIHEt6U2N5hNGdwA5Ut7q4Btg3M29rNLARLCK2AZ4AfpCZmzQdz0gXEWsBa2Xm9RGxEnAdsJvf0aGJiABWyMwnImIscAXw8cz8fcOhjXgR8X+BKcDKmblz0/GMdBExC5iSmd7HaxhExEzgN5l5crmrwvKZOa/TcVjj5SOMhl1mXg483HQco0VmzsnM68vrx4Hb8WkPQ5aVJ8rk2DIs2f+BDoOIWBfYCTi56Vik3iJiZWAb4LsAmflsE0kXmHiBjzDSCBIRE4HNgKsaDmVEK01iNwD3AxdnpuW5+L4BHA680HAco0kCF0XEdeVpLhq6VwEPAN8vzeEnR8QKTQRi4gXRxzz/+1XXiYgVgZ8Cn8jMx5qOZyTLzOczczLVkzOmRoRN4oshInYG7s/M65qOZZTZKjM3B94JfKx049DQLA1sDpyYmZsBTwKN9Ok28WrzEUZSk0pfpJ8CP8zMs5uOZ7QoTQ2XAe9oNpIRbytg19In6XTgnyLitGZDGvky874yvh84h6prjIZmNjC7pXb7LKpErONMvHyEkbpc6Qz+XeD2zPxa0/GMdBGxRkSML6+XA7YH7mg0qBEuM4/MzHUzcyLVOfTSzNyv4bBGtIhYoVxMQ2kS2xHwSvEhysy/A/dGxEZl1jSgkQuUGrlzfTfxEUbDLyJ+DGwLrB4Rs4EvZOZ3m41qRNsK2B+4ufRLAvi38gQIDd5awMxyRfNSwJmZ6e0P1G0mAOdU/3exNPCjzPxFsyGNeIcAPyyVLHcB728iiCX+dhKSJEmdYlOjJElSh5h4SZIkdYiJlyRJUoeYeEmSJHWIiZckSVKHmHhJS6iIeGLgpTojInaLiI07tK+jIuKwGrb7iYhYvmW6rfItx/7vg9zXhS33IhvS5xgRp0fEBkNZV9LQmXhJ6ga7AQMmXhHRzfce/ASw/EAL9eFw4ITBrJCZ7xqGB/yeWPYtqYNMvCS9KCImR8TvI+KmiDgnIlYt8y+LiC9HxNURcWdEvLXMXz4izizLnxERV0XElPLejhFxZURcHxE/Kc+aJCKmR8RtZZ3jImJLYFfgKxFxQ0S8uldMp0TE1yLiV8CXe9dYRcQtETGxDLdHxHci4taIuKjcmX5Rx/vqiPhFeQjxbyLitS37PD4ifhcRd0XEHmX+UhFxQtn+BaXmaY+IOBRYG/hVibNn+8dExI2lTCf0sf8NgWcy88GW/Z4YEb8q+31bRHyvHNcpLevNiojV+9jepyPimlK2XyzzVoiIn5U4bomIvcvivwG27/JkVhp1TLwktfoB8JnMnATcDHyh5b2lM3MqVc1Oz/yPAo+U5Y8GtgAoScHngO3LQ36vBf5vRLwM+Gfg9WWdL2Xm76ge0/XpzJycmX/pI64Ny7Y+NUD8GwD/k5mvB+YB/zLA8jOAQzJzC+AwFq55WgvYGtgZmF7m7Q5MBN4AHAi8BSAzj6d6xut2mbldWXYF4PeZuSlwOfChPva/FXB9r3mrAv8EfBI4H/g68HrgDRExub8DiYgdqY5/KjAZ2CKqhyq/A7gvMzfNzE2AX5SYXwD+DGza3zYlDT8TL0kARMQqwPjM/HWZNRPYpmWRnodzX0eVfECVmJwOkJm3ADeV+W+majr8bXnM0QHA+sBjwHzg5IjYHXiqzfB+kpnPt7HcXzPzhj7ifIlSA7cl8JMS40lUyVaP/83MFzLzNqrHt0B1vD8p8/8O/Ir+PQv0PIqov1jWAh7oNe/8rB4pcjMwNzNvLknSrYs6Hqpn+e0I/IEqmXstVSJ2M1XN1pcj4q2Z+WjLOvdT1dRJ6hCrmCW165kyfp5/nDuin2UDuDgz933JGxFTqR5Quw9wMFXtzkCebHn9HAv/0ziujxh74lxUU+NSwLzMnNzP+63bil7jdizIfzyTrbXMWj0NrNLPfl/oFcML/WyjNcb/ysyTXvJGxBbAu4D/ioiLMvM/ylvjSgySOsQaL0kAlJqQR3r6b1E9mPvXi1gF4ApgL4ByVeIbyvzfA1tFxGvKe8tHxIallmmV8oDvT1A1iQE8DqzUZqizgM3LdjcHXtnmegvJzMeAv0bEnmVbEREDNbtdAfxL6es1geph8D0Gcww9bgdeM8h1+vNL4AMtfenWiYg1I2Jt4KnMPA04jlJ2xYZUNWmSOsQaL2nJtXxEzG6Z/hpVk+C3o7otwl3A+wfYxgnAzIi4iaqJ6ybg0cx8ICLeB/w4IpYty36OKjk5NyLGUdXQfLK8dzrwndJJfY9++nn1+Cnw3tI8eA1wZ1tH27f3ACdGxOeAsSWOGwfY9zTglrLfq4CeprsZwM8jYk5LP6+BXA58NSKipXZsSDLzooh4HXBlRAA8AexHldh9JSJeABYAHwEoiePTmTlncfYraXBiMX/rkpZgETEGGJuZ88vViJcAG2bmsw2HVpuIWDEzn4iI1YCrga1Kf6+hbu+/qfp1/b9hC7K9/X4SeCwzv9vJ/UpLOmu8JC2O5aluoTCWqgbrI6M56SouiOrmpcsARy9O0lX8J/CmxY5q8OYBpzawX2mJZo2XJElSh9i5XpIkqUNMvCRJkjrExEuSJKlDTLwkSZI6xMRLkiSpQ0y8JEmSOuT/A+NWGlV0P1lpAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -911,12 +911,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -947,12 +947,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -998,7 +998,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1007,7 +1007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -1065,7 +1065,7 @@
        "124            3.3      0             3000.0  "
       ]
      },
-     "execution_count": 28,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1078,7 +1078,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1103,7 +1103,7 @@
     "    \n",
     "    bm2 = X_bm.copy()\n",
     "    for f, d in zip(features, deltas):\n",
-    "        bm2[___] += ___\n",
+    "        bm2[f] += d\n",
     "    return model.predict(bm2).item() - model.predict(X_bm).item()"
    ]
   },
@@ -1123,7 +1123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1132,7 +1132,7 @@
        "[-1, -2, -3, -4, -5, -6, -7, -8, -9, -10]"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1143,7 +1143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1153,7 +1153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1171,7 +1171,7 @@
        " -1.8115942028985472]"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1182,9 +1182,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "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 +1206,12 @@
     "#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 * x for x 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 * y for y in price_deltas] #2\n",
     "ax[1].plot(runs_closed, revenue_deltas, 'o-')\n",
     "ax[1].set(xlabel='Runs closed', ylabel='Change ($)', title='Revenue');"
    ]
@@ -1226,20 +1239,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
     "#Code task 4#\n",
     "#Call `predict_increase` with a list of the features 'Runs', 'vertical_drop', and 'total_chairs'\n",
     "#and associated deltas of 1, 150, and 1\n",
-    "ticket2_increase = ___(['Runs', ___, ___], [1, ___, ___])\n",
+    "ticket2_increase = predict_increase(['Runs', 'vertical_drop', 'total_chairs'], [1, 150, 1])\n",
     "revenue2_increase = 5 * expected_visitors * ticket2_increase"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -1272,19 +1285,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "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"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -1324,13 +1337,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 43,
+     "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 +1382,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**A: 1** Your answer here"
+    "**A: 1** \n",
+    "\n",
+    "- The adult weekend ticket price of Big Mountain Resort is currently \\$81. Compared with the national average price, $64.2, it seems that they sell a ticket at a high price. Big Mountain Resort also charges the highest price in Montana and the state average price is \\$51.9. However, the pricing prediction model suggests that Big Mountain resort can charge up to \\$95.87 with the current facilities. This price is estimated by considering all the impacts of each facility on the price put together. Since the expected mean absolute error is \\$10.39, this suggests consumers are willing to pay more.\n",
+    "\n",
+    "\n",
+    "- The results of reviewing 4 possible scenarios for either cutting costs without undermining the ticket price or charging an even higher ticket price are as follows.\n",
+    "\n",
+    "    1. After analyzing the effect of closing runs on price when all other things are unchanged, the model shows that closing one run makes no difference. Therefore, Big mountain Resort can successfully keep costs down without lowering the price.\n",
+    "\n",
+    "    2. If Big Mountain Resort adds a run with the increased vertical drop by 150 feet, they also need to install an additional chair lift. In this case, the model supports a markup of \\$1.99, and the expected revenue increase is \\$3474683.\n",
+    "\n",
+    "    3. However, adding 2 acres of snow making area on top of the previous scenario2 or increasing the longest run by .2 miles with additional 4 acres of snow making make no difference in the ticket price.\n",
+    "     \n",
+    "- Every year about 350,000 visitors come to ski or snowboard at Big Mountain Resort and spend five days on average. This number includes the recently installed chair lift which increases operating costs by  \\$1,540,000 this season. Since the additional operating cost per ticket is \\$0.88, an increase in price of more than $0.88 generates an increase in revenue and profit."
    ]
   },
   {
@@ -1379,7 +1416,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**A: 2** Your answer here"
+    "**A: 2** \n",
+    "\n",
+    "- The limitations of the data and analytics.\n",
+    "\n",
+    "    1. The data includes ticket prices only for adults and the information about the total number of visitors is given. This can cause inaccurate cost-benefit analysis considering the proportion of family visitors.\n",
+    "    2.  The operating cost for an additional chair lift is only provided. According to scenario 2, the model suggests that Big Mountain Resort is capable of increasing the current prices. In order to build it up convincingly, the costs for managing runs and snow-making equipment are necessary.\n",
+    "    3. Based on the result of pre-processing data, `fastQuads`, `Runs`, `Snow Making_ac`, and `vertical_drop` are the top 4 important features. But, in the case of `Snow Making_ac`, 13.4% of data are still missing after data cleaning. Missing values replaced with the median can cause bars in the model prediction. Also, 51 resorts that don’t provide `AdultWeekend` price are dropped. Then we can question the representativeness of the samples that might lead to the loss of prediction power.\n",
+    "\n",
+    "\n",
+    "- Though Big Mountain Resort already charges a high price compared to the market average, our analysis shows that its facilities can support further price increases. It helps that the business leaders decide the range of increase considering an accurate prediction of the costs."
    ]
   }
  ],
@@ -1399,7 +1445,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.8.8"
   },
   "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..c6ca5c3e9
Binary files /dev/null and b/models/ski_resort_pricing_model.pkl differ