lewiuberg/tutorial_2.ipynb

## tutorial_2.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Activity sheet 2\n# Data pre-processing\n**Objective**\n\nThe aim of this activity sheet is to familiarise yourselves with all the necessary steps that need to be done on a dataset, before you start developing your learning models and train them on your data. Those steps comprised of tasks such as fetching the data, exploring it to gain insights and prepare it accordingly for better exposing the underlying data patterns.\n\n**Dataset description**\n\nWe are going to use the California Housing Prices dataset from the StatLib repository. This dataset was based on data from the 1990 California census. It is not exactly recent, but it has many qualities for learning, so we are going to use it although it is outdated. This data has metrics such as the population, median income, median housing price, and so on for each districts in California. The median housing price is the target value of the dataset."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import os\nimport tarfile\nimport pandas as pd\nimport requests\nimport geopandas as gpd\nimport seaborn as sns\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as colors\nfrom shapely.geometry import Point, Polygon\nfrom sklearn.model_selection import train_test_split\nfrom geopandas import GeoDataFrame\nfrom zipfile import ZipFile\nfrom sklearn import preprocessing\nfrom sklearn.impute import SimpleImputer\nfrom pandas.plotting import scatter_matrix\nfrom sklearn.preprocessing import LabelBinarizer\n%matplotlib inline",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Fetch the data\nYou could download the data using your browser by simply visiting (https:// github.com/ageron/handson-ml) and then extract the zipped Nile. Trying to automate things as much as you can, you are asked to write a small function to do so for you. Your function should: a) create a directory in your workspace, b) download the housing.tgz Nile in it from the repository: (https:// raw.githubusercontent.com/ageron/handson-ml/master/), and c) extract the housing.csv from it in your directory."
    },
    {
      "metadata": {
        "code_folding": [
          0
        ],
        "hide_input": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def create_folder(directory):\n    try:\n        if not os.path.exists(directory):\n            os.makedirs(directory)\n    except OSError:\n        print('Error: Creating directory. ' + directory)",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "code_folding": [
          0
        ],
        "hide_input": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def only_csv(path):\n    for file in os.listdir(path):\n        if file.endswith('.csv'):\n            return file",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "code_folding": [
          0
        ],
        "hide_input": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def missing_values(df, compare_value=\"isnull\", size_fig=(12, 8)):\n    plt.subplots(figsize=size_fig)\n    if compare_value == \"isnull\":\n        print(f\"Number of missing values: {df.isnull().sum().sum()}\")\n        ax = sns.heatmap(df.isnull(), yticklabels=False,\n                         cbar=False, cmap=\"copper\")\n    else:\n        print(f\"Number of missing values: {(df == compare_value).sum().sum()}\")\n        ax = sns.heatmap(df == compare_value, yticklabels=False,\n                         cbar=False, cmap=\"copper\")\n    ax.set_xticklabels(\n        ax.get_xticklabels(),\n        rotation=45,\n        horizontalalignment=\"right\")",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "code_folding": [
          0
        ],
        "hide_input": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "def missing_data(df):\n    print(f\"Columns with missing data: {df.isnull().any().sum()}\")\n    print(\n        f\"Columns names with missing data: {df.columns[df.isnull().any()]}\")\n    print(f\"Total instances of data: {len(df.index)}\")\n    print(\n        f\"Instances without missing data: {df.total_bedrooms.notnull().sum()}\")\n    print(f\"Instances of missing data, isnull: {df.isnull().sum().sum()}\")\n    print(f\"Instances of missing data, isnan: {df.isna().sum().sum()}\")",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Make directory"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "dir_name = \"housing\"\ncreate_folder(dir_name)",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Download files"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "tgz_filename = \"housing.tgz\"\nurl = \"https://github.com/ageron/handson-ml/blob/master/datasets/housing/housing.tgz?raw=true\"\nfile = requests.get(url)\nopen(f\"{dir_name}/{tgz_filename}\", \"wb\").write(file.content)\nfile.close()",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "zip_filename = \"us_state.zip\"\nurl = \"https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_state_500k.zip\"\nfile = requests.get(url)\nopen(f\"{dir_name}/{zip_filename}\", \"wb\").write(file.content)\nfile.close()",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Extract csv"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "tf = tarfile.open(f\"{dir_name}/{tgz_filename}\")\ntf.extractall(dir_name)\ntf.close()",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Extract shp"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "zf = ZipFile(f\"{dir_name}/{zip_filename}\", \"r\")\nzf.extractall(dir_name)\nzf.close()",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Load the data\nCreate a small function in order to load your dowloaded data into a Pandas DataFrame object"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "extracted_file = only_csv(dir_name)",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = pd.read_csv(f\"{dir_name}/{extracted_file}\")",
      "execution_count": 12,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Take a glance at the data\nTake a quick look at the data and understand its structure using some helpful Pandas methods such as head(), info(), value_counts() and describe(). Are the data attributes numerical or not? Moreover you can plot a histogram for the numerical attributes of your data using hist() method to get a better feel of the type of the data you dealing with."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.head()",
      "execution_count": 13,
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-122.23</td>\n      <td>37.88</td>\n      <td>41.0</td>\n      <td>880.0</td>\n      <td>129.0</td>\n      <td>322.0</td>\n      <td>126.0</td>\n      <td>8.3252</td>\n      <td>452600.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-122.22</td>\n      <td>37.86</td>\n      <td>21.0</td>\n      <td>7099.0</td>\n      <td>1106.0</td>\n      <td>2401.0</td>\n      <td>1138.0</td>\n      <td>8.3014</td>\n      <td>358500.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-122.24</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1467.0</td>\n      <td>190.0</td>\n      <td>496.0</td>\n      <td>177.0</td>\n      <td>7.2574</td>\n      <td>352100.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1274.0</td>\n      <td>235.0</td>\n      <td>558.0</td>\n      <td>219.0</td>\n      <td>5.6431</td>\n      <td>341300.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1627.0</td>\n      <td>280.0</td>\n      <td>565.0</td>\n      <td>259.0</td>\n      <td>3.8462</td>\n      <td>342200.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0    -122.23     37.88                41.0        880.0           129.0   \n1    -122.22     37.86                21.0       7099.0          1106.0   \n2    -122.24     37.85                52.0       1467.0           190.0   \n3    -122.25     37.85                52.0       1274.0           235.0   \n4    -122.25     37.85                52.0       1627.0           280.0   \n\n   population  households  median_income  median_house_value ocean_proximity  \n0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.describe()",
      "execution_count": 14,
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20433.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n      <td>20640.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>-119.569704</td>\n      <td>35.631861</td>\n      <td>28.639486</td>\n      <td>2635.763081</td>\n      <td>537.870553</td>\n      <td>1425.476744</td>\n      <td>499.539680</td>\n      <td>3.870671</td>\n      <td>206855.816909</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>2.003532</td>\n      <td>2.135952</td>\n      <td>12.585558</td>\n      <td>2181.615252</td>\n      <td>421.385070</td>\n      <td>1132.462122</td>\n      <td>382.329753</td>\n      <td>1.899822</td>\n      <td>115395.615874</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-124.350000</td>\n      <td>32.540000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>0.499900</td>\n      <td>14999.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>-121.800000</td>\n      <td>33.930000</td>\n      <td>18.000000</td>\n      <td>1447.750000</td>\n      <td>296.000000</td>\n      <td>787.000000</td>\n      <td>280.000000</td>\n      <td>2.563400</td>\n      <td>119600.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>-118.490000</td>\n      <td>34.260000</td>\n      <td>29.000000</td>\n      <td>2127.000000</td>\n      <td>435.000000</td>\n      <td>1166.000000</td>\n      <td>409.000000</td>\n      <td>3.534800</td>\n      <td>179700.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>-118.010000</td>\n      <td>37.710000</td>\n      <td>37.000000</td>\n      <td>3148.000000</td>\n      <td>647.000000</td>\n      <td>1725.000000</td>\n      <td>605.000000</td>\n      <td>4.743250</td>\n      <td>264725.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>-114.310000</td>\n      <td>41.950000</td>\n      <td>52.000000</td>\n      <td>39320.000000</td>\n      <td>6445.000000</td>\n      <td>35682.000000</td>\n      <td>6082.000000</td>\n      <td>15.000100</td>\n      <td>500001.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "          longitude      latitude  housing_median_age   total_rooms  \\\ncount  20640.000000  20640.000000        20640.000000  20640.000000   \nmean    -119.569704     35.631861           28.639486   2635.763081   \nstd        2.003532      2.135952           12.585558   2181.615252   \nmin     -124.350000     32.540000            1.000000      2.000000   \n25%     -121.800000     33.930000           18.000000   1447.750000   \n50%     -118.490000     34.260000           29.000000   2127.000000   \n75%     -118.010000     37.710000           37.000000   3148.000000   \nmax     -114.310000     41.950000           52.000000  39320.000000   \n\n       total_bedrooms    population    households  median_income  \\\ncount    20433.000000  20640.000000  20640.000000   20640.000000   \nmean       537.870553   1425.476744    499.539680       3.870671   \nstd        421.385070   1132.462122    382.329753       1.899822   \nmin          1.000000      3.000000      1.000000       0.499900   \n25%        296.000000    787.000000    280.000000       2.563400   \n50%        435.000000   1166.000000    409.000000       3.534800   \n75%        647.000000   1725.000000    605.000000       4.743250   \nmax       6445.000000  35682.000000   6082.000000      15.000100   \n\n       median_house_value  \ncount        20640.000000  \nmean        206855.816909  \nstd         115395.615874  \nmin          14999.000000  \n25%         119600.000000  \n50%         179700.000000  \n75%         264725.000000  \nmax         500001.000000  "
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.index",
      "execution_count": 15,
      "outputs": [
        {
          "data": {
            "text/plain": "RangeIndex(start=0, stop=20640, step=1)"
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.columns",
      "execution_count": 16,
      "outputs": [
        {
          "data": {
            "text/plain": "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n       'total_bedrooms', 'population', 'households', 'median_income',\n       'median_house_value', 'ocean_proximity'],\n      dtype='object')"
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Gives the same information as the two above.\ndf.info()",
      "execution_count": 17,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 20640 entries, 0 to 20639\nData columns (total 10 columns):\n #   Column              Non-Null Count  Dtype  \n---  ------              --------------  -----  \n 0   longitude           20640 non-null  float64\n 1   latitude            20640 non-null  float64\n 2   housing_median_age  20640 non-null  float64\n 3   total_rooms         20640 non-null  float64\n 4   total_bedrooms      20433 non-null  float64\n 5   population          20640 non-null  float64\n 6   households          20640 non-null  float64\n 7   median_income       20640 non-null  float64\n 8   median_house_value  20640 non-null  float64\n 9   ocean_proximity     20640 non-null  object \ndtypes: float64(9), object(1)\nmemory usage: 1.6+ MB\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.longitude.value_counts()",
      "execution_count": 18,
      "outputs": [
        {
          "data": {
            "text/plain": "-118.31    162\n-118.30    160\n-118.29    148\n-118.27    144\n-118.32    142\n          ... \n-115.72      1\n-123.68      1\n-116.09      1\n-123.72      1\n-116.69      1\nName: longitude, Length: 844, dtype: int64"
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.hist(bins=40, figsize=(14, 10));",
      "execution_count": 19,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 1008x720 with 9 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Split the data\nUse Scikit-Learn functions such as train_test_split() to split your data into training and testing datasets"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train, test = train_test_split(\n    df, test_size=0.2, random_state=1, shuffle=True)",
      "execution_count": 20,
      "outputs": []
    },
    {
      "metadata": {
        "cell_style": "split",
        "trusted": true
      },
      "cell_type": "code",
      "source": "train.info()",
      "execution_count": 21,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 16512 entries, 15961 to 235\nData columns (total 10 columns):\n #   Column              Non-Null Count  Dtype  \n---  ------              --------------  -----  \n 0   longitude           16512 non-null  float64\n 1   latitude            16512 non-null  float64\n 2   housing_median_age  16512 non-null  float64\n 3   total_rooms         16512 non-null  float64\n 4   total_bedrooms      16349 non-null  float64\n 5   population          16512 non-null  float64\n 6   households          16512 non-null  float64\n 7   median_income       16512 non-null  float64\n 8   median_house_value  16512 non-null  float64\n 9   ocean_proximity     16512 non-null  object \ndtypes: float64(9), object(1)\nmemory usage: 1.4+ MB\n"
        }
      ]
    },
    {
      "metadata": {
        "cell_style": "split",
        "trusted": true
      },
      "cell_type": "code",
      "source": "test.info()",
      "execution_count": 22,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 4128 entries, 4712 to 11878\nData columns (total 10 columns):\n #   Column              Non-Null Count  Dtype  \n---  ------              --------------  -----  \n 0   longitude           4128 non-null   float64\n 1   latitude            4128 non-null   float64\n 2   housing_median_age  4128 non-null   float64\n 3   total_rooms         4128 non-null   float64\n 4   total_bedrooms      4084 non-null   float64\n 5   population          4128 non-null   float64\n 6   households          4128 non-null   float64\n 7   median_income       4128 non-null   float64\n 8   median_house_value  4128 non-null   float64\n 9   ocean_proximity     4128 non-null   object \ndtypes: float64(9), object(1)\nmemory usage: 354.8+ KB\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Visualise your data\nCreate a scatterplot of all districts to visualise the data using the attributes ‘latitude’ and ‘longitude’. (Hint: Since those attributes contain geographical information the scatterplot should have the shape of California)"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Showing the most valuable locations."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "x = train.longitude\ny = train.latitude\ns = train.population/100\nc = train.median_house_value\n\nplt.figure(figsize=(10, 8))\nax = plt.scatter(x, y, s=s, c=c, alpha=0.5, cmap=\"turbo\")\nplt.colorbar(ax);",
      "execution_count": 23,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 720x576 with 2 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "In the geopandas section, I used a lot of Dayans work."
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Generate geodata\ngdf = GeoDataFrame(train, geometry=[Point(xy) for xy in zip(\n    train[\"longitude\"], train[\"latitude\"])])\n# Import the shape file\nusa = gpd.read_file(f\"{dir_name}/cb_2018_us_state_500k.shp\")\n# Ploat\nax = usa[usa.STUSPS == \"CA\"].plot(\n    figsize=(10, 8), color=\"whitesmoke\", edgecolor=\"lightgrey\")\ngdf.plot(ax=ax, alpha=0.25, markersize=10, cmap=\"hot\", legend=True);",
      "execution_count": 24,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 720x576 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "usa.head()",
      "execution_count": 25,
      "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>STATEFP</th>\n      <th>STATENS</th>\n      <th>AFFGEOID</th>\n      <th>GEOID</th>\n      <th>STUSPS</th>\n      <th>NAME</th>\n      <th>LSAD</th>\n      <th>ALAND</th>\n      <th>AWATER</th>\n      <th>geometry</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>28</td>\n      <td>01779790</td>\n      <td>0400000US28</td>\n      <td>28</td>\n      <td>MS</td>\n      <td>Mississippi</td>\n      <td>00</td>\n      <td>121533519481</td>\n      <td>3926919758</td>\n      <td>MULTIPOLYGON (((-88.50297 30.21523, -88.49176 ...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>37</td>\n      <td>01027616</td>\n      <td>0400000US37</td>\n      <td>37</td>\n      <td>NC</td>\n      <td>North Carolina</td>\n      <td>00</td>\n      <td>125923656064</td>\n      <td>13466071395</td>\n      <td>MULTIPOLYGON (((-75.72681 35.93584, -75.71827 ...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>40</td>\n      <td>01102857</td>\n      <td>0400000US40</td>\n      <td>40</td>\n      <td>OK</td>\n      <td>Oklahoma</td>\n      <td>00</td>\n      <td>177662925723</td>\n      <td>3374587997</td>\n      <td>POLYGON ((-103.00257 36.52659, -103.00219 36.6...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>51</td>\n      <td>01779803</td>\n      <td>0400000US51</td>\n      <td>51</td>\n      <td>VA</td>\n      <td>Virginia</td>\n      <td>00</td>\n      <td>102257717110</td>\n      <td>8528531774</td>\n      <td>MULTIPOLYGON (((-75.74241 37.80835, -75.74151 ...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>54</td>\n      <td>01779805</td>\n      <td>0400000US54</td>\n      <td>54</td>\n      <td>WV</td>\n      <td>West Virginia</td>\n      <td>00</td>\n      <td>62266474513</td>\n      <td>489028543</td>\n      <td>POLYGON ((-82.64320 38.16909, -82.64300 38.169...</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "  STATEFP   STATENS     AFFGEOID GEOID STUSPS            NAME LSAD  \\\n0      28  01779790  0400000US28    28     MS     Mississippi   00   \n1      37  01027616  0400000US37    37     NC  North Carolina   00   \n2      40  01102857  0400000US40    40     OK        Oklahoma   00   \n3      51  01779803  0400000US51    51     VA        Virginia   00   \n4      54  01779805  0400000US54    54     WV   West Virginia   00   \n\n          ALAND       AWATER  \\\n0  121533519481   3926919758   \n1  125923656064  13466071395   \n2  177662925723   3374587997   \n3  102257717110   8528531774   \n4   62266474513    489028543   \n\n                                            geometry  \n0  MULTIPOLYGON (((-88.50297 30.21523, -88.49176 ...  \n1  MULTIPOLYGON (((-75.72681 35.93584, -75.71827 ...  \n2  POLYGON ((-103.00257 36.52659, -103.00219 36.6...  \n3  MULTIPOLYGON (((-75.74241 37.80835, -75.74151 ...  \n4  POLYGON ((-82.64320 38.16909, -82.64300 38.169...  "
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Investigate attribute correlation\nCompute the linear correlation between each attribute and the ‘median_house_value’ using the corr() method. What’s the most promising attribute to predict the median house value?"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train.corr()",
      "execution_count": 26,
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>longitude</th>\n      <td>1.000000</td>\n      <td>-0.925246</td>\n      <td>-0.106156</td>\n      <td>0.037578</td>\n      <td>0.065412</td>\n      <td>0.095861</td>\n      <td>0.049854</td>\n      <td>-0.018227</td>\n      <td>-0.050893</td>\n    </tr>\n    <tr>\n      <th>latitude</th>\n      <td>-0.925246</td>\n      <td>1.000000</td>\n      <td>0.009373</td>\n      <td>-0.029916</td>\n      <td>-0.063107</td>\n      <td>-0.105325</td>\n      <td>-0.066939</td>\n      <td>-0.076734</td>\n      <td>-0.139374</td>\n    </tr>\n    <tr>\n      <th>housing_median_age</th>\n      <td>-0.106156</td>\n      <td>0.009373</td>\n      <td>1.000000</td>\n      <td>-0.361784</td>\n      <td>-0.319623</td>\n      <td>-0.296996</td>\n      <td>-0.301496</td>\n      <td>-0.123550</td>\n      <td>0.105758</td>\n    </tr>\n    <tr>\n      <th>total_rooms</th>\n      <td>0.037578</td>\n      <td>-0.029916</td>\n      <td>-0.361784</td>\n      <td>1.000000</td>\n      <td>0.929219</td>\n      <td>0.854458</td>\n      <td>0.916737</td>\n      <td>0.201318</td>\n      <td>0.135514</td>\n    </tr>\n    <tr>\n      <th>total_bedrooms</th>\n      <td>0.065412</td>\n      <td>-0.063107</td>\n      <td>-0.319623</td>\n      <td>0.929219</td>\n      <td>1.000000</td>\n      <td>0.875187</td>\n      <td>0.979268</td>\n      <td>-0.006820</td>\n      <td>0.050560</td>\n    </tr>\n    <tr>\n      <th>population</th>\n      <td>0.095861</td>\n      <td>-0.105325</td>\n      <td>-0.296996</td>\n      <td>0.854458</td>\n      <td>0.875187</td>\n      <td>1.000000</td>\n      <td>0.905040</td>\n      <td>0.007521</td>\n      <td>-0.023884</td>\n    </tr>\n    <tr>\n      <th>households</th>\n      <td>0.049854</td>\n      <td>-0.066939</td>\n      <td>-0.301496</td>\n      <td>0.916737</td>\n      <td>0.979268</td>\n      <td>0.905040</td>\n      <td>1.000000</td>\n      <td>0.013801</td>\n      <td>0.066801</td>\n    </tr>\n    <tr>\n      <th>median_income</th>\n      <td>-0.018227</td>\n      <td>-0.076734</td>\n      <td>-0.123550</td>\n      <td>0.201318</td>\n      <td>-0.006820</td>\n      <td>0.007521</td>\n      <td>0.013801</td>\n      <td>1.000000</td>\n      <td>0.688194</td>\n    </tr>\n    <tr>\n      <th>median_house_value</th>\n      <td>-0.050893</td>\n      <td>-0.139374</td>\n      <td>0.105758</td>\n      <td>0.135514</td>\n      <td>0.050560</td>\n      <td>-0.023884</td>\n      <td>0.066801</td>\n      <td>0.688194</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "                    longitude  latitude  housing_median_age  total_rooms  \\\nlongitude            1.000000 -0.925246           -0.106156     0.037578   \nlatitude            -0.925246  1.000000            0.009373    -0.029916   \nhousing_median_age  -0.106156  0.009373            1.000000    -0.361784   \ntotal_rooms          0.037578 -0.029916           -0.361784     1.000000   \ntotal_bedrooms       0.065412 -0.063107           -0.319623     0.929219   \npopulation           0.095861 -0.105325           -0.296996     0.854458   \nhouseholds           0.049854 -0.066939           -0.301496     0.916737   \nmedian_income       -0.018227 -0.076734           -0.123550     0.201318   \nmedian_house_value  -0.050893 -0.139374            0.105758     0.135514   \n\n                    total_bedrooms  population  households  median_income  \\\nlongitude                 0.065412    0.095861    0.049854      -0.018227   \nlatitude                 -0.063107   -0.105325   -0.066939      -0.076734   \nhousing_median_age       -0.319623   -0.296996   -0.301496      -0.123550   \ntotal_rooms               0.929219    0.854458    0.916737       0.201318   \ntotal_bedrooms            1.000000    0.875187    0.979268      -0.006820   \npopulation                0.875187    1.000000    0.905040       0.007521   \nhouseholds                0.979268    0.905040    1.000000       0.013801   \nmedian_income            -0.006820    0.007521    0.013801       1.000000   \nmedian_house_value        0.050560   -0.023884    0.066801       0.688194   \n\n                    median_house_value  \nlongitude                    -0.050893  \nlatitude                     -0.139374  \nhousing_median_age            0.105758  \ntotal_rooms                   0.135514  \ntotal_bedrooms                0.050560  \npopulation                   -0.023884  \nhouseholds                    0.066801  \nmedian_income                 0.688194  \nmedian_house_value            1.000000  "
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, ax = plt.subplots(figsize=(8, 6))\nax = sns.heatmap(train.corr(), linewidths=0.5, annot=True)\nax.set_xticklabels(\n    ax.get_xticklabels(),\n    rotation=45,\n    horizontalalignment=\"right\");",
      "execution_count": 27,
      "outputs": [
        {
          "data": {
            "image/png": 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E0uZwpN6ALiGN1P0ncWp5c/uw9rM96bBpHh02zSMnLhm7AvmK9rZA8V4Zuxr5NblXUsEoQUqivt+MS1DATWWocKoHRrkJOQW+N1C+K7n05P8M7G6iXgAbzD04gVLKplLKYm/jpJRLpJRtpJRtRo4cWY54lWvRF9/Spm1P2rTtyZo1//DUEw8D0L5dK9JS04iNjS+2TPXqpgOTq6sLL744jK+WmdqSAQF+eTUP9O3BmYjiV0XcqrPfbGBjyHQ2hkwnel0odYeYBrW6twpAl55V4liX2+HqoTPY+fliU9sLYW2F+4BOpGwofFVTyoZ9eAwx9VC4PdCR9F2mK2VsanuB1vQraVOzOnb1a5EbGU9u9BUcgxqaxsAATp1akB0RVaZ8mYfOYOtXA5va3ghrK9z6dSZtw95CNWkb9+H+UHcAXPveR/puUxs+fdtB7BrXRZjH4ji2b0bOmUjTP2fzqRGstDgHtyX71MUy5cs6fBqbejWxruUN1la4PNiFjE2F86Vv2ovL4GAAnPt04uoeUz5hZ4uwtwWg2n2BSL2B3IhI0zKb9+LQ4R7TvI6BedPL6uqhM9jWy/85u/XvTGoJP2f3h0370e2B+0jfZcpp5e4MGvPPuY43tn41yLlU/A3BzYr++h9CgycTGjyZhHX78R5i6p1zbt0AfXomuUX+FnLjU9BnZOHc2jQ+yHvI/ST8vf+628i+nIBrp+YAaBxscW7VkMyIyzeVL/Lr9XmDbuPXhVJjSBcAXFoHXDefS2tT46TGkC5c+TsUoNB4Ga++bUk/Wb6fo1J26jLqku0AXhBCfAu4Y+oRmYypp6UkpwB/IUQ9KeUFoLQT8OlAwZGwFzCdHtoHPFxg+nbgcWCzEKI5cG3k3b/AQiFEgJQyQghRDagppTx9i6/vlk1+cx77ww6TkpJG8MAnGT38KR7qV/5ejJu1dt0mevfuzqkTu8jMymLEiPwzZ6H719OmbU/AdKl1ixam8/mz53yY1wMzetQzBAd3RqfTk5KcynPDx1s0X+ymcHyCA+m9ZwGGrFxCJyzOm9djw1w2hkwH4J4Zj1F7UEe09jb0PfApF37YwvEPfsGtpT/3LpuAjasDviFBNJ38EBu6TilbGIORS68vpeHyN0GjJfHHjWSfjqTGpMe4eiiC1A37SVi5Eb+Px9N85yIMKemcHf0BAI7tmuI7ejBSb0AajVx6bTH65HT0yekkr91Nk78XgN5A5rHzXFn+T5nzRb2xGP/vZiK0GpJWbST7TCQ+rzxO5uEI0jbuI/HHDdT98BWabFuMPiWdiy+9Z1o07SpXvvydhn8sAClJ23KAtM2haOxtqf+/txBWVqDVkLEznMQV68ucL/atRdT55m2ERkPKTxvIOXOJ6uOfJOvIGTI27SVl1XpqfjCJgM1LMaSkEzXuXQCsPFyo883bYJTo4hKJnvh+3mrj539NjQ8moZ0xEkNSKpdf/ahs+QrkjHx9CQHfm/Zj4o+byD4die9E035M3bCPxJUbqPfRBJru+AJDSjrnx5jyOLZvhu/Ex5F6PRglkdMWYSjDeJKSJG08iEdwEO33foohK5dT4xbmzWuz6T1CgycDcGbKUhp/MgaNnQ1Jm8JJ2hQGgGefdjSY+xzWHs7cs3waGUcvcHjoHKKX/UOjj0fTdtsCEILYlVu4evzSLedL2BiGZ3AgnfZ+jCErh2Pj8j8WosOmefwbPBWAE1OW0fyTUWjsbEjYFJ53tVHDN57AqXldkJKsyCscn/Rl3vKd93+KlZM9wsYKrz5tOPDoXK6evrlGVrlVwUG45SUsfTVGVSaEyJBSOpoH5r4L9MF0HcZsKeWPQoiuwCQp5YPm+s+AUCnlN0KIfsB7wFVgP+AkpXxCCPEM0EZK+ZIQ4j5gKaZenIcBa2AVpp6cv4AnpZT1hBD2wNeYxuCcAGoCY6SUoUKI7sB8wNYce4aUcs11XpbUJRQ/jXInsPb0x8qm7N3WFU2fe5mffJ+o7BglejhmOQChtQZWbpBStIn6jfC6/Ss7RqkCL67heP0HKjtGiZqe/QuAg7UHVHKSkrWK/J2t3kMqO0apusatZr330MqOUaKecSvB1JNucTlHN5T7n7lt85AKyVZRVA9MAVJKR/NXianHZXKR+VuBrQWev1Rg9hYpZWNz42chpgG2SCm/Ab4xf7+L4pdRF7yucYa5Lgso8S9QSrmZ/PE4iqIoinJX9sCoMTCW87x5YO0xwAXTVUmKoiiKolQA1QNjIVLKD4EPKzuHoiiKcveRsupdBl1eqgGjKIqiKFVdFbwMurxUA0ZRFEVRqrq7cAyMasAoiqIoSlV3F/bAqEG8iqIoiqJUOaoHRlEURVGquip4L6PyUg0YRVEURanq7sJTSKoBoyiKoihV3V04iFeNgVEURVEUpcpRPTCKoiiKUtWpU0iKoiiKolQ5d+EpJNWAURRFUZSq7i5swAjTjZeV/zD1A1YURblziIpYadb2b8p9rLfv8kyFZKsoqgfmLmBlU7OyI5RIn3sZXcK5yo5RKmtPf3b7PlTZMUrUMeZnADZ7P1LJSUrWPW4Va72HVnaMUvWNW8kvPo9XdowSDY79AeCO3X9941byh89jlR2jVP1iV7C8xpOVHaNET0R/X9kR/lNUA0ZRFEVRqrq78BSSuoxaURRFUao6aSz/4waEEL2FEKeEEBFCiKklzP9QCBFufpwWQqQUmGcoMG+NJV6y6oFRFEVRlKqugntghBBaYCEQAkQB+4UQa6SUx6/VSCknFKh/GQgqsIosKWWgJTOpHhhFURRFqeoqvgemHRAhpTwnpcwFVgIDrlP/GLDCQq+uRKoBoyiKoijKjdQEIgs8jzJPK0YIURfwAzYXmGwnhAgVQvwrhBhoiUDqFJKiKIqiVHUWOIUkhBgJjCwwaYmUckkZVjUU+ElKWfAW2XWllJeFEP7AZiHEESnl2fLkVQ0YRVEURanqLHArAXNjpbQGy2WgdoHntczTSjIUGFNk3ZfNX88JIbZiGh9TrgaMOoWkKIqiKFWd0Vj+x/XtBxoIIfyEEDaYGinFriYSQjQG3IA9Baa5CSFszd97AvcBx4sue6tUD4yiKIqiKNclpdQLIV4C/gG0wDIp5TEhxCwgVEp5rTEzFFgpC3/MfxNgsRDCiKnjZF7Bq5fKSjVgFEVRFKWquw0fZCelXAusLTLtjSLPZ5aw3G7gHkvnUQ0YRVEURanqLDAGpqpRDRhFURRFqeruwlsJqAaMkufDBbPo07s7mVlZDB8+gbDwo8Vqhgzpz7SpL6PValm7diPTps8FYOTzTzFq1DAMBiNXM67y4uhXOXHizG3JPWPuArbv2oe7myu/ff9FhW3HtVsgfrOeA62G+B82cfmzXwvNFzZWNPhkLNVa+KNPTuf0CwvIiboCQM2XB+H1WDAYjJx/fRkpW8MB8B3xAN5P9AAhiFu+gZilfxVaZ40X+lFv5jPsa/YM+qT0cuVvMOdZPIKDMGblcHzs52QcOV+sxqmFH00+GYPGzobETWGcee1rAPymPEr13m2QRokuIZXjYz8nNy65XHmazhlG9eAgDFk5HB67iLQjF4rVOLfwo+Uno9DY2XBlUxjHX/s2b17d4b2o+2xPpMFI/MYwTr39Ay5B9bnn/edNBUJw5r2fiFu3v1w5AVrMfhqf4EAMWbkcGPcFKSVkbTr1EeoM6YyNazXW1H8ub7pHh8a0nPUUzk3rsO/FT4n+c1+581TEvqvx0H34j+6XV+PUtA47e0wj/djFW87XbPYwvM37K3zcIlJLyOfSwo/Aj19Ea2dD3KZwjs0w5Wu1eCyO9X0BsHaphi71Ktt7TKPm4PuoP/rB/NfXtA7bQ6aTVoZ8BbV++ylqdg9En5XDnglLSC4ha8spQ/Ab0gkbl2qsajCi2PzafdvS5ctxrOv9OkmHi/9d3RZ3YQ+MugrpFgghMm4w31UIMbrA8xpCiJ/M3wcKIfqWYZszhRCTbj3trenTuzsNAvxo3LQTo0ZNYeFn7xSrcXd3Y/47M+jZ61FaBnbH29uL7t06AbBi5a8EtepBm7Y9ee+Dz3n/3TcrOnKegX1D+GLB7IrdiEaD/9znOf7EHMLvH4/nwE7YN6xVqMT7sWD0qRmEdXyJ6CV/UnfGUwDYN6yF54BOhHcdz/HHZ+P/zvOg0eDQqDbeT/TgcN8phAe/gluPNtjV88lbn00ND1y6BuY1gsrDIzgIBz8f/u0wlpOTltDo3eIHYYBG7z7PyYmL+bfDWBz8fHDvHgjApYVr2NdtMvuDXyVhw0H8Jj5crjzVgwNx8PNlW4fxHJ20lOal5Gn+7nCOTFzCtg7jcfDzpbo5j/t9TfHu3Yad3aew4/7JnF/0JwDpJyPZ1XM6O4Onsn/oOzR/fwRCW77DnHdwII7+Pqy/9xUOTvqSwPnPlVgXs/4gW/u8Xmx61uUEQsd9QeSvu8uV45qK2nfRP+9iZ/BUdgZP5dBLC8m8dKVMjRcv8/7afO8EDk1ayj3zh5dYd8/85zg0cSmb752Ao78PXt1bAnDwhU/Y3mMa23tMI+avfcSsNTVAL/+yK2962Eufk3npSrkbLzW6t8TZz4c1901k76tf0e6dZ0qsi9pwkL/7lnxMs6pmR+MRvUg4EFGuLMqtUw0Yy3IF8howUspoKeW1I30gcMsNmNulX79e/G/5TwDs3XcQF1cXfHy8CtX4+9UhIuI8CQlJAGzavINBg0wvKT09v21XrZoDhQegV6w2gffg4uxUodtwDAog60IsOZfikDo9Cb/vxL1X20I1br3bEb9qKwCJf+7BpbNpzJp7r7Yk/L4TmasnJzKerAuxOAYFYN+gFukHz2DMygWDkbR/j+Het33e+vzeepaLb39nkX3p2bsNsau3A5B24AxWztWw8XItVGPj5YrW0Z60A6aes9jV26nex/QaDRlZeXVaB9tyZ/Lu3YbL5jwpByKwcnbAtkgeWy9XrBztSTH/Y7i8ejvefdoAUHdYCGc//R1jrh6A3IQ0AIxZuUiD6Z2oxs4aLLDvavRqzaVVOwBIPhiBtbMDdkWyXpuXHZ9SbHpmZAJpJyIt1sVfUfuuIN9B9xHzW9kaXD69WhNp3l8p5v1VUj5rR3tSDpryRa7agU/vNsXWVaNfB6JLaPjVHNSR6DLmK6hWr9ac+2knAIkHz2LjUq3En23iwbMl/mwBWr76MMcW/okhR1fuPOVS8ZdR33FUA6YMhBCOQohNQoiDQogjQohr94OYB9Q3323zPSFEPSHEUfM187OAR83zHi3as2Kuq2f+/jXznTx3Ao0K1NQXQvwthDgghNhhvt7eImrW8CEqMjrv+eWoGGrW8ClUE3H2Ag0b1qdu3VpotVoG9O9F7do18uaPenEYp07sYt7cGYx/pdDA9CrP1sed3MsJec9zY5Kw8fEoXhNtrjEYMaRlYuXuhI2PBznRifnLRidi6+NO5qlLOLdvgpWbIxp7G9y6t8K2hicAbr3akhObRObx8r3DzMvm6052gfw5MYnY+roXq8mJyc+ZHV24xn/aUDoe/Bzvhzpx/t0fy5XHzted7MsFthWThF2RPHa+7mTHJBXIk19Trb4v7u0b03HdbNr/+gYugf55dS6tAui87T06b32Po5O/ymvQlD2rG1nR+TmyYpKw83Ur1zrLl6fi9t01vgPuJfrXXWXPV+D3PauUfFkF88UkFqtx79CYnIRUrp6PLbaNGgPu5bIFGjAOPm5kFsiaGZ2Eg8/N/2zd7qmHQw13ojeFlztLud2Gu1HfaVQDpmyygUFSylZAN+ADIYQApgJnpZSBUsrJ14rNN756A/jRPK/Uo78QojWm6+gDMfXYFHybvwR4WUrZGpgEfG7Zl3V9KSmpvPTyNFYsX8S2Lb9y8UIUBkP+J0Uv+uJbGjW5j2mvzWH6tHG3M1qVlHXmMpcX/kbTlW/Q5IfXuXrsAtJoRGNvQ62xg4l8d2VlRyzk3Dsr2d1qNHE/76TWc70rNYuw0mLt5sjuPjM4OWs5QUvH581LPRjBjvsns6vXdOqPG4DG1rrygt6BrrfvwNQANGblkHEyqnICmtUc1JHLJfS+uAbVx5CVQ3ol50MIWr/5BAff+qFyc1xzF/bAqEG8ZSOAuUKILoAR0w2tvC207s7Ar1LKTAAhxBrzV0egI7Da1FYCwLbEcAXuZ7F48eJSNzTqxWEMH/4EAKGh4dQq0JtSs5Yvl6OLv/P5868N/PnXBgBGDH8Cg9FQrObHH39n4afFx9BUZTmxSdjU9Mx7buPrTm5sYvGaGp7kxiSBVoPW2QF9Ujq5sYnY1sjvrbGp4UFOrOndZ/yKTcSv2ARAnWmPkxudiF1dH+zqeNNy0wcA2Pp60HL9exzuMxXdlZSbzlzz2V7UeDIYgPTws9jV9CSVU3nrzCnwDhggJyYJW9/8nHY1itcAxP68g5Y/TOP8e6tvOgtA3Wd7UvvJ7gCkhJ/FrmaBbRXpMYDiPQt2NfJrsqMTif3LNBg2News0iix8XAiNzF/oPPVM9Hor2bj1Lg2qYfO3VJW/2dDqPdENwCSw89hXyM/h72vO9kx5RvAfKtu576rMbBjiadtrqfesyHUeeJavnPYFfh9ty8ln33BfL4ehWqEVoNv33Zs7zm92LZqDiy5YXOzGj7Tg/rmn21S+DkcCmR1qOFOZuzN/WytHe1waVyLHj+/BoB9dRfu/+YVtj2zoHIG8lbBBkh5qR6YsnkCqA60llIGAnGA3S2uQ0/h/X+j5TVAirkH59qjSUmFUsolUso2Uso2I0eOLKkEMPWYtGnbkzZte7JmzT889YRpuE77dq1IS00jNja+2DLVq5v+2F1dXXjxxWF8tcx0t/SAAL+8mgf69uBMRCWNxK8gGeER2Pv5YlvbC2FtheeATiT9E1qoJvmf/Xg90hUAjwfvJXWn6SqupH9C8RzQCWFjhW1tL+z9fMkIM537t/ZwBsCmpifufTtw5dcdZJ68xP57nuNgu1EcbDeKnJhEDvWcfEuNF4DLX//D/uBX2R/8KlfW7cNnSBcAnFs3wJCeSW6Rc/q58SkYMrJwbt0AAJ8hXUj42/Qa7f3yTydW792WzDPR3KqLX6/PGyQaty6UmuY8rq0D0KdnklMkT058CvqMLFxbBwBQc0gX4sx54taF4nFfMwCq+fuisbYiNzEd+zrV8wbt2tXyxDGgBpmRtz4I+tzXG9jcYzqbe0wn5u9Q6jzSGQC3VgHo0rNKHQ9RUW7HvgNACHz7d7jl8SUXvt6QN8A29u9Qapv3l2urAHSl5NNlZOHaypSv9iOdif3nQN58zy73kBERXazhk59vD2V1+puNrAt5jXUhrxH59wH8HzZdiODRqj65aZk3/bPVpWfxc/NR/N5+Ar+3n0DCwbOV13i5S6kemLJxAeKllDohRDegrnl6OlDaaNKi8y4ADwIIIVphuvU4wHbgGyHEO5h+Pv2AxVLKNCHEeSHEECnlavMpqxZSykOWeEFr122id+/unDqxi8ysLEaMeCVvXuj+9bRp2xMwXWrdokVTAGbP+ZAzZ0zvbEePeobg4M7odHpSklN5bvh4S8S6KZPfnMf+sMOkpKQRPPBJRg9/iof69bLsRgxGzk3/kqYrXkdoNcSt3EzW6UhqTx5KxqEIkteHErdiEw0+HUvQ7s/Qp2Rw+sUPAcg6HUnCH7sJ2vYxUm/g3PSlee+WGn01GSs3J6TOwPlpSzGkZVo2t1nixjA8gltx795PMGTlcmJc/tnHtpveZX/wqwCcmvIlTT4ZjdbOhsRN4SRuCgOg/owncAjwBaMkOyqBk5PLcoPafFc2huEVHMj9ez/GmJXD4XH5l7932jSPncFTATg2ZRkt8i4FDueKeaxB5IottPjoRTpvew9jrp7DY02vx61dY+q/3B+pNyCNkmNTl6Er5+XnsRvD8Q4OpOe/H2LIyuHA+Pxeze4b57K5h6mXoPnrj1F7UEe09jb0OfgpF37Yyon3f8Yt0J8OyyZg7VoNn5BWNJ38MBvvf7XMeSpq3wG439uErOhEsi4Wf/Nys+LN+br/+xGGrBzCC+yvLhvfYXuPaQAcmfp13mXU8ZvDiS8wjqTmwHtL7GXxuLcx2dGJZF4qe76CojeFUzO4Jf13f4AhK5c9E/J/r/tsmMO6EFPvStCModQb2BErexsGhX5CxIqtHPngF4tksJjbeOHEnULczqtFqjohRIaU0tF8M6o/AEcgFOgA9JFSXhBC/AC0ANYBC4E/pZTNhRDumO4hYQ28g+kmWL9jOv20F7i3wDpeA4YB8cAl4KCU8n0hhB+wCPA1r2ellHLWDWJLK5uaFtwLlqPPvYwu4da69m8na09/dvs+VNkxStQx5mcANns/UslJStY9bhVrvYdWdoxS9Y1byS8+j1d2jBINjjWNqbhT91/fuJX84fNYZccoVb/YFSyv8WRlxyjRE9Hfg2kIgsVlrXiz3P/M7R97q0KyVRTVA3MLpJSO5q8JmBocJdUUPSo2N09PovCAXICepaxjDjCnhOnngcodPakoiqLcedQYGEVRFEVRlDuf6oFRFEVRlKquCn6OS3mpBoyiKIqiVHV34Skk1YBRFEVRlKruLrwgRzVgFEVRFKWquwt7YNQgXkVRFEVRqhzVA6MoiqIoVd1d2AOjGjCKoiiKUtWpq5AURVEURalqpFEN4lUURVEUpaq5C08hqUG8iqIoiqJUOaoHRlEURVGqurtwDIy6G/V/n/oBK4qi3Dkq5I7PmQtfKvex3mHMZ+pu1Mqd5SffJyo7QokejlnObt+HKjtGqTrG/Iwu4VxlxyiRtac/AK/UG1rJSUq24MJKZtQremP2O8fsCz8wq+6d+XfxxsXlAMy8Q/PNvLicH+/QYwrAozHLOdmwb2XHKFHj02srbuVqDIyiKIqiKMqdT/XAKIqiKEpVdxf2wKgGjKIoiqJUdXfheFbVgFEURVGUqk71wCiKoiiKUuXchZ/EqwbxKoqiKIpS5ageGEVRFEWp6u7CD7JTPTCKoiiKUtUZZfkfNyCE6C2EOCWEiBBCTC1h/jNCiCtCiHDzY0SBecOEEGfMj2GWeMmqB0ZRFEVRqjhZwYN4hRBaYCEQAkQB+4UQa6SUx4uU/iilfKnIsu7Am0AbTJ8Of8C8bHJ5MqkeGEVRFEWp6iq+B6YdECGlPCelzAVWAgNuMl0vYIOUMsncaNkA9C7zazVTDRhFURRFUW6kJhBZ4HmUeVpRDwkhDgshfhJC1L7FZW+JasAoiqIoSlUnjeV+CCFGCiFCCzxG3mKKP4B6UsoWmHpZvrX8C82nxsAoeVq+/TS+wS3RZ+USOn4xKUcuFKtpNnUIdR/ujI1rNX4LGJ433bNDY1rOehKXJnXY++JnXP5rX5kyuHYLxG/Wc6DVEP/DJi5/9muh+cLGigafjKVaC3/0yemcfmEBOVFXAKj58iC8HgsGg5Hzry8jZWs4AL4jHsD7iR4gBHHLNxCz9K9C66zxQj/qzXyGfc2eQZ+UXqbc1zNj7gK279qHu5srv33/hcXXfzMGvTmMJt2CyM3KYcWkRVw+dqFYzchvp+Ls5YZGq+Hc/pP8/PoypLlbudOwXtz3dE+kwcjxzWH8Oe8Hi+Z74M2nadgtEF1WLj9P+oKYEvI9/e0UnLxc0Wi1XNx/kj9e/xpplPSa9jiNe7TCkKsn6VIcv0xeTHZapkXzFdRr5tM06NYSXVYuv09aTOzR4lkf//ZVHL1c0VhpubTvFOvMWW+HPgXy/TZpMTEl5HuySL6/KjBfkPm4YsjKZd/4xSSXcFy5Z+oQ6j3cGWvXavxS4LjS8IU++D/eDak3kJOYxr5XlpIZlWCxbNU6t8brtRcQWg0pq/8hacnqQvPt2zTH+7WR2DbyI3rCPNL/2ZU3r9aXs7APbEzWgeNEvTDTYpnKzAI/PynlEmBJKbMvA7ULPK9lnlZw+cQCT78E3i2wbNciy24tR1RA9cAoZj7dW+Lk78PfHSdycPJXtJr3bIl1MevD2Nz3jWLTM6MSCB23mMhfd5c9hEaD/9znOf7EHMLvH4/nwE7YN6xVqMT7sWD0qRmEdXyJ6CV/UnfGUwDYN6yF54BOhHcdz/HHZ+P/zvOg0eDQqDbeT/TgcN8phAe/gluPNtjV88lbn00ND1y6BuY1girCwL4hfLFgdoWt/0aadA3E08+XuV3Hs3r6Uh6eM6LEum/HfMz7fabwbs/JOLo70/KBDgAE3NuU5iFt8uZtXfqnRfM17BqIh58PH3Z9hd+mf0n/Oc+VWPfjmE9Y2Gcan/Z8lWruzjQ35zu78wif9nyVz/pMJeF8DF1G97dovoICurXEw8+Hz+6fyJ/TvuKB2SX/nfw05lOW9JnOFyFTqObhRNMH2ldYpoIadGuJu58Pn9w/kT+uk2/1mE/5os90Pg+ZgoOHE80qKJ+v+biytuNEQid/RetSjivR68PYUMJxJeXIRTb0nsE/wdOI/HMfLWc8ZrlwGg3eb44m6vk3ONf3RZwfvB+b+rULlehj4omZuoC0P7cWWzzpq5+Jmfy+5fKUl9FY/sf17QcaCCH8hBA2wFBgTcECIYRvgaf9gRPm7/8Begoh3IQQbkBP87RyuWEDRghRTwhxtLwbus76y/Efr2IVfO1CiDZCiE8qO1NFqdG7NRdX7wAg6WAE1s4O2Hm5FqtLOhhBdnxKsemZUQmknogs17s4x6AAsi7EknMpDqnTk/D7Ttx7tS1U49a7HfGrtgKQ+OceXDrfA4B7r7Yk/L4TmasnJzKerAuxOAYFYN+gFukHz2DMygWDkbR/j+HeN/9g7ffWs1x8+ztkBd5HpE3gPbg4O1XY+m+kec82hP6yHYCLYRHYOzngVN21WF1ORhYAGistWmsr07UCQMcnQti06HcMuXoAMhLTLJqvSc/WhP9i+t2LCovAzskBx5vId+1nFrHjCEaD6eAbGRaBi4+HRfMV1CikNYd+NmW9HBaBrbMDjiX8neQWy1phkUrNFxUWgV0p+Yrvy4rJU7N3ay6YjyuJ1zmuJJZyXInffRxDVm5ejYOvu8Wy2bVoSO7FaHSRsaDTk/bXdhx73FuoRnc5npxTF0r855655xDGq1kWy3Onk1LqgZcwNTxOAKuklMeEELOEENfeNYwVQhwTQhwCxgLPmJdNAt7G1AjaD8wyTyuXSj+FJKXsWNkZboaUMhQIrewcFcXex53M6Pzev6yYJOx93Uo8qFQUWx93ci/ndw/nxiThGNSgeE20ucZgxJCWiZW7EzY+HqQfPJ2/bHQitj7uZJ66RJ2pj2Pl5ogxOxe37q3IOHQWALdebcmJTSLz+MWKf3GVyNnbnZQCP9uU2CRcfNxJv5JSrHbkd9Oo07I+J7ce4tDafwGo7u+Lf7vG9J08FH1OLmvmfE/k4XMWy+fk7UZqdP6xLC02CWcfNzJKyDfsu6nUalmf01vDObZ2b7H5rYd05cifeyyWrVhWH3fSCuzL9NgknLzdyCjh7+SJ76ZQI7A+EVsPcaKErBXBuUi+tNgknEvJ9+R3U6hpzne8gvJZ8rji/1hXYrYcslg2a28P9LH5xxt9bAL2LRtZbP233W04RSmlXAusLTLtjQLfTwOmlbLsMmCZJfPc7CkkrRBiqblltV4IYS+ECBRC/GsebfyruVsIIcRWIUQb8/eeQogL5u+bCSH2mT/c5rAQooF5eob5a1fzsj8JIU4KIZYLIYR5Xl/ztANCiE+EEKX2YQshZgohvhVC7BBCXBRCDBZCvCuEOCKE+FsIYW2uay2E2GZe5z/Xur7M0w+ZW5BjCqy367XtCiHaCSH2CCHChBC7hRCNzNOfEUL8Yt7OGSHEuyVELJh1kXmg1DEhxFsFppf4eoUQ1YQQy8z7MUwIcbOXsN21ss5c5vLC32i68g2a/PA6V49dQBqNaOxtqDV2MJHvrqzsiHeUJU+/w8x2o7CysaJBx+YAaLRaHFwc+XjgDP6Yu5ynF46vtHzfPj2P+e1GY2VjjX/HZoXm3T9mAEaDgUO/7Spl6dtr+dPzWdB2DFY2VvgVyXon+P7p+XzQdgzaOzRfQXUfug/3lv6c/Nyypy//UywwiLequdkemAbAY1LK54UQq4CHgFeBl6WU24QQszB9SM3466zjReBjKeVy8/kzbQk1QUAzIBrYBdwnhAgFFgNdpJTnhRArbiJvfaAb0BTYAzwkpXxVCPEr8IAQ4i/gU2CAlPKKEOJRYA7wHPA18JKUcrsQ4r1S1n8S6Cyl1AshegBzzfsEIND8OnKAU0KIT6WUkSWvhteklEnmDwjaJIRoAZy+zut9DdgspXxOCOEK7BNCbJRSXi24UvPI8ZEAixcvprRO1/rPhOD3RDcAkg6dw6GGB9feK9n7upMVU67PGLplObFJ2NT0zHtu4+tObmxi8ZoanuTGJIFWg9bZAX1SOrmxidjWyD91YFPDg5xY07v6+BWbiF+xCYA60x4nNzoRu7o+2NXxpuWmDwCw9fWg5fr3ONxnKroS3vlXNfc91ZMOj3UHIPLQWVwL7BtXH3dSY0vvvdXn6Di6IZTmIW04vfMIqbGJHPnHNCj70qGzSKOkmrsTV8sx4Ln9UyG0ecz0u3f50DlcauT/ljr7uJMWW/rvnj5Hx4kNB2gS0oazO01nt4Me7kKj4FZ8/ficMmcqTZunQ2g11JQ1+vA5nAvsSycfd9LjSs9qyNFxav0BGvZszbmdFXMmvu3TIbQ257tcJJ+zjztp18mnN+drbMF8Ac+E4F/kuHJNWY4r3p2b0XTcADYPmo3RfBrTEnRxiVj55B9vrHw80cUlXmeJO9xdeDPHm23AnJdShpu/P4CpgeAqpdxmnvYtsLqkBQvYA7wmhKgF/CKlPFNCzT4pZRSAECIcqAdkAOeklOfNNSsw/3O+jnVSSp0Q4gimhtLf5ulHzOtsBDQHNpg7ebRAjLlR4Cql3G6u/x/Qp4T1uwDfmnuRJGBdYN4mKWWq+TUcB+pS+Pr3gh4xNzasAF9MDS7NdV5vT6C/EGKS+bkdUIf8gVJAsZHk8qc3t1GSs99s4Ow3GwDwCQ4k4LmeRP62B/dWAejSs27r6SOAjPAI7P18sa3tRW5sEp4DOnF69EeFapL/2Y/XI13JOHAajwfvJdV80E36J5SGn48nevEf2Hi7Y+/nS0ZYBADWHs7oEtOwqemJe98OHHlgKoa0TPbfkz9YtNW+RRzu/WqFXIVUGXb9bz27/rcegCbdgug0rBdha3ZTNyiA7PTMYqePbBxssa1mT/qVFDRaDU26t+L8vpMAHFkfSkCHZkTsOU51P1+01lblarwA7P3fBvb+z/S717BbIB2G9eTwmj3UCgogJz2r2OkjGwdbbKrZk2HO16h7IBf2nQKgwf0t6PzCg3z56NvosnPLlaskod9tIPQ7U9YG3QNpO6wnx9bsoea1rEX+TqwdbLF1tCcjPgWh1dCgexCX9p+0eK5r9n+3gf0F8rUb1pOjBfdlkXw2DrbYmPNpKiBfxDcbiDAfV3yDA2nwXE8u/bYHjzIcV1yb16XNu8PZ9vh8ciw89ir7yGls6tXAupY3urhEnB/oQvQr1+00v6NV9Cfx3olutgGTU+B7A+B6nVo9+aem7K5NlFL+IITYCzwArBVCvCCl3HyD7ZR1jE6OeZtGIYRO5o/QNJrXKYBjUspCI7bMDZib8TawRUo5SAhRj8KXg93UaxBC+AGTgLZSymQhxDcU2F+lEJh6k07dZM6bFrspHJ/gQHrvWYAhK5fQCYvz5vXYMJeNIdMBuGfGY9Qe1BGtvQ19D3zKhR+2cPyDX3Br6c+9yyZg4+qAb0gQTSc/xIauU24thMHIuelf0nTF6withriVm8k6HUntyUPJOBRB8vpQ4lZsosGnYwna/Rn6lAxOv/ghAFmnI0n4YzdB2z5G6g2cm740b+Bdo68mY+XmhNQZOD9tKYYKvMS2JJPfnMf+sMOkpKQRPPBJRg9/iof69bpt2z+xJYwm3QKZvu1jdFk5rJicfyn3xLXz+KDvVGwc7Bj+5WSsbKwQGg0Re46xe7npn9C+VVsY+u6LTP7nPQw6PSsmfm7RfKe3hNOwWyCvbPuQ3Kwcfpmc/7s3Zu1cFvadjrWDLU9+ORErG2uERnBuz3H2L98IwINvPYOVjTXPfm869R4ZFsGa1yx6qj3Pmc3hBHQL5KXtC9Bl5bJmUn7WkWvnsqTvdGwcbHn0y1fysl7Yc5zQ7zdVSJ6S8jXoFshYc77fC+R7ce1cvjDvy8cK5DtfgfliNoXjGxzIA3sWoM/KZV+B40rPDXNZbz6utJjxGHUHdcTK3oZ+Bz7l3A9bOPbBL7R8/XGsqtnRcck4ADIvJ7DzmQWWCWcwEjdrEbW/mg1aDak/rSc34hKeY58k++gZMjbvxe6eBtRc+DpaZ0ccu7XHc+yTnH9gFAB1fngXG//aaBzsqL/9O2Knf8TVnQctk025KeJGV1+Y/0H/KaVsbn4+CXAEBmE61bJDCDETcJFSThBCfAkckFIuEkKMB8ZLKesJIfwx9eRIIcT7QJSU8iMhRIaU0lEI0RWYJKV80LydzzANmv0R02mVzlLKC0KI5eZtPVhK3plAhpTyffPzDCmlY8F5wCfAceApKeUe87iYhuYR1YeB0VLKnUKI+cADUsrmBfOZT0V9L6X82bzOZ8yv8RmgzbX7QJjHrrwvpdxaQs6WwHeYTjdVBw4DU673eoUQcwFnTKfupBAiSEoZdt0fIMiffJ+4QUnleDhmObt9H7pxYSXpGPMzugTLDVa1JGtPfwBeqTe0kpOUbMGFlcyo93hlxyjV7As/MKvunfl38cbF5QDMvEPzzby4nB/v0GMKwKMxyznZsG9lxyhR49NrwfRG1OIypgwu9zkkx/m/VEi2ilKez4EZBrxn/ocfCMwyT38fGCWECAM8C9Q/Ahw1nxpqjumf9w1JKbOA0cDfQogDQDqQWo7cmO/j8DAw3zxYNxy4djXUs8BCc87SfpjvAu+YX2OZeomklIeAMEzjaX7ANObnRq/3bUynqw4LIY6ZnyuKoih3u9twN+o7zQ3/+UopL2BqcFx7XvCTezqUUH8SaFFg0gzz9HnAvBLqHc1ft1LgVEyRu1lukVI2Nl+VtJDrXM4spZxZ0vqLzjOP6elSwvIHgJYFJr1aNJ+Ucg/QsITX+A3wTYF1ldhLVGD+M6XMKvH1mhs3L1xvnYqiKMpdqApeRVReVeWTeJ8394gcwzSAdvH1y6u8u+31KoqiKOWhemDuTFLKD4EPC04TQjwLjCtSuktKOYY7jHnwsm2RyU9JKY+UVF/S61UURVEUJV+VaMCUREr5NabPbLnjSSlvz41QFEVRlLvS7bpZ6J2kyjZgFEVRFEUxUw0YRVEURVGqnLvwg+yqyiBeRVEURVGUPKoHRlEURVGqOnUKSVEURVGUKkc1YBRFURRFqWpudFug/yLVgFEURVGUqu4u7IFRg3gVRVEURalybng3aqXKUz9gRVGUO0eF3PE5bXhIuY/1zl9tqFJ3o1ankBRFURSlilOfxKv8J4XWGljZEUrUJuo3Nns/UtkxStU9bhWv1Bta2TFKtODCSgB0CecqOUnJrD39yT23r7JjlMrGvx3Z4X9WdowS2QWabmKfc2Z3JScpmW2Djly+t3tlxyhVzT2bOdu8V2XHKFH9o/9U3MpVA0ZRFEVRlCrn7vsgXjWIV1EURVGUqkf1wCiKoihKFafGwCiKoiiKUvWoBoyiKIqiKFWOGgOjKIqiKIpy51M9MIqiKIpSxakxMIqiKIqiVD134Skk1YBRFEVRlCpO9cAoiqIoilL13IU9MGoQr6IoiqIoVY7qgbmLOXcNos5bI0CrIWHFBmIX/lJovrCxwu+j8Ti0qI8+OZ1zo94nNyqeaoENqDt/tLkIohesJOXvvQB4j+iH52MhICWZJy9yYeKnyBydRfI2mPMsHsFBGLNyOD72czKOnC9W49TCjyafjEFjZ0PipjDOvPY1AH5THqV67zZIo0SXkMrxsZ+TG5dskVzXDHpzGE26BZGblcOKSYu4fOxCsZqR307F2csNjVbDuf0n+fn1ZXldv52G9eK+p3siDUaObw7jz3k/WDRfaWbMXcD2Xftwd3Plt++/uC3bLGhn6GHmf/E/DEYjg3t3ZcQj/QrNj45L4I0Pl5KUmo6LUzXemTwKn+ruRMclMP7tjzBKiV5v4PH+ITzyQLDF8+0KP8n8b37DaDQyqHt7hg8svI2YhGRmLFxBemYWRqNk3OMP0DmoCZfjkxj0ynzq1fAC4J4GdXn9+Yctmm3ngSPMX/IDRqORwT27MHzIA4XmR8cn8MZHy0hOS8fFsRpzJ43Ex9Odk+cuMXvhd1zNykKj0fD8Iw/Su0t7i2YryrZDW1zGv4TQari6Zi0Z/1tRaL7j0Idx6N8XDAYMKamkzHkPQ2xchWayv68NnlNfRGi1pP28jpSvVhWab9e6OZ5TXsSmoT9xk+dydcPOQvNFNQfq/L6Eq5v3kDB3YYVmvRF5F/bAqAbM3Uqjoc7sFzj9+JvoYhJp8td7pKzfR/aZqLwSz6Eh6FMzONppFG79O1Fr+tOcG/0+WScvcrzvRDAYsfZyo+n6D0nZsB/r6q54PfcgR7u/jMzOxX/RZNz7dyZx9eZyx/UIDsLBz4d/O4zFuXUDGr07ggN9XitW1+jd5zk5cTFpB87Q8odpuHcPJGlzOJcWruH8/B8BqDWiD34TH+bUq0vLneuaJl0D8fTzZW7X8dQNCuDhOSP4eOCMYnXfjvmYnIwsAJ5ZNIGWD3Qg/I89BNzblOYhbXi/zxQMuXocPZwtlu1GBvYN4fGH+jP97fdv2zavMRiMzFn4LUvmTsHH052h496gW/tW1K9bM6/m/S9/oF9wJwaEdGZv+DE+/mYV70x+kerurny/4E1sbKzJzMpm0IvT6NqhFV4ebpbLZzQyd9kvLH7tBbw9XHh82kd0bdOM+rV88mqW/rKRXvcG8kjPjpyNiuWleV+y7jPTz76Wtyer3p1osTyFshmMzF30P5bMnoS3hzuPTZhF1/aB1K+Tv+8++OpH+gV3ZEBwJ/YeOs4n3/7E3IkjsbO1Yc4rI6hb04f4xGSGjn+Ljq3uwdnRoUKyotHgOnEcCeMmY4i/gteyRWTv2I3+wsW8ktzTEVx9dhQyJ4dqg/rjPGYkya+/XTF5zJmqzxhD9PPT0McmUOvHT7m65V905y7llehjrhA/4wNcnym54en+8tNkHThacRlvxW1owAghegMfA1rgSynlvCLzXwFGAHrgCvCclPKieZ4BOGIuvSSl7F/ePP/pU0hCCFchxOgb1NQTQjx+E+uqJ4S4Q35Ty69aYANyLsSQeykOqdOT9PtOXHsWfgfm2rMdiau3AJD8126cOrUAwJidCwbTX4uwtYYCY8eElRaNnQ1oNWjsbdDFJVkkr2fvNsSu3g5A2oEzWDlXw8bLtVCNjZcrWkd70g6cASB29Xaq92kLgMHcaADQOtgipWUHvDXv2YbQX0z5LoZFYO/kgFN112J11xovGistWmurvH3X8YkQNi36HUOuHoCMxDSL5rueNoH34OLsdNu2V9CR02epU8Ob2r5eWFtb0ef+Dmz590ChmnOXomkf2BSAdi2bsmWPab61tRU2NtYA5Op0GC38MwU4GnGJ2t4e1PL2wNrKit4dg9i6/1ixuoysbNPXzGyqu92exufR0+eo4+tFLR/TvuvdpR1b/g0rVHMuMpr2LZoA0K5Fk7z59Wr6ULemqRHm5eGGu4szyakV9ztn07Qx+qjLGKJjQK8nc+Nm7Lp0LFSTezAcmZNj+v7YcbRe1SssD4DtPY3QXYpGHxULej0Z67ZSrfu9hWr00XHknj6PNBZvHdg0DUDr4UbW7gPF5lUGaSz/43qEEFpgIdAHaAo8JoRoWqQsDGgjpWwB/AS8W2BelpQy0Pwod+MF/uMNGMAVuG4DBqgH3LABcyuEEHd8z5aNrzu5MQl5z3NjE7HxdS9c41OgxmDEkJaJlZvpH121oAY02/QJzTZ+zMVpi8BgRBebROzi32ixdyktD36NIT2TtO3hFslr6+tO9uX8vDkxidgWyWvr605OTGLe8+zowjX+04bS8eDneD/UifPv/miRXNc4e7uTEp2/7ZTYJFx83EusHfndNGYdWEzO1WwOrf0XgOr+vvi3a8y432Yz5sc3qN3C36L57lTxCcn4VM/fT96e7sQlFj6119C/Dht3hQKwaXcoV7OySUlLByD2SiKDR00n5OnxPDfkAYv2vgDEJ6Xi4+Ga99zLw4W45NRCNaOG9OKvHQcIGTWLMfO+ZOqzg/LmXb6SxCNTPuC5mQs5eOKcRbPFJSbjXWTfxRfdd3612Wj+B7tpzwHzvssoVHPk1Dl0ej21fb0smq8gTXVPDPHxec8N8Qloq5feQHHo15ecPfsqLA+AlZcH+tgrec/1cQlYeXne3MJC4Dl5JInvW64XtwpoB0RIKc9JKXOBlcCAggVSyi1Sykzz03+BWhUZ6L/egJkH1BdChAsh3jM/jgohjgghHi1Q09lcM8Hc07JDCHHQ/Oh4nfXnEUI8I4RYI4TYDGwSQrgLIX4TQhwWQvwrhGhhritt+kwhxLfmbV8UQgwWQrxrzvq3EMLaXDdPCHHcvPzt7/M3uxp2hmPBYznxwGR8X3oIYWuN1qUarj3bceTeFzjc+jk09na4D76/siIWc+6dlexuNZq4n3dS67nelZZjydPvMLPdKKxsrGjQsTkAGq0WBxdHPh44gz/mLufpheMrLd+dZtKIxwg9cpIhY2YQeuQkXh5uaDSmQ5dPdQ9+WTSXv756nzUbd5JQpHFxO6zbFUb/+9uyYdEbLJw6gtc+W4HRaKS6mzP/LJzBqvkTmfR0f6Z++j0Zmdm3NdvE5x7lwNFTPDL2TUKPnCq07wCuJKUwfcFSZo0fXmh6ZbLv1QObxg1JX27ZNxmW5Dy0H5nb92OIS7hx8e1itMDj+moCkQWeR5mnlWY4sK7AczshRKj5/97Am3lJN3LH9xSU01SguZQyUAjxEPAi0BLwBPYLIbabayZJKR8EEEI4ACFSymwhRANgBdDmJrfXCmghpUwSQnwKhEkpBwohugPfAYHAW6VMB6gPdMPUPbcHeEhK+aoQ4lfgASHEDmAQ0FhKKYUQriWFEEKMBEYCLF68mFYl1OTGJGHjm/9uw8bHg9yYwqd7cmNNNbqYRNBq0Do7oE9OL1STHRGF4Wo29o3qYFvbm5zIePRJpq7olHV7cGzdmKRftt3Uziuq5rO9qPGkacBkevhZ7Gp6ksopAGx9PcgpkjcnJglbX4+853Y1itcAxP68g5Y/TOP8e6vLlOua+57qSYfHugMQeegsrjXyt+3q405qbOmnz/Q5Oo5uCKV5SBtO7zxCamwiR/4xveO8dOgs0iip5u7E1aT0UtfxX+Dl6Ubslfz9FJeQhHeRXhQvDzc+en0cAJlZ2WzYuR9nx2rFagLq1uLg0VP07NzOcvncXYhNTMl7Hp+YirebS6GaX7fsZdG05wFo2bAeOTodyelX8XBxwsbadIht6l+b2t6eXIy5QrP6tS2SzdvDjbgi+65oD5SXhxsfvvYyYNp3G3cfyBvnkpGZxZi3PuTlpwbTsnF9i2QqjfFKAlqv/B4erZcnhitXitXZtm2F0zNPkDB6AugsM/i/NPr4RKx88nuBrLw90cffXIPErmUT7Fo3x3nog2gc7BHWVhgzs0j6aFlFxb0hSwziLfi/w2yJlHJJGdbzJKb/mwXfwdaVUl4WQvgDm4UQR6SUZ8uT985oct8enYAVUkqDlDIO2Aa0LaHOGlgqhDgCrMbUmLhZG6SU144onYD/AUgpNwMeQgjn60wHWCel1GEa6KQF/jZPP4LpVFcqkA18JYQYDFzrqitESrlEStlGStlm5MiRJZVw9dAZ7Px8santhbC2wn1AJ1I2FO6yTdmwD48h3QBwe6Aj6btM469sanuB1vSrY1OzOnb1a5EbGU9u9BUcgxqaxsAATp1akB0RRVld/vof9ge/yv7gV7mybh8+Q7oA4Ny6AYb0THLjUwrV58anYMjIwrl1AwB8hnQh4W/TqQd7v/xBl9V7tyXzTHSZc12z63/r+aDvVD7oO5Uj60NpM9iUr25QANnpmaRfKZzPxsE2b1yMRquhSfdWxJ815TiyPpSADs1M+fx80Vpb/ecbLwDNG/pzMTqWqNh4dDo967b9S9cOhZvcyanpGM1jEL788Q8G9TQdE2OvJJGdkwtAavpVwo6fpl4tX4vma1a/NpdiE4iKT0Sn1/P37jDub9OsUI2vpxt7j5rGXZ2LiiNXp8fd2ZGktAwM5txRcYlcjLlCLW+PYtsoc7aGflyMjicq9go6nZ6/t++ja/ugQjWF9t3qvxgU0hkAnU7P+Nmf0q/7ffTsVNJh0LJyT5zEqnZNtL4+YGWFQ4/uZO/YU6jGumEArq++QuLkGRiTUyo8U87RU1jXqYlVTW+wssKxT1eubvn3ppaNnzqfSyFPcanXMBLfX0r6mk2V2ngBy4yBKfi/w/wo2Hi5DBRsfdcyTytECNEDeA3oL6XMycsn5WXz13PAViCo6LK36r/eA1MWE4A4TD01GkwNhpt1tZzbzgGQUhqFEDqZP9LUCFhJKfVCiHZAMPAw8BLQvUxbMhi59PpSGi5/EzRaEn/cSPbpSGpMeoyrhyJI3bCfhJUb8ft4PM13LsKQks7Z0R8A4NiuKb6jByP1BqTRyKXXFqNPTkefnE7y2t00+XsB6A1kHjvPleX/lHOXmCRuDMMjuBX37v0EQ1YuJ8Z9njev7aZ32R/8KgCnpnxJk09Go7WzIXFTOImbTIMW6894AocAXzBKsqMSODn5lt9UXNeJLWE06RbI9G0fo8vKYcXk/MuRJ66dxwd9p2LjYMfwLydjZWOF0GiI2HOM3cs3ALBv1RaGvvsik/95D4NOz4qJn5e2KYub/OY89ocdJiUljeCBTzJ6+FM81K/Xbdm2lVbL9FFP8+KM9zAYjAzq2YWAurX47LufadbQj24dWrH/8Ak+/mYVQghaN2/Ea6OHAXAu8jLvL12BECAlDBvch4Z+lundKJhv2nODGTV3CUajZGDXdgTU9mHhqr9p5l+Lrm2aM/GpfsxavJrv/9qOEIJZo4YihODgiXMsXPU31lotQghmPP8wLha8ysdKq2X6i08w6o0PMBiNDAzpTEDdmiz8/leaNqhHt/ZB7D9ykk++/QkhBK2aN+S1UU8B8M/OfRw8dprU9AzWbDRdGvz2hBE09q9jsXyFGIykfPApnh/NB42Wq3+uQ3/+Ak7PP4PuxGmyd+7G+aUXEA52uM9507RIXDxJrxa/ks+SmRLmLsR38VyEVkPar+vRnb2I25inyTl2msyt/2LbvCE+H72BxtmJal074D7maSIHlvymsLLdhsuo9wMNhBB+mBouQykyflQIEQQsBnpLKeMLTHcDMqWUOUIIT+A+Cg/wLRNh6asx7iRCCA/goJSyrrnH4gWgL+AOhALtMZ3DWyClvN+8zIdAlJTyAyHEs8Ay09kaUQ/4U0rZvJRtPYNp9PVL5uefAFeklG8LIboCH0opg64zfSaQIaV837x8hpTS0fz9TCAD+AJwkFLGCyFcgHNSyhu9pZOhtQbe0n67XdpE/cZm70cqO0apuset4pV6Qys7RokWXFgJgC7BsgNDLcXa05/ccxU7CLM8bPzbkR3+Z2XHKJFd4IMA5JzZXclJSmbboCOX7y3b+6bboeaezZxtfnsa4Leq/tF/AERFrDuu2/3l/mfuvWXbdbMJIfoCH2E6Q7BMSjlHCDELCJVSrhFCbATuAWLMi1ySUvY3jyVdjOnNuAb4SEr5VXnz/qd7YKSUiUKIXebLn9cBh4FDmC5efVVKGSuESAQMQohDwDfA58DPQoinMZ3CKWuvykxgmRDiMKZTPcNuMP1mOAG/CyHsMP0RvFLGbIqiKMp/iayQdlHhTUi5FlhbZNobBb7vUcpyuzE1bCzqP92AAZBSFr1EenKR+TqKn4ZpUeD7Kea6C0CJvS/m+d9gagBde54EDCyhrrTpM4s8dyxlnuVGKCqKoij/CeqTeBVFURRFqXKkseJ7YO40qgFzi4QQvYD5RSafl1IOKqleURRFUSqa6oFRbkhK+Q9gmUtrFEVRFEUpE9WAURRFUZQqTt6GQbx3GtWAURRFUZQqTp1CUhRFURSlyrkbB/HeTbcSUBRFURTlP0L1wCiKoihKFfcf/lD9UqkGjKIoiqJUcXfjKSTVgFEURVGUKk41YBRFURRFqXLuxlNI/+m7USuA6caViqIoyp2hQrpKzrcMKfex3u/QhirVjaN6YBRFURSlilOnkJT/pPC6/Ss7QokCL65hrffQyo5Rqr5xK5lRr+jNzO8Msy/8AEDuuX2VnKRkNv7t0CWcq+wYpbL29Cfn6IbKjlEi2+YhAOjiTlVykpJZezdih8/DlR2jVJ1jf+Jg7QGVHaNErSJ/r7B1q0/iVRRFURSlylGfxKsoiqIoSpVjvAt7YNQn8SqKoiiKUuWoHhhFURRFqeLUGBhFURRFUaocdRWSoiiKoihVzt34kW5qDIyiKIqiKFWO6oFRFEVRlCpOnUJSFEVRFKXKuRsvo1YNGEVRFEWp4tRVSIqiKIqiVDl34yBe1YC5iznd34qab45AaLUkrlxP/KKfC80XNlbUWTABh3sC0CencfGl98iNigfArnE9ar8zGo2jAxiNnO4/EZmjI2DlHKy83JDZuQCcfepN9ImpZc7YdM4wqgcHYcjK4fDYRaQduVCsxrmFHy0/GYXGzoYrm8I4/tq3efPqDu9F3Wd7Ig1G4jeGcertH3AJqs897z9vfpGCM+/9RNy6/WXOeM0Dbz5Nw26B6LJy+XnSF8QcK5716W+n4OTlikar5eL+k/zx+tdIo6TXtMdp3KMVhlw9SZfi+GXyYrLTMsudCWBn6GHmf/E/DEYjg3t3ZcQj/QrNj45L4I0Pl5KUmo6LUzXemTwKn+ruRMclMP7tjzBKiV5v4PH+ITzyQLBFMt2sGXMXsH3XPtzdXPnt+y9u67av2Rl2nPnLfsJoNDI4uCPDB/csND/mShIzPv0f6ZlZGAxGxj85gM6tm6HTG5i5aDknzkViMBjp17UdIwb3smy2vQeY98mXGIwGHnqgJyOeLHyPoujYeF6f9wlJKam4ODsxb8Yr+Hh5AvDBoq/ZvicUo1Fyb9tApo19HiHK/i7ef/ZzuAcHYczK5dS4z7h65HyxGscW/jT8eAwaOxuSNoVxbsYyAKxcHWm8eAJ2tb3Ijozn5MgF6FOvAuDSsRn+s55BWFuhT0rj8KA3Aagx8kF8nggGKbl64hKnxy9E5uhumNO5axC1Zj4PWg2JKzYQ93nx4169jyZgf099DMnpnB9tOu7Z1PKi6ZbPyD57GYCrB08TOX2RKcurT+L+UDe0LtU41PjOvb/bf41qwNytNBpqvf0CZ594A11sIg3XfEDqxn3knInMK3F/NARDagYn7n8B136d8Z06jIsvvQdaDXU/eoWLExaQfeICWlcnpM6Qt9zFcQvIOhJR7ojVgwNx8PNlW4fxuLYOoPm7I9jdZ0axuubvDufIxCWkHIigzQ9Tqd49kCubw3G/rynevduws/sUjLl6bDydAUg/GcmuntORBiO2Xq502jKf+PUHkIay30ykYddAPPx8+LDrK9QKCqD/nOdYPPCNYnU/jvmEnIwsAB5bNJ7mD3TgyB97OLvzCBveXYnRYKTn1KF0Gd2f9fNWljnPNQaDkTkLv2XJ3Cn4eLozdNwbdGvfivp1a+bVvP/lD/QL7sSAkM7sDT/Gx9+s4p3JL1Ld3ZXvF7yJjY01mVnZDHpxGl07tMLLw63cuW7WwL4hPP5Qf6a//f5t22ZBBoORuUtXseSNl/D2cOWxKe/Rte091K/tm1ez5Ke/6dmxFY/27szZyBjGzFnE361nsX7PQXQ6Pb98+BpZObkMGjebPp3aUNPLw0LZDMz+cDFLF8zCp7oHj46cSLdO7ahfr05ezfufL6N/r24M6BPM3gOH+GjJd8yb8QphR04QduQEv3z9CQBPvzSV/eFHaRd0T5myuAUHYe/vS+i9L+PUqgEB80dyqO+0YnUB85/nzMQvSD94hmY/vIZb9yCSN4dR6+WBpOw4QtRnv1HrpYHUenkQF2Z/j9bZgYB5Izj62BxyLidgbf4btvFxp+aIPhzoMgFjdi6Nl7xC9YH3Ef/j1usH1WioPfsFzjz+JrqYRBr9+T6pG/aRXeC45zE0BH1KBsc7v4hb/87UnD6M86PfAyDnYiwne08ottqUDfuI/+Yvmm1fVKb9Zwl34xiYO+4yaiGEqxBi9A1q6gkhbnibYHPd0evMf0YI8VlZclpi+crkENiAnAsx5EbGIXV6kv/YgUtI+0I1LiHtSfp5MwApa3fhdF9LAJy6BJF18gLZJy4AYEhJB6Pl7yTm3bsNl1dvN23/QARWzg7YerkWqrH1csXK0Z6UA6YG0+XV2/Hu0waAusNCOPvp7xhz9QDkJqQBYMzKzWusaOysLdL32qRna8J/2QFAVFgEdk4OOFZ3LVZ3rfGisdKitbZCmrcdseMIRnOmyLAIXHws80/uyOmz1KnhTW1fL6ytrehzfwe2/HugUM25S9G0D2wKQLuWTdmyxzTf2toKGxtrAHJ1OoyV0EfdJvAeXJydbvt2rzkacYE6Pp7U8vHE2tqK3p1asWX/4UI1QgiuZmUDkJGZRXV3F9N0BJnZuegNBnJyc7G20uJob2exbEdOnKFOTV9q1/DB2tqaPsGd2bxzb6GasxciadeqBQDtWrVgi3m+EILcXB06vZ5cnR6d3oCHm2uZs3j0akv8qq0ApB88g5WzA9ZF/latvVzROjqQfvAMAPGrtuLRu23e8nHm5eMKTPca3JmEv/aSczkBAJ35bxhAaLVo7GxAq0Fjb0tubPINc1YLbEDOhVhyL5mPe2t24NKzXaEa157tSfrJdNxL/msXTve1uOF6M8NOo4+/8fYrkpSi3I+q5o5rwACuwHUbMEA94IYNmMoihLjje7asfTzQxSTkPdfFJGBd5J+mtY8HumhzjcGIIf0qWjcn7PxqgpT4fzeThn99iNcLgwstV+f9sTRa+xHeYx8tV0Y7X3eyLyfmPc+OScLO1714TUxSfk10fk21+r64t29Mx3Wzaf/rG7gE+ufVubQKoPO29+i89T2OTv6qXL0vAE7ebqRG5+dIi03C2afknoph301l2oEvyLmaxbG1e4vNbz2kK6e3hpcrzzXxCcn4VM/fZ96e7sQlFj7QNvSvw8ZdoQBs2h3K1axsUtLSAYi9ksjgUdMJeXo8zw154Lb2vtwJ4pJS8fbMf83e7m7EFzklOurRvvy5fR89np/B6DmLmDZ8CAAh9wbhYGdD8IjX6PnCGwzrH4yLUzWLZYtPSMw7HQTgXd2T+CuJhWoaBfixcfseADZu38PVzCxSUtMIbN6YtkH30G3QM3QbNIz72gVRv17tMmex8fUgJzp/27kxSdj6Fj6e2Pp6kBOTX5MTk4SNucamuiu6+BQAdPEp2Jgb//b+vli5OnLPL28R+M98vIbcb1p/bBJRi9bQ7sAiOhxeiiEtk5Rth26Y09rHg9zogse9xBKOe+75NQWOewA2tb1pvO5DGqyeQ7V2TW9iz9w+Upb/UdXciQ2YeUB9IUS4EOI98+OoEOKIEOLRAjWdzTUTzD0tO4QQB82PjrewvdpCiK1CiDNCiDevTRRCPCmE2GfexmIhhNY8/VkhxGkhxD7gvgL13wghvhBC7AXeFUIECiH+FUIcFkL8KoRwM9eVNn2rEOJDIUSoEOKEEKKtEOIXc67Z5ppqQoi/hBCHzPukfC2EsrLSUK1tUy6O+4AzD03BpXcHHM3vUi6O+4BTvcZyZsg0qrVtitvgbpUSEUBYabF2c2R3nxmcnLWcoKXj8+alHoxgx/2T2dVrOvXHDUBja33bcn379DzmtxuNlY01/h2bFZp3/5gBGA0GDv2267blmTTiMUKPnGTImBmEHjmJl4cbGo3p0OBT3YNfFs3lr6/eZ83GnSQkl30803/Vuh2hDOjWgY1LZ/P5a6OY/sl3GI1GjkZcQKPRsHHpHNYteotv/9hMVGzCjVdoQZNGP0to+FEeHj6O0PBjeFf3QKPRcCkqmnMXo9j00zI2//w1+w4e5sChY7c12/Vc65kUVlocW/hz7Mm5HH1sNnUmPGxq1LhUw6N3W/a3G8PeliPRONhS/aHOFZpJF5/E0fYjONlnAlGzluH36UQ0jvYVus1bYZSi3I+q5k7sKZgKNJdSBgohHgJeBFoCnsB+IcR2c80kKeWDAEIIByBESpkthGgArADa3OT22gHNgUzz+v8CrgKPAvdJKXVCiM+BJ4QQG4C3gNZAKrAFCCuwrlpARymlQQhxGHhZSrlNCDELeBMYD3xXynSAXCllGyHEOOB383aSgLNCiA+BrkC0lPIB8+t2KekFCSFGAiMBFi9eTLsSanSxiVj75r97s/b1RBebWLymhnm6VoPWqRqG5HR0MYlc3XsMQ7LpXXralgPYN69Pxq7D6OJMvRDGq1mk/L4Nh8CGJP+ypZRdX1zdZ3tS+8nuAKSEn8WuZv67o6K9LVC8V8auRn5NdnQisX/tAyA17CzSKLHxcCI3MT2v/uqZaPRXs3FqXJvUQ+duOidA+6dCaPOYqYF2+dA5XGrk53D2cSftOl3a+hwdJzYcoElIG87uNJ3lDHq4C42CW/H143NuKcf1eHm6EXslf5/FJSThXaQXxcvDjY9eHwdAZlY2G3bux9mxWrGagLq1OHj0FD07l/Qb9d/k7e5CXEL+zzEuKRkvj8J/dr9u2sOi18cA0LKRPzm5OpLTr7J2Ryj3BTbF2kqLh4sTQY39OXb2ErV8PLEEL08PYuPzG0RxVxLwqu5RrObjOdMByMzMYuP23Tg7OfLTn+tp2awhDg6mf8Cd2rfm0LGTtG5ZuEF9Pb7P9jYNogXSw89iWyN/2za+7oV6WwByYhIL9crY+rqTa67JvZKCtZepF8bayxVdgqmhnBOdiC45HWNmDsbMHFL/PU61ZvUAyL4Ujy7RdEopce1enNs24srPO66bWRebiE2Ngsc9jxKOe0nYlHDcAzDkmr5mHTlLzsUY7Pxrknm4/OP9lLK5E3tgCuoErJBSGqSUccA2oG0JddbAUiHEEWA1cCt9exuklIlSyizgF/M2gzE1HvYLIcLNz/2B9sBWKeUVKWUu8GORda02N15cAFcp5Tbz9G+BLqVNL7D8GvPXI8AxKWWMlDIHOAfUNk8PEULMF0J0llKW+HZYSrlEStlGStlm5MiRJb7ozENnsPWrgU1tb4S1FW79OpO2ofDpjLSN+3B/yNSYcO17H+m7Tef+07cdxK5xXYT5/LNj+2amwb9aTV5XK1ZanIPbkn3qYonbL83Fr9ezM3gqO4OnErculJpDTLvHtXUA+vRMcszdzNfkxKegz8jCtXUAADWHdCHub9PpkLh1oXjcZzogV/P3RWNtRW5iOvZ1qiO0pl99u1qeOAbUIDPyyi3lBNj7vw0s7DudhX2nc3x9KIGDTe8AawUFkJOeRcaVwlltHGzzxsVotBoadQ/kytloABrc34LOLzzI9yPeR2e+gssSmjf052J0LFGx8eh0etZt+5euHVoVqklOTcdoHsP05Y9/MKinqZs+9koS2TmmLKnpVwk7fpp6tXy5mzQLqMvFmCtExSWg0+n5e+dBurYpPCbCp7o7ew+fAuBcVCy5Oh3uzo74erqz76hpemZ2DodPX8CvprfFsjVv3IBLUdFERcei0+lYt2kH3e4rPI4tOSUt72e7dPlPDOrbAwBfr+qEhh9Drzeg0+sJDT+Kf91bO4UU8/XfhPWYTFiPyST+vQ+vR7oC4NSqAYb0zLxTQtfo4lMwZGTi1KoBAF6PdCXxH9PVf0nrQ/E2L+9dYHriP/txadfEPM7FBqdWDcg8E0VOVAJOrRuisbcBwLXzPWSduXzDzFcPncG2ni82tb1Mx73+nUndsK9QTcqGfbg/bDruuT1wH+m7TMc9K3dnMPdM2tTxxtavBjmXYm9pn1Wku3EMzJ3YA1MWE4A4TD01GiD7FpYteuZPAgL4VkpZaBi9EGLgDdZ19Ra2W5Ic81djge+vPbeSUp4WQrQC+gKzhRCbpJSzyrQlg5GoNxbj/91MhFZD0qqNZJ+JxOeVx8k8HEHaxn0k/riBuh++QpNti9GnpJuuQAIMaVe58uXvNPxjAUhJ2pYDpG0ORWNvS/3/vYWwsgKthoyd4SSuWF/mnXFlYxhewYHcv/djjFk5HB6Xfxltp03z2Bk8FYBjU5bRIu8y6nCubAoHIHLFFlp89CKdt72HMVfP4bGfA+DWrjH1X+6P1BuQRsmxqcvQJaUX2/6tOL0lnIbdAnll24fkZuXwy+TFefPGrJ3Lwr7TsXaw5ckvJ2JlY43QCM7tOc7+5RsBePCtZ7CysebZ702/cpFhEax5bVm5MgFYabVMH/U0L854D4PByKCeXQioW4vPvvuZZg396NahFfsPn+Djb1YhhKB180a8NnoYAOciL/P+0hUIYTo/PmxwHxr6lX2cRFlMfnMe+8MOk5KSRvDAJxk9/Cke6mfZS5Gvx0qrZfqIRxj19kIMRsnA7h0IqOPLwhV/0jSgDt3atmDSsEG8tWgF//tzC0LA2y89hRCCob278PrC7xk0bjYSGNCtAw3r1bzhNm86m5WW6eNf4IVJMzEYjQzq24MAvzp89tVymjUKoFun9uwPP8JHi78z/WxbNmPGhBcB6Nm1I/sOHmbQMy8jhKBT+1Z0va/sPWvJGw/iHtyKNv9+hjErh9PjP8+bF7TxPcJ6TAYgYuqXeZdRJ28OI3mTqQM78tNfabJkIj6PB5MddYWTIxcAkHXmMklbwmi95QOkURK7fBOZJ01XDCX8uYeg9e8hDQYyjpwn5n8bbhzUYCTy9SUEfG867iX+uIns05H4TjQd91I37CNx5QbqfTSBpju+wJCSzvkxpivgHNs3w3fi40i9HoySyGmLMKRkAFBz+jDcBnZBY29L831fkbhiAzEflv8qwltRFU8BlZeQd9jIHSGEB3BQSllXCDEYeAHTP2x3IBRTL0hNYIGU8n7zMh8CUVLKD4QQzwLLpJRCCFEP+FNK2byUbT0DzMV0CikL2As8h+l00u+YTiHFCyHcAScgF/gXaAWkAZuBQ1LKl4QQ35i39ZN53YeAl6SUO4QQMwEXKeWE60zfium0WKgQoiuFT5FtBSYB0UCS+VTZg8AIKeXAG+xSGV63/w1KKkfgxTWs9b5zPzOhb9xKZtS7M8eKz77wAwC55/bdoLJy2Pi3Q5dwa6fkbidrT39yjt7EP7xKYNs8BABd3KlKTlIya+9G7PB5+MaFlaRz7E8crD2gsmOUqFXk72B6g2xx/9YYXO5/5h2if7luNiFEb+BjQAt8KaWcV2S+LaZhEq2BROBRKeUF87xpwHDAAIyVUv5T3rx3XA+MlDJRCLHLfPnzOuAwcAhTz8irUspYIUQiYDA3Br4BPgd+FkI8DfzNrfWE7AN+xjR+5XspZSiAEGIGsF4IoQF0wBgp5b/mRsceIAUIv856hwFfmMfnnAOevcH0m3EP8J4QwmjONOoWllUURVGUMjFfyLIQCAGiMA2xWCOlPF6gbDiQLKUMEEIMBeYDjwohmgJDgWZADWCjEKKhlNJAOdxxDRgAKWXRt72Ti8zXAd2L1BQ8MT3FXHcBU+9Kadv5BlMDqKR5P1J8jAtSyq+Br0uY/kyR5+FAhxLqSpvetcD3W4GtJc0Dyt1qVRRFUf5bbsMppHZAhJTyHIAQYiUwACjYgBkAzDR//xPwmRBCmKevNI/pPC+EiDCvb095At3pg3gVRVEURbmB2zCItyYQWeB5lHlaiTVSSj2mq3U9bnLZW3ZH9sBYmhCiF6aurILOSykHVUYeRVEURbEkS3wWesGP4DBbIqVcYoFVV4i7ogFjHiykTr0oiqIo/0nSAmODzY2V0hoslzF9nMc1tczTSqqJMn8ivQumwbw3s+wtU6eQFEVRFEW5kf1AAyGEnxDCBtOg3DVFatZgulAF4GFgszRd6rwGGCqEsBVC+AENMF1AUy53RQ+MoiiKovyXGSv4E1GklHohxEuYzmZoMX1cyTHzJ8qHSinXAF8B/zMP0k3C1MjBXLcK04BfPaarest1BRKoBoyiKIqiVHnGivl4mUKklGuBtUWmvVHg+2xgSCnLzgEsd58UVANGURRFUao8S4yBqWpUA0ZRFEVRqjhLXIVU1ahBvIqiKIqiVDmqB0ZRFEVRqjh1CklRFEVRlCrnbjyFpBowiqIoilLF3Y0NGGH6jBnlP0z9gBVFUe4cFXKuZ6330HIf6/vGraxS56FUD8xd4Hj9Byo7Qomanv2LX3yK3nj8zjE49gdm1X2ismOU6I2LywHIDv+zkpOUzC7wQXKObqjsGKWybR6CLuFcZccokbWnPwA5p3dWcpKS2TbsxG7fhyo7Rqk6xvzMZu9HKjtGibrHraqwdasxMIqiKIqiVDnGu6/9ohowiqIoilLV3Y5P4r3TqAaMoiiKolRxd+NgR/VBdoqiKIqiVDmqB0ZRFEVRqri78TJq1YBRFEVRlCrOKNQYGEVRFEVRqpi7cQyMasAoiqIoShV3N55CUoN4FUVRFEWpclQPjKIoiqJUceqD7BRFURRFqXLUB9kpiqIoilLlqEG8yl2lWpfW+Lw+EqHVkPzjehIXry40X9hYUeP9idg3D8CQnE7U2HnoLseDtRU1Zr+E3T0NwGgk9u0lZO49YlrI2grfmaNwaH8PGI3Ef/Ad6f/stkjeFrOfxic4EENWLgfGfUHKkQvFappOfYQ6Qzpj41qNNfWfy5vu0aExLWc9hXPTOux78VOi/9xnkUyl6TXzaRp0a4kuK5ffJy0m9mjxrI9/+yqOXq5orLRc2neKda9/jTRa/jC0K/wk87/5DaPRyKDu7Rk+MLjQ/JiEZGYsXEF6ZhZGo2Tc4w/QOagJl+OTGPTKfOrV8ALgngZ1ef35hy2eb2fYceYv+wmj0cjg4I4MH9yzcL4rScz49H+kZ2ZhMBgZ/+QAOrduhk5vYOai5Zw4F4nBYKRf13aMGNzL4vmuZ8bcBWzftQ93N1d++/6L27ptgJ0HjjB/6QqMRsngkM4MH9K30Pzo+ATe+PhrktMycHGsxtyJI/DxdAfgxTc/5MipswQ1acBnb44rVw7XboH4zXoOtBrif9jE5c9+LTRf2FjR4JOxVGvhjz45ndMvLCAn6goANV8ehNdjwWAwcv71ZaRsDceufg0affFK3vK2db2JfG8lMUv/ovbER/B6ogf6xDQALr7zAymbD95S3gZznsUjOAhjVg7Hx35OxpHzxWqcWvjR5JMxaOxsSNwUxpnXvgag/htP4tmzNVKnJ+tCHCfGfY4+LTM/a00P2u/4kPPvrSZy0R+3lEu5NWoQbwUQQtQTQhy9iZrHCzxvI4T4pOLTmWk0+M4cxaXn3iSi1yhc+nXBJqB2oRLXIb0wpGYQ0f15Er/+Da8pzwLg9qjpn8S5vmO4OGwG3tNHgPkzCKqPfhR9Ygpne4zkbK9RZO677m64ad7BgTj6+7D+3lc4OOlLAuc/V2JdzPqDbO3zerHpWZcTCB33BZG/WqYxdT0B3Vri4efDZ/dP5M9pX/HA7GdLrPtpzKcs6TOdL0KmUM3DiaYPtLd4FoPRyNxlv/D5tOf5dcGr/L0rjLNRsYVqlv6ykV73BrJq/kTmj3uSuV/9nDevlrcnq96dyKp3J1ZI48VgMDJ36SoWvTaa3z6awbqdBzgbGVOoZslPf9OzYytWvT+Vd195ljlLfwRg/Z6D6HR6fvnwNVa+N4Wf1u/icnyixTNez8C+IXyxYPZt3eY1BoORuV8sZ9HMCfy28G3Wbd/L2UvRhWo+WLaKft078vOnb/HC0H588m3+z/aZwb2Y88qI8gfRaPCf+zzHn5hD+P3j8RzYCfuGtQqVeD8WjD41g7COLxG95E/qzngKAPuGtfAc0InwruM5/vhs/N95HjQass9GcyhkkunR61WMWTkkrct/0xGz5M+8+bfaePEIDsLBz4d/O4zl5KQlNHq35H3Q6N3nOTlxMf92GIuDnw/u3QMBSN52mH33T2Rft8lkno2h7thBhZZr8NYwkjaF3VImSzCK8j+qGtWAqTz1gLwGjJQyVEo59nZt3L5lQ3IvRqOLjAWdntQ/t+PUo0OhGqce7Un9ZRMAaet2Uu3elgDYBtTh6p5DABgSUzGmZZh6YwDXISEkLDLfMl5KDMlpFslbo1drLq3aAUDywQisnR2w83ItVpd8MILs+JRi0zMjE0g7EQnGir/YsFFIaw79bMp6OSwCW2cHHEvImpuRBYDGSovW2gpZAX3ARyMuUdvbg1reHlhbWdG7YxBb9x8rVpeRlW36mplNdTdnywcpNd8F6vh4UsvHE2trK3p3asWW/YcL1QghuJqXL4vq7i6m6Qgys3PRGwzk5OZibaXF0d7utmUHaBN4Dy7OTrd1m9ccPXOOOr5e1PKpbtp3XdqxZW/hf5znLsXQvkUTANq1aMyWveF58zq0bEo1C+wvx6AAsi7EknMpDqnTk/D7Ttx7tS1U49a7HfGrtgKQ+OceXDrfA4B7r7Yk/L4TmasnJzKerAuxOAYFFFrWpfM9ZF+Iy+uxKS/P3m2IXb0dgLQDZ7ByroZNkb9PGy9XtI72pB04A0Ds6u1U72N6TUnbDiMNpuNI6oHT2NZwz193n7ZkXYrn6qkoi2S9FUYLPKqau7IBY+79OCmEWC6EOCGE+EkI4SCECBZChAkhjgghlgkhbM31F4QQ75qn7xNCBJinfyOEeLjAejNK2dYOIcRB86OjedY8oLMQIlwIMUEI0VUI8ad5GXchxG9CiMNCiH+FEC3M02eac20VQpwTQpS5wWPl7YEuJiHvuT42AWtvj8I1Ph7oYswHDYMRY3omWjdnsk+exym4A2g1WNfyxq55ANa+nmicqgHgNeEp/H7/mFqfTkPr4VrWiIXY+bqRFZ2U9zwrJgk7XzeLrNvSnHzcSYvO7wlIj03CybvkrE98N4WJBxeRczWbE2v3WjxLfFIqPgV+Bl4eLsQlpxaqGTWkF3/tOEDIqFmMmfclU5/Nf0d5+UoSj0z5gOdmLuTgiXMWzxeXlIq3Z/6+8XZ3Iz6xSL5H+/Ln9n30eH4Go+csYtrwIQCE3BuEg50NwSNeo+cLbzCsfzAu5t/Bu0FcYgrenvn/PL093IhPTClU09CvNhv3HABg056DXM3KJiWt2GGqXGx93Mm9nH8syY1JwsbHo3hNtLnGYMSQlomVuxM2Ph7kFPhbyY1OxNbHvdCyngPuI+G3nYWm+TzXh5abFlB/wWi0Lrf2M7f1dSe7QN6cmERsfd2L1eTE5OfKji5eA1Dj8e4kbgoHQOtgS92XBnDh/dXF6m4HaYFHVXNXNmDMGgGfSymbAGnAK8A3wKNSynswjQ8aVaA+1Tz9M+CjW9hOPBAipWwFPApcO000FdghpQyUUn5YZJm3gDApZQtgOvBdgXmNgV5AO+BNIYR10Q0KIUYKIUKFEKFLliy5hag3J2X1enSxCfj/9jE+M0aSefAEGI0IKy3WvtXJPHiC8wPGkRl2Au9pwy2+/f+S5U/PZ0HbMVjZWOHXsVmlZFi3K4z+97dlw6I3WDh1BK99tgKj0Uh1N2f+WTiDVfMnMunp/kz99HsyMrNvf74doQzo1oGNS2fz+WujmP7JdxiNRo5GXECj0bBx6RzWLXqLb//YTFRswo1XeBeZ+NwQDhw9zSPjZhJ69BReHm5oNFXnsC+srXDv1ZbEP/JP/cZ++w8HO4zhUI+J6OJTqPfmsErJVnf8IKTeQJy5t9Vv8iNELv4LQ2ZOpeS5G08h3c2DeCOllLvM338PvA6cl1KeNk/7FhhDfmNlRYGvRRsc12MNfCaECAQMQMObWKYT8BCAlHKzEMJDCHGtX/8vKWUOkCOEiAe8gUL9lVLKJcC1los8Pv/3YhvQxyVi7euZ99zKxxNdXOHxA/rYRKx9q6OPTQStBo2TQ94pobg5S/Pq6q1+n5zzlzEkp2HMzM4btJu2bieuQwoPyLwV/s+GUO+JbgAkh5/DvkBXrb2vO9kxyWVet6W1eTqEVkNNWaMPn8O5Rv47UCcfd9LjSs9qyNFxav0BGvZszbmdlhkzdI2XuwuxBd6Vxyem4u3mUqjm1y17WTTteQBaNqxHjk5HcvpVPFycsLE2HSKa+temtrcnF2Ou0Kx+4bFS5eHt7kJcQv6+iUtKxsujSL5Ne1j0+hhTvkb+5OSa8q3dEcp9gU2xttLi4eJEUGN/jp29RC0fT+4G3h6uxCXk90rGJSbjVaTH08vDjQ+nm/ZdZlY2G3cfxNnRwaI5cmKTsKmZv89tfN3JjU0sXlPDk9yYJNBq0Do7oE9KJzc2EdsCfys2NTzIic1/Ta7dg7h65By6hPxeuYLfx32/gSb/m37DjDWf7UWNJ02D19PDz2JX05NUTgFg6+tBTkxSofqcmCRsffNz2dUoXOPz6P14hrQm7OFZedOcWwVQ/cH21H/9CaxcqoFRYszJ5fKyf26YTymbqtMUt7yiPWYpt1B/7Xs95n0ohNAANiUsNwGIA1oCbUqpuRUFm/cGytgIzTp8Gpt6NbGu5Q3WVrg82IWMTYVPYaRv2ovLYNMfvXOfTlzdYxqbIOxsEfa2AFS7LxCpN5AbEWlaZvNeHDqYzm9X6xiYN70szn29gc09prO5x3Ri/g6lziOdAXBrFYAuPavEsS6VJfS7DSzpO50lfadzan0oLR8yZa0ZFEBOehYZRbJaO9jmjYsRWg0NugeReDYaS2tWvzaXYhOIik9Ep9fz9+4w7m9TuKfH19ONvUdN5/rPRcWRq9Pj7uxIUloGBvOYoai4RC7GXKFWkdOM5c4XUJeLMVeIiktAp9Pz986DdG3TolCNT3V39h4+Zc4XS65Oh7uzI76e7uw7apqemZ3D4dMX8KvpbdF8d7JmDfy4GB1HVOwV077bvo+u7QIL1SSnpmM0/wy/XL2WQT06WTxHRngE9n6+2Nb2Qlhb4TmgE0n/hBbO8c9+vB7pCoDHg/eSam6oJ/0TiueATggbK2xre2Hv50tGWETectUHdiLh18Knj6wLjFdx79uezJOXbpjx8tf/sD/4VfYHv8qVdfvwGdIFAOfWDTCkZ5Jb5O8zNz4FQ0YWzq1NY/t8hnQh4W/Ta3Lv1pK6YwZw+On5GLNy85Y5OOBN9rR9iT1tXyJqyVoufPzrbW283I1jYO7mHpg6Qoh7pZR7MA2mDQVeEEIESCkjgKeAbQXqH8U0buVRYI952gWgNbAK6I+pt6UoFyBKSmkUQgwDtObp6UBpo/92AE8AbwshugIJUso0Ycm7jRqMxL61iDrfvI3QaEj5aQM5Zy5RffyTZB05Q8amvaSsWk/NDyYRsHkphpR0osa9C4CVhwt1vnkbjBJdXCLRE9/PW238/K+p8cEktDNGYkhK5fKrH1kkbuzGcLyDA+n574cYsnI4MH5x3rzuG+eyuYfpXVjz1x+j9qCOaO1t6HPwUy78sJUT7/+MW6A/HZZNwNq1Gj4hrWg6+WE23v+qRbIVdWZzOAHdAnlp+wJ0WbmsmZSfdeTauSzpOx0bB1se/fIVrGysERrBhT3HCf1+k8WzWGm1THtuMKPmLsFolAzs2o6A2j4sXPU3zfxr0bVNcyY+1Y9Zi1fz/V/bEUIwa9RQhBAcPHGOhav+xlqrRQjBjOcfxsXC796ttFqmj3iEUW8vxGCUDOzegYA6vixc8SdNA+rQrW0LJg0bxFuLVvC/P7cgBLz90lMIIRjauwuvL/yeQeNmI4EB3TrQsF5Ni+a7kclvzmN/2GFSUtIIHvgko4c/xUP9bs+l3FZaLdNffIJRb36IwWhkYI9OBNStycLvf6Npg3p0ax/I/qOn+OTbnxFC0KpZQ14b9UTe8sOmzONCVAyZ2Tn0eGYSb419hvtaNb/1IAYj56Z/SdMVryO0GuJWbibrdCS1Jw8l41AEyetDiVuxiQafjiVo92foUzI4/aKpEzvrdCQJf+wmaNvHSL2Bc9OX5g2019jb4tKlJWdfXVxoc3Vff5pqzeqBhJzIeM6+emuXryduDMMjuBX37v0EQ1YuJ8Z9njev7aZ32R9sOi6cmvIlTT4ZjdbOhsRN4SSaryxq+M5wNDZWBK4yXe2YduAMp15dWnxDt1lVbICUl5AVcenDHU4IUQ/4G1OjpTVwHFOD5V7gfUwNu/3AKClljhDiAvAj0AdTD8hjUsoIIYQ38Dtgb17fGCmlo3n9f0opmwshGgA/Y+q1KVhjDfwDeGAaexMGTJJSPiiEcAeWAf5AJjBSSnlYCDETyJBSvm9+HUeBB6WUF67zcuXx+g+Uc49VjKZn/+IXn8dvXFhJBsf+wKy6T9y4sBK8cXE5ANnhf1ZykpLZBT5IztENlR2jVLbNQ9AlWH5QsiVYe/oDkHN65w0qK4dtw07s9n2osmOUqmPMz2z2fqSyY5Soe9wqoGI+MveL2k+W+5/5i5HfV6mRMHdzD4xeSvlkkWmbgKBS6t+TUk4pOEFKGQcUvPZ4inn6BaC5+fszQIsSanRA9yLb2GqelwQMLBpASjmzyPMyvF1SFEVR/mvuxh6Yu3kMjKIoiqIoVdRd2QNTsIfkJuvrVVgYRVEURSmnu7EH5q5swCiKoijKf8ndN5pVnUJSFEVRlCqvsj/IzvwJ8huEEGfMX4t9/LgQIlAIsUcIccz8SfOPFpj3jRDivPnT6cPNn512XaoBoyiKoihKeU0FNkkpG2C6IGZqCTWZwNNSymZAb+AjIYRrgfmTzZ9OHyilDL/RBlUDRlEURVGquDvgg+wGYPoEe8xfBxYtkFKeNl+Zi5QyGtOtdqqXdYOqAaMoiqIoVdwd0IDxllLGmL+PxXSbm1IJIdph+mT6swUmzzGfWvrw2s2Ur0c1YBRFURSlirPE3agL3gjY/BhZcBtCiI1CiKMlPAYUymL6hNxSxxULIXyB/wHPSimvtZ2mYbpZcVvAHfNnpl2PugpJURRFUao4S9xNusiNgEua36O0eUKIOCGEr5QyxtxAiS+lzhn4C3hNSvlvgXVf673JEUJ8DUy6UV7VA6MoiqIoSnmtAYaZvx+G6TY7hQghbIBfge+klD8Vmedr/iowjZ85eqMNqgaMoiiKolRxd8AYmHlAiBDiDNDD/BwhRBshxJfmmkeALsAzJVwuvVwIcQQ4AngCs2+0QXUKSVEURVGquMr+IDspZSIQXML0UGCE+fvvge9LWb7ovQFv6K68G/VdRv2AFUVR7hwVcsfnOXWfKPex/rWLy9XdqJU7y8HaA25cVAlaRf7OWu+hlR2jVH3jVjKz7hOVHaNEMy8uByDnzO5KTlIy2wYd0cWdquwYpbL2bkTO6Z2VHaNEtg07AaBLOFfJSUpm7enPbt+HKjtGqTrG/Ex43f6VHaNEgRfXVHaE/xTVgFEURVGUKk7dzFFRFEVRlCrnbhwroBowiqIoilLFqR4YRVEURVGqHEt8kF1Voz4HRlEURVGUKkf1wCiKoihKFWe8C0fBqAaMoiiKolRxd1/zRTVgFEVRFKXKU4N4FUVRFEWpcu7GU0hqEK+iKIqiKFWO6oFRFEVRlCru7ut/UQ0YRVEURany1BgY5a7l3DWIWjOfB62GxBUbiPv850LzhY0V9T6agP099TEkp3N+9HvkRsVjU8uLpls+I/vsZQCuHjxN5PRFFsvVdM4wqgcHYcjK4fDYRaQduVA8ews/Wn4yCo2dDVc2hXH8tW/z5tUd3ou6z/ZEGozEbwzj1Ns/UOOh+/Af3S+vxqlpHXb2mEb6sYsWy91n5tM06NYSXVYuv01aTMzR4rmf/PZVHL1c0VhpubTvFH+9/jXSaPn3UTsPHGH+kh8wGo0M7tmF4UMeKDQ/Oj6BNz5aRnJaOi6O1Zg7aSQ+nu6cPHeJ2Qu/42pWFhqNhucfeZDeXdpbPt/eA8z75EsMRgMPPdCTEU8+XDhfbDyvz/uEpJRUXJydmDfjFXy8PAH4YNHXbN8TitEoubdtINPGPo8Qlv1Er50HjjB/6QqMRsngkM4MH9K3cL74BN74+GuS0zJM+2/iCHw83YH/t3fe8VGUzx9/TzoQICSBEDpIU5Hei1JF+EpRLNg79oJd7A079oJdf3axYUWKCipKbypdOgkkIYQSUuf3x2zCJSS0JHeJPG9e9+Ju97nbT/Zud2dn5pmBK+59msXLVtHu6Ga8cO/1parrYLhr7Dim/zaL6BpRfPneK37ZZlSftjR+4GIIDmLLB1PZ+MIXBdZLWAjNnruOKq2bkL1tB8svH0fGhq2E1IikxWu3ENn2KLZ8/DP/3vn6Pp/d8u3biWgYx4I+ow9bX9UT2lP33kuR4GCSP/qRLS/ve65rMG40lY9rSva2NNZeY+c6gIiWjaj/yFUERVaG3FyWD70JCQmh2aeP5L8/ND6WbV/8zMYH9tVflhyJOTBHhAEjIo2Ab1S1lZ+3u1NVIw9h/H3ATlV9stDyRpSl/qAg6j90OSvOvpeszcm0+OZJtk+exZ4V6/OHxIwcQHbqTv7udQU1hvai7pgL+PeqJwDIWJvA0pMO/4RSHDX7taVy43h+6XoDUR2a0urxS/l90F37jGv1+CUsvulVUueupOMHt1Ozb1u2TltAdI9jiDupI7/2vY3czGzCYqsBsOmz39j02W8AVD26Pu3fvrlUjZdmfdoQ3bg2z51wE/XaNeV/D13E68Pv3Wfcp1c/T8bOdADOeOV6jv1fF5Z8/Uep6QDIycll7Mv/x6sP3UxcTDRnjX6A3l3aclSDuvljnnrjY4b0686wfj35c+HfPPfOBMbeNIqI8DAevvFSGtatzZbkbYy84X66tz+OapGVS1FfDg89PZ7Xxj1A7ZoxnDnqJvr07MxRjRrkj3nypTcZOrAPwwb148+5C3nm1Xd59K4bmb/4H+Yv/ofP33oOgPOvuZ3ZC5bQud1xpagvl7GvvM+rD95EXEwNzrrxQW//1ckf89SbnzCkb3eG9evBnwv/4bl3PmPsTZcBcOGpA9mTkcmE738pNU2HwvDBAzh7xFDGPPjkgQeXBkFBNBl7GX+d+QCZm5Np/f1jpPw4m/TlG/KHxJ3Vj+ztO5nf/RpihvWg4V3nsfyKceTuyWLd4x9SuWUDKrdosM9HRw/uQs6uPSXWV+/By1l1zj1kJSTTfOJTbJ8yiwyfc130mQPI2b6Tf064nKghvYi//QLWXvMEBAfR8JkbWTt6HHv+WUNwVFU0KwfNyGLZ4Bvy39/8m3Gk/jCzZDodB4VL4nVQpW0zMtYkkLkuEc3KZtvEGVQ/sXOBMVEndiFlwjQAtn37G1V7tC5zXXEndWTjp9MBSJ27kpBqlQmvFVVgTHitKEIiK5E6dyUAGz+dTtygjgA0vGAAq57/itzMbAAyk9L22Ub8KT3Y/OXvpaq7xYAOLPxsBgAb5q8kolplIgvpBvKNl6CQYIJDQ9AyuIFasnw1DeJrUa92LUJDQzjp+M789Mf8AmNWr99El9ZHA9C59dH56xvVrU3DurUBqBVTg+jq1di2fd99WBIW/7OCBnXjqV+nNqGhoQzq14tpv/5ZYMyqNevp3N5+b53bt+Ynb72IkJmZRVZ2NplZ2WRl5xBTI6pU9S1Zkbf/au7df38W2n/rNvvsv5b89OeC/HVd2xxDlUoRparpUOjY9jiqV6vqt+1FtmtK+poEMrxzSdJXvxI9sFOBMTVO6syWT34GIPmbmVTvZQZnbnoGO2YtJXdP1j6fG1Q5gjqXD2HDsxNKpK9y22ZkrNlM5nrvXPf1DKoPKOhVrD6gCymf2bku9bvfqNqjDQBVj29H+tI17PlnDQA5qTsgt2DgJrxxHUJiqrNr1l8l0nk4aCk8KhpHkgETLCKvichfIvKjiFQSkbYi8oeILBKRL0SkBoCI/CwiHb3nsSKyxnt+rIjMEpEF3nuaecvP9Vk+XkSC8zYqIg+LyEJvO3HeskYiMs37jKkiss/thoh08N63ELjaZ3mRGkpCaO0YMjcl5b/O2pxMaO2YQmOi947JySVnxy6Ca9iJMax+HC2/f5pmnz5Mlc7HlFROPhHx0ezZmJz/es/mFCLio/cdszll75hNe8dUOSqe6C4t6f79Q3T54h6qt22yzzbih3Vj0xe/lZpmgGq1o0nbtFd3WkIK1eJqFDn23Hdv45Z5L5O5aw9/f/dnkWNKQmLyNuJq7t1ncbHRbEneVmBM88b1mfL7XACmzpzLrvQ9pKbtLDBm8bLVZGVnUz++Vqnq25KUnB8OAoirGcuWrckFxrRo2pgp0+2Odsr0mezanU7q9jTatmpJp3bH0eeUC+lzygX06NyOoxrVL1V9icmpxMX67L+YGmxJTi0wpnnj+kyZmbf/5hW5/44UwmtHk7lx77kkc3MKYYXOJeGFzyVpuwmJ3r+R1eC2kWx6ZSK5uzNKpC+0dgxZm33PdUlFnOtiyCriXBfRuC6o0uTd+2j+7dPUuvzUfT4/akgvUr/5tUQaD5fcUnhUNI4kA6YZ8KKqHgukAiOAd4HbVLU1sBjY189fkCuAZ1W1LdAR2CAiRwNnAj285TnAOd74KsAfqtoGmA5c5i1/HnjH2+77wHNFbOst4FrvvfvVcMC/vAzJ2pLCki6XsnTQaDY88CaNn7+JoMhKgZSUj4QEE1ojkt8H3cXSB96n3Ws3FFhfvX1TctMz2Lk0cLvwvfMf46lOVxMcFkLj7scGRMNNF5/J3CXLOOO6e5mzeBm1YmoQFLT31LA1JZUx417jgRsuKbDcX9x81UXMWbCE0y65njkL/iKuZgxBQUGs27CJ1Ws3MHXCm0z77C1mzVvE3IX+v/O96eLTmbtkOWdcfx9zluy7/xwlo/KxjYhoWJuU72cFVkhIEFU6HcPa659ixYjbqH5SVyILeaJrDO3Ftq+mB0SelsK/isYRkQPj8a+qLvCezwWOAqJUNS84/Q7w6QE+YyZwp4jUAz5X1RUi0g/oAMz2kgcrAVu88ZnANz7bHOA97wbkme//BzzuuxERifK0TfcZM6g4DYVFisgoYBTA+PHj6XiAPyorIZmwOnvvgkPjY8hKSC40JoWwOrG2PDiI4KpVyNm2A4CcTPs/ffEqMtZuJqJJXXYvWnmArRZNw4tOpP65fQFIXbCKiLp7744Ke1tgX69MRJ29Y/ZsSibhWzvpbZ+/Cs1VwmKqkplseusM786mL0onfNTp/AF0GNkHgI2LVlOtzl7d1WpHk5a4rbi3kp2RxbIf59LyxA6s/nVJqejJIy6mBolb9+6zxKQUasUU9AbViqnB03deC8Du9D1M+X1ufp7Lzt3pXH3/01x73qm0aXlUqWoDqBUbQ8KWvXfEiVuTqFUzZp8xzz48xvTtTmfK9N+pVjWSCd/8SJtjm1O5shnMPbt0YOFfS+nQpvQMwbiYKBKTfPZf8jZqxUQV1BdTg6fHmJPU9t+8Us0TqkhkJKQQVnfvuSQsPprMQueSDO9ckrk5xc4l1SqTnbKj2M+s2qEFkW2Oov2sl5HgYEJjq3HsZ/fz14gD3W/uS1ZCMqHxvue62CLOdcmEFnGuy9qczK4//8o/76X9NJdKrY5i52+LAIg4uhEEB5O+ZNUh6yoNKqIHpaQcSbcJvr7HHCBqP2Oz2btv8gPYqvoBMBRIB74Tkb6AYN6Utt6jhare570lSzU/syGHUjAYi9FQeMyrqtpRVTuOGjXqgJ+5a+EKwhvFE1a/FhIaQo2hvdg+ueDdTurkWUSfZpuq8b8e7PAO2pDoauDdbYY1iCO8cR0y1iUc9t+39q0f+bXf7fza73YSv59D3dOPByCqQ1Oyd+wmY0tqgfEZW1LJ3plOVIemANQ9/XgSf5gDQOL3c4jpYRezKk3iCQoNyTdeECF+aFc2lVL+y+x3J/PK4DG8MngMS3+cQ5sRvQCo164pGTvS2VlId1jl8Py8mKDgIJr1bUfSqk2losWXY5s3Zu2mLWxI2EpWVjY/TJ9F7y7tCozZtn0HuV4s//VPv+WUAaY9KyubGx56niF9e3Biz077fHZp0KplM9Zt2MSGTQlkZWXx/dQZ9OlRMCdhW2pavr7X3p/AKYP7AxBfqyZzFvxFdnYOWdnZzFmwhCYNSzeEdGyzxqzdlFhw/3VuW1Bfgf33Haf071mqGioSOxespFLjeMK9c0nssJ6kTJpTYMy2SbOpdUZvAGJO7sb2Axjtie9OYk67y5jX+UqWDLuTPas3H5bxArB74QrCG9chrH6cneuG9CJtcsHQbdqUWUSPsHNd1OAe7PjdznU7fplHRMuGSEQYBAcR2eXYAsm/NYYeT+rEwHhfjlSOJA9MYbYD20Skl6rOAM4D8rwxazCvyiwgf06niDQBVqvqc17eSmvgR+ArEXlaVbeISDRQVVX3N63ld2Ak5lk5B5jhu1JVU0UkVUR6quqv7A1JFadh2uHvBiAnl/V3v0rT9+5DgoNI/ngqe5avJ/6ms9m9aCXbJ88i+aPJNHpmNMfMeIWc1B38e7XNaojscizxN52NZmdDrrL+jpfJSS2d+P/WKfOp1a8tJ/z5LLnpGSy6fu800J5TH+XXfrcD8Ndtb9I6fxr1ArZOXQDA+g9/ovUzV9DrlyfIzcxm0XUv5b8/utvRpG9KJn3tFkqbFdMW0KxPW66bPo6s9Ey+unl8/rorvhvLK4PHEFo5nLNev5GQsFAkSPh35t/MeW9qqWsJCQ5mzBXncOU9T5GTm8vwAb1o2rAuL773Bcc0a0SfLu2YvXgpz70zARGhfavm3HnleQBM+nUW8/5azvYdO5k4xeL6D46+lJZN9p0hctj6QoIZc8PlXH7zfeTk5nLK4P40bdyAF954n2NbNKVPzy7MXrCYZ8a/i4jQoc2x3DX6CgBO7N2dWfMWccqF1yIi9OzSnt49Oh9gi4eoL2//3fu07b/+Pb3996W3/9oye8kynnvnM9t/xzbnzivzD1cuuO1R1mzYzO49GfS/8Gbuv+5CerT332TIW+59lNnzF5Gamka/4edy1SXnMWLIwLLbYE4uq8e8zjEf3o0EB5H40TTSl6+n/i0j2blwJdt+nEPih1Np9vx1tPv9BbJTd7L8iqfz395+1ssER1YiKCyE6JM68/dZDxSYwVQa+jbcM54m79q5LuWTKexZsZ7aN9q5Lm3KLJI/nkzDp2/k6F/Gk526w2YgATlpu9j6+lc0/3ocqJL201zSpu01zqJO7snqC+8vPa2HyJE4jVq0LKY+lDMKT0MWkZuBSOBL4BWgMrAauEhVt4lIS+ATzGvyLXCuqjYSkdsxQycLSADOVtUUETkTuAPz2mQBV6vqH77TqEXkNOBkVb1QRBpiOS6xwFZvu+t8p1GLSAfgTSw5/EdgsKq2Kk7Dfv58nVd/WIn3YVnQfv1XfBc3MtAyimVw4kfc1/CcAw8MAPetfR+AjBWlO4OqtAhv1p2sxGWBllEsoXEtyFgemGTLAxHe3Dw4WUmrA6ykaEJjm/B7/IhAyyiW7ps/Y0HDoYGWUSRt104E89qXOlc2OqPEF/OX13xSJtrKiiPCA6Oqa4BWPq99iyJ0LWL8Usyzkcdd3vJHgUeLGP8x8HERyyN9nk8AJnjP1wJFhX7u83k+F/BN4L11fxocDofDceRyJHpgjggDxuFwOByO/zIuidfhcDgcDoejAuA8MA6Hw+FwVHAqYh2XkuIMGIfD4XA4KjhHYgjJGTAOh8PhcFRwjkQPjMuBcTgcDofDUeFwHhiHw+FwOCo4LoTkcDgcDoejwpF7BBSlLYwzYBwOh8PhqOAceeaLy4FxOBwOh6PCk4uW+FESRCRaRCaLyArv/xrFjMsRkQXeY6LP8sYi8qeIrBSRj0Uk7EDbdAaMw+FwOByOknI7MFVVmwFTvddFka6qbb2Hb9Oqx4CnVbUpsA245EAbdAaMw+FwOBwVHC2FfyVkGPCO9/wdYPjBvlFEBOsPOOFQ3n9EdKM+wnFfsMPhcJQfyqTj85kNh5f4XP/x2i8PW5uIpKpqlPdcgG15rwuNywYWANnAo6r6pYjEAn943hdEpD7wvaq2Kvx+X1wSr8PhcDgcFZzS6EYtIqOAUT6LXlXVV33WTwFqF/HWO31fqKqKSHGCGqrqRhFpAkwTkcXA9sPR6wyYI4Cf404PtIQi6Z34KV/XPivQMoplSMKHfBx/TqBlFMmZm98HYGO3vgFWUjR1Z05jRu3TAi2jWHolTOD3+BGBllEk3Td/BlCu9WUlrQ60jGIJjW1C+udjAy2jSCqdOqbMPrs0KvF6xsqr+1nfv7h1IpIoIvGqullE4oEtxXzGRu//1SLyM9AO+AyIEpEQVc0G6gEbD6TX5cA4HA6Hw+EoKROBC7znFwBfFR4gIjVEJNx7Hgv0AP5Wy2X5CThtf+8vjDNgHA6Hw+Go4OSWwqOEPAoMEJEVQH/vNSLSUURe98YcDcwRkYWYwfKoqv7trbsNuFFEVgIxwBsH2qALITkcDofDUcEJ9IQcVU0G+hWxfA5wqff8d+C4Yt6/Guh8KNt0BozD4XA4HBWc0kjirWi4EJLD4XA4HI4Kh/PAOBwOh8NRwXHdqB0Oh8PhcFQ4SmMadUXDGTAOh8PhcFRwjsQcGGfAOBwOh8NRwQn0LKRA4JJ4HQ6Hw+FwVDicB8bhcDgcjgqOS+J1HHE0ffgiYvq1Jyc9g6XXvcjOxf/uMyaydRNaPnc1wRFhJE+dx8o73wKg5pCuNLr5DCo3r8u8k+5gx0LrjyIhwbQYdwWRrZsgwUEkfvoL65778rD0HfvQBcT1a0tOeiYLrn+Z7YvX7DOmeuvGtH32CoIjwkicuoC/7rKO7u3HX0fkUfEAhFavQtb2XUzvfwd1T+3BUVednP/+asc0YPqAMaT9tfawNObR7sHzie/Xhpz0TGbdMJ5tRWg97vbTaXRaL0KjqvB500vylze/fBBNzu6DZueQkZzGrBtfY/eGpBLpKY7wrp2ofsM1SHAQuyZ+x87/+7DA+siRp1F56GDIySEndTupDz9BTkJiqWy7yUMXE92vHbnpmSy7/gV2FfN7a/7s1QRFhJEydT6r73oTgJCoSFqOH01E/VrsWb+FpaPGkb19FwDVux9LkwcuREJDyE5JY9Ep9wJQZ9TJ1D6nH6iy6591LL/hRTQja59tRvVpS+MHLobgILZ8MJWNL3xRYL2EhdDsueuo0roJ2dt2sPzycWRs2ApA3WtPodZZ/SAnl3/vfpPUnxcQcVQdWrxyY/77wxvGsf6Jj9j82rfUv+kMap3Tn+zkNADWPvIBqdPmHfQ+PFytITUiafHaLUS2PYotH//Mv3e+vs9nt3z7diIaxrGgz+iD1nO43DV2HNN/m0V0jSi+fO+VMt9eUfy2bCOPfzOL3FzllE7NuLj3vjXWJi1aw/ipCwBoHh/NoyOPB+CZ7+cyY9kGAEb1bc3A1o39prsojsQkXhdCOoKJ7teOSo3j+bPrtSy/eTzNH7+syHHNH7+M5Te9wp9dr6VS43ii+7YFYNfS9Sy5+Em2z/ynwPiaQ7sRFB7KnN43MffE26hz3gAi6tc8ZH21+rUlskltpnUbzcKbX+O4xy4pctxxj13MwpteY1q30UQ2qU2tvm0AmHf5c0zvfwfT+9/B5m9nsfm72QBs/Py3/OXzr3mJ3eu2lth4ie/bhqpNavNd95uYc8sbdHj0oiLHbfpxPpMH37PP8tTFa5l80l1M6ncH67+ZRZu7yqjJZVAQUTddT/KNt5N41kVUHtCXkEYNCwzJXL6SrRddyZbzLmPPtOlUu3pUMR92aNTo145KTeKZ0+1aVtz8Ck0fK/pzmz52GStueoU53a6lUpN4avRtB0C9a4eTOmMxc7pfS+qMxdS79hQAgqtVpumjl/L3BY8x74TR/HPZUwCE1Y6m7qWDWDDwNub1vhEJDqLm8B5F7pMmYy/j73MeZsEJNxA7vCeVmtcrMCTurH5kb9/J/O7XsOnVb2h413kAVGpej9hhPVnQ+wb+PvshmjxyGQQFsWfVJhYOuNkeA28lNz2DlO9n5X/e5le/yV9/KMZLSbTm7sli3eMfsuaBd4v86OjBXcjZtefgtZSQ4YMH8Mq4h/y2vcLk5ObyyMQ/ePGi/nw+ehg/LPyXVYmpBcasTUrjzZ8X8/YVg/h89HBuPbkTANOXbuCfTcl8fO0Q3rtqMO9M/4udezID8FfsJRct8aOi8Z81YETkZxHp6D3/TkSiSvGzrxCR80vr8wJF7EmdSPz0FwDS5q4gpFoVwmpFFRgTViuKkMhKpM1dAUDip78QO8iqPe9esZH0VZv2/WBVgiqHI8FBBEWEkZuVTfaO9EPWV3tgB9Z/MgOA1HkrCa1WmfBC+sJrRREaWYnUeSsBWP/JDGqf1HGfz6ozpCubvvh9n+V1T+nOpi/3XX6o1D2pA2s+Na3JntaIQlrz1u3ZkrrP8i2//01Oemb+mMrx0SXWVBRhx7Qke8NGcjZthuxsdk+ZRsTx3QuMyZy3AM3IsOd//U1wrUM3PosiZmAntnzyMwA75q0gpFplQgvto9BaUQRHVmbHPPu9bfnkZ2JO6pT//kTv/Yk+y2ud2oukb/8kY6N5rLKS0vI/T4KDCYoIg+AggiqFk5mwbR9dke2akr4mgYx1iWhWNklf/Ur0wE4FxtQ4qXO+9uRvZlK9l92pRw/sRNJXv6KZ2WSs30L6mgQi2zUt8N7qvY5jz5rEfI9NSSiJ1tz0DHbMWkrunn09UEGVI6hz+RA2PDuhxBoPlo5tj6N6tap+215hlqxPon5MNepFVyU0JJiBbRrz8z/rC4z5fPZyzuzWgmqVwgGIjqwEwOotqXRoFEdIcBCVwkJpHl+D35YXcS70I6pa4kdF4z9rwPiiqoNVNbUUP+8VVS36NqYCER4fTcbG5PzXGZuTCS904QyPjyZjs8+YTfuOKczWr/8gd3cG3Ra9Rrd5L7P+5a/JTt15yPoi4qPZs2nvttM3pxBRaNsR8dGkb07Jf71nc/I+Y6K7tiQjaTu7/k3YZxt1hnVjYykYMJVqR7O7kNZK8TUO67OanNWbzT8tLLGmogiqGUvOlr1d7nO2JBFcs3gDpfKQwWTMnFXs+kMhLD6GDJ99lLk5hfD4mAJjwuNjCv7eNqcQ5o0JqxlFlmf8ZW1JJaxmFACVmsQTEhXJcZ/fT9tJj1Hr9BPs8xNS2PDyRDrPfZmui14jJ203qb/su1/Da0eTuXFvuC5zcwphtWP2HbPJG5OTS07abkKiqxJWu9DftCmZ8NoFf3+xw3qQ9OWvBZbVvngQbaaO46hxVxFcvUqx+6w0te6PBreNZNMrE8ndnXHQWio6W9J2U9tn38dVq8wWLySZx9qkNNYmpXHBK99x3kvf8tuyjQA0r12D31ZsJD0zm2279jB7VQKJhd7rKHvKlQEjIo1EZKmIvC0iy0XkfRHpLyK/icgKEeksIlVE5E0RmSUi80VkmPfeSiLykYj8IyJfAJV8PneN17obEflSROaKyF8iMspnzE4ReVhEForIHyIStx+d94nIzd7zn0XkMU/PchHp5S0PFpEnRWSJiCwSkWu95f083Yu9vyOvtfgaEXlERBaIyBwRaS8ik0RklYhc4bPtW0RktveZ95fqF1BKVG3XFM3JZWabUfzR6WrqXzGEiIa1Aqan7ind2ViE9yWq3VHkpGewY+mGAKgqmoYjehDdpglLX/om0FKoNLA/YS2bs+P9jwMtpUjy7hglJJjI1k3469yxLDnrIRqMPs2MmupViDmpE7M7X82fbUYRVDmcmiN6+VWjhIYQPbATyV/v/f0lvDOJeV2vZmH/m8jakkqjey/wq6bCVD62ERENaxcIcTmMnBxlXVIar192Eo+OPJ4HvvidtPRMujevS88W9bjgle+4/aPptG5QkyCRgGo9EkNI5TGJtylwOnAxMBs4G+gJDAXGAH8D01T1Yi8sNEtEpgCXA7tV9WgRaQ0UF1i+WFVTRKQSMFtEPvO6aFYB/lDVO0XkceAy4GADtCGq2llEBgP3Yq3ERwGNgLaqmi0i0SISAbwN9FPV5SLyLnAl8Iz3OetUta2IPO2N6wFEAEuAV0TkRKAZ1rFTgIkicryqTvcV4xlmowDGjx9Pc591dS4aSJ1z+wOQtmAl4XX33r3Z3W+K70eRUeguObzOvmMKE3dqT1KmLUCzc8hKSmP77KVUbXMUe9Zu2e/7ABpdNIAG5/QFIHXBaiLq7N12pfho9hTa9p7NKVTy8bhExMcUGCPBQcQP7sz0E8fss626w4s2bA6WphcOoMk5fQBIWbiayoW0pm/eN1yxP+J6Hcsx1w9j2ikPkZuZfdi69kfu1iSCa+01JoNrxZKzdd/QRnin9lS98BySrhoNWfuGHA6W+ItOsiRaYMeCVYT77KOwQt49yPMC+v4mo8n0xmRuTSW0lnlhQmtFkZW03d6zKZmsbTvI3Z1B7u4Mtv/xN1WObQTAnnVbyPKSZZO/+5NqnVqw9bMZBbeZkEJY3dgCujITkvcdUyeWzM0pEBxEcLXKZKfsIDMhueDfVCeGjIS9v7+ovu3YtXh1vlagwPPE9yZz9P/t+9ssjpJoLY6qHVoQ2eYo2s96GQkOJjS2Gsd+dj9/jbj3oHVVRGpVq0yCj9ckMW03tQp5w+KqV6ZV/VhCg4OoG12VhrHVWJeURqv6sVzWpzWX9WkNwO0fTadhbDW/6i+MS+ItH/yrqotVNRf4C5iqdqu1GDMITgRuF5EFwM/YBb4BcDzwHoCqLgIWFfP514nIQuAPoD5mEABkAnm3vXO9bR0snxfxvv7AeFXN9jSlAC28v2+5N+YdT3ceE73/FwN/quoOVd0KZHjG2oneYz5moLX00Z+Pqr6qqh1VteOoUQUTJTe9NYk5/W5hTr9bSPp+NnGeu71ah2Zk79hNZqH8jMwtqWTvTKdaB9tM3OknkPTD7P3ujD0bk4jq2QqAoMrhVGvfnN0rN+73PXmseWtyfoJtwg9zqH+G3TFHtW9K1o7dZBTSl7Ellayd6US1t7yD+mf0ImHS3Pz1sccfx86Vm/YxfBAhfmhXNn0586B0FcXKtyfz44Ax/DhgDBu/n0Oj001rTPumZO1ILzLXpTiiWjWk4+OXMOOCp8hITjvwGw6TzH+WElK/LsHxtSEkhMr9+7JnRsF9ENq8KVG33kjyLXeRuy21RNvb/NYPzO9/C/P730LyD7OodUZvAKq2b0bOjt35IaE8srakkrNzN1Xb2++t1hm9SZ5kv7eUH+cQ570/zmd58qTZVO98tJfnEkbV9s3YvWIDGRuSqNqhOUGVwgCI6nUc6Sv2/R3uXLCSSo3jCa9fCwkNIXZYT1ImzSkwZtuk2fnaY07uxvZfl5imSXOIHdYTCQshvH4tKjWOZ+f8lfnvqzm8J0lfFAwf+eb9RA/uwu6l6w5yb5ZMa3EkvjuJOe0uY17nK1ky7E72rN78nzdeAI6tF8u6pDQ2puwgKzuHSQv/5YSjCyZE9zmmAXNW2wy8bbv2sDYpjXrRkeTk5pLqJTwv35zCioRtdGtWx+9/gy+5qiV+VDTKowfGNwib6/M6F9ObA4xQ1WW+b5KDcN+JSG/MsOimqrtF5GfMAALI0r1ZTDkc2r7J03io7yvuc3z/7rzXIZjX5RFVHV+CbeSTMmUeMf3a0eXP58lJz2TZ9S/mr+s49Qnm9LsFgBW3vUbL5/KmtS4gZep8AGIHdabZ2IsJjanGce/fwc4la1g08mE2vTmJFs9eRadfxoEICR/9xK6/D/4knceWKfOp1a8tff94hpz0DBbcsPfPPn7KI0zvfwcAi29/K38a9ZZpC9jiTXkEqDu8W5FelphuLdmzKZnd6w7sFToYNk9dQHy/tvxv5jiy0zOZNXqv1hMnj+XHAXaX3fqus2h4SndCKoUxZO7zrP7gJ/566nPa3H02IVUi6P7q9QDs3pjErxeOKxVtBcjJJfWp54l95jEICmbXN9+T/e8aql52IVn/LGfPr79T7ZrLkcoRRD9sF7GcxC2k3HpXiTe9bco8ovu1p+MfL5CbnsHyG17KX9duyhPM72+/t5W3v54/jXrbtPls835v65//gqNfvYnaZ/djz4atLB1l+yd9xUZSfppPh5+eQnOVhPensnupJWMmfTOTdj8+gebksHPxv2z+v8lF7pPVY17nmA/vtmn/H00jffl66t8ykp0LV7LtxzkkfjiVZs9fR7vfXyA7dSfLr3jatr18PUlf/067X55Fs3NYPeY1yLWKHEGVwql+fBtW3VrwcG149/nmIVLIWL+FVbcewhTiEmgFaD/rZYIjKxEUFkL0SZ35+6wHSF8emBDqLfc+yuz5i0hNTaPf8HO56pLzGDFkoN+2HxIcxO1Du3Dlm1PI1VyGdWxG07gavDR5PsfUjaH3MQ3o3rwOM1ds4tSnvyRIhNGDOhJVJYKMrBwufvUHAKqEh/LwGb0ICS6P/oD/NlKeMo9FpBHwjaq28l6/7b2ekLcO81JUA65VVRWRdqo6X0RuBI5R1UtFpBWwAOiqqnNEZA3QEQvJXKqqQ0SkpTfmJFX9WUR2qmqkt93TgJNV9cJidN4H7FTVJz0j6GZvO7HAHFVt5OWt9AdG5oWQgN3AcqCvqq70/r75qvpsnkZVTRKRC73n13jby9PfHngQC0HtFJG6mOG1v6uw/hx3+kHtf3/TO/FTvq5dRtOFS4EhCR/ycfw5gZZRJGdufh+Ajd36BlhJ0dSdOY0ZtU8LtIxi6ZUwgd/jRwRaRpF03/wZQLnWl5W0OtAyiiU0tgnpn48NtIwiqXTqGLAb0VKnV91+Jb6Yz9g4NbCJPIdIRTQZHwRCgUUi8pf3GuBlIFJE/gEewMI5hfkBCPHGPIqFkcqK14F1ns6FwNmquge4CPhURBZjnpWDvv1S1R+BD4CZ3vsnAIGbh+hwOByOcoFL4g0wqroGaOXz+sJi1l1exHvTgZHFfG4jn5eDihkT6fN8AmYcFKfzPp/nvX2eJ+HlwHi5Lzd6D9/3TgXa7U+jqr6NJfEWte5Z4NnitDkcDofjyKMiGiAlpVwZMA6Hw+FwOA6d8pQO4i+cAbMfRORObEq3L5+q6sOB0ONwOBwOh8NwBsx+8AwVZ6w4HA6Ho1zjQkgOh8PhcDgqHEdiITtnwDgcDofDUcFxOTAOh8PhcDgqHEdiCKki1oFxOBwOh8NxhOM8MA6Hw+FwVHBcCMnhcDgcDkeF40gMITkDxuFwOByOCs6ROAvJ5cA4HA6Hw+GocJSrbtSOMsF9wQ6Hw1F+KJOOz63iupb4XL8k8Y8K1Y3ahZAcDofD4ajgHIkhJGfAHAH8GFdkk+6Ac2LiR7xf59xAyyiWcza9x9LmgwMto0haLv8OgFWtBgZYSdEctWQS8+oPC7SMYmm//iumxZ0RaBlF0jfxEwAWNBwaYCVF03btRNI/HxtoGcVS6dQxZCWtDrSMIgmNbVJmn517BEZTnAHjcDgcDkcF50j0wLgkXofD4XA4HCVCRKJFZLKIrPD+r1HEmD4issDnsUdEhnvr3haRf33WtT3QNp0B43A4HA5HBSdXtcSPEnI7MFVVmwFTvdcFUNWfVLWtqrYF+gK7gR99htySt15VFxxog86AcTgcDoejgqOl8K+EDAPe8Z6/Aww/wPjTgO9VdffhbtAZMA6Hw+FwVHDKgQcmTlU3e88TgLgDjB8JfFho2cMiskhEnhaR8ANt0BkwDofD4XA4EJFRIjLH5zGq0PopIrKkiEeBKYdqBeaKtYhEJB44Dpjks/gOoCXQCYgGbjuQXjcLyeFwOByOCk5pzEJS1VeBV/ezvn9x60QkUUTiVXWzZ6Bs2c+mzgC+UNUsn8/O895kiMhbwM0H0us8MA6Hw+FwVHBUc0v8KCETgQu85xcAX+1n7FkUCh95Rg8iIlj+zJIDbdAZMA6Hw+FwVHBy0RI/SsijwAARWQH0914jIh1F5PW8QSLSCKgP/FLo/e+LyGJgMRALPHSgDboQksPhcDgcFZxA9zVU1WSgXxHL5wCX+rxeA9QtYlzfQ92m88A4HA6Hw+GocDgPzBFOi4cvoGa/duSkZ7DkupfZsXjNPmOqtm5Mq+euJDgijK1T57PsTpvqf9TNp1H33L5kJqcBsHLsRyRNXUBojUjavDGaam2PYtNHv7B0zFulorXDg+dRt29bstMzmDn6VbYVobXNbafT+PSehFWvwifNLt1nff3BnTj+9ev5/qS7SVn0b6noAqjSqwO17rwcCQ4i9dNJpLz6aYH1lTq2Iu7OUYS3aMym0Y+yY9Jv+evqvf4Aldq2JH3u32y4/L5S05S/7R4dib39CiQ4mLTPvif1jU8KrI/o0IrY264grHkTEm8Zy67JvxZYL1Uq0+CrV9k1bSZJY18sFU3Verej3n2XQXAQyR9OJvGlzwpuMyyERs+MptJxR5GzbQf/XvUEmRu2EFavFsf89AJ7Vm0EYNe85awf8zIAdW49l+gRfQiuXoWFLUve/6vZwxcR068duekZ/H3dS+xcvO/vpWrrxhz93NUERYSRPHU+K+603/pR95xL7Ikd0Kxs0tck8s/1L5GdtrfcRXjdGLrMeJp/n/iU9S9/fUi6qp7Qnrr3XooEB5P80Y9seXnffddg3GgqH9eU7G1prL3G9h1ARMtG1H/kKoIiK0NuLsuH3oSEhNDs00fy3x8aH8u2L35m4wOvU1J+W7aRx7+ZRW6uckqnZlzc+7h9xkxatIbxUxcA0Dw+mkdHHg/AM9/PZcayDQCM6tuaga0bl1jPoXDX2HFM/20W0TWi+PK9V/y67cOhFEJAFQ5nwBzBxPZrS5XG8fza9Qaqd2jKMY9fyp+D7tpn3DGPX8LfN73K9rkraf/B7cT2bUvStAUArB3/HWtf/qbA+NyMLFY++gmRLesT2bJ+qWit07cN1RrXZmKPm4hpfxSdH7mQSSfft8+4DZPnseytyQz97cl91oVUiaDlpQNJmruyVDTlExRE3L1Xsf6iO8lKSKLRZ8+wc+ofZK5anz8ke/MWNt8+juhLRuzz9pQ3PiMoIpyokWXQODIoiJp3Xc2my+4gOyGJeh8/z66f/iBr9TofbVvZctdTRF14WpEfEX3t+aTPPWA+3SFpqv/Q5aw4+16yNifT4psn2T55FntW7N1fMSMHkJ26k797XUGNob2oO+YC/r3qCQAy1iaw9KTR+3xs6uRZbHn7W46d/nKJJcb0a0flxrX5o+t1VOvQjBaPX8rcQXfuM67F45ex9KbxpM1dQZsP7iC6b1tSpi1g2y+LWP3wB2hOLkfddQ4NrzuFVQ+9n/++ZvdfQMrU+YcuLCiIeg9ezqpz7iErIZnmE59i+5RZZPjsu+gzB5CzfSf/nHA5UUN6EX/7Bay95gkIDqLhMzeydvQ49vyzhuCoqmhWDpqRxbLBN+S/v/k340j9YeahaytETm4uj0z8g1cuOZG4apU558VvOeHo+hwVF5U/Zm1SGm/+vJi3rxhEtUrhpOxMB2D60g38symZj68dQlZODpe8OokezesSGRFWYl0Hy/DBAzh7xFDGPLjvuaQ8EugQUiDwewhJRH4WkY7e8+9EJKoUP/ttESn6LFzOEZELReQFf26z5kkd2fTpdAC2z11JSLXKhNWKKjAmrFYUIZGV2O5d9Dd9Op2agzru93NzdmeQOmsZuRlZ+x13KNQb2IHVE8wzkDxvFWHVqxBRSGveuj1bUov8jDa3nsZfL35DTinqAoho3ZzMtZvIWp8AWdmkfTudyP7dCozJ2riFjGVrIHffTP/dMxeSuyu9VDXlEX5cC7LWbSJ7QwJkZ7Pz+5+p0regtuxNiWQu/xctQlvYMU0JjqlB+u9zS01TlbbNyFiTQOa6RDQrm20TZ1D9xM4FxkSd2IWUCdMA2Pbtb1Tt0fqAn7t7/nKyt2wrFY2xJ3UkwTs20uauIKRalSKPjeDISqTNXQFAwqfTqTmoEwApvyxCc2x/bp+7nPA60Xs/e1An0tdtYZfnXTgUKrdtRsaazWSu9/bd1zOoPqBLgTHVB3Qh5TPbd6nf/UbVHm0AqHp8O9KXrmHPP2sAyEndsc/vMbxxHUJiqrNr1l+HrK0wS9YnUT+mGvWiqxIaEszANo35+Z/1BcZ8Pns5Z3ZrQbVKVrMsOrISAKu3pNKhURwhwUFUCguleXwNflu+qcSaDoWObY+jerWqft1mSSgHhez8TkBzYFR1sKqmBlLDkUxEfDR7Nibnv96zOYWI+Oh9x2xO2TtmU8ExDS4eSLefHuPYZy4npHqVMtNauXYNdm/aq3X3phQq196nV1ix1DiuEZXrRLPJc1WXJqFxMWQnJOW/zk5IIjQuptS3cziE1IohO2Fr/uvsxCRCasUe3JtFiL1lFMlPvlaqmkJrx5C5ae/+ytqcTGjtmEJjoveOycklZ8cugmvYxSSsfhwtv3+aZp8+TJXOx5SqtjzC46PZs3GvxozNyYQXOjbC46PJ2Oxz/GzadwxAnbP7kuz97oIrh9PwmmGsefLTfcYdDKG1Y8ja7LvvkorYdzFkFbHvIhrXBVWavHsfzb99mlqXn7rP50cN6UXqN7/us/xw2JK2m9o+54S4apXZsn1XgTFrk9JYm5TGBa98x3kvfctvyyw02Lx2DX5bsZH0zGy27drD7FUJJBZ6r6Mg5aCVgN85KANGRBqJyFLPw7FcRN4Xkf4i8pvXebKziFQRkTdFZJaIzM+rzCcilUTkIxH5R0S+ACr5fO4aEYn1nn8pInNF5C/f6n8islNEHhaRhSLyh4gcqDzx8SLyu4iszvPGiPGEVzFwsYic6S3vLSL58Q8ReUFELvSePyoif3tljZ/0ltUUkc9EZLb36FHM/gry/rYon2UrRCRORIaIyJ/ePppS1N9T2JMkIjt9nt/ibXuRiNx/gH1Rpqx/ZzIzulzHzL63k5GYSov7zw2knOIRocO95zDv/g8CraRCUW3kEHZPn01OYtKBB/uJrC0pLOlyKUsHjWbDA2/S+PmbCIqsdOA3BoiGN5yCZueQ+NkMABrfcgbrx39Lzu4M/4sJCaJKp2NYe/1TrBhxG9VP6kpkIc9WjaG92PbVdL9JyslR1iWl8fplJ/HoyON54IvfSUvPpHvzuvRsUY8LXvmO2z+aTusGNQkS8ZsuR8XgUHJgmgKnAxcDs4GzgZ7AUGAM8DcwTVUv9i7cs0RkCnA5sFtVjxaR1sC8Yj7/YlVNEZFKwGwR+cybllUF+ENV7xSRx4HL2P/88HhPV0ussM4E4FSgLdAGm18+W0SKPUpFJAY4BWipqupjiDwLPK2qv4pIA6wM8tGF36+quSLylfcZb4lIF2CtqiaKyK9AV+9zLwVuBW7az9/jq+tEoBnQGRBgoogcr6rTC40bBYwCGD9+PI181tW/6ETqnmuz1dIWrCKi7t67t8LeFtjXKxNRZ++YzK3b85dveG8a7d+79WD+jIOm+YX9OeqcPgCkLFhN5Tp7tVauE83uhIMLF4RGRlC9ZT36f2Y5DJVqVueEt2/klwvHlUoib1ZiMiG193o1QmrHkpWYvJ93+I/sLcmE1K6Z/zokLpbsLQdnkES0OZqIDq2oNvJkgipXQkJDyN2dTsozb5ZIU1ZCMmF19u6v0PgYshKSC41JIaxOrC0PDiK4ahVytu0AICfT/k9fvIqMtZuJaFKX3YtKntdU96KB1DnXZoHuWLCKiLqxbGcZAOHxMWQUOjYyNqcQHu9z/NQpOKb2mScQO6AD8097IH9ZtfZNqXlyF466+xzzWOYquRmZbHxzEgdDVkIyofG++y62iH2XTGgR+y5rczK7/vwrfz+m/TSXSq2OYudvi0z/0Y0gOJj0JasOSsuBqFWtMgk+XpPEtN3UKuSljatemVb1YwkNDqJudFUaxlZjXVIarerHclmf1lzWxwys2z+aTsPYaqWi67/KkZgDcygGzL+quhhARP7C2marV3imEVAPGCoieeV/I4AGwPHAcwCqukhEFhXz+deJyCne8/rYhToZyATyvCRzgQEH0PmlWknBv328Gz2BD1U1B0gUkV+wfgtpxXzGdmAP8Ibnocnbfn/gGNl7J1BNRCJVdWcRn/ExcA/wFta06mNveT3gY6/qYBhwKFfQE71HXvZfJLafChgwhcpB6493T8tft/6tH1n/lnUvj+3fjgYXDyThi9+p3qEp2Tt2k1kofyRzSyrZO9Op3qEp2+eupM7px7PujR8AywHIG19rcCd2LC0Y3y4py9+ewvK3pwBQp19bWlw0gLVfziSm/VFkpu0uNtelMFk70vms1ZX5r/tPuJN5D3xQarOQ9ixeTlijOoTWiyMrMZlq/zueTTc+XiqfXVIyliwjtEFdQurGkZ2YTOSg3iTe+uhBvXfL7Y/lP686bADhxzYvsfECsGvhCsIbxRNWvxZZCSnUGNqLNdc+VWBM6uRZRJ/Wl13zllHjfz3Y4V1kQ6KrkZ26E3JzCWsQR3jjOmSsSyixJoCNb01i41tmSMT0b0e9i08i8YvfqNahGTnFHBs5O9Op1qEZaXNXUPv049ngHRvRfdrQ8OphzDvlXnLTM/PfM2/YvfnPG998Otm79hy08QKwe+EKwhvXIax+HFkJydQY0ou11xVMMk2bMovoEX3ZPW8ZUYN7sON323c7fplHrStORSLC0KxsIrscy9Y3Jua/r8bQ40mdWHrel2PrxbIuKY2NKTuoVa0ykxb+y9iRvQqM6XNMA75f+C/DOzZj2649rE1Ko150JDm5uexIzySqSgTLN6ewImEb3ZrVKTVt/0XcLKT94+vzzPV5net9Tg4wQlWX+b5JDsLtJyK9MeOgm6ruFpGfMQMIIEv3mpY5B6HZV+eBNp5NwTBaBICqZotIZ6woz2nANUBfb2xXVd1zgM8FmAk0FZGaWFnkPK/R88A4VZ3o/d337U+XiARhhk7e3/OIqo4/iO0fkKQp84nt15aefz5LTnoGf12/d6pg16mP8ke/2wH457Y3afXclQRFhJE0dQFJeVMe7zmHqq0agirp67fy9817p132mv08IVUrIWEh1BrUkblnjmXX8o2HrXXT1AXU7deGob8/RU56JjNH723XMWjyw3w/wLwr7e4aSaPh3QmpFMYpc55j5Yc/s/ipzw97uwdFTi6JD7xM/TceguAgtk/4kcyV64i97lz2LFnBzml/EnFcM+q+eDfB1SKJ7NOF2OvO5d//mVHV4IPHCWtSn6DKERw1/V0SxjzDrl+Lc1QeuraksS8SP34sEhxE2hc/krVqLTWuPp+Mv5az++c/CG/VnNrP3ENQtapU6d2V6KvPZ/3wUQf+7BJoWn/3qzR97z4kOIjkj6eyZ/l64m86m92LVrJ98iySP5pMo2dGc8yMV8hJ3cG/V9tFOrLLscTfdDaanQ25yvo7XiYn1e4f6o65gBrDjyeoUjitZr1B8oeT2fz0R4clMXnKfGL6tafbn8+Rk57JP9e/lL+u09THmd3PvI3Lbnudo5+7iuCIMJKnLiDZm1nU/JFLCAoLoe0ndwOWCLzs1lLIJcrJZcM942nyru27lE+msGfFemrfaPsubcoskj+eTMOnb+ToX8aTnbrDZiABOWm72Pr6VzT/ehyokvbTXNKmzcn/6KiTe7L6wtKLSocEB3H70C5c+eYUcjWXYR2b0TSuBi9Nns8xdWPofUwDujevw8wVmzj16S8JEmH0oI5EVYkgIyuHi181Y7BKeCgPn9GLkGD/pmzecu+jzJ6/iNTUNPoNP5erLjmPEUMG+lXDoXAkemDkYP5or/TvN6raynv9tvd6Qt46LFxTDbjW88y0U9X5InIjcIyqXioirYAFmBEwR0TWAB2BHsClqjpERFp6Y05S1Z9FZKeqRnrbPQ04WVUvLEZnvi7v9U5VjRSRU7FQ1mCsy+UcoAsQCswAWmC5OfOB+7GwU2VV3SIi1YHVqhojIh8A81X1Ce/z26rqgv3styeA2kCMqg72ls33/ta5Yg2rGqtqby/3pqOqXiMidwFVVfU2ERmONb0SL4T0INBPVXeKSF3MwNtf0yz9Ma7kNTHKghMTP+L9OuU0bwY4Z9N7LG1eBlObS4GWy78DYFWr8nlCPWrJJObVH3bggQGi/fqvmBZ3RqBlFEnfRKvTs6Dh0AArKZq2ayeS/vnYQMsolkqnjiEraXWgZRRJaGwTOPCN9WERW615iS2YpLTlFSrRqDTrwDwIPAMs8rwG/wInAy9jeSD/AP9gYaDC/ABc4Y1ZBvxRiroAvgC6AQuxFt+3qmoCgIh8gjWN+pe9oZmqwFciEoH92G70ll8HvOiFwUKw0M0V+9nux1i+0IU+y+4DPhWRbcA0oKjqTK9521+I7ZtdAKr6o4gcDcz0PFs7gXPZf9dPh8PhcPzHqYjToEvKQRkwXu+CVj6vLyxm3eVFvDcdywEp6nMb+bwcVMyYSJ/nEzDvSHE6Lyz0OtL7X4FbvEfh99yKJdIWpnMRY5OAM4vbfhHj51DI2lbVryiiS6eqvg287T1PBLr6rL7NZ9yzWDKxw+FwOBzAkRlCcpV4HQ6Hw+Go4Lgk3gqCiNyJTen25VNVfTgAWi4Cri+0+DdVvdrfWhwOh8NxZOI8MBUEz1Dxu7FSFKr6FjZV2uFwOBwOh5+okAaMw+FwOByOvbgkXofD4XA4HBWOitjLqKQ4A8bhcDgcjgqO88A4HA6Hw+GocByJSbz+rc3scDgcDofDUQo4D4zD4XA4HBUclwPjcDgcDoejwnEkhpCcAeNwOBwORwXnSDRgXA6Mw+FwOByOCocciVab4/ARkVGq+mqgdRRFedYGTl9JKc/6yrM2cPpKQnnWdqTjPDCOQ2VUoAXsh/KsDZy+klKe9ZVnbeD0lYTyrO2IxhkwDofD4XA4KhzOgHE4HA6Hw1HhcAaM41Apz7Hg8qwNnL6SUp71lWdt4PSVhPKs7YjGJfE6HA6Hw+GocDgPjMPhcDgcjgqHM2AcDofD4XBUOJwB43A4HA6Ho8LhDBhHAUREAq2hKMqrroqI25f/HXy/S/e9Oo40nAHjAAqc/MpdfywREfWyzUXkaBGp707WB0/evhKRY0WkprrM/UPCZ/+FBVqLL4WOi2uAc8qbxvKOz3frzicVEGfAOPJPhCJyIvCWiNwgImcEWlcehU7S44FbgKnl5aTjcxKsIyINA62nMN53OxR4CWict7w87D+ffddFRPqISLk7J3n773/A8yIyLtB6CuMdF+cBM1U1M9B6fPH5fluIyHEiUi3QmvLwOe/1Bu4SkWEiclSAZTkOgXJ3snD4H+8g7gc8CbwF9ASGBPpuTkQq+zwfCJwC/A9IA7IDpasw3v4bAnwJPCkin4lIzQDLykdEWgEPAZeo6iwRiRWRep7ugJ4DPA2DgfeAPaqam7cu0NryEJFOwIPAJKCziLwbyAuxiPQQkb7evqsBDAYuB5JF5FwRuUtETgiUPl88jcOBd4GbgZdFpHNgVRk+N20vA8ux898ZIhIcWGWOg6VcnCAc/kdEaotIE59FTYCLgT1AQ2CMqmaKSL0A6WsB3Oj9D5CKeV8uBToDJ/ucgAKKiByHnZwHA98CLYEMn/UB8XT4bDceSABiRORu7GKyWESO8zUYAoHnsXoAOEVVZ4pIGxEZISI1Aq3N09cMuBL4TFU/V9WeQBXgORGpHiBZTYE3ROQEVd0GrATuBt4BugDNgG4B0lYAEWkEXAX0AX4HmgMrAu39E5EgEakEDAROB1YBO4F3VDXH9+bJUX5xBsyRy03AM94JGiAX+Bh4BhisqutF5CTgNBEJD4C+WkBt4BQRaQzsxu6UzlHVEz3j6kLg0gBeSPLIBD4CTgWuwIyrNBHpBntDYP6ikOGCqk4GlgLPA2uAkcBTQCt/6iqGdcAfwM0i8jQwFjgLuDWgqvZSA8sL6yIibQFUdQT223zFn3freR4pVX0HOxaeF5FjMO/au8B1qnot8DPQI0DHbVEswhoiXgCM9IyuLp4B4Vfyjg1VzVXVdGAD8AQWXh2qqptE5FSgt7+1OQ4dZ8AcYfgcwLcAKdiFoxHwOTATmKWqW0WkJ/A0sExVM4r7vDLUNwO7K2qPeYZWYSfAOp6b/E7gOuABVd3uL32FtLbxjKud2F3cpcCpqvqviPTBLjB+z4nxPFMnAZNF5C0RGa6q16lqZ1X9P+Bo4FzMmPErhXIiunvG3ceYgfo1th/fB0L9ra2QvmNFpCWwFjOmVmBh1dYAqnoS8KSq5vhRXl4u2HWYFyYFM5xbqOpXwCYRuRi7ObnNn8dtHj77r4qIhKjqGiAKuAS4QlVXiUh/4BU8A9ufeMdGZxGZ5C2aD1QDnvVu2toDD+PjQXWUX1wrgSMUETkeuAFoi92FjAJigAuBYzGPzGOq+nWA9F0HnAbMBjoAU7C7pI7AidjJ/A1VXepnXXmJf62Au4BwzLA6EbgdeBaoirnNb1fVbwKgLRbzpL2KeVlaAouBL4A4LFdntD+1FdI5GPMAZWHeggdVdau37nhM+z3+1iciQaqa6+VbPQf8AgzCPFZJmIGaBXysqgv9qCtWVZO85+0xo6U3dgwMwMKXo7Bj5X7gfVX9x1/6CiMiw4DzgTDs2OiMGaa7sVyTq4BbAvX78zQuAJar6hkicq2nsQ5mbN2vqhMDpc1x8DgD5ghERJpjF7ORWGhhPHbHe4/nPYjFblaS8y6KftYXi7nEL/fuivoAZwKJwHOqmuxPPUXoGwbcC/yIGXu7gMuA4zFjKxr4WlWn+nv/ic2oaAj0UdULvTvi84E2mBfrUyBKVZf7U5uPcSWYofeVqv4tIp9iBvRTWG7J3cAEVf3SX/pEJCbvNyUitYDPsGPhJxE5DXgcM6Z3Y4bCS6q6sqx1eXoaA9cCP6vqRBFpit1YjPDWh2PHb38sB2tJIHKHfL7fKOAb4A5sn3Xx/o8BegGRwJ/evi3z79fzAmV7z+sDQaq61nv9O7BZVUd44axjgW2el8jv5z3HoeNCSEcIhZLm9gCrgRRVzVLVi4H6wCci0lxVk/JO6IE4iL27zWzsrg1V/QmYi4U9LhSRsEAlAXo5D6cCN6rq7cBoLInyFSz89gBwk6pO9bT703jpAbyNJWSfJyK3qvEO8A9wDBCmqsv9rc27uA3FDKjBWAgEzCCIB27Dcomu97PxUgkLozb0dG7Bvs9kEQlW1QmYR2i05+170F/Gi8cuYDvQUyxhfTUWRn3S05uB5Zh8DewMhPHi6VAR6QX0BWao6gxVvR6YDnziaXtBVR/1jucy//2JSAzwoIgc4+UPPQ4MF5EG3va7A8eJyCRVTVfVOaq6yh/aHKWDM2COELwTzPEi0ltV12Gzejp6d0xgCZ4hBPA3ISKnisiz3su3gJoiMsJ7nYi5yP9PVTMDeIIRzMPSA8C7mP0OHAU8IiJxfs6LMFE2W+sKbPbYvZjH5RYRudXT+RowVlU3+Fubp68lFrL8FvgNuFxEjldL6LwSM6DDA2A45wCPAtkicq+3LBOrq5KnYRV7cyJS/SFKPDyD6hUgGRiCeTSGA91E5AMRuQvLEXtIVVf7Q1thnd7/nbCp8COBwSJyKYCq3grMAr4UkUri36nxdbBz2qXYMfsc5iEd7Hm2wIya9iLSzo+6HKWECyEdQYjIJcAj2F1SFDAGmIedlE/HvAq/BVBfPSw/4wtsSugw7KStQCNghKr+7WdNea7xhkCOqm7wTnYPA5+o6tsi0hUzHgR4TVV/9adGT+dwzBDYBNzt6TwGS1K8X1XH+luTj7ZjMS/GbFUdIyLR2IXuRCwkOM3X1e8nTZWwcMIuL7TQGEvWnYj99r7EjOYE4CRsn/olH8zX+yQi1dRmtIVjoaSGWNLzPOz7DgK+V9Ul/tBWjN7jsfPHBFX9RUTOwcJFf6rqW96YFqq6LADa2mEe02pYLZ+8kNwK7Lw3ELjV3+cVR+ngDJj/MD4X33DP1YzYLIUxmGGQi8XOj8VyEiYV/2llqrMNsF1V14hIXSzM8AN2ZxwJHAesVNWNAdI3DLu4JWFTfidi02hfwEJbJ2AF9i4F5nohm7LW5GtY7cRmpHTC8l2WYnVLNnvGQx21qdQBwbvrfhHzUl2ulmdVA0sYPxE4G0jzl+fK8xr0wC5e/2A1Sh4C6gE3Yr+9t7DvtDbwj6pOD0A+05XAUCyE9JuqPi8iN2Peqol5YcpAIyJXY8fqDar6hhe6GYgZfjNU9TXxEqT9pKfA9+R5/87HcqwexEKWA7Hj9nW1GVyOioiqusd/7IFnmHrP22Dhofo+yy7DTtwdfMf7vq+M9VXyeX6cp++WPI2Y6zcBGF8O9mV3bHp5NDbDYw2WwFsXiMUKhjXGLohLgKP8qG0Q8BeWY/AnNsOoP+YqvxkzXPb5Tfjr94fN7BgGdPZeP4Pl6DT0XtfIex6A77UqZoimAMO9ZZWxKtSfAHcE+Hd3GmYct/d+Y6ux8CDAfZgHMDJA2vK+33ggxHs+EvNq5J1TYrAwXKsAaeuHhSzPBCKwvLCxwDif31+o73vco+I9XA7MfwzPNd7ce94SmznREKtqW9dLQn0P2Ah85t0J5xXI8kfSZCQwUKz3zUjM+/MzdjIcISINVXUTFvdv693NBZJw4HqgK2YwXIlNXX0AiFPVmZiX6AasyN4qf4gSq93zAHCZqp6B7cOvsHyD77CQW34tFX98t77bEusdNB5L2L1TRF5W1RuAbcBjItJIVbepNyPE36jqDswYnQycLCJ1VHW3WvjvJSy5s9n+PqOMCcZClPO831gP7PhohnmzxqnqzkAI877fwcAHwIdiBQinYobV/4lIF7Vcpg/Vz6EtT9sQLLdlB3ARZjhvwX6PiuWGVcHyn/x6bDhKFxdC+g/hucbrYUl9VYBzgNaYEfM2lh/xAmYsnIKVzV7kR321VHWLd4J5AMvDOU5Vd3phmuOxpLtt2J3n1aq63l/6ikNEQrE+Kd+o6mQReQjzHN2gFg4JB6qoaoofNUVh3+XNqprgLXsLSFDVO0Skdt5yf+Ptr4+Al1V1iohUBV7DZss8gv0Wn1TVxX7WlRd2awQkq+oOEQnBLnZxqnqOiMRjxurPagnGZa4JOw/nFlo+AvO4dPBZ9gbwvKouKGtd+8Mzor7FZgWGYYmxJ2ChwCuAa7Djd4e/jQOxafDjgDuxmlG3Y7+7YKz+TC18ZuI5KjbOA/MfwTtwr/Au+Huw6b2vqWqyWsnsS7FEtjuxi8vP/jZegBc8D9BqLPwyC5tVgVoc+jMstNUaO3kH3HgBUNUsLA/hAS9Ztj/wuGe8BKlqRlkbLz6zPSI8gykNC8EM9Bk2BW+2TKCMFw/BLhgR3utdmEetvhoXBMB4CfLxDE3Emm5+oZY4/BCwVUR+A34CEv1hvHjUzzNeRORKEXlERM5Q1c+AmSIyT0S6icgozCjwm5G8H4KABao6y/NYfYTlh/VX1WeAE1U1LQDGS3Ns/9wJVMcKTZ4BvI6FBt8A1jnj5b+D88D8RxCr0LkLm27ZFKv50QWrJjpNVRO8O+FQrJDZ6gAkJUZ6mkIxl/MILInzW1X9TGwq8BogWwMwFXl/eKG2G7FE2Rc1ABWKvYvv1ViNnC+wHIl3sbvhVCzn4DZV/d7PuvI8G00xt30q9r0+A5ymqvPFqtvejM1W2eGv71dEqqjqLu95T6yH0FDMY/Am8Ltag0ZE5Cxgo6pO94MuwUKPGzAvwR+YkfctlgOWrKp3ishtmFe1IZaX81dZaytKq/f9tsdCqgs9va+p6vPemCcw799TYvVz/Hb8evsyCLsh+lBVnxTreH2Oql4vVtxxGOZxXuAvXY6yxxkw/yG8O/NHsem+N3vegtOxpMQIzHi4Q/3b2yhKVVO95w0w78o4rPHcDyJyOeayD8FCW2epV1a+PCJ7p7Xm9WzyVyXbjtgF9wbMw/Ym1oTue2yaaBzmVfN79V9P32AsLLgcS3h+BAsRvowZWadgxeC+86OmSGz/PK2qn3szsoKxmUX3Y0bWT0CWqgake7NY/ZSvMM/jtWrVibtiyae7sdo9u0QkTFUzA6HR03ky9p1epaozPGPwdmAZZnQ9h3mA/VZCoIjZRl2xCQp3YyH0HzxtI4HzNECzLB1liJaDTGL3KPkDaOr93xVrwng/Fp8ejuVKLMLqqPhTUwg2ffFOrCfKS5j3ZTjWm2eQN64/Xt+eAOy3PCO+DXAyEFzcOKxuSCC01cBybl7zWVcf6+R8Qjn47cV6v69umPEyBLtLb46FPXoCnQKk7RysFs7/vNdB2Ky3y7zX12EJ7X7T5/O9Bnn/t8ZCgo/7/Na6YB6ZsZ7mgM2UwTxC04COPsuCgGZYIu8zWCfnQGjr6h0fEZjR8h4WygJrWjoY6BqofeceZfsIKc6wcZR/fFy7zYHnRGSWqt4jIpmYwXAHdpc+EailFkby2925qmaLyATs4lYN65qbhVXlBBgrIlVV9RMsf8PvePtvAGbkJQOXiMh9wGItmFgZpKo5IlIdm1o9Scu4roWPtoew77GueI391HpEvUWAujbn4XmGgoC/1WbLAHwtIscBJ6vquADpEjXeFxEFHvXyYL4Wkc1AM7FqsSdjFzy/ND8sdPx1E5FkzJjvDPwhIutU9QURmYXNkllX1r+zg2CX90gHa6fhHQubVfXsvEGB8PxhXa5rYR6sdzBv81gRWeR9pwFraukoe1wSbwXGu8ANx9z0yUAvERmrqvOwmR51gHswr0JC3nvKWldeeMUjBJiBuZrv8NH+JRZyuFZEqhV6j98Qq1Z7PTBErTfKv9hd+XHilT33OWFXByZhRdfK/KIiIkdjBfSuVOvJsxF4Q0ROEpvJNRILM/gVn/3SFbv73g7UEpHxPsN2Yl6igOAdG33Eent9gHkyxoo1Bn0PuxiPAF7xl/GSpwtARG7Ciqpdh3kxNmAVsu+WvT2s5qi1EvArhRLGI1R1O1aX6RgRifSOhW7A42KNV4GA9U27DDuPrMKaSDbA6vl08bcWRwAItAvIPQ7/gSUB/sTe4lFdsLuQe73XnYBj/azJt4heQ6C697wq1r35Be91byy8UDmA+68ylpi7Hks2zVv+JPAh0A4vpITNapgK9PSTtnCs5sx6LDcib/m9WA7Rt3ghOD/ur5pAPe95W2xq9M3e6xpY477PMe/fEjxXfgC/37sxQ6WZ9/psLJyUF2IIK/yb9ZOu9sAP3vNXsMrTeVo6YLP0avhbVyGNw7zf2Oue3g6YgfACdlO0AvOwBfL7DSr0uj12M7IS68YeMG3u4Z+HCyFVbEKw2G9eGGEx1iPlfBHZpapP+ktI4aRW7w5zMFBZRL5R1YfFejF9KCLTsXj1aarqVw+Cr5tbVXeLyOtAJay4XrKq/qSWAP0s3mwob/bWVKzui1+SFFU1wwu/CdBJRIar6peqer/3d+TPrvEHIhKBJUg28kJsVbDWACEiUl8tpNUPM7BisI7cAQkL5iVaq+qDIpIN/CYivVT1AxEJA54XkROwqb/5v9ky1FNUaGW5iNyJeQxOVdVMERmgVmfoaPVjon1hvJD0dZhnNxrrvXQGcDnmJaoHXKIBaK/gi/p4QT0d84B5IvKqqqYHUpvDP7hZSBUIn5yXBlitigxvFs9w4BZVXSIig7C7p8rAw+qnBmri04zPM1TOV9UTRORFrOfNM2rTQsOxO/Sp6qeqtT4a8/bfYCzRdBvmsdqAFbk6CushNLnQ+6phSdLzylpbEcvrYNN+2wBT1OqDBCTfwMtrGYl5h+4BWmGzoqZgOUEB6VXlixd2uxD4XFX/9JaNwTxtPVR1mYjEq+pmP+nxbcw4EJiDTYP/P8wQ6K6qe0TkCmzm0SnqzdrzF2I1mpqp6m9iM7WeAuao6l3e+nMww/SqQBilRf3WZT9TtX2Oc7/1X3IEBpcDU0HwOSgHYa7c78RK8f+CJel+IyL3YrN53sO+W7+U4ffi4CvFugyD1XI5T0Suw6ZGtwdGicizakXfXvW38QIFStw/iBXfaoq575tiM1PWAyNFJNonDyDYu5svE+NFRBqJSDdP2z55QGptFfKm2Q72LjZl7jUopDHvPBGHtSgYgJWNX4qFQHoDQ0Wktr80+VJov23Dwi+niEhH77gZi+U2/SzWaiPRX9p8jJersdBfpFpOyafYsfua5628EgsVpvpLm6crCPOUJnpetqWYQd9BRBp6++99rPfSWyJSU6wYpb/05Z33ThYr8veyiNQsynjx0ZXXGsUZL/9xnAFTQfAO4k5Y1v05WLfc3ljb+g+wnh/LsQ6wWViPoQ1+0paEtaifKSLRal1y07BiYY94XqBPga4+Ro5fEOv/dIn3PALrKXMOVqOkNjZr4UNsSug4zGuV4hNmKuuCXN2BT0Wk536MmM3ABOB+DUBSp6rmikg7LB/iBawXD9hd+RzMi9UbAhOS9vZbD884bYgZA2FYQ8TuYgmn0zDvRrq/L2wicjxWCbu3qq71PFnLsN/dfKx68pnq575BkH+Rfx9LxH4Cm/I+Cms7cgdeIrZah/XOqrrVD8eErz4Vkf7Yb+3/sGnTz/kY1cA+ifbPibXacPzHcSGkCoJ3QL4ENFav6JaInIbVUPkL+EBVk0WkC1ZwarSqLvSzxkHYBa6TqqaIyFOYuzwFSygerX5uDyAirbE8kk2qulWsiVssdvG4WFWXishszFvVWgPQIE9ELgBuAq452LwCf4WQ8tzwXtjtHFU9x1veEwsj/YOVbA9R/5Xfz9OWd3feHTNEJ2CG1HdY7aH7sC7EJ2Azub71pz4fnc2xG48cLF9tIJbk/Jqq/hQITYXxPHtXYeUOJmDdzV/Gcuzu1gA03fT5fu/DvtNamFF1lqquywtb+/xfHfNW3qV+LKjnCBzOA1OO8b0b91zLrwJBInK3t2wC1oW4HZbzAjbV9mx/Gy+enu+xRm6zxRJf/w+bBTIIuM+fxovn/j5frd/TMmC8iDyglviainmrdohIL8yVP9yfxkuh7/Yd4FmsV9QJhT0xea5xEakuVrvEL4mn3tM8r8pCoKmInOpt/1dgLZbkWc/fxounQb3v7zTgIrVu192xnLCbVPVezBvTJxDGi4ic7oVNlwObsX01AQupJmKF1gKOZyhswW6QtmC5OJ2wfZeLzSD0qx7vaQ3v/0wsh+l64ALPeDkDuMnzvGR7N3ifY8aWM16OFLQcTIVyj30f7PWOnYD1v7kEOwH2wnJc7vAZWzvQegtpH4zNiIryXlcKgIau2HTUvIqrx2NelzHe67ewEvcJWA2YQHy3RwMt2DvV/DKs6F+vvHHsncYdhZ+mcfvoG4BNlb4SC02eg4WRrsc6/c4B2gboN5an8QGsyNr5Pus6YHWQQgKhyed1Y8w7emeh5ad5+655IPbdAfZnLeAWYDyW9BwoHQO931plzJhKxDyUYBWf/8ZnOjxeGDPQ+9E9/Px7CbQA9yjiS/FOvEAfLCH2Ksylez92h9kTS+S92xsXsHoR+/kbhmPx/SACV4L/FCwp8SzvdXfsLu1y73Vd4OhA7EPMyPsHK7C2CKjpLb8EM7x6+4yNwmro9PKjvr7exXcA5uV7Cpvy2x8r5jcBm/7r799V3ndb02fZ9ZiH6CifffsrUNXf+rztxwOh3vOGnrGS1yagj7f/jguEtoPct3HAGOCYAOnoh9WZ6emzrD1WIuI94Hd8atBgU/rjA73/3MP/D5cDU44QkcZAiqpu9xJOH8NK2r8uInHAbUCGqt7hJQbuUNX5gdS8P8Sqdvo9p8Tb9lDMFR6BTfd9UlVf8xI67wF+VdWH/awpL6bfGvMGDcG8BW9gXoR2au0eLsdK88/wZs1MwTxuZd4l2dMZjIUCZ2E5TOOxXjcbRKQylnQapgGqtSEiJ2EhhS1YBdZHsCTyqzDDPh7rSvyZn3UJVuDveSwH53dVzRKRZp6uj9VafUSraoo/teXp835/oWotPQosLzQmXL1aNP78jr19+ARmkL4PnIUdx59hIekooJqqrvHGirrZRkcszoApR3jZ9p8CTVR1m4jcgt313qeWoFsbOxEO1gDMRqkoiEhNbNbJlcACLNzxNPCUqr4nIj2AdC3Dui6F9FTC7sjTRKQ+oJhrvC7whKp2FJH3Mbf5saqa6L1PsCTUUFVdWsYaC9TVEJHzsTYGGVgjxASx9gX1gFfVjzNRCuk8FkvUvAjLzeiE1eg5T0Ruxb7zC9SSoYutFVKKeoqqUXIlVovpIeBPz4h5Dgtj9lPV5LLUtD+8mVqXY161OXlGcSEjJi8pNgzIVa++kx+01cdmL3bFZlbOAf7ActhuxfLU1vhDi6Ni4CrxliNUdYqInAXMFZu2+i1WKKy3iEzFTtgZ2AXQUTxZWKfm+aq6S6zy76fAOO+i9o6/hPjclQ8TkaVYCOZ2VV0uIqd7usC+66OBlnh1SrwLSpnWy8nzkqlNQW2P1Xn5EwtrzQbWeMZLZ+BRLDnWr8ZLISMhHJjseaeCPJ0Picjxqvq4NxPlDRHpp6rrylqbz0X/HGwq/hbMU5CKzc56XazwZA1gQICNlxaY8ZJ3LrlERCqp6iTP65LXcT0vKfYZzJNUpgUKve3GYrPGlrC3ttAOz9PSCJsMsKcsdTgqHs6AKWeo6g8icg12EWmP3W0Ox9zjVbA79q2BU1j+8HF71wWSVDVVRNZjxsFgtWnAy7C8jX/9qc3TtQi4GauvcYUXignCGnC2E5GbgdOB81T1L3+57L2L/X0i8gPWfPFtLC/nAmxm1lygiYj8iRmFd6jqD2WtqzDePuyBJcWGAqeLyNeq+h2wQUTSgebAdLVqz1mAP4utXQ2ch4UFW2D5Sv/Dbja6YV6i6wJ53Hphy8lYEvsbXrh6AHCW53H5loId1z/H6g6VeXVl77e+VUS+xbyQl2AVsTeLyDDMcB6jXkNahyMPF0Iqp3iu3iew4lE7RaQVlv+yIhB5B+UdsRo092Kho7pYDscbWEfuDzEDYqSq/ulHA8HXLT8WKwqmWEhwtRcSPAtrYTBVVb8oa02F9OVtv6n3uENV53kXjQ7AbFX9WkQaYiG3Lf7Oh/CMl+7YdzkX8071wabWvoUlGr+IGYYz/aQrrzZOnr5XgDdVdZa3fgyWUJxfQFFVA+49EJHJQANVbeG9boC1qegOXK9WJykKu2m6U/0wHdkzrM7QvW0LTsZu2BZiRtTR2Ey8Se685yiMM2DKMd5F+S1sNoDfk/4qCt5J8B0s2e904GRsFk+Gd3ecC6xW1Ul+1JR3cWuJeQPWYTOyrsFCShdgCcbHA1/lufD9fYIW67U0BJs6+6KqPu0tvx3rcn66P/UUoa8zlsx+h6r+ISJN2HvRjcZq0Xytql8GQFtzzKP3Beb9edxb3hozCC7xtyYfbXm/v0bYbKzF3vJvgRqq2t173RDLc1kvIiHYvv5SVWf4SWdXrDjdArW6PYjVOroL6wr/qqpm+kOLo+LhCtmVY9QKw12CNfJzFMKLnefxIubhGIZVjM3w8oheUdWX8+7g/KXLu3gMwfpUPYJVEu2HJWH/A/yEJSj6ti3w+92EWq+lz7BCem3EK1SHhZDCxRpZBpLqmJHX13u9Hptmvg44UVUvUdUv/fHdikh3sf5jiMi12Hc6FvMWXCciF3tDjwMae6GYgOD9/oZiXoyHRORtEYlV1f8Bm0VkoTdurXoFJtWSdR8uS+Ml73sSkVoiUltV/8D6LDUQkQe9Yb/iHSPOeHHsD5cDU87xYtMB6T5cXvHZF4KFZHZiNXKysLou6SLSG5uRch0Fk2LLUleoqmb53Pmej9Wgmetd3E4ExqtNpZ0FpPrDTV9I4z6/I1VNEpEJ2P67U0RGYPVLnlDVNH/qK4yqTvaMqqdE5F9V/VBEtmMFHmNFZKt6+EFODeARz6tWD8vXOBErvz8FMxTaYSGuM9WaNvoN31CVF3a729N4MpaQmyUiY1R1hIh8IyLdVfV3b7x4u7FMPb3esTEcuB0IFpHfsVy18Vg+1mRs5uV1qvpXWWpxVHxcCMlRIRGRgVgYZiLmzeiB5bmMwy7E9wP3qupXftITgoWwVmEG1blYEvaLeRpE5AmgvqqO9IemQvoaYcW+ZhZnDIt1Fb8A6Aw87YVsyoXh7Hmz3scSZHOB91R1YgB0DMB+Y3+o6mUiEg6MwLx/NbB2H9vVz7ONRKQG8CZWkXiHF96KwsJs92PTzl/GcocuUlW/NHotQmdj7Hu8BGuvMAY7Xp/Akp6HAytVdXYg9DkqFi6E5KhwiMjR2PTOxViJ+8ux7rl3YRfgwdisha/8FTby3O9/YfkQk7GKoZ8DLcVql4AZW0kiEuoPTYU4mK7XScBHwM3lyXgBUNWvMaOwKZZcPFE8/KxjMvY7GyYiI9WKvX0EbMUMq5QAGC9har2orgLqiEg3tf5Lc7Cw5Zuq+jdmOFQDKvlTXyEEM1RS1fq7PYK1R7lYrVP4h854cRwsLoTkqBD45JU0weptvKeqr4pV1h2CGTJvqeqQwu/xo8wVmAcmDrtQfIXNjLpDRLZgrvxb1KcKqr9Q1Q88w+klESm267X6TJvNM3TKkREzUUT2AG+KyCpV/TxAOr4SkWwsnISqfiQibwNVVHWHP7V4XrNnxJpGzvbCf3eJyBDv9T/A2WJF6c7AOsKv8KdGT2c4liy8WqykwfEi8pPazLY3ME+Rw3FIuBCSo8IgVkL+Lax4WVtVjfOWd8IaDe7CZlHsCNRFV6zqbgfMXX+LWl2fS7G4/kRVneNPo6DwtkTkEqx30LWq+ovvevEq13rJp6er6uv+0HioeGGcVaq6OsA6BmEho9FqneEDpWMse3OWFohVAh6NTZFfjYU2/4flX33jJ00xQJyq/i02NfpcLF/tWqA1cDbmiVmE5cNcpqpT/KHN8d/BGTCOCoFYFdGHgUe8pNgvsHySjt76LphbelkgdebhnbSfxdz2PTGDwa9JiT5eq6Ox8EaCWp+ty7ALydVqFW3zKrDmiNUB+QzLH/JrgnFFJJDGlOwt+d8Oq14bCZyrqvNF5CospHSJWu2jcLWZeWVuPMve6dg5wHQs5PYAFs4ajrX2iMFCvY2Ab1V1allqcvw3cQaMo9wj1kDwTiwE84B6TfpE5GOgjaq2DKS+4vDCW5cAn6ofa9AU0jAY6yT9Bbb/+qkVLLsE26cXq+rP3tgo4BPgQfVTHRBHyRDrn/YE3neJhS5v84yY0Zih2h5IUz82PfRCvTdh+TaJqnqHt/wx4FTgBFXdJH7oV+X47+IMGEeFQESOAi7E8ra+171N6L7EOk2XS2+Bz11yICrYluuu147Dx+c7fhhLV8qrZPsIZqhe5IUrG6gfekIVo60BcBtWDfsBVZ3vrX8GC281BLKcAeM4XJwB46gweFNDz8NmMkxT1WkBllSukArQ9dpRuog1kewEPOTNIkNE/gKWYlOnA5IP5mPE1MPq0WzGKvwu8Na3KC/hXkfFxU2jdpQ7ipsa600N/T8gDBgoIjX8PY22vOLth7bAGBG5EJuemrfPulOw6/U6rOs13hhV1VXOeCnf5P3WRaSXiIwUkb5Yf6imwGAROVpEjsOMlydVNS1Qyew+M9g2YNWKa2GNI9t5Q/w+E8rx38MZMI7ySNXiVngX5DeAN1R1W3mZ4htovP2wCJtiPg6b8eTb9bqJWNfr67Gu1784469i4RkFJ2JVaxUL+TXGquz2BB7CksbfUj81ttwfPkbMWixPJwYr8og/83Ec/11cCMlRLvBxObfELsD3qOqcQOuqCBSaCl3uul47So5niEZi07Yfxoz8l4FBXjJsOBYujFbVVX7UlXfchqpPfaNCv8m8MZVVdXfh9Q7H4eIMGEe5wZsxcwbQCtiITZn+o9CYvFol1YCTVPWTAEgtNxQy/Mpt12vH4eHz/eZNg74YOz56AGer6ipvRtk/6vU1CoDG/2HVsH8G5vgk2PsaMXnJ7GFYQbvsQGh1/LdwISRHuUBEmgEvAc9jUz+nYY0FO/iMCdG9tUq+BNb4X2n5wefiVu67XjsOD+/7HQ58ISLPYUns3bD6Lqu8mWY3AoFoT5FXn+lyYCrmAbpErE9ZfgjJu+nI9o7bV7FK1Q5HiXGtBBwBxecurRKwUFXnesvXA12xDrV3qepCn5PgJ1h4ZFbAhAcQqQBdrx0lw8c4jcLKB7znreqJhQgvEpE6WDL2Har6SwA0tsb6fo1R1TfEGjUOwJJ1Q1T1W/YWSKyO9Qa7X33aVTgcJcGFkBwBwecEXUlV08X69MzAplo+6o25BTgGa5R3JxYK+Ra4S4/QWiVSzrteO0oPserS7YFYVX3QWzYU6681Ces+XVWtaF1AwoIiMhlooKotvNcNgKHYzLfr1YomRmF9we50hrSjNHEGjCNgiPU2ugr4HfgAcy3fgs2a+Qp4Engai/dfjsX+M9XPJfnLGyLSFvjeezkAuyuvDnyjqn+JSC/MyBmtAWgc6Th8fAz77ljfr5XYFORbgV9VNUtEzgPGAB1VdVcAtDXCDKfF3vJvgRqq2t173RDLc1kve9sKfKmuurOjlHEGjMOv+CTzdcZyNt7DOkkvB37E8loeANKB14Aq2PTQoaqaFhDR5QwRqYLdgcdhSbr/YnflkYBv1+uvAibScdh4npeHgBtVdbGIPAhEAROA3z0jpm4gQjGeB+g+YD2wDbhZVZNE5DOgqaq2KeI90aqa4l+ljiMBl8Tr8Asi0sTL3cj27tCexpq4vYXdXYYDg7BpoBep6lWYV+EV4DpnvOzFu+segFVaHY/1g7oCmwWyE5ud8pWr81JhqQ70wb5jMIM+BTNWewL4y3gRkQif592xqroDsST6U4BHRKSmqo4A1ntj8saLp9UZL44ywRkwDn9xPtaPByAR+BubsdDMK3T1HFbb4jwvZg7mfRmqqov8Lba8o6rpXj7BHcCLIvIAcDbwcV79HDfbqGKiqj8CI7Dj42wvDPggkIB52PyCiNQAPhSRvMKSScDV2HF8BTYbqjnwgYjUU9WTfadyu9+fo6xxISSH3xDri/Ij0A7IxO4sj8ZmUazw1keqK2l/SEg56HrtKH28ukgPAs+r6tt+3naYqmaKSDzW4TpaVWd6BfUeA1aq6ngRGYX99s5VVdcewOFXnAfGUabkuZG93JcNwBJgJla34iFgMfCciDRX1Q3OeDl01MrGX6Gqk1zY6L+Dqn4H3A/cKiJ1RCTYH9sVkVjgTRHppKqbsVDWF97rXKy+0Okici1Wl2a0M14cgcAZMI4yw2fWwlDgKS8H5gzMaJmLlbt/1HseGUCpFZ68yqbObf/fQlUnAieo6iZVzfHTNpOwis43iEhbVX0BM6Te9wpLfoElFJ8IPKYBqgDscLgQkqNM8aZKj8Xu0n7xWf4i8D8shp7lLrwOR+DxmSXYDkugj8TCQ/NF5Cqs7MElqvqn7G1v4FpTOAKC88A4ygSfUMb/sE60f4vIcBF5U0ROUdWrscJ1XdzJz+EoH3jGS3+sSN79WLjoMRFpp6ovYZ3gP/QS7bO897jj1xEQnAfGUSaISAtVXSYi12FVORthfXpqYb+7q33Gujs4hyPA+IR8H8bskru85Y9gtYUuUtU5ItJAVdcFVKzDgTNgHGWAiIRjDQS/V9UxItIJ2Kmq/4hIG+wu7jRgrTNcHI7yhYicA3QCHvLyYRCRv4ClWO2hHe64dZQHXDNHR6njxcVPBT4REVR1DORPCx2HVe9cE0iNDoejgNelF1AXqzMzFzgLGCwis7HrxFLgSVdQ0lGecB4YR6nhzVCoAsz0yp3Xx2YsfKOq94l1Sl6jqtMCKtThcOQjIicCz2A5Lx9ieWtZwBlADNAM6zj9TaA0OhxF4TwwjhLjk8PSBxgO3CEif3jN3MYCb4tIhqo+ElChDocjH68oXSRwMdb8sypW4mChqm4SkRlAZayI3arAKXU4isbNQnIcNj4zjeqISJCqPgm8DNwDdPXWbfGW/REAiQ6HoxA+x22oFxL6EctteRo41TNeLgE6qOo2Z7w4yivOA+M4bLzY+UlYJ+SV3h3ddUAOMEZEVgPDgDNV9Tc328jhCDzecTscGCUiK4HjgAisvssqEWkN3IjVfHE4yi0uB8Zx2IhIc2AicBnWoPEUYCjWrfZoIB5IU9WfA6XR4XAYPgm7UcDbwCfeqpuB+sC7QB2gJXCvVwXY4Si3OA+M45AQkWCfkuYZwAxVneGFkB4TkQbAcFV9L4AyHQ5HITzjpQvQHpirqh8AiMhOzIuaAdwNVPUq7zqPqaNc43JgHAeFiFQFUNUcEekjIpdjXpb/ichFXpM3gGQgLlA6HQ5HQXwaqnbHvCwnA0O94zjU87Q8g3lQN6vqfHAVdh3lHxdCchwQEakM/AA8BywEvgSWAX8DUcCpwLPACiyB9wYXNnI4yg+e5+Uh4EZVXSwiD2LH7gTgd6/sQV1V3RhInQ7HoeA8MI4Doqq7sRkKt2BGzNmqeipW3GoLZtB0BNoCdznjxeEod1THyhwM8F4/AKQAFwA9AZzx4qhoOAPGcVCo6hfAXdj06BO9xR8BK4FU4Hcs8e8bn2maDoejHKCqPwIjgEtE5GxVzQIeBBKwmxCHo8LhkngdB42qThaRi4CHRWSDqn4oIh97qxfkxcxd7NzhKH+o6lcikgU8KCJhqvo2MCbAshyOw8blwDgOGa+n0YPAc6r6TqD1OByOg0dEhgKPAv2BRJ9ZhQ5HhcIZMI7DotBJMMFnFpLD4SjniEhNVd0aaB0OR0lwBozjsHEnQYfD4XAECmfAOBwOh8PhqHC4WUgOh8PhcDgqHM6AcTgcDofDUeFwBozD4XA4HI4KhzNgHA6Hw+FwVDicAeNwOBwOh6PC4QwYh8PhcDgcFQ5nwDgcDofD4ahw/D9yskbGvo8M/AAAAABJRU5ErkJggg==\n",
            "text/plain": "<Figure size 576x432 with 2 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "scrolled": false,
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.figure(figsize=(4, 6))\nplt.title(\"Correlation\", fontsize=18)\n\nax = sns.heatmap(train.corr()[[\"median_house_value\"]].sort_values(\n    by=[\"median_house_value\"], ascending=False),\n    vmin=-1,\n    cmap='coolwarm_r',\n    annot=True,\n    annot_kws={\"size\": 12})\nax.set_yticklabels(\n    ax.get_yticklabels(),\n    rotation=0,\n    horizontalalignment=\"right\");",
      "execution_count": 28,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 288x432 with 2 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "attributes = [\"median_house_value\", \"median_income\", \"total_rooms\", \"housing_median_age\"]\nscatter_matrix(train[attributes], figsize=(10,8));",
      "execution_count": 29,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 720x576 with 16 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The most promising attribute to predict the median house value is the median income."
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Dealing with missing values\nAre there any attributes with missing values? If so, you need to Nind ways to a) get rid of instances with missing values, b) get rid of the entire lacking attribute and c) Nill in the missing values with values that make sense to you, using some Pandas methods."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "missing_data(train)",
      "execution_count": 30,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Columns with missing data: 1\nColumns names with missing data: Index(['total_bedrooms'], dtype='object')\nTotal instances of data: 16512\nInstances without missing data: 16349\nInstances of missing data, isnull: 163\nInstances of missing data, isnan: 163\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "It seems that all missing values are np.nan objects."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train.total_bedrooms[208:211]",
      "execution_count": 31,
      "outputs": [
        {
          "data": {
            "text/plain": "987       23.0\n18786      NaN\n10406    256.0\nName: total_bedrooms, dtype: float64"
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "missing_values(train)",
      "execution_count": 32,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Number of missing values: 163\n"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 864x576 with 1 Axes>"
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Dropping rows with missing values"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "drop_row = train.dropna(axis=0, how='any')",
      "execution_count": 33,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "missing_data(drop_row)",
      "execution_count": 34,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Columns with missing data: 0\nColumns names with missing data: Index([], dtype='object')\nTotal instances of data: 16349\nInstances without missing data: 16349\nInstances of missing data, isnull: 0\nInstances of missing data, isnan: 0\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Dropping columns with missing values"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "drop_col = train.dropna(axis=1, how='any')\n# Alternative: drop_col.dropna(subset=[\"total_bedrooms\"], inplace=True);",
      "execution_count": 35,
      "outputs": []
    },
    {
      "metadata": {
        "cell_style": "split",
        "trusted": true
      },
      "cell_type": "code",
      "source": "print(train.shape)\ncount = 0\nfor col in train.columns:\n    count += 1\n    print(count, \":\", col)",
      "execution_count": 36,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "(16512, 11)\n1 : longitude\n2 : latitude\n3 : housing_median_age\n4 : total_rooms\n5 : total_bedrooms\n6 : population\n7 : households\n8 : median_income\n9 : median_house_value\n10 : ocean_proximity\n11 : geometry\n"
        }
      ]
    },
    {
      "metadata": {
        "cell_style": "split",
        "trusted": true
      },
      "cell_type": "code",
      "source": "print(drop_col.shape)\ncount = 0\nfor col in drop_col.columns:\n    count += 1\n    print(count, \":\", col)",
      "execution_count": 37,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "(16512, 10)\n1 : longitude\n2 : latitude\n3 : housing_median_age\n4 : total_rooms\n5 : population\n6 : households\n7 : median_income\n8 : median_house_value\n9 : ocean_proximity\n10 : geometry\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Filling in missing values"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### fillna"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# train = train.copy() or pd.options.mode.chained_assignment = None\n# train[\"total_bedrooms\"].fillna(\n#     value=train[\"total_bedrooms\"].mean(), inplace=True)",
      "execution_count": 38,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "`inplace = True` has warning after performing `train_test_split`\nSome issue with chaining.\n\nhttps://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing-view-versus-copy\n\nhttps://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#indexing-and-selecting-data\n\nhttps://www.dataquest.io/blog/settingwithcopywarning/\n\n\n```\n/Users/lewiuberg/Documents/Utdanning/Applied Data Science/17.MAL - UC3MAL101/venv/lib/python3.8/site-packages/pandas/core/series.py:4517: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  return super().fillna(\n```\n\nAnother solution is to use df.loc"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "missing_data(train)",
      "execution_count": 39,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Columns with missing data: 1\nColumns names with missing data: Index(['total_bedrooms'], dtype='object')\nTotal instances of data: 16512\nInstances without missing data: 16349\nInstances of missing data, isnull: 163\nInstances of missing data, isnan: 163\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_fill = train.copy()",
      "execution_count": 40,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "missing_data(train_fill)",
      "execution_count": 41,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Columns with missing data: 1\nColumns names with missing data: Index(['total_bedrooms'], dtype='object')\nTotal instances of data: 16512\nInstances without missing data: 16349\nInstances of missing data, isnull: 163\nInstances of missing data, isnan: 163\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_fill[32:35]",
      "execution_count": 42,
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity</th>\n      <th>geometry</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1368</th>\n      <td>-122.13</td>\n      <td>38.01</td>\n      <td>51.0</td>\n      <td>1262.0</td>\n      <td>309.0</td>\n      <td>608.0</td>\n      <td>298.0</td>\n      <td>2.1974</td>\n      <td>139300.0</td>\n      <td>NEAR BAY</td>\n      <td>POINT (-122.13000 38.01000)</td>\n    </tr>\n    <tr>\n      <th>19559</th>\n      <td>-120.98</td>\n      <td>37.60</td>\n      <td>36.0</td>\n      <td>1437.0</td>\n      <td>NaN</td>\n      <td>1073.0</td>\n      <td>320.0</td>\n      <td>2.1779</td>\n      <td>58400.0</td>\n      <td>INLAND</td>\n      <td>POINT (-120.98000 37.60000)</td>\n    </tr>\n    <tr>\n      <th>13262</th>\n      <td>-117.65</td>\n      <td>34.10</td>\n      <td>19.0</td>\n      <td>1688.0</td>\n      <td>365.0</td>\n      <td>622.0</td>\n      <td>322.0</td>\n      <td>3.6000</td>\n      <td>136400.0</td>\n      <td>INLAND</td>\n      <td>POINT (-117.65000 34.10000)</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n1368     -122.13     38.01                51.0       1262.0           309.0   \n19559    -120.98     37.60                36.0       1437.0             NaN   \n13262    -117.65     34.10                19.0       1688.0           365.0   \n\n       population  households  median_income  median_house_value  \\\n1368        608.0       298.0         2.1974            139300.0   \n19559      1073.0       320.0         2.1779             58400.0   \n13262       622.0       322.0         3.6000            136400.0   \n\n      ocean_proximity                     geometry  \n1368         NEAR BAY  POINT (-122.13000 38.01000)  \n19559          INLAND  POINT (-120.98000 37.60000)  \n13262          INLAND  POINT (-117.65000 34.10000)  "
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_fill.loc[:, \"total_bedrooms\"].fillna(value=train_fill[\"total_bedrooms\"].mean(),inplace=True)",
      "execution_count": 43,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_fill[32:35]",
      "execution_count": 44,
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity</th>\n      <th>geometry</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1368</th>\n      <td>-122.13</td>\n      <td>38.01</td>\n      <td>51.0</td>\n      <td>1262.0</td>\n      <td>309.000000</td>\n      <td>608.0</td>\n      <td>298.0</td>\n      <td>2.1974</td>\n      <td>139300.0</td>\n      <td>NEAR BAY</td>\n      <td>POINT (-122.13000 38.01000)</td>\n    </tr>\n    <tr>\n      <th>19559</th>\n      <td>-120.98</td>\n      <td>37.60</td>\n      <td>36.0</td>\n      <td>1437.0</td>\n      <td>539.049544</td>\n      <td>1073.0</td>\n      <td>320.0</td>\n      <td>2.1779</td>\n      <td>58400.0</td>\n      <td>INLAND</td>\n      <td>POINT (-120.98000 37.60000)</td>\n    </tr>\n    <tr>\n      <th>13262</th>\n      <td>-117.65</td>\n      <td>34.10</td>\n      <td>19.0</td>\n      <td>1688.0</td>\n      <td>365.000000</td>\n      <td>622.0</td>\n      <td>322.0</td>\n      <td>3.6000</td>\n      <td>136400.0</td>\n      <td>INLAND</td>\n      <td>POINT (-117.65000 34.10000)</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n1368     -122.13     38.01                51.0       1262.0      309.000000   \n19559    -120.98     37.60                36.0       1437.0      539.049544   \n13262    -117.65     34.10                19.0       1688.0      365.000000   \n\n       population  households  median_income  median_house_value  \\\n1368        608.0       298.0         2.1974            139300.0   \n19559      1073.0       320.0         2.1779             58400.0   \n13262       622.0       322.0         3.6000            136400.0   \n\n      ocean_proximity                     geometry  \n1368         NEAR BAY  POINT (-122.13000 38.01000)  \n19559          INLAND  POINT (-120.98000 37.60000)  \n13262          INLAND  POINT (-117.65000 34.10000)  "
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "missing_data(train_fill)",
      "execution_count": 45,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Columns with missing data: 0\nColumns names with missing data: Index([], dtype='object')\nTotal instances of data: 16512\nInstances without missing data: 16512\nInstances of missing data, isnull: 0\nInstances of missing data, isnan: 0\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "### Imputation"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "missing_data(train)",
      "execution_count": 46,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Columns with missing data: 1\nColumns names with missing data: Index(['total_bedrooms'], dtype='object')\nTotal instances of data: 16512\nInstances without missing data: 16349\nInstances of missing data, isnull: 163\nInstances of missing data, isnan: 163\n"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "print(train.shape)\ntrain[32:35]",
      "execution_count": 47,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "(16512, 11)\n"
        },
        {
          "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity</th>\n      <th>geometry</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1368</th>\n      <td>-122.13</td>\n      <td>38.01</td>\n      <td>51.0</td>\n      <td>1262.0</td>\n      <td>309.0</td>\n      <td>608.0</td>\n      <td>298.0</td>\n      <td>2.1974</td>\n      <td>139300.0</td>\n      <td>NEAR BAY</td>\n      <td>POINT (-122.13000 38.01000)</td>\n    </tr>\n    <tr>\n      <th>19559</th>\n      <td>-120.98</td>\n      <td>37.60</td>\n      <td>36.0</td>\n      <td>1437.0</td>\n      <td>NaN</td>\n      <td>1073.0</td>\n      <td>320.0</td>\n      <td>2.1779</td>\n      <td>58400.0</td>\n      <td>INLAND</td>\n      <td>POINT (-120.98000 37.60000)</td>\n    </tr>\n    <tr>\n      <th>13262</th>\n      <td>-117.65</td>\n      <td>34.10</td>\n      <td>19.0</td>\n      <td>1688.0</td>\n      <td>365.0</td>\n      <td>622.0</td>\n      <td>322.0</td>\n      <td>3.6000</td>\n      <td>136400.0</td>\n      <td>INLAND</td>\n      <td>POINT (-117.65000 34.10000)</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n1368     -122.13     38.01                51.0       1262.0           309.0   \n19559    -120.98     37.60                36.0       1437.0             NaN   \n13262    -117.65     34.10                19.0       1688.0           365.0   \n\n       population  households  median_income  median_house_value  \\\n1368        608.0       298.0         2.1974            139300.0   \n19559      1073.0       320.0         2.1779             58400.0   \n13262       622.0       322.0         3.6000            136400.0   \n\n      ocean_proximity                     geometry  \n1368         NEAR BAY  POINT (-122.13000 38.01000)  \n19559          INLAND  POINT (-120.98000 37.60000)  \n13262          INLAND  POINT (-117.65000 34.10000)  "
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_numerical = train.drop([\"ocean_proximity\", \"geometry\"], axis=1)",
      "execution_count": 48,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_numerical[32:35]",
      "execution_count": 49,
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1368</th>\n      <td>-122.13</td>\n      <td>38.01</td>\n      <td>51.0</td>\n      <td>1262.0</td>\n      <td>309.0</td>\n      <td>608.0</td>\n      <td>298.0</td>\n      <td>2.1974</td>\n      <td>139300.0</td>\n    </tr>\n    <tr>\n      <th>19559</th>\n      <td>-120.98</td>\n      <td>37.60</td>\n      <td>36.0</td>\n      <td>1437.0</td>\n      <td>NaN</td>\n      <td>1073.0</td>\n      <td>320.0</td>\n      <td>2.1779</td>\n      <td>58400.0</td>\n    </tr>\n    <tr>\n      <th>13262</th>\n      <td>-117.65</td>\n      <td>34.10</td>\n      <td>19.0</td>\n      <td>1688.0</td>\n      <td>365.0</td>\n      <td>622.0</td>\n      <td>322.0</td>\n      <td>3.6000</td>\n      <td>136400.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n1368     -122.13     38.01                51.0       1262.0           309.0   \n19559    -120.98     37.60                36.0       1437.0             NaN   \n13262    -117.65     34.10                19.0       1688.0           365.0   \n\n       population  households  median_income  median_house_value  \n1368        608.0       298.0         2.1974            139300.0  \n19559      1073.0       320.0         2.1779             58400.0  \n13262       622.0       322.0         3.6000            136400.0  "
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "imp_mean = SimpleImputer(missing_values=np.nan, strategy=\"mean\")",
      "execution_count": 50,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_numerical = pd.DataFrame(imp_mean.fit_transform(train_numerical),\n                                        columns=train_numerical.columns,\n                                        index=train_numerical.index)",
      "execution_count": 51,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "imp_mean.statistics_",
      "execution_count": 52,
      "outputs": [
        {
          "data": {
            "text/plain": "array([-1.19574288e+02,  3.56321936e+01,  2.86044695e+01,  2.63959066e+03,\n        5.39049544e+02,  1.42525715e+03,  5.00593508e+02,  3.87614927e+00,\n        2.07735064e+05])"
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_numerical.mean().values",
      "execution_count": 53,
      "outputs": [
        {
          "data": {
            "text/plain": "array([-1.19574288e+02,  3.56321936e+01,  2.86044695e+01,  2.63959066e+03,\n        5.39049544e+02,  1.42525715e+03,  5.00593508e+02,  3.87614927e+00,\n        2.07735064e+05])"
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "scrolled": true,
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_numerical[32:35]",
      "execution_count": 54,
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1368</th>\n      <td>-122.13</td>\n      <td>38.01</td>\n      <td>51.0</td>\n      <td>1262.0</td>\n      <td>309.000000</td>\n      <td>608.0</td>\n      <td>298.0</td>\n      <td>2.1974</td>\n      <td>139300.0</td>\n    </tr>\n    <tr>\n      <th>19559</th>\n      <td>-120.98</td>\n      <td>37.60</td>\n      <td>36.0</td>\n      <td>1437.0</td>\n      <td>539.049544</td>\n      <td>1073.0</td>\n      <td>320.0</td>\n      <td>2.1779</td>\n      <td>58400.0</td>\n    </tr>\n    <tr>\n      <th>13262</th>\n      <td>-117.65</td>\n      <td>34.10</td>\n      <td>19.0</td>\n      <td>1688.0</td>\n      <td>365.000000</td>\n      <td>622.0</td>\n      <td>322.0</td>\n      <td>3.6000</td>\n      <td>136400.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n1368     -122.13     38.01                51.0       1262.0      309.000000   \n19559    -120.98     37.60                36.0       1437.0      539.049544   \n13262    -117.65     34.10                19.0       1688.0      365.000000   \n\n       population  households  median_income  median_house_value  \n1368        608.0       298.0         2.1974            139300.0  \n19559      1073.0       320.0         2.1779             58400.0  \n13262       622.0       322.0         3.6000            136400.0  "
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "columns_to_overwrite = train_numerical.columns",
      "execution_count": 55,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "columns_to_overwrite",
      "execution_count": 56,
      "outputs": [
        {
          "data": {
            "text/plain": "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n       'total_bedrooms', 'population', 'households', 'median_income',\n       'median_house_value'],\n      dtype='object')"
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "pd.options.mode.chained_assignment = None\ntrain[columns_to_overwrite] = train_numerical[columns_to_overwrite]\npd.options.mode.chained_assignment = \"warn\"",
      "execution_count": 57,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train[32:35]",
      "execution_count": 58,
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity</th>\n      <th>geometry</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1368</th>\n      <td>-122.13</td>\n      <td>38.01</td>\n      <td>51.0</td>\n      <td>1262.0</td>\n      <td>309.000000</td>\n      <td>608.0</td>\n      <td>298.0</td>\n      <td>2.1974</td>\n      <td>139300.0</td>\n      <td>NEAR BAY</td>\n      <td>POINT (-122.13000 38.01000)</td>\n    </tr>\n    <tr>\n      <th>19559</th>\n      <td>-120.98</td>\n      <td>37.60</td>\n      <td>36.0</td>\n      <td>1437.0</td>\n      <td>539.049544</td>\n      <td>1073.0</td>\n      <td>320.0</td>\n      <td>2.1779</td>\n      <td>58400.0</td>\n      <td>INLAND</td>\n      <td>POINT (-120.98000 37.60000)</td>\n    </tr>\n    <tr>\n      <th>13262</th>\n      <td>-117.65</td>\n      <td>34.10</td>\n      <td>19.0</td>\n      <td>1688.0</td>\n      <td>365.000000</td>\n      <td>622.0</td>\n      <td>322.0</td>\n      <td>3.6000</td>\n      <td>136400.0</td>\n      <td>INLAND</td>\n      <td>POINT (-117.65000 34.10000)</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n1368     -122.13     38.01                51.0       1262.0      309.000000   \n19559    -120.98     37.60                36.0       1437.0      539.049544   \n13262    -117.65     34.10                19.0       1688.0      365.000000   \n\n       population  households  median_income  median_house_value  \\\n1368        608.0       298.0         2.1974            139300.0   \n19559      1073.0       320.0         2.1779             58400.0   \n13262       622.0       322.0         3.6000            136400.0   \n\n      ocean_proximity                     geometry  \n1368         NEAR BAY  POINT (-122.13000 38.01000)  \n19559          INLAND  POINT (-120.98000 37.60000)  \n13262          INLAND  POINT (-117.65000 34.10000)  "
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Dealing with categorical attributes\nIf there are any categorical attributes in your dataset, see how you can handle them and create on-hot vectors using the LabelBinarizer() class of Scikit-Learn.\n\nNote! dtypes can be changed as shown below.\n```python\ndf = dtype_change(df, nominal_col, \"category\")\ndf = dtype_change(df, ordinal_col, \"category\")\ndf = dtype_change(df, discrete_col, \"int\")\ndf = dtype_change(df, continuous_col, \"float\")\n```"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "for i in range(1,6):\n    globals()[\"train%s\" % i] = train.copy()\n",
      "execution_count": 59,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_categorical = train[\"ocean_proximity\"]",
      "execution_count": 62,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "lb_encoder = LabelBinarizer()",
      "execution_count": 63,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_encoded = lb_encoder.fit_transform(train_categorical)",
      "execution_count": 64,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "type(train_encoded)",
      "execution_count": 65,
      "outputs": [
        {
          "data": {
            "text/plain": "numpy.ndarray"
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_encoded[32:35]",
      "execution_count": 66,
      "outputs": [
        {
          "data": {
            "text/plain": "array([[0, 0, 0, 1, 0],\n       [0, 1, 0, 0, 0],\n       [0, 1, 0, 0, 0]])"
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "type(train_encoded[0][0])",
      "execution_count": 67,
      "outputs": [
        {
          "data": {
            "text/plain": "numpy.int64"
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train_encoded = pd.DataFrame(train_encoded, columns=lb_encoder.classes_, index=train.index)",
      "execution_count": 68,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train = pd.concat([train, train_encoded], axis=1).drop([\"ocean_proximity\"], axis=1)",
      "execution_count": 69,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train[\"INLAND\"].head(5)",
      "execution_count": 70,
      "outputs": [
        {
          "data": {
            "text/plain": "15961    0\n1771     0\n16414    1\n5056     0\n8589     0\nName: INLAND, dtype: int64"
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train",
      "execution_count": 71,
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>geometry</th>\n      <th>&lt;1H OCEAN</th>\n      <th>INLAND</th>\n      <th>ISLAND</th>\n      <th>NEAR BAY</th>\n      <th>NEAR OCEAN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>15961</th>\n      <td>-122.43</td>\n      <td>37.71</td>\n      <td>52.0</td>\n      <td>1410.0</td>\n      <td>286.0</td>\n      <td>879.0</td>\n      <td>282.0</td>\n      <td>3.1908</td>\n      <td>255600.0</td>\n      <td>POINT (-122.43000 37.71000)</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1771</th>\n      <td>-122.35</td>\n      <td>37.95</td>\n      <td>42.0</td>\n      <td>1485.0</td>\n      <td>290.0</td>\n      <td>971.0</td>\n      <td>303.0</td>\n      <td>3.6094</td>\n      <td>114600.0</td>\n      <td>POINT (-122.35000 37.95000)</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>16414</th>\n      <td>-121.24</td>\n      <td>37.90</td>\n      <td>16.0</td>\n      <td>50.0</td>\n      <td>10.0</td>\n      <td>20.0</td>\n      <td>6.0</td>\n      <td>2.6250</td>\n      <td>137500.0</td>\n      <td>POINT (-121.24000 37.90000)</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5056</th>\n      <td>-118.35</td>\n      <td>34.02</td>\n      <td>34.0</td>\n      <td>5218.0</td>\n      <td>1576.0</td>\n      <td>3538.0</td>\n      <td>1371.0</td>\n      <td>1.5143</td>\n      <td>118800.0</td>\n      <td>POINT (-118.35000 34.02000)</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8589</th>\n      <td>-118.39</td>\n      <td>33.89</td>\n      <td>38.0</td>\n      <td>1851.0</td>\n      <td>332.0</td>\n      <td>750.0</td>\n      <td>314.0</td>\n      <td>7.3356</td>\n      <td>422700.0</td>\n      <td>POINT (-118.39000 33.89000)</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>10955</th>\n      <td>-117.88</td>\n      <td>33.76</td>\n      <td>17.0</td>\n      <td>1768.0</td>\n      <td>474.0</td>\n      <td>1079.0</td>\n      <td>436.0</td>\n      <td>1.7823</td>\n      <td>205300.0</td>\n      <td>POINT (-117.88000 33.76000)</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>17289</th>\n      <td>-119.63</td>\n      <td>34.42</td>\n      <td>42.0</td>\n      <td>1765.0</td>\n      <td>263.0</td>\n      <td>753.0</td>\n      <td>260.0</td>\n      <td>8.5608</td>\n      <td>500001.0</td>\n      <td>POINT (-119.63000 34.42000)</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5192</th>\n      <td>-118.26</td>\n      <td>33.93</td>\n      <td>42.0</td>\n      <td>1433.0</td>\n      <td>295.0</td>\n      <td>775.0</td>\n      <td>293.0</td>\n      <td>1.1326</td>\n      <td>104800.0</td>\n      <td>POINT (-118.26000 33.93000)</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>12172</th>\n      <td>-117.16</td>\n      <td>33.73</td>\n      <td>10.0</td>\n      <td>2381.0</td>\n      <td>454.0</td>\n      <td>1323.0</td>\n      <td>477.0</td>\n      <td>2.6322</td>\n      <td>140700.0</td>\n      <td>POINT (-117.16000 33.73000)</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>235</th>\n      <td>-122.20</td>\n      <td>37.79</td>\n      <td>35.0</td>\n      <td>1802.0</td>\n      <td>459.0</td>\n      <td>1009.0</td>\n      <td>390.0</td>\n      <td>2.3036</td>\n      <td>126000.0</td>\n      <td>POINT (-122.20000 37.79000)</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>16512 rows × 15 columns</p>\n</div>",
            "text/plain": "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n15961    -122.43     37.71                52.0       1410.0           286.0   \n1771     -122.35     37.95                42.0       1485.0           290.0   \n16414    -121.24     37.90                16.0         50.0            10.0   \n5056     -118.35     34.02                34.0       5218.0          1576.0   \n8589     -118.39     33.89                38.0       1851.0           332.0   \n...          ...       ...                 ...          ...             ...   \n10955    -117.88     33.76                17.0       1768.0           474.0   \n17289    -119.63     34.42                42.0       1765.0           263.0   \n5192     -118.26     33.93                42.0       1433.0           295.0   \n12172    -117.16     33.73                10.0       2381.0           454.0   \n235      -122.20     37.79                35.0       1802.0           459.0   \n\n       population  households  median_income  median_house_value  \\\n15961       879.0       282.0         3.1908            255600.0   \n1771        971.0       303.0         3.6094            114600.0   \n16414        20.0         6.0         2.6250            137500.0   \n5056       3538.0      1371.0         1.5143            118800.0   \n8589        750.0       314.0         7.3356            422700.0   \n...           ...         ...            ...                 ...   \n10955      1079.0       436.0         1.7823            205300.0   \n17289       753.0       260.0         8.5608            500001.0   \n5192        775.0       293.0         1.1326            104800.0   \n12172      1323.0       477.0         2.6322            140700.0   \n235        1009.0       390.0         2.3036            126000.0   \n\n                          geometry  <1H OCEAN  INLAND  ISLAND  NEAR BAY  \\\n15961  POINT (-122.43000 37.71000)          0       0       0         1   \n1771   POINT (-122.35000 37.95000)          0       0       0         1   \n16414  POINT (-121.24000 37.90000)          0       1       0         0   \n5056   POINT (-118.35000 34.02000)          1       0       0         0   \n8589   POINT (-118.39000 33.89000)          1       0       0         0   \n...                            ...        ...     ...     ...       ...   \n10955  POINT (-117.88000 33.76000)          1       0       0         0   \n17289  POINT (-119.63000 34.42000)          1       0       0         0   \n5192   POINT (-118.26000 33.93000)          1       0       0         0   \n12172  POINT (-117.16000 33.73000)          0       1       0         0   \n235    POINT (-122.20000 37.79000)          0       0       0         1   \n\n       NEAR OCEAN  \n15961           0  \n1771            0  \n16414           0  \n5056            0  \n8589            0  \n...           ...  \n10955           0  \n17289           0  \n5192            0  \n12172           0  \n235             0  \n\n[16512 rows x 15 columns]"
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "train.info()",
      "execution_count": 73,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 16512 entries, 15961 to 235\nData columns (total 15 columns):\n #   Column              Non-Null Count  Dtype   \n---  ------              --------------  -----   \n 0   longitude           16512 non-null  float64 \n 1   latitude            16512 non-null  float64 \n 2   housing_median_age  16512 non-null  float64 \n 3   total_rooms         16512 non-null  float64 \n 4   total_bedrooms      16512 non-null  float64 \n 5   population          16512 non-null  float64 \n 6   households          16512 non-null  float64 \n 7   median_income       16512 non-null  float64 \n 8   median_house_value  16512 non-null  float64 \n 9   geometry            16512 non-null  geometry\n 10  <1H OCEAN           16512 non-null  int64   \n 11  INLAND              16512 non-null  int64   \n 12  ISLAND              16512 non-null  int64   \n 13  NEAR BAY            16512 non-null  int64   \n 14  NEAR OCEAN          16512 non-null  int64   \ndtypes: float64(9), geometry(1), int64(5)\nmemory usage: 2.0 MB\n"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Below is some of my classmates attempts, found in Thomas' gist. I may alter and experiment with the code, so the original can be seen here: https://gist.github.com/ImUnlikely/3d7798d381f1df3b771277e8b49e0fa9"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Method 1 from Thomas (float values (1.0 and 0.0))\n# import numpy as np\n# from sklearn.preprocessing import OneHotEncoder\ndf = train1  # load_csv_to_dataframe(file_path) # Load data\n\ndata = np.asarray(df[\"ocean_proximity\"])\ndata = data.reshape(-1, 1)\n\nohe = preprocessing.OneHotEncoder(categories=\"auto\", sparse=False)\nohe.fit(data)\ntransformed = ohe.transform(df[\"ocean_proximity\"].to_numpy().reshape(-1, 1))\ntransformed = pd.DataFrame(transformed, columns=ohe.categories_[0])\ndf=pd.concat([df, transformed], axis=1).drop([\"ocean_proximity\"], axis=1)\n\ndf.head()",
      "execution_count": 99,
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>geometry</th>\n      <th>&lt;1H OCEAN</th>\n      <th>INLAND</th>\n      <th>ISLAND</th>\n      <th>NEAR BAY</th>\n      <th>NEAR OCEAN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-122.23</td>\n      <td>37.88</td>\n      <td>41.0</td>\n      <td>880.0</td>\n      <td>129.0</td>\n      <td>322.0</td>\n      <td>126.0</td>\n      <td>8.3252</td>\n      <td>452600.0</td>\n      <td>POINT (-122.23000 37.88000)</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-122.22</td>\n      <td>37.86</td>\n      <td>21.0</td>\n      <td>7099.0</td>\n      <td>1106.0</td>\n      <td>2401.0</td>\n      <td>1138.0</td>\n      <td>8.3014</td>\n      <td>358500.0</td>\n      <td>POINT (-122.22000 37.86000)</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-122.24</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1467.0</td>\n      <td>190.0</td>\n      <td>496.0</td>\n      <td>177.0</td>\n      <td>7.2574</td>\n      <td>352100.0</td>\n      <td>POINT (-122.24000 37.85000)</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1274.0</td>\n      <td>235.0</td>\n      <td>558.0</td>\n      <td>219.0</td>\n      <td>5.6431</td>\n      <td>341300.0</td>\n      <td>POINT (-122.25000 37.85000)</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>None</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0    -122.23     37.88                41.0        880.0           129.0   \n1    -122.22     37.86                21.0       7099.0          1106.0   \n2    -122.24     37.85                52.0       1467.0           190.0   \n3    -122.25     37.85                52.0       1274.0           235.0   \n4        NaN       NaN                 NaN          NaN             NaN   \n\n   population  households  median_income  median_house_value  \\\n0       322.0       126.0         8.3252            452600.0   \n1      2401.0      1138.0         8.3014            358500.0   \n2       496.0       177.0         7.2574            352100.0   \n3       558.0       219.0         5.6431            341300.0   \n4         NaN         NaN            NaN                 NaN   \n\n                      geometry  <1H OCEAN  INLAND  ISLAND  NEAR BAY  \\\n0  POINT (-122.23000 37.88000)        0.0     0.0     0.0       1.0   \n1  POINT (-122.22000 37.86000)        0.0     0.0     0.0       1.0   \n2  POINT (-122.24000 37.85000)        0.0     1.0     0.0       0.0   \n3  POINT (-122.25000 37.85000)        1.0     0.0     0.0       0.0   \n4                         None        1.0     0.0     0.0       0.0   \n\n   NEAR OCEAN  \n0         0.0  \n1         0.0  \n2         0.0  \n3         0.0  \n4         0.0  "
          },
          "execution_count": 99,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Method 2 (integers (1 and 0))\n# Method 2 from Dayana\n# from sklearn.preprocessing import LabelBinarizer\ndf = train2  # load_csv_to_dataframe(file_path) # Load data\n\nlb = preprocessing.LabelBinarizer()\nlb.fit(df[\"ocean_proximity\"])\ntransformed = lb.transform(df[\"ocean_proximity\"])\nohe_df = pd.DataFrame(transformed, columns=lb.classes_)\ndf = pd.concat([df, ohe_df], axis=1).drop([\"ocean_proximity\"], axis=1)\n\ndf.head()",
      "execution_count": 78,
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>geometry</th>\n      <th>&lt;1H OCEAN</th>\n      <th>INLAND</th>\n      <th>ISLAND</th>\n      <th>NEAR BAY</th>\n      <th>NEAR OCEAN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-122.23</td>\n      <td>37.88</td>\n      <td>41.0</td>\n      <td>880.0</td>\n      <td>129.0</td>\n      <td>322.0</td>\n      <td>126.0</td>\n      <td>8.3252</td>\n      <td>452600.0</td>\n      <td>POINT (-122.23000 37.88000)</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-122.22</td>\n      <td>37.86</td>\n      <td>21.0</td>\n      <td>7099.0</td>\n      <td>1106.0</td>\n      <td>2401.0</td>\n      <td>1138.0</td>\n      <td>8.3014</td>\n      <td>358500.0</td>\n      <td>POINT (-122.22000 37.86000)</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-122.24</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1467.0</td>\n      <td>190.0</td>\n      <td>496.0</td>\n      <td>177.0</td>\n      <td>7.2574</td>\n      <td>352100.0</td>\n      <td>POINT (-122.24000 37.85000)</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1274.0</td>\n      <td>235.0</td>\n      <td>558.0</td>\n      <td>219.0</td>\n      <td>5.6431</td>\n      <td>341300.0</td>\n      <td>POINT (-122.25000 37.85000)</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>None</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0    -122.23     37.88                41.0        880.0           129.0   \n1    -122.22     37.86                21.0       7099.0          1106.0   \n2    -122.24     37.85                52.0       1467.0           190.0   \n3    -122.25     37.85                52.0       1274.0           235.0   \n4        NaN       NaN                 NaN          NaN             NaN   \n\n   population  households  median_income  median_house_value  \\\n0       322.0       126.0         8.3252            452600.0   \n1      2401.0      1138.0         8.3014            358500.0   \n2       496.0       177.0         7.2574            352100.0   \n3       558.0       219.0         5.6431            341300.0   \n4         NaN         NaN            NaN                 NaN   \n\n                      geometry  <1H OCEAN  INLAND  ISLAND  NEAR BAY  \\\n0  POINT (-122.23000 37.88000)        0.0     0.0     0.0       1.0   \n1  POINT (-122.22000 37.86000)        0.0     0.0     0.0       1.0   \n2  POINT (-122.24000 37.85000)        0.0     1.0     0.0       0.0   \n3  POINT (-122.25000 37.85000)        1.0     0.0     0.0       0.0   \n4                         None        1.0     0.0     0.0       0.0   \n\n   NEAR OCEAN  \n0         0.0  \n1         0.0  \n2         0.0  \n3         0.0  \n4         0.0  "
          },
          "execution_count": 78,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Method 3 (integers (1 and 0))\n# Method 3 from Dayana\n# from sklearn.preprocessing import MultiLabelBinarizer\ndf = train3  # load_csv_to_dataframe(file_path) # Load data\n\nmlb = preprocessing.MultiLabelBinarizer()\nlocation = df.ocean_proximity.apply(lambda x: list(x.split(\", \")))\nlocation_df = pd.DataFrame({\"ocean_proximity\": location})\nlocation_encoded = pd.DataFrame(mlb.fit_transform(location_df[\"ocean_proximity\"]), columns=mlb.classes_)\ndf_encoded = pd.concat([df, location_encoded], axis=1)\n\ndf_encoded.head()",
      "execution_count": 79,
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity</th>\n      <th>geometry</th>\n      <th>&lt;1H OCEAN</th>\n      <th>INLAND</th>\n      <th>ISLAND</th>\n      <th>NEAR BAY</th>\n      <th>NEAR OCEAN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-122.23</td>\n      <td>37.88</td>\n      <td>41.0</td>\n      <td>880.0</td>\n      <td>129.0</td>\n      <td>322.0</td>\n      <td>126.0</td>\n      <td>8.3252</td>\n      <td>452600.0</td>\n      <td>NEAR BAY</td>\n      <td>POINT (-122.23000 37.88000)</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-122.22</td>\n      <td>37.86</td>\n      <td>21.0</td>\n      <td>7099.0</td>\n      <td>1106.0</td>\n      <td>2401.0</td>\n      <td>1138.0</td>\n      <td>8.3014</td>\n      <td>358500.0</td>\n      <td>NEAR BAY</td>\n      <td>POINT (-122.22000 37.86000)</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-122.24</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1467.0</td>\n      <td>190.0</td>\n      <td>496.0</td>\n      <td>177.0</td>\n      <td>7.2574</td>\n      <td>352100.0</td>\n      <td>NEAR BAY</td>\n      <td>POINT (-122.24000 37.85000)</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1274.0</td>\n      <td>235.0</td>\n      <td>558.0</td>\n      <td>219.0</td>\n      <td>5.6431</td>\n      <td>341300.0</td>\n      <td>NEAR BAY</td>\n      <td>POINT (-122.25000 37.85000)</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>None</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0    -122.23     37.88                41.0        880.0           129.0   \n1    -122.22     37.86                21.0       7099.0          1106.0   \n2    -122.24     37.85                52.0       1467.0           190.0   \n3    -122.25     37.85                52.0       1274.0           235.0   \n4        NaN       NaN                 NaN          NaN             NaN   \n\n   population  households  median_income  median_house_value ocean_proximity  \\\n0       322.0       126.0         8.3252            452600.0        NEAR BAY   \n1      2401.0      1138.0         8.3014            358500.0        NEAR BAY   \n2       496.0       177.0         7.2574            352100.0        NEAR BAY   \n3       558.0       219.0         5.6431            341300.0        NEAR BAY   \n4         NaN         NaN            NaN                 NaN             NaN   \n\n                      geometry  <1H OCEAN  INLAND  ISLAND  NEAR BAY  \\\n0  POINT (-122.23000 37.88000)        0.0     0.0     0.0       1.0   \n1  POINT (-122.22000 37.86000)        0.0     0.0     0.0       1.0   \n2  POINT (-122.24000 37.85000)        0.0     1.0     0.0       0.0   \n3  POINT (-122.25000 37.85000)        1.0     0.0     0.0       0.0   \n4                         None        1.0     0.0     0.0       0.0   \n\n   NEAR OCEAN  \n0         0.0  \n1         0.0  \n2         0.0  \n3         0.0  \n4         0.0  "
          },
          "execution_count": 79,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Method 4 (integers (1 and 0))\n# Method 4 from Leon\ndf = train4  # load_csv_to_dataframe(file_path) # Load data\n\nocean_proxy = pd.get_dummies(df['ocean_proximity'], drop_first=False)\ndf = df.drop('ocean_proximity', axis=1)\ndf = pd.concat([df, ocean_proxy], axis=1)\n\ndf.head()",
      "execution_count": 80,
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>geometry</th>\n      <th>&lt;1H OCEAN</th>\n      <th>INLAND</th>\n      <th>ISLAND</th>\n      <th>NEAR BAY</th>\n      <th>NEAR OCEAN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>15961</th>\n      <td>-122.43</td>\n      <td>37.71</td>\n      <td>52.0</td>\n      <td>1410.0</td>\n      <td>286.0</td>\n      <td>879.0</td>\n      <td>282.0</td>\n      <td>3.1908</td>\n      <td>255600.0</td>\n      <td>POINT (-122.43000 37.71000)</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1771</th>\n      <td>-122.35</td>\n      <td>37.95</td>\n      <td>42.0</td>\n      <td>1485.0</td>\n      <td>290.0</td>\n      <td>971.0</td>\n      <td>303.0</td>\n      <td>3.6094</td>\n      <td>114600.0</td>\n      <td>POINT (-122.35000 37.95000)</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>16414</th>\n      <td>-121.24</td>\n      <td>37.90</td>\n      <td>16.0</td>\n      <td>50.0</td>\n      <td>10.0</td>\n      <td>20.0</td>\n      <td>6.0</td>\n      <td>2.6250</td>\n      <td>137500.0</td>\n      <td>POINT (-121.24000 37.90000)</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5056</th>\n      <td>-118.35</td>\n      <td>34.02</td>\n      <td>34.0</td>\n      <td>5218.0</td>\n      <td>1576.0</td>\n      <td>3538.0</td>\n      <td>1371.0</td>\n      <td>1.5143</td>\n      <td>118800.0</td>\n      <td>POINT (-118.35000 34.02000)</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8589</th>\n      <td>-118.39</td>\n      <td>33.89</td>\n      <td>38.0</td>\n      <td>1851.0</td>\n      <td>332.0</td>\n      <td>750.0</td>\n      <td>314.0</td>\n      <td>7.3356</td>\n      <td>422700.0</td>\n      <td>POINT (-118.39000 33.89000)</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n15961    -122.43     37.71                52.0       1410.0           286.0   \n1771     -122.35     37.95                42.0       1485.0           290.0   \n16414    -121.24     37.90                16.0         50.0            10.0   \n5056     -118.35     34.02                34.0       5218.0          1576.0   \n8589     -118.39     33.89                38.0       1851.0           332.0   \n\n       population  households  median_income  median_house_value  \\\n15961       879.0       282.0         3.1908            255600.0   \n1771        971.0       303.0         3.6094            114600.0   \n16414        20.0         6.0         2.6250            137500.0   \n5056       3538.0      1371.0         1.5143            118800.0   \n8589        750.0       314.0         7.3356            422700.0   \n\n                          geometry  <1H OCEAN  INLAND  ISLAND  NEAR BAY  \\\n15961  POINT (-122.43000 37.71000)          0       0       0         1   \n1771   POINT (-122.35000 37.95000)          0       0       0         1   \n16414  POINT (-121.24000 37.90000)          0       1       0         0   \n5056   POINT (-118.35000 34.02000)          1       0       0         0   \n8589   POINT (-118.39000 33.89000)          1       0       0         0   \n\n       NEAR OCEAN  \n15961           0  \n1771            0  \n16414           0  \n5056            0  \n8589            0  "
          },
          "execution_count": 80,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Method 5 (values 0-4 in same column)\nfrom sklearn.preprocessing import LabelEncoder\ndf = train5  # load_csv_to_dataframe(file_path) # Load data\n\nle = preprocessing.LabelEncoder()\nle.fit(df[\"ocean_proximity\"])\ndf[\"ocean_proximity\"] = le.transform(df[\"ocean_proximity\"])\n\ndf.head()",
      "execution_count": 81,
      "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>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity</th>\n      <th>geometry</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>15961</th>\n      <td>-122.43</td>\n      <td>37.71</td>\n      <td>52.0</td>\n      <td>1410.0</td>\n      <td>286.0</td>\n      <td>879.0</td>\n      <td>282.0</td>\n      <td>3.1908</td>\n      <td>255600.0</td>\n      <td>3</td>\n      <td>POINT (-122.43000 37.71000)</td>\n    </tr>\n    <tr>\n      <th>1771</th>\n      <td>-122.35</td>\n      <td>37.95</td>\n      <td>42.0</td>\n      <td>1485.0</td>\n      <td>290.0</td>\n      <td>971.0</td>\n      <td>303.0</td>\n      <td>3.6094</td>\n      <td>114600.0</td>\n      <td>3</td>\n      <td>POINT (-122.35000 37.95000)</td>\n    </tr>\n    <tr>\n      <th>16414</th>\n      <td>-121.24</td>\n      <td>37.90</td>\n      <td>16.0</td>\n      <td>50.0</td>\n      <td>10.0</td>\n      <td>20.0</td>\n      <td>6.0</td>\n      <td>2.6250</td>\n      <td>137500.0</td>\n      <td>1</td>\n      <td>POINT (-121.24000 37.90000)</td>\n    </tr>\n    <tr>\n      <th>5056</th>\n      <td>-118.35</td>\n      <td>34.02</td>\n      <td>34.0</td>\n      <td>5218.0</td>\n      <td>1576.0</td>\n      <td>3538.0</td>\n      <td>1371.0</td>\n      <td>1.5143</td>\n      <td>118800.0</td>\n      <td>0</td>\n      <td>POINT (-118.35000 34.02000)</td>\n    </tr>\n    <tr>\n      <th>8589</th>\n      <td>-118.39</td>\n      <td>33.89</td>\n      <td>38.0</td>\n      <td>1851.0</td>\n      <td>332.0</td>\n      <td>750.0</td>\n      <td>314.0</td>\n      <td>7.3356</td>\n      <td>422700.0</td>\n      <td>0</td>\n      <td>POINT (-118.39000 33.89000)</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n15961    -122.43     37.71                52.0       1410.0           286.0   \n1771     -122.35     37.95                42.0       1485.0           290.0   \n16414    -121.24     37.90                16.0         50.0            10.0   \n5056     -118.35     34.02                34.0       5218.0          1576.0   \n8589     -118.39     33.89                38.0       1851.0           332.0   \n\n       population  households  median_income  median_house_value  \\\n15961       879.0       282.0         3.1908            255600.0   \n1771        971.0       303.0         3.6094            114600.0   \n16414        20.0         6.0         2.6250            137500.0   \n5056       3538.0      1371.0         1.5143            118800.0   \n8589        750.0       314.0         7.3356            422700.0   \n\n       ocean_proximity                     geometry  \n15961                3  POINT (-122.43000 37.71000)  \n1771                 3  POINT (-122.35000 37.95000)  \n16414                1  POINT (-121.24000 37.90000)  \n5056                 0  POINT (-118.35000 34.02000)  \n8589                 0  POINT (-118.39000 33.89000)  "
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          "execution_count": 81,
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