California_Housing_Prices.ipynb

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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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    "colab": {
      "name": "California Housing Prices.ipynb",
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    "accelerator": "TPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Tejas-TA/Machine-Learning-Prediction-of-California-House-Prices/blob/main/California_Housing_Prices.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1tbevuZExOf"
      },
      "source": [
        "# **California Housing Prices Prediction - Machine Learning**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3zT5YKE6ExOg"
      },
      "source": [
        "### Exploratory Data Analysis (EDA)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "llJRbIncGN9f",
        "outputId": "8f906bc0-c35c-49b0-e590-aeb43763bb03"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "amLePjDeExOh"
      },
      "source": [
        "# Using pandas for Data Analysis and Data Manipulation\n",
        "import pandas as pd\n",
        "df = pd.read_csv(\"/content/drive/MyDrive/housing.csv\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "pJK1-fLnExOi",
        "outputId": "6278f391-6374-4d9c-9550-8982aff8ed74"
      },
      "source": [
        "#Examining first 10 values of all features\n",
        "df.head(10)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
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              "        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>ocean_proximity</th>\n",
              "      <th>median_house_value</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</td>\n",
              "      <td>880</td>\n",
              "      <td>129.0</td>\n",
              "      <td>322</td>\n",
              "      <td>126</td>\n",
              "      <td>8.3252</td>\n",
              "      <td>NEAR BAY</td>\n",
              "      <td>452600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-122.22</td>\n",
              "      <td>37.86</td>\n",
              "      <td>21</td>\n",
              "      <td>7099</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>2401</td>\n",
              "      <td>1138</td>\n",
              "      <td>8.3014</td>\n",
              "      <td>NEAR BAY</td>\n",
              "      <td>358500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-122.24</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52</td>\n",
              "      <td>1467</td>\n",
              "      <td>190.0</td>\n",
              "      <td>496</td>\n",
              "      <td>177</td>\n",
              "      <td>7.2574</td>\n",
              "      <td>NEAR BAY</td>\n",
              "      <td>352100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52</td>\n",
              "      <td>1274</td>\n",
              "      <td>235.0</td>\n",
              "      <td>558</td>\n",
              "      <td>219</td>\n",
              "      <td>5.6431</td>\n",
              "      <td>NEAR BAY</td>\n",
              "      <td>341300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52</td>\n",
              "      <td>1627</td>\n",
              "      <td>280.0</td>\n",
              "      <td>565</td>\n",
              "      <td>259</td>\n",
              "      <td>3.8462</td>\n",
              "      <td>NEAR BAY</td>\n",
              "      <td>342200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52</td>\n",
              "      <td>919</td>\n",
              "      <td>213.0</td>\n",
              "      <td>413</td>\n",
              "      <td>193</td>\n",
              "      <td>4.0368</td>\n",
              "      <td>NEAR BAY</td>\n",
              "      <td>269700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.84</td>\n",
              "      <td>52</td>\n",
              "      <td>2535</td>\n",
              "      <td>489.0</td>\n",
              "      <td>1094</td>\n",
              "      <td>514</td>\n",
              "      <td>3.6591</td>\n",
              "      <td>NEAR BAY</td>\n",
              "      <td>299200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.84</td>\n",
              "      <td>52</td>\n",
              "      <td>3104</td>\n",
              "      <td>687.0</td>\n",
              "      <td>1157</td>\n",
              "      <td>647</td>\n",
              "      <td>3.1200</td>\n",
              "      <td>NEAR BAY</td>\n",
              "      <td>241400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-122.26</td>\n",
              "      <td>37.84</td>\n",
              "      <td>42</td>\n",
              "      <td>2555</td>\n",
              "      <td>665.0</td>\n",
              "      <td>1206</td>\n",
              "      <td>595</td>\n",
              "      <td>2.0804</td>\n",
              "      <td>NEAR BAY</td>\n",
              "      <td>226700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.84</td>\n",
              "      <td>52</td>\n",
              "      <td>3549</td>\n",
              "      <td>707.0</td>\n",
              "      <td>1551</td>\n",
              "      <td>714</td>\n",
              "      <td>3.6912</td>\n",
              "      <td>NEAR BAY</td>\n",
              "      <td>261100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   longitude  latitude  ...  ocean_proximity  median_house_value\n",
              "0    -122.23     37.88  ...         NEAR BAY              452600\n",
              "1    -122.22     37.86  ...         NEAR BAY              358500\n",
              "2    -122.24     37.85  ...         NEAR BAY              352100\n",
              "3    -122.25     37.85  ...         NEAR BAY              341300\n",
              "4    -122.25     37.85  ...         NEAR BAY              342200\n",
              "5    -122.25     37.85  ...         NEAR BAY              269700\n",
              "6    -122.25     37.84  ...         NEAR BAY              299200\n",
              "7    -122.25     37.84  ...         NEAR BAY              241400\n",
              "8    -122.26     37.84  ...         NEAR BAY              226700\n",
              "9    -122.25     37.84  ...         NEAR BAY              261100\n",
              "\n",
              "[10 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "qFl_kCqeExOj",
        "outputId": "18ee07b8-f9c9-4351-cf21-d117f99568f7"
      },
      "source": [
        "#Examining last 10 values of all features\n",
        "df.tail(10)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>ocean_proximity</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20630</th>\n",
              "      <td>-121.32</td>\n",
              "      <td>39.29</td>\n",
              "      <td>11</td>\n",
              "      <td>2640</td>\n",
              "      <td>505.0</td>\n",
              "      <td>1257</td>\n",
              "      <td>445</td>\n",
              "      <td>3.5673</td>\n",
              "      <td>INLAND</td>\n",
              "      <td>112000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20631</th>\n",
              "      <td>-121.40</td>\n",
              "      <td>39.33</td>\n",
              "      <td>15</td>\n",
              "      <td>2655</td>\n",
              "      <td>493.0</td>\n",
              "      <td>1200</td>\n",
              "      <td>432</td>\n",
              "      <td>3.5179</td>\n",
              "      <td>INLAND</td>\n",
              "      <td>107200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20632</th>\n",
              "      <td>-121.45</td>\n",
              "      <td>39.26</td>\n",
              "      <td>15</td>\n",
              "      <td>2319</td>\n",
              "      <td>416.0</td>\n",
              "      <td>1047</td>\n",
              "      <td>385</td>\n",
              "      <td>3.1250</td>\n",
              "      <td>INLAND</td>\n",
              "      <td>115600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20633</th>\n",
              "      <td>-121.53</td>\n",
              "      <td>39.19</td>\n",
              "      <td>27</td>\n",
              "      <td>2080</td>\n",
              "      <td>412.0</td>\n",
              "      <td>1082</td>\n",
              "      <td>382</td>\n",
              "      <td>2.5495</td>\n",
              "      <td>INLAND</td>\n",
              "      <td>98300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20634</th>\n",
              "      <td>-121.56</td>\n",
              "      <td>39.27</td>\n",
              "      <td>28</td>\n",
              "      <td>2332</td>\n",
              "      <td>395.0</td>\n",
              "      <td>1041</td>\n",
              "      <td>344</td>\n",
              "      <td>3.7125</td>\n",
              "      <td>INLAND</td>\n",
              "      <td>116800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20635</th>\n",
              "      <td>-121.09</td>\n",
              "      <td>39.48</td>\n",
              "      <td>25</td>\n",
              "      <td>1665</td>\n",
              "      <td>374.0</td>\n",
              "      <td>845</td>\n",
              "      <td>330</td>\n",
              "      <td>1.5603</td>\n",
              "      <td>INLAND</td>\n",
              "      <td>78100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20636</th>\n",
              "      <td>-121.21</td>\n",
              "      <td>39.49</td>\n",
              "      <td>18</td>\n",
              "      <td>697</td>\n",
              "      <td>150.0</td>\n",
              "      <td>356</td>\n",
              "      <td>114</td>\n",
              "      <td>2.5568</td>\n",
              "      <td>INLAND</td>\n",
              "      <td>77100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20637</th>\n",
              "      <td>-121.22</td>\n",
              "      <td>39.43</td>\n",
              "      <td>17</td>\n",
              "      <td>2254</td>\n",
              "      <td>485.0</td>\n",
              "      <td>1007</td>\n",
              "      <td>433</td>\n",
              "      <td>1.7000</td>\n",
              "      <td>INLAND</td>\n",
              "      <td>92300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20638</th>\n",
              "      <td>-121.32</td>\n",
              "      <td>39.43</td>\n",
              "      <td>18</td>\n",
              "      <td>1860</td>\n",
              "      <td>409.0</td>\n",
              "      <td>741</td>\n",
              "      <td>349</td>\n",
              "      <td>1.8672</td>\n",
              "      <td>INLAND</td>\n",
              "      <td>84700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20639</th>\n",
              "      <td>-121.24</td>\n",
              "      <td>39.37</td>\n",
              "      <td>16</td>\n",
              "      <td>2785</td>\n",
              "      <td>616.0</td>\n",
              "      <td>1387</td>\n",
              "      <td>530</td>\n",
              "      <td>2.3886</td>\n",
              "      <td>INLAND</td>\n",
              "      <td>89400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  ...  ocean_proximity  median_house_value\n",
              "20630    -121.32     39.29  ...           INLAND              112000\n",
              "20631    -121.40     39.33  ...           INLAND              107200\n",
              "20632    -121.45     39.26  ...           INLAND              115600\n",
              "20633    -121.53     39.19  ...           INLAND               98300\n",
              "20634    -121.56     39.27  ...           INLAND              116800\n",
              "20635    -121.09     39.48  ...           INLAND               78100\n",
              "20636    -121.21     39.49  ...           INLAND               77100\n",
              "20637    -121.22     39.43  ...           INLAND               92300\n",
              "20638    -121.32     39.43  ...           INLAND               84700\n",
              "20639    -121.24     39.37  ...           INLAND               89400\n",
              "\n",
              "[10 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E-Ordf51ExOj",
        "outputId": "f51e894f-4e15-401c-f300-e1b3a4a6c84f"
      },
      "source": [
        "#Checking number of rows and columns (m*n)\n",
        "df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20640, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MBeNQArKExOk",
        "outputId": "459974b3-3fa9-4ee3-fcfe-45321a828363"
      },
      "source": [
        "#Feature names listed\n",
        "df.columns"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
              "       'total_bedrooms', 'population', 'households', 'median_income',\n",
              "       'ocean_proximity', 'median_house_value'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "7K-uFo1TExOk",
        "outputId": "830247d0-59e8-43e5-8863-8909ed74762d"
      },
      "source": [
        "#25%, 75%, standard deviaiton etc are displayed for all features\n",
        "df.describe()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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  ...  median_income  median_house_value\n",
              "count  20640.000000  20640.000000  ...   20640.000000        20640.000000\n",
              "mean    -119.569704     35.631861  ...       3.870671       206855.816909\n",
              "std        2.003532      2.135952  ...       1.899822       115395.615874\n",
              "min     -124.350000     32.540000  ...       0.499900        14999.000000\n",
              "25%     -121.800000     33.930000  ...       2.563400       119600.000000\n",
              "50%     -118.490000     34.260000  ...       3.534800       179700.000000\n",
              "75%     -118.010000     37.710000  ...       4.743250       264725.000000\n",
              "max     -114.310000     41.950000  ...      15.000100       500001.000000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3eXJpD7aExOl",
        "outputId": "0544d20f-e606-48df-bde7-799bb01ddc22"
      },
      "source": [
        "#To check non-null count\n",
        "df.info()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20640 entries, 0 to 20639\n",
            "Data 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  int64  \n",
            " 3   total_rooms         20640 non-null  int64  \n",
            " 4   total_bedrooms      20433 non-null  float64\n",
            " 5   population          20640 non-null  int64  \n",
            " 6   households          20640 non-null  int64  \n",
            " 7   median_income       20640 non-null  float64\n",
            " 8   ocean_proximity     20640 non-null  object \n",
            " 9   median_house_value  20640 non-null  int64  \n",
            "dtypes: float64(4), int64(5), object(1)\n",
            "memory usage: 1.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FP1tMu_JExOl"
      },
      "source": [
        "#Using matplotlib and seaborn for visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "8B3v2f9yExOl",
        "outputId": "f4e76285-2a9c-4342-ca90-29340eb6c8a9"
      },
      "source": [
        "#Visualizing missing data\n",
        "sns.heatmap(df.isnull(),cmap='Greens',cbar=True,yticklabels=False)\n",
        "plt.title('Missing Data in Total Bedrooms')\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fCwVc7fTExOm",
        "outputId": "c93e7ec6-d172-4ad1-868c-edec07aa65b3"
      },
      "source": [
        "#Checking as to which feature has missing values using boolean commands\n",
        "df.isnull().any()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "longitude             False\n",
              "latitude              False\n",
              "housing_median_age    False\n",
              "total_rooms           False\n",
              "total_bedrooms         True\n",
              "population            False\n",
              "households            False\n",
              "median_income         False\n",
              "ocean_proximity       False\n",
              "median_house_value    False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JtwS2r9KExOm",
        "outputId": "e8b12dcd-c4d2-4bee-f33e-5d487277d877"
      },
      "source": [
        "#So, total_bedrooms has missing values\n",
        "#Counting how many rows do not have bedrooms \n",
        "df.isnull().sum()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "longitude               0\n",
              "latitude                0\n",
              "housing_median_age      0\n",
              "total_rooms             0\n",
              "total_bedrooms        207\n",
              "population              0\n",
              "households              0\n",
              "median_income           0\n",
              "ocean_proximity         0\n",
              "median_house_value      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NF7P1GplExOm"
      },
      "source": [
        "<b>We have 207 missing values for 'total_bedrooms' which will be handled during the next phase"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5t_si0jdExOn"
      },
      "source": [
        "### Data Cleaning, Visualization, Feature Engineering and Selection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gfOOYK3yExOn",
        "outputId": "51ed77f2-1dbf-4224-a11c-2617159ffb38"
      },
      "source": [
        "#Another version of df.head() but without feature names\n",
        "df.values"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-122.23, 37.88, 41, ..., 8.3252, 'NEAR BAY', 452600],\n",
              "       [-122.22, 37.86, 21, ..., 8.3014, 'NEAR BAY', 358500],\n",
              "       [-122.24, 37.85, 52, ..., 7.2574, 'NEAR BAY', 352100],\n",
              "       ...,\n",
              "       [-121.22, 39.43, 17, ..., 1.7, 'INLAND', 92300],\n",
              "       [-121.32, 39.43, 18, ..., 1.8672, 'INLAND', 84700],\n",
              "       [-121.24, 39.37, 16, ..., 2.3886, 'INLAND', 89400]], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "jNs7txYYExOn",
        "outputId": "894aef93-ad62-4b1d-c014-114b3b05e30e"
      },
      "source": [
        "# Shuffling all rows(frac=1)\n",
        "df = df.sample(frac=1)\n",
        "df.head(10)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>ocean_proximity</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1726</th>\n",
              "      <td>-122.37</td>\n",
              "      <td>37.95</td>\n",
              "      <td>32</td>\n",
              "      <td>1298</td>\n",
              "      <td>363.0</td>\n",
              "      <td>716</td>\n",
              "      <td>268</td>\n",
              "      <td>0.9797</td>\n",
              "      <td>NEAR BAY</td>\n",
              "      <td>76400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12942</th>\n",
              "      <td>-121.27</td>\n",
              "      <td>38.65</td>\n",
              "      <td>25</td>\n",
              "      <td>2787</td>\n",
              "      <td>601.0</td>\n",
              "      <td>1247</td>\n",
              "      <td>522</td>\n",
              "      <td>2.9016</td>\n",
              "      <td>INLAND</td>\n",
              "      <td>159800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15016</th>\n",
              "      <td>-117.03</td>\n",
              "      <td>32.77</td>\n",
              "      <td>19</td>\n",
              "      <td>4819</td>\n",
              "      <td>1492.0</td>\n",
              "      <td>2572</td>\n",
              "      <td>1336</td>\n",
              "      <td>2.3393</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "      <td>118200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14577</th>\n",
              "      <td>-117.20</td>\n",
              "      <td>32.82</td>\n",
              "      <td>35</td>\n",
              "      <td>1217</td>\n",
              "      <td>220.0</td>\n",
              "      <td>643</td>\n",
              "      <td>237</td>\n",
              "      <td>3.9464</td>\n",
              "      <td>NEAR OCEAN</td>\n",
              "      <td>171600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6477</th>\n",
              "      <td>-118.08</td>\n",
              "      <td>34.09</td>\n",
              "      <td>32</td>\n",
              "      <td>3214</td>\n",
              "      <td>718.0</td>\n",
              "      <td>2316</td>\n",
              "      <td>751</td>\n",
              "      <td>3.7066</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "      <td>206800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18096</th>\n",
              "      <td>-122.04</td>\n",
              "      <td>37.33</td>\n",
              "      <td>26</td>\n",
              "      <td>2690</td>\n",
              "      <td>401.0</td>\n",
              "      <td>1264</td>\n",
              "      <td>429</td>\n",
              "      <td>7.7643</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "      <td>474700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8043</th>\n",
              "      <td>-118.16</td>\n",
              "      <td>33.84</td>\n",
              "      <td>36</td>\n",
              "      <td>2831</td>\n",
              "      <td>573.0</td>\n",
              "      <td>1462</td>\n",
              "      <td>569</td>\n",
              "      <td>3.8646</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "      <td>214600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15068</th>\n",
              "      <td>-116.97</td>\n",
              "      <td>32.78</td>\n",
              "      <td>37</td>\n",
              "      <td>1255</td>\n",
              "      <td>238.0</td>\n",
              "      <td>671</td>\n",
              "      <td>278</td>\n",
              "      <td>3.7019</td>\n",
              "      <td>&lt;1H OCEAN</td>\n",
              "      <td>138600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19603</th>\n",
              "      <td>-121.04</td>\n",
              "      <td>37.60</td>\n",
              "      <td>27</td>\n",
              "      <td>958</td>\n",
              "      <td>184.0</td>\n",
              "      <td>580</td>\n",
              "      <td>177</td>\n",
              "      <td>2.1875</td>\n",
              "      <td>INLAND</td>\n",
              "      <td>82800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13468</th>\n",
              "      <td>-117.38</td>\n",
              "      <td>34.09</td>\n",
              "      <td>8</td>\n",
              "      <td>3955</td>\n",
              "      <td>815.0</td>\n",
              "      <td>2184</td>\n",
              "      <td>725</td>\n",
              "      <td>3.3438</td>\n",
              "      <td>INLAND</td>\n",
              "      <td>127600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  ...  ocean_proximity  median_house_value\n",
              "1726     -122.37     37.95  ...         NEAR BAY               76400\n",
              "12942    -121.27     38.65  ...           INLAND              159800\n",
              "15016    -117.03     32.77  ...        <1H OCEAN              118200\n",
              "14577    -117.20     32.82  ...       NEAR OCEAN              171600\n",
              "6477     -118.08     34.09  ...        <1H OCEAN              206800\n",
              "18096    -122.04     37.33  ...        <1H OCEAN              474700\n",
              "8043     -118.16     33.84  ...        <1H OCEAN              214600\n",
              "15068    -116.97     32.78  ...        <1H OCEAN              138600\n",
              "19603    -121.04     37.60  ...           INLAND               82800\n",
              "13468    -117.38     34.09  ...           INLAND              127600\n",
              "\n",
              "[10 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GfpX9TZuExOo",
        "outputId": "22e24a68-8994-448a-fc86-86db4542cc4d"
      },
      "source": [
        "#Categorical Data, so, will enumerate and replace all data with unique values for each type of data\n",
        "df['ocean_proximity'].unique()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['NEAR BAY', 'INLAND', '<1H OCEAN', 'NEAR OCEAN', 'ISLAND'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "DsKVDPyPExOo",
        "outputId": "13e0bddd-5910-44b9-b3c0-8a74bcbcb03b"
      },
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.countplot(data=df,x='ocean_proximity')\n",
        "plt.plot()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aD8LMyc8ExOo"
      },
      "source": [
        "# Replace ocean proximity with values to help our model\n",
        "ocean_proximity = {a:b for b,a in enumerate(df['ocean_proximity'].unique())}\n",
        "df.replace(ocean_proximity, inplace=True)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vg0MMirMExOp",
        "outputId": "23c23fcf-324f-4e2d-cf31-232847156f3e"
      },
      "source": [
        "df.ocean_proximity.unique()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gb4CRLX-ExOp"
      },
      "source": [
        "# The feature 'total_bedrooms' has NaN values so I will replace them with the mean of the feature\n",
        "df = df.apply(lambda x: x.fillna(x.mean()))"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z0wq1oESExOp"
      },
      "source": [
        "#Run this cell to check if mean values are populated into the csv. \n",
        "#Note that data frame has been shuffled\n",
        "#new=df.to_csv(\"total_bedrooms\".csv\")"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nnTarZs3ExOp",
        "outputId": "7c933c72-ccc4-4416-a3dd-ebd10318caf3"
      },
      "source": [
        "#Verifying Data is balanced\n",
        "df.isnull().any()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "longitude             False\n",
              "latitude              False\n",
              "housing_median_age    False\n",
              "total_rooms           False\n",
              "total_bedrooms        False\n",
              "population            False\n",
              "households            False\n",
              "median_income         False\n",
              "ocean_proximity       False\n",
              "median_house_value    False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "kuH-26VdExOq",
        "outputId": "743ae19f-2ae4-4ae6-c0ef-605a8bbe284a"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "df.hist(bins=50,figsize=(20,15))\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "xp03ZUq0ExOq",
        "outputId": "847cb1a1-5117-4cf1-ae99-2e3da5f9b321"
      },
      "source": [
        "#Analyzing the median income range\n",
        "df[\"median_income\"].hist(bins=40)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcc9e4ad490>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Mpw0SYQnExOq",
        "outputId": "227dced0-2e9b-4de0-e6a7-0907fa47cf27"
      },
      "source": [
        "# Calculating correlation\n",
        "corr_matrix=df.corr()\n",
        "corr_matrix"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>ocean_proximity</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>-0.924664</td>\n",
              "      <td>-0.108197</td>\n",
              "      <td>0.044568</td>\n",
              "      <td>0.069260</td>\n",
              "      <td>0.099773</td>\n",
              "      <td>0.055310</td>\n",
              "      <td>-0.015176</td>\n",
              "      <td>0.399266</td>\n",
              "      <td>-0.045967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latitude</th>\n",
              "      <td>-0.924664</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.011173</td>\n",
              "      <td>-0.036100</td>\n",
              "      <td>-0.066658</td>\n",
              "      <td>-0.108785</td>\n",
              "      <td>-0.071035</td>\n",
              "      <td>-0.079809</td>\n",
              "      <td>-0.521292</td>\n",
              "      <td>-0.144160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>housing_median_age</th>\n",
              "      <td>-0.108197</td>\n",
              "      <td>0.011173</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.361262</td>\n",
              "      <td>-0.318998</td>\n",
              "      <td>-0.296244</td>\n",
              "      <td>-0.302916</td>\n",
              "      <td>-0.119034</td>\n",
              "      <td>-0.049827</td>\n",
              "      <td>0.105623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_rooms</th>\n",
              "      <td>0.044568</td>\n",
              "      <td>-0.036100</td>\n",
              "      <td>-0.361262</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.927253</td>\n",
              "      <td>0.857126</td>\n",
              "      <td>0.918484</td>\n",
              "      <td>0.198050</td>\n",
              "      <td>-0.000911</td>\n",
              "      <td>0.134153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_bedrooms</th>\n",
              "      <td>0.069260</td>\n",
              "      <td>-0.066658</td>\n",
              "      <td>-0.318998</td>\n",
              "      <td>0.927253</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.873910</td>\n",
              "      <td>0.974725</td>\n",
              "      <td>-0.007682</td>\n",
              "      <td>0.018246</td>\n",
              "      <td>0.049454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>population</th>\n",
              "      <td>0.099773</td>\n",
              "      <td>-0.108785</td>\n",
              "      <td>-0.296244</td>\n",
              "      <td>0.857126</td>\n",
              "      <td>0.873910</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.907222</td>\n",
              "      <td>0.004834</td>\n",
              "      <td>0.046390</td>\n",
              "      <td>-0.024650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>households</th>\n",
              "      <td>0.055310</td>\n",
              "      <td>-0.071035</td>\n",
              "      <td>-0.302916</td>\n",
              "      <td>0.918484</td>\n",
              "      <td>0.974725</td>\n",
              "      <td>0.907222</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.013033</td>\n",
              "      <td>0.029373</td>\n",
              "      <td>0.065843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median_income</th>\n",
              "      <td>-0.015176</td>\n",
              "      <td>-0.079809</td>\n",
              "      <td>-0.119034</td>\n",
              "      <td>0.198050</td>\n",
              "      <td>-0.007682</td>\n",
              "      <td>0.004834</td>\n",
              "      <td>0.013033</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.098940</td>\n",
              "      <td>0.688075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ocean_proximity</th>\n",
              "      <td>0.399266</td>\n",
              "      <td>-0.521292</td>\n",
              "      <td>-0.049827</td>\n",
              "      <td>-0.000911</td>\n",
              "      <td>0.018246</td>\n",
              "      <td>0.046390</td>\n",
              "      <td>0.029373</td>\n",
              "      <td>0.098940</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.203778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>median_house_value</th>\n",
              "      <td>-0.045967</td>\n",
              "      <td>-0.144160</td>\n",
              "      <td>0.105623</td>\n",
              "      <td>0.134153</td>\n",
              "      <td>0.049454</td>\n",
              "      <td>-0.024650</td>\n",
              "      <td>0.065843</td>\n",
              "      <td>0.688075</td>\n",
              "      <td>0.203778</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    longitude  latitude  ...  ocean_proximity  median_house_value\n",
              "longitude            1.000000 -0.924664  ...         0.399266           -0.045967\n",
              "latitude            -0.924664  1.000000  ...        -0.521292           -0.144160\n",
              "housing_median_age  -0.108197  0.011173  ...        -0.049827            0.105623\n",
              "total_rooms          0.044568 -0.036100  ...        -0.000911            0.134153\n",
              "total_bedrooms       0.069260 -0.066658  ...         0.018246            0.049454\n",
              "population           0.099773 -0.108785  ...         0.046390           -0.024650\n",
              "households           0.055310 -0.071035  ...         0.029373            0.065843\n",
              "median_income       -0.015176 -0.079809  ...         0.098940            0.688075\n",
              "ocean_proximity      0.399266 -0.521292  ...         1.000000            0.203778\n",
              "median_house_value  -0.045967 -0.144160  ...         0.203778            1.000000\n",
              "\n",
              "[10 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "hse3k3XpExOr",
        "outputId": "97757c9a-2290-47db-dcd6-afb8e5578178"
      },
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.displot(df['median_house_value'],color='magenta')\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zOp248HtExOr"
      },
      "source": [
        "As we see from above data, house values > 500000 are outliers, so we remove them"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cx-B3iNRExOr",
        "outputId": "ec549aee-d311-414f-f9e2-bd3018ab58ff"
      },
      "source": [
        "df[df['median_house_value']>450000]['median_house_value'].value_counts().head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "500001    965\n",
              "500000     27\n",
              "475000      8\n",
              "483300      6\n",
              "466700      4\n",
              "Name: median_house_value, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4fPPcQk1ExOs"
      },
      "source": [
        "df=df.loc[df['median_house_value']<500001,:]"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0d-PSHQvExOs"
      },
      "source": [
        "df=df[df['population']<25000]"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "gSbHjmnsExOs",
        "outputId": "d5cd0001-6be7-4bbf-fed4-ff6908fa5a67"
      },
      "source": [
        "plt.figure(figsize=(14,8))\n",
        "sns.displot(df['median_house_value'],color='magenta')\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x576 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3VCWVzqyExOs"
      },
      "source": [
        "<b>The outliers of median house value >500000 are removed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dgqxxZ4qExOs",
        "outputId": "1df85736-a490-476f-d522-b3f54b0fa358"
      },
      "source": [
        "plt.figure(figsize=(11,7))\n",
        "sns.heatmap(cbar=False,annot=True,data=df.corr()*100,cmap='coolwarm')\n",
        "plt.title('% Corelation Matrix')\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6s8aeWVfMebVd7pkHqPTK0wD4VKlAg58+xZGZSUZMLJEPd7/oscVmo86M0cR8PJ2MmNiC6UA+mzF7P691rcoXY+uxe18a0XuOY3eY7O1enm4M7VuDsVN2k55htzCp+i/WrVtHYGAg4eHhbN26Nc82jRo1omWrVhTz8GDBggWMGjWK998vevNrL1VmZiZr1q/nuWeesjqKJUQEofD/UBek0R98QEhIMMnJybzV/x3Kly/HzTVrZm+32+1E79rNiGHvcfLkSXr07kP1GyMoV7ashakLhq+fHy9078GHwwfj5uZGRPUaHD4UZ3WsfGVzc+N/PZ/gWMZJXp82n11HEhnRqR0j5/3OqSw7TatVwGZBMVpQvpwyjic7d7vgoEpOq1cu4ZVe7xRwqrxpQZ63ZsA3xhg7cEREVgANgGPAemNMLICIbAYqAanAHmNMjGv/b4CuV3D+FsA4AGPMVhE5XQ00xjnlZbWrmC8GnDOcLCJdT5+/u1so7dzy5yNGcRMyk4+xqv65V5qx0+YSO22uM2Qec8hPxB2hRMuG2cue5UqRuOLMR783fzKEtF172TtuWr5kLSgP3F2Gjm1LA9B70F8MHxuVve27zxoRd/gEADab8F7fGixa/i8r1yTkeayiZPHP37F88Q8A9H7nI4KCS57TJii4JIkJR7KXE4/+m2e7ouKff/5h7dq1bNiwgczMTNLT0/lg5Eje6NMnu42/v3/2123btuXzqVOtiFpoNkT+SdUqlQkKKprTFv6LEoEBHE1MIrhEEEcTkwgK9L/4TkVISEgw4JyKcmuTJuyI2pmrIC8ZHIy/nx+enp54enpyc42a7NkTUyQK8gXzfmDRwp8BCA+PyDXafTQhnhKum7FzatioKQ0bNQVg4S/zL7mQK2r8vYrToEo5/tixj2da1eXLlx4C4I+ofeyLT7I43ZX5df5cfls4D4D09DQ++mAg4LxHYNPGtdhsNho2OXeO+N49u3DY7VSpGlGYcbNpQX75Tub42s6VfQ+zODNtyPMS2guw2Bjz+IUaGWM+BT4F+Nkjwlyo7eXIOp5Gxt5Ywh5sx+E5zo82/WpFcHxr1EX2hPhFq4gY0hN31x+zknc0I+pt51MMqg3qgbu/L1u7vp1fUQvM3AVxzF3gHDHx9bHh7i5kZRk6tgljy7bk7JHwvq9WY9+BdL79sWiM9l/Mne0f5s72D1+wTeXwmzh86AD/HjlIiRKhrP19ES/1GlJICfNfly5d6NKlCwBbt25lzpw5uYpxgMTEREqUKAHA2nXrKF++fKHnLEzLVvzObS0K/2YnKzVtWJ+FS5fzxEP3s3Dpcpo2zPvGsKLoxIkTOBwOvL29OXHiBJF/bqLT44/latOkcWM+/uQT7HY7mZmZ7NgZxQP33WtR4stzd8f7uLujcwBp4/q1LJj3A81btmZn1HZ8fHwoUSL4nH2Sk5MIDAwi9fhxfvn5R/q89W5hxy4wianpuNts+HsV50RmFmujD9ClVT2OpqYT7OvNqawsvlgeyf/dXrTf4+06PEC7Dg+cs37CmKHUa9A0z2IcYNXK37i15R0FHe+8tCDP2+/ACyIyDSiBc8S6D3DjedpHAZVFpJIxZi/w6HnaHQf8cizvBeoB64GHcqxfCTwBLBWRmkAt1/q1wMciUtUYs0tEfICyxpidl9m/S1J7+iiCWzakWEgQrWNWED14PJue7kPNCQMJ79cNcXcn7n8LLqkgz0xKIXrYRJqtmQ1A9NCPyUxKwbNsKcL7dSN1+26abfgegH0TZ3Bg6uyC6FK+qljOh/6vR2AMxOxP4/1xzpeh1k3+tGsdxq6YVL4YWw+AyV/FsDYy0cq4+SY5KYF3e3UmIz0NNzdh4bxZjJgwCy9vX57u2oeRA1/F4XDQ4vaOlKtQxeq4+e6r6dOpFh5O48aN+fHHH1m7bh02mw0/Pz969expdbwCk3HiBJGbt9CjezeroxSYISM/YvPf20g5dpyHu7xA58cf4fEH72fQB6NZsHgppUJLMuCN162OmW+SkpIZNHQo4JyaclvLljSoX4/5C34BoMPdd1GhQnnq16vHiy+/grgJ7dq0oVKlilbG/k/qNWhE5IZ1vPjck87HHr7+Rva2Ht2f56MJUwD4fPIEYvbsAeDRJ56ibDnnRXb0zh28P+RdUlNT2bhuDd/M+JLxn3xx7omuYgnH0un/7SIcDoPDGNrcEk7Lm25g9PxVrNweg8MYHmlyM42qXtsDC6f1fqVLrqerrPl9Kf0GjrzAHgVLjMm3AdQi7/RjD103an4A3AUY4D1jzLci0grn4wc7uNpPADYaY74UkY7ASCAN2AD4GWM6iUhnoL4xpruI3ApMwTnK/hDgAfwP50j7z8CTxphKIuIFfIFzDvt2oCzwsjFmo4i0BkYAp+8e7G+M+el8fcrPEfKiYni7T62OUOhGj6xtdQRLhHgU/elAl8vdccrqCIXO3ZFpdQRLnLJdygen15YM8bE6QqG7YdsPVkewxM7qF/7U9VpVKzw0z0n6OkKew+lnkBvnVUof17+c25cDy3Ms57xbcZkx5kZXMf8xsNHV5kvgS9fXqzn3sYe1cnzd39UuA3iMPBhjluKcz66UUkoppa4B1+bdCtZ43nWT5zYgAOdTV5RSSimllLogHSHPJ8aYMcC5/+ewUkoppZRSF6Aj5EoppZRSSllIC3KllFJKKaUspAW5UkoppZRSFtKCXCmllFJKKQtpQa6UUkoppZSFtCBXSimllFLKQlqQK6WUUkopZSEtyJVSSimllLKQFuRKKaWUUkpZSAtypZRSSimlLKQFuVJKKaWUUhYSY4zVGVQBatZxxXX3Avf9tavVEQpd1R2/WR3BEpnGw+oIhe6U4/rr80n79ddngOK2TKsjFDqDWB2h0P201s/qCJa4vd4pqyNYokUNnzzf5DpCrpRSSimllIW0IFdKKaWUUspCWpArpZRSSillIS3IlVJKKaWUspAW5EoppZRSSllIC3KllFJKKaUspAW5UkoppZRSFtKCXCmllFJKKQtpQa6UUkoppZSFtCBXSimllFLKQlqQK6WUUkopZSEtyJVSSimllLKQFuRKKaWUUkpZSAtypZRSSimlLORudQB1bfDzcafvaxGUCfPkVKaD4WOjiNmfTmhIcfq/fiNBgR4A/PTrIb6bd9DitOdXa8owQu9uxal/j7KyTscrPl7Zp+4jvG83AKKHT+Lg9B9w8/Kk3qyxeFeugLHbOfLzMqLeHnXF5yoIkRvX89nkidgdDtq0vYuHHnn8nDarVi7nm5lfgQg33FCZ3m++DUD8v0cYP3Y0CQnxCPDu4GGUKhVWyD24cuvXrOKbGZ/jJm7YbDa6dO1O9Rq1zmk35J0+JCUdxW63c1ONWvxftx7YbDYLEuePf/76k6+mjCUrKws//wAGvD/xnDZ/b9nIzKkTMMbg6enFiz36E1amnAVp809c7F4+Hz+Yfbt38OCT3bjrvqcAOBp/mCljB3IsOREEWrW5nzYdz/15KGrmzZ3J6uWLALDbszgYu49PZyzA188/V7vJ44axJ3oHBkPpMuXp1qM/nl7eVkS+YvPmzuCP7D7bORi7l8kzFuDrF5Cr3d9bNvC16/1dPPv9Xd6KyPmmUinh8dtsJKU6l7fvd7BiqwN/b3igmQ0fTwEgcqeDtTscFibNP4diY/hywkD279nBfU+8TNv7ns7e9tYL7fH08kHcnL/f+4+caVlOLcgvg4ikGmN8L7A9EHjCGDPRtVwGGGeMeUhEagNljDELLvOcA4FUY8yHVxC9wD31SAWi96TSb9g2KpTzoueL4fTovxW73TBh6m527k7Fy8vG1DF12bA5ib0H0q2OnKfYaXPZO3EGtaeOuKz9Gv/2FVue60vGvjMXGx5BAVTr351VjR/EGEPzdXM5Mm8pjpOn2DN6KkdXrEM8PGi86EtKtm1B/MKV+d2dK2K325k8cTyDh44gOKQkvXq8TMPGTalQoWJ2m7iDsXz3v28Y8eFYfP38SE5Oyt42ZtQIHn60E3Xq1iMjIwM3ESu6ccVurl2XBo1vRUTYG7ObUe8PZPzk6ee069V3IN7ePhhjGDnsXdasWk6zlrdbkPjKpaUeZ+qkD3lr4GhCQsNISU7Ms93nE0fSu/8IypavxKKf5/D9t1/S7fX+hZw2f/n6+tPp/3rx57oVudbbbO481qUHlarcSEZGGgN7PU2N2o0oW76yRUnzR8cHOtHxgU4ARK5fxYIfZ51TjAM89X+v4e3tA8D0z8aycP5s7n346XPaFQUdH3iSjg88CUDk+t/55cdvzynGAaZOHEmv/h9QtnwlFv88hx++/ZIXX3+nsOPmu33/Gr5eas+1zmFg4UY7hxKhmDu80MGd3YccxKdYFDIf+fgG8Nhzb7B5/bI8t/caPBk//6BCTnUunbKSvwKBl04vGGPijDEPuRZrA3dbkqoQVCrvTeTWZAD2x2ZQOtSToEAPjiadYudu56V4RoadvQfSCQkubmXUC0pctZHMxNy/gbwrl6fB/M9otm4OTZbNxCfi0v4Al2zTjPglq8lMSiEr+RjxS1YT2rY5jowTHF2xDgCTmUnKpn/wLFcq3/typaJ3RlG6TBnCSpfBw8OD5i1asW7N6lxtFv66gPYd7sXXzw+AwEDnL7X9+/dht9upU7ceAF5eXhT39CzcDuQTLy9vxHUxcfJEBue7rDhdrNjtdrKyMrP3KYpWr1hEgyYtCQl1fqIREFgiz3YiQkZ6GgDp6WkEBYcUWsaC4h9YgsrhNbDZco9XBZYIoVKVGwHw8vKhTLlKJB2NtyJigfljxWKatrgzz22n39/GGE6dOlWk3985rblAn3O/v1MJvAbe3+eTmgGHXNfdp7IgIcXg531tvMb+gSW4IY+f6avN1Z3uKiUivsCPQBDgAfQ3xvwIvA9UEZHNwGLgY2A+UBcYDHiJSDNgOFCdHCPfIvI30MEYs1dE3gaeAf4FDgCRrjZVXMcsCaQDzxtjdhROry9sV0waLZuGsPWfFKqH+1Eq1JPQ4OIkJWdmtwkLLU61Kr78E3XMwqSX7+ZJQ/jr5QGk79pHYMNa1Bw/gHVtnrnofp5lSnHiwOHs5ROxR/Ask7vwdg/wo1T724gZPy3fc1+po0cTCAkJzV4OCSlJVFTut1vcwVgA3uj1Gg6Hncc7PU29+g2Ji43Fx8eXYe8N5MjhQ9SuU5enO/9fkZ3Cse6PlcyYNoVjyUn0G/j+edsNfqc3u6K2U6d+Ixrf2rIQE+avQ3EHsGdlMbjvy5zISKfdPY/QovVd57Tr+spbjBjUi2LFiuPl7cPgD6dYkLbwxR+JY9+eKKpUq2F1lHxz8sQJtvy5li4v9jpvm08+eo9NkWsoV/4Gnnz2lUJMVzBO97nzefr8/Ct9+WBQz+z396APPyvkhAWjfEmhWwd3jmcYFm60nzMKHugDYSWEgwnGmoCFSYSPBr0MAi3bPEiLNg9aFkUL8v/mBHC/MeaYiIQAa0XkJ+AtoKYxpjaAiFQCMMacEpF3gfrGmO6ubQPzOrCI1AMewzmi7g78iasgBz4FXjTGRItII2Ai0LpAeniZZszez2tdq/LF2Hrs3pdG9J7j2B1nfpi9PN0Y2rcGY6fsJj3DfoEjXV1sPt4ENalD3Vljz6wrVgyAcs88QKVXnB/Z+lSpQIOfPsWRmUlGTCyRD3e/6LHFZqPOjNHEfDydjJjYgulAAbPb7RyKO8iwEaNISIin3xs9GTdxCnaHnX+2/cVH4z+hZGgpPhg+hCW/LaJN23OLuqKgUdMWNGragm1/b+Gb6VMZOGx0nu3eHfIhp06d5KOR7/H31j+5pU6DQk6aPxx2OzG7o3j7vXGcOnmSAX26Eh5Rg9JlK+Rqt+DHb3lzwCiqRtRg3tyZzPhsHF1f7WtR6sJxIiOdCSPe5InneuLlfd4ZjEXOnxtWEVG9Vp7TVU57sUd/HHY7X0wezZpVv9Hqjg6FmDD//blhFdWq18pzugrALz/O4o0Bo13v7xnM+GwsXV/tV8gp89ehRMOYOVmcyoLwssLjt7kz7oes7O3F3OHRVu78usHOycwLHOga8ebQqQQFh3IsOZExg7oRVrYS1WrUsySLFuT/jQDDRKQF4ADKAvk156A58L0xJh3AVXyR9x4AACAASURBVOifHpVvCnyX46PCPOd+iEhXoCtAlZt7EVbxym9OzMsDd5ehY9vSAPQe9BfDx0Zlb/vus0bEHT4BgM0mvNe3BouW/8vKNQkFkqWgiJuQmXyMVfXvO2db7LS5xE6bC+Q9h/xE3BFKtGyYvexZrhSJK9ZnL9/8yRDSdu1l77irb3QcIDg4hISEf7OXExLiCQ4OztUmJKQk1SJuxN3dnbCw0pQpW45DcbEEh4RwQ+WqhJUuA0DjJrcStWM7FJGC/Jf53/Pbr/MBeHvQCEq4PqquUfMWJhyO41hKMv4BgXnuW6xYcRo2vpX1a1cXqYJ80c9zWLrwJwAaN2tNrTqN8PT0wtPTixtr1mZfzK5cBfmxlCT2xURTNcI5Styk2e28P7CnJdmv1G8L/seKRT8A0PPdsQSVKJlnu6ysLCaMeJMmLdtRv8lVMRbyn+R8rd8Y8CElgkvyx8rfzjt1Iyc3m42mLe5g3pyZRaogX/TzbJZl93kUQcElWbPy/NNVnO/vXTne33cwYuDrhZY3PzWMcKNuuHOG8swlWRzPcK6PPmho3wi8i0P6SXATeLSVja17HGzfX7RHx5f98i0rF38PwGv9xxN4np/poGDnp8D+gSWo0+g2YqK3aUFexHTCOW2knjEmU0T2Apc7QTaL3HP4L7a/G5B8evT9Qowxn+IcTadZxxUF9lM1d0EccxfEAeDrY8PdXcjKMnRsE8aWbcnZI+F9X63GvgPpfPtj0RsFzjqeRsbeWMIebMfhOb8C4FcrguNboy6yJ8QvWkXEkJ64BzpHnEre0Yyot50jq9UG9cDd35etXd8uuPBXKLxaBHFxBzl8+BDBwSH8vnI5vd/IPTrUqElTVq5Yxh1t2nEsJYW4g7GUCiuNj48vaWmppKQkExAQyNYtm6kaXs2inly+uzrcz10d7gfgUFwsxhhEhD27dpKVlYmff+4RtYyMdE5kZBBUIhi7PYvIDWvzfBLL1axN+wdp0975ce3BA3v54pNR2O1ZZGVmsStqG3ff+2iu9j6+fqSnpXHo4H5Kl63AX5s3ULZcJQuSX7k77n6EO+5+5IJtjDFMnTCE0uUq0e7eToWUrGDkfK0B0tNS2f73Jl7uNSDP9sYYjhw6SFiZchhjiFy3ijLlKubZ9mrVpv1DtGn/UPby6T6/1Gtgnu2d7+/UHO/v9ZQpou/v9VEO1kc5n5jim6PSKBssiDiLcYB7m9qITzas2V70n65y212Pcttdj16wzckTGRjjwNPLh5MnMvhny1o6PPx8ISU8lxbk/00A8K+rGL8NOP2b6Tjgd559zt62F+gAICJ1gRtc61cCX4rIcJyvT0dgsmt6TIyIPGyM+U6cw+S1jDFb8rNj/1XFcj70fz0CYyBmfxrvj9sJQK2b/GnXOoxdMal8MdZ51Tn5qxjWRub91Aar1Z4+iuCWDSkWEkTrmBVEDx7Ppqf7UHPCQML7dUPc3Yn734JLKsgzk1KIHjaRZmtmAxA99GMyk1LwLFuK8H7dSN2+m2YbnFfw+ybO4MDU2QXat8tls9l4odsrDOz/Fg6HgzvatKNCxUrMnP4lVcOr0ahxU+rWa8DmPyN5+YVncXNzo/NzXfF3FatdnnuB/n37gDFUCa9Gm3ZF857mtatXsnzpQtxt7hQrXoyebw7IvqGtV/fnGDXhc06eOMHwwX3JzMzEGEPNm2vT9u57LE7+35UtX4lb6jXmzVeeRkS4rc09lK9YBYARA3vx/CtvUSK4JF1feYsxw/sh4oaPrx8vvFa0P84HSE5KYFDvZ8hIT0NEWDRvFsPGf8uBvbv4Y/kCylWsyjs9ngDgoSdf5pb6t1qc+MptWLOCWnUa4unplWv96dc6MCiYSR8NISM9DWMMFW8I59mX+liUNn9sWLOCm12fAuU0YmBPur7Sl6Dgkjz/ylt8NLxv9vu762tX7wDKpbqpohsNItxwOCDTbpi90jlwViFUqF3FjcNJhhc7OMcKl2yyE32waI+UA6QkJfBenyc5keH8mf5t/tcMHjeb1GPJTBzhvH/A7rDTqHk7ata17udZjCn63+zCcvqxh6554/MAX2Aj0Bi4y3VD5tdALeAXXDd1GmNqikgJYCHOm0CHAz/hvDG0LLAOaJLjGDlv6twP/GmM+VBEbgAmAaVdx5lljBl8ocwFOUJ+ter7a1erIxS6qjt+szqCJTKNh9URCt0px/XX55P266/PAMVt18Ek3rOY8z7H6Nr109rzjeNd226vd8rqCJZoUcMnzze5jpBfhtPPIDfGJOAsoPNq88RZq2q61icCZ08obXOeYwwFhuaxPgZod3mplVJKKaXU1UyfQ66UUkoppZSFtCBXSimllFLKQlqQK6WUUkopZSEtyJVSSimllLKQFuRKKaWUUkpZSAtypZRSSimlLKQFuVJKKaWUUhbSglwppZRSSikLaUGulFJKKaWUhbQgV0oppZRSykJakCullFJKKWUhLciVUkoppZSykBbkSimllFJKWUiMMVZnUAVo/Y6U6+4FDvA4ZnWEQrfrxjusjmCJuF+irI5Q6DyLWZ2g8LnbrrtfYwBcj3+eS/qdsjpCoQvzSbY6giX8uT77XbFqhOS1XkfIlVJKKaWUspAW5EoppZRSSllIC3KllFJKKaUspAW5UkoppZRSFtKCXCmllFJKKQtpQa6UUkoppZSFtCBXSimllFLKQlqQK6WUUkopZSEtyJVSSimllLKQFuRKKaWUUkpZSAtypZRSSimlLKQFuVJKKaWUUhbSglwppZRSSikLaUGulFJKKaWUhdytDqCuDXGxe5kybjB7d0fx0JPdaH//k9nbtv65hulTRuFwOGh15710fOgZC5P+d5Eb1/PZ5InYHQ7atL2Lhx55/Jw2q1Yu55uZX4EIN9xQmd5vvg1A/L9HGD92NAkJ8Qjw7uBhlCoVVsg9OL9aU4YRencrTv17lJV1Ol7x8co+dR/hfbsBED18Egen/4Cblyf1Zo3Fu3IFjN3OkZ+XEfX2qCs+V2HYteU3Vs0fi4gbbjYbtz3Yj3JV67N/51qWzR6e3S7xyB46PDuG8FvusDBt/ona/BsrfjjT7zsf7UeF8PokHz3I7IndMQ4HdnsWDVo/Sb1W5/48FEU7Ni1h6feuPrvZaPd4PypWqwfA5tXfs3LeJwC06PgitW+938qo+WbHpiUs+yF3nyuEO/s8Y8z/Ebt7CxXC6/LEa5MtTpq/Dh+MYcbEd4iN2U6Hx17hjns6Z29btmAGfyyZgzFw6+0PcFv7p6wLmg/Wr/mdWTM+x831s9yl6ytUr1HrnHarVy5hzrfTcTgc1GvQhKee7WZB2vyTmprK6HET2LtvH4LQq8er3FT9xuztaWlpvP/haOLj47Hb7Tz0wP20vdOa399akKt84ePrz1PP9yZy7fJc6x12O9Mmf8CbgyZQIjiUd3s/Q92GzSlbobI1Qf8ju93O5InjGTx0BMEhJenV42UaNm5KhQoVs9vEHYzlu/99w4gPx+Lr50dyclL2tjGjRvDwo52oU7ceGRkZuIlY0Y3zip02l70TZ1B76ojL2q/xb1+x5bm+ZOw7mL3OIyiAav27s6rxgxhjaL5uLkfmLcVx8hR7Rk/l6Ip1iIcHjRd9Scm2LYhfuDK/u5PvKkQ04ZlatyMixB/cwbzPe/Dsu79SoVpjnun3IwAZacl8PrANlarfanHa/HPDjU2oNsDZ7yOxO5g7uQfdhvyKX0BJOr/1Le4exTh1Io3JAztSrXZr/AJLWR35it1QvTHdardGRDh8IIrvJvXglWG/kJ6azPIfP6bru7MRESYPepCI2q3x8gmwOvIVq1y9MRGuPh85EMV3n/Sg+9BfAGja9jkyT2UQueJbi1PmPx9ffx7u8hZbNizNtT5ufzR/LJlDn2FfY3P3YOKwbtSs15KSYRUsSnrlbq5djwaNmyEi7I3Zzej3BzBu8oxcbY4fS2H61EmMGPsZAQGBjB89lK2bI6lVu55Fqa/cxE+n0KBeXd7t9xaZmZmcPHky1/af5v9MxfLlGTLgHZJTUniuazdat2qJh4dHoWe96JQVEakkIn8XVAAR+aOgjn2lcvZdROqLyDirM12tAgJLUDn8Jmzuua/xdkdvo1RYOULDyuLu4UHj5m2IXH/1F2Bni94ZRekyZQgrXQYPDw+at2jFujWrc7VZ+OsC2ne4F18/PwACA4MA2L9/H3a7nTp1nb/UvLy8KO7pWbgduIjEVRvJTEzJtc67cnkazP+MZuvm0GTZTHwiLu0iqmSbZsQvWU1mUgpZyceIX7Ka0LbNcWSc4OiKdQCYzExSNv2DZ7miUcAV8/RBXBdRmSczgHMvqHZuWsgNNzXHo5hXIacrOOfrt829GO4exQDIyjqFMQ6rIua74rn6nJ799e6/V1GlRlO8fQPx8gmgSo2m7Prrdyuj5pucr/OpHH0GqHxTE4p7+lgVrUD5BQRTsWpNbLbcf7cOH4yhUtVaFCvuhc3mTtXq9dm87jeLUuYPLy/v7Nf15ImMPH6DwZHDcYSVKUdAQCAAtWrXZ93qFYWYMn+lpaXx19/baNfmTgA8PDzw9fXN3UiE9IwMjDFkZGTg5+eLzWazIO1VMEJujGlqdYZLYYzZCGy0OkdRk3Q0nhIhZ4quEsGh7N65zcJE/83RowmEhIRmL4eElCQqakeuNnEHYwF4o9drOBx2Hu/0NPXqNyQuNhYfH1+GvTeQI4cPUbtOXZ7u/H+W/dBfqpsnDeGvlweQvmsfgQ1rUXP8ANa1ufh0I88ypThx4HD28onYI3iWyV14uwf4Uar9bcSMn5bvuQtK9ObF/P7TKNKPJ/JAt3M/ut8R+TP1W3exIFnB2vHnYpZ9P4q0Y4k89uqZfqckHuLbcV1JjN/PHQ+9cU2Mjp+2PXIxv80ZTdrxRDq95pyiciz5CP4lSme38Q8K41jyEasi5rvtfy5myZzRpB1L5AlXn69XZcpXZd6s8aQeT6ZYseJs2/Q7FarUsDrWFVv3x0pmTvuUY8lJ9B147qehYaXLERd7gH+PHCI4pCTr1/xOVlaWBUnzx+HDRwgMCODDMWPZExNDeNWqdHvhebxyDIjd26E9AwYP5fGnOpOekcHbb/bBzc2a2ysvtSC3icgUoClwELgXiAA+AbyB3cCzxpgkEVkO9DbGbBSREGCjMaaSiNQAvgCK4RyZf9AYEy0iqcYYXxFpBQwEEoCaQCTwpDHGiMjdwGggDVgNVDbGdMgrqIgMBG4AKgMVgNeBxsBdruwdjTGZIlLPdUxf1zk7G2MOudZPdR1uUY7jtnL1q4OINATGAp5ABtDFGBMlIp2Be1zfkyrA98aYN873TRWRSUADwAuYbYwZ4FqfZ39FxAcY7/r+eAADjTE/nu/4qnDZ7XYOxR1k2IhRJCTE0++NnoybOAW7w84/2/7io/GfUDK0FB8MH8KS3xbRpu1dVkc+L5uPN0FN6lB31tgz64o5R0TLPfMAlV55GgCfKhVo8NOnODIzyYiJJfLh7hc9tths1JkxmpiPp5MRE1swHSgA4bXvJLz2nRyI3sCq+WN55NUvs7elpvxLQtxOKt3UzLqABeTGundyY9072bdzA8t/HMuTPb8EIKBEaboOnMfx5CN89/HL3FivLb7+IdaGzSfV691J9Xp3sjdqA0u/H8czfb6wOlKBq173TqrXvZN9URtY9sM4nu597ff5fMLKVebOe7vw8XsvUMzTi3KVIiwr0vJTo6YtaNS0Bf/8vZlZ0z9nwLAxubb7+vnR9eWejH5/IG5ubkRUr8HhQ3EWpb1ydoed6F27eemFrlS/MYKJk6fw7Xez6fzUmXvcNv65icqVb+CD4e8Rd+gQb/V/l5o1a+Dj7V3oeS/1HRYOfGyMqQEkAw8CXwFvGmNqAX8BAy5yjBeBscaY2kB9IK+/xHWAHsBNOAvqW0XEE5gM3GWMqQeUvIS8VYDWOIvjGcAyY8zNOIvn9iLigbOwfch1zKnAUNe+XwCvGGNuucDxdwDNjTF1gHeBYTm21QYeBW4GHhWR8hc4ztvGmPpALaCliNS6SH/fBpYaYxoCtwEjXUV6LiLSVUQ2isjG7//35QVOf2UW//wdb/foxNs9OpF0ND7PNkHBJUlMODOKlHj0X4KCL+UlvLoEB4eQkPBv9nJCQjzBwcG52oSElKRhoya4u7sTFlaaMmXLcSguluCQEG6oXJWw0mWw2Ww0bnIre3ZFF3YXLou4CZnJx1hV/77sfytq3Q0455ufXpcS+Tcb7unKqvr3ZRfjJ+KO4Fn+zA2rnuVKcSLuzHvg5k+GkLZrL3vHXd2j45tWzGTasHuZNuxeUnOMhJYPb0BKwgHSUxOz10X9+Qvht9yJzVb48w7z28ZlM5ky6F6mDLqX4zn6XbFaA5LjD5B+PDFXe7/AUpQsG86B6KL7AeL6JTOZNOA+Jg24j2NJZ/pcKaIBSfEHSDuehH9gKY4lHsredizpMP5F+FOB9Utn8snA+/hk4H0cz9Hniq4+px9PusDeRdeKX2cxvM/DDO/zMMmJ/563XdPWD/DmiG95fdCXePv4E1q64nnbXq1+mT+X3t2fpXf3Z0k8mpC9/qaatTlyOI5jKcnn7FO/0a28P2Yyw0ZNokzZCpQpe6ES5uoWEhxCyZAQqt8YAUDzW5uya9eeXG0WLV5Cs6ZNEBHKlilDWKlSHDhgzUDRpY6QxxhjNru+jsRZ8AYaY05PLpoGfHeRY6wB3haRcsBcY0xeFcl6Y0wsgIhsBioBqcAeY0yMq803QNeLnOsX1yj4X4AN+NW1/i/XMSNwjjIvds2psgGHRCTQ1a/Tk5yn4xxZP1sAME1EwgGDc7T6tCXGmBRXH/4BKgIHzpPzERHpivN1KI3zQsTtAv1tA9wjIr1dy544PwXYnvOgxphPgU8B1u9IMec59xW7s/3D3Nn+4Qu2qRx+E4cPHeDfIwcpUSKUtb8v4qVeQwoqUoEJrxZBXNxBDh8+RHBwCL+vXE7vN/rlatOoSVNWrljGHW3acSwlhbiDsZQKK42Pjy9paamkpCQTEBDI1i2bqRpezaKeXJqs42lk7I0l7MF2HJ7j/PHxqxXB8a1RF903ftEqIob0xD3QH4CSdzQj6u3RAFQb1AN3f1+2dn274MLnkzotO1GnZScAkv7dhzHGedPb/m3Ys07h5ROU3XbHxp9pfm9Pq6Lmq/q3daL+bc5+J+bo96F9rn77BnEs8TBevoF4FPMkIy2FA9F/0uiOztYGvwINb+9Ew9udfT565Eyf41x99vYNpErNZiyZO4aMNOe9Fru3reb2B4vua96wdScatna9zkfyep0DLU5YMFq2e4yW7R67aLvjKUfxCwgmMeEQW9YvodfQGRfd52pzV4cHuKvDAwAciovNfo337IoiKysTP/9zb0hOSU4iIDCI1OPHWfjzD/TsO6iwY+ebEiWCKFkyhAOxsZQvV45NW7ZQoULuC4zQ0BA2bdnCzTVrkJSUROzBg5QOs+YJaJdakOe8LdUOXOgnNYszI+/ZE3WMMV+LyDqgPbBARF4wxiw9a9+zz/Nf57ifdJ3TISKZxpjTRanDdUwBthljmuTcyVWQX4ohOEfd7xeRSsDys8/tct4+iMgNQG+ggWuqz5fk+H6dh+Cc6nPxqqiQJScl8G6vzmSkp+HmJiycN4sRE2bh5e3L0137MHLgqzgcDlrc3pFyFapYHfey2Ww2Xuj2CgP7v4XD4eCONu2oULESM6d/SdXwajRq3JS69Rqw+c9IXn7hWdzc3Oj8XFf8Xb/wujz3Av379gFjqBJejTbt7ra4R7nVnj6K4JYNKRYSROuYFUQPHs+mp/tQc8JAwvt1Q9zdifvfgksqyDOTUogeNpFma2YDED30YzKTUvAsW4rwft1I3b6bZhu+B2DfxBkcmDq7QPuWH3ZuXsg/637EzeaOezFPOjw7JvsGqZSjsRxPOkT5qg0tTpn/dkQuZOuaH7G5+n1/V2e/Ew7v5rf/vQ8iYAyN2z5LaLkIq+Pmi+2Ri9jyh/O19ihWnIdedPbZ2zeQFh1f4tMhzkGIlh1fwvsaKVr/iVzE1jWuPnuc6TPAF+93IuHQHk6dTGd075bc0/k9qtZsbnHi/HEsOYEP3nqMExlpiLixfMEM3h79A17evnw2qidpx1OwubvzyHP98PbxtzruFVm7egUrli7E3eZOseLFef3Ngdmvce/uz/LhBOdM3amTx7EvZhcADz3euUiPkAO8/EJX3h85mqysTMLCwujd4zXmL3A+QajD3XfR6bFHGTlmLF1fegWD4bnOzxAQYM1rLWdq1fM0cBac840xNV3LvXHOu74f6G6M+d01bzvAGPO6iHwGRBpjJolID6CHaw55ZZwj7UZEPgRijTEfnTWHvPfpueEiMgHnTZTfAjtxThHZKyIzXee60BzyVGPMh67lVGOMb85twDjgH+ApY8wa1xSWasaYbSKyFXjJGLNKREYA7Y0xNc+aQ/49MMMYM8d1zM6uPnYG6htjurvONx/40BizPI+ct+Cc9lMH57SUrcCbF+qviAwD/HFOqTEiUscYs+lCr19BjpBfrQI8jlkdodDtuvHaeO715Yr75aq7Ni1wnsWsTlD43G3X3a8xAC7y5/maVNLvlNURCl2Yz7lTR64H/lyf/a5YNSLP5x5fyV0Kz+Ccw7wV57zpwa71HwLdRGQTkPMOn0eAv11TUWriLEYvyhiTAbwE/CoikcBxIOXCe130mKeAh4ARIrIF2IzzhlWALsDHrpzne1j0B8BwVx//0yi+MWYLsAnnfPSvcd68ebH+DsE5PWariGxzLSullFJKqSLsoiPkVwMR8TXGpIrz85WPgWhjzJiL7VdU5Wd/dYT8+qAj5NcPHSG/fhSBP8/5TkfIrx86Qp5bUXmOz/OuEettOG+ovLb+/95zXW/9VUoppZS6bln+HwNdCtfocK4RYhHpArx2VtPVxpiXCy3YJXLdzFr8rNVPGWP+yqt9Xv1VSimllFLXpiJRkOfFGPMFzmeGX/WMMY2szqCUUkoppa5ORWXKilJKKaWUUtckLciVUkoppZSykBbkSimllFJKWUgLcqWUUkoppSykBblSSimllFIW0oJcKaWUUkopC2lBrpRSSimllIW0IFdKKaWUUspCWpArpZRSSilloSL7P3WqSxPikWB1hEKXbnysjlDo4n6JsjqCJcrcFWF1hELnF+FtdYRC51Py+uszQEZihtURCt0tnw+zOkLhS4bYoJutTlHovE8kWx3hqqIj5EoppZRSFrkei3F1Li3IlVJKKaWUspAW5EoppZRSSllIC3KllFJKKaUspAW5UkoppZRSFtKCXCmllFJKKQtpQa6UUkoppZSFtCBXSimllFLKQlqQK6WUUkopZSEtyJVSSimllLKQFuRKKaWUUkpZSAtypZRSSimlLKQFuVJKKaWUUhbSglwppZRSSikLuVsdQBV9drudV197jZDgYAYNGpRr2+LFi/ns888JCQkBoGOHDrRr186KmPlq/ZpVfDPjc9zEDZvNRpeu3aleo9Y57Ya804ekpKPY7XZuqlGL/+vWA5vNZkHi/LNry2+smj8WETfcbDZue7Af5arWZ//OtSybPTy7XeKRPXR4dgzht9xhYdrzqzVlGKF3t+LUv0dZWafjFR+v7FP3Ed63GwDRwydxcPoPuHl5Um/WWLwrV8DY7Rz5eRlRb4+64nMVhjJPPk7pBx8AEQ7PnsvBGV9TsftLBLduCQ5DZmIiUW8P4FR8vNVR81XoQ48S0r4jGEPGnt3sHTGM8A8/wubtDYB7YBDpO/5h9zt9LU6af8o88TilHrwfRDgy53viZn5Npdd7UKJlc0xmFidiD7Dz3YHYj6daHTXfHD6axIBJM0hMOY6IcH/rJjzerhWTvvuZFZF/4SZuBPn7MvDFTpQMCrA67n9ijOGzyROI3LCO4sU9ebXnG1SpWu2cdiuXL2H2t18jIpQIDub13v3wDwhg9e/LmTVzGrEH9jNyzESqVouwoBdXbtiEKfyxcTNBAf5MH+v8G/X5rLnM+20Fgf5+ALzQ6WGa1LvFypiIMcbSAKpg7dm9u8Bf4Llz5xIdHU16enqeBXl0dDQvvfRSQcfIlm58CvwcGRnpeHp6ISLsjdnNqPcHMn7y9HOzpKfh7e2DMYaRw96labNWNGt5e77nWbM3LN+PeT6nTqThUdwbESH+4A7mfd6DZ9/9NVebjLRkPv9/9u47PKpijeP4d7IJbBLSA0EgEEoAJSBIETAUqRZUBK9KkarYULCgYgNpgjTpRUUQrBcQFFG4itKEUCXU0BIglJCQ3jebuX/sEgmEomz2BHg/z8PD7pw55/xONiTvzs4Zhnfg2dFrcSvlXmxZKtz/739B+Ic3Ii8jk/rzxv2jgrzpr1+wq/9Qso6dLGhz8/MhfPMSNjTtitaaFhFLWX93F/JzcvFrcifn1kag3Nxouno+h8fOIX7Vun+d26uWx7/e91p51KjO7ePHsrPbU+RbLNSdPYNDI0ZjSUzEmpEBQIUe3fCoXo3DI0YXex7PssV/zQBugYHUmjqLvX16oHNzqTpsBKmbN3Nu1cqCPtU+GE3yxvUkrv7lCkdyjKzErGI/h0eN6tQa9yG7evQi32IhbOZ0Do8ag7lSRZK3bAWrlZDBLwMQ8/HUYs9z52djiv0cAAlJKSQkp1K7ajAZWdk89e4EJrzyNOX8fSnjYQbgm1/WcvTkGd7u/0SxZon1q1ssx922dTMrf1jGeyM+5GDUfj6dPZ3xH88s1MdqtdKv53+YNvtzvH18mP/ZHEqXLk23nn04cfwYLi6KmdMm07f/cw4vyAOzYx16vMv5a+8B3M1mRk2dU6ggdzeb6d75AadkuFDZOnerotpv6ikrSilfpdQVK0GlVIhSqvs1HCtEKbXHceluDvEJCWzZupWOHTsaHcWp3N1tBSlATnYWRf7rAjw8bG8OrFYreXmWroV1DwAAIABJREFUgn1uZKXMngXXYcnJgiKu/uDOVVS9o0WxFuPXK3HDNiyJKYXaPKoF03jFp4RHLKHZ71/iWavaNR2rbIdw4n/biCUphbzkVOJ/20i5ji3Iz8rm3NoIALTFQsrOfZgrBTn8WhzNo1pV0nbvIT87G6xWUrZtJ7Bdm4JiHMDk7g434YCOMplwKV0aXEy4lDaTey6hYJuLhwdeDe4iecO/f0NV0rhXvei13r6dgLZtSN60GaxWANIid1OqXDmDkzpWoJ8PtasGA+DpbiakQhBnk5ILinGArJzcG/pn9pbNf9K6bXuUUtSqfQcZGekkJp4r1Edrjdaa7OwstNZkZWbiH2D7RDu4chUqVqpsRHSHql+nNt5exT9Qd71u9ikrvsALwMwr9AkBugNfOeqkSilXrXWeo45Xks2ZM4f+/fqRlXX5kZwNGzeye88eKlasyLMDBlC2bFknJiw+EX+uY9GCT0hNTuLt4WMv22/Ee69zOGo/DRrdTdN7WjkxYfE59Nf/WP/DRDLTEuny/JxLth/Y/hON2vQ1INn1qTtrJLtfHEbm4WP4NqlH2LRhRHTofdX9zBWCyD5xpuB5dmwc5gqFC29XHy+CHryX6GkLHJ7b0TIOHyHk5YG4+viQn5ODf4tw0vbuAyDk5RcJergTeWnpRPYbYHBSx7IkJBD33dfU/XYp+Tk5pG7bStq2LQXbfcNbkrZjO/mZmQamdKzMw0cIeenFgtfaLzyc9H37CvUJ6vwI8atWG5Sw+J2KP0fUsVjCqocAMOO7FaxcvxVPDzNz3nnJ2HDXITEhgcCyf7+RCggsS2JCAv7+AQVtrq6uPDdwMINeeBqz2cxtFSoy4IWXjYjrdEt//pVVazdSq3oIA/t0x7uMsUX7TT1CDowFqiul/lJKjbf/2aOU2q2UeuKCPi3sfV6xj4SvV0rtsP9pfi0nUkr1UUr9oJRaA/ymlPJXSi1TSkUqpTYrperZ+12ufbhSaoH93MeUUl2UUh/Zs/6ilHKz9xurlNpn33+C479k1y4iIgJfX19CQ0Mv2+fuu+9m/vz5zJo5k7saNGDixBtj/uy1uLt5S6bNWcgb743m64XzLtvv/ZET+HTRUiwWC3sidzgxYfEJrd+efu//wiMDZrBhxZRC29JTzpJw6iAhd4QblO7fMXl64NesAXd9M4XwbcsImzkCc3nbm8dKvbsQvm0Z4duW4dMwjMY/zCV82zIa/nf6NR1bmUw0WDSJ6BkLyYp2zse01yPraDSx8+ZTd+5M6s6eQXpUFDrfNloaM3UGEe3u5+xPP1Ohe/F+lO9spjJe+DRvwZ5u/yHysUcwmc34t+tQsN2/TTsS1/xqYELHy4qOJvbz+YTNnkmdmdPJiIpCW/MLtld6uj/amkf8TyuvcJQbV2Z2Dm98PI/XnupSMDr+4uOd+GnaB9zfvBHfrb55Pg0pSl5eHj//9AOTps9h3qL/ElK1Gku+c9j4ZIn16H1t+XbmBD6fOJIAP1+mzzf+mm/2gvwt4IjWuj6wGagP3Am0A8YrpW6z91mvta6vtZ4MnAXaa63vAp4A/smkubuAx7TWrYAPgJ1a63rA28AX9j6XaweoDrQBHgYWAb9rresCWcCDSqkA4FGgjn3/UUWFUEoNUEptU0pt+/qbb/5B/H9m3759bN68md59+jB23Dh2RUby0fjxhfp4e3tTys0NgI4dO3Lo8OFiy1Pcfl7xPa8N7M9rA/uTeMHH2HXC7iTuzClSU5Ivu2+pUqVp0vQetmze6IyoDrdz7ZcsGPMIC8Y8QnpyXEF7cGhjUhJOkJmeWNAWteNnQu9sj8nkZkTUf025KCzJqWxo1Lngz9p6tvmFsQuWFrSlbN/D1ocHsKFRZ7b/ZyAA2afiMAf/PY/fXCmI7FN/f53qzh5JxuEYYqaW/NHx884sXcbOJ3qwq09/8lJTyYo5Vmj72RUrCWzn+PshjOTVsBG5Z06Rl5IMVitJ69fiGWab32vy9sGz9h2kbPrT4JSOF/f9cv7q1oPd/Z4mLzWNrGO217rcww/h37IFUUPfNThh8cjLs/LGx/O4755GtGl86Q1999/TkN+27jIg2b+38sdlDB74DIMHPoOfvz8J8WcLtp1LiMffvsDCedFHbb+Tb7utIkop7mnRmgP79zo1sxH8fX0wmVxwcXHh4fat2X/oqNGRbvopKxcKB77WWluBOKXUWqAxkHpRPzdgulKqPmAFLr0l+fL+p7U+X5mEA10BtNZrlFIBSinvK7QD/Ky1tiildgMm4PxdQ7uxTa1ZAWQDnymlVtifX0JrPReYC8V7U2ffvn3p29c2LSEyMpIlS5bwxpAhhfokJibi7+8PwOaICIKDg4srTrG7v9Oj3N/pUQBOn4pFa41SiqOHD5KXZ8HLu/Cd+FlZmWRnZeHnH4DVmsf2rZuLXInlRtCgVQ8atOoBQNLZYwXXHnd8L9a8XNw9/Qr6Htj2Ey0eedWoqP9aXloGWTGxlO96H2eW2P7pedWrRVpk1FX3jV+9gVojX8XV1/ZPuWy7cKLemQRAzQ8G4+pdhsgB7xRf+GLg5u+HJTGJ0uXLE9i2DTt79MJcuTLZx48DENCmNZnRMYZmdLTcs3F43hGGKl0anZOD912NyIg6AIBfq3tJ2fwn2pJrcErHu/C1Dmh7L7ue6o1v8+ZU6tObyP5P2+aX32S01oz45GuqVgyi5wP3FrQfP3OWyuVt0zz+2L6HkNtK/j0fF3rgoc488FBnALZt2czKH5fRolUbDkbtx9PTs9B0FYCAgEBijx8jJSUZHx9f/tq5nUrBVYyI7lQJickE+vsCsC5iO9UqVzI40a1VkF+rV4A4bCPpLtgK4GuVcfUuV5QDoLXOV0pZ9N9L4OQDrlrrPKVUE6At8BgwENuIeonyxcKF1AwNpWnTpixfvpzNERGYTCa8vLx47dUbr1AryuaN6/hjzSpcTa6UKl2KV98cVnDzz2sD+zNx+mfkZGfz4YihWCwWtNaE1a1PxwceNjj59Tv41yr2RSzHxeSKaykznfpNLrj2lHOxpCWdJrhGE4NTXl39hRMJaNWEUoF+tIley6ER09jZawhh04cT+vbzKFdXTn238poKcktSCofGzCR802IADo2egSUpBXPFIELffp70/UcI3/o9AMdmLuLEvMXFem2OcMfkCbj6+qLz8jg8eizWtHRqjhiOR0gVtM4n59RpDjlhhRVnyty/j6S1v3PH3M/RViuZhw6SsGI5AP5t2nLmq0UGJywetSdOwM3HB52Xx5Ex47CmpVN96Ju4lHIjbPYsANJ27+bIKOesgOIMuw4eZeWGrdQIvo3uQz8C4IUnHmT5H5s5dvosLkpxW6A/Q/s9bnDSf69h47vZvjWC5/r3tC17+MobBdsGD3yGj6d/gn9AIE9078U7bwzGZHKlbLlyvPzqmwBs/nM9n8yaRkpKCiOHv03VatUZPuojoy7nXxs2aSZ/7dlPclo6jz49iP5PdmHn3v0cij6OUoryZQMZ8pzx9zzd1Mse2qd47NBaV1FKdQGeBR4A/IFtwN1ARWCSfZoJSqnJQKzWeqJSqi8wT2utlFIhwAqtddhlztUHaKS1Hmh/PhWI11qPVEq1BiZrrRtcoX04kK61nmDfP11rXcb+eDiQDswGPLTWZ5VSPsBRrXXht7sXccayhyWNM5Y9LGmcuexhSXI9yx7eqJyx7GFJ46xlD0saZyx7WNI4a9nDkqS4lj0s6Zy17GFJc7llD2/qEXKt9Tml1Eb7coU/A5HALkADb2itzyilzgFWpdQuYD62FVmWKKV6YZsy8m9HvYcD85RSkUAm0Psq7dfCC1iulDJjW2vu5hhuFkIIIYS4hd3UI+RCRshvFTJCfuuQEfJbh4yQ3xpkhPzWckv+x0BCCCGEEEKUdDf1lJXioJTqCIy7qDlaa/2oEXmEEEIIIcSNTQryf0hrvQpYZXQOIYQQQghxc5ApK0IIIYQQQhhICnIhhBBCCCEMJAW5EEIIIYQQBpKCXAghhBBCCANJQS6EEEIIIYSBpCAXQgghhBDCQFKQCyGEEEIIYSApyIUQQgghhDCQFORCCCGEEEIYSP6nzpuca36u0RGcLlf7Gh3B6cyljE5gDK9aHkZHcLq0qEyjIzidu29poyMYIiM2x+gIThfnV8voCE7nRi76FhwfzS7lZXSEEuXW+w4QQgghhCghbsViXFxKvguEEEIIIYQwkBTkQgghhBBCGEgKciGEEEIIIQwkBbkQQgghhBAGkoJcCCGEEEIIA0lBLoQQQgghhIGkIBdCCCGEEMJAUpALIYQQQghhICnIhRBCCCGEMJAU5EIIIYQQQhhICnIhhBBCCCEMJAW5EEIIIYQQBpKCXAghhBBCCAO5Gh1A3FxOxJ5k1LjxBc/PnImjd89udHnkYQNTFY99u3fwxSdTyMvLw8vbh2FjZ17SZ8+ubXw5bzpaa8xmd54b/C7lK1QyIK3jRP31K2uXTUEpF1xMJto/8TaVQxuRfO4ki2cOROfnY7Xm0bhNTxq27mZ0XIeo0LMbt3XtAkpxZvFSTi76iioDXyCgTSvI11gSE4l6Zxi58fFGR72qep+ModwDrck9e451DR667uNVfKozoUOfB+DQh7M4uXAZLu5mGn4zBY9qldFWK3E//U7UOxOv+1zOEPT4k5R96GG01mQdPUL0mFGUqVuP4BdeQrkorFlZRI8eSc7JWKOjOkzlAT2p1OsxlFLEfrGYY3MWUu/TCXjWqAqAm48XlpQ0NrXuanDS6xcfH8/EieNJTkpGKbjvvgd4pHPnQn02bdrEooULUC4umFxMDHj2WerUCTMosWN8PHkiW7dsxsfXl5mzPrlke3paGh9/PJEzp0/jVqoUgwa/SkhIVQOSOo6tHplQ8Py0vR7p+kjhn3t/Re5h1iefkWe14uPtxaSxo50dFQCltTbkxMI5jh/ab9gLbLVa6da7P9MmfURQuXJOO2+CLv5zZaSnMeyNZ3lr+CQCy5UnJTkRH1//S/q98uwTvP7uOCoGh7D6pyUcObif51951+F59p4JcPgxLyc3OwO30h4opYiLPcDSOYN5fuQvWPNy0Rpc3UqRm53BnOEP0eetr/HyDSq2LFVeaFBsxz7Po0Z1bh8/lp3dniLfYqHu7BkcGjEaS2Ii1owMACr06IZH9WocHlH8P8jTojKva3//8EbkZWRSf964f1SQN/31C3b1H0rWsZMFbW5+PoRvXsKGpl3RWtMiYinr7+5Cfk4ufk3u5NzaCJSbG01Xz+fw2DnEr1r3rzKXu9vvX+33T7kFluX2mXPY3bMbOjeH6iNGkbxpExV69ebQW2+QfSyGso92pcztdxA9ZmSx50ncn1bs5yhTuwb1Pp3A5vZPonMtNPzvHPa9NoLM6OMFfWqNGEJeajpHJswq9jzVt6wo1uMnJp4jMTGRGjVCyczMZNDLL/He++9TuXKVgj5ZWVmYzWaUUkRHH2Xsh2OYM/fTYsuknTBZYc/uSMzu7kya+FGRBfm8z+ZiNrvTvcdTnDhxnFkzpzPmw4+KNZM5P6NYj38hq9XKk72fZvqkcYXqkfT0DF4e8hYffvA+QeXKkpScjJ+vb7FmCQ69QxXVXuKmrCilfJVSL1ylT4hSqvs1HCtEKbXnCtv7KKWm/5ucjtj/ZrdzVyS33VbeqcW4s2xcu5rGzVoRWK48QJHFOIBSiqxM2w+dzMwM/AICnZaxuJQye6KU7eeJJScLsD02uZbC1a0UAHl5uWidb1REh/KoVpW03XvIz84Gq5WUbdsJbNemoBgHMLm7ww0yuJG4YRuWxJRCbR7Vgmm84lPCI5bQ7Pcv8axV7ZqOVbZDOPG/bcSSlEJecirxv22kXMcW5Gdlc25tBADaYiFl5z7MlYrvjZkjKZMJl9KlwWTCpbQZS0I8WmtMnp4AuHp6kptQ8j8JuVaeNauRsj2S/KxstNVK4sZtlOvUrlCfoM4dOb30J4MSOpa/fwA1aoQC4OHhQXDlYM4lnCvUx93dveBnXHZ2Nqgi66cbSljdenh5eV12+/Hjx6l3Z30AgoMrczYujqSkJGfFK3Y7d+2mQhH1yG9r1xHevClB5coCFHsxfiUlccqKL/ACcOnn/38LAboDXzkj0D+llHLVWucZncNof6zbwL0tWxgdo1icPnUCa14eI4a+SHZWJvc9/Dgt29x/Sb8BL73FuA9eo1Sp0rh7eDJiwqUjEzeiAzv+x+/fTyQjNZEnX55T0J6SeJpvpw4gMf447R57o1hHx50l4/ARQl4eiKuPD/k5Ofi3CCdt7z4AQl5+kaCHO5GXlk5kvwEGJ/336s4aye4Xh5F5+Bi+TeoRNm0YER16X3U/c4Ugsk+cKXieHRuHuULh19zVx4ugB+8letoCh+d2NEtCPGe++ZI7lywjPyeH1K1bSN26hZixY6g5fhL5OTlYMzLY92x/o6M6TPqBw4S+Mwg3Px+s2TmUbd+ClL/2Fmz3a9aQ3PhzZB49foWj3Jji4s5w9MgRatWudcm2P//cyIL5n5OcnMzwD0YYkM65qlatxqY/NxAWVpeoqAOcPRvHuYR4/Pyc8+lUcft93foi65GTJ0+RZ83j1bfeJSsri0cf7kSHtvcakLBkFuRjgepKqb+A/9nb7gc0MEpr/a29z+32PguA74GFgKe9/0Ct9Z/XeL5gpdQfQEVgkdb6AwClVE/gZaAUEAG8oLW2KqX6AkOBZGAXkGPvPx/IBhoAG5VSXwCzAQ/gCNBPa52klKp/mfY/gJ1AC/t19LKfpy7wrdb6XaWUJ/AdUAkwASPtX48Sx2KxsGnLFvr3fsroKMUi32ol+kgU74yaSm5ODsOGDCC0Vh1uq1i5UL+Vy7/lzWETqVGrDj8u/ZJFn05lwMtDDUrtOLXvak/tu9pz7OBW/lg+hZ6vzgfAx/82Bgz/kbTkOP4740VqN+xIGe8b+1OBrKPRxM6bT925M8nPyiY9KgqdbwUgZuoMYqbOIPjpflTo/gTHZsw2OO0/Z/L0wK9ZA+76ZsrfbaVsn3RU6t2FkJd6AeBZvTKNf5hLvsVCVnQs2/8z8KrHViYTDRZNInrGQrKiS/6ca5OXF77hLYl8vAvWtDSqjxxDQIf78GvVmoNDXiVj317Kd+tB5ZcGEzNujNFxHSLj4FGip35Gw8WfYM3MInXPAbD+/elW+a4PcHrJSgMTFo+srCxGjx7FMwOexcPD85LtzZvfQ/Pm97Bn924WLvyCMWPGGpDSef7z+BPMnT2LlwY+R0iVqlSvXgMXF5PRsRzCVo9s5eki6hGr1crBw0cZP/oDcnNyefn1t7ijdk0qVazo9JwlsSB/CwjTWtdXSnUFngPuBAKBrUqpdfY+r2utOwEopTyA9lrrbKVUKPA10Ogaz9cECAMy7cf/CcgAngDu0VpblFIzgR5Kqf8BHwANgRTgd2xF9HmVgOb2wj0SeElrvVYpNQIYBgwGvrhMO0Cu1rqRUmoQsNx+nkTgiFJqMtAaOKW1ftB+3T5FXZBSagAwAODDEcPp/uTj1/ilcJyt23dQo3o1/PyM+/jH0Vb/tIQ1q34AoGl4G+o1uBuz2R2z2Z3aYfU5Fn24UEGempLEsehD1KhVB4Bm4W0ZO/xVQ7Jfr22/f8nOdd8B8OSguQUj31VqNubH+BNkpiXi4fX3tB0v3yDKVgzlxKFt3N7wPkMyO9KZpcs4s3QZACGDBpJzJq7Q9rMrVhI2a9oNWZArF4UlOZUNjTpfsi12wVJiFywFip5Dnn0qDv9WTQqemysFkbh2S8HzurNHknE4hpipJX90HMC7UWNyTp8iLzkZgKR1f1Cmbj3ca9QgY59t1Dhxza/UnPCxkTEd7uSXSzn5pe11Dn13ENmnbN/fymQi6MF2bGrr/N8hxSkvL48xo0dyb+t7ueee8Cv2DatblzOTz5CSkoKPT5G/cm8KHh6eDH71dQC01vTv24vyt5U3OJVjbNm+g9DL1COBgQF4e3vhbjbjbjZTN+wOjkTHGFKQl7g55BcJB77WWlu11nHAWqBxEf3cgE+UUruB/wJ3/INz/E9rfU5rnQUstZ+zLbZieKt9FL4tUA24G/hDax2vtc4FLh6d/q+9GPcBfLXWa+3tC4CWl2u/YP8f7H/vBvZqrU9rrXOAo0Cwvb29UmqcUqqF1rrwRFA7rfVcrXUjrXUjI4pxgN/Xrufeli2v3vEG0uHBroyduoCxUxfQuFkrovbtwmrNIyc7m8NRe6kYXKVQf88yXmRmZHD6pO2j3t1/baVipRADkl+/Rvf24Jlhy3lm2HIsudmcvxn89LG9WPNycS/jR2riGSy52QBkZaRw4tAOAoJu7Lv0z3Pzt31sW7p8eQLbtuHsyp8xV/77zVdAm9ZkRscYE+465aVlkBUTS/muf79x8qp36Uf4RYlfvYGy7cJx9fXG1debsu3CiV+9AYCaHwzG1bsM+169cUaSc+PiKFMnzDaHHPBu2IismGhMnmUoHRxsa2vUhKxjMQamdLxSgbY30+aKt1GuUztOL7bNFw9o1YyMQ9HknIq70u43FK01Uz6eTHBwZR7tUvSqMadOnSr4GXf48CHyLBa8vb2dGdPp0tPTsVgsAKxa9TN1wuoW+cnBjej3tZefPtu8aRP27N2P1WolOzuHA1EHqVzJmJXQSuII+b/xChCHbSTdBdvUkWt18Z1YGttdagu01oXmFiilLh1CKux6bxnOsf+df8Hj889dtdYHlVJ3AQ8Ao5RSv2mtS9zktqzsbLb/tYvBA583OkqxqRgcwp0Nm/LmS71QSnFvh4cJrlIdgHHDX+OZl97CP6AsA156i8kfvo1SLniW8eLZQW8bnPz6Hdi+ishNyzGZXHEtZebRAZNRSpFw5gi/fjfWdgOU1jTt2I9yla6tsCvp7pg8AVdfX3ReHodHj8Walk7NEcPxCKmC1vnknDrNISessOII9RdOJKBVE0oF+tEmei2HRkxjZ68hhE0fTujbz6NcXTn13UrSIqOueixLUgqHxswkfNNiAA6NnoElKQVzxSBC336e9P1HCN/6PQDHZi7ixLzFxXpt1ytj314Sf1/DHfMWoK1WMg8eJP6HZeTGn6XGqA9Ba/LS0oj5cJTRUR2q/vyPcfP3RVvy2P/GKPJSbau7lO9yP6eX3lzTVfbt28uaNb8REhLCwIG29SN69+5D/FnbjboPPPggGzduYM1vv2JydaV0qVK8+dbQgps8b1QfjRvD7shIUlNT6P1Ud3r0fIq8PNvUuwce7MSJE8eZPHE8SikqV6nCoEE35qe5F7PVI38xeOBzBW0/rvwFgIceuI8qwcE0atiAZwYOxkUp7u/YnqohVS53uGJV4pY9VEoFADu01lWUUl2AZ7EVoP7ANmyj1BWBSVrrVvZ9JgOxWuuJ9jne87TWSikVAqzQWhe5gKhSqg8wBtuUlSxsc8X7YZu+shzblJWzSil/wAvIBTYDdwGpwBpgl9Z6oH0O+Qqt9WL7sXdhm8u+Xik1HPDRWr9yhfY/sE3D2aaUak3hKTl/AK8Dp4BE+9ScTsDTWusrvkkwctlDozhj2cOSxpnLHpYkzlj2sKS53mUPb0TOWvawpHHGsoclTXEve1gSOWPZw5LImcseliSXW/awxI2Qa63PKaU22pcr/BmIxHbzpAbe0FqfUUqdA6z24nY+thVZliilegG/8M9GqrcAS7DN/16ktd4GoJR6F1itlHIBLMCLWuvN9iJ6E7abOv+6wnF7A7Pt89uPAn2v0n4t6gLjlVL59kw37xC0EEIIIcQtosSNkAvHkhHyW4OMkN86ZIT81iEj5LcGGSG/tdww/zGQEEIIIYQQt5ISN2WlOCilOgLjLmqO1lo/akQeIYQQQgghzrslCnKt9SpgldE5hBBCCCGEuJhMWRFCCCGEEMJAUpALIYQQQghhICnIhRBCCCGEMJAU5EIIIYQQQhhICnIhhBBCCCEMJAW5EEIIIYQQBpKCXAghhBBCCANJQS6EEEIIIYSBpCAXQgghhBDCQLfE/9R5K3PNtxgdwely8t2MjuB0riZtdARDeJb1MDqC07n7ljY6gtOdjUgyOoIhfMPKGB3B6UxYjY5ggFvxmiHL5db7/r4SGSEXQgghhBDCQFKQCyGEEEIIYSApyIUQQgghhDCQFORCCCGEEEIYSApyIYQQQgghDCQFuRBCCCGEEAaSglwIIYQQQggDSUEuhBBCCCGEgaQgF0IIIYQQwkBSkAshhBBCCGEgKciFEEIIIYQwkBTkQgghhBBCGEgKciGEEEIIIQwkBbkQQgghhBAGcjU6gLjxjZsyk83btuPr48Pn0ycBkJqWxoiPJnPmbDzly5Vl2Juv4lWmjMFJHetUbAyfTRvBsSMH6Nrzee7v/BQA5+LP8MmU4aQmJ4KC1h0epcND3QxO6zgHdv7Gmu+noJQLLi4m7uv2NlVqNgTgr43fs+7H2QC0fOg56t/zqJFRHabcY08Q+OBDoDVZR48QM24MoRM+xuThAYCrrx+ZB/Zx5L2hBid1rKDHn6TsQw+j7dcdPWYUZerWI/iFl1AuCmtWFtGjR5JzMtboqFdU75MxlHugNblnz7GuwUPXfbyKT3UmdOjzABz6cBYnFy7Dxd1Mw2+m4FGtMtpqJe6n34l6Z+J1n8sZbuvWjaAunVFKcWbp95z+6mtCBg/Cr2VLtMVCdmwsh4YNx5qebnRUh7Farbw8aDABAQGM+GD4JdvXrVvPoi+/BKWoVrUqb735hvNDFrNeffri4e6Oi8kFk4uJaVOnGB3JoaZMnsDWLRH4+PoyY9Ynl+138GAUQ159mTfeeod7wls6MWFhUpAXA6VUCLBCax12lT7NtdZf2Z83AnpprV92QkSHuq9tax7tdB8fTp5e0PbV4mXcdWdduj/2KF8t/p6vFi/j2T49DUzpeGXKeNPj6dfYEbG2ULvJ5MqTfQcTUr02WVkZDH+tF3Xq303F4Gr/jOObAAAgAElEQVQGJXWsqrc35fn6bWy/vE9E8d9Zg3lpzM9kpifzx/IZDHh/MUop5nzQlVr12+Du6WN05OviFhhIuS6PsbdPD3RuLlWHjcC/TTsODnqhoE+1D0aTvHG9gSkdzy2wLEGPPc7unt3QuTlUHzEK/7btqdCrN4feeoPsYzGUfbQrFXr3JXrMSKPjXlHsgqXEzFxE/Xnj/tF+TX/9gl39h5J17GRBm5ufDzXfHciGpl3RWtMiYilxP64hPyeXo5PmcW5tBMrNjaar51O2Y0viV61z9OU4lEf16gR16UzkU73Jt1ioM2MaSevXk7w5gphp08FqpcrLL1GpX1+OTZ1mdFyHWbb8B4KDg8nMzLxk28mTJ/n2u++YOGE8Xl5eJCcnG5DQOcaN/RAfnxv7Z/TltG3XgQcfeoTJEz+6bB+r1cqCeZ/S4K6GTkxWNJmyYpwQoPv5J1rrbTdiMQ5wZ9gdeF80+v3nlq10bNMagI5tWrMxYovTcxU3b19/qoXWwWQq/L7W1z+QkOq1AXB396RCpRCSzsUbEbFYlDZ7opQCwJKTWfD4yJ4NVK/THI8yvrh7+lC9TnMO7745ilRlMuFSujS4mHApbSb3XELBNhcPD7wa3EXyhpJdeP0bBddtsl23JSEerTUmT08AXD09yU0o+d/biRu2YUlMKdTmUS2Yxis+JTxiCc1+/xLPWtf2hrlsh3Dif9uIJSmFvORU4n/bSLmOLcjPyubc2ggAtMVCys59mCsFOfxaHM29alXS9+whPzsbrFZStu8goE0bkjdvBqsVgLTdeygdVPKv5VrFJySwdetW7uvYscjtP/+yik6dOuHl5QWAr6+vM+MJBwmrW6/gNbycFT8up/k94fiUgNf4lhwht49O/wJsB+4C9gK9gGbABGxfl63A81rrHKVUDPAdcD+QBXTXWh9WSs3HNhK+2H7cdK11mSLOtRDwtDcN1Fr/CYwFbldK/QUsAHYCr2utOyml/IF5QDUgExigtY5USg0HKtvbKwMfa62nOvJr4yiJySkE+PsB4O/nS2JyylX2uDnFx53i2NEoqtesY3QUh9q//X/8umQSGWmJ9Bhkm6KSmhyHt/9tBX28/cqTmhxnVESHsSQkEPfd19T9din5OTmkbttK2ra/32D6hrckbcd28osYabuRWRLiOfPNl9y5ZJnturduIXXrFmLGjqHm+Enk5+Rgzchg37P9jY76r9SdNZLdLw4j8/AxfJvUI2zaMCI69L7qfuYKQWSfOFPwPDs2DnOFwsWqq48XQQ/eS/S0BQ7P7WiZRw5TZeALuPr4kJ+Tg1/4PaTv21eoT9AjD5OwerVBCR1vzpy59O/Xl8ysrCK3nzxp+0Tk1ddeJz8/n549utOoUSNnRnQKpRRvv/seSsED99/PA/ffb3QkpzqXkMCmPzcwZuwEDn4cZXScW7Mgt6sF9Ndab1RKzQNeBZ4F2mqtDyqlvgCeBz6290/RWtdVSvWyt3W6xvOcBdprrbOVUqHA10Aj4C3sBTiAUqr1Bft8AOzUWndWSrUBvgDq27fVBu4FvIAopdQsrbXl33wBnEUphUIZHcPpsrMymT7uTbr3fxV3j5tr/vztDdtze8P2xERtZc33U+k95HOjIxUbUxkvfJq3YE+3/5CXnkb14aPwb9eBxF9tBYp/m3YkrFxhcErHM3l54RveksjHu2BNS6P6yDEEdLgPv1atOTjkVTL27aV8tx5UfmkwMePGGB33HzF5euDXrAF3ffP3nFlTqVIAVOrdhZCXegHgWb0yjX+YS77FQlZ0LNv/M/Cqx1YmEw0WTSJ6xkKyokv23HqArOgYYucvoM7MGVizs8iIOgjW/ILtlfr3Q1utxK/82cCUjhMRsQVfXx9CQ0PZFRlZZB+r1cqpU6f4aNxYEhISeP2NN5k9cwZlbrL7oCaO/4jAwECSk5MZ+s67BFcKpm7dy860vel8Mncmffo9jYtLyZgscisX5Ce01hvtjxcB7wHRWuuD9rYFwIv8XZB/fcHfk//BedyA6Uqp+oAVqHkN+4QDXQG01muUUgFKKW/7tp+01jlAjlLqLBAEFPqpr5QaAAwAGPfBe/R84rF/ENcx/H19OJeYRIC/H+cSk/Dz9b76TjeAX1d+x9rVywB49f0p+PmXLbJfXl4e08e9SbNW99GoWRtnRiwWW377ku3r/gtAj8Fz8PazjQiG1GrMsvgTZKQl4e0bREzU3yPHqUlnCKnVxJC8juTVsBG5Z06Rl2KbR5q0fi2eYXVJ/HU1Jm8fPGvfwZH33jY4peN5N2pMzulT5Nnnzyat+4MydevhXqMGGfv2ApC45ldqTvj4SocpkZSLwpKcyoZGnS/ZFrtgKbELlgJFzyHPPhWHf6u/v6/NlYJIXPv3933d2SPJOBxDzNSSPzp+3tllyzm7bDkAlQe+SG7cWQDKPfQQfi1bsPfZ542M51B79+1j8+YItmzdhsWSS2ZmFuPGj+fNIUMK+gQGBlK7Vi1cXV0pX748lSpW5OSpU9SqeS2/vm8cgYGBgG1KTvNmzYg6GHVLFeSHDh1i/FjbYEJqagrbt27FxcVEs+b3GJLnVi7I9UXPk4GAa+x//nEe9nn4SikXoFQR+70CxAF32vtm/5uwF8i54LGVIl5DrfVcYC7AqajIi6/TKZo3acSqNX/Q/bFHWbXmD5o3aWxEDIdr98DjtHvg8Sv20Vozb/pIbqsUwn2P9HBSsuLVpG0PmrS1Xcu5uGNorVFKcerYXqx5uXiU8aV6WDi/LZ1MVoZtetKRvRtp2/VVI2M7RO7ZODzvCEOVLo3OycH7rkZkRB0AwK/VvaRs/hNtyTU4pePlxsVRpk4YLqVLk5+Tg3fDRmQcOIDfvW0oHRxMzokTeDdqQtaxGKOj/mN5aRlkxcRSvut9nFnyCwBe9WqRFnn1j63jV2+g1shXcbUPMpRtF07UO7bVpWp+MBhX7zJEDnin+MIXAzc/PyxJSZQqX56ANm2I7NUb3+bNqNinF7uffsY2v/wm0a9vH/r17QPArshIlixZWqgYB2jerCl/rF1Hhw7tSUlJIfbkSW4rX96AtMUnOzub/Px8PDw8yM7OZsfOHfTodvOsBnYtPvt8YcHjyZM+okmTpoYV43BrF+SVlVLNtNabsN1cuQ14VilVQ2t9GHgKuHD5jCewzft+Athkb4sBGmKbX/4wttHwi/kAsVrrfKVUb8Bkb0/DNu2kKOuBHsBI+1SWBK116vmb50qakeM/5q89e0lJTeM/fZ+lT7fH6db1UT74aBIr/7eGoHJlGfbGK0bHdLjkpAQ+eL03WZkZKKVY/eM3jJn2LSdiDvPnHyupVKUG7w223bf7WM8XubORcf/QHWn/9tXs+nM5LiZX3EqV5rHnJqOUwqOMLy0feoG5I/8DQKuHXsCjjPE3ylyvzP37SFr7O3fM/RxttZJ56CAJK2yjif5t2nLmq0UGJyweGfv2kvj7Gu6Yt8B23QcPEv/DMnLjz1Jj1IegNXlpacR8OMroqFdVf+FEAlo1oVSgH22i13JoxDR29hpC2PThhL79PMrVlVPfrbymgtySlMKhMTMJ37QYgEOjZ2BJSsFcMYjQt58nff8Rwrd+D8CxmYs4MW9xsV6bI9SaMB43Xx90Xh5Hx47Fmp5OtTffxKWUG3VmzQQgffdujoz+0OCkxeeLhQsJDQ2lWdOmNGzYkO07djLg2edwcXHh6f798Pa+OT7lPS8pKYkRo0YDtik697ZuddPNkx8/bjS7IyNJTU2hz1Pd6N6zF9a8PADuf/D6lz91NKW1IQOohrrgps5t2ArqfdgK8Cvd1Pkttps6c4Bu9ps6g4DlgLv9eC9qrctcuOyhfd74Emyj6hf2cQNWYRuVn8+139SZrrWeYL+OPUAnrXXM5a7VqBFyIx3LDzE6gtPFJF75TvKbVc13w42O4HT5FqvREZzubESS0REM4Rt2c81ZvhYVFn9rdAThJJYiJxXc/GpWr1zk6OqtXJBfcZ3wi/rHAI201glX61vSSEF+a5CC/NYhBfmtQwpycTOTgrywknFrqRBCCCGEELeoW3IOuX2KxzXfSqy1Dim2MEIIIYQQ4pYmI+RCCCGEEEIYSApyIYQQQgghDCQFuRBCCCGEEAaSglwIIYQQQggDSUEuhBBCCCGEgaQgF0IIIYQQwkBSkAshhBBCCGEgKciFEEIIIYQwkBTkQgghhBBCGEgKciGEEEIIIQwkBbkQQgghhBAGcjU6gCheuSaz0RGcrrSyGB3B6bQ2OoExshKzjI7gdBmxOUZHcDrfsDJGRzBE8p50oyM4XQWjAxgkX91646OeeSlGRyhRbr3vACGEEEKIEuJWLMbFpeS7QAghhBBCCANJQS6EEEIIIYSBpCAXQgghhBDCQFKQCyGEEEIIYSApyIUQQgghhDCQFORCCCGEEEIYSApyIYQQQgghDCQFuRBCCCGEEAaSglwIIYQQQggDSUEuhBBCCCGEgaQgF0IIIYQQwkBSkAshhBBCCGEgKciFEEIIIYQwkBTkQgghhBBCGMjV6ADixtarb3/c3d1xcXHBZDIxfcrkS/rsitzN7LmfkGfNw8fbmwnjxhqQ1LF+XPolG/9YDYDVmsfJ2GPMXbSSMl7ehfrNmTqGo4cOoNHcViGY5we/i9ndw4jIDnNg52/8vmwKSrng4mLivm5vUzm0IQCLJj9N7JFdVA69i+6D5hic1HEqdO9GUNdHQSnilnzPqS+/IuSVwfi3aoG25JEde4KD7w/HmpZudFSHqTygJ5V6PYZSitgvFnNszkLqfToBzxpVAXDz8cKSksam1l0NTupYt3XrRlCXziilOLP0e05/9TUhgwfh17Il2mIhOzaWQ8OGY00v2a91vU/GUO6B1uSePce6Bg9d9/EqPtWZ0KHPA3Dow1mcXLgMF3czDb+Zgke1ymirlbiffifqnYnXfS5nsFqtvDxoMAEBAYz4YHihbWfPnmXCpElkpGdgzc+nX98+NGnc2JigDpCbm8sbQ4ZgsViwWq2Eh4fT86mnCvVZunQpq375BZPJhI+PD4NfeYWgoCCDEjvOkh9+4qdVv6K15sGO7XjskU6Ftn+zdDm//bEesH1PHI89ydJFn+Ht5eX0rLdEQa6UCgFWaK3DnHzedK11mX/QfziQrrWecFF7CAbkv1YffTgaHx+fIrelp6czfeYsRo8YTrly5UhOTnZyuuLxUJcePNSlBwDbt2xg5fJvLinGAZ56ehAeHp4ALPx0CqtWLOaR//RyalZHq3Z7U2rVb4NSirgTUfx39mAGjv4ZgOYd+2PJzWL72m8NTuk4HjWqE9T1UXb16EW+xULYzOkkrltP8ubNxEydBlYrIYNfJrh/P2I+nmp0XIcoU7sGlXo9xub2T6JzLTT87xziV68l8unXC/rUGjGEvNSSXZT+Ux7VqxPUpTORT/Um32KhzoxpJK1fT/LmCGKmTQerlSovv0Slfn05NnWa0XGvKHbBUmJmLqL+vHH/aL+mv37Brv5DyTp2sqDNzc+Hmu8OZEPTrmitaRGxlLgf15Cfk8vRSfM4tzYC5eZG09XzKduxJfGr1jn6chxu2fIfCA4OJjMz85JtX3/zDS1btKDTgw9y7Phx3nt/GF/M/9yAlI7h5ubGh2PH4u7uTl5eHq+//jqNGjWi9u23F/SpXr06U6ZOxWw289OKFcybN4+hQ4camPr6RR87zk+rfmXmxLG4ubny5rBRNGvckIoVbivo82SXR3iyyyMA/LllG4uXrzCkGAeZsiKK2e9/rOWe5s0oV64cAL6+vgYncrw/1/6P5i3bF7ntfDGutSY3NxellDOjFYtSZs+C68jNySx0TdXuaEZps6dR0YqFe9WqpO3eQ352NlitpGzfTkDbNiRv2gxWKwBpkbspZf8evxl41qxGyvZI8rOy0VYriRu3Ua5Tu0J9gjp35PTSnwxKWDzcq1Ylfc+Fr/UOAtq0IXnzBa/17j2UvgFGDhM3bMOSmFKozaNaMI1XfEp4xBKa/f4lnrWqXdOxynYIJ/63jViSUshLTiX+t42U69iC/Kxszq2NAEBbLKTs3Ie5Usn/2sQnJLB161bu69ix6A5KFRTqGRkZBAT4OzGd4ymlcHd3ByAvLw9rXh5c9LvozjvvxGw2A1C7dm0SEhKcntPRjp2I5fZaoZjNpTGZTNwZdgfrN0Vctv+atRto0/IeJyYs7FYqyE1KqU+UUnuVUquVUu5KqfpKqc1KqUil1PdKKT8ApdQfSqlG9seBSqkY++M6SqktSqm/7PuE2tt7XtA+RyllOn9SpdRopdQu+3mC7G0hSqk19mP8ppSqfHFYpVRD+367gBcvaC8yg2EUvP3e+7z48mBW/vzLJZtjT50iPT2dIW8N5cWXB/O/39YYELL45GRns2vHZu5ufu9l+8z+eBTP9erEqdhjdOz0HyemKz77d/yP6e/cz1dTnuPhPqONjlOsMg8fweeuBrj6+OBiNuMXHk7p8oWLjqDOj5C08U+DEjpe+oHD+DVtiJufDy7uZsq2b4G5YvmC7X7NGpIbf47Mo8cNTOl4mUcO493gwtf6Hkpd/Fo/8jBJGzcalPD61J01kr2DR7Lh7q7sf3McYdOGXdN+5gpBZJ84U/A8OzYOc4XCXxdXHy+CHryXhDWbHJq5OMyZM5f+/fqiXIoeIOnZowdr1vxOz6d68f6wYbzw3HNOTuh4VquVgS++SPdu3WjQoAG1a9e+bN9Vq1fTqFEjJ6YrHlWrVGb33v2kpKaRnZ1DxLadnE04V2Tf7Owctu74i5bNmzo55d9uiSkrdqFAN631M0qp74CuwBvAS1rrtUqpEcAwYPAVjvEcMEVr/aVSqhS2Iv924AngHq21RSk1E+gBfAF4Apu11u8opT4CngFGAdOABVrrBUqpfsBUoPNF5/ocGKi1XqeUGn+lDBeHVEoNAAYAjB45gu5PPvEPvkz/zKSPPiIwMIDk5GTeevc9goMrUTfs75k1VquVQ4ePMG7MKHJychj8+hBur12LShUrFlsmZ9qxdQO1bq9X5HSV854b/C75Viufz5nEpg2/0rpdp8v2vVHcfld7br+rPceitvL7sqn0ev3G/Tj3arKio4n9fD5hs2dizcoiIyoKbc0v2F7p6f5oax7xP600MKVjZRw8SvTUz2i4+BOsmVmk7jkAF1xz+a4PcHrJzXO952VFxxA7fwF1Zs7Amp1FRtTBQtddqX8/tNVK/MqfDUz575g8PfBr1oC7vpnyd1upUgBU6t2FkJdsU+k8q1em8Q9zybdYyIqOZft/Bl712MpkosGiSUTPWEhWdGzxXICDRERswdfXh9DQUHZFRhbZ548/1tK+fTu6dunCvv37GT9hIrNnzcTF5cYdwzSZTEyfMYP09HRGjRxJTEwMISEhl/Rbs2YNhw4e5KOPPnJ+SAerElyJJ7t25o33R2I2l6Z6tZDLvoabtm6jzu21DJuuArdWQR6ttf7L/ng7UB3w1VqvtbctAP57lWNsAt5RSlUClmqtDyml2gINga32j+7dgbP2/rnAigvOeX5eQzOgi/3xQqDQd75Syteebd0Ffe6/XIaLQ2qt5wJzAWIOH9RXuabrEhgYANimotzTrBkHog4WKsjLBgTg7eWF2WzGbDZTt04YR49G35AF+eqflrBm1Q8AvDFsAv4BZflz3a+Xna5yIReTieYt2/Hjki9vyIJ8y5ov2bHO9s+jx6A5ePnZRseq1GpM0rwTZKYl4eHlZ2TEYhX3/XLivl8OQJWXBpITFwdAuYcfwr9lC/YMuPFH0C528sulnPxyKQCh7w4i+5TtmpXJRNCD7djU9nEj4xWbs8uWc3aZ7bWuPPBFcuNsP87LPfQQfi1bsPfZ542M968pF4UlOZUNjS4e+7HNN49dYHuti5pDnn0qDv9WTQqemysFkbh2S8HzurNHknE4hpipC4rxChxj7759bN4cwZat27BYcsnMzGLc+PG8OWRIQZ9Vq1czeuQIAO64/XZyLbmkpqbeFFMuy5QpQ7169di+bdslBfnOnTv59ptvGPfRR7jZ36zd6B7o0JYHOrQF4NMvvqRsQECR/das20jbluHOjHaJG/ft3j+Xc8FjK3Clf1l5/P21MZ9v1Fp/BTwMZAErlVJtAIVttLu+/U8trfVw+y4WrfX5gtiKA94AXSaDIbKzswvm2WVnZ7N9x05CqlQp1KdZ06bs3bcPq9VKdnY2Bw5GUTk42Ii4163Dg10ZO3UBY6cuwD+gLJkZ6ezfs5OGTVsU2V9rzZlTsQWPt0dsoEKlKkX2LematOnBc8OX8dzwZVhyszn/bX362F6sebm4l7nxf1FdiZu/7c1G6fLlCWh7L/E//4xv8+ZU6tObfYMG2+Yc32RKBdrmzZor3ka5Tu04vdg2XzygVTMyDkWTYy/QbzZufrbXulT58gS0aWN/rZtRsU8v9g9+5YZ9rfPSMsiKiaV81/sK2rzq1bqmfeNXb6Bsu3Bcfb1x9fWmbLtw4ldvAKDmB4Nx9S7DvlfHFEtuR+vXtw+LFn7BF/M/56033+TOevUKFeMA5cqWZedftvG748ePk5truezCBTeClORk0u2rAuXk5LBz504qXfR7+Mjhw0ybOpX3hw27Kd54nJeUbLuPIu5sPOv/jKBtq0t/X6dnZBC5Zx/Nmxq7ks6tNEJ+sRQgSSnVQmu9HngKOD9aHoNt1HsL8Nj5HZRS1YCjWuup9nnf9YDVwHKl1GSt9VmllD/gpbU+doVz/wk8iW3kuwew/sKNWutkpVSyUipca73B3udKGQyZmJ2UlMwHo23zh61WK/e2akXjRg1ZYf84t9MD91O5cjCNGjbkuRdfQrko7uvQgZCQG7MovdjWTWup16AJZrN7ofZxw1/jmZfewtcvgFkfjyQrMwOtNVWqhtLvhSGXOdqNY9/21URuWo6LyRU3t9I89tzkghs7Px/bg4TTR8nNyWTS6614uM8oaoQV/YblRlJ74gTcfHzQeXkcGTMOa1o61Ye+iUspN8JmzwIgbfdujoy6MYqSa1F//se4+fuiLXnsf2MUealpAJTvcj+nl95801XOqzVhPG6+ttf66NixWNPTqfam7bWuM2smAOm7d3Nk9IcGJ72y+gsnEtCqCaUC/WgTvZZDI6axs9cQwqYPJ/Tt51Gurpz6biVpkVFXPZYlKYVDY2YSvmkxAIdGz8CSlIK5YhChbz9P+v4jhG/9HoBjMxdxYt7iYr224vDFwoWEhobSrGlTnnnmaaZMmcr3y5ajFLz26is39A35iUlJTJwwgfz8fNsqOS1acPfdd7Pwiy8IrVmTpk2b8tlnn5Gdnc2HY2w/w8qWLcuw4cONDe4Awz8cT2paOiaTiUHPP02ZMp788PMqAB6+33ZT74ZNW2jUoB7uZvOVDlXs1N8DuDevi5cNVEq9DpQBlgGzAQ/gKNBXa52klKoNfIdtVPsnoKfWOkQp9Ra2wt0CnAG6a60TlVJPAEOxjapbgBe11pv/z959h0dRdXEc/55sgFQSSEJowVADCgjSISCgIM2GvWNDsADyKoqgoIio9CIICIKgYgERAQWl994EBFRaiHSSkEqyue8fu8QEQpPNTsr5PE8edmbuzv7ubsqZO3eGzLc9FJH7gQ7GmE4icgOOOeLBwAnn6x7KfNtDEakDTAYMjqK/nTGm+qUyXKrvOT1lJTc6nZ79Kan87I/jefsuAP9VuZfqWB3B7RKiUq7cKJ/xK2ftH0qrxPyev24reTVu/CN/3bnnaqRLQZqs8C+vtASrI1iiTJUa2R7dFYiCvCDTgrxg0IK84NCCvODQgrxg0IK8YLlUQV4wvwuUUkoppZTKJbQgV0oppZRSykJakCullFJKKWUhLciVUkoppZSykBbkSimllFJKWUgLcqWUUkoppSykBblSSimllFIW0oJcKaWUUkopC2lBrpRSSimllIW0IFdKKaWUUspCWpArpZRSSillIS3IlVJKKaWUspAYY6zOoHLQ7r+OFLgPONHubXUEtzud5Gt1BEs0TF1idQS3O1YswuoIbmfDbnUE5Sa7qra3OoIlbt450+oIbuc1fbjVESwR/N4kyW69jpArpZRSSlmkIBbj6mJakCullFJKKWUhLciVUkoppZSykBbkSimllFJKWUgLcqWUUkoppSykBblSSimllFIW0oJcKaWUUkopC2lBrpRSSimllIW0IFdKKaWUUspCWpArpZRSSillIS3IlVJKKaWUspAW5EoppZRSSllIC3KllFJKKaUspAW5UkoppZRSFtKCXCmllFJKKQt5Wh1A5T3GGD4bP4ZNG9ZRpIgX3Xr2omKlKhe1W7lsCd998yXp6Xbq1m/EU890BmDnjm1MmvAJB/b/zWtvvk3jyFvd3YXr9tOs6axeuhAAu93OkagDjJ8+Hz//gCztft+2ga8mj8EYQxEvb7r06EvJ0mFWRHaZo0f2M33s20Tt302Hh1/h9rs6ZWxbMn86qxfNxBhocltHWrR/wrqgLnL01Bn6jZvO6diziAj3tmzEI22aM+67eSzbtAMP8aBYUT/6d3mMkGIBV95hLnfixAmGDh1MzJkYRKBNm3bcfc89WdqsWbOG6dOmIh4e2DxsdH7hBW66qbpFiV3HbrfTrXsPgoKCeO/d/hdtX758BdO//BJEqFC+PG++0cv9IV3scn0+fvw4Q4YNIyE+AXt6Os883Yn69epZE/Qq1Zz4ASXaNefc8VMsr33nde+vzBP3ULl3VwD2DRrHkWmz8fD2os6MkfhUKIex2zk2bwl7+gy97tfKSd/P/on5C39DRCgfXo5e3V+mcOHCWdosXbGKqV9/iwAVy4fT5/VXLcnqauLljd/dnbCVKAMY4mdPIe3wXwB4N26Nb5uHOPVhd0xivKU5821BLiJLgdeMMRtFZD7wqDEmxkX77gIkGmO+cMX+8ppNG9fxz5EjjPtsGnv37ObTMSMYPKMBqNIAACAASURBVGJsljZxcbFMmTyeoaM+JSAgkJFDP2Tb1s3cXOsWgkuE0q3nG8ye+a1FPbh+d3Z8nDs7Pg7ApvUr+PnHby4qxgEmjx3M//p+TJmwcH6dN5PZ30yhy6tvuzuuS/n6FeWBp99k24bFWdZHH9rH6kUzef2Dr7B5FmLsB12pXudWQkqWsyipa3h6ePDqY/dQtXwYCUnJPNF3CA2qV+WJ9rfR9YH2AMz4ZRkTZ/3CW88+ZHHa62ezefDcc89TqVJlEhMT6d7tFWrfUpty5W7IaFOrVi0aNmyIiLB//998OOgDxk/4zMLUrjH7xzmEhYWRmJh40bYjR47wzbffMnTIYPz9/YmJccmfE8tdrs9fz5hBs6ZN6dC+PQcPHeLtd/rxxZTPLUh59aKmzuLA2OnUmvzRNT2v4W9fsO3Z3iQdPJKxrlCxAKr0fZmVDe/DGEPTdbM49tNi0lPO8fewyZxatg4pVIiGC6cQckczTixY7uruuMSJU6f44af5TB47giJFivDeh0NYvHwlbW5vmdEmKjqar7//gVEfD8Tfz48zMbEWJnYt37aPcG7f76R8Mw5sNqSQ40DEo2gxClW6CXvMKYsTOhSIKSvGmHauKsad+/u0oBbjAOvXrqb5ba0QESKq3khCQjynT2f9hj529B9KlS5DQEAgADVr3cKaVY5fVqGhJQkvXxHxyB/ffmuW/UrjZq2y3SYiJCUmAJCYGE9gULA7o+UI/4AgbqhUHZst6/H80SP7Ca9Uk8JFvLHZPKlUrS5b1/1mUUrXCS4WQNXyjrMavt5ehJcO5fiZGPx8vDLaJKWcQ0SsiuhSxYsHUalSZQB8fHwIKxfGqZNZf769vb0z+pucnAz5oO8nTp5kw4YNtLnjjmy3//zLAjp06IC/vz8AgYGB7oyXI67UZ0QyCvWEhASCgoq7Md1/c3rlRlJPZy0mfSqEUW/uZ0Sum0mjJV/iG1HhqvYV0jqSE4tWkXomlrSYOE4sWkWJO5qSnpTMqWXrADCpqcRu2YVX2VCX98WV7Ol2Us6dw263k5xyjuDiWT/LeQt+4652bfD38wOgWGDeP9sHIEW8KRRehZTNKxwr7HZMchIAvm0fJmHBd2CMhQn/latGyEUkHPgFWAs0BjYAnwPvAiWAx4CdwGigOlAI6G+M+VFEvJ1tbwb+ALwz7fcAUNcYc1JEZgNhgBcw0hgzwdkmHhgJdACSgLuNMccukbM/EG+MGeIciV8HtAACgWeNMStExAZ8BLQB0oGJxpjRInIbMATHe78B6GqMSXFm/BpoC6QBnYFBQCVgsDHmU+drvw48CBQBfjDG9LvW9/l6nT55kuCQEhnLQcEhnD55kuLFgzLWlSpVhuiowxw7dpTg4BDWrVlFWlqqu6PmuJTkZLZtXkunLv/Ldvvzr/Tm43d7UrhwEbx9fHl3SN4fRbyU0mGV+GnGaOLPxlC4cBF2bllBuYo3WR3LpaJPnGLPwSiqVwwH4JNv5zJ/xQZ8fbwY3+cVa8PlgGPHjvL3X38RUTXiom2rV69i6pTPiYmJof+771mQzrXGj5/As888TWJSUrbbjxxxjJz2/N9rpKen8/hjj1K3bl13RnS5K/X58cceo0+fvsyZ8xPJKckMGjjQzQldo8a4Aex4qR+Jfx4ksH5Nqo/ux7rWT13xeV6lQ0k+fDRjOTnqGF6lsxbengH+hLZvwf7RU12e21VCgoJ44N67eOSZLhQpXJi6tW+m7i21srSJOhINQLdeb5Gens6TjzxE/Tq1rYjrUh7FgklPOIvfvc/gWTKMtOgDxM//msIVbyQ9Lgb7sSirI2bIjUOUlYChQFXn16NAJPAa8BbQB1hsjKmPowgeLCK+QFcc00iqAf2AOpfY/zPGmDpAXaCbiJyvIn2BtcaYm4HlwPPXkNnTmaeH87XBUVCHA7WMMTWBL0XEC5gCPGSMqYGjKO+aaT+HjDG1gBXOdvcDDXEckCAirYHKQH2gFlBHRJpdGEZEOovIRhHZ+O2M6dfQDdfx8/fnhZd7MGTQe7z1endKhJbEw8NmSZactHnDSqpUq5ntdBWAn3+cQa9+wxgzZQ7Nbm/P9M9Gujmh+5QsW4FWdz/NJ++/wCcfdKVseAQe+eQsCEBicgq9Rkzmf090zBgdf+nBDswb/S5tG9fl24W583T1f5WUlMTAge/zfOcX8PHxvWh748ZNGD/hM95+ux/TpuXtE4br1q0nMDCAypUrX7KN3W4nOjqajz/6kDff6MWIUaOJj7d2zun1uJo+L126jFatbmf6tC947913GTxkKOnp6W5Mef1svj4Ua1SbW2aMJHLjbKqPfQ+vkiEAlH2qI5EbZxO5cTYBdapTb84EIjfOps53Y65q32KzUXv6MPZ/Mo2k/bmnsLvQ2fh4Vq/bwJefjeXbqRNJSk7m1yXLsrSx29M5Ev0Pwz54jz6vvcqwMeOIj0+wKLHriIcHnqVuIHnDEmLGvYs5dw6fFnfj3aw9iYtnWx0vi1w1Qu603xizA0BEdgKLjDFGRHbgKHDLAneJyGvO9l5AOaAZMArAGLNdRLZfYv/dRORe5+MwHAXuKeAcMNe5fhOQ/RyE7M3K9Lxw5+PbgU+NMWnOTKdF5GZn//Y620wFXgJGOJfnOP/dAfgZY84CZ0UkRUQCgdbOry3Odn7O/FkqAeeo/wSA3X8dccm5mPk/zWbhgnkAVK4cwckTxzO2nTp5guLBF0/FqN+gMfUbNAZgwc9z83xxtnDe9yxZ4PiIevUbSrGgENYsv/R0lbjYMxzc/yeVIhyjxI0ib+ej/nnzIpllv8xg9aKZAHTt/QmBxUtk265xy440btkRgDlfjSQwKHefxr1aaWl2eo2YTJsmdWlZ7+aLtrdtUodug8fzwv3tLEjnemlpaXwwcAAtmregSZPIy7atXqMGR4cfJTY2loCAvHmae+euXaxdu471GzaSmnqOxMQkPho8mDdefz2jTXBwMFUjIvD09KRkyZKULVOGI9HRRFS5+IL2vOBq+rxg4UIGDnCc/bixWjXOpZ4jLi4uT03XEQ8hNSaOlXXvuWhb1NRZRE11/PnObg55cvQxit9aP2PZq2wop5etz1iu8ekAEv48wIFRuXd0HGDz1u2UDC1BoPPns2njhuzavYdWLf69oUJIcBDVIirj6elJqZKhlC1dmqjof6hapZJVsV3CHneG9LgzpEXtByBl10Z8WtyNLTCYwBf7A4655IFd3iFmwvuY+DjLsubGgjwl0+P0TMvpOPLagfuMMXsyP+lq5m+KSHMchXIjY0yic7rJ+YmgqcZkTCSyc23vzfmM1/q8S+0nc7/PL3sCAgwyxoy/jtf4T9rdeQ/t7nT8Qtu4fi3zf5pN01tbsnfPbnx9fbNMVzkvJuYMgYHFiD97lp/n/cjrb77j7tgu1br9/bRuf3/GcmJCPLt/38KL/+ufbXtfP38SE+L558ghSpUpx46t6yldNtw9YV3s1jYPc2ubh6/Y7mzsKfwDgjh98h+2rV/E/wZac4bGlYwxvDfxa8qXCeXxdi0y1h86epxyJR0HJks3/U54qfxx8GGMYeSI4YSFlePejvdl2yY6OppSpUohIvz55z7SUlMpWrSom5O6zjNPd+KZpzsBsG37dmbOnJWlMAVo3KghS5ctp3XrVsTGxhJ15AilSpa0IK1rXE2fS4SEsGXrVlq3asWhQ4c4dy41zx10pZ1NIOlAFCXva8PRmb8A4F8zgrPb91zhmXBi4UoiBvTEM9DxvR1yeyR7+gwDoMq7PfAs6sf2zn1yLryLlAgJZvcfe0lOTqFIkcJs3raDiEoVs7Rp0rB+xoWesbFxREVHU6pk3v+dZuLjSI87jS0oFPupYxSuUI206IPETRmS0abYqx8RM36A3mXlP1gAvCIirzhHzmsbY7bgGCV+FFgsItWBmtk8NwA44yzGq+KYDpJTfgVeEJElxpg0ESkO7AHCRaSSMeZP4Alg2WX3ktUCYICIfGmMiReRMjgOJI5f6YmuVKdeAzZtWEeXZx933Pbw1X9v/dXj5ecZMWYiAJPGj2H/338D8NCjT1CmrOPCuH17/+DDAe8QHx/PxnVr+Hr6FEZ/mruv3M/OhjXLqFG7AV5e3lnWf9S/J51f6U2xoBCef+VNRgzqjYgHvn7+dO6e+395X0lczEk+fvNhkpMSEPFg6fzp9Bk2G28fPz4b2pOEs7HYPD158Nm38PHNu0Xaedv2/s38lRuoFFaKR3t/DMCLD7Xnx6VrOfjPcTxEKBVcnN7PPGhxUtfYtWsnixcvIjw8nJdffhGAp57qxInjJwBo1749q1atZPGi37B5elKkcGHeeLN3vrmoNbMvpk2jcuXKNGrYkDp16rBp8xY6v9AFDw8Pnnv2mTx9EHIpmfv8/PPPMXLkKH6Y/SMi8L+er+b6z7nWtKEE3VqfwsHFaLl/GfveG82WJ1+n+pj+VH6rK+LpSfS386+qIE89E8u+D8YSueZ7APYN/ITUM7F4lQml8ltdid/9F5EbfgDg4NjpHJ78fY727b+qFlGFZk0a0aXHa9hsNipVKE/7Nq34fPrXRFSuROMG9ah3Sy02btnK0y92x+bhQeennySgqL/V0V0ift5X+N3fGbHZsJ85SfwPk62OlC0xueTqUsi4qHOuMaa6c3mKc/n789uAejimeDTGMQd+vzGmwwUXde4GygAvOW97eADHnPGzwGwc00r24LgIs78xZqmIxBtj/Jyvez/QwRjT6RI5+5P1os7zt1cMBjYaY8JFxBP4GMdFnak4Luocc4WLOs9feNrJ+fhl5+tl3tYdeM4ZJR543Bjz16XeU1dNWclLEu3eV26Uz5xOuniOb0HQMHWJ1RHc7lixiy+wzO9s2K2OoNxkV9X2Vkdwu5t3zrQ6giW8pg+3OoIlgt+blO1Rba4qyJXraUFeMGhBXnBoQa7yMy3ICw4tyLPK21fZKaWUUkoplcflxTnkbiMifYAHLlj9nTEmb96MVSmllFJK5TpakF+Gs/DW4lsppZRSSuUYnbKilFJKKaWUhbQgV0oppZRSykJakCullFJKKWUhLciVUkoppZSykBbkSimllFJKWUgLcqWUUkoppSykBblSSimllFIW0oJcKaWUUkopC2lBrpRSSimllIXEGGN1BpWDkud8UuA+4EExna2O4Hb3Nz5tdQRLeEqa1RHczpOC2OdUqyNYIl0K3phZkbREqyNYYttN91kdwe2arh1ldQRLFK1zh2S3vuD9tCullFJK5RIFsRhXF9OCXCmllFJKKQtpQa6UUkoppZSFtCBXSimllFLKQlqQK6WUUkopZSEtyJVSSimllLKQFuRKKaWUUkpZSAtypZRSSimlLKQFuVJKKaWUUhbSglwppZRSSikLaUGulFJKKaWUhbQgV0oppZRSykJakCullFJKKWUhLciVUkoppZSykBbkSimllFJKWcjT6gAq70tJTePpcTNJTbOTlp5OqxqVePGOhqz78zDD5q4kNc3OjWVL0P+B2/G05Z9jwPBQ4ZEWNs7EO5Z3H0pn2fZ0ivpAx0gbvl4CwKa96az9I93CpK6xfs0KZkyfhId44GGz8XTnV6h2U82L2q1avoiZ30wjPT2dOvUa8cQzXS1I+98ZY/hs/Bg2bVhHkSJedOvZi4qVqlzUbvnSRXz/zVeICMWDgnj1tbcoGhDAqhVLmfHlVKIOH2Lw8LFUqhJhQS/+uxHDh7Jh/VoCAgMZO27iRdvjz55lxIihHP3nHwoVLkz3Hj0JDy9vQdKc92Snp/Hx9sbD5oHNw8boUSOtjuRS586do9frr5OamordbicyMpLHn3giS5tZs2ax4JdfsNlsBAQE0OPVVwkNDbUosWt8P/sn5i/8DRGhfHg5enV/mcKFC2dps3TFKqZ+/S0CVCwfTp/XX7Uk69WoOfEDSrRrzrnjp1he+87r3l+ZJ+6hcm/H7+19g8ZxZNpsPLy9qDNjJD4VymHsdo7NW8KePkOv+7Vy2tFTZ+g/bhqnY88Cwr0tG/NI2+aM/HI2Kzb/TiFPT8qGBvPOC4/i7+tjaVYtyNV1K+xp47MX7sWnSGFS7XY6ffI9jSNu4O0ZvzLhhXsJDynGJwvWMmfTbjrWv8nquC518Ljhq8X2LOvSDSzYaOef01DYE17o4Mlf/6RzItaikC5So1Yd6jWMREQ4sP8vhn3Yj1Hjp2dpczYulmmTx/HRyM8ICAhk9LCBbN+6iZq16liU+tpt2riOf44cYdxn09i7ZzefjhnB4BFjs7Sx2+1MGv8Joz/9nKIBAUyZNJ55P/3AI493otwN5Xmz77uMHT3coh5cn9tvb0WHO+9i2NCPs93+7bdfU6FCRfq+3Z/Dhw8xbuwYPhiUfdv84KMPBxEQEGB1jBxRqFAhBn34Id7e3qSlpfHaa69Rt25dqlarltGmYsWKjBw1Ci8vL+bNncvkyZPp3bu3hamvz4lTp/jhp/lMHjuCIkWK8N6HQ1i8fCVtbm+Z0SYqOpqvv/+BUR8PxN/PjzMxufuXd9TUWRwYO51akz+6puc1/O0Ltj3bm6SDRzLWFSoWQJW+L7Oy4X0YY2i6bhbHflpMeso5/h42mVPL1iGFCtFw4RRC7mjGiQXLXd0dl/L08KDHY/dStXwYCUnJPNlnMA1qRNCgRgQvPXwnnjYbo7/+kSlzfuWVR+62NGv+Ga7MhUSktIh8f43PeU9Ebnc+7iEi1h6yXQURwaeIY3QhzZ5OWno6Hh5CIZsH4SHFAGhUOYxFO/60MqbbxCfBP6cdj8+lwclYg7+PWBvKBby9fRBx9CMlOYnsenTsaDQlS5clICAQgJq16rJu1TI3prx+69eupvltrRARIqreSEJCPKdPn8rSxhiDMYbk5CSMMSQlJlI8KBiAsHI3UKZsOSuiu0T1GjXx9/e/5PZDhw5R8+ZaAISFleP4sWOcOXPGXfGUC4kI3t7eAKSlpWFPSwPJ+pN988034+XlBUDVqlU5efKk23O6mj3dTsq5c9jtdpJTzhFcvHiW7fMW/MZd7drg7+cHQLHA3H1AdnrlRlJPZz1o8KkQRr25nxG5biaNlnyJb0SFq9pXSOtITixaReqZWNJi4jixaBUl7mhKelIyp5atA8CkphK7ZRdeZXP/mZLgYgFULR8GgK+3F+FlQjlxJpaGNavhabMBUL1SOMdOxVgZE9AR8msiIp7GmLSrbW+MiQbuv5bXMMa8k2mxBzAdSLyWfVjBnp7OIyNmcOhULA81rkmNsFDs6Yadh49xU1gov+74k6Mx8VbHdLmwEKFrB0/OJhkWbLRfNAoe6AsliwtHThprArrYutXL+XLqBOJiztC7/8WjMSVLlSU66jDHj/1DUHAI69esIC3tqn9kcoXTJ08SHFIiYzkoOITTJ09SvHhQxjpPT0+6vNyD7i8+h5eXF6VKl6Hzi92siOt25ctXYM3qlVSvXoM9e/7g+PFjnDp5gmLFilkdzeVEhLf6vo0ItGvblnZt21odyeXsdjvdu3UjOjqaDh06ULVq1Uu2XbBwIXXr1nVjOtcLCQrigXvv4pFnulCkcGHq1r6ZurfUytIm6kg0AN16vUV6ejpPPvIQ9evUtiLuf1Zj3AB2vNSPxD8PEli/JtVH92Nd66eu+Dyv0qEkHz6asZwcdQyv0lkLb88Af0Lbt2D/6Kkuz52Tok+cYs+BI9xU8YYs6+csXUurRrdYlOpflhfkItITeMa5+JkxZoSIPAm8BhhguzHmCREJAT4Fzg899TDGrBKR+sBIwAtIAp42xuwRkU7AXYAPUBH4wRjT6zI54oGJQGvgKPCwMeaEiCwFtgKRwNcishUYguO92wB0BWoCk4D6gA1YDzwExANzjTHVnXnuAXyBys59FAaeAFKAdsaY0yIyBZgLlHZ+LRGRk8A0oKYxpocz7/PAjcaYiya2iUhnoDPAmBcf5tk7Ii/zCbiGzcODb3s+SlxSCq9Oncufx07z0WNtGPzTCs6l2WlcpRw2yfujxJn9c9owfGYa59KgchnhkRaejJr9b/FZ2BMeau7JLxvspKRaGNSFGjRuRoPGzdj1+1ZmTJtEvw+yTsvw8/en80s9GfZhfzw8PIiodhNH/4m2KG3OSUtL4+d5cxg2ZjwlS5Zm4rhRzPz2Kx585IkrPzmPe+DBh5jw6TheebkL4TeUp2LFSnh42KyOlSOGDv6Y4OBgYmJi6N2nL2Flw6hRo7rVsVzKZrMx5pNPiI+P5/0BAzhw4ADh4eEXtVu8eDH79u7l44/z9vSks/HxrF63gS8/G4ufry/vfjiEX5cso1WLWzPa2O3pHIn+h2EfvMeJk6d4tffbfDZ6OH5+vhYmv3o2Xx+KNarNLTP+vebB5pwjX/apjoS/8iQAvhXLUW/OBNJTU0naH8WmB16+4r7FZqP29GHs/2QaSfujcqYDOSAxOYU3hk+i5xMd8fPxzlg/efYCPG022jax/kDT0oJcROoATwMNAAHWicgGoC/Q2BhzUkTOn0saCQw3xqwUkXLAAqAa8AfQ1BiT5pzq8QFwn/M5tYDaOArePSIy2hhz+BJxfIGNxphXReQdoB9w/ruzsDGmroh4AfuA24wxe0XkC6Cr8yBiDvA+4A1MN8b8LiLhF7xGdWceL+BP4A1jTG0RGQ48CYw439AYM8p5sNLC+T74AX1E5HVjTKrzfXshu44YYyYAEwCS53zi1qHZot5FqFexLKv/OMhTzW9hyouOEwSr9xzk4Im8f1q7foQHt1R2zPT6clEaZ5Mc6/cdMbRvAD5FIDEFPAQeam5j+9/p7D6Ud0fHf547i0W/zAXgrXc/zpiWcWP1Whw7Ooi42BiKOqennFe3QRPqNmgCwK8/z8kTxdr8n2azcME8ACpXjuDkieMZ206dPEHx4OAs7ff/7Zh+VapUGQCaNG3OzO++dlNaa/n4+NKj52uAY+rOs08/SclSJS1OlTOCnZ97YGAgjRs1Ys/ePfmuID/Pz8+PmjVrsmnjxosK8i1btvDNjBl89PHHFLrg4se8ZvPW7ZQMLUGg87qApo0bsmv3niwFeUhwENUiKuPp6UmpkqGULV2aqOh/qFqlklWxr4l4CKkxcayse89F26KmziJq6iwg+znkydHHKH5r/Yxlr7KhnF62PmO5xqcDSPjzAAdG5Z3R8bQ0O28Mn0SbJnVpWf/mjPU/LVvHys07Gdvn5YzpmFayeg55JI6R6wRjTDwwC6gLfGeMOQlgjHHOxuV2YIxzhHoOUNRZpAYA34nI78BwIPNVg4uMMbHGmGRgF5D1PEVW6cA3zsfTndnOO78+AthvjNnrXJ4KNHM+fg9o5cx/qSGEJcaYs8aYE0As8JNz/Q4g/DLZcL4/i4EOIlIVKGSM2XG557jL6fhE4pJSAEhOTWPtvsOElyjGqXjHTJtzaWl8vnQT9zeqYWVMl1i/J51P56bx6dw0TKY6u0yQIOIoxgHubmzjRIxhze68fXeVth06MmTMZIaMmUxKSjLG2em//9xDWloq/kUvnlsZG+M48Io/e5YF82Zz2x0d3Jr5v2h35z2MGDOREWMm0qBRJEsX/Yoxhj1/7MLX1zfLdBWAoKBgog4dJDbWMe9w65ZNlA273K+X/CM+Pp7UVMcpnwULfuam6jXw8ckbI4fXIjk5mcTExIzHm7dsJvyG/PUZx8bEEB/vmEqYkpLCli1bKBsWlqXNX3/+yehRo3inXz8CAwOz202eUiIkmN1/7CU5OQVjDJu37aBcWNksbZo0rM/WHTsBiI2NIyo6mlIlc/986fPSziaQdCCKkve1yVjnX/Pq7vZ0YuFKQm6PxDOwKJ6BRQm5PZITC1cCUOXdHngW9WNXzw9yJHdOMMYwYMJXhJcJ5bH2/164u3rbLqbN/Y2hrz2PV5HccZBp+ZSVa+ABNHQW1xlEZAyOQvde54j00kybUzI9tnNt/c08rJlwFe2DAD+gEI4R8OyekzlPeqbl9KvM9hnwFo6zAp9fRXu3OBmXSN9vFpKebkg3htY3V+bWG8szbO5Klu/eT7oxPNioBg0qhV15Z3nIjTd4UC/Cg/R0SLUbvl/uuNtKuRJCrYoeHD1j6NLBccy7aIudfUfy7kg5wNpVy1i2eAGeNk8KFynCq2/0zxhVeO3lZxgyZjIAk8eP4uB+xwjy/Y90onSZvPW516nXgE0b1tHl2ccdtz189d+Zbj1efp4RYyZSPCiYhx59kj69emCzeRJSogTder4BwNrVK5g4bjSxsbEM6P8W5StUpP/7eec0/8cffcCO7duJi4vlqSce5bHHnyAtzfG93a59Bw4fPsTwoYMREcrdcAPdu/e0OHHOOHPmDO+9PxBwzLNu0fzWPD9/+kKnz5xh6JAhpKenO+6o0bQpDRo0YNoXX1C5ShUaNmzIpEmTSE5OZtAHjiIsJCSEfv37Wxv8OlSLqEKzJo3o0uM1bDYblSqUp32bVnw+/WsiKleicYN61LulFhu3bOXpF7tj8/Cg89NPElD00hc6W63WtKEE3VqfwsHFaLl/GfveG82WJ1+n+pj+VH6rK+LpSfS38zm7fc8V95V6JpZ9H4wlco3jfhT7Bn5C6plYvMqEUvmtrsTv/ovIDT8AcHDsdA5Pvqb7Vrjdtj1/M3/lBiqFlebR3o7rnl56sANDvpjJudQ0XhrkuINWjUrh9H72ISujIsZYVySIyC3AFKAhzikrOKZhfA40MsacEpHizrnVXwFbjDGDnc+tZYzZKiI/4JgiMlNE+gOdjDHhzjnbdY0xLzvbzwWGGGOWXiKLAR4xxswQkb5AqDHmFecc8teMMRudU1b2Ai2NMX8653tvMcaMdE5ZmQGUB0oZY152HiBknkOeOc8B5/LJzNvOzyE3xnwvIjuAu4wx+zPl3AyE4JhPfsU5IO6espIbDIrpbHUEt7u/8ekrN8qHPCVvXTDqCp4UxD7nk4swrlG6WH0S2/2KpOX6exi43Lab7rtyUrCo5AAAIABJREFUo3yo6dpRVkewRNE6d2Q7P8bSn3ZjzGYcBfl6HMX4Z8aYVcBAYJmIbAOGOZt3A+qKyHYR2QV0ca7/GBgkIlu4vhH/BKC+c+pLSxxTUC7Mm4xj7vZ3zmI5HfjUeRFqqjHmK+BDoJ6ItLzw+f/BBOAXEVmSad23wKqrKcaVUkoppVTuZ+kIeW4iIvHGGD+rc1yJc6R/uDFm0dW01xHygkFHyAsOHSEvOHSEvGDQEfKCJVeOkKurJyKBIrIXSLraYlwppZRSSuV+eemiTpcQkXVAkQtWP5HbR8eNMTFAFatzKKWUUkop1ypwBbkxpoHVGZRSSimllDpPp6wopZRSSillIS3IlVJKKaWUspAW5EoppZRSSllIC3KllFJKKaUspAW5UkoppZRSFtKCXCmllFJKKQtpQa6UUkoppZSFtCBXSimllFLKQgXuPwYqaPZWe8DqCO63Bm6rc87qFG5XlBirI7hdIn4EJ0dZHcOtYrxK4pWeYHUMt0rzKEwqha2O4XY20vBKK1iftUHwmj7c6hhu13TtKKsjWGJFw25WR3Cry33OYoxxYxTlbtv3HS9wH3DMOV+rI7jdDUUKVlF6nk9KwTsISS7sb3UEt0vy8LM6giV87bFWR3C7ItNHWB3B7Qrf/aDVESxR0Irx89qn7pHs1uuUFaWUUkoppSykBblSSimllFIW0oJcKaWUUkopC2lBrpRSSimllIW0IFdKKaWUUspCWpArpZRSSillIS3IlVJKKaWUspAW5EoppZRSSllIC3KllFJKKaUspAW5UkoppZRSFtKCXCmllFJKKQtpQa6UUkoppZSFtCBXSimllFLKQlqQK6WUUkopZSFPqwOo/OHPvbvp81pXevTqR6PIFlm2JSUm8vYbL2Usnz51gqbNW/N0527ujulS/0TtZ8qY/hz6+w/uefQl7rjnyYxtb77QHi9vX8TDA5vNRt/BX1qY1DXi4+MZNmoMBw4eRBD+16MbN1armrE9ISGBD4cM48SJE9jtdu7veC93tLrdwsSu88GYiazeuJViAUWZNnIQAJNmzOKn35YRWNQfgBcee4BGdW62MqZLHI46wvsfDclY/ufoMZ56/BHuu/vOLO22bv+dcRMnkWa3E1DUn2EfDnR3VJcaOXwIG9avIyAwkE/GTbxku7179/B6z270erMPTSKbuTFhzpg5Zx7zFvyGMYb2d9zO/Xd3yLJ9xqwfWbR0BQB2u51DUUeYNX0SRf39rYjrUuLljd/dnbCVKAMY4mdPIe3wXwB4N26Nb5uHOPVhd0xivLVBXeToqTP0HzeN07FnAeHelo15pG1zRn45mxWbf6eQpydlQ4N554VH8ff1sTruZdWc+AEl2jXn3PFTLK9955WfcAVlnriHyr27ArBv0DiOTJuNh7cXdWaMxKdCOYzdzrF5S9jTZ+h1v9alaEGurpvdbmf6lE+5uXa9bLd7+/gwZPTnGcu9uj9Lg8Z5/w+Zr18ADz/bi63rl2S7/X/vjce/aDE3p8o5YydMpF6dW3jnrTdJTU0lJSUly/Y5c+dxQ1gYA/q9TUxsLM927krL5rdSqFAhixK7TrsWTbmvbSveHzU+y/oHO9zBo/e0syhVzggrW4bxo4cDjp/th596jshGDbK0iY9PYNS48Qx69x1CS4RwJibGiqguddvtrWl/590MH/rxJdvY7XamTv6M2rfUcWOynLP/4CHmLfiNsUM/pFAhT97o9z6N6tWhTOlSGW0e7ng3D3e8G4DV6zfy/Y9z80UxDuDb9hHO7fudlG/Ggc2GFCoMgEfRYhSqdBP2mFMWJ3QtTw8Pejx2L1XLh5GQlMyTfQbToEYEDWpE8NLDd+JpszH66x+ZMudXXnnkbqvjXlbU1FkcGDudWpM/uqbnNfztC7Y925ukg0cy1hUqFkCVvi+zsuF9GGNoum4Wx35aTHrKOf4eNplTy9YhhQrRcOEUQu5oxokFy13dHcCCKSsislRE6jofzxeRQBfue4qI3O+q/bmTiHQSkTFW5/gvfpk7k4aNb6Vo4JU/yugjh4iLjaHaTXl/JLFoYHHKV74Jmy3/H9cmJCSw4/edtGndCoBChQrh5+eXtZEIiUlJGGNISkrC398Pm81mQVrXq3VTVYr6+1odw+22bNtB6VIlCS1RIsv6RcuWE9m4IaElQgAodhU/+7ld9Ro18b9CoTn3px9p3CSSgHzQX4CDh6OoFlEZL68i2Gw2bq5+IyvWrLtk+8XLVtKyWRM3Jsw5UsSbQuFVSNnsGP3HbsckJwHg2/ZhEhZ8B8ZYmND1gosFULV8GAC+3l6ElwnlxJlYGtashqfzd3X1SuEcO5X7D7BPr9xI6unYLOt8KoRRb+5nRK6bSaMlX+IbUeGq9hXSOpITi1aReiaWtJg4TixaRYk7mpKelMypZY6fB5OaSuyWXXiVDXV5X86zdA65MaadMSb3f/Lqkk6dPMG6Nctp3e6eq2q/avkiGjdtiYjkcDKLiTDi3ZcY8NqjLF840+o01+3o0WMEBgQwZPhIur7SnWEjR5OUnJylzd0d2nP4cBSPPNGJF17qRtfOz+Phkb8vU5n182889WofPhgzkbj4BKvjuNyS5Sto0azpReuPHIkmPj6enm/2pWv3/7FwUfZnifKTUydPsmb1Stq2v/7T47lF+RvKsWPnbmLjzpKcnMK6jVs4fjL7UeHk5BQ2bN5Ks8YN3ZwyZ3gUCyY94Sx+9z5DYNd++N39FBQqTOGqtUiPi8F+LMrqiDkq+sQp9hw4wk0Vb8iyfs7StTSudaNFqa5PjXED2NljACsb3MfuNz6i+uh+V/U8r9KhJB8+mrGcHHUMr9JZC2/PAH9C27fg5OI1Ls2c2VX9tRSRcBH5wzkCvVdEvhSR20VklYjsE5H6IuIrIpNFZL2IbBGRu53P9RaRGSKyW0R+ALwz7feAiAQ7H88WkU0islNEOmdqEy8iA0Vkm4isFZErHZ40E5HVIvL3+dFycRgsIr+LyA4Reci5vrmIzM30WmNEpJPz8YcisktEtovIEOe6EBGZKSIbnF/ZDhWIiIezb4GZ1u0TkVARuVNE1jnfo9+y68+FI/0iEp/p8evO194uIu9e4vU7i8hGEdn4/YwvrvB2XZ8pE0fxeKeuV114rVq+iCa35o95xZfzxsDJvD30K7r3HcOSn79l785NVke6LvZ0O/v+/IsO7doybvRIvLy8+Oa777O02bh5CxUqlOfraVMYN3oEYz4dT0JiokWJc969bW7jm7FD+HzoAIKKBTJmyldWR3Kp1NRU1qzfwK2RjS/aZrfb2fvn3wzs35cP3+vHlzO+I+rIkWz2kn9MnDCWTs88l68OMm8IK8vD991Dr3cG8Eb/96lYIfyS/VuzYSM3VYvIN9NVxMMDz1I3kLxhCTHj3sWcO4dPi7vxbtaexMWzrY6XoxKTU3hj+CR6PtERP5+MkozJsxfgabPRtkldC9P9NzZfH4o1qs0tM0YSuXE21ce+h1dJxxm8sk91JHLjbCI3ziagTnXqzZlA5MbZ1Pnu6iYliM1G7enD2P/JNJL259yB2rWca68EPAA8A2wAHgUigbuAt4BdwGJjzDPOQnS9iPwGvAAkGmOqiUhNYPMl9v+MMea0iHgDG0RkpjHmFOALrDXG9BGRj4Hngfcvk7OUM1dVYA7wPdARqAXcDAQ793/JSUAiEgTcC1Q1xphMhfVIYLgxZqWIlAMWANUufL4xJl1EfnTu43MRaQAcNMYcE5GVQEPnfp8DegH/u0x/MudqDVQG6gMCzBGRZsaYLH0xxkwAJgBs33fc5efcfpk7i98W/ARAYmICIz7uD0BcXCxbNq7FZrNRv9HFc8QP/P0n6XY7FStFuDqS2yz5+RuW//oDAN37jiaweEi27YoFOU7xFw0sTu0GLdi/bydVbsq7806Dg4IJCQ6mWlXHZ9e0SWO++S7ryP/CXxfx0AP3ISKUKV2akqGhHD4cRdWIKlZEznHFAwMyHt/Vqjm9Bg6zMI3rrd+0mcoVK1Cs2MXTM4KDgyha1B9vLy+8vbyoUf1G/tp/gLJlyliQ1D327dvH4A8/ABy/6zZt2ICHh41GjfP2FI52rW+jXevbAPjsiy8JCQrKtt3i5au4rVmkO6PlKHvcGdLjzpAWtR+AlF0b8WlxN7bAYAJf7A845pIHdnmHmAnvY+LjLEzrOmlpdt4YPok2TerSsv6/U0d/WraOlZt3MrbPy3nyDLZ4CKkxcayse/HZ+qips4iaOgvIfg55cvQxit9aP2PZq2wop5etz1iu8ekAEv48wIFRU3OwB9dWkO83xuwAEJGdwCJnUbkDCAfKAneJyGvO9l5AOaAZMArAGLNdRLZfYv/dRORe5+MwHIXnKeAccH4UexPQ6go5Zxtj0oFdmUafI4GvjTF24JiILAPqAZf6CYsFkoFJzhH0869/O3Bjpm/WoiLiZ4zJ7hLsb4B3gM+Bh53L4HifvhGRUkBhYP8V+pNZa+fXFueyH473KWeuMLiENh060qZDx4vWjxk+kDr1GmdbjAOsXP5bnh8db9H2IVq0feiybVKSkzAmHS9vX1KSk9i1bS0dHnjeTQlzRvHixQgJCeZwVBRhZcuyZds2ypULy9KmRIlgtmzbRo3qN3HmzBmijhyhVMmSFiXOeSdPxxBc3FGsLl+3iQrlylqcyLWWLFuZ7XQVgMYN6zNm3ETsdjupqWn8sWfvRXdhyW8mfT4t4/HwYR9Tv37DPF+MA5yJiaVYYADHjp9gxep1fDJk0EVt4hMS2P77Lt76X96+M1ZmJj6O9LjT2IJCsZ86RuEK1UiLPkjclH/vMFTs1Y+IGT8g39xlxRjDgAlfEV4mlMfat8xYv3rbLqbN/Y3xb3fDq0hhCxP+d2lnE0g6EEXJ+9pwdOYvAPjXjODs9j1XfO6JhSuJGNATz8CiAITcHsmePo4Blirv9sCzqB/bO/fJufBO11KQZ76lQnqm5XTnfuzAfcaYLL2/miMtEWmOo9htZIxJFJGlOAp6gFRjMq6ssF9F5sw5r/TiaWSdtuMFYIxJE5H6wG3A/cDLQEtn24bGmOQLd5SNNUAlEQkB7uHfUf3RwDBjzBxnv/tfLpeIeOAo3M/3Z5AxZnw2z8l1Xnvl6Sx3V1mzYjFv9R9sYSLXij1zkvdff5zkpAREhN/mfsV7o74nPi6GsR85TnrY0+00aNqG6rfk/T/cL73QmQ8HDyMtLZWSJUvyWo/uzJ3/MwAd2rXlsYcfYvDwkXR+8RUMhmc7PUVAQFGLU7tGv2Fj2fr7bmLOxnPvc9159uGObNm5m337DyEilAwJ5vUuT1sd02WSkpPZtHUrPV7ukrHup/mOP3J3tmvDDWFh1K1Tm+df7oGHCG3vaEX58Bsutbs8YfBHA9mxfTtxcbF0euIRHn38SexpaQD5at74hfoPGkzc2XhsNhvduz6Hn58vc35eAMBdbe8AYOWa9dStXRNvL6/L7SrPiZ/3FX73d0ZsNuxnThL/w2SrI+WobXv+Zv7KDVQKK82jvR13J3npwQ4M+WIm51LTeGnQWABqVAqn97OXH3iyWq1pQwm6tT6Fg4vRcv8y9r03mi1Pvk71Mf2p/FZXxNOT6G/nX1VBnnomln0fjCVyjWMa5r6Bn5B6JhavMqFUfqsr8bv/InKD48z4wbHTOTz5+8vt7j8TcxVXEYtIODDXGFPduTzFufz9+W04pocUBV5xjpzXNsZsEZGewI3GmOdEpDqwFUdRu1FEDgB1gSbAc8aYO0WkqrNNG2PMUhGJN8b4OV/3fqCDMabTJXJm5HIuxxtj/ESkI46pM+2A4sBGoAFQCFgBROCY274FeBfHNBcfY8xxEQkA/jbGBInIV8AWY8xg5/5rGWO2XuZ9GwyUBIKMMe2c67Y4+7pJRD4Hyhtjmjvnrtc1xrwsIn0Bf2PMGyJyD/CD4y2V1sAA4DZjTLyIlMFxwHL8UhlyYspKbhdzruDdDeOGIvn7AqRL8UkpeNeEJxfOH3N4r0WSh9+VG+VDvvbYKzfKZ4pMH2F1BLcrfPeDVkewxIqG+eeMy7Von7on28FiV96vbQAwAtjuHNXdD3QAxuGYR70b2I1j2smFfgG6ONvsAda6MBfAD0AjYBtggF7GmKMAIvIt8Lsz7/mpIP7AjyLihWNUuqdzfTfgE+e0G08cU0X+HUK62Dc45tt3yrSuP/CdiJwBFgPls3neROfrb8Px3iQAGGMWikg1YI3zzEM88DhwyYJcKaWUUkrlblc1Qq7yLh0hLxh0hLzg0BHygkNHyAsGHSEvWC41Qp5/7t+klFJKKaVUHpQn/4tBEemD4xaMmX1njBloQZange4XrF5ljHnJ3VmUUkoppVTekycLcmfh7fbiOzvGmM9x3NpQKaWUUkqpa6ZTVpRSSimllLKQFuRKKaWUUkpZSAtypZRSSimlLKQFuVJKKaWUUhbSglwppZRSSikLaUGulFJKKaWUhbQgV0oppZRSykJakCullFJKKWUhLciVUkoppZSykBhjrM6g8ikR6WyMmWB1DnfSPhccBbHfBbHPUDD7rX0uOApiv3Njn3WEXOWkzlYHsID2ueAoiP0uiH2Ggtlv7XPBURD7nev6rAW5UkoppZRSFtKCXCmllFJKKQtpQa5yUq6an+Um2ueCoyD2uyD2GQpmv7XPBUdB7Heu67Ne1KmUUkoppZSFdIRcKaWUUkopC2lBrpRSSimllIW0IFdKKaWUUspCWpArpf4zEfEWkQircyillFJ5mRbkyqVExEdE3haRic7lyiLSwepcOUlEqojIIhH53blcU0T6Wp0rp4nIncBW4Bfnci0RmWNtqpwjIh+LSFERKeT8vE+IyONW58ppIvKAiPg7H/cVkVkicovVuXKaiNhEpLSIlDv/ZXWmnCYiFUWkiPNxcxHpJiKBVudyBxGJFJGnnY9DRKS81ZlykogMFZGbrM7hbrn5c9aCXLna50AK0Mi5fAR437o4bjER6A2kAhhjtgMPW5rIPfoD9YEYAGPMViDX/HLLAa2NMXFAB+AAUAl43dJE7vG2MeasiEQCtwOTgHEWZ8pRIvIKcAz4FZjn/JpraSj3mAnYRaQSjtvChQFfWRsp54lIP+ANHL/HAQoB061L5Ba7gQkisk5EuohIgNWBclpu/5y1IFeuVtEY8zH/FqeJgFgbKcf5GGPWX7AuzZIk7pVqjIm9YF1+vo+qp/Pf9sB32fQ9v7I7/20PTDDGzAMKW5jHHboDEcaYm4wxNZxfNa0O5Qbpxpg04F5gtDHmdaCUxZnc4V7gLiABwBgTDfhbmiiHGWM+M8Y0AZ4EwoHtIvKViLSwNlmOytWfsxbkytXOiYg3zsJMRCriGDHPz046+3m+z/cD/1gbyS12isijgM05NWk0sNrqUDloroj8AdQBFolICJBscSZ3OCIi44GHgPnOKQ35/W/HYaCgHHBllioijwBP8e8ZgUIW5nGXc8bxn7Kc/x3ua3EetxARG1DV+XUS2Ab0FJEZlgbLObn6c9b/GEi5lIi0AvoCNwILgSZAJ2PMUitz5SQRqYDj9G5j4AywH3jcGHPAylw5TUR8gD5AaxxnQRYAA4wx+bZIFZHiQKwxxu7sf1FjzFGrc+UkZz/bADuMMftEpBRQwxiz0OJoOUZEJgEROKaqZAwoGGOGWRbKDUTkRqALsMYY87Vzfu2DxpiPLI6Wo0TkNaAy0AoYBDwDfGWMGW1psBwkIsNxTL9bDEzKfJZXRPYYY/Ldxfq5/XPWgly5nIgEAQ1xFGlrjTEnLY7kFs6jbQ9jzFmrsyjXc44mtcdxevf89JV8X6QBiEgxHPOJM/d7s3WJcpZzrulFjDHvujuLcg/nYFLG4IIx5leLI+Uo54WN3xpjErLZFpBfp+Tl5s9ZC3LlEle660J+/OMtIj0vtz2/F2oi8hMXzxmPBTYC4/PbSLmIzMcxRWUHkH5+fX4v0kRkANAJ+It/P29jjGlpWSg3ERE/AGNMvNVZcpKI7OAy138UkPnziEhRsh50nrYwTo4SkUXGmNuutE65j+eVmyh1VYY6//UC6uKYiyZATRwFWqNLPC8vO38xSARQDzh/y787gQsv8syP/gZCgK+dyw8BZ4EqOO4884RFuXJK2YJSmFzgQRwXa5+zOoi7iEh1YBpQ3Ll8EnjSGLPT0mA55/ytaV9y/jvN+e/j5O8LtQEQkReAd3EccKfj+NtlgApW5soJIuIF+ADBzjNf52+6UBQoY1kwNxCRs/z7/VwYx/URCcaYotal+peOkCuXEpFZQD9jzA7ncnWgvzHmfmuT5RwRWQ60Pz9VxXnP5nnGmGbWJstZIrLBGFMvu3UistMYk6/ucSsiHwGL8vPc6eyIyEygqzHmuNVZ3EVEVgN9jDFLnMvNgQ+MMY0tDZbDRGSLMab2Bes2G2Py9X3nRWQf0KggTK8Uke5AD6A0EJ1pUxww0RgzxpJgbiYiAtwNNDTGvGl1HtARcuV6EeeLcQBjzO8iUs3KQG4QCmQePTznXJff+YlIOWPMIQDnf5zi59yWH0dT1wI/iIgHjtt6Co6pG7lidCUHDQK2OP/jq8wXON5lXaQc53u+GAcwxizNbXdkyCEiIk2MMaucC43J/3fUAcd0rESrQ7iDMWYkMFJEXsktFzNawXm3ldnO60W0IFf50nYR+Yx/b7b/GLDdwjzu8AWwXkR+cC7fA0y1MI+7/A9YKSJ/4ShOywMvOguX/Nj/YTimXu0wBevU4lTgIy6YO5/P/S0ib5N16sbfFuZxl2eByc7/JEZw3DXqGWsjuUVvYLWIrCPrQWc36yLlDBFpaYxZjON2ph0v3G7M/9u782g5qzLf499fEASBgAitt1XGy2CQBCFhEFrBgXsdmAdFRETltsEhraDoohcIIjQgCEalbQdmVCYVbKFRmjkgEAhhiLmtpBG4dNONDBEBE/jdP/YuTp1KnRMsq2rX2fV81qpV532ravEU56Rqv3s/+3l8aYGw+qLl/U4ipdcOzF6nSFkJXZXz02YCjXSN64Ezatvg1ypvav2bfHi97TtLxtMvuSb1ZvlwYc2/55yatJPtYRmUAu1Tk2qXc2uPAXbMp24gpd49Xi6q/ml0bay10kYrSbcCN7Lshu3qJhYkHWP7aElntnnYtqu9AGt5z0tJHZe/MyjpeDEgD+EvlFM1ltFI5ahZ3iMwhbSZFwDb55SLqHcknUXa5HUFw1Wb+lTS+72M0e+7uspJwyoqRi2bOx9Cv0XKSugqSYtosyvfdnW71Zv8MyPveRVS6sZCoKpNja1y7t1OpAH5z4F3kWaZqhyQkxo+LSLtzq+9dXyzxkBlu6ZzBqoreyjpNNt/N0ZJz5rz5gemfXghV0j6P8DljL7orLns4ZrAh1i2r0KNaTqzGb+s50C855ghD12VmwI1rAzsC6xl+6hCIfVdTl851PbHSsfSS7l28TTgTtvTJL0aOM/2OwuH1lPDUpt6GEna2vZcSW9t97jt6/odU+i9PJHUyjVPJOVKQrcwHGk6B433+KC85xiQh56TNNf21qXj6CdJd9veonQcvSTpVtvbSJoL7EyqQb7A9mbLeemE1FqbGqi9NjXwYj7x0YzsC7kOOLbm/GJJs3I1inHP1UbS64DZwA751A3ALNsPlYsq9MIwlLOcaCJlJXRVS8fOxi7mqv/OWvIvJwFbMbq+a61uz8ue3wHmAn8Abi4bUk/9E/DZltrU3wGqrk0NfB+4h9QgCFLDpzOBZSo0VOQgoHXw/eE252pzJnABaWUTUnWZM4HaV71WZHQxgmtJ3YaXFAuq986VdAjwM4YnTWcd4AiW3fc0EOl3MUMeukrSNU2HS0k5t6fYXlgopJ7LudQNjZ3bl9RccaSVpPWBybbnN53bvKbZY0l32Z62vHO1kTTP9pbLO1cDSfsDHyBVV7mh6aHVgRdqbys+TL/rZrlU74qMlGs9EHi+5rRDSZ8AvgI8wUh+de1pOlcBPwIOBz5OuvD+L9tHFA0sq3rmMhTxUduj6vVK2qBUMH1yn+2Lmk9I2he4aIznV8f2v7c5fS5ptaAWw1qb+hlJO9q+EUDSDsAzhWPqlTnAI8DawClN5xdTfz8FgMckfRD4QT7eH3isYDz9MqPlwvpfJd1VLJr+OAz4n8PQnbTJq2x/L6efXQdcJ+m20kE1xIA8dNvFLDsIuxioOYf8iyw7+G53btiodABd9hFSbepG44wbGI6mKTOBs5uaxfyeNLNUHdsPAA+QGkANo4+Qcsi/Rpo1nQMcXDSi/nhe0ka2fwsgaUPg+cIx9dpvGJLupE0aKUiPSHoPKbV0rXGe31cxIA9dIWkzUpm/NVq6YU2mKVerJpLeBbwbeK2krzc9NJmUujLsqsqHy01hPi1p9XQ4HFVWbM8DpkmanI+fKhxSz0najjQwfQOpxOUKwNO2JxcNrMfyBUmtpR3H8zngGkn3ky4616P+C5GngXk5zbTq7qRNjssTC4eR/n1PBj5TNqQRMSAP3bIp8F5gTWDXpvOLgUOKRNR7/w+4nfQFNrfp/GIG6B956A5JW5BqrK+Vj/8bOMj2PUUD67HWKiuSqq+yAnwDeD9plWs6qV7zJkUj6oO86e0Qlq1NXfVKkO2rJW1M+h6D1HX4ufFeU4Gf5Nsw+VX+3HqSVBlsoMSmztBVkra3XXOljWVIepntmBFvIekW29st/5kTQ67be2RLlZXjbVddZUXSJaQqK80b3qbZrrbKiqTbbU+XNN/21Hyu+m6O+W/8BtIEw4spG7YvKRZUH+QNjufbfiIfvxLY3/a3ykYWuknS/yUVXfgRcGle9RwYMSAPXSHp87ZPGqsjVo3LYJIutL1fbpDT7j1PLRBWX0l6LWl5t3k27fpyEfVOVFkZ/1xNJF0PvAP4LvAfpI2eHx7G3/UwGONvvMoLsGH/3pK0DWn1aw/gPuCHts8rG1USKSuhWxbk+9uLRtFfs/IiazzoAAAXeElEQVT9e4tGUYikE4H3kT7UGrNpBqockBNVVoahykrDgaS88U+S0s9eD+xdNKL++Jmkd9v+eelA+mwFSXKeoZS0AmnvQI2G+nvL9q3ArZKOB04lrfwNxIA8ZshD+AtJOrG1jmm7c7WRtBCYOgS5lsCLy9jHkGpUm7S0f8ygLXt2m6RppNz5NfKpx0m588NQBnAoSFpM+psWsCrwp3wTaQNz1ZtZJZ1MWun7dj71t8CDtg8rF1VvSZpi+76WczvZvrZQSD2XN6bvSZoh3wj4MXCh7bnjvrBPYkAeukrS5Sy7DPYkaeb82zU2y2nXgrg597RWkq4A9h2GaiN5xuyXtgduI1Av5fd9ou3Dh6HKyljL+A21/5seVpImkQbhjcZPvwC+a7va0oeS7iGt9p1EqoR2EjDddrUlPyUtIm1kvXAQ97pFykrotvuBdRhpLPE+UtWRTUhtxg8sFFfXSZoJHApsKKl5tnB14KYyUfXVH0lls66m8rJZtp+X9IKkNSqvLjJKft875p+rHYg3Gcpl/AZJAg4ANrD9ZUmvB/5HXuavlu0XgDPybVhsC5xIqjW/OnA+sEPRiHpvQ48zCy1ptu1P9TOgZjEgD932Ztszmo4vl3Sb7RmSqmmjnl0AXAGcAHyh6fxi278vE1JfXZZvw+IPwN2SfkGq4QvUeQHS4k5Jl5FKADa/70vHfsnElOtwD7NvAS8AbwO+TPqb/yYwY7wXTXR5X8SXGNmg3kjVqbaNPKlJzjPAKqQZ8kX5wqRa4w3Gs6IXJDEgD922mqR1bf8OQNK6wGr5sT+VC6v7muqZ7g8g6a9IH2yrSVqt8f+gVrbPXv6zqnIpI106h8nKpPbpb2s6Zyr+f9GUUw1pc9+KDEFjIGBb21tJuhNSMyxJtW5ubPY90ubdUeUeK3cb8FPSxdbawD9K2tv2vmXDGl4xIA/ddhhwo6TfkmYZNgAOlbQqI3WMqyJpV9Ju7b8GHiXNsiwgdS6tVm6kcQIwhaZurLXOKi3vAkTSJbarq8Rhu/aOhcuwvXrj55zGsTtQTU39cSzJ+wYa1UbWIc2Y1+5J21eUDqLPPmq7URXtEWB3SdWklE5EsakzdJ2klwOb5cOFNW7kbCbpLtLs4S9tv0nSzsAHbX+0cGg9JelGUgfHr5G6sx4MTLJ9VNHACqmtbvFYPQUahiBVZ5Tafr/tSDqAtO9nK9IEyj7A39u+qGhgPSbpH0hlLi9l9H6YO4oF1WOSVgRmkjvwAteSCi8sKRZUYaX/jccMeeiFrRlpvTxNErbPKRtSTy2x/ZikSZIm2b5G0mmlg+qDVXLLaeXc2y9JmgsM5YCccQavE1Rj9mwH0irIj/LxvqTa89WS1NyFdBIwHah6YgHA9vn53/DbSSuce9hesJyX1WDbfD+96ZwZnaZVmzNIqViNbqQH5p8PKRZRn0h6he0/tnno9L4H0yQG5KGrJJ1Lqu85j9HNYmoekD8haTVSQ5zzJT1K0+a3ij2Xy4X9m6RPAg8zsl8gTHCNFJ1cTWhH20vz8T+SarDXbNemn5eS2m3vXiaU/pG0EWlz3zcl7QS8U9IjjZbytRq2cqbZjJbOs/+aV3urJenNpO67qwHr5h4Lf2v7UADbZxUML1JWQndJWgBMeQm7mauR8+OfJc0oHUBqoHK+7ceKBtZjkmaQcuXXJFVkWAM4yfYtRQMrpPRyZ6/kBlDbNyoH5QZJt9jetGxkodskzSPNEq8P/DOpitLmtt9dMq5ekfRB2+dJ+my7x22f2u+Y+kXSHaQ+Er/NxxsCF7f21KiJpF+R0rAua3xWS7rH9hvLRpbEDHnotnuA15A2iQwF282z4VVuXG3H9m35xz+Q8seHXa2dWf+BVPrwGtJF51tIJeKqlQcnp5M2chq4GfiM7fuLBtZ7L9hemlN2vmF7dqPiSqVWzferj/usOh0OXCPpftK/6/UYgs9x2w+mfdovGpiqOjEgD922NnCfpFsZvTlmt3Ih9UZLabRRD1Fxu2lJp9n+uzG6slb3ux6ne2Pj9zyV9MNVfQ2sT2yfmbuyNvJsj7D9HyVj6oMLSPW398zH7yc1O9t2zFfUYYmk/YEPMZK2s2LBeHrK9rfz/THjPU/SF22f0J+oei9X0pkGbAw0VroW2n5u7FdV4cGctuK8qXUWaZV3IETKSugqSW9td972df2OJfSGpK1tzx2W37Wk9cZ7vPZmMk3dGze0fWzuLfCamrs3SprfuNBqOndXS85tdSRNAT4O3Gz7B5I2APazfWLh0IqSdEdtqRySbrW9Tek4+knS2qSVr3eQJlSuAmYNSnppDMhD10l6NSOd3W61/WjJeEIInZN0Brl7o+035Bzyq1o68lZF0onA48APSasj7wNeCZwMMCSdeENW4/4QSV8jrX78iNEdeKst9TjoYkAeukrSfqQvrWtJV6B/A3zO9sUl4wrdM04KBwCtM4u1kLQdMBt4A6l74woMQffGxuxg86Ck9tliSYvGebjalur5fbdLQ6vy/b5Ulc6QX9PmtG1XW+pR0knAccAzwJXAVNLekPOKBpZFDnnotiNJ5ZQehRc7vf0SiAF5Pd6b7z+R78/N9x+kvlrczb5ByiW+iFSJ4kPAJkUj6o+h695oe4PSMRTSXId7ZVLN+bUKxTJItPynTCxDWupxF9ufl7QnqZTpXqRyxQMxIJ9UOoBQnUktKSqPEX9nVbH9QM6bfqftz9u+O9+OAHYpHV8v2f4NsILt522fCfzv0jH1wdeBHwOvlvQV4Ebg+LIh9ZakFSV9WtLF+fbJvAmsarYfa7o9bPs04D2l4xoA1XUqlfQqSV+XdIekuZJOl/Sq0nH1WGMS+j3ARbafLBlMq5ghD912paR/IVUkgJR7+fOC8YTekaQdbN+UD95M3Rdff5S0EjAvL30+Qt3vF1imeyMMR/fGdl0MzwA+ViyiPpDUnJbR6FBa/Tghr/ocwkiHaQBsfyTf13gB+kPS7PDe+fgAUj75O4pF1Hs/k/RrUsrKzPx7H5gOvJFDHrpO0t6kdtsAN9j+ccl4Qm9I2hr4PqkhEMATwEdq3RSUq638Jyl//DOk9/3NRmONmuWB2o6ktJWbav0dN7TLka89bx6WyStudCj9qu2FZSLqD0lzSN1n59JUl9r2JcWC6rF2DXEk3W17i1Ix9YOktYAnbT8v6RXA5EEp4xoD8hDCX0TSGgCDtvzXbZJm2T59eedqI+koUi7xJaRc2j1Iy73HFQ2sh4axi+EwkzTP9pal4+gnSacCtwIX5lP7ANvYPrxcVL0l6UPtzts+p9+xtBMD8tAVw9okZ5jl8pbHA39t+125hvH2tr9XOLSeaFdpocZyaK0kLQSm2X42H68CzLO96fivnLgkvR04E2h05lwfONh2u8oU1cgX10eTurECXAccOwQX28cBc2wPTXpl/s5elZEVgRUYKX9Y5Xe2pNlNhyuT0vDusL1PoZBGiQF5CKEjuXvjmcCRtqdJehlwZ21Lnrlz4QdIKRs3ND00GXje9tvbvrASOY1hT9tP5OM1gUsrL4+2MnAY6Qv7CeA24GuNi5JaSboEuAc4O586kHQxtle5qHqvaXD6HLCEmEhC0ua27y0dRy/lz7If2h6IzfnVb9YIIfTM2rYvlPRFANtLJT2/vBdNQHNIGzjXBk5pOr8YmF8koj7Is0kGngTulfSLfPxO0lJ3zc4BngK+nI8/QCrvuW+xiPpjI9t7Nx0fI2lesWj6xPbqpWMYQOcCtadoPQ0MTInTGJCHEDr1dC6T1ahPvR1p8FaVXOLxAWD7li60C2wvLRdZz92e7+eSyh42XNv/UPrujbanNB1fI+m+YtH0zzOSdrR9I4CkHUgVKaqXO9BuTEplAMD29eUiKq662uuSLmcktXYFUpO3C8d+RX/FgDyE0KnPApcBG0m6CViHtDGoSpL2Bb7KSBfa2ZKq7UJr++zlPyulObTMqtbgDknb2b4FQNK2jFyg1GwmcHZjozbwOHBQwXj6QtLHgFnA64B5wHbAzUC1aVkvQY35zF9t+nkp8IDth0oF0ypyyEMIHct545uSBqgLbS8pHFLPSLqL1AxpVBfa2kvhLU+NG1slLSD9Xf8un1oXWEj6ErftqaVi6yVJLyddVG8ErEla8bLtY4sG1mOS7iatfN1ie0tJmwHH1547P552m9hr0LLKeWtLI8OiYoY8hNCR3E793Yw009hFErZPLRpY70QX2vZqnNUZiE1eBfyUtIn1DuDhwrH007O2n5WEpJfb/rWkaqsIvUR/Kh1At0naDziZAV3ljAF5CKFTl5O6nN0NvFA4ln64IrrQDoe8b2AYvW5QKk702UO54sZPgF9Iepy0b6Rqkl4LrMfo7qTX5/vtSsXVQ0cCM1pXOYEYkIcQJrTX1bp0PwYD3yaVPwT4J1Ku6bCrbvPXEJsjaQvbd5cOpJ9s75l//FIu87kGcGXBkHpO0omkSYX7GKlFbqDmjawDvcoZOeQhhI7kD/SrbV9VOpZ+GKMx0PwhuyhZhqRdhuVvoFY5h9qkSbqNSQ2RnmOkHneVf+OSJtt+KrdTX4bt3/c7pn7JDb+m2n6udCz9IulkYCqjVznn2z6iXFQjYkAeQuiIpD2B80gzDNU205A0EzgU2BD4bdNDqwM32f5gkcB6rGmQtsxDVDxIG0aS1hvv8VpTeCT9zPZ7JS0i/a03r/bY9oaFQuu53NhtX9t/KB1LP0naG9ghH95g+8fjPb+fYkAeQuhI/hLbHbjbFX+Q5BJwrwROAL7Q9NDiymfQhnKQFsIwyF1ZpwFXk1ZDALD96WJBDbkYkIcQOiLpemAn28OwoTOEUBlJ45b1s31Hv2LpN0lt68u/1P4DE5GkvYATgb8irYYM1KpuDMhDCB2RdBYpjeMKRs+w1Fr2cCjlDqyzSV3tViJ1uHt6UL7EQuhU3sAJqTvndOAu0iBtKnC77e1LxRa6T9JvgF1tLygdSztRZSWE0KlF+bZSvoU6fQN4P3ARadDyIWCTohGF0AW2dwaQdCmwVaO6jKQ3Al8qGFrPSdqYlIY3hXRBAkDNefPAfw7qYBxihjyE0COSZtv+VOk4wl9G0u22pzdXlKmxO2cYXpLutb358s7VRNKNwNHA14BdgYNJZQGPKhpYD+RUFYC3Aq8h1ZtvXtW9tERcrWKGPITQKzss/ylhAvijpJWAeZJOAh5hgGr3htAF8yV9l1Q1CuAAYH7BePphFdtXS1LeoP0lSXOB6gbkpAuOhj8CuzQdG4gBeQghhIF3IGkA/kngM8Drgb3GfUUIE8vBwExgVj6+HjijXDh98ZykScC/Sfok8DCwWuGYesL2wS/leZK+aPuEXscz5n8/UlZCCL3QrpFOmHgkzbJ9+vLOhTCRSVoFWNf2wtKx9IOkGcACYE3gy8Bk4GTbtxQNrKDS31mx7BhC6JVoqV6HduXRPtzvIELoFUm7AfOAK/PxlpIuKxtVb9m+LTcF+r3tg23vPcyD8azod1YMyEMIvRIzqBOYpP0lXQ5sIOmyptu1QLUNkcJQOhrYBngCwPY8YIOiEfWYpO0l3Qf8Oh9Pk/StwmGVVjRlJHLIQwgdyYO11g+wJ4HbgW/bPqvvQYVumkPawLk2cErT+cXUv+EtDJcltp+URk2Q1p7Pexrwv4DLAGzfJektZUMqLmbIQwgT0v3AH4Dv5NtTpMHaJvk4TGC2H7B9bW6O8mtg9Xx7yPbSstGF0FX3SvoAsIKkjSXNJl2QVs32gy2nni8SyOC4qOR/PGbIQwiderPtGU3Hl0u6zfYMSfcWiyp0laR9ga8C15JmkGZL+pzti4sGFkL3fAo4klSb+gLgX0gbHWv2oKQ3A5a0IqnCzMA2zekGSesAhwDr0zT+tf2RfH98mciSGJCHEDq1mqR1bf8OQNK6jJTN+lO5sEKX/T0ww/aj8OKX2i+BGJCHWkzJt5fl2+7AbsDUkkH12MdJ+3xeSyp5eBXwiaIR9d5PgRtIn18DtxoQA/IQQqcOA26U9FvSzOkGwKGSVgXOLhpZ6KZJjcF49hiR7hjqcj5wOHAP8ELhWPrC9n+TGiANk1fYPqJ0EGOJOuQhhI5JejmwWT5caPvZkvGE7svdOacBP8in3gfMH+QvthD+HJJutL1j6Tj6SdLZwCzbT+TjVwKnNNI3aiTpOGCO7Z+XjqWdGJCHEDqWcxDXZ3Q+3jnFAgpdJ+lE4FdAY8ByA7BdDMhDLSS9HdgfuJqURw6A7YFoqd4Lku60/ablnauJpMXAqqTf8RLSyq5tTy4aWBYD8hBCRySdC2xEaqjRyMez7U+Xiyp0W7vudZLm2645vzYMEUnnkVb67mUkZcWVzxbfBexk+/F8vBZwne0tykY2vCKHPITQqenAFMdVfZUkzQQOBTaU1Fx3fHXgpjJRhdATM2xvWjqIPjsFuEXShaSZ4n2Ar5QNqfdyas7GwMqNc7avLxfRiBiQhxA6dQ/wGlLzmFCfC4ArgBOALzSdX2w7OnWGmsyRNMX2faUD6Rfb50j6DWlixcDBtm8uHFZPSfoYqbzj60gru9sBNwNvKxlXQ6SshBA6IukaYEvgVkbnXe5WLKgQQvgzSVpASr9bRPosa+QWV5uWJWkW8DHgUtL73QP4ju3ZRQPrIUl3AzOAW2xvKWkz4HjbexUODYgBeQihQ5Le2u687ev6HUsIIXRK0nrtztt+oN+x9EtOQ9ve9tP5eFXg5sovQhqN6+YB29p+TtK9tjcvHRtEykoIoUMx8A4h1KDmgfc4xOjmOM/nczV7SNKawE+AX0h6HBiY333MkIcQ/iyNmr25hFTzB8hAlZAKIYTQnqTPAgcBP86n9gDOsn1auaj6J6/wrgFcaXsgOkvHgDyEEEIIYchI2oqm/gK27ywZT69Immz7qVzacRmDskk9BuQhhI5I2gh4KOfh7QRMBc5pdH4LIYQQSpP0M9vvlbSItKrbnJpj2xsWCm2UGJCHEDqSN8ZMJ3Xq/DnwU2Bz2+8uGVcIIYQw0cSmzhBCp16wvVTSnsBs27MlVbnkGUIIYWLKqTljsn1Hv2IZTwzIQwidWiJpf9LGoF3zuRULxhNCCCG0OiXfr0xa1b2LlLYyFbgd2L5QXKNMKh1ACGHCOpj0QfYV24skbQCcWzimEEII4UW2d7a9M6mr9Fa2p9veGngT8HDZ6EZEDnkIIYQQQqhauyZA0RgohDDhNe1YH2VQdqyHEEIITeZL+i5wXj4+AJhfMJ5RYoY8hNARSa9qOlwZ2BdYy/ZRhUIKIYQQ2pK0MjATeEs+dT1whu1ny0U1IgbkIYSukTQ35+aFEEIIA0XSKsC6theWjqVVpKyEEDrSUkpqEmn3enymhBBCGDiSdgNOBlYCNpC0JXCs7d3KRpbEl2cIoVOnNP28FPh3YL8yoYQQQgjjOhrYBrgWwPa8XB1sIMSAPITQkVxGKoQQQpgIlth+UlLzuYHJ24465CGEjkhaQ9Kpkm7Pt1MkrVE6rhBCCKGNeyV9AFhB0saSZgNzSgfVEAPyEEKnvg8sJqWp7Ac8BZxZNKIQQgihvU8BmwPPARcATwKzikbUJKqshBA6Imme7S2Xdy6EEEIoTdJ04EhgfUZStm17arGgmkQOeQihU89I2tH2jQCSdgCeKRxTCCGE0M75wOHAPcALhWNZRgzIQwidmgmc3ZQ3/jhwUMF4QgghhLH8l+3LSwcxlkhZCSF0RNLLgX2AjYA1Sfl4tn1s0cBCCCGEFpLeDuwPXE3KIwfA9qXFgmoSM+QhhE79FHgCuAN4uHAsIYQQwngOBjYDVmQkZcXAQAzIY4Y8hNARSffYfmPpOEIIIYTlkbTQ9qal4xhLlD0MIXRqjqQtSgcRQgghvARzJE0pHcRYYoY8hPBnkXQ3aZnvZcDGwP2kfDwxQCWkQgghhAZJC0h7nhYxgN9ZMSAPIfxZJK033uO2H+hXLCGEEMJLMdZ316B8Z8WAPIQQQgghhIIihzyEEEIIIYSCYkAeQgghhBBCQTEgDyGEEEIIoaAYkIcQQgghhFBQDMhDCCGEEEIo6P8DL4VBFxjE+Q8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 792x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "wUa6QPXpExOt",
        "outputId": "5f0bca30-5dd6-465e-bd33-fa11afc18f76"
      },
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.stripplot(data=df,x='ocean_proximity',y='median_house_value',jitter=0.3)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcc9e685e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Cxb8vKjkExOt",
        "outputId": "d374c3d3-4642-4965-ab9b-7e11722f651f"
      },
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sns.boxplot(data=df,x='ocean_proximity',y='median_house_value',palette='viridis')\n",
        "plt.plot()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3VFTmi27ExOt"
      },
      "source": [
        "### Modelling and Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFjksRXiExOu"
      },
      "source": [
        "X = df.drop('median_house_value',axis=1)\n",
        "y = df['median_house_value']"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "vC6GSXzpExOu",
        "outputId": "beb37d8e-4ca9-4817-8f53-9f91a7d6284c"
      },
      "source": [
        "X.head(10)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>ocean_proximity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1726</th>\n",
              "      <td>-122.37</td>\n",
              "      <td>37.95</td>\n",
              "      <td>32</td>\n",
              "      <td>1298</td>\n",
              "      <td>363.0</td>\n",
              "      <td>716</td>\n",
              "      <td>268</td>\n",
              "      <td>0.9797</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12942</th>\n",
              "      <td>-121.27</td>\n",
              "      <td>38.65</td>\n",
              "      <td>25</td>\n",
              "      <td>2787</td>\n",
              "      <td>601.0</td>\n",
              "      <td>1247</td>\n",
              "      <td>522</td>\n",
              "      <td>2.9016</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15016</th>\n",
              "      <td>-117.03</td>\n",
              "      <td>32.77</td>\n",
              "      <td>19</td>\n",
              "      <td>4819</td>\n",
              "      <td>1492.0</td>\n",
              "      <td>2572</td>\n",
              "      <td>1336</td>\n",
              "      <td>2.3393</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14577</th>\n",
              "      <td>-117.20</td>\n",
              "      <td>32.82</td>\n",
              "      <td>35</td>\n",
              "      <td>1217</td>\n",
              "      <td>220.0</td>\n",
              "      <td>643</td>\n",
              "      <td>237</td>\n",
              "      <td>3.9464</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6477</th>\n",
              "      <td>-118.08</td>\n",
              "      <td>34.09</td>\n",
              "      <td>32</td>\n",
              "      <td>3214</td>\n",
              "      <td>718.0</td>\n",
              "      <td>2316</td>\n",
              "      <td>751</td>\n",
              "      <td>3.7066</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18096</th>\n",
              "      <td>-122.04</td>\n",
              "      <td>37.33</td>\n",
              "      <td>26</td>\n",
              "      <td>2690</td>\n",
              "      <td>401.0</td>\n",
              "      <td>1264</td>\n",
              "      <td>429</td>\n",
              "      <td>7.7643</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8043</th>\n",
              "      <td>-118.16</td>\n",
              "      <td>33.84</td>\n",
              "      <td>36</td>\n",
              "      <td>2831</td>\n",
              "      <td>573.0</td>\n",
              "      <td>1462</td>\n",
              "      <td>569</td>\n",
              "      <td>3.8646</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15068</th>\n",
              "      <td>-116.97</td>\n",
              "      <td>32.78</td>\n",
              "      <td>37</td>\n",
              "      <td>1255</td>\n",
              "      <td>238.0</td>\n",
              "      <td>671</td>\n",
              "      <td>278</td>\n",
              "      <td>3.7019</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19603</th>\n",
              "      <td>-121.04</td>\n",
              "      <td>37.60</td>\n",
              "      <td>27</td>\n",
              "      <td>958</td>\n",
              "      <td>184.0</td>\n",
              "      <td>580</td>\n",
              "      <td>177</td>\n",
              "      <td>2.1875</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13468</th>\n",
              "      <td>-117.38</td>\n",
              "      <td>34.09</td>\n",
              "      <td>8</td>\n",
              "      <td>3955</td>\n",
              "      <td>815.0</td>\n",
              "      <td>2184</td>\n",
              "      <td>725</td>\n",
              "      <td>3.3438</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  ...  median_income  ocean_proximity\n",
              "1726     -122.37     37.95  ...         0.9797                0\n",
              "12942    -121.27     38.65  ...         2.9016                1\n",
              "15016    -117.03     32.77  ...         2.3393                2\n",
              "14577    -117.20     32.82  ...         3.9464                3\n",
              "6477     -118.08     34.09  ...         3.7066                2\n",
              "18096    -122.04     37.33  ...         7.7643                2\n",
              "8043     -118.16     33.84  ...         3.8646                2\n",
              "15068    -116.97     32.78  ...         3.7019                2\n",
              "19603    -121.04     37.60  ...         2.1875                1\n",
              "13468    -117.38     34.09  ...         3.3438                1\n",
              "\n",
              "[10 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TYjLBOh6ExOu",
        "outputId": "b84d9424-30a7-4951-ec12-38b48ac5f43a"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19673, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yJ_fsUfVExOu",
        "outputId": "30e83392-d668-4943-97e9-c0c77c56a6ae"
      },
      "source": [
        "y.head(10)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1726      76400\n",
              "12942    159800\n",
              "15016    118200\n",
              "14577    171600\n",
              "6477     206800\n",
              "18096    474700\n",
              "8043     214600\n",
              "15068    138600\n",
              "19603     82800\n",
              "13468    127600\n",
              "Name: median_house_value, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VJC38nPJExOv",
        "outputId": "019e5a98-a8f2-44de-dd0f-0c891bb37772"
      },
      "source": [
        "y.shape"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19673,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BliMF6sHExOv"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.6)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hSuqu-C6ExOv",
        "outputId": "222663e6-cc6e-408a-dbc4-6960f729b987"
      },
      "source": [
        "print(\"X_train shape is \",X_train.shape)\n",
        "print(\"X_test shape is \",X_test.shape)\n",
        "print(\"y_train shape is \",y_train.shape)\n",
        "print(\"y_test shape is \",y_test.shape)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train shape is  (11803, 9)\n",
            "X_test shape is  (7870, 9)\n",
            "y_train shape is  (11803,)\n",
            "y_test shape is  (7870,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fcqcpuBnExOv"
      },
      "source": [
        "### 1. Random Forest Regressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ADy6uPpExOw"
      },
      "source": [
        "#Using this class to make my print statement look bold\n",
        "class color:\n",
        "    BOLD = '\\033[1m'"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Twn2fc2pExOw",
        "outputId": "1e07811c-d6ea-439a-f6af-4bd78fdd448e"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import numpy as np\n",
        "\n",
        "rf = RandomForestRegressor(random_state=0)\n",
        "rf.fit(X_train, y_train)\n",
        "\n",
        "y_pred = rf.predict(X_test)\n",
        "\n",
        "accuracies = {}\n",
        "\n",
        "accuracy = rf.score(X_test, y_test)\n",
        "accuracy_rounded = round(accuracy*100,2)\n",
        "accuracies['Random Forest'] = accuracy_rounded\n",
        "\n",
        "r2 = metrics.r2_score(y_test, y_pred)\n",
        "Adj_r2 = 1-(1-r2)*(9-1)/(9-1-1)\n",
        "\n",
        "print(color.BOLD + '\\nR2 score is ',r2)\n",
        "\n",
        "print(color.BOLD + '\\nAdjusted R2 score is ',Adj_r2)\n",
        "\n",
        "print(color.BOLD + '\\nMean Absolute Error is',round(metrics.mean_absolute_error(y_test,rf.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nMean Squared Error is',round(metrics.mean_squared_error(y_test,rf.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nRoot Mean Squared Error is',round(np.sqrt(mean_squared_error(y_test,rf.predict(X_test))),2))\n",
        "\n",
        "print(color.BOLD + \"\\nAccuracy of Random Forest Regressor is \", accuracy_rounded,'%')"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1m\n",
            "R2 score is  0.7832358051464153\n",
            "\u001b[1m\n",
            "Adjusted R2 score is  0.7522694915959032\n",
            "\u001b[1m\n",
            "Mean Absolute Error is 30721.65\n",
            "\u001b[1m\n",
            "Mean Squared Error is 2092687538.84\n",
            "\u001b[1m\n",
            "Root Mean Squared Error is 45745.9\n",
            "\u001b[1m\n",
            "Accuracy of Random Forest Regressor is  78.32 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xsKntNzWExOw"
      },
      "source": [
        "### 2. Decision Tree Regressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N6aIrD7LExOx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e63396f-d646-462a-d0ac-5a60da97f304"
      },
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "dt = DecisionTreeRegressor(random_state=42)\n",
        "dt.fit(X_train,y_train)\n",
        "\n",
        "y_pred = dt.predict(X_test)\n",
        "\n",
        "accuracy = dt.score(X_test, y_test)\n",
        "accuracy_rounded = round(accuracy*100,2)\n",
        "accuracies['Decision Tree'] = accuracy_rounded\n",
        "\n",
        "r2 = metrics.r2_score(y_test, y_pred)\n",
        "Adj_r2 = 1-(1-r2)*(9-1)/(9-1-1)\n",
        "\n",
        "print(color.BOLD + '\\nR2 score is ',r2)\n",
        "\n",
        "print(color.BOLD + '\\nAdjusted R2 score is ',Adj_r2)\n",
        "\n",
        "print(color.BOLD + '\\nMean Absolute Error is',round(metrics.mean_absolute_error(y_test,dt.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nMean Squared Error is',round(metrics.mean_squared_error(y_test,dt.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nRoot Mean Squared Error is',round(np.sqrt(metrics.mean_squared_error(y_test,dt.predict(X_test))),2))\n",
        "\n",
        "print(color.BOLD + \"\\nAccuracy of Decision Tree Regressor is \", accuracy_rounded,'%')"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1m\n",
            "R2 score is  0.5566886018214638\n",
            "\u001b[1m\n",
            "Adjusted R2 score is  0.4933584020816729\n",
            "\u001b[1m\n",
            "Mean Absolute Error is 43319.38\n",
            "\u001b[1m\n",
            "Mean Squared Error is 4279822317.61\n",
            "\u001b[1m\n",
            "Root Mean Squared Error is 65420.35\n",
            "\u001b[1m\n",
            "Accuracy of Decision Tree Regressor is  55.67 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WWXA1UPaExOx"
      },
      "source": [
        "### 3. AdaBoost Regressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uIgHs3GzExOx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21d84e48-e10f-45a9-e7b5-da9f5f48c961"
      },
      "source": [
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "\n",
        "ada = AdaBoostRegressor(random_state=0)\n",
        "\n",
        "ada.fit(X_train,y_train)\n",
        "\n",
        "y_pred = ada.predict(X_test)\n",
        "\n",
        "accuracy = ada.score(X_test, y_test)\n",
        "accuracy_rounded = round(accuracy*100,2)\n",
        "accuracies['AdaBoost'] = accuracy_rounded\n",
        "\n",
        "r2 = metrics.r2_score(y_test, y_pred)\n",
        "Adj_r2 = 1-(1-r2)*(9-1)/(9-1-1)\n",
        "\n",
        "print(color.BOLD + '\\nR2 score is ',r2)\n",
        "\n",
        "print(color.BOLD + '\\nAdjusted R2 score is ',Adj_r2)\n",
        "\n",
        "print(color.BOLD + '\\nMean Absolute Error is',round(metrics.mean_absolute_error(y_test,ada.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nMean Squared Error is',round(metrics.mean_squared_error(y_test,ada.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nRoot Mean Squared Error is',round(np.sqrt(mean_squared_error(y_test,ada.predict(X_test))),2))\n",
        "\n",
        "print(color.BOLD + \"\\nAccuracy of AdaBoost Regressor is \", accuracy_rounded,'%')"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1m\n",
            "R2 score is  0.3626153065393227\n",
            "\u001b[1m\n",
            "Adjusted R2 score is  0.2715603503306545\n",
            "\u001b[1m\n",
            "Mean Absolute Error is 67663.64\n",
            "\u001b[1m\n",
            "Mean Squared Error is 6153447096.52\n",
            "\u001b[1m\n",
            "Root Mean Squared Error is 78443.91\n",
            "\u001b[1m\n",
            "Accuracy of AdaBoost Regressor is  36.26 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "crwxCa3eExOy"
      },
      "source": [
        "### 4. XGBoost Regressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7-7TsCiaExOy",
        "outputId": "bf81bdaa-7148-492b-c069-8a11e96edab9"
      },
      "source": [
        "from xgboost import XGBRegressor\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "xg = XGBRegressor()\n",
        "xg.fit(X_train,y_train)\n",
        "\n",
        "y_pred = xg.predict(X_test)\n",
        "\n",
        "accuracy = xg.score(X_test, y_test)\n",
        "accuracy_rounded = round(accuracy*100,2)\n",
        "accuracies['XGBoost'] = accuracy_rounded\n",
        "\n",
        "r2 = metrics.r2_score(y_test, y_pred)\n",
        "Adj_r2 = 1-(1-r2)*(9-1)/(9-1-1)\n",
        "\n",
        "print(color.BOLD + '\\nR2 score is ',r2)\n",
        "\n",
        "print(color.BOLD + '\\nAdjusted R2 score is ',Adj_r2)\n",
        "\n",
        "print(color.BOLD + '\\nMean Absolute Error is',round(metrics.mean_absolute_error(y_test,xg.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nMean Squared Error is',round(metrics.mean_squared_error(y_test,xg.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nRoot Mean Squared Error is',round(np.sqrt(mean_squared_error(y_test,xg.predict(X_test))),2))\n",
        "\n",
        "print(color.BOLD + \"\\nAccuracy of XGBoost Regressor is \", accuracy_rounded,'%')"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[12:56:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "\u001b[1m\n",
            "R2 score is  0.7394561294472903\n",
            "\u001b[1m\n",
            "Adjusted R2 score is  0.702235576511189\n",
            "\u001b[1m\n",
            "Mean Absolute Error is 35371.47\n",
            "\u001b[1m\n",
            "Mean Squared Error is 2515345818.97\n",
            "\u001b[1m\n",
            "Root Mean Squared Error is 50153.22\n",
            "\u001b[1m\n",
            "Accuracy of XGBoost Regressor is  73.95 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VM2qHHYBExOz"
      },
      "source": [
        "### 5. Gradient Boost Regressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4HtUdLgFExOz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "422479f2-236f-410d-e7d8-9f75086ef3e5"
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "\n",
        "gboost = GradientBoostingRegressor(random_state=42)\n",
        "\n",
        "gboost.fit(X_train,y_train)\n",
        "\n",
        "y_pred = gboost.predict(X_test)\n",
        "\n",
        "accuracy = gboost.score(X_test, y_test)\n",
        "accuracy_rounded = round(accuracy*100,2)\n",
        "accuracies['Gradient Boost'] = accuracy_rounded\n",
        "\n",
        "r2 = metrics.r2_score(y_test, y_pred)\n",
        "Adj_r2 = 1-(1-r2)*(9-1)/(9-1-1)\n",
        "\n",
        "print(color.BOLD + '\\nR2 score is ',r2)\n",
        "\n",
        "print(color.BOLD + '\\nAdjusted R2 score is ',Adj_r2)\n",
        "\n",
        "print(color.BOLD + '\\nMean Absolute Error is',round(metrics.mean_absolute_error(y_test,gboost.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nMean Squared Error is',round(metrics.mean_squared_error(y_test,gboost.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nRoot Mean Squared Error is',round(np.sqrt(mean_squared_error(y_test,gboost.predict(X_test))),2))\n",
        "\n",
        "print(color.BOLD + \"\\nAccuracy of Gradient Boost Regressor is \", accuracy_rounded,'%')"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1m\n",
            "R2 score is  0.7332654000851206\n",
            "\u001b[1m\n",
            "Adjusted R2 score is  0.6951604572401379\n",
            "\u001b[1m\n",
            "Mean Absolute Error is 35823.29\n",
            "\u001b[1m\n",
            "Mean Squared Error is 2575112434.03\n",
            "\u001b[1m\n",
            "Root Mean Squared Error is 50745.57\n",
            "\u001b[1m\n",
            "Accuracy of Gradient Boost Regressor is  73.33 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0BLssVV1ExO0"
      },
      "source": [
        "### 6. Ridge Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMHNvzb2ExO0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5afd7c7-9188-4a78-fbcf-92f5bdc8525f"
      },
      "source": [
        "from sklearn.linear_model import Ridge\n",
        "\n",
        "rid = Ridge(alpha=0.1)\n",
        "\n",
        "rid.fit(X_train,y_train)\n",
        "\n",
        "y_pred = rid.predict(X_test)\n",
        "\n",
        "accuracy = rid.score(X_test, y_test)\n",
        "accuracy_rounded = round(accuracy*100,2)\n",
        "accuracies['Ridge Regression'] = accuracy_rounded\n",
        "\n",
        "r2 = metrics.r2_score(y_test, y_pred)\n",
        "Adj_r2 = 1-(1-r2)*(9-1)/(9-1-1)\n",
        "\n",
        "print(color.BOLD + '\\nR2 score is ',r2)\n",
        "\n",
        "print(color.BOLD + '\\nAdjusted R2 score is ',Adj_r2)\n",
        "\n",
        "print(color.BOLD + '\\nMean Absolute Error is',round(metrics.mean_absolute_error(y_test,rid.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nMean Squared Error is',round(metrics.mean_squared_error(y_test,rid.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nRoot Mean Squared Error is',round(np.sqrt(mean_squared_error(y_test,rid.predict(X_test))),2))\n",
        "\n",
        "print(color.BOLD + \"\\nAccuracy of Ridge Regression is \", accuracy_rounded,'%')"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1m\n",
            "R2 score is  0.6008881616883058\n",
            "\u001b[1m\n",
            "Adjusted R2 score is  0.5438721847866352\n",
            "\u001b[1m\n",
            "Mean Absolute Error is 45897.34\n",
            "\u001b[1m\n",
            "Mean Squared Error is 3853110386.62\n",
            "\u001b[1m\n",
            "Root Mean Squared Error is 62073.43\n",
            "\u001b[1m\n",
            "Accuracy of Ridge Regression is  60.09 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oc89QA2kExO1"
      },
      "source": [
        "### 7. Lasso Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHZR3S32ExO1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15816275-1771-4b6b-ac8d-6789bf1bfc1a"
      },
      "source": [
        "from sklearn import linear_model\n",
        "\n",
        "lasso = linear_model.Lasso(alpha=0.3)\n",
        "\n",
        "lasso.fit(X_train,y_train)\n",
        "\n",
        "y_pred = lasso.predict(X_test)\n",
        "\n",
        "accuracy = lasso.score(X_test, y_test)\n",
        "accuracy_rounded = round(accuracy*100,2)\n",
        "accuracies['Lasso Regression'] = accuracy_rounded\n",
        "\n",
        "r2 = metrics.r2_score(y_test, y_pred)\n",
        "Adj_r2 = 1-(1-r2)*(9-1)/(9-1-1)\n",
        "\n",
        "print(color.BOLD + '\\nR2 score is ',r2)\n",
        "\n",
        "print(color.BOLD + '\\nAdjusted R2 score is ',Adj_r2)\n",
        "\n",
        "print(color.BOLD + '\\nMean Absolute Error is',round(metrics.mean_absolute_error(y_test,lasso.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nMean Squared Error is',round(metrics.mean_squared_error(y_test,lasso.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nRoot Mean Squared Error is',round(np.sqrt(mean_squared_error(y_test,lasso.predict(X_test))),2))\n",
        "\n",
        "print(color.BOLD + \"\\nAccuracy of Lasso Regression is \", accuracy_rounded,'%')"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1m\n",
            "R2 score is  0.6008881496276757\n",
            "\u001b[1m\n",
            "Adjusted R2 score is  0.5438721710030578\n",
            "\u001b[1m\n",
            "Mean Absolute Error is 45897.34\n",
            "\u001b[1m\n",
            "Mean Squared Error is 3853110503.06\n",
            "\u001b[1m\n",
            "Root Mean Squared Error is 62073.43\n",
            "\u001b[1m\n",
            "Accuracy of Lasso Regression is  60.09 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MohqDtvSExO2"
      },
      "source": [
        "### 8. ElasticNet Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w94Y8-kTExO2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79c8a56d-0544-4dae-b436-9245f6c3dc09"
      },
      "source": [
        "from sklearn.linear_model import ElasticNet\n",
        "\n",
        "ela = ElasticNet(random_state=0)\n",
        "\n",
        "ela.fit(X_train,y_train)\n",
        "\n",
        "y_pred = ela.predict(X_test)\n",
        "\n",
        "accuracy = ela.score(X_test, y_test)\n",
        "accuracy_rounded = round(accuracy*100,2)\n",
        "accuracies['Elastic Net Regression'] = accuracy_rounded\n",
        "\n",
        "r2 = metrics.r2_score(y_test, y_pred)\n",
        "Adj_r2 = 1-(1-r2)*(9-1)/(9-1-1)\n",
        "\n",
        "print(color.BOLD + '\\nR2 score is ',r2)\n",
        "\n",
        "print(color.BOLD + '\\nAdjusted R2 score is ',Adj_r2)\n",
        "\n",
        "print(color.BOLD + '\\nMean Absolute Error is',round(metrics.mean_absolute_error(y_test,ela.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nMean Squared Error is',round(metrics.mean_squared_error(y_test,ela.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nRoot Mean Squared Error is',round(np.sqrt(mean_squared_error(y_test,ela.predict(X_test))),2))\n",
        "\n",
        "print(color.BOLD + \"\\nAccuracy of Elastic net Regression is \", accuracy_rounded,'%')"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1m\n",
            "R2 score is  0.5564862201303187\n",
            "\u001b[1m\n",
            "Adjusted R2 score is  0.49312710872036425\n",
            "\u001b[1m\n",
            "Mean Absolute Error is 49133.97\n",
            "\u001b[1m\n",
            "Mean Squared Error is 4281776153.41\n",
            "\u001b[1m\n",
            "Root Mean Squared Error is 65435.28\n",
            "\u001b[1m\n",
            "Accuracy of Elastic net Regression is  55.65 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-55vOHQ0ExO2"
      },
      "source": [
        "### 9. CatBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lWRZ6r-KF3Hp",
        "outputId": "16343f52-484e-418f-e19b-2525b7505c9b"
      },
      "source": [
        "!pip install catboost"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: catboost in /usr/local/lib/python3.7/dist-packages (0.25.1)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from catboost) (4.4.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.1.5)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.19.5)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost) (1.3.3)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.3.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (2.8.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (2.4.7)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "BLAKNVDmExO3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d357a3e8-442a-4b89-d373-5573af16d615"
      },
      "source": [
        "from catboost import CatBoostRegressor\n",
        "\n",
        "cat = CatBoostRegressor()\n",
        "\n",
        "cat.fit(X_train,y_train)\n",
        "\n",
        "y_pred = cat.predict(X_test)\n",
        "\n",
        "accuracy = cat.score(X_test, y_test)\n",
        "accuracy_rounded = round(accuracy*100,2)\n",
        "accuracies['CatBoost'] = accuracy_rounded\n",
        "\n",
        "r2 = metrics.r2_score(y_test, y_pred)\n",
        "Adj_r2 = 1-(1-r2)*(9-1)/(9-1-1)\n",
        "\n",
        "print(color.BOLD + '\\nR2 score is ',r2)\n",
        "\n",
        "print(color.BOLD + '\\nAdjusted R2 score is ',Adj_r2)\n",
        "\n",
        "print(color.BOLD + '\\nMean Absolute Error is',round(metrics.mean_absolute_error(y_test,cat.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nMean Squared Error is',round(metrics.mean_squared_error(y_test,cat.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nRoot Mean Squared Error is',round(np.sqrt(mean_squared_error(y_test,cat.predict(X_test))),2))\n",
        "\n",
        "print(color.BOLD + \"\\nAccuracy of CatBoost is \", accuracy_rounded,'%')"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Learning rate set to 0.060564\n",
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            "999:\tlearn: 31922.2122180\ttotal: 4.64s\tremaining: 0us\n",
            "\u001b[1m\n",
            "R2 score is  0.8136268647998726\n",
            "\u001b[1m\n",
            "Adjusted R2 score is  0.7870021311998543\n",
            "\u001b[1m\n",
            "Mean Absolute Error is 28581.86\n",
            "\u001b[1m\n",
            "Mean Squared Error is 1799285799.35\n",
            "\u001b[1m\n",
            "Root Mean Squared Error is 42417.99\n",
            "\u001b[1m\n",
            "Accuracy of CatBoost is  81.36 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mMKJqBBEExO3"
      },
      "source": [
        "### 10. Light GBM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_A94tylsMVuv",
        "outputId": "b4c16c56-288a-4187-bc02-1c6b25106902"
      },
      "source": [
        "!pip install lightgbm"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: lightgbm in /usr/local/lib/python3.7/dist-packages (2.2.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from lightgbm) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from lightgbm) (0.22.2.post1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from lightgbm) (1.19.5)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->lightgbm) (1.0.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pnVsmZAQExO3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa1ca885-63fc-4df9-f267-1d30e88cbaed"
      },
      "source": [
        "import lightgbm as lgb\n",
        "\n",
        "lg = lgb.LGBMRegressor()\n",
        "\n",
        "lg.fit(X_train,y_train)\n",
        "\n",
        "y_pred = lg.predict(X_test)\n",
        "\n",
        "accuracy = lg.score(X_test, y_test)\n",
        "accuracy_rounded = round(accuracy*100,2)\n",
        "accuracies['Light GBM'] = accuracy_rounded\n",
        "\n",
        "r2 = metrics.r2_score(y_test, y_pred)\n",
        "Adj_r2 = 1-(1-r2)*(9-1)/(9-1-1)\n",
        "\n",
        "print(color.BOLD + '\\nR2 score is ',r2)\n",
        "\n",
        "print(color.BOLD + '\\nAdjusted R2 score is ',Adj_r2)\n",
        "\n",
        "print(color.BOLD + '\\nMean Absolute Error is',round(metrics.mean_absolute_error(y_test,lg.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nMean Squared Error is',round(metrics.mean_squared_error(y_test,lg.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nRoot Mean Squared Error is',round(np.sqrt(mean_squared_error(y_test,lg.predict(X_test))),2))\n",
        "\n",
        "print(color.BOLD + \"\\nAccuracy of Light GBM is \", accuracy_rounded,'%')"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1m\n",
            "R2 score is  0.7957184078382149\n",
            "\u001b[1m\n",
            "Adjusted R2 score is  0.7665353232436742\n",
            "\u001b[1m\n",
            "Mean Absolute Error is 30362.45\n",
            "\u001b[1m\n",
            "Mean Squared Error is 1972177843.39\n",
            "\u001b[1m\n",
            "Root Mean Squared Error is 44409.21\n",
            "\u001b[1m\n",
            "Accuracy of Light GBM is  79.57 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-J3YdiTExO4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5149aed0-8473-43d7-bef6-800142137433"
      },
      "source": [
        "#Verifying how untuned models predict house value\n",
        "print('\\nPrediction of Random Forest is ',rf.predict([[-122.22,37.86,21,7099,1106,2401,1138,8.3014,0]]))      \n",
        "print('\\nPrediction of Ada Boost is ',ada.predict([[-122.22,37.86,21,7099,1106,2401,1138,8.3014,0]])) \n",
        "print('\\nPrediction of Gradient Boost is ',gboost.predict([[-122.22,37.86,21,7099,1106,2401,1138,8.3014,0]]))\n",
        "print('\\nPrediction of Ridge Regression is ',rid.predict([[-122.22,37.86,21,7099,1106,2401,1138,8.3014,0]]))\n",
        "print('\\nPrediction of Lasso Regression is ',lasso.predict([[-122.22,37.86,21,7099,1106,2401,1138,8.3014,0]]))\n",
        "print('\\nPrediction of Elasticnet Regression is ',ela.predict([[-122.22,37.86,21,7099,1106,2401,1138,8.3014,0]]))\n",
        "print('\\nPrediction of Decision Tree is ',dt.predict([[-122.22,37.86,21,7099,1106,2401,1138,8.3014,0]]))\n",
        "print('\\nPrediction of CatBoost is ',cat.predict([[-122.22,37.86,21,7099,1106,2401,1138,8.3014,0]]))\n",
        "print('\\nPrediction of Light GBM is ',lg.predict([[-122.22,37.86,21,7099,1106,2401,1138,8.3014,0]]))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Prediction of Random Forest is  [415482.]\n",
            "\n",
            "Prediction of Ada Boost is  [335364.57564576]\n",
            "\n",
            "Prediction of Gradient Boost is  [408704.15479083]\n",
            "\n",
            "Prediction of Ridge Regression is  [401869.56241675]\n",
            "\n",
            "Prediction of Lasso Regression is  [401870.22914012]\n",
            "\n",
            "Prediction of Elasticnet Regression is  [376429.54743047]\n",
            "\n",
            "Prediction of Decision Tree is  [361600.]\n",
            "\n",
            "Prediction of CatBoost is  [451460.49067526]\n",
            "\n",
            "Prediction of Light GBM is  [432786.20765921]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5bLdMPdExO5"
      },
      "source": [
        "### All accuracies together"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kjoT8Ht4ExO5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f42fba0d-b7b6-4352-be5c-83a940460340"
      },
      "source": [
        "accuracies"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'AdaBoost': 36.26,\n",
              " 'CatBoost': 81.36,\n",
              " 'Decision Tree': 55.67,\n",
              " 'Elastic Net Regression': 55.65,\n",
              " 'Gradient Boost': 73.33,\n",
              " 'Lasso Regression': 60.09,\n",
              " 'Light GBM': 79.57,\n",
              " 'Random Forest': 78.32,\n",
              " 'Ridge Regression': 60.09,\n",
              " 'XGBoost': 73.95}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N9oc_NMvExO6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "09ee228a-fcd4-4d1b-df93-87dc3494b7e7"
      },
      "source": [
        "#Plotting accuracies of all the models\n",
        "colors = [\"green\", \"yellow\", \"black\", \"magenta\", \"#0e76a8\", \"red\", \"#34558b\",\"#f0daa4\",\"#3b3d4b\",\"#fd823e\"]\n",
        "\n",
        "sns.set_style(\"whitegrid\")\n",
        "plt.figure(figsize=(18,10))\n",
        "plt.yticks(np.arange(0,100,10))\n",
        "plt.ylabel(\"\\nAccuracy %\",fontsize=20)\n",
        "plt.xlabel(\"\\nAlgorithms\",fontsize=20)\n",
        "sns.barplot(x=list(accuracies.keys()), y=list(accuracies.values()), palette=colors)\n",
        "plt.show()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "86enmCvj75s1"
      },
      "source": [
        "#CatBoost performs the best without tuning."
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zhtXxCfZ2q9d"
      },
      "source": [
        "### Hyperparameter Tuning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XBlspCKB2Nso"
      },
      "source": [
        "\n",
        "> Let's try to increase the accuracy of the top performing models on this dataset - \n",
        "\n",
        "> 1.   RandomForest\n",
        "2.   CatBoost\n",
        "1.   LightGBM\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pquKfsls2zrb"
      },
      "source": [
        "from sklearn.model_selection import RepeatedKFold\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "import numpy as np"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vclEaCSCMB8"
      },
      "source": [
        "### Random Forest Tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iw6XU5vgGTn8",
        "outputId": "3c638893-d398-4cfc-cdab-c0078d00f301"
      },
      "source": [
        "#Randomized Search CV\n",
        "\n",
        "# Number of trees in the random forest\n",
        "n_estimators = [int(x) for x in np.linspace(start = 5, stop = 300, num = 6)]\n",
        "\n",
        "# Number of features to consider at every split\n",
        "max_features = ['auto', 'sqrt', 'log2']\n",
        "\n",
        "# Maximum number of levels in tree\n",
        "max_depth = [int(x) for x in np.linspace(10, 120, num = 12)]\n",
        "max_depth.append(None)\n",
        "\n",
        "# Minimum number of samples required to split a node\n",
        "min_samples_split = [2, 5, 10, 13, 16]\n",
        "\n",
        "# Minimum number of samples required at each leaf node\n",
        "min_samples_leaf = [1, 2, 4, 8]\n",
        "\n",
        "# Method of selecting samples for training each tree\n",
        "bootstrap = [True, False]\n",
        "\n",
        "# Create the random grid\n",
        "random_grid = {'n_estimators': n_estimators,\n",
        "               'max_features': max_features,\n",
        "               'max_depth': max_depth,\n",
        "               'min_samples_split': min_samples_split,\n",
        "               'min_samples_leaf': min_samples_leaf,\n",
        "               'bootstrap': bootstrap}\n",
        "\n",
        "\n",
        "print(random_grid)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_estimators': [5, 64, 123, 182, 241, 300], 'max_features': ['auto', 'sqrt', 'log2'], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, None], 'min_samples_split': [2, 5, 10, 13, 16], 'min_samples_leaf': [1, 2, 4, 8], 'bootstrap': [True, False]}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_e0MRVsgHA-S"
      },
      "source": [
        "rf_random = RandomizedSearchCV(estimator = rf, \n",
        "                               param_distributions = random_grid, \n",
        "                               n_iter = 9, \n",
        "                               cv = 4, \n",
        "                               verbose=0,\n",
        "                               random_state=42, \n",
        "                               n_jobs = -1)"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oxzmUYqrHINW",
        "outputId": "3f73acb5-d8ab-499b-df9d-a00e8d4cb999"
      },
      "source": [
        "rf_random.fit(X_train, y_train)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=4, error_score=nan,\n",
              "                   estimator=RandomForestRegressor(bootstrap=True,\n",
              "                                                   ccp_alpha=0.0,\n",
              "                                                   criterion='mse',\n",
              "                                                   max_depth=None,\n",
              "                                                   max_features='auto',\n",
              "                                                   max_leaf_nodes=None,\n",
              "                                                   max_samples=None,\n",
              "                                                   min_impurity_decrease=0.0,\n",
              "                                                   min_impurity_split=None,\n",
              "                                                   min_samples_leaf=1,\n",
              "                                                   min_samples_split=2,\n",
              "                                                   min_weight_fraction_leaf=0.0,\n",
              "                                                   n_estimators=100,\n",
              "                                                   n_jobs=None, oob_score=Fals...\n",
              "                   iid='deprecated', n_iter=9, n_jobs=-1,\n",
              "                   param_distributions={'bootstrap': [True, False],\n",
              "                                        'max_depth': [10, 20, 30, 40, 50, 60,\n",
              "                                                      70, 80, 90, 100, 110, 120,\n",
              "                                                      None],\n",
              "                                        'max_features': ['auto', 'sqrt',\n",
              "                                                         'log2'],\n",
              "                                        'min_samples_leaf': [1, 2, 4, 8],\n",
              "                                        'min_samples_split': [2, 5, 10, 13, 16],\n",
              "                                        'n_estimators': [5, 64, 123, 182, 241,\n",
              "                                                         300]},\n",
              "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
              "                   return_train_score=False, scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HkSRAxKaHbcL",
        "outputId": "867c9c7b-a127-4fa0-d13e-32940bce1407"
      },
      "source": [
        "rf_random.best_params_"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bootstrap': False,\n",
              " 'max_depth': 20,\n",
              " 'max_features': 'sqrt',\n",
              " 'min_samples_leaf': 2,\n",
              " 'min_samples_split': 2,\n",
              " 'n_estimators': 64}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U3flN-hgJos3",
        "outputId": "220425d8-3254-4f97-e953-847de59d8104"
      },
      "source": [
        "#Predict on test data\n",
        "y_pred = rf_random.predict(X_test)\n",
        "\n",
        "#Metrics Calculation\n",
        "r2 = round(metrics.r2_score(y_test, y_pred),2)\n",
        "Adj_r2 = round(1-(1-r2)*(9-1)/(9-1-1),2)\n",
        "\n",
        "#Display results\n",
        "print(color.BOLD + '\\nR2 score is ',r2)\n",
        "\n",
        "print(color.BOLD + '\\nAdjusted R2 score is ',Adj_r2)\n",
        "\n",
        "print(color.BOLD + '\\nMean Absolute Error is',round(metrics.mean_absolute_error(y_test,rf_random.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nMean Squared Error is',round(metrics.mean_squared_error(y_test,rf_random.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nRoot Mean Squared Error is',round(np.sqrt(mean_squared_error(y_test,rf_random.predict(X_test))),2))"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1m\n",
            "R2 score is  0.79\n",
            "\u001b[1m\n",
            "Adjusted R2 score is  0.76\n",
            "\u001b[1m\n",
            "Mean Absolute Error is 30619.87\n",
            "\u001b[1m\n",
            "Mean Squared Error is 2066902252.04\n",
            "\u001b[1m\n",
            "Root Mean Squared Error is 45463.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "trr0kN_8H6Rl",
        "outputId": "07488036-1fda-4076-f813-59c912fcad84"
      },
      "source": [
        "#Grid Search CV\n",
        "\n",
        "param_grid = {\n",
        "    'bootstrap': [True],\n",
        "    'max_depth': [80, 90, 100],\n",
        "    'max_features': [2, 3],\n",
        "    'min_samples_leaf': [3, 4, 5],\n",
        "    'min_samples_split': [4, 8, 10],\n",
        "    'n_estimators': [100, 200, 300]\n",
        "}\n",
        "\n",
        "# Instantiate the grid search model\n",
        "grid_search = GridSearchCV(estimator = rf, param_grid = param_grid, cv = 2, n_jobs = -1, verbose = 0)\n",
        "\n",
        "grid_search.fit(X_train,y_train)\n",
        "\n",
        "grid_search.best_params_"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bootstrap': True,\n",
              " 'max_depth': 80,\n",
              " 'max_features': 3,\n",
              " 'min_samples_leaf': 3,\n",
              " 'min_samples_split': 4,\n",
              " 'n_estimators': 300}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UzvSrsRxINaA",
        "outputId": "58677f8e-4099-432e-a731-5182d8a716db"
      },
      "source": [
        "best_grid = grid_search.best_estimator_\n",
        "best_grid"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=80, max_features=3, max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=3,\n",
              "                      min_samples_split=4, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=300, n_jobs=None, oob_score=False,\n",
              "                      random_state=0, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0LD-s9XFODaO",
        "outputId": "f19fea51-812d-4d1a-be03-28c8116ba71b"
      },
      "source": [
        "#Predict on test data\n",
        "y_pred = best_grid.predict(X_test)\n",
        "\n",
        "#Metrics Calculation\n",
        "r2 = round(metrics.r2_score(y_test, y_pred),2)\n",
        "Adj_r2 = round(1-(1-r2)*(9-1)/(9-1-1),2)\n",
        "\n",
        "#Display results\n",
        "print(color.BOLD + '\\nR2 score is ',r2)\n",
        "\n",
        "print(color.BOLD + '\\nAdjusted R2 score is ',Adj_r2)\n",
        "\n",
        "print(color.BOLD + '\\nMean Absolute Error is',round(metrics.mean_absolute_error(y_test,rf_random.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nMean Squared Error is',round(metrics.mean_squared_error(y_test,rf_random.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nRoot Mean Squared Error is',round(np.sqrt(mean_squared_error(y_test,rf_random.predict(X_test))),2))"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1m\n",
            "R2 score is  0.78\n",
            "\u001b[1m\n",
            "Adjusted R2 score is  0.75\n",
            "\u001b[1m\n",
            "Mean Absolute Error is 30619.87\n",
            "\u001b[1m\n",
            "Mean Squared Error is 2066902252.04\n",
            "\u001b[1m\n",
            "Root Mean Squared Error is 45463.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BlhhyCA6EJYw"
      },
      "source": [
        "### CatBoost Tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mvo9-WUNENLZ",
        "outputId": "d7ba8706-7d45-4f75-8dc4-ab8afdafce14"
      },
      "source": [
        "#Randomized Search CV\n",
        "n_estimators = [int(x) for x in np.linspace(start = 5 , stop = 150, num = 2)]\n",
        "\n",
        "max_depth = [int(x) for x in np.linspace(2, 120, num = 2)] \n",
        "\n",
        "max_depth.append(None)\n",
        "\n",
        "grid = {'n_estimators': n_estimators, 'learning_rate' : np.linspace(0,0.3,5)}\n",
        "\n",
        "print(grid)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_estimators': [5, 150], 'learning_rate': array([0.   , 0.075, 0.15 , 0.225, 0.3  ])}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F3SD5g8FEXBN",
        "outputId": "3f81b6c1-4f0a-47e4-90e0-2f744bc2e9e7"
      },
      "source": [
        "rscv = RandomizedSearchCV(      estimator=cat, \n",
        "                                param_distributions=grid, \n",
        "                                n_iter = 10, \n",
        "                                scoring='r2',\n",
        "                                cv = 5, \n",
        "                                verbose=0, \n",
        "                                random_state=42,\n",
        "                                n_jobs=-1,\n",
        "                                return_train_score=True)\n",
        "\n",
        "rscv.fit(X_train, y_train)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=5, error_score=nan,\n",
              "                   estimator=<catboost.core.CatBoostRegressor object at 0x7fcc9e668950>,\n",
              "                   iid='deprecated', n_iter=10, n_jobs=-1,\n",
              "                   param_distributions={'learning_rate': array([0.   , 0.075, 0.15 , 0.225, 0.3  ]),\n",
              "                                        'n_estimators': [5, 150]},\n",
              "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
              "                   return_train_score=True, scoring='r2', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hKxz1qm5EaZj",
        "outputId": "7500d759-d91b-49d6-bb8f-7979f8e0bfc1"
      },
      "source": [
        "# summarize result\n",
        "print(rscv.best_params_)\n",
        "print(rscv.score(X_test , y_test))"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_estimators': 150, 'learning_rate': 0.3}\n",
            "0.7998492587728515\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ixsqP3IIEeAp",
        "outputId": "5ec8c227-89a8-4e9c-d9af-1a69e8a12307"
      },
      "source": [
        "#Predict on test data\n",
        "y_pred = rscv.predict(X_test)\n",
        "\n",
        "#Metrics Calculation\n",
        "r2 = round(metrics.r2_score(y_test, y_pred),2)\n",
        "Adj_r2 = round(1-(1-r2)*(9-1)/(9-1-1),2)\n",
        "\n",
        "#Display results\n",
        "print(color.BOLD + '\\nR2 score is ',r2)\n",
        "\n",
        "print(color.BOLD + '\\nAdjusted R2 score is ',Adj_r2)\n",
        "\n",
        "print(color.BOLD + '\\nMean Absolute Error is',round(metrics.mean_absolute_error(y_test,rscv.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nMean Squared Error is',round(metrics.mean_squared_error(y_test,rscv.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nRoot Mean Squared Error is',round(np.sqrt(mean_squared_error(y_test,rscv.predict(X_test))),2))"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1m\n",
            "R2 score is  0.8\n",
            "\u001b[1m\n",
            "Adjusted R2 score is  0.77\n",
            "\u001b[1m\n",
            "Mean Absolute Error is 30108.2\n",
            "\u001b[1m\n",
            "Mean Squared Error is 1932297731.82\n",
            "\u001b[1m\n",
            "Root Mean Squared Error is 43957.91\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iwS1FfCANahe"
      },
      "source": [
        "### Light GBM Tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XXehILI_L8Zq",
        "outputId": "36210654-e41f-4c82-86a9-22a916a0b02b"
      },
      "source": [
        "#Randomized Search CV\n",
        "n_estimators = [int(x) for x in np.linspace(start = 5 , stop = 350, num = 2)]\n",
        "\n",
        "max_depth = [int(x) for x in np.linspace(3, 450, num = 3)] \n",
        "\n",
        "max_depth.append(None)\n",
        "\n",
        "grid = {'n_estimators': n_estimators,'learning_rate' : np.linspace(0,0.5,5)}\n",
        "\n",
        "print(grid)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_estimators': [5, 350], 'learning_rate': array([0.   , 0.125, 0.25 , 0.375, 0.5  ])}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FcTBU4HKMQy2",
        "outputId": "29f5bae7-1282-42e1-fa3a-28172d6d85e4"
      },
      "source": [
        "rscv = RandomizedSearchCV(      estimator=lg, \n",
        "                                param_distributions=grid, \n",
        "                                n_iter = 15, \n",
        "                                scoring='r2',\n",
        "                                cv = 10, \n",
        "                                verbose=0, \n",
        "                                random_state=42,\n",
        "                                n_jobs=-1,\n",
        "                                return_train_score=True)\n",
        "\n",
        "rscv.fit(X_train, y_train)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:281: UserWarning: The total space of parameters 10 is smaller than n_iter=15. Running 10 iterations. For exhaustive searches, use GridSearchCV.\n",
            "  % (grid_size, self.n_iter, grid_size), UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=10, error_score=nan,\n",
              "                   estimator=LGBMRegressor(boosting_type='gbdt',\n",
              "                                           class_weight=None,\n",
              "                                           colsample_bytree=1.0,\n",
              "                                           importance_type='split',\n",
              "                                           learning_rate=0.1, max_depth=-1,\n",
              "                                           min_child_samples=20,\n",
              "                                           min_child_weight=0.001,\n",
              "                                           min_split_gain=0.0, n_estimators=100,\n",
              "                                           n_jobs=-1, num_leaves=31,\n",
              "                                           objective=None, random_state=None,\n",
              "                                           reg_alpha=0.0, reg_lambda=0.0,\n",
              "                                           silent=True, subsample=1.0,\n",
              "                                           subsample_for_bin=200000,\n",
              "                                           subsample_freq=0),\n",
              "                   iid='deprecated', n_iter=15, n_jobs=-1,\n",
              "                   param_distributions={'learning_rate': array([0.   , 0.125, 0.25 , 0.375, 0.5  ]),\n",
              "                                        'n_estimators': [5, 350]},\n",
              "                   pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
              "                   return_train_score=True, scoring='r2', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6oUoP6ESMFMC",
        "outputId": "694a2a91-dc36-4165-f8ee-fb5a01cc2bcb"
      },
      "source": [
        "# summarize result\n",
        "print(rscv.best_params_)\n",
        "print(rscv.score(X_test , y_test))"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_estimators': 350, 'learning_rate': 0.125}\n",
            "0.8066438082211866\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_l6LsUYMHRX",
        "outputId": "22696190-2e1c-4439-9a49-77ef1823f9eb"
      },
      "source": [
        "#Predict on test data\n",
        "y_pred = rscv.predict(X_test)\n",
        "\n",
        "#Metrics Calculation\n",
        "r2 = round(metrics.r2_score(y_test, y_pred),2)\n",
        "Adj_r2 = round(1-(1-r2)*(9-1)/(9-1-1),2)\n",
        "\n",
        "#Display results\n",
        "print(color.BOLD + '\\nR2 score is ',r2)\n",
        "\n",
        "print(color.BOLD + '\\nAdjusted R2 score is ',Adj_r2)\n",
        "\n",
        "print(color.BOLD + '\\nMean Absolute Error is',round(metrics.mean_absolute_error(y_test,rscv.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nMean Squared Error is',round(metrics.mean_squared_error(y_test,rscv.predict(X_test)),2))\n",
        "\n",
        "print(color.BOLD + '\\nRoot Mean Squared Error is',round(np.sqrt(mean_squared_error(y_test,rscv.predict(X_test))),2))"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1m\n",
            "R2 score is  0.81\n",
            "\u001b[1m\n",
            "Adjusted R2 score is  0.78\n",
            "\u001b[1m\n",
            "Mean Absolute Error is 28981.8\n",
            "\u001b[1m\n",
            "Mean Squared Error is 1866701709.51\n",
            "\u001b[1m\n",
            "Root Mean Squared Error is 43205.34\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "64BPpQR-27-b"
      },
      "source": [
        "### Exporting the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zPvnVPK9NwSx"
      },
      "source": [
        "\n",
        "\n",
        "> Based on hyperparameter tuning, we can either choose Catboost or LightGBM(top-performer)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kk6fLKUpExO6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1836503-1822-4145-85da-1d9b9f979059"
      },
      "source": [
        "#Exporting the model using joblib library\n",
        "import joblib\n",
        "joblib.dump(lg,\"California_Model.pkl\")"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['California_Model.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    }
  ]
}