{
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "DGPlYumZnO1t"
      },
      "source": [
        "# Simple Neural Network using Keras\n",
        "\n",
        "\n",
        "## Introduction\n",
        "Our use case is to build, train, and evaluate a prediction model for sales analysis.\n",
        "\n",
        "In this model, we need to feed the advertising budget of TV, radio, and newspapers to the model and the model will forecast the possible sales.\n",
        "\n",
        "## Dataset\n",
        "The advertising dataset captures the sales revenue generated with respect to advertisement costs across numerous platforms like radio, TV, and newspapers.\n",
        "\n",
        "### Features:\n",
        "\n",
        "#### Digital: advertising dollars spent on Internet.\n",
        "#### TV: advertising dollars spent on TV.\n",
        "#### Radio: advertising dollars spent on Radio.\n",
        "#### Newspaper: advertising dollars spent on Newspaper.\n",
        "\n",
        "### Target (Label):\n",
        "#### Sales budget"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YBXAUbijjNBF"
      },
      "source": [
        "# Step 1: Data Preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AsHg6SD2nO1v"
      },
      "source": [
        "### Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "gEXV-RxPnO1w"
      },
      "outputs": [],
      "source": [
        "# Import the necessary libraries\n",
        "\n",
        "# For Data loading, Exploraotry Data Analysis, Graphing\n",
        "import pandas as pd   # Pandas for data processing libraries\n",
        "import numpy as np    # Numpy for mathematical functions\n",
        "\n",
        "import matplotlib.pyplot as plt # Matplotlib for visualization tasks\n",
        "import seaborn as sns # Seaborn for data visualization library based on matplotlib.\n",
        "%matplotlib inline\n",
        "\n",
        "import sklearn        # ML tasks\n",
        "from sklearn.model_selection import train_test_split # Split the dataset\n",
        "from sklearn.metrics import mean_squared_error  # Calculate Mean Squared Error\n",
        "\n",
        "# Build the Network\n",
        "from tensorflow import keras\n",
        "from keras.models import Sequential\n",
        "#from tensorflow.keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "\n",
        "\n",
        "#from keras.callbacks import EarlyStopping\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "wSDtqpd-Dk9H"
      },
      "outputs": [],
      "source": [
        "# Next, you read the dataset into a Pandas dataframe.\n",
        "\n",
        "url = 'https://github.com/LinkedInLearning/artificial-intelligence-foundations-neural-networks-4381282/blob/main/Advertising_2023.csv?raw=true'\n",
        "advertising_df= pd.read_csv(url,index_col=0)"
      ]
    },
    {
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      "execution_count": 3,
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        "id": "Ue_jPccWnT30",
        "outputId": "70f4b09a-14b7-4d90-b4fc-1d9731ac527a"
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            "text/plain": [
              "    digital     TV  radio  newspaper  sales\n",
              "1    345.15  156.0   37.8       69.2   22.1\n",
              "2     66.75   46.0   39.3       45.1   10.4\n",
              "3     25.80   18.3   45.9       69.3    9.3\n",
              "4    227.25  145.1   41.3       58.5   18.5\n",
              "5    271.20  165.2   10.8       58.4   12.9\n",
              "6     13.05    8.7   48.9       75.0    7.2\n",
              "7     86.25   57.5   32.8       23.5   11.8\n",
              "8    180.30  120.2   19.6       11.6   13.2\n",
              "9     12.90    8.6    2.1        1.0    4.8\n",
              "10   299.70  199.8    2.6       21.2   10.6"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>digital</th>\n",
              "      <th>TV</th>\n",
              "      <th>radio</th>\n",
              "      <th>newspaper</th>\n",
              "      <th>sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>345.15</td>\n",
              "      <td>156.0</td>\n",
              "      <td>37.8</td>\n",
              "      <td>69.2</td>\n",
              "      <td>22.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>66.75</td>\n",
              "      <td>46.0</td>\n",
              "      <td>39.3</td>\n",
              "      <td>45.1</td>\n",
              "      <td>10.4</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>25.80</td>\n",
              "      <td>18.3</td>\n",
              "      <td>45.9</td>\n",
              "      <td>69.3</td>\n",
              "      <td>9.3</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>227.25</td>\n",
              "      <td>145.1</td>\n",
              "      <td>41.3</td>\n",
              "      <td>58.5</td>\n",
              "      <td>18.5</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>271.20</td>\n",
              "      <td>165.2</td>\n",
              "      <td>10.8</td>\n",
              "      <td>58.4</td>\n",
              "      <td>12.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>13.05</td>\n",
              "      <td>8.7</td>\n",
              "      <td>48.9</td>\n",
              "      <td>75.0</td>\n",
              "      <td>7.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>86.25</td>\n",
              "      <td>57.5</td>\n",
              "      <td>32.8</td>\n",
              "      <td>23.5</td>\n",
              "      <td>11.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>180.30</td>\n",
              "      <td>120.2</td>\n",
              "      <td>19.6</td>\n",
              "      <td>11.6</td>\n",
              "      <td>13.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>12.90</td>\n",
              "      <td>8.6</td>\n",
              "      <td>2.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>299.70</td>\n",
              "      <td>199.8</td>\n",
              "      <td>2.6</td>\n",
              "      <td>21.2</td>\n",
              "      <td>10.6</td>\n",
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              "  </tbody>\n",
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          "metadata": {},
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      ],
      "source": [
        "advertising_df.head(10)"
      ]
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    {
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      "execution_count": 4,
      "metadata": {
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        },
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        "outputId": "8501c8e5-3ced-45eb-afa7-46c697d3185c"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1199 entries, 1 to 1197\n",
            "Data columns (total 5 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   digital    1199 non-null   float64\n",
            " 1   TV         1199 non-null   float64\n",
            " 2   radio      1199 non-null   float64\n",
            " 3   newspaper  1199 non-null   float64\n",
            " 4   sales      1199 non-null   float64\n",
            "dtypes: float64(5)\n",
            "memory usage: 56.2 KB\n"
          ]
        }
      ],
      "source": [
        "# Pandas info() function is used to get a concise summary of the dataframe.\n",
        "advertising_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
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            "text/plain": [
              "           digital          TV        radio    newspaper        sales\n",
              "count  1199.000000  1199.00000  1199.000000  1199.000000  1199.000000\n",
              "mean    135.472394   146.61985    23.240617    30.529942    14.005505\n",
              "std     135.730821    85.61047    14.820827    21.712507     5.202804\n",
              "min       0.300000     0.70000     0.000000     0.300000     1.600000\n",
              "25%      24.250000    73.40000     9.950000    12.800000    10.300000\n",
              "50%      64.650000   149.70000    22.500000    25.600000    12.900000\n",
              "75%     256.950000   218.50000    36.500000    45.100000    17.400000\n",
              "max     444.600000   296.40000    49.600000   114.000000    27.000000"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>digital</th>\n",
              "      <th>TV</th>\n",
              "      <th>radio</th>\n",
              "      <th>newspaper</th>\n",
              "      <th>sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1199.000000</td>\n",
              "      <td>1199.00000</td>\n",
              "      <td>1199.000000</td>\n",
              "      <td>1199.000000</td>\n",
              "      <td>1199.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>135.472394</td>\n",
              "      <td>146.61985</td>\n",
              "      <td>23.240617</td>\n",
              "      <td>30.529942</td>\n",
              "      <td>14.005505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>135.730821</td>\n",
              "      <td>85.61047</td>\n",
              "      <td>14.820827</td>\n",
              "      <td>21.712507</td>\n",
              "      <td>5.202804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.70000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>1.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>24.250000</td>\n",
              "      <td>73.40000</td>\n",
              "      <td>9.950000</td>\n",
              "      <td>12.800000</td>\n",
              "      <td>10.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>64.650000</td>\n",
              "      <td>149.70000</td>\n",
              "      <td>22.500000</td>\n",
              "      <td>25.600000</td>\n",
              "      <td>12.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>256.950000</td>\n",
              "      <td>218.50000</td>\n",
              "      <td>36.500000</td>\n",
              "      <td>45.100000</td>\n",
              "      <td>17.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>444.600000</td>\n",
              "      <td>296.40000</td>\n",
              "      <td>49.600000</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>27.000000</td>\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-c5a69589-ddb3-4e03-a4e8-cec86963fa9d\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c5a69589-ddb3-4e03-a4e8-cec86963fa9d')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-c5a69589-ddb3-4e03-a4e8-cec86963fa9d button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7a85ac93-eaba-495b-8bf6-7b2532d23d8e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7a85ac93-eaba-495b-8bf6-7b2532d23d8e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "### Get summary of statistics of the data\n",
        "advertising_df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IzyfOhGaEzlL",
        "outputId": "a4a882d9-5740-4b8f-c8ef-8a1feac902df"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1199, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "#shape of dataframe - 1199 rows, five columns\n",
        "advertising_df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6nNrSEBnBk71"
      },
      "source": [
        "Let's check for any null values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P7gHdfMdBk71",
        "outputId": "58cd17b2-5760-4c4d-c8a0-2a56fc080e21"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "digital      0\n",
              "TV           0\n",
              "radio        0\n",
              "newspaper    0\n",
              "sales        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# The isnull() method is used to check and manage NULL values in a data frame.\n",
        "advertising_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hGbU2YpwFBIC",
        "outputId": "f2bea7ec-3bcf-4fce-a20c-7f8fc55ee802"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "#check there are any NAN values\n",
        "advertising_df.isnull().values.any()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QWVdsrmgnO1_"
      },
      "source": [
        "## Exploratory Data Analysis (EDA)\n",
        "\n",
        "Let's create some simple plots to check out the data!  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "qqsAMR1rov7F",
        "outputId": "b93dc775-49dc-4d17-e30a-e7d842e8b3bf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# The heatmap is a way of representing the data in a 2-dimensional form. The data values are represented as colors in the graph.\n",
        "# The goal of the heatmap is to provide a colored visual summary of information.\n",
        "sns.heatmap(advertising_df.corr())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "i8bsyyVPm13-",
        "outputId": "08b9e815-6937-4d2e-dc5d-c3db9cf77d28"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "## Another option is to plot the heatmap so that the values are shown.\n",
        "\n",
        "plt.figure(figsize=(10,5))\n",
        "sns.heatmap(advertising_df.corr(),annot=True,vmin=0,vmax=1,cmap='ocean')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "8UdKVi-2tNKI",
        "outputId": "530307ee-f17f-4bd3-f6ce-67a3690f273c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "#create a correlation matrix\n",
        "corr = advertising_df.corr()\n",
        "plt.figure(figsize=(10, 5))\n",
        "sns.heatmap(corr[(corr >= 0.5) | (corr <= -0.7)],\n",
        "            cmap='viridis', vmax=1.0, vmin=-1.0, linewidths=0.1,\n",
        "            annot=True, annot_kws={\"size\": 8}, square=True)\n",
        "plt.tight_layout()\n",
        "display(plt.show())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dNlslaNtuO2Q",
        "outputId": "f8417a52-c743-41eb-c4aa-02d093abeda9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            digital        TV     radio  newspaper     sales\n",
              "digital    1.000000  0.474256  0.041316   0.048023  0.380101\n",
              "TV         0.474256  1.000000  0.055697   0.055579  0.781824\n",
              "radio      0.041316  0.055697  1.000000   0.353096  0.576528\n",
              "newspaper  0.048023  0.055579  0.353096   1.000000  0.227039\n",
              "sales      0.380101  0.781824  0.576528   0.227039  1.000000"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-c10f1b06-840e-4a74-ba4c-3d66a97ff452\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>digital</th>\n",
              "      <th>TV</th>\n",
              "      <th>radio</th>\n",
              "      <th>newspaper</th>\n",
              "      <th>sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>digital</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.474256</td>\n",
              "      <td>0.041316</td>\n",
              "      <td>0.048023</td>\n",
              "      <td>0.380101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV</th>\n",
              "      <td>0.474256</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.055697</td>\n",
              "      <td>0.055579</td>\n",
              "      <td>0.781824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>radio</th>\n",
              "      <td>0.041316</td>\n",
              "      <td>0.055697</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.353096</td>\n",
              "      <td>0.576528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>newspaper</th>\n",
              "      <td>0.048023</td>\n",
              "      <td>0.055579</td>\n",
              "      <td>0.353096</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.227039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sales</th>\n",
              "      <td>0.380101</td>\n",
              "      <td>0.781824</td>\n",
              "      <td>0.576528</td>\n",
              "      <td>0.227039</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c10f1b06-840e-4a74-ba4c-3d66a97ff452')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1141e4a8-0600-4097-b6f0-e7d87aadeb2e')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-1141e4a8-0600-4097-b6f0-e7d87aadeb2e button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c10f1b06-840e-4a74-ba4c-3d66a97ff452 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c10f1b06-840e-4a74-ba4c-3d66a97ff452');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "advertising_df.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 842
        },
        "id": "WKFX9SCauZp4",
        "outputId": "64de2ac5-4703-428f-a738-454b72adb51e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-6c77a4103e7b>:4: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  mask = np.zeros_like(advertising_df.corr(), dtype=np.bool)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x900 with 2 Axes>"
            ],
            "image/png": 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AgoGTGocZ3C0AAAAAtkMiBAAAAMB2aI0DAAAAggGTJZhCRQgAAACA7ZAIAQAAALAdWuMAAACAYEBrnClUhAAAAADYDokQAAAAANuhNQ4AAAAIAg4WVDWFuwUAAADAdqgIAQAAAMGAyRJMoSIEAAAAwHZIhAAAAADYDq1xAAAAQDBw0hpnBhUhAAAAALZDIgQAAADAdmiNAwAAAIKBgxqHGdwtAAAAALZDRQgAAAAIBkyWYAoVIQAAAAC2QyIEAAAAwHZojQMAAACCgYPWODOoCAEAAACwHRIhAAAAALZDaxwAAAAQBBysI2QKdwsAAACA7ZAIAQAAALAdWuMAAACAYMCCqqZQEQIAAABgOwFVETp04EBNh4AA1bBRo5oOAQAAILCxjpApVIQAAAAA2A6JEAAAAADbCajWOAAAAAAWOalxmMHdAgAAAGA7JEIAAAAAbIfWOAAAACAYMGucKVSEAAAAANgOFSEAAAAgCDicVITMoCIEAAAAwHZIhAAAAADYDq1xAAAAQDBwUOMwg7sFAAAAwHZIhAAAAADYDokQAAAAEAwcjsDZLJgzZ45iYmIUHh6uxMRErV279oT7Jicny+FwHLf95S9/8fl6JEIAAAAAatTixYvlcrmUmZmpdevWKTY2Vqmpqdq1a1eV+y9ZskQ7d+70bF999ZVCQkL017/+1edrkggBAAAAwcDpCJzNpOzsbI0fP15paWnq1q2bcnNzVa9ePS1YsKDK/Zs0aaLmzZt7tuXLl6tevXokQgAAAABqTnl5uUpLS7228vLyKvetqKhQYWGhUlJSPGNOp1MpKSlavXq1T9d7+umndeWVV6p+/fo+x0giBAAAAMCvsrKyFBkZ6bVlZWVVue+ePXtUWVmp6Ohor/Ho6GgVFxef8lpr167VV199pXHjxpmKkXWEAAAAgGAQQOsIZWRkyOVyeY2FhYVVy7Wefvpp9ejRQ3369DF1HIkQAAAAAL8KCwvzOfFp1qyZQkJCVFJS4jVeUlKi5s2bn/TYsrIyvfjii5o5c6bpGAMnbQQAAABgO6GhoYqPj1d+fr5nzO12Kz8/X3379j3psS+//LLKy8t1zTXXmL4uFSEAAAAgCDgszNYWKFwul8aMGaOEhAT16dNHOTk5KisrU1pamiRp9OjRatWq1XHPGT399NMaPny4mjZtavqaJEIAAAAAatTIkSO1e/duTZs2TcXFxYqLi1NeXp5nAoWioiI5nd7NbJs3b9aqVau0bNkyS9d0GIZhnHbkfnLowIGaDgEBqmGjRjUdAgAAQEDb+di8mg7Bo8Wt/1fTIZwSFSEAAAAgGDhqb2tcTWCyBAAAAAC2Q0UIAAAACAZOahxmcLcAAAAA2A6JEAAAAADboTUOAAAACAa0xpnC3QIAAABgOyRCAAAAAGyH1jgAAAAgGLCOkClUhAAAAADYDhUhAAAAIAg4nFSEzKAiBAAAAMB2SIQAAAAA2A6tcQAAAEAwcFDjMIO7BQAAAMB2SIQAAAAA2A6tcQAAAEAwYB0hU6gIAQAAALAdEiEAAAAAtkNrHAAAABAMWFDVFCpCAAAAAGyHihAAAAAQDFhHyBTuFgAAAADbIRECAAAAYDu0xgEAAABBwMFkCaZQEQIAAABgOyRCAAAAAGyH1jgAAAAgGDhojTODihAAAAAA26EiBAAAAAQDJzUOM7hbAAAAAGyHRAgAAACA7dAaBwAAAAQDJkswhYoQAAAAANshEQIAAABgOz4nQpdffrny8vJkGEZ1xgMAAADACocjcLZawOdEaP/+/frLX/6iNm3aaNq0adq6dWt1xgUAAAAA1cbnRCg/P19bt27V2LFj9fzzz6tjx4666KKL9MILL6i8vLw6YwQAAAAAvzL1jFDbtm01ffp0bd26VcuXL1fLli01fvx4tWjRQrfccosKCwurK04AAAAAJ+FwOgNmqw0cxmk+9HPo0CG98MILuuuuu3Tw4EH98ssv1s914MDphIIg1rBRo5oOAQAAIKDtWrykpkPwiBp5aU2HcEqntY7Qtm3btHDhQi1cuFAHDx5USkqKv+ICAAAAYEYtmaQgUJhOhI4ePapXXnlFCxYs0MqVK9W6dWuNHTtWaWlpat26tU/nKC8vP+65orCwMLOhAAAAAIAlPjfwrV27VjfeeKNatGih8ePHq3nz5srLy9PWrVs1bdo0n5MgScrKylJkZKTXlpWVZekNAAAAAIBZPj8j5HQ6FRsbq7Fjx2rUqFFq3Lix5YueqCJU8fPPls+J4MYzQgAAACe365XXazoEj6jLh9d0CKfkc2vcxRdfrBdffFH16tU77YuGhYVV2QpHIgQAAADgTPC5Ne6tt97S4cOHqzMWAAAAADgjfK4IneYs2wAAAACqk6N2rN8TKEzdLQdT8gEAAAAIAqamz+7UqdMpk6F9+/adVkAAAAAALHBStDDDVCI0Y8YMRUZGVlcsAAAAAHBGmEqErrzySkVFRVVXLAAAAABwRvicCPF8EAAAABC4+HndHJ8nS2DWOAAAAADBwueKkNvtrs44AAAAAOCMMfWMEAAAAIAAxTpCpnC3AAAAANgOFSEAAAAgGLCOkClUhAAAAADYDokQAAAAANuhNQ4AAAAIBqwjZAoVIQAAAAC2QyIEAAAAwHZojQMAAACCgZMahxncLQAAAAC2QyIEAAAAwHZojQMAAACCAbPGmUJFCAAAAIDtUBECAAAAgoDDSUXIDCpCAAAAAGyHRAgAAACA7ZAIAQAAAMHA4QyczYI5c+YoJiZG4eHhSkxM1Nq1a0+6/4EDB3TLLbeoRYsWCgsLU6dOnfT222/7fD2eEQIAAABQoxYvXiyXy6Xc3FwlJiYqJydHqamp2rx5s6Kioo7bv6KiQgMHDlRUVJReeeUVtWrVSjt27FCjRo18vqbDMAzDj+/htBw6cKCmQ0CAamjiQw0AAGBHe5a9X9MheDQbdJGp/RMTE9W7d289/vjjkiS3263WrVvr1ltv1eTJk4/bPzc3V7NmzdKmTZtUp04dSzHSGgcAAAAEA6cjYLby8nKVlpZ6beXl5VWGXVFRocLCQqWkpPz3rTidSklJ0erVq6s85s0331Tfvn11yy23KDo6Wt27d9f999+vyspK32+XubsLAAAAACeXlZWlyMhIry0rK6vKfffs2aPKykpFR0d7jUdHR6u4uLjKY7Zu3apXXnlFlZWVevvttzV16lTNnj1b9957r88x8owQAAAAEAwcgbOOUEZGhlwul9dYWFiY387vdrsVFRWlJ598UiEhIYqPj9ePP/6oWbNmKTMz06dzkAgBAAAA8KuwsDCfE59mzZopJCREJSUlXuMlJSVq3rx5lce0aNFCderUUUhIiGesa9euKi4uVkVFhUJDQ095XVrjAAAAANSY0NBQxcfHKz8/3zPmdruVn5+vvn37VnlMv379tGXLFrndbs/YN998oxYtWviUBEkkQgAAAEBwqOm1g05jHSGXy6X58+frmWee0caNG3XTTTeprKxMaWlpkqTRo0crIyPDs/9NN92kffv2aeLEifrmm2/01ltv6f7779ctt9zi8zVpjQMAAABQo0aOHKndu3dr2rRpKi4uVlxcnPLy8jwTKBQVFcnp/G+C1bp1a7377ru67bbb1LNnT7Vq1UoTJ07UnXfe6fM1WUcItQLrCAEAAJzcnvdX1nQIHs0uurCmQzglKkIAAABAEHA4A2fWuNqAZ4QAAAAA2A6JEAAAAADboTUOAAAACAYBtKBqbUBFCAAAAIDtUBECAAAAgoGTGocZ3C0AAAAAtkMiBAAAAMB2aI0DAAAAggGTJZhCRQgAAACA7ZAIAQAAALAdWuMAAACAYOCkNc4MKkIAAAAAbIeKEAAAABAEHA5qHGZwtwAAAADYDokQAAAAANuhNQ4AAAAIBqwjZAoVIQAAAAC2QyIEAAAAwHZojQMAAACCAesImUJFCAAAAIDtUBECAAAAggHrCJnC3QIAAABgOyRCAAAAAGyH1jgAAAAgGDBZgilUhAAAAADYDokQAAAAANuhNQ4AAAAIAg4HrXFmUBECAAAAYDsBVRFq2KhRTYcAAAAAwAYCKhFKnv54TYeAAFUwPV2D7p1b02EggC2bclNNhwAAQM1y0uxlBncLAAAAgO0EVEUIAAAAgEVMlmAKFSEAAAAAtkMiBAAAAMB2aI0DAAAAggGtcaZQEQIAAABgOyRCAAAAAGyH1jgAAAAgGLCOkCncLQAAAAC2Q0UIAAAACAIOJkswhYoQAAAAANshEQIAAABgO7TGAQAAAMHASWucGVSEAAAAANgOiRAAAAAA26E1DgAAAAgGDmocZnC3AAAAANgOiRAAAAAA26E1DgAAAAgGzBpnChUhAAAAALZDRQgAAAAIBg4qQmZQEQIAAABgOyRCAAAAAGyH1jgAAAAgGLCOkCncLQAAAAC2QyIEAAAAwHZojQMAAACCgIN1hEyhIgQAAADAdqgIAQAAAMGAdYRMoSIEAAAAwHZIhAAAAADYDq1xAAAAQDBwUuMwg7sFAAAAwHZIhAAAAADYDq1xAAAAQDBg1jhTqAgBAAAAsB0SIQAAAAC2Q2scAAAAEAyctMaZQUUIAAAAgO2QCAEAAABBwOFwBsxmxZw5cxQTE6Pw8HAlJiZq7dq1J9x34cKFcjgcXlt4eLip65EIAQAAAKhRixcvlsvlUmZmptatW6fY2FilpqZq165dJzwmIiJCO3fu9Gw7duwwdU0SIQAAAAA1Kjs7W+PHj1daWpq6deum3Nxc1atXTwsWLDjhMQ6HQ82bN/ds0dHRpq5JIgQAAAAEA4cjYLby8nKVlpZ6beXl5VWGXVFRocLCQqWkpHjGnE6nUlJStHr16hO+3cOHD6tt27Zq3bq1LrnkEv3nP/8xdbtIhAAAAAD4VVZWliIjI722rKysKvfds2ePKisrj6voREdHq7i4uMpjOnfurAULFuiNN97Q888/L7fbrfPPP18//PCDzzEyfTYAAAAAv8rIyJDL5fIaCwsL89v5+/btq759+3q+P//889W1a1fNmzdP99xzj0/nIBECAAAAgkEArSMUFhbmc+LTrFkzhYSEqKSkxGu8pKREzZs39+kcderUUa9evbRlyxafY6Q1DgAAAECNCQ0NVXx8vPLz8z1jbrdb+fn5XlWfk6msrNSXX36pFi1a+HxdKkIAAABAMLC4fk8gcLlcGjNmjBISEtSnTx/l5OSorKxMaWlpkqTRo0erVatWnueMZs6cqfPOO08dOnTQgQMHNGvWLO3YsUPjxo3z+ZokQgAAAABq1MiRI7V7925NmzZNxcXFiouLU15enmcChaKiIjmd/0309u/fr/Hjx6u4uFiNGzdWfHy8Pv74Y3Xr1s3nazoMwzD8/k4sSp7+eE2HgABVMD1dg+6dW9NhIIAtm3JTTYcAAECNKi058eKjZ1pEdFRNh3BKVIQAAACAYBBAkyXUBrW3kRAAAAAALCIRAgAAAGA7llvjvvvuO+Xk5Gjjxo2SpG7dumnixIlq376934IDAAAA4BuHg9Y4MyxVhN59911169ZNa9euVc+ePdWzZ0+tWbNGf/zjH7V8+XJ/xwgAAAAAfmWpIjR58mTddttteuCBB44bv/POOzVw4EC/BAcAAADAR06eejHD0t3auHGjxo4de9z49ddfr6+//vq0gwIAAACA6mQpETr77LO1YcOG48Y3bNigqKjAnzMcAAAAgL1Zao0bP368brjhBm3dulXnn3++JOmjjz7Sgw8+KJfL5dcAAQAAAPiAyRJMsZQITZ06VQ0bNtTs2bOVkZEhSWrZsqWmT5+uCRMm+DVAAAAAAPA3S4mQw+HQbbfdpttuu02HDh2SJDVs2NCvgQEAAABAdbG8jtBvSIAAAACAAEBrnCk+J0Lnnnuu8vPz1bhxY/Xq1eukCzatW7fOL8EBAAAAQHXwORG65JJLFBYWJkkaPnx4dcUDAAAAANXO50QoMzOzyq8BAAAABAAWVDXltJ8RsqK8vFzl5eVeY79VmwAAAACguvmcCDVu3PikzwX93r59+076elZWlmbMmOE19muVqZmv4QAAAAD4HTeTJZjicyKUk5Pj+Xrv3r269957lZqaqr59+0qSVq9erXfffVdTp0495bkyMjKOW3g1LCxMBVnzfQ0HAAAAACzzOREaM2aM5+vLLrtMM2fOVHp6umdswoQJevzxx/Xee+/ptttuO+m5wsLCaIUDAAAAUGMsPVH17rvvavDgwceNDx48WO+9995pBwUAAADAHLcROFttYCkRatq0qd54443jxt944w01bdr0tIMCAAAAgOpkada4GTNmaNy4cSooKFBiYqIkac2aNcrLy9P8+TznAwAAACCwWUqErrvuOnXt2lWPPvqolixZIknq2rWrVq1a5UmMAAAAAJw5bqOW9KQFCMvrCCUmJmrRokX+jAUAAAAAzojTXlD16NGjqqio8BqLiIg43dMCAAAAMMGgImSKpckSjhw5ovT0dEVFRal+/fpq3Lix1wYAAAAAgcxSIjRp0iS9//77mjt3rsLCwvTUU09pxowZatmypZ599ll/xwgAAAAAfmWpNe7f//63nn32WSUnJystLU0XXHCBOnTooLZt22rRokUaNWqUv+MEAAAAcBJ0xpljqSK0b98+tWvXTtKvzwPt27dPktS/f3+tXLnSf9EBAAAAQDWwlAi1a9dO27ZtkyR16dJFL730kqRfK0WNGjXyW3AAAAAAUB0stcalpaXp888/V1JSkiZPnqyhQ4fq8ccf17Fjx5Sdne3vGAEAAACcAusImWM6ETp27JiWLl2q3NxcSVJKSoo2bdqkwsJCdejQQT179vR7kAAAAADgT6YToTp16uiLL77wGmvbtq3atm3rt6AAAAAAoDpZekbommuu0dNPP+3vWAAAAABYZBhGwGy1gaVnhH755RctWLBA7733nuLj41W/fn2v13lOCAAAAEAgs5QIffXVVzr33HMlSd98843Xaw6H4/SjAgAAAGBKbanEBApLidCKFSv8HQcAAAAAnDGWnhECAAAAgNrMUkUIAAAAQGBx0xlnChUhAAAAALZDIgQAAADAdmiNAwAAAIIAs8aZQ0UIAAAAgO1QEQIAAACCgFtUhMygIgQAAADAdkiEAAAAANgOrXEAAABAEGCyBHOoCAEAAACwHRIhAAAAALZDaxwAAAAQBOiMM4eKEAAAAADboSIEAAAABAE3JSFTqAgBAAAAsB0SIQAAAAC2Q2scAAAAEARYR8gcKkIAAAAAbIdECAAAAIDt0BoHAAAABAFmjTOHihAAAAAA2yERAgAAAGA7tMYBAAAAQYDOOHOoCAEAAACwHSpCAAAAQBBgHSFzqAgBAAAAsB0SIQAAAAC2Q2scAAAAEARYR8gcKkIAAAAAbIdECAAAAIDt0BoHAAAABAFmjTOHihAAAAAA2yERAgAAAIKAEUCbFXPmzFFMTIzCw8OVmJiotWvX+nTciy++KIfDoeHDh5u6HokQAAAAgBq1ePFiuVwuZWZmat26dYqNjVVqaqp27dp10uO2b9+u22+/XRdccIHpa5IIAQAAAPCr8vJylZaWem3l5eUn3D87O1vjx49XWlqaunXrptzcXNWrV08LFiw44TGVlZUaNWqUZsyYoXbt2pmOkUQIAAAACAJuwwiYLSsrS5GRkV5bVlZWlXFXVFSosLBQKSkpnjGn06mUlBStXr36hO935syZioqK0tixYy3dL2aNAwAAAOBXGRkZcrlcXmNhYWFV7rtnzx5VVlYqOjraazw6OlqbNm2q8phVq1bp6aef1oYNGyzHSCIEAAAAwK/CwsJOmPicrkOHDunaa6/V/Pnz1axZM8vnIRECAAAAgkBtXUeoWbNmCgkJUUlJidd4SUmJmjdvftz+3333nbZv366hQ4d6xtxutyTprLPO0ubNm9W+fftTXpdnhAAAAADUmNDQUMXHxys/P98z5na7lZ+fr759+x63f5cuXfTll19qw4YNnm3YsGH605/+pA0bNqh169Y+XZeKEAAAAIAa5XK5NGbMGCUkJKhPnz7KyclRWVmZ0tLSJEmjR49Wq1atlJWVpfDwcHXv3t3r+EaNGknSceMnQyIEAAAABAF3LW2Nk6SRI0dq9+7dmjZtmoqLixUXF6e8vDzPBApFRUVyOv3bzEYiBAAAAKDGpaenKz09vcrXCgoKTnrswoULTV+PRAgAAAAIArW4IFQjmCwBAAAAgO2QCAEAAACwHYdRWyccBwAAAOCxbsuOmg7B49wObWs6hFMKqGeE/jRjTk2HgAC1IvMWDbp3bk2HgQC2bMpNKvhyc02HgQCV3KNzTYcAAAgwtMYBAAAAsJ2AqggBAAAAsKY2ryNUE6gIAQAAALAdKkIAAABAEGAONHOoCAEAAACwHRIhAAAAALZDaxwAAAAQBNx0xplCRQgAAACA7ZAIAQAAALAdWuMAAACAIGCI3jgzqAgBAAAAsB0qQgAAAEAQYB0hc6gIAQAAALAdEiEAAAAAtkNrHAAAABAE3LTGmUJFCAAAAIDtkAgBAAAAsB1a4wAAAIAgQGecOVSEAAAAANgOiRAAAAAA26E1DgAAAAgCLKhqDhUhAAAAALZDRQgAAAAIAqwjZA4VIQAAAAC2QyIEAAAAwHZojQMAAACCAJMlmENFCAAAAIDtkAgBAAAAsB1a4wAAAIAg4KYzzhQqQgAAAABsh4oQAAAAEASYLMEcKkIAAAAAbMd0IvTLL7/o2WefVUlJSXXEAwAAAADVznRr3FlnnaUbb7xRGzdurI54AAAAAFhAa5w5llrj+vTpow0bNvg5FAAAAAA4MyxNlnDzzTfL5XLp+++/V3x8vOrXr+/1es+ePf0SHAAAAABUB0uJ0JVXXilJmjBhgmfM4XDIMAw5HA5VVlb6JzoAAAAAPnGL1jgzLCVC27Zt83ccAAAAAHDGWEqE2rZt6+84AAAAAOCMsbyO0HPPPad+/fqpZcuW2rFjhyQpJydHb7zxht+CAwAAAOAbwwicrTawlAjNnTtXLpdLQ4YM0YEDBzzPBDVq1Eg5OTn+jA8AAAAA/M5SIvTYY49p/vz5uvvuuxUSEuIZT0hI0Jdffum34AAAAAD4xjCMgNlqA0uJ0LZt29SrV6/jxsPCwlRWVnbaQQEAAABAdbKUCJ1zzjlVLqial5enrl27nm5MAAAAAFCtLM0a53K5dMstt+jo0aMyDENr167Vv/71L2VlZempp57yd4wAAAAATsFdS1rSAoWlRGjcuHGqW7eupkyZoiNHjujqq69Wy5Yt9cgjj3gWWwUAAACAQGUpEZKkUaNGadSoUTpy5IgOHz6sqKgof8YFAAAAANXGciIkSbt27dLmzZslSQ6HQ2effbZfggIAAABgTm2ZrS1QWJos4dChQ7r22mvVsmVLJSUlKSkpSS1bttQ111yjgwcP+jtGAAAAAPArS4nQuHHjtGbNGr311ls6cOCADhw4oKVLl+qzzz7T//3f//k7RgAAAACn4DYCZ6sNLLXGLV26VO+++6769+/vGUtNTdX8+fM1ePBgvwUHAAAAANXBUkWoadOmioyMPG48MjJSjRs3Pu2gAAAAAKA6WUqEpkyZIpfLpeLiYs9YcXGxJk2apKlTp/otOAAAAAC+MQwjYLbawFJr3Ny5c7Vlyxa1adNGbdq0kSQVFRUpLCxMu3fv1rx58zz7rlu3zj+RAgAAAICfWEqEhg8f7ucwAAAAAODMsZQIZWZm+jsOAAAAAKehtrSkBQpLzwgBAAAAQG1mqSJUWVmpf/zjH3rppZdUVFSkiooKr9f37dvnl+AAAAAAoDpYqgjNmDFD2dnZGjlypA4ePCiXy6VLL71UTqdT06dP93OIAAAAAE7FbRgBs9UGlhKhRYsWaf78+fr73/+us846S1dddZWeeuopTZs2TZ988om/YwQAAAAAv7KUCBUXF6tHjx6SpAYNGujgwYOSpIsvvlhvvfWW/6IDAAAA4BPDCJytNrCUCP3hD3/Qzp07JUnt27fXsmXLJEmffvqpwsLC/BcdAAAAAFQDS4nQiBEjlJ+fL0m69dZbNXXqVHXs2FGjR4/W9ddf79cAAQAAAMDfLM0a98ADD3i+HjlypNq0aaPVq1erY8eOGjp0qN+CAwAAAOAbt2pJT1qA8Ms6Qn379pXL5SIJAgAAAGDJnDlzFBMTo/DwcCUmJmrt2rUn3HfJkiVKSEhQo0aNVL9+fcXFxem5554zdT1LFSFJ2rx5sx577DFt3LhRktS1a1fdeuut6ty5s9VTAgAAALChxYsXy+VyKTc3V4mJicrJyVFqaqo2b96sqKio4/Zv0qSJ7r77bnXp0kWhoaFaunSp0tLSFBUVpdTUVJ+uaaki9Oqrr6p79+4qLCxUbGysYmNjtW7dOnXv3l2vvvrqKY8vLy9XaWmp11ZeXm4lFAAAAACSDMMImM2s7OxsjR8/XmlpaerWrZtyc3NVr149LViwoMr9k5OTNWLECHXt2lXt27fXxIkT1bNnT61atcrna1pKhO644w5lZGRo9erVys7OVnZ2tj7++GPddddduuOOO055fFZWliIjI722rKwsK6EAAAAACDBmCh8VFRUqLCxUSkqKZ8zpdColJUWrV68+5bUMw1B+fr42b96sCy+80OcYLSVCO3fu1OjRo48bv+aaazzTap9MRkaGDh486LVlZGRYCQUAAACAAqsiZKbwsWfPHlVWVio6OtprPDo6WsXFxSd8vwcPHlSDBg0UGhqqv/zlL3rsscc0cOBAn++XpWeEkpOT9eGHH6pDhw5e46tWrdIFF1xwyuPDwsJYbwgAAAAIUhkZGXK5XF5j/v75v2HDhtqwYYMOHz6s/Px8uVwutWvXTsnJyT4dbykRGjZsmO68804VFhbqvPPOkyR98sknevnllzVjxgy9+eabXvsCAAAAsA8zhY9mzZopJCREJSUlXuMlJSVq3rz5CY9zOp2ewkxcXJw2btyorKys6k2Ebr75ZknSE088oSeeeKLK1yTJ4XCosrLSyiUAAAAAmOCupcsIhYaGKj4+Xvn5+Ro+fLgkye12Kz8/X+np6T6fx+12m5qAzVIi5Ha7rRwGAAAAAMdxuVwaM2aMEhIS1KdPH+Xk5KisrExpaWmSpNGjR6tVq1ae54yysrKUkJCg9u3bq7y8XG+//baee+45zZ071+drWl5H6H8dOHBAjRo18tfpAAAAANjEyJEjtXv3bk2bNk3FxcWKi4tTXl6eZwKFoqIiOZ3/neetrKxMN998s3744QfVrVtXXbp00fPPP6+RI0f6fE1LidCDDz6omJgYz4X++te/6tVXX1WLFi309ttvKzY21sppAQAAAFhkZf2eQJKenn7CVriCggKv7++9917de++9p3U9S9Nn5+bmqnXr1pKk5cuX67333lNeXp7+/Oc/a9KkSacVEAAAAABUN0sVoeLiYk8itHTpUl1xxRUaNGiQYmJilJiY6NcAAQAAAJxaba8InWmWKkKNGzfW999/L0nKy8vzrAJrGAazxAEAAAAIeJYqQpdeeqmuvvpqdezYUXv37tWf//xnSdL69euPW2QVAAAAAAKNpUToH//4h2JiYvT999/roYceUoMGDSRJO3fu9FpHCAAAAMCZ4aY1zhRLiVCdOnV0++23Hzd+2223nXZAAAAAAFDdLCVCbdq0UXJyspKSkpScnKz27dv7Oy4AAAAAqDaWJku4//77FR4ergcffFAdO3ZU69atdc0112j+/Pn69ttv/R0jAAAAgFMwjMDZagNLFaFrrrlG11xzjaRfnwv64IMPtHTpUt18881yu93MHAcAAAAgoFlKhCTpyJEjWrVqlQoKCrRixQqtX79e3bt3V3Jysh/DAwAAAAD/s5QInX/++Vq/fr26du2q5ORkTZ48WRdeeKEaN27s7/gAAAAA+IBZ48yx9IzQpk2bVL9+fXXp0kVdunRR165dSYIAAAAA1BqWEqG9e/fq/fff13nnnad3331X/fr1U6tWrXT11Vdr/vz5/o4RAAAAwCkYAfRfbWApEXI4HOrZs6cmTJigV155Re+8844GDhyol19+WTfeeKO/YwQAAAAAv7L0jNC6detUUFCggoICrVq1SocOHVKPHj106623Kikpyd8xAgAAAIBfWUqE+vTpo169eikpKUnjx4/XhRdeqMjISH/HBgAAAMBHBpMlmGIpEdq3b58iIiL8HQsAAAAAnBGWnhGKiIjQgQMH9NRTTykjI0P79u2T9GvL3I8//ujXAAEAAADA3yxVhL744gsNGDBAjRo10vbt2zV+/Hg1adJES5YsUVFRkZ599ll/xwkAAADgJNx0xpliqSLkcrmUlpamb7/9VuHh4Z7xIUOGaOXKlX4LDgAAAACqg6WK0Keffqp58+YdN96qVSsVFxefdlAAAAAAzGGyBHMsVYTCwsJUWlp63Pg333yjs88++7SDAgAAAIDqZCkRGjZsmGbOnKljx45J+nWB1aKiIt1555267LLL/BogAAAAAPibpURo9uzZOnz4sKKiovTzzz8rKSlJHTp0UIMGDXTffff5O0YAAAAAp2AYRsBstYGlZ4QiIyO1fPlyffTRR/r88891+PBhnXvuuUpJSfF3fAAAAADgd5YSIUnKz89Xfn6+du3aJbfbrU2bNumFF16QJC1YsMBvAQIAAACAv1lKhGbMmKGZM2cqISFBLVq0kMPh8HdcAAAAAExw15KWtEBhKRHKzc3VwoULde211/o7HgAAAACodpYmS6ioqND555/v71gAAAAA4IywlAiNGzfO8zwQAAAAgJrnNoyA2WoDS61xR48e1ZNPPqn33ntPPXv2VJ06dbxez87O9ktwAAAAAFAdLCVCX3zxheLi4iRJX331lddrTJwAAAAAnHm1Zf2eQGEpEVqxYoW/4wAAAACAM8bSM0IAAAAAUJtZXlAVAAAAQOBw0xlnChUhAAAAALZDIgQAAADAdmiNAwAAAIIAs8aZQ0UIAAAAgO1QEQIAAACCABUhc6gIAQAAALAdEiEAAAAAtkNrHAAAABAE3LTGmUJFCAAAAIDtkAgBAAAAsB1a4wAAAIAgQGecOVSEAAAAANgOFSEAAAAgCDBZgjlUhAAAAADYDokQAAAAANuhNQ4AAAAIAoZojTODihAAAAAA2yERAgAAAGA7tMYBAAAAQcBg1jhTqAgBAAAAsB0SIQAAAAC2Q2scAAAAEATcdMaZQkUIAAAAgO1QEQIAAACCAJMlmENFCAAAAIDtOAxSRwAAAKDWe+C15TUdgsfkEQNrOoRTCqjWuM++3V7TISBAJXSMUdGEO2o6DASwNo8+pL0ff1LTYSBANT3/PD38Zn5Nh4EAdfuwATUdAuAXbuobptAaBwAAAMB2SIQAAAAA2E5AtcYBAAAAsIZH/82hIgQAAADAdqgIAQAAAEGAgpA5VIQAAAAA2A6JEAAAAADboTUOAAAACAKsI2QOFSEAAAAANW7OnDmKiYlReHi4EhMTtXbt2hPuO3/+fF1wwQVq3LixGjdurJSUlJPuXxUSIQAAAAA1avHixXK5XMrMzNS6desUGxur1NRU7dq1q8r9CwoKdNVVV2nFihVavXq1WrdurUGDBunHH3/0+ZokQgAAAEAQMAwjYDazsrOzNX78eKWlpalbt27Kzc1VvXr1tGDBgir3X7RokW6++WbFxcWpS5cueuqpp+R2u5Wfn+/zNUmEAAAAAPhVeXm5SktLvbby8vIq962oqFBhYaFSUlI8Y06nUykpKVq9erVP1zty5IiOHTumJk2a+BwjiRAAAAAAv8rKylJkZKTXlpWVVeW+e/bsUWVlpaKjo73Go6OjVVxc7NP17rzzTrVs2dIrmToVZo0DAAAAgkAgTRqXkZEhl8vlNRYWFlYt13rggQf04osvqqCgQOHh4T4fRyIEAAAAwK/CwsJ8TnyaNWumkJAQlZSUeI2XlJSoefPmJz324Ycf1gMPPKD33ntPPXv2NBUjrXEAAABAEHDLCJjNjNDQUMXHx3tNdPDbxAd9+/Y94XEPPfSQ7rnnHuXl5SkhIcH0/aIiBAAAAKBGuVwujRkzRgkJCerTp49ycnJUVlamtLQ0SdLo0aPVqlUrz3NGDz74oKZNm6YXXnhBMTExnmeJGjRooAYNGvh0TRIhAAAAADVq5MiR2r17t6ZNm6bi4mLFxcUpLy/PM4FCUVGRnM7/NrPNnTtXFRUVuvzyy73Ok5mZqenTp/t0TRIhAAAAIAhYWb8nkKSnpys9Pb3K1woKCry+3759+2lfj2eEAAAAANgOiRAAAAAA26E1DgAAAAgC7lreGnemURECAAAAYDtUhAAAAIAgQEHIHCpCAAAAAGyHRAgAAACA7dAaBwAAAASB2r6O0JlGRQgAAACA7ZAIAQAAALAdWuMAAACAIMA6QuZQEQIAAABgO1SEAAAAgCDAZAnmUBECAAAAYDskQgAAAABsh9Y4AAAAIAjQGWcOFSEAAAAAtkMiBAAAAMB2aI0DAAAAggDrCJlDRQgAAACA7ZAIAQAAALAdWuMAAACAIGCI1jgzqAgBAAAAsB0qQgAAAEAQYLIEc6gIAQAAALAdEiEAAAAAtkNrHAAAABAE6Iwzh4oQAAAAANshEQIAAABgO7TGAQAAAEHAoDfOFCpCAAAAAGyHihAAAAAQBFhHyBxLFaHvv/9eP/zwg+f7tWvX6m9/+5uefPJJvwUGAAAAANXFUiJ09dVXa8WKFZKk4uJiDRw4UGvXrtXdd9+tmTNn+jVAAAAAAPA3S4nQV199pT59+kiSXnrpJXXv3l0ff/yxFi1apIULF/ozPgAAAAA+MAwjYLbawFIidOzYMYWFhUmS3nvvPQ0bNkyS1KVLF+3cudN/0QEAAABANbCUCP3xj39Ubm6uPvzwQy1fvlyDBw+WJP30009q2rSpXwMEAAAAAH+zlAg9+OCDmjdvnpKTk3XVVVcpNjZWkvTmm296WuYAAAAAnDluI3C22sDS9NnJycnas2ePSktL1bhxY8/4DTfcoHr16vktOAAAAACoDpYXVDUMQ4WFhZo3b54OHTokSQoNDSURAgAAABDwLFWEduzYocGDB6uoqEjl5eUaOHCgGjZsqAcffFDl5eXKzc31d5wAAAAATqK2zNYWKCxVhCZOnKiEhATt379fdevW9YyPGDFC+fn5fgsOAAAAAKqDpYrQhx9+qI8//lihoaFe4zExMfrxxx/9EhgAAAAA31ERMsdSRcjtdquysvK48R9++EENGzY87aAAAAAAoDpZSoQGDRqknJwcz/cOh0OHDx9WZmamhgwZ4q/YAAAAAKBaWGqNmz17tlJTU9WtWzcdPXpUV199tb799ls1a9ZM//rXv/wdIwAAAIBTcNMaZ4qlROgPf/iDPv/8c7344ov64osvdPjwYY0dO1ajRo3ymjzhRMrLy1VeXu41FhYWZiUUAAAAADDNUiIkSWeddZauueYaS8dmZWVpxowZXmOZmZm6eNR1VsMBAAAAAJ/5nAi9+eabPp902LBhJ309IyNDLpfLaywsLExfFu30+RoAAAAA/ovGOHN8ToSGDx/u034Oh6PKGeV+LywsjFY4AAAAADXG50TI7XZXZxwAAAAATgPrCJljafpsAAAAAKjNLE+WUFZWpg8++EBFRUWqqKjwem3ChAmnHRgAAAAAVBdLidD69es1ZMgQHTlyRGVlZWrSpIn27NmjevXqKSoqikQIAAAAOMNYR8gcS61xt912m4YOHar9+/erbt26+uSTT7Rjxw7Fx8fr4Ycf9neMAAAAAOBXlhKhDRs26O9//7ucTqdCQkJUXl6u1q1b66GHHtJdd93l7xgBAAAAwK8sJUJ16tSR0/nroVFRUSoqKpIkRUZG6vvvv/dfdAAAAAB8YhhGwGy1gaVnhHr16qVPP/1UHTt2VFJSkqZNm6Y9e/boueeeU/fu3f0dIwAAAAD4laWK0P33368WLVpIku677z41btxYN910k/bs2aN58+b5NUAAAAAA8DdLFaE//vGPnpJXVFSUcnNz9dprr6lbt26Ki4vzZ3wAAAAAfOCuHR1pAcNSReiSSy7Rs88+K0k6cOCAzjvvPGVnZ2v48OGaO3euXwMEAAAAAH+zlAitW7dOF1xwgSTplVdeUXR0tHbs2KFnn31Wjz76qF8DBAAAAHBqNT1BQm2bLMFSInTkyBE1bNhQkrRs2TJdeumlcjqdOu+887Rjxw6/BggAAAAA/mYpEerQoYNef/11ff/993r33Xc1aNAgSdKuXbsUERHh1wABAAAAwN8sJULTpk3T7bffrpiYGCUmJqpv376Sfq0O9erVy68BAgAAADi1mm6Hq22tcZZmjbv88svVv39/7dy5U7GxsZ7xAQMGaMSIEX4LDgAAAACqg6VESJKaN2+u5s2be4316dPntAMCAAAAgOpmORECAAAAEDjctaQlLVBYekYIAAAAAGozKkIAAABAEKAgZA4VIQAAAAA1bs6cOYqJiVF4eLgSExO1du3aE+77n//8R5dddpliYmLkcDiUk5Nj+nokQgAAAABq1OLFi+VyuZSZmal169YpNjZWqamp2rVrV5X7HzlyRO3atdMDDzxw3ARuviIRAgAAAIKAEUD/lZeXq7S01GsrLy8/YezZ2dkaP3680tLS1K1bN+Xm5qpevXpasGBBlfv37t1bs2bN0pVXXqmwsDBL94tECAAAAIBfZWVlKTIy0mvLysqqct+KigoVFhYqJSXFM+Z0OpWSkqLVq1dXW4xMlgAAAADArzIyMuRyubzGTlS52bNnjyorKxUdHe01Hh0drU2bNlVbjCRCAAAAQBAIpHWEwsLCLLesnSm0xgEAAACoMc2aNVNISIhKSkq8xktKSixPhOALEiEAAAAgCBiGETCbGaGhoYqPj1d+fr5nzO12Kz8/X3379vX3bfKgNQ4AAABAjXK5XBozZowSEhLUp08f5eTkqKysTGlpaZKk0aNHq1WrVp4JFyoqKvT11197vv7xxx+1YcMGNWjQQB06dPDpmiRCAAAAAGrUyJEjtXv3bk2bNk3FxcWKi4tTXl6eZwKFoqIiOZ3/bWb76aef1KtXL8/3Dz/8sB5++GElJSWpoKDAp2uSCAEAAABBwB04cyVYkp6ervT09Cpf+9/kJiYmxnQL3v/iGSEAAAAAtkMiBAAAAMB2aI0DAAAAgsDptorZDRUhAAAAALZDIgQAAADAdmiNAwAAAIIArXHmUBECAAAAYDtUhAAAAIAg4KYiZAoVIQAAAAC2QyIEAAAAwHZojQMAAACCAJ1x5lARAgAAAGA7JEIAAAAAbIfWOAAAACAIMGucOVSEAAAAANgOFSEAAAAgCBhUhEyhIgQAAADAdkiEAAAAANgOrXEAAABAEDBEa5wZVIQAAAAA2A6JEAAAAADboTUOAAAACAJuOuNMoSIEAAAAwHZIhAAAAADYDq1xAAAAQBBgQVVzqAgBAAAAsB0qQgAAAEAQoCJkDhUhAAAAALZDIgQAAADAdmiNAwAAAIKAm9Y4U6gIAQAAALAdEiEAAAAAtkNrHAAAABAE6Iwzh4oQAAAAANtxGEw4HnDKy8uVlZWljIwMhYWF1XQ4CDB8PnAqfEZwMnw+cCp8RmqvP82YU9MheKzIvKWmQzglEqEAVFpaqsjISB08eFARERE1HQ4CDJ8PnAqfEZwMnw+cCp+R2otEyBxa4wAAAADYDpMlAAAAAEGARi9zqAgBAAAAsB0SoQAUFhamzMxMHlBElfh84FT4jOBk+HzgVPiMwC6YLAEAAACA7VARAgAAAGA7JEIAAAAAbIdECAAAAIDtkAgBAAAAsB0SoWqUnJysv/3tb5KkmJgY5eTk+Hzs9u3b5XA4tGHDhtOKwV/nAVD7LFy4UI0aNfJ8P336dMXFxdVYPABqB4fDoddff72mwwCqHYnQGfLpp5/qhhtu8Hn/1q1ba+fOnerevbskqaCgQA6HQwcOHKimCBEoHA7HSbehQ4fK4XDok08+qfL4AQMG6NJLLz3DUaM2uP3225Wfn1/TYQAAEBDOqukA7OLss882tX9ISIiaN29eTdEgkO3cudPz9eLFizVt2jRt3rzZM9agQQP1799fCxYs0Hnnned17Pbt27VixQr9+9//PmPxovpVVFQoNDT0tM/ToEEDNWjQwA8RAf537Ngx1alTp6bDAGAjVIT8pKysTKNHj1aDBg3UokULzZ492+v1/22N27Rpk/r376/w8HB169ZN7733nlcp+vctbdu3b9ef/vQnSVLjxo3lcDh03XXXSZLy8vLUv39/NWrUSE2bNtXFF1+s77777ky8ZVST5s2be7bIyEg5HA6vsQYNGmjs2LFavHixjhw54nXswoUL1aJFCw0ePLiGooc/JCcnKz09XX/729/UrFkzpaamKjs7Wz169FD9+vXVunVr3XzzzTp8+LDXcQsXLlSbNm1Ur149jRgxQnv37vV6/X9b49xut2bOnKk//OEPCgsLU1xcnPLy8s7EW7SV5ORkTZgwQXfccYeaNGmi5s2ba/r06Z7XDxw4oHHjxunss89WRESELrroIn3++eeSpIMHDyokJESfffaZpF//zJo0aeL1S5Dnn39erVu3lvRr0pyenq4WLVooPDxcbdu2VVZWlmdfh8OhuXPn6s9//rPq1q2rdu3a6ZVXXvGK984771SnTp1Ur149tWvXTlOnTtWxY8c8r//2OZo3b55at26tevXq6YorrtDBgwe9zvPUU0+pa9euCg8PV5cuXfTEE094Xvvt37jFixcrKSlJ4eHhWrRo0WneafzeK6+8oh49eqhu3bpq2rSpUlJSVFZWpk8//VQDBw5Us2bNFBkZqaSkJK1bt+6k5/r+++91xRVXqFGjRmrSpIkuueQSbd++3fN6QUGB+vTpo/r166tRo0bq16+fduzYUc3vEDh9JEJ+MmnSJH3wwQd64403tGzZMhUUFJzwL5bKykoNHz5c9erV05o1a/Tkk0/q7rvvPuG5W7durVdffVWStHnzZu3cuVOPPPKIpF8TMJfLpc8++0z5+flyOp0aMWKE3G63/98kAsaoUaNUXl7u9QOMYRh65plndN111ykkJKQGo4M/PPPMMwoNDdVHH32k3NxcOZ1OPfroo/rPf/6jZ555Ru+//77uuOMOz/5r1qzR2LFjlZ6erg0bNuhPf/qT7r333pNe45FHHtHs2bP18MMP64svvlBqaqqGDRumb7/9trrfnu0888wzql+/vtasWaOHHnpIM2fO1PLlyyVJf/3rX7Vr1y698847Kiws1LnnnqsBAwZo3759ioyMVFxcnAoKCiRJX375pRwOh9avX+9JhD/44AMlJSVJkh599FG9+eabeumll7R582YtWrRIMTExXrFMnTpVl112mT7//HONGjVKV155pTZu3Oh5vWHDhlq4cKG+/vprPfLII5o/f77+8Y9/eJ1jy5Yteumll/Tvf/9beXl5Wr9+vW6++WbP64sWLdK0adN03333aePGjbr//vs1depUPfPMM17nmTx5siZOnKiNGzcqNTXVL/cav3YWXHXVVbr++uu1ceNGFRQU6NJLL5VhGDp06JDGjBmjVatW6ZNPPlHHjh01ZMgQHTp0qMpzHTt2TKmpqWrYsKE+/PBDffTRR2rQoIEGDx6siooK/fLLLxo+fLiSkpL0xRdfaPXq1brhhhvkcDjO8LsGLDBw2g4dOmSEhoYaL730kmds7969Rt26dY2JEycahmEYbdu2Nf7xj38YhmEY77zzjnHWWWcZO3fu9Oy/fPlyQ5Lx2muvGYZhGNu2bTMkGevXrzcMwzBWrFhhSDL2799/0lh2795tSDK+/PLLKs+D2uWf//ynERkZWeVrV155pZGUlOT5Pj8/35BkfPvtt2cmOFSbpKQko1evXifd5+WXXzaaNm3q+f6qq64yhgwZ4rXPyJEjvT4/mZmZRmxsrOf7li1bGvfdd5/XMb179zZuvvlm68HjOElJSUb//v29xnr37m3ceeedxocffmhEREQYR48e9Xq9ffv2xrx58wzDMAyXy2X85S9/MQzDMHJycoyRI0casbGxxjvvvGMYhmF06NDBePLJJw3DMIxbb73VuOiiiwy3211lLJKMG2+80WssMTHRuOmmm04Y/6xZs4z4+HjP95mZmUZISIjxww8/eMbeeecdw+l0ev5da9++vfHCCy94neeee+4x+vbtaxjGf/9tysnJOeF1YV1hYaEhydi+ffsp962srDQaNmxo/Pvf//aM/f7nkeeee87o3Lmz12eqvLzcqFu3rvHuu+8ae/fuNSQZBQUFfn8fQHWjIuQH3333nSoqKpSYmOgZa9KkiTp37lzl/ps3b1br1q29ngHq06ePpWt/++23uuqqq9SuXTtFRER4fvNXVFRk6XyoPa6//nqtXLnS0wq5YMECJSUlqUOHDjUcGfwhPj7e6/v33ntPAwYMUKtWrdSwYUNde+212rt3r6c9cuPGjV5/B0lS3759T3j+0tJS/fTTT+rXr5/XeL9+/byqA/CPnj17en3fokUL7dq1S59//rkOHz6spk2bep7hatCggbZt2+b5fzspKUmrVq1SZWWlPvjgAyUnJys5OVkFBQX66aeftGXLFiUnJ0uSrrvuOm3YsEGdO3fWhAkTtGzZsuNi+d/PRd++fb3+zBcvXqx+/fp5WnGnTJly3L8pbdq0UatWrbzO4Xa7tXnzZpWVlem7777T2LFjvd7Tvffee1zrdkJCgvmbiVOKjY3VgAED1KNHD/31r3/V/PnztX//fklSSUmJxo8fr44dOyoyMlIRERE6fPjwCX9u+Pzzz7VlyxY1bNjQ82fZpEkTHT16VN99952aNGmi6667TqmpqRo6dKgeeeQRr2ddgUBGIlTLDR06VPv27dP8+fO1Zs0arVmzRtKvfeIIbgMGDFCbNm20cOFClZaWasmSJRo7dmxNhwU/qV+/vufr7du36+KLL1bPnj316quvqrCwUHPmzJHE/+u1xf9OAuBwOOR2u3X48GG1aNFCGzZs8No2b96sSZMmSZIuvPBCHTp0SOvWrdPKlSu9EqEPPvhALVu2VMeOHSVJ5557rrZt26Z77rlHP//8s6644gpdfvnlPse5evVqjRo1SkOGDNHSpUu1fv163X333aY+Z7+17M2fP9/rPX311VfHzXb5+885/CckJETLly/XO++8o27duumxxx5T586dtW3bNo0ZM0YbNmzQI488oo8//lgbNmxQ06ZNT/hnfPjwYcXHxx/3Gf3mm2909dVXS5L++c9/avXq1Tr//PO1ePFiderU6YQzmwKBhFnj/KB9+/aqU6eO1qxZozZt2kiS9u/fr2+++cbTt/17nTt31vfff6+SkhJFR0dL+nV67ZP5bcaoyspKz9jevXu1efNmzZ8/XxdccIEkadWqVX55Twh8TqdTaWlpevrpp9WqVSuFhoaa+oEHtUdhYaHcbrdmz54tp/PX31+99NJLXvt07drV84uQ35zsB5GIiAi1bNlSH330kdffUx999JHlCjXMO/fcc1VcXKyzzjrruGd5ftOoUSP17NlTjz/+uOrUqaMuXbooKipKI0eO1NKlS4/7dyYiIkIjR47UyJEjdfnll2vw4MHat2+fmjRpIunXz8Xo0aM9+3/yySfq1auXJOnjjz9W27ZtvZ5breqh96KiIv30009q2bKl5xxOp1OdO3dWdHS0WrZsqa1bt2rUqFGndX9gncPhUL9+/dSvXz9NmzZNbdu21WuvvaaPPvpITzzxhIYMGSLp14kQ9uzZc8LznHvuuVq8eLGioqIUERFxwv169eqlXr16KSMjQ3379tULL7xw3MymQKAhEfKD32bxmjRpkpo2baqoqCjdfffdnh9Y/tfAgQPVvn17jRkzRg899JAOHTqkKVOmSNIJHy5s27atHA6Hli5dqiFDhqhu3bpq3LixmjZtqieffFItWrRQUVGRJk+eXG3vE4EnLS1NM2fO1F133aWrrrpKdevWremQUA06dOigY8eO6bHHHtPQoUM9Eyj83oQJE9SvXz89/PDDuuSSS/Tuu++ecga4SZMmKTMzU+3bt1dcXJz++c9/asOGDczedQalpKSob9++Gj58uB566CF16tRJP/30k9566y2NGDHC0zqWnJysxx57zPPLjiZNmqhr165avHixpzooSdnZ2WrRooV69eolp9Opl19+Wc2bN/daWPfll19WQkKC+vfvr0WLFmnt2rV6+umnJUkdO3ZUUVGRXnzxRfXu3VtvvfWWXnvttePiDg8P15gxY/Twww+rtLRUEyZM0BVXXOFp+Z4xY4YmTJigyMhIDR48WOXl5frss8+0f/9+uVyu6rqd+P/WrFmj/Px8DRo0SFFRUVqzZo12796trl27qmPHjnruueeUkJCg0tJSTZo06aT/dowaNUqzZs3SJZdc4pllcseOHVqyZInuuOMOHTt2TE8++aSGDRumli1bavPmzfr222+9km0gUNEa5yezZs3SBRdcoKFDhyolJUX9+/c/rsf/NyEhIXr99dd1+PBh9e7dW+PGjfP89i08PLzKY1q1aqUZM2Zo8uTJio6OVnp6upxOp1588UUVFhaqe/fuuu222zRr1qxqe48IPG3atFFKSor279+v66+/vqbDQTWJjY1Vdna2HnzwQXXv3l2LFi3ymhJZks477zzNnz9fjzzyiGJjY7Vs2TLPL1hOZMKECXK5XPr73/+uHj16KC8vT2+++aanzQrVz+Fw6O2339aFF16otLQ0derUSVdeeaV27Njh6RiQfn1OqLKy0vMskPRrcvS/Yw0bNtRDDz2khIQE9e7dW9u3b9fbb7/t9Yu5GTNm6MUXX1TPnj317LPP6l//+pe6desmSRo2bJhuu+02paenKy4uTh9//LGmTp16XNwdOnTQpZdeqiFDhmjQoEHq2bOn1/TY48aN01NPPaV//vOf6tGjh5KSkrRw4UKdc845frx7OJGIiAitXLlSQ4YMUadOnTRlyhTNnj1bf/7zn/X0009r//79Ovfcc3XttddqwoQJioqKOuG56tWrp5UrV6pNmza69NJL1bVrV40dO1ZHjx5VRESE6tWrp02bNumyyy5Tp06ddMMNN+iWW27R//3f/53BdwxY4zAMw6jpIPBrO0r//v21ZcsWtW/fvqbDAQAEIYfDoddee03Dhw+3fI7p06fr9ddf14YNG/wWFwDUBFrjashrr72mBg0aqGPHjtqyZYsmTpyofv36kQQBAAAAZwCJUA05dOiQ7rzzThUVFalZs2ZKSUnR7NmzazosAAAAwBZojQMAAABgO0yWAAAAAMB2SIQAAAAA2A6JEAAAAADbIRECAAAAYDskQgAAAABsh0QIAAAAgO2QCAEAAACwHRIhAAAAALbz/wDiFkJOBxYd/QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "### Visualize Correlation\n",
        "\n",
        "# Generate a mask for the upper triangle\n",
        "mask = np.zeros_like(advertising_df.corr(), dtype=np.bool)\n",
        "mask[np.triu_indices_from(mask)] = True\n",
        "\n",
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(11, 9))\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(advertising_df.corr(), mask=mask, cmap=cmap, vmax=.9, square=True, linewidths=.5, ax=ax)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2eDQ9CNw3uej"
      },
      "source": [
        "Since Sales is our target variable, we should identify which variable correlates the most with Sales.\n",
        "\n",
        "As we can see, TV has the highest correlation with Sales.\n",
        "Let's visualize the relationship of variables using scatterplots."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "SOsTLClWnO2B",
        "outputId": "a734e4e1-4d91-4f3a-bd44-5ae4df3815b1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7af11a8ad300>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# It is used basically for univariant set of observations and visualizes it through a histogram i.e. only one observation\n",
        "# and hence you choose one particular column of the dataset.\n",
        "sns.displot(advertising_df['sales'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ajx0zjPt4DIa"
      },
      "source": [
        "Let's visualize the relationship of variables using scatterplots. -- Separately"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3f7bN--N5TYF"
      },
      "source": [
        "Another way to view the linear relationsips between variables is to use a \"for loop\" that does the same as above.\n",
        "\n",
        "It seems there's no clear linear relationships between the predictors.\n",
        "\n",
        "At this point, we know that the variable TV will more likely give better prediction of Sales because of the high correlation and linearity of the two."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "otYqhhMapHhY",
        "outputId": "bb3439aa-0e92-4ec9-dfb8-c2702aeb141d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1800 with 5 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "'''=== Show the linear relationship between features  and sales Thus, it provides that how the scattered\n",
        "      they are and which features has more impact in prediction of house price. ==='''\n",
        "\n",
        "# visiualize all variables  with sales\n",
        "from scipy import stats\n",
        "#creates figure\n",
        "plt.figure(figsize=(18, 18))\n",
        "\n",
        "for i, col in enumerate(advertising_df.columns[0:13]): #iterates over all columns except for price column (last one)\n",
        "    plt.subplot(5, 3, i+1) # each row three figure\n",
        "    x = advertising_df[col] #x-axis\n",
        "    y = advertising_df['sales'] #y-axis\n",
        "    plt.plot(x, y, 'o')\n",
        "\n",
        "    # Create regression line\n",
        "    plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1)) (np.unique(x)), color='red')\n",
        "    plt.xlabel(col) # x-label\n",
        "    plt.ylabel('sales') # y-label\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ThNRxdAmA5fQ"
      },
      "source": [
        "Concluding results after observing the Graph\n",
        "The relation bw TV and Sales is stong and increases in linear fashion\n",
        "The relation bw Radio and Sales is less stong\n",
        "The relation bw TV and Sales is weak"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OIPKB4hanO2F"
      },
      "source": [
        "## Training a Linear Regression Model\n",
        "\n",
        "Regression is a supervised machine learning process.  It is similar to classification, but rather than predicting a label, you try to predict a continuous value.   Linear regression defines the relationship between a target variable (y) and a set of predictive features (x).  Simply stated, If you need to predict a number, then use regression.\n",
        "\n",
        "Let's now begin to train your regression model! You will need to first split up your data into an X array that contains the features to train on, and a y array with the target variable, in this case the Price column. You will toss out the Address column because it only has text info that the linear regression model can't use."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KFDhhs1KA22t"
      },
      "source": [
        "#### Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l0NjRPeFBk74"
      },
      "source": [
        "##### Split: X (features) and y (target)\n",
        "Next, let's define the features and label.  Briefly, feature is input; label is output. This applies to both classification and regression problems."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "ZEEGuBAnnO2F"
      },
      "outputs": [],
      "source": [
        "X = advertising_df[['digital', 'TV', 'radio', 'newspaper']]\n",
        "y = advertising_df['sales']\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lXgBbCFcBCfG"
      },
      "source": [
        "##### Scaling (Normalization)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hVOPQyd2HtxP",
        "outputId": "29307080-cd33-406f-e5c6-c2b2c499ccd3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.89211961 0.4032179  0.0977028  0.17886333]\n",
            " [0.66254734 0.45658693 0.39008405 0.44765371]\n",
            " [0.29009225 0.20576311 0.51609436 0.77920128]\n",
            " ...\n",
            " [0.06744611 0.99272247 0.05163843 0.08536149]\n",
            " [0.19480049 0.91868871 0.08898294 0.33188231]\n",
            " [0.06744611 0.99272247 0.05163843 0.08536149]]\n"
          ]
        }
      ],
      "source": [
        "'''=== Noramlization the features. Since it is seen that features have different ranges, it is best practice to\n",
        "normalize/standarize the feature before using them in the model ==='''\n",
        "\n",
        "#feature normalization\n",
        "normalized_feature =  keras.utils.normalize(X.values)\n",
        "print(normalized_feature)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X97FWdDOnO2H"
      },
      "source": [
        "##### Train - Test - Split\n",
        "\n",
        "Now let's split the data into a training and test set.  Note:  Best pracices is to split into three - training, validation, and test set.\n",
        "\n",
        "By default - It splits the given data into 75-25 ration\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "fS99Llq8nO2J"
      },
      "outputs": [],
      "source": [
        "# Import train_test_split function from sklearn.model_selection\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split up the data into a training set\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=101)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K4p_3UGEcqPb",
        "outputId": "3b78c906-bae0-4d7d-9c06-7c62ef11bd38"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(719, 4) (480, 4) (719,) (480,)\n"
          ]
        }
      ],
      "source": [
        "print(X_train.shape,X_test.shape, y_train.shape, y_test.shape )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mdc8pL4TIb9k"
      },
      "source": [
        "# Step 2:  Build Network\n",
        "Because so few samples are available, we will be using a very small network with two hidden layers, each with 64 units. In general, the less training data you have, the worse overfitting will be, and using a small network is one way to mitigate overfitting."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J10hkM7BAhbh"
      },
      "source": [
        "#### Build and Train the Network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K-_Vage0th27",
        "outputId": "00f5710f-4b1c-45f7-d0b2-e45ba47759f5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/32\n",
            "23/23 [==============================] - 2s 20ms/step - loss: 6355.9458 - mse: 6355.9458 - val_loss: 5807.6504 - val_mse: 5807.6504\n",
            "Epoch 2/32\n",
            "23/23 [==============================] - 0s 10ms/step - loss: 4806.4727 - mse: 4806.4727 - val_loss: 4447.7969 - val_mse: 4447.7969\n",
            "Epoch 3/32\n",
            "23/23 [==============================] - 0s 9ms/step - loss: 3695.4465 - mse: 3695.4465 - val_loss: 3428.7375 - val_mse: 3428.7375\n",
            "Epoch 4/32\n",
            "23/23 [==============================] - 0s 8ms/step - loss: 2848.5457 - mse: 2848.5457 - val_loss: 2607.1289 - val_mse: 2607.1289\n",
            "Epoch 5/32\n",
            "23/23 [==============================] - 0s 7ms/step - loss: 2141.7068 - mse: 2141.7068 - val_loss: 1864.4614 - val_mse: 1864.4614\n",
            "Epoch 6/32\n",
            "23/23 [==============================] - 0s 6ms/step - loss: 1493.2397 - mse: 1493.2397 - val_loss: 1210.5009 - val_mse: 1210.5009\n",
            "Epoch 7/32\n",
            "23/23 [==============================] - 0s 12ms/step - loss: 950.9180 - mse: 950.9180 - val_loss: 668.1801 - val_mse: 668.1801\n",
            "Epoch 8/32\n",
            "23/23 [==============================] - 0s 12ms/step - loss: 535.9688 - mse: 535.9688 - val_loss: 365.2287 - val_mse: 365.2287\n",
            "Epoch 9/32\n",
            "23/23 [==============================] - 0s 11ms/step - loss: 318.2729 - mse: 318.2729 - val_loss: 231.7376 - val_mse: 231.7376\n",
            "Epoch 10/32\n",
            "23/23 [==============================] - 0s 8ms/step - loss: 230.9233 - mse: 230.9233 - val_loss: 193.1924 - val_mse: 193.1924\n",
            "Epoch 11/32\n",
            "23/23 [==============================] - 0s 7ms/step - loss: 200.3414 - mse: 200.3414 - val_loss: 181.3994 - val_mse: 181.3994\n",
            "Epoch 12/32\n",
            "23/23 [==============================] - 0s 9ms/step - loss: 185.2456 - mse: 185.2456 - val_loss: 171.7923 - val_mse: 171.7923\n",
            "Epoch 13/32\n",
            "23/23 [==============================] - 0s 8ms/step - loss: 173.6612 - mse: 173.6612 - val_loss: 162.9434 - val_mse: 162.9434\n",
            "Epoch 14/32\n",
            "23/23 [==============================] - 0s 9ms/step - loss: 164.4381 - mse: 164.4381 - val_loss: 155.8642 - val_mse: 155.8642\n",
            "Epoch 15/32\n",
            "23/23 [==============================] - 0s 8ms/step - loss: 156.3019 - mse: 156.3019 - val_loss: 149.8550 - val_mse: 149.8550\n",
            "Epoch 16/32\n",
            "23/23 [==============================] - 0s 11ms/step - loss: 149.5408 - mse: 149.5408 - val_loss: 144.7365 - val_mse: 144.7365\n",
            "Epoch 17/32\n",
            "23/23 [==============================] - 0s 10ms/step - loss: 143.6699 - mse: 143.6699 - val_loss: 140.9443 - val_mse: 140.9443\n",
            "Epoch 18/32\n",
            "23/23 [==============================] - 0s 15ms/step - loss: 138.9901 - mse: 138.9901 - val_loss: 136.9614 - val_mse: 136.9614\n",
            "Epoch 19/32\n",
            "23/23 [==============================] - 0s 15ms/step - loss: 134.6012 - mse: 134.6012 - val_loss: 133.7686 - val_mse: 133.7686\n",
            "Epoch 20/32\n",
            "23/23 [==============================] - 0s 19ms/step - loss: 130.8806 - mse: 130.8806 - val_loss: 130.9590 - val_mse: 130.9590\n",
            "Epoch 21/32\n",
            "23/23 [==============================] - 0s 20ms/step - loss: 127.4660 - mse: 127.4660 - val_loss: 128.1422 - val_mse: 128.1422\n",
            "Epoch 22/32\n",
            "23/23 [==============================] - 0s 10ms/step - loss: 123.8663 - mse: 123.8663 - val_loss: 125.6087 - val_mse: 125.6087\n",
            "Epoch 23/32\n",
            "23/23 [==============================] - 0s 9ms/step - loss: 120.4207 - mse: 120.4207 - val_loss: 122.6469 - val_mse: 122.6469\n",
            "Epoch 24/32\n",
            "23/23 [==============================] - 0s 13ms/step - loss: 116.7625 - mse: 116.7625 - val_loss: 119.3021 - val_mse: 119.3021\n",
            "Epoch 25/32\n",
            "23/23 [==============================] - 0s 9ms/step - loss: 112.5525 - mse: 112.5525 - val_loss: 115.5973 - val_mse: 115.5973\n",
            "Epoch 26/32\n",
            "23/23 [==============================] - 0s 15ms/step - loss: 107.6662 - mse: 107.6662 - val_loss: 110.8775 - val_mse: 110.8775\n",
            "Epoch 27/32\n",
            "23/23 [==============================] - 0s 9ms/step - loss: 101.2922 - mse: 101.2922 - val_loss: 103.4047 - val_mse: 103.4047\n",
            "Epoch 28/32\n",
            "23/23 [==============================] - 0s 12ms/step - loss: 91.9364 - mse: 91.9364 - val_loss: 91.9141 - val_mse: 91.9141\n",
            "Epoch 29/32\n",
            "23/23 [==============================] - 0s 8ms/step - loss: 80.0998 - mse: 80.0998 - val_loss: 79.4774 - val_mse: 79.4774\n",
            "Epoch 30/32\n",
            "23/23 [==============================] - 0s 9ms/step - loss: 66.3701 - mse: 66.3701 - val_loss: 61.2325 - val_mse: 61.2325\n",
            "Epoch 31/32\n",
            "23/23 [==============================] - 0s 8ms/step - loss: 49.9562 - mse: 49.9562 - val_loss: 43.2350 - val_mse: 43.2350\n",
            "Epoch 32/32\n",
            "23/23 [==============================] - 0s 9ms/step - loss: 34.8417 - mse: 34.8417 - val_loss: 31.0812 - val_mse: 31.0812\n"
          ]
        }
      ],
      "source": [
        "## Build Model (Building a three layer network - with one hidden layer)\n",
        "model = Sequential()\n",
        "model.add(Dense(4,input_dim=4, activation='relu'))                                                  # You don't have to specify input size.Just define the hidden layers\n",
        "model.add(Dense(3,activation='relu'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "# Compile Model\n",
        "model.compile(optimizer='adam', loss='mse',metrics=['mse'])\n",
        "\n",
        "#  Fit the Model\n",
        "history = model.fit(X_train, y_train, validation_data = (X_test, y_test),\n",
        "                    epochs = 32)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eZ_b0LGtE0By"
      },
      "source": [
        "#### Model Summary\n",
        "Once we've run data through the model, we can call .summary() on the model to get a high-level summary of our network."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0u5rBxABCtw7",
        "outputId": "57e05bec-cea9-49fc-9b8d-490edbac79e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 4)                 20        \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 3)                 15        \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 4         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 39\n",
            "Trainable params: 39\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#inspect the model\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JLoJRfUtn27m",
        "outputId": "693b7de0-cd5b-46f9-b375-b887fd37937d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15/15 [==============================] - 0s 3ms/step - loss: 31.0812 - mse: 31.0812\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "31.08115005493164"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "model.evaluate(X_test, y_test)[1]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RHBzoonKnFfj"
      },
      "source": [
        "### Visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q5_YlrlTFc92"
      },
      "source": [
        "Running .fit (or .fit_generator) returns a History object which collects all the events recorded during training. You can plot the training and validation curves for the model loss and mse by accessing these elements of the History object."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "D0q6HDS_FMx-",
        "outputId": "ff295d84-2cc3-4258-cc48-7a5ff9dc4b74"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "MSE_COLS = [\"mse\", \"val_mse\"]\n",
        "\n",
        "pd.DataFrame(history.history)[MSE_COLS].plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ktMqgjoDGAe7"
      },
      "source": [
        "You can add more 'flavor' to the graph by making it bigger and adding labels and names, as shown below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "jB1VUt-bmTPH",
        "outputId": "ffcb6a85-79d8-4008-bfba-7660bb9a4f58"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": 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s57du3aq0tLRs5+vVq6fq1atr48aNkqSNGzeqUaNGCggIsNZERkYqKSlJu3fvttb8+9qRkZHWa+TmypUrSkpKyvZAKdNkkCSTdGitdP6QzbK+jYMkSQt3nFBGJtNNAQAAAACA/RQ6eFuyZImmT5+u+vXrW8+Fh4frf//7nxYvXlyoa3399dfatm2b3nzzzRyvxcXFycXFRT4+PtnOBwQEKC4uzlpzdeiW9XrWa3nVJCUl6fLly7n29eabb8rb29v6qFatWqHuC3bgU00K7Wg53v61zbJOdf1kdnPSqaQr2nIo3k7NAQAAAAAAXEPwlpmZKWdn5xznnZ2dlZmZWeDrHD16VE8++aS+/PJLubm5FbaNYvX8888rMTHR+jh69GhJt4TcNLvb8hz9pWTju+fq5KheDQMlMd0UAAAAAADYV6GDt65du+rJJ5/UiRP/hBjHjx/XqFGj1K1btwJfZ+vWrTp9+rSaN28uJycnOTk5afXq1Zo6daqcnJwUEBCg1NRUJSQkZHvfqVOnFBhoCVICAwNz7HKa9XN+NWazWRUqVMi1N1dXV5nN5mwPlEL1bpZczVLCEenwOptlWbubLt55UmkZBQ+HAQAAAAAArkehg7dp06YpKSlJNWrUUM2aNVWzZk2FhoYqKSlJ77//foGv061bN+3cuVPR0dHWR8uWLTVkyBDrsbOzs1auXGl9z/79+3XkyBFFRERIkiIiIrRz506dPn3aWrN8+XKZzWaFh4dba66+RlZN1jVwA3Nxlxrcajn+40ubZRFhvqrs6aLzl9K07sBZOzUHAAAAAADKO6fCvqFatWratm2bVqxYoX379kmS6tevn2MDg/x4eXmpYcOG2c55eHjI19fXen7o0KEaPXq0KlWqJLPZrCeeeEIRERFq27atJKlnz54KDw/XPffcowkTJiguLk4vvviihg8fLldXV0nSI488omnTpumZZ57Rgw8+qF9++UXffvutFi1aVNhbR2nU7G5p26fSnp+kPu9IbjlHJzo5OiiqUZA+3XhYC6JPqEtd/xJoFAAAAAAAlDeFCt7S0tJUoUIFRUdHq0ePHurRo0dx9SVJmjx5shwcHDRgwABduXJFkZGRmj59uvV1R0dHLVy4UI8++qgiIiLk4eGh++67T6+99pq1JjQ0VIsWLdKoUaP03nvvqWrVqvroo48UGRlZrL3DTqq2knxrS+f+kvb8KDW/N9eyfk2D9enGw1q6O04paRlyc3a0b58AAAAAAKDcMRmGYRTmDWFhYfrhhx/UpEmT4uqp1ElKSpK3t7cSExNZ7600WjdZWvGKVK2tNHRpriWZmYY6TFil4wmXNX1Ic/VpFGTfHgEAAAAAQJlR0Kyo0Gu8jR07Vi+88ILi4+Ovq0GgyDS+SzI5SEc3SecO5lri4GDSzU0sYdsCdjcFAAAAAAB2cE2bK6xZs0bBwcGqW7eumjdvnu0B2J05SKr594660bY3Wej39+6mK/ed1oWUNHt0BgAAAAAAyrFCb67Qv3//YmgDuE7NhkgHlkvRX0ldxkoOOddwCw8yK8zPQzFnkrVs9ykNaFG1BBoFAAAAAADlRaGCt/T0dJlMJj344IOqWpXQAqVI3T6Sm4904YQUs0qqlXOXXZPJpH5NgjVlxV9asOMEwRsAAAAAAChWhZpq6uTkpHfeeUfp6enF1Q9wbZxcpUa3W46j59gsy5puuu6vs4pPTrVHZwAAAAAAoJwq9BpvXbt21erVq4ujF+D6NBtied67ULp8PteSMD9PNaxiVnqmoZ93nrRjcwAAAAAAoLwp9BpvvXv31nPPPaedO3eqRYsW8vDwyPZ6v379iqw5oFCCmkr+DaTTu6Vd86RWD+Va1rdxsHYdT9L87Sd0d9sQ+/YIAAAAAADKDZNhGEZh3uDgYHuQnMlkUkZGxnU3VdokJSXJ29tbiYmJMpvNJd0O8rJhmrRsrBTcXBq2KteS4wmX1f6tX2QySRue66og7wp2bhIAAAAAANzICpoVFXqqaWZmps1HWQzdcINpfKfk4CSd2Cad3ptrSRWfCmpVo6IMQ1q0g+mmAAAAAACgeBQ6eANKNU8/qXak5Tj6S5tlff/eZGH+9hP26AoAAAAAAJRDBQ7e+vTpo8TEROvPb731lhISEqw/nzt3TuHh4UXaHHBNsjZZ2P6NlJGWa0mfRkFydDBpx7FEHTqbbMfmAAAAAABAeVHg4G3p0qW6cuWK9efx48crPj7e+nN6err2799ftN0B16J2T8m9spR8WjqwIteSyp6ualfTV5K0gFFvAAAAAACgGBQ4ePv3HgyF3JMBsB9HZ8tab1Ke0037XTXdlO8zAAAAAAAoaqzxhrIpa7rp/iVS8rlcS3o2CJSLo4P+On1R++Iu2LE5AAAAAABQHhQ4eDOZTDKZTDnOAaVSQAMpqKmUmSbt/DbXEu8Kzupc108S000BAAAAAEDRcypooWEYuv/+++Xq6ipJSklJ0SOPPCIPDw9Jyrb+G1AqNB0inYy2TDdt+2iuJf2aBmvZnlNasOOExkTWJUwGAAAAAABFpsDB23333Zft57vvvjtHzb333nv9HQFFpdFAadlYKW6ndHKHFNQ4R0m3egFyd3HU0fjL+uNogppXr1gCjQIAAAAAgLKowMHbrFmzirMPoOi5V5Lq9pH2/GgZ9ZZL8FbBxVE9wgP0U/QJLdh+guANAAAAAAAUGTZXQNnW9O9NFnZ8K6Wn5lqStbvpwh0nlZHJ7qYAAAAAAKBoELyhbKvZVfIMlC7HS38uybWkQ20/eVdw1pkLV7Q5JvcdUAEAAAAAAAqL4A1lm6OT1OQuy3H0l7mWuDg5qE+jIEnSF5sP26szAAAAAABQxhG8oezLmm7613LpwqlcS+5vV0OStGRXnA6fS7ZTYwAAAAAAoCwjeEPZ51dHqtpKMjKkHV/nWlI30Eud6/op05A+Xhdr5wYBAAAAAEBZVKBdTefPn1/gC/br1++amwGKTdMh0rEtUvQcqd0IyWTKUTKsQ5h+3X9G3/5+VKO611FFD5cSaBQAAAAAAJQVBQre+vfvn+1nk8kkwzCy/ZwlIyOjaDoDilLD26Qlz0tn9knHt0lVW+QoiajpqwbBZu0+kaTPNx3WiG61S6BRAAAAAABQVhRoqmlmZqb1sWzZMjVt2lSLFy9WQkKCEhIS9PPPP6t58+ZasiT3XSOBEufmLdXvazmO/iLXEpPJpGEdwyRJn244pJQ0QmQAAAAAAHDtCr3G28iRI/Xee+8pMjJSZrNZZrNZkZGRmjRpkkaMGFEcPQJFo+lgy/POeVLa5VxL+jQKUhWfCjqXnKof/jhux+YAAAAAAEBZU+jg7eDBg/Lx8clx3tvbW4cOHSqCloBiEtpJ8q4mXUmU9i3KtcTZ0UEPtK8hSZq5NkaZmUaudQAAAAAAAPkpdPDWqlUrjR49WqdOnbKeO3XqlMaMGaPWrVsXaXNAkXJwkJoMshxHf2mz7K7W1eXl5qSYM8laue+0nZoDAAAAAABlTaGDt08++UQnT55U9erVVatWLdWqVUvVq1fX8ePH9fHHHxdHj0DRafp38HZwlZR4LNcST1cnDWkTIkn6cM1Be3UGAAAAAADKmALtanq1WrVqaceOHVq+fLn27dsnSapfv766d++ebXdToFSqFCaFtJcOr5e2fy11fDrXsgfa19DH62K05dB5bTtyXs2rV7RzowAAAAAA4EZX6BFvkmX3x549e2rYsGF64okn1KNHD0I33DiaDrE8R38pGbmv4RZgdtMtTatIkj5aG2OvzgAAAAAAQBlS6OAtMzNTr7/+uqpUqSJPT0/FxsZKkl566SWmmuLGEH6L5OwhxcdIRzbZLHu4Q5gkacmuOB0+l2yv7gAAAAAAQBlR6ODtv//9r2bPnq0JEybIxcXFer5hw4b66KOPirQ5oFi4ekoN+luOo7+wWVY30Eud6/op05A+Xhdrn94AAAAAAECZUejg7bPPPtOHH36oIUOGyNHR0Xq+SZMm1jXfgFIva7rp7h+lVNuj2Yb9Pert29+PKj451Q6NAQAAAACAsqLQwdvx48dVq1atHOczMzOVlpZWJE0BxS6knVQxVEq9KO2Zb7MsoqavGlYxKyUtU19sOmzHBgEAAAAAwI2u0MFbeHi41q5dm+P8d999p2bNmhVJU0CxM5myb7Jgs8xkXevt0w2HlJKWYY/uAAAAAABAGeBU2DeMGzdO9913n44fP67MzEx9//332r9/vz777DMtXLiwOHoEikeTu6RVb0iH1krxsVKl0FzL+jQK0oQl+3U84bK+33Zcg9tUt3OjAAAAAADgRlToEW+33HKLFixYoBUrVsjDw0Pjxo3T3r17tWDBAvXo0aM4egSKh081KayT5Xj71zbLnB0d9OBNllDuo7Uxysw07NEdAAAAAAC4wRUqeEtPT9drr72m0NBQLV++XKdPn9alS5e0bt069ezZs7h6BIpP07stz9FzpMxMm2V3tqomLzcnxZxN1sp9p+3UHAAAAAAAuJEVKnhzcnLShAkTlJ6eXlz9APZVL0pyNUuJRyxTTm3wdHXSkDYhkqQP1xy0V3cAAAAAAOAGVuippt26ddPq1auLoxfA/lzcpYa3WY7z2GRBkh5oX0POjiZtOXRe246ct0NzAAAAAADgRlbozRV69+6t5557Tjt37lSLFi3k4eGR7fV+/foVWXOAXTS9W9o6W9ozX+rzruRmzrUswOymW5pW0Xdbj2nmmhjNuLuFffsEAAAAAAA3FJNhGIVaKd7BwfYgOZPJpIyMjOtuqrRJSkqSt7e3EhMTZTbnHsrgBmYY0v9aS2f/lG6eLLV80Gbpn6cuqOfkNTKZpF+f7qwQXw+btQAAAAAAoGwqaFZU6KmmmZmZNh9lMXRDOWAySS3utxxvmCZl2v4e1wnwUue6fjIM6eN1sfbpDwAAAAAA3JAKHbwBZVLzeyU3Hyn+oLR3QZ6lwzqESZK+/f2o4pNT7dAcAAAAAAC4ERV6jbfXXnstz9fHjRt3zc0AJcbVS2o9TFozQVo3WQq/xTISLhcRNX3VsIpZu44n6YtNhzWiW207NwsAAAAAAG4EhV7jrVmzZtl+TktLU2xsrJycnFSzZk1t27atSBssDVjjrZxIPitNbiilX5bu+VGq2cVm6U/Rx/Xk19Hy9XDR+ue6ys3Z0X59AgAAAACAElVsa7z98ccf2R67du3SyZMn1a1bN40aNeq6mgZKlEdly5RTyTLqLQ99GgWpik8FnUtO1ffbjtuhOQAAAAAAcKMpkjXezGazXn31Vb300ktFcTmg5LR7XDI5SrGrpeNbbZY5OzrowZtCJUkfrY1RZmahBo4CAAAAAIByoMg2V0hMTFRiYmJRXQ4oGT7VpUa3W47XTcmz9M5W1eTl5qSYs8lasfdU8fcGAAAAAABuKIXeXGHq1KnZfjYMQydPntTnn3+u3r17F1ljQIlp/6S042vL7qZn/5Iq5755gqerk+5uG6IZvx7UzLUx6tkg0M6NAgAAAACA0qzQwdvkydnXvnJwcJCfn5/uu+8+Pf/880XWGFBiAsKlOr2lPxdL69+Tbplms/T+djX00doYbTl0XtuOnFfz6hXt2CgAAAAAACjNCh28xcbGFkcfQOly0yhL8Lb9a6nLC5I5ONeyALObbmlaRd9tPaaZa2I04+4Wdm4UAAAAAACUVkW2xhtQplRvI1VvJ2WmSZum51k6rGOYJGnJ7jgdPpdsj+4AAAAAAMANoNAj3m699VaZTKYC1X7//feFbggoNW4aJc3ZIP0+S+rwlFQh92mkdQK81Lmun37df0YfrY3V6/0b2rlRAAAAAABQGhV6xJu3t7dWrlyp33//3Xpu69at+uWXX2Q2m+Xt7W19ADe02j0k/wZS6kVpy0d5lmaNepu79ajik1Pt0R0AAAAAACjlCj3iLSAgQHfccYc++OADOTo6SpIyMjL02GOPyWw265133inyJoESYTJZRr19/5C06QOp7XDJxT3X0ogwXzWsYtau40n6YtNhjeiW+06oAAAAAACg/Cj0iLdPPvlETz/9tDV0kyRHR0eNHj1an3zySZE2B5S4BrdKPtWlS2el6C9tlplMJj3cwTLq7dMNh5SSlmGvDgEAAAAAQClV6OAtPT1d+/bty3F+3759yszMLJKmgFLD0UlqN8JyvH6qlJFmszSqUZCq+FTQueRUfb/tuJ0aBAAAAAAApVWhg7cHHnhAQ4cO1aRJk7Ru3TqtW7dOEydO1EMPPaQHHnigOHoESlazuyX3ylLiEWn3DzbLnBwd9OBNoZKkj9bGKDPTsFeHAAAAAACgFCr0Gm/vvvuuAgMDNXHiRJ08eVKSFBQUpDFjxuipp54q8gaBEudcQWr7qPTL69K6yVKj2y3rv+XizlbVNGXFn4o5m6wVe0+pZ4NAOzcLAAAAAABKC5NhGNc8LCcpKUmSZDabi6yh0igpKUne3t5KTEws8/cKGy4nSJMbSqkXpMHfSnUibZa+vWSfZvx6UK1qVNTcR9rZr0cAAAAAAGAXBc2KCj3V9Gpms1l//PGHFi9erPPnz1/PpYDSrYKP1PLvqdTrJudZen+7GnJ2NGnLofPadoR/FwAAAAAAlFcFDt7efvttvfTSS9afDcNQr1691KVLF0VFRal+/fravXt3sTQJlAptH5McXaQjG6XDG22WBZjd1L9pFUnSzDUx9uoOAAAAAACUMgUO3r755hs1bNjQ+vN3332nNWvWaO3atTp79qxatmypV199tViaBEoFc5DUZJDleP2UPEsf7hgmSVqyO06HziYXc2MAAAAAAKA0KnDwFhsbq8aNG1t//vnnnzVw4EC1b99elSpV0osvvqiNG22PAgLKhPZPSjJJfy6RTtke4VknwEud6/rJMKSP18Xarz8AAAAAAFBqFDh4S09Pl6urq/XnjRs3ql27fxaODw4O1tmzZ4u2O6C08a0phd9iOV7/Xp6lw/4e9TZ361HFJ6cWd2cAAAAAAKCUKXDwVrNmTa1Zs0aSdOTIEf3555/q2LGj9fVjx47J19e36DsESpubRlqed34nnT9ssywizFcNq5iVkpapzzfargMAAAAAAGVTgYO34cOH6/HHH9fQoUPVu3dvRUREKDw83Pr6L7/8ombNmhVLk0CpEtxMCusiGRnSxmk2y0wmk4Z1rClJ+mzjIaWkZdirQwAAAAAAUAoUOHh7+OGHNXXqVMXHx6tjx46aN29ettdPnDihBx98sMgbBEqlm0ZZnrd9Jl08Y7OsT8NAVfGpoHPJqfp+23E7NQcAAAAAAEoDk2EYRkk3UdolJSXJ29tbiYmJMpvNJd0OSgPDkGZ2lU5skzqOkbq+aLP043Wxen3hHoVV9tCK0Z3k4GCyY6MAAAAAAKCoFTQrKvCINwBXMZn+GfX224fSlQs2S+9sVU1mNyfFnE3Wir2n7NQgAAAAAAAoaQRvwLWqd7PkW1tKSZS2zrZZ5unqpCFtQyRJH66JsVNzAAAAAACgpBG8AdfKwUFq/6TleOP/pPQrNkvvb1dDzo4m/X74vLYePm+nBgEAAAAAQEkieAOuR+M7JK8g6cJJacc3NssCzG7q37SKJOmjtYx6AwAAAACgPCB4A66Hk6sUMdxyvP49KTPDZunDHcMkSUt2x+nQ2WR7dAcAAAAAAEpQoYO35ORkvfTSS2rXrp1q1aqlsLCwbA+g3Glxv+TmLZ07IO1baLOsToCXutT1k2FYdjoFAAAAAABlW6GDt4ceekgff/yxOnTooMcff1xPPvlktkdhzJgxQ40bN5bZbJbZbFZERIQWL15sfT0lJUXDhw+Xr6+vPD09NWDAAJ06lX1XyCNHjigqKkru7u7y9/fXmDFjlJ6enq3m119/VfPmzeXq6qpatWpp9uzZhb1twDZXL6n1MMvxuimSYdgszRr1NnfrUcUnp9qhOQAAAAAAUFKcCvuGxYsXa9GiRWrfvv11f3jVqlX11ltvqXbt2jIMQ59++qluueUW/fHHH2rQoIFGjRqlRYsWae7cufL29tbjjz+u2267TevXr5ckZWRkKCoqSoGBgdqwYYNOnjype++9V87Ozho/frwkKTY2VlFRUXrkkUf05ZdfauXKlXrooYcUFBSkyMjI674HQJLU5hFpwzTpxDYpdo0U1inXsogwXzWsYtau40n6fONhPdm9tp0bBQAAAAAA9mIyjDyG5+QiNDRUP//8s+rXr18sDVWqVEnvvPOOBg4cKD8/P82ZM0cDBw6UJO3bt0/169fXxo0b1bZtWy1evFg333yzTpw4oYCAAEnSBx98oGeffVZnzpyRi4uLnn32WS1atEi7du2yfsZdd92lhIQELVmypEA9JSUlydvbW4mJiTKbzUV/0ygbfh4j/fahFNZFuvdHm2Xzt5/QiK/+kK+Hi9Y/11Vuzo726xEAAAAAAFy3gmZFhZ5q+vrrr2vcuHG6dOnSdTX4bxkZGfr666+VnJysiIgIbd26VWlpaerevbu1pl69eqpevbo2btwoSdq4caMaNWpkDd0kKTIyUklJSdq9e7e15uprZNVkXSM3V65cUVJSUrYHkK+IxyWToxSzSjrxh82yPg0DVcWngs4lp2retmN2bBAAAAAAANhToYO3iRMnaunSpQoICFCjRo3UvHnzbI/C2rlzpzw9PeXq6qpHHnlEP/zwg8LDwxUXFycXFxf5+Phkqw8ICFBcXJwkKS4uLlvolvV61mt51SQlJeny5cu59vTmm2/K29vb+qhWrVqh7wvlUMUQqZFldKbWTbFZ5uTooKE3hUqSPlobq8zMQg06BQAAAAAAN4hCr/HWv3//Im2gbt26io6OVmJior777jvdd999Wr16dZF+RmE9//zzGj16tPXnpKQkwjcUTPsnpR3fSHt+ks4dlHxr5lp2R6tqmrLiT8WeTdaKvafUs0GgnRsFAAAAAADFrdDB28svv1ykDbi4uKhWrVqSpBYtWmjLli167733dOeddyo1NVUJCQnZRr2dOnVKgYGWkCIwMFC//fZbtutl7Xp6dc2/d0I9deqUzGazKlSokGtPrq6ucnV1LZL7QzkT0ECq00v6c4m0/j2p39RcyzxdnTSkbYhm/HpQH66JIXgDAAAAAKAMKvRU0+KWmZmpK1euqEWLFnJ2dtbKlSutr+3fv19HjhxRRESEJCkiIkI7d+7U6dOnrTXLly+X2WxWeHi4tebqa2TVZF0DKHI3jbI8b/9KSjpps+yBdjXk7GjS74fPa+vh83ZqDgAAAAAA2Euhg7eMjAy9++67at26tQIDA1WpUqVsj8J4/vnntWbNGh06dEg7d+7U888/r19//VVDhgyRt7e3hg4dqtGjR2vVqlXaunWrHnjgAUVERKht27aSpJ49eyo8PFz33HOPtm/frqVLl+rFF1/U8OHDrSPWHnnkEcXExOiZZ57Rvn37NH36dH377bcaNWpUYW8dKJjqbaXqEVJGqrRpus0yf7Ob+jetIkmauSbGXt0BAAAAAAA7KXTw9uqrr2rSpEm68847lZiYqNGjR+u2226Tg4ODXnnllUJd6/Tp07r33ntVt25ddevWTVu2bNHSpUvVo0cPSdLkyZN18803a8CAAerYsaMCAwP1/fffW9/v6OiohQsXytHRUREREbr77rt177336rXXXrPWhIaGatGiRVq+fLmaNGmiiRMn6qOPPlJkZGRhbx0ouKxRb79/Il22PZrt4Y5hkqSle+J06GyyPToDAAAAAAB2YjIMo1BbKtasWVNTp05VVFSUvLy8FB0dbT23adMmzZkzp7h6LTFJSUny9vZWYmKizGZzSbeDG4FhSDPaSaf3SF1fkjo+bbP0gVm/adX+M7qnbYhe79/Qjk0CAAAAAIBrUdCsqNAj3uLi4tSoUSNJkqenpxITEyVJN998sxYtWnSN7QJljMn0z6i3TTOktMs2S7NGvc3delTxyan26A4AAAAAANhBoYO3qlWr6uRJy4LxNWvW1LJlyyRJW7ZsYSdQ4GoNbpN8qkuXzkp/fGGzLCLMV42qeCslLVOfbzxsxwYBAAAAAEBxKnTwduutt1p3CX3iiSf00ksvqXbt2rr33nv14IMPFnmDwA3L0UlqN8JyvGGqlJGea5nJZLKOevts4yGlpGXYq0MAAAAAAFCMCr3G279t3LhRGzduVO3atdW3b9+i6qtUYY03XLPUS9KURpZRb7d9JDW+Pdey9IxMdXrnVx1PuKw3bm2oIW1C7NwoAAAAAAAoqGJb4+3fIiIiNHr06DIbugHXxcVdavuI5XjdZMumC7lwcnTQ0JtCJUkfrY1VZuZ15eEAAAAAAKAUuKbg7fPPP1f79u0VHBysw4cta1JNmTJFP/30U5E2B5QJrR6SXDyl07ulv5bbLLujVTWZ3ZwUezZZy/acsmODAAAAAACgOBQ6eJsxY4ZGjx6tPn36KCEhQRkZlvWofHx8NGXKlKLuD7jxVagotXzAcrxuss0yT1cn3d3WMsV0yoo/lcGoNwAAAAAAbmiFDt7ef/99zZw5U2PHjpWjo6P1fMuWLbVz584ibQ4oM9oOlxxdpCMbpCObbJYN6xgms5uT9sVd0PfbjtmxQQAAAAAAUNQKHbzFxsaqWbNmOc67uroqOTm5SJoCyhxzkNTkLsvxuik2y3zcXTS8Sy1J0qTlf7LDKQAAAAAAN7BCB2+hoaGKjo7OcX7JkiWqX79+UfQElE3tnpRkkv5cLJ3aY7PsvnY1FOztppOJKZq1/pDd2gMAAAAAAEWr0MHb6NGjNXz4cH3zzTcyDEO//fab3njjDT3//PN65plniqNHoGyoXEsK72c5Xv+ezTI3Z0c91bOuJGn6rwd0PjnVHt0BAAAAAIAiVujg7aGHHtLbb7+tF198UZcuXdLgwYM1Y8YMvffee7rrrruKo0eg7Gg/0vK8c66UcMRmWf9mVVQv0EsXUtI1bdUB+/QGAAAAAACKVKGDN0kaMmSI/vrrL128eFFxcXE6duyYhg4dWtS9AWVPleZSWGfJyJA2TLNZ5uhg0vN9LFO3P994WEfjL9mpQQAAAAAAUFSuKXjL4u7uLn9//6LqBSgfbhpled72mZR81mZZx9qV1b6Wr1IzMjVx2X47NQcAAAAAAIqKU0ELu3btWqC6X3755ZqbAcqF0E5ScDPpxB/Sbx9KXV7ItcxkMun53vV18/vr9GP0CT3UIUwNq3jbuVkAAAAAAHCtChy8/frrrwoJCVFUVJScnZ2LsyegbDOZLKPevr1X2vx/UrsRkqtnrqUNq3jrlqbB+in6hN5cvFdfDG0jk8lk54YBAAAAAMC1KHDw9vbbb2vWrFmaO3euhgwZogcffFANGzYszt6AsqvezZJvLencAWnbp1LEcJulT/esq8U747T+wDmt+eusOtXxs2OjAAAAAADgWhV4jbcxY8Zoz549+vHHH3XhwgW1b99erVu31gcffKCkpKTi7BEoexwcpfZPWo43TJPSU22WVqvkrnsiQiRJby3ep4xMwx4dAgAAAACA61TozRUiIiI0c+ZMnTx5UsOHD9cnn3yi4OBgwjegsBrfKXkFSRdOSDu/zbP08S615OXmpL0nk/TjH8ft1CAAAAAAALge17yr6bZt27R69Wrt3btXDRs2ZN03oLCcXP+ZYrpuipSZabO0ooeLHutcS5I0cdl+paRl2KFBAAAAAABwPQoVvJ04cULjx49XnTp1NHDgQFWqVEmbN2/Wpk2bVKFCheLqESi7WtwvuXlL5/6S9i/Ks/SB9jUU5O2mE4kp+nTDIbu0BwAAAAAArl2Bg7c+ffqoZs2a2rx5s9555x0dO3ZM7777rsLDw4uzP6Bsc/WSWj1sOV43WTJsr9/m5uyo0T3qSJL+t+qAEi7ZXhcOAAAAAACUPJNh5PG/9K/i4OCgoKAg+fv7y2Qy2azbtm1bkTVXWiQlJcnb21uJiYkym80l3Q7KmotnpCkNpfQU6b4FUmhHm6UZmYaipq7VvrgLGtYxTC/0qW/HRgEAAAAAgFTwrMipoBd8+eWXi6QxAP/i6Sc1u0faMtMy6i2P4M3RwaRne9fTA7O2aPb6Q7o3IkRVK7rbsVkAAAAAAFBQBR7xVp4x4g3F7vwhaWpzyciQhq2WgpvaLDUMQ4NnbtbGmHO6rVkVTbrTdi0AAAAAACh6Bc2KrnlXU0l66623lJCQcD2XACBJFWtIDQdYjtdPybPUZDLp+T71JEk/RB/X7hOJxdsbAAAAAAC4JtcVvI0fP17x8fFF1QtQvt000vK85yfp3ME8SxtX9VHfJsEyDOmtxfuKvzcAAAAAAFBo1xW8MUsVKEIBDaTakZKRKa2dlG/5mJ515exo0tq/zmrtX2fs0CAAAAAAACiM6wreABSxTs9Ynrd/JZ09kGdpdV933d02RJL05s/7lJlJEA4AAAAAQGlyXcHbnj17VKNGjSJqBYCqtpTq9LZssvDr+HzLn+haW16uTtpzMkk/bT9uhwYBAAAAAEBBFTp4O3r0qI4dOyZJqlatmn7//XeNHDlSH374YZE3B5RLXV6wPO+aJ8XtyrO0koeLHulcU5L07tI/lZKWUdzdAQAAAACAAip08DZ48GCtWrVKkhQXF6cePXrot99+09ixY/Xaa68VeYNAuRPUWGpwq+V4Vf6j3h5sH6pAs5uOJ1zW5xsPF3NzAAAAAACgoAodvO3atUutW7eWJH377bdq2LChNmzYoC+//FKzZ88u6v6A8qnzC5LJQdq/SDq2Nc/SCi6OGt2jjiRp2qoDSryUZo8OAQAAAABAPgodvKWlpcnV1VWStGLFCvXr10+SVK9ePZ08ebJouwPKK786UpNBluNfXs+3fECLqqoT4KnEy2mavjrvTRkAAAAAAIB9FDp4a9CggT744AOtXbtWy5cvV69evSRJJ06ckK+vb5E3CJRbnZ6RHJylmFXSoXV5ljo6mPRc73qSpFnrD+l4wmV7dAgAAAAAAPJQ6ODt7bff1v/93/+pc+fOGjRokJo0aSJJmj9/vnUKKoAiULGG1Pxey/Ev/5UMI8/yLnX91TasklLTMzVp2Z/F3x8AAAAAAMiTyTDy+V/zucjIyFBSUpIqVqxoPXfo0CG5u7vL39+/SBssDZKSkuTt7a3ExESZzeaSbgflSdJJaWpTKT1FGjJPqt09z/LtRxN0y//Wy2SSFj3RQeHBfF8BAAAAAChqBc2KCj3i7fLly7py5Yo1dDt8+LCmTJmi/fv3l8nQDShR5iCp1UOW419ez3fUW5NqPopqHCTDkN5ess8ODQIAAAAAAFsKHbzdcsst+uyzzyRJCQkJatOmjSZOnKj+/ftrxowZRd4gUO7dNEpy8ZRORkv7FuZb/kxkXTk7mrT6zzNaf+Bs8fcHAAAAAAByVejgbdu2berQoYMk6bvvvlNAQIAOHz6szz77TFOnTi3yBoFyz6Oy1PZRy/Evb0iZGXmWh/h6aEibEEnSm4v3KjOz0LPJAQAAAABAESh08Hbp0iV5eXlJkpYtW6bbbrtNDg4Oatu2rQ4fPlzkDQKQFPG45OYtndkr7ZqXb/kTXWvJ09VJu44nacGOE3ZoEAAAAAAA/Fuhg7datWrpxx9/1NGjR7V06VL17NlTknT69Gk2HgCKSwUfqd0Iy/Gq8VJGWp7lvp6ueqRTmCTpnaX7dSU971FyAAAAAACg6BU6eBs3bpyefvpp1ahRQ61bt1ZERIQky+i3Zs2aFXmDAP7W5hHJw086HytFz8m3fOhNYQowu+rY+cv6YtMROzQIAAAAAACuVujgbeDAgTpy5Ih+//13LV261Hq+W7dumjx5cpE2B+Aqrp7STaMtx6snSOlX8iyv4OKoUd3rSJLe/+UvJV7Oe5QcAAAAAAAoWoUO3iQpMDBQzZo104kTJ3Ts2DFJUuvWrVWvXr0ibQ7Av7R8UPIKlpKOSb/Pyrd8YIuqqu3vqYRLafpg9UE7NAgAAAAAALIUOnjLzMzUa6+9Jm9vb4WEhCgkJEQ+Pj56/fXXlZmZWRw9Asji7CZ1GmM5XvuulJqcZ7mTo4Oe7WUJxD9ZF6sTCZeLu0MAAAAAAPC3QgdvY8eO1bRp0/TWW2/pjz/+0B9//KHx48fr/fff10svvVQcPQK4WrN7pIo1pOQz0m8f5lverb6/WodW0pX0TE1e/mfx9wcAAAAAACRJJsMwjMK8ITg4WB988IH69euX7fxPP/2kxx57TMePHy/SBkuDpKQkeXt7KzExkZ1bUTps/1r64T+Sm480cofk5p1n+R9HzuvW6RtkMkmLn+ygeoF8jwEAAAAAuFYFzYoKPeItPj4+17Xc6tWrp/j4+MJeDsC1aHS7VLmulJIgbfxfvuXNqldUVKMgGYb09uJ9xd8fAAAAAAAofPDWpEkTTZs2Lcf5adOmqUmTJkXSFIB8ODhKXV6wHG/8n5R8Lt+3jImsKycHk1btP6MNB88Wc4MAAAAAAMCpsG+YMGGCoqKitGLFCkVEREiSNm7cqKNHj+rnn38u8gYB2FC/nxTYWIrbIa2fIvV8Pc/yGpU9NLhNdX228bDeWrxPPz7WXg4OJvv0CgAAAABAOVToEW+dOnXSn3/+qVtvvVUJCQlKSEjQbbfdpv3796tDhw7F0SOA3Dg4SF3/3tDkt5nShbh83zKiW215uDhqx7FELdx5spgbBAAAAACgfCv05gq2HDt2TK+99po+/DD/XRZvNGyugFLLMKSPe0rHfpNaPSxFvZvvW6au/EuTlv+p6pXctWJ0J7k4FTp/BwAAAACgXCu2zRVsOXfunD7++OOiuhyAgjCZpG5/j3rbOltKOJLvWx7qECo/L1cdib+kLzcfLt7+AAAAAAAoxxjqAtzoQjtKoZ2kzDRp9dv5lru7OGlU9zqSLKPfklLSirtDAAAAAADKJYI3oCzIWust+ivp7IF8y+9oWVU1/Tx0/lKa/m/1wWJuDgAAAACA8ongDSgLqrWS6vSSjAzp1/H5ljs5OujZXvUkSR+vi1VcYkpxdwgAAAAAQLnjVNDC2267Lc/XExISrrcXANejy1jpzyXSrnnSTaOlwIZ5lvcID1CrGhW15dB5TV7+p94e2NhOjQIAAAAAUD4UeMSbt7d3no+QkBDde++9xdkrgLwENZYa3Go5XpX/qDeTyaTneteXJM3delR/nrpQnN0BAAAAAFDumAzDMEq6idKuoFvEAiXuzJ/S9DaSkSk99ItUtUW+b3n0i61avCtO3er56+P7W9mhSQAAAAAAbmwFzYpY4w0oS/zqSI3vshz/8nqB3jImsq4cHUxaue+0NsWcK8bmAAAAAAAoXwjegLKm87OSg7MUs0o6tC7f8jA/Tw1uXV2S9ObifWIQLAAAAAAARYPgDShrKtaQmv+93uIv/5UKEKSN6FZbHi6O2n40QT/vjCve/gAAAAAAKCcI3oCyqOPTkpObdGSjdGBlvuV+Xq4a1rGmJGnC0n1KTc8s7g4BAAAAACjzCN6AssgcLLV6yHL8y+sFGvX2UIdQVfZ01eFzl/TVb0eKuUEAAAAAAMo+gjegrLpplOTiKZ2MlvYtzLfcw9VJI7vXliS9t/IvXUhJK+YGAQAAAAAo2wjegLLKo7LU9lHL8S9vSJkZ+b7lzlbVFObnofjkVH24JqaYGwQAAAAAoGwjeAPKsojHJTdv6cxeade8fMudHR30TGQ9SdLMtTE6lZRS3B0CAAAAAFBmEbwBZVkFH6ndCMvxr29KGflPH41sEKAWIRWVkpapicv2F29/AAAAAACUYQRvQFnX5hHJw0+Kj5Gi5+RbbjKZ9EKf+pKkb38/puijCcXcIAAAAAAAZRPBG1DWuXpKN422HK+eIKVfyfctLUIqakDzqpKkcT/tUmZm/ruiAgAAAACA7AjegPKg5YOSV7CUdEz6fVaB3vJs77rycnXSjmOJ+vb3o8XcIAAAAAAAZQ/BG1AeOLtJncZYjtdOlFKT832Lv5ebRvaoI0l6e8k+JVxKLc4OAQAAAAAocwjegPKi2T1SxRpS8mnptw8L9JZ7I0JUJ8BT5y+ladLyP4u3PwAAAAAAyhiCN6C8cHSWOj1nOV43RUpJzPctzo4OeqVfA0nSF5sOa/eJ/N8DAAAAAAAsCN6A8qTxHVLlulJKgrTxfwV6S7ualXVz4yBlGtLLP+2WYbDRAgAAAAAABUHwBpQnDo5SlxcsxxunS8nnCvS2sVH1VcHZUb8fPq8fo48XY4MAAAAAAJQdBG9AeVO/nxTYWEq9IK2fUqC3BHlX0BPdakmSxv+8TxdS0oqxQQAAAAAAyoYSDd7efPNNtWrVSl5eXvL391f//v21f//+bDUpKSkaPny4fH195enpqQEDBujUqVPZao4cOaKoqCi5u7vL399fY8aMUXp6eraaX3/9Vc2bN5erq6tq1aql2bNnF/ftAaWTg4PU9UXL8W8zpQtxBXrb0JtCFVrZQ2cuXNHUlX8VY4MAAAAAAJQNJRq8rV69WsOHD9emTZu0fPlypaWlqWfPnkpOTrbWjBo1SgsWLNDcuXO1evVqnThxQrfddpv19YyMDEVFRSk1NVUbNmzQp59+qtmzZ2vcuHHWmtjYWEVFRalLly6Kjo7WyJEj9dBDD2np0qV2vV+g1KjdU6raWkq/LK2dWKC3uDo56uW+4ZKkWesP6a9TF4qzQwAAAAAAbngmoxStlH7mzBn5+/tr9erV6tixoxITE+Xn56c5c+Zo4MCBkqR9+/apfv362rhx4/+3d+9xVtX1/sdfa+3LXIAZQGSAQERRFBUsVCQveSFB0zKxtKzQKH+WejKzvJSXzHMsPZVdvJyy8pzMMivNvBveyvBGoaCCihcwHC4CMzDDzOzL+v2x9+yZAdRh2JuZYV7Px2M91net9f1+92c4u92j91lrfTnwwAO59957OfbYY1m2bBk1NTUA3HDDDZx//vmsXLmSZDLJ+eefz913382CBQsKn3XyySezdu1a7rvvvk3qaG5uprm5uXBcX1/PqFGjqKuro6qqqsT/CtI28tpj8L/HQZiA//gnDNypU8O++H/P8OALyzlo7A7cPGsyQRCUuFBJkiRJknqW+vp6qqur3zMr6lHveKurqwNg8ODBAMydO5dUKsXUqVMLffbYYw922mkn5syZA8CcOXPYZ599CqEbwLRp06ivr+f5558v9Gk/R2uf1jk2duWVV1JdXV3YRo0aVbw/UuopxhwKYz4E2RQ8+r1OD7vk2PEk4yGPv/I29y7o3GOqkiRJkiT1RT0meMtms5xzzjkcdNBB7L333gDU1taSTCYZOHBgh741NTXU1tYW+rQP3Vqvt157tz719fVs2LBhk1ouvPBC6urqCtvSpUuL8jdKPc4RF+f2834Lq17p1JBRgyv50od2BeCKu16gsSX9HiMkSZIkSeqbekzwduaZZ7JgwQJ+97vfdXcplJWVUVVV1WGTtkuj9ofdp0OUgUeu7PSwLx22KyMHVbCsronrHl5cwgIlSZIkSeq9ekTwdtZZZ3HXXXfx8MMPM3LkyML5YcOG0dLSwtq1azv0X758OcOGDSv02XiV09bj9+pTVVVFRUVFsf8cqXc5/Ju5/YI/wvLnOzWkPBHj4mNzCy387LFXeX1Vw3uMkCRJkiSp7+nW4C2KIs466yxuv/12HnroIcaMGdPh+qRJk0gkEsyePbtwbtGiRSxZsoQpU6YAMGXKFObPn8+KFSsKfR588EGqqqoYP358oU/7OVr7tM4h9WnDJ8D444EIHvrPTg87anwNh+6+Iy2ZLJff9ULJypMkSZIkqbfq1uDtzDPP5Oabb+aWW25hwIAB1NbWUltbW3jvWnV1NbNmzeLcc8/l4YcfZu7cuZx22mlMmTKFAw88EICjjjqK8ePH89nPfpZnn32W+++/n29961uceeaZlJWVAXDGGWfw6quv8o1vfIOFCxdy3XXX8fvf/56vfvWr3fa3Sz3K4d+EIIRFd8Obczs1JAgCLj1uPIlYwEMLVzD7xeXvPUiSJEmSpD6kW4O366+/nrq6Og477DCGDx9e2G699dZCnx/+8Icce+yxzJgxg0MPPZRhw4bxpz/9qXA9Fotx1113EYvFmDJlCp/5zGf43Oc+x+WXX17oM2bMGO6++24efPBBJk6cyPe//31uvPFGpk2btk3/XqnH2nF3mHByrv3wFZ0etuuO/Zl18C4AfPsvL9CUypSiOkmSJEmSeqUgiqKou4vo6err66murqaurs6FFrT9WvM6/GQSZNNw6j2w80GdGtbQnOaI7z/C8vpmvvbh3Tn7yN1KW6ckSZIkSd2ss1lRj1hcQVIPMGhn+MDncu2HvgOdzOT7lcX55kdy71O89pFXeHNNY4kKlCRJkiSpdzF4k9Tm0K9DvByWzIFXZr93/7zjJgxn8pjBNKWy/OfdL5awQEmSJEmSeg+DN0ltqkbA/l/ItbfgrrcgCPj2x/YiFgbcu6CWv728soRFSpIkSZLUOxi8Sero4K9Coh+8NQ8W3tXpYXsMq+JzU0YDcNmdz9OSzpaoQEmSJEmSegeDN0kd9RsCB34p137wEkg3d3roOVN3Z0j/JItXNnDTP14rUYGSJEmSJPUOBm+SNnXQV6B/Dax+FZ64rtPDqisSnD99DwB+9NeXWV7fVKoKJUmSJEnq8QzeJG2qvAo+fHmu/ejVUL+s00NnfGAk799pIA0tGa68x4UWJEmSJEl9l8GbpM3b55Mw8gBINeQeOe2kMAy4/KN7EwRwx7xlPPnq2yUsUpIkSZKknsvgTdLmhSEcczUQwPzb4I1/dHroPiOr+dQBOwFw6Z3Pk8640IIkSZIkqe8xeJP0zkbsC5Nm5tr3fAOymU4P/fpR4xhYmWBh7Tp+8+SS0tQnSZIkSVIPZvAm6d0dcTGUV8Py+TD3V50eNqhfkvOOGgfA9x9YxKr1nV8dVZIkSZKk7YHBm6R3128IHP6tXPuhK6BxdaeHfuqAndhrRBX1TWmuvm9RiQqUJEmSJKlnMniT9N72+zwM3Qs2rIGHvtPpYbEw4PKP7QXArc8sZd7StSUqUJIkSZKknsfgTdJ7i8XhmKty7Wd+BW892+mhk0YPZsYHRgJwyZ8XkM1GpahQkiRJkqQex+BNUufsfDDsdQIQwb3nQ9T5AO38o8cxoCzOc2/W8ftnlpauRkmSJEmSehCDN0mdd9QVkKiEJXNg/m2dHjZ0QDnnfHh3AL5330LWNraUqkJJkiRJknoMgzdJnVf9Pjjka7n2AxdD87pOD/3clNHsXtOfNY0pfvDgSyUqUJIkSZKknsPgTdKWmXIWDBoD62vhsf/u9LBELOSyj+YWWrj5iTd4flldqSqUJEmSJKlHMHiTtGUS5TD9ylx7zrWw6pVOD/3grkM4dsJwshFc+ufnibbgPXGSJEmSJPU2Bm+Sttzu02HshyGbgvsu2KKFFr75kT2pSMR45o013DHv3yUsUpIkSZKk7mXwJmnLBQFM/y6ECXjlQXjp/k4PHV5dwdlHjgXgv+5ZyLqmVKmqlCRJkiSpWxm8SeqaIWNhypdz7fsugFRTp4fOOngMY4b0Y+W6Zn48++USFShJkiRJUvcyeJPUdYd+HfoPgzWvwZyfdnpYWTzGpceNB+BXj7/Oy8s7vzqqJEmSJEm9hcGbpK4rGwBHfSfX/tv3oe7NTg89bNxQPjy+hnQ24rK/uNCCJEmSJGn7Y/Amaevs8wkYdSCkGuHBS7Zo6CXHjicZD3n8lbe5d0FtiQqUJEmSJKl7GLxJ2jpBAMdcBQSw4I/w+t87PXTU4Eq+9KFdAbjirhdobEmXqEhJkiRJkrY9gzdJW2/4RNjvtFz7nm9ApvMB2pcO25WRgypYVtfEdQ8vLlGBkiRJkiRtewZvkorjiIuhYhCseB7m/qrTw8oTMS4+NrfQws8ee5XXVzWUqkJJkiRJkrYpgzdJxVE5GI74Vq790BXQ8Hanhx41voZDd9+RlkyWy+96oUQFSpIkSZK0bRm8SSqeSadBzT7QtBYeurzTw4Ig4NLjxpOIBTy0cAWzX1xeuholSZIkSdpGDN4kFU8Yyy+0AMz9X1j2r04P3XXH/sw6eBcAvv2XF2hKZUpRoSRJkiRJ24zBm6TiGv1B2OcTQJRbaCGKOj307CPGUlNVxpLVjfz8sVdLV6MkSZIkSduAwZuk4vvw5ZDoB28+Bc/d2ulh/crifPMjuYUWrn3kFd5c01iqCiVJkiRJKjmDN0nFVzUCDj0v137wEmiq7/TQ4yYMZ/KYwTSlsvzn3S+WqEBJkiRJkkrP4E1SaUw5EwbvCuuXw2NXdXpYEAR8+2N7EQsD7l1Qy99eXlnCIiVJkiRJKh2DN0mlES+D6d/NtZ+4Hla+1Omhewyr4nNTRgNw2Z3P05LOlqJCSZIkSZJKyuBNUunsfhTsNg2yabjvgi1aaOGcqbszpH+SxSsb+OXjr5WwSEmSJEmSSsPgTVJpTb8SYklYPBsW3dPpYdUVCc6fvgcAP3jwJRbWdv49cZIkSZIk9QQGb5JKa4ddYcpZufZ9F0KqqdNDT5w0kiP2GEpLOstXfjuPplSmREVKkiRJklR8Bm+SSu+Qr8GAEbD2DfjHTzo9LAgCrjpxAkP6J1m0fB1X3beohEVKkiRJklRcBm+SSq+sPxz1nVz7b9+HtUs7PXRI/zKuPnEiAL98/DUefclVTiVJkiRJvYPBm6RtY+8ZMPogSG+AB761RUMP32MoM/OrnJ5327O8vb65FBVKkiRJklRUBm+Sto0ggKO/B0EIL9wBrz22RcMvPGZPdhvan5Xrmjn/j/OJtmCFVEmSJEmSuoPBm6RtZ9g+sN+sXPve8yGT7vTQ8kSMH538fpKxkL++uJzfPtX5x1UlSZIkSeoOBm+Stq3DL4KKwbDiBXj6xi0aOn5EFd+YPg6Ay+96nldWrC9FhZIkSZIkFYXBm6Rtq3IwHHlxrv3wf8H6LVss4fMHjeHgsUNoSmU559Z/0ZLOlqBISZIkSZK2nsGbpG3vAzNh2ARoroOHLt+ioWEY8P1PTmRgZYIF/67nBw++VKIiJUmSJEnaOgZvkra9MAbHXJ1r//PX8O9/btHwmqpyvnvCBAD+57HFzFn8drErlCRJkiRpqxm8SeoeOx0IE04CIrjn65DdskdGp+89jJP3H0UUwbm/n0ddY6o0dUqSJEmS1EUGb5K6z4cvh2R/+Pcz8Nzvtnj4xceOZ8yQfrxV18RFt88niqISFClJkiRJUtcYvEnqPgOGwYe+kWs/eCk01W3R8H5lca45aV/iYcDd89/ij//8dwmKlCRJkiSpawzeJHWvyV+CHcZCwwp49KotHj5x1EC++uHdAbj0zwt44+2GYlcoSZIkSVKXGLxJ6l7xJEz/Xq795A2wctEWT3HGh3blgDGDaWjJcM6t80hntux9cZIkSZIklYLBm6Tut9tUGHcMZNNw7zdgC9/VFgsDfnjSvgwoj/OvJWv5yUOvlKhQSZIkSZI6z+BNUs8w7b8gVgavPgIL79ri4e8bWMF/fnwfAH7y0MvMfWN1kQuUJEmSJGnLGLxJ6hkGj4EPnp1r338RpDZs8RQfnTiCE97/PrIRfOV381jXlCpykZIkSZIkdZ7Bm6Se45BzoWokrF0Cj/+4S1N8+2N7MWpwBW+u2cCldz5f5AIlSZIkSeo8gzdJPUeyHxz1nVz77z/IBXBbaEB5gh9+cl/CAP70z39z57PLilykJEmSJEmdY/AmqWfZ6+Ow8yGQboL7v9mlKfbbeTBnHbEbAN+8fT7/Xrvlj61KkiRJkrS1DN4k9SxBAEd/D4IYvHhnbrGFLviPI8by/p0Gsq4pzVdvnUcmu2UrpUqSJEmStLUM3iT1PDV7wf5fyLXvPR8yW75IQjwWcs1J+9IvGeOp11bzP48tLnKRkiRJkiS9O4M3ST3T4RdC5Q6wciE89bMuTTF6h35c9tG9APjBAy/x3Jtri1igJEmSJEnvzuBNUs9UMQiOvDTXnv0dWLmoS9OcOGkkH9lnOOlsxFd+N4/GlnQRi5QkSZIk6Z0ZvEnqud7/Wdj1CEhvgD/OgnTzFk8RBAH/+fG9GV5dzmurGvjOXS+UoFBJkiRJkjZl8Cap5wpDOP56qBgMtfPhoSu6NM3AyiTf/+REggB++9RS7n++tsiFSpIkSZK0KYM3ST3bgGHwsZ/m2v/4Cbz6aJem+eCuQzj90F0AuOCPz7G8vqlYFUqSJEmStFkGb5J6vj0+ApNOBSK4/QxoXN2lab724XHs/b4q1jSmOO+2Z8lmo6KWKUmSJElSewZvknqHaf8FO4yFdcvgL1+BaMtDs2Q85JqT3k95IuRvL6/il4+/VoJCJUmSJEnKMXiT1Dsk+8GMGyGMw4t3wrzfdGmasUP7c/Gx4wG46r5FvPhWfTGrlCRJkiSpwOBNUu8x4v1wxLdy7Xu+AW8v7tI0nz5gJ6buWUNLJstXfvcvmlKZIhYpSZIkSVKOwZuk3uWD/wGjD4ZUA/zpdMiktniKIAj43ox92HFAGS8tX893711YgkIlSZIkSX2dwZuk3iWMwQn/A+XV8O9n4NGrujTNDv3LuPrECQDc9I/XeXjRimJWKUmSJEmSwZukXqh6JBx7Ta79t/+GN+Z0aZrDxg3ltIN2BuDrtz3HqvXNxalPkiRJkiQM3iT1VnufABM/DVE298hpU12Xpjl/+h6MqxnAqvXNnP+H54i6sFqqJEmSJEmbY/Amqfc6+nswcDTULYF7vt6lKcoTMX70qX1JxkNmL1zBzU8uKXKRkiRJkqS+yuBNUu9VXgUzboQgBs/dCs/d1qVp9hhWxQXT9wDgirte4JUV64pZpSRJkiSpjzJ4k9S7jToAPvSNXPvuc2HNG12a5tQP7syhu+9IczrLf/x2Hs3pTBGLlCRJkiT1Rd0avD322GMcd9xxjBgxgiAIuOOOOzpcj6KISy65hOHDh1NRUcHUqVN5+eWXO/RZvXo1p5xyClVVVQwcOJBZs2axfv36Dn2ee+45DjnkEMrLyxk1ahRXXdW1VRAl9VCHnAcjD4Dmerj9DMhueWgWhgH/feIEBvdL8sJb9fzggZdKUKgkSZIkqS/p1uCtoaGBiRMncu211272+lVXXcWPf/xjbrjhBp588kn69evHtGnTaGpqKvQ55ZRTeP7553nwwQe56667eOyxxzj99NML1+vr6znqqKMYPXo0c+fO5eqrr+ayyy7jZz/7Wcn/PknbSCwOJ/wMkgNgyT/g7z/s0jRDq8r53owJAPzPY6/y+CurilmlJEmSJKmPCaIesoRfEATcfvvtHH/88UDubrcRI0bwta99jfPOOw+Auro6ampquOmmmzj55JN58cUXGT9+PE8//TT77bcfAPfddx/HHHMMb775JiNGjOD666/nm9/8JrW1tSSTSQAuuOAC7rjjDhYuXNip2urr66murqauro6qqqri//GSimPeb+GOMyCMw+cfgJGTujTNRbfP55YnlzCsqpx7v3IIg/oli1yoJEmSJKk362xW1GPf8fbaa69RW1vL1KlTC+eqq6uZPHkyc+bMAWDOnDkMHDiwELoBTJ06lTAMefLJJwt9Dj300ELoBjBt2jQWLVrEmjVrNvvZzc3N1NfXd9gk9QITT4a9ToBsGv70BWhe/95jNuNbH9mTXXbsR219ExfdPp8e8v+fkCRJkiT1Mj02eKutrQWgpqamw/mamprCtdraWoYOHdrhejweZ/DgwR36bG6O9p+xsSuvvJLq6urCNmrUqK3/gySVXhDAsT+AqpGw+lW474IuTVOZjPPjk99PIhZw74Jabpv7ZpELlSRJkiT1BT02eOtOF154IXV1dYVt6dKl3V2SpM6qGAQn/A8QwL9+DS/c2aVp9n5fNV87ahwAl935PK+vaihikZIkSZKkvqDHBm/Dhg0DYPny5R3OL1++vHBt2LBhrFixosP1dDrN6tWrO/TZ3BztP2NjZWVlVFVVddgk9SI7HwwHn5Nr/+U/oH5Zl6b54iG7cOAug2lsyfCVW+eRymSLV6MkSZIkabvXY4O3MWPGMGzYMGbPnl04V19fz5NPPsmUKVMAmDJlCmvXrmXu3LmFPg899BDZbJbJkycX+jz22GOkUqlCnwcffJBx48YxaNCgbfTXSNrmDrsIhu8LG9bA7WdAdstDs1gY8INP7ktVeZxnl67lBw++VPw6JUmSJEnbrW4N3tavX8+8efOYN28ekFtQYd68eSxZsoQgCDjnnHO44ooruPPOO5k/fz6f+9znGDFiRGHl0z333JPp06fzxS9+kaeeeorHH3+cs846i5NPPpkRI0YA8OlPf5pkMsmsWbN4/vnnufXWW/nRj37Eueee201/taRtIp6EGTdCohJeexSeuLZL04wYWMGVJ0wA4PpHFvPrOa8XsUhJkiRJ0vYsiLpxub5HHnmEww8/fJPzM2fO5KabbiKKIi699FJ+9rOfsXbtWg4++GCuu+46dt9990Lf1atXc9ZZZ/GXv/yFMAyZMWMGP/7xj+nfv3+hz3PPPceZZ57J008/zZAhQzj77LM5//zzO11nZ5eIldQDPfMruOsciCXhC7Nh+IQuTfPDB1/iR7NfJgjgmpP25WP7vq+4dUqSJEmSeo3OZkXdGrz1FgZvUi8WRfC7U2DR3TBkHJz+CCQruzBNxGV3Ps//znmDeBjw88/tx+F7DH3vgZIkSZKk7U5ns6Ie+443SSqKIICP/gT618CqRfDgJV2cJuDS4/bi+H1HkM5GnHHzXJ56bXWRi5UkSZIkbU8M3iRt//rtAMdfn2s//XN46YEuTROGAVd/YiJH7jGU5nSWWTc9zfPL6opYqCRJkiRpe2LwJqlvGHskHPjlXPvPX4b1K7o0TSIWcu0pH+CAMYNZ15xm5i+f4rVVDUUsVJIkSZK0vTB4k9R3HHkpDN0LGlbCn8/Mvf+tC8oTMW6cuR97jahi1foWPnPjk7xVt6HIxUqSJEmSejuDN0l9R6IcZtwIsTJ4+QF4+sYuT1VVnuB/P38Auwzpx7/XbuCzv3iK1Q0tRSxWkiRJktTbGbxJ6ltqxsNR38m1H/gWrFjY5amG9C/j/2YdwPDqcl5ZsZ7TfvUU65vTRSpUkiRJktTbGbxJ6nsOOB3GToV0E/zxC5Bu7vJUIwdV8utZBzCoMsGzb9Zx+v89Q1MqU8RiJUmSJEm9lcGbpL4nCOBj10HlDrB8Psy+fKumGzt0AP/7+QPol4zxj8Vv8x+//RfpTLZIxUqSJEmSeiuDN0l904Aa+Ni1ufacn8Lih7dqugkjB/LzmfuRjIc88MJyLvzTfKIuLt4gSZIkSdo+GLxJ6rvGHQ37zcq17/gSNK7equk+uOsQfvqp9xMLA26b+yb/efeLhm+SJEmS1IcZvEnq2466AobsDuvegjvPhq0Myo7aaxjfmzEBgBv//hrXPbK4GFVKkiRJknohgzdJfVuyEk74OYQJWHgX/OvXWz3liZNGcvGx4wG4+v5F3PzEG1s9pyRJkiSp9zF4k6QR+8KRF+fa954Pb2/9XWqzDh7D2UeMBeDiPy/gzmeXbfWckiRJkqTexeBNkgCmnA1jDoVUI/zxC5BJbfWU5354dz574GiiCM69dR4PL1pRhEIlSZIkSb2FwZskAYQhHH8DlA+EZf+ER7671VMGQcC3P7oXH9t3BOlsxJdunsszr2/dAg6SJEmSpN7D4E2SWlW/D467Jtf+2/fh9ce3esowDPjvT0zk8HE70pTKctpNT/PCsvqtnleSJEmS1PMZvElSe3t9HPb9DBDB7f8PNqzd6ikTsZDrTpnE/jsPYl1Tms/98ileX9Ww1fNKkiRJkno2gzdJ2tjR34VBY6BuKdxzXlGmrEjGuHHm/uw5vIpV65v5zC+epLauqShzS5IkSZJ6JoM3SdpY2QCYcSMEMZh/Gzz3+6JMW12R4P8+fwA771DJm2s28NlfPMmahpaizC1JkiRJ6nkM3iRpc0buB4ddkGvf/TVY83pRpt1xQBm/njWZYVXlvLxiPafe9DQNzemizC1JkiRJ6lkM3iTpnRx8Low6EJrr4U//DzLFCchGDa7k17MOYGBlgmeXruX0Xz9DczpTlLklSZIkST2HwZskvZNYHE74GZRVwdIn4KHLIYqKMvVuNQO46bQDqEzGePyVt/nKb+eRzmSLMrckSZIkqWcweJOkdzNoNHzk+7n24z+C206F5vVFmXrfUQP5+ef2IxkLue/5Wr55+wKiIgV7kiRJkqTuZ/AmSe9lwidz4VuYgBfugBuPhFUvF2Xqg8YO4cefej9hALc+s5Qr711o+CZJkiRJ2wmDN0nqjP2/AKfeDf2HwcqF8LPD4cW7ijL19L2H8d0TJgDws8de5fpHFxdlXkmSJElS9zJ4k6TO2mky/L/HYKcPQss6uPUUmP0dyG79wgif3H8U3/rIngBcdd8ibnlyyVbPKUmSJEnqXgZvkrQlBtTAzDth8pdyx3/7b/jNJ6Bx9VZP/YVDduHMw3cF4Jt3zOcvzy7b6jklSZIkSd3H4E2StlQsAUd/F2b8AuIVsHg2/OxDsGzeVk993lHjOGXyTkQRnPv7eTz60sqtr1eSJEmS1C0M3iSpq/Y5Eb7wVxg0BtYugV9Og3m3bNWUQRBw+cf25tgJw0llIs749VzmvrH1d9NJkiRJkrY9gzdJ2hrD9obTH4bdpkG6Ce74Etx1LqRbujxlLAz4wSf35UO778iGVIbTfvU0L75VX8SiJUmSJEnbgsGbJG2tikHwqd/BYRcBATzzC7jpI1Df9Xe0JeMhN3xmEpNGD6K+Kc3nfvkUb7zdULyaJUmSJEklZ/AmScUQhnDY+fDpW6G8Gt58Cv7nQ/D6412esiIZ45cz92ePYQNYua6Zz/ziSZbXNxWxaEmSJElSKRm8SVIx7T4NTn8EavaGhhXwv8fBnOsgiro0XXVlgv+bdQCjd6hk6eoNfO4XT7G2seuPsUqSJEmSth2DN0kqtsG7wKwHYZ9PQJSB+y+EP34BWrr2qOjQAeXcPGsyQweUsWj5Ok68YQ5/f3lVkYuWJEmSJBWbwZsklUKyEk74OUz/HoRxWPAHuHEqvL24S9ONGlzJzV+YzA79kryyYj2f+cWTzLrpaRavXF/kwiVJkiRJxRJEUReff+pD6uvrqa6upq6ujqqqqu4uR1Jv88Y/4Pczc4+ellXDCT+DcdO7NNWahhZ+NPtlbn7iDdLZiHgY8JkDR/OVI3djUL9kkQuXJEmSJG1OZ7Mig7dOMHiTtNXq34LbZsLSJ3PHHzofPnRBblGGLli8cj1X3vMif31xBQDVFQn+48jd+OyBo0nGvZlZkiRJkkrJ4K2IDN4kFUW6Be6/CJ7+ee54t6Nyd79VDOrylH9/eRVX3P0CC2vXATBmSD8uPHoPPjy+hiAIilG1JEmSJGkjBm9FZPAmqajm/RbuOgfSTTBoZzjpZhi2T5eny2Qjfv/MUr7/wCJWrc+teDpllx341rF7steI6uLULEmSJEkqMHgrIoM3SUX31rNw62dg7RKIV8BHfwwTPrlVU65rSnH9I4u58e+v0ZLOEgTwyUmj+NpRuzO0qrxIhUuSJEmSDN6KyOBNUkk0roY/fgEWz84dH/D/YNp/QiyxVdMuXd3I9+5byF3PvQVAZTLGlw/blS8csgvlidjWVi1JkiRJfZ7BWxEZvEkqmWwGHrkSHrs6d7zTFPjETTBg2FZPPfeN1Vx+14s8u3QtACOqyzn/6D346MQRvv9NkiRJkraCwVsRGbxJKrmF98Dt/w+a66F/DXzy/2CnA7d62mw24i/PLeN79y5kWV0TAPuOGsjFx45n0uiuL+ogSZIkSX2ZwVsRGbxJ2iZWvZJ779vKFyGMw7Qr4YAvQhHuTtvQkuHGv73K9Y8uprElA8BxE0dw/vRxjBxUudXzS5IkSVJfYvBWRAZvkraZ5vVw51nw/O254wknwbHXQLI44djy+ib++/5F/OGfbxJFkIyHfOHgMXz58LH0L4sX5TMkSZIkaXtn8FZEBm+StqkogjnXwoOXQJSBmn3gpF/D4DFF+4gF/67jirtf4IlXVwMwpH8Z5x21O5/YbxSx0Pe/SZIkSdK7MXgrIoM3Sd3itb/BbadC4yooHwgzboTdPly06aMo4oEXlnPlPS/y+tuNAOwxbAAXHzueg8YOKdrnSJIkSdL2xuCtiAzeJHWbun/D7z8L/54LBHD4RXDIeRCGRfuIlnSW/5vzOj+e/TL1TWkApu45lIuO2ZNdduxftM+RJEmSpO2FwVsRGbxJ6lbpZrj3fJj7q9zx+/aDfT8N44+HfjsU7WNWN7Two7++xM1PLiGTjYiHAZ+dMpqvHLkbAyuTRfscSZIkSertDN6KyOBNUo/wr5vhrnMh05w7DuOwy+Gw9wzY4yNQXpzfp1dWrOe/7nmRhxauAKC6IsFXjtyNz04ZTSJWvDvtJEmSJKm3MngrIoM3ST1G3b9hwR9hwR/grWfbzsfLYbejYJ8Tc/tExVZ/1N9eXskVd73IouXrANhlSD8uOmZPjtxzKEHgAgySJEmS+i6DtyIyeJPUI616ORfCzf8DvP1y2/nkgNwdcPucCLscBrFElz8incly6zNL+cEDL/F2QwsAB43dgW8eM57xI/w9lCRJktQ3GbwVkcGbpB4tiqB2fu4uuAV/grqlbdcqBsNex+ceR93pg11elGFdU4prH17ML//+Gi2ZLEEA08YPY/Iug5k0ehB7Dq/yMVRJkiRJfYbBWxEZvEnqNbJZePOp3J1wz98ODSvbrg0YAXufkAvhRrwfuvC46NLVjXz3voXc/dxbHc5XJGJMHFXNpNGDmDR6EO8fNYhB/VyQQZIkSdL2yeCtiAzeJPVKmTS8/lguhHvhL9Bc13Zt8C65AG7vE2HoHls89fw363j0pRXMfWMN/1yylroNqU367Lpjv0IQN2n0IHYZ0p8w9N1wkiRJkno/g7ciMniT1Oulm+GVv+beB7foXkhvaLtWs3fbnXCDdt7iqbPZiFdXrWfuG2sK2+KVDZv0q65I8IGdBjJp9CA+MHoQ+44aSGUyvhV/lCRJkiR1D4O3IjJ4k7RdaV4PL92XC+Fe+Stk292tNnL/3F1wex0PA4Z1+SPWNLTwr6W5EO6Z19fw7JtraUplO/SJhQHjh1cVgrhJowcxorrcFVMlSZIk9XgGb0Vk8CZpu9W4Gl78S+5x1Nf/BlE+HAtC2PngXAg3/qNQMWirPiaVyfLiW/WFO+L++cYaltU1bdJvWFV5hyBu/PAqknEXbZAkSZLUsxi8FZHBm6Q+Yd3y3IIMC/4Abz7ddj5MwNgjcyHcuKOhrH9RPm7Z2g38c0lbEPf8snrS2Y7/lVQWD5k4cmAhiPvATgPZoX9ZUT5fkiRJkrrK4K2IDN4k9TlrXocFf8rdCbd8Qdv5RCXsPh32ORF2OQyS/Yr2kRtaMjz35lqeyQdxc5esYW3jpos2jBnScdGGsTu6aIMkSZKkbcvgrYgM3iT1aSsW5u6Cm/8HWPNax2tl1TCgJvc+uAHDoX9Nbj+gdT8M+g+DZOUWf2wURby6qqFwR9zcN9bw8or1m/SLhwEDK5Ps0C/JoH4JBvdL5rbKJINa2/2SDKpMskP/3L48Eevqv4YkSZIkGbwVk8GbJAFRBMv+lbsL7vnbof7fnR9bpICurjHFP5e2BXHzlq6lsSWzxX9KZTLWMZDrt2lIN7jdcXVFgph31UmSJEnKM3grIoM3SdpIFEHzOlhXC+trc/t1b+XeE7fuLVif36+rhVRj5+ctq86HczUbBXTD2rZ2AV06k2XV+hbebmhmTUOK1Y0trF7fzOrGFGsaWlid39Y0tvB2QwtrGlo2eY9cZwQBDKpMMqgy0SGQax/QVVckqEjEKE/GqEjEqMzvW48TMReJkCRJkrYXBm9FZPAmSV3UPqDbOJBb1z6wq4X0hs7P2z6gqxgM8XKIl220T+b3beeiWJINUZz6VIy6VIy6VMjq5pA1zQFvNwes2hCwsgmWN8KqhgyrG1PUN6WL8k8RD4MOQVxbO8wdJ2OUJ9qudThuDfHatXPnQyqS8cJxWTz0fXeSJEnSNtDZrCi+DWuSJPU1QQDlVbltx93fuV8UQXN92x1z73QnXWtA11yX21Yt2rJygMr8Nuw9O4eQKCcqT5KNlZEJy0iHSVIkaQ4SNEcJmqI4jdk4jZk4jdkYG7JxmrIxNrRumZDmKEELcVLEaUnHSaXjtBTOxWghwQbi1EXxtn4kSBGnOcodt52L5f+Kd1YWDymLhyTjuSCuLBFSFo+RzJ9v29quJ2MhZfngLje27XryXfom28/Vei0eEgSGf5IkSRIYvEmSeoIggPLq3NapgK5dKNdUB+nm/NaU3/LtTEvH40K/5k3PZ5pz/QuflYVUI0GqkRgQA5Jb9DdRkv+WbQ3iUsRpJkFLFKM5SuTP5YK8VDZOS3OcdHOsXXAXJxXl+rTNkWu35AO+dcRZne+bjjYa+w7j2+Zt6xvGE8RiCRLxGMlYSCIekIjlgrlELCQRyx/H252L58639Wm9HrS73u44fz23bztX+Jx4x7nihXEB8TC3NyCUJElSqRm8SZJ6jw4B3bjiz5/N5gK4zoR0mwR6TZBJtwV46Zbcvv3WlXNRx8UjEqRJ0O7x14D3ugmu27SkY2TSMVLNMdLEyBAjRawQ6mUISecDvHTrFrVrt/ZvHZu/1kSM9cQKYzOEpKJ4h3GtYzNRfk+MNGGHfRTECMI4USxOEOY2wjhhPA5BnCCWIIzF8vs4YSxOEIsTiyXy4WKcWDxeCBLjYT5kDDsGfbngLyQRBsTC3HFuHxDL941vfC1s36ftfDwfHLaOiYch8TDwEWNJkqQeyuBNkqRWYQhhBSQquruSNtlMPoxrhkwqH8jl2+90Lptqd76lXfudzm/Uzqbf/XqHeVJEmRaCbGqT0pNBBsiwyb9mT8yIsvkNoOXdOm4qFcXyIWL7fT7gi9qCvkxhHxSO08TIRiEZAlLEaOrQN+g4LtrMOUKyhGQIiYJcmBiFMci3aT1uPRfGCYIQwlh+H0IQIwhDgjBGEMRy+9bjwj63hYV9WDjOhZK541gYI4i1nm+7Hssfx9pfi7VdC8LWa7mAMRYEhCG5sDEMCINcANm679AOAmKxdmPy572jUZIk9QQGb5Ik9WRhrOeFgRsJIPcY8MbhXDadDwHT7dqt59P5diofLm6mnckfb3bcxu3Wz0l1PJ9JQZQhyqaJMunCnnb73Jbp0A6itn2QzRBG77zIRiLIkCADbBo+dlvIGOU3gMy7dexZslFQCBOzvHs7RbhR/3wAmb/e2s4GIdn8+xGzQUhEWNhHQW4cheMAWvvn21GQm4cgLBy3tqHtOAhCorDdtdZzQf69h0EsH3SGHcPPIBdaEgQEQUgQdhwfhK3jW8PRoBCWhmGYG5efq7V/2HochgRhkA9Uc9fDMAZhkAtAw9YxuXYuUA0L58PWIDX/GWHY2g7zAWpIEMQK7dY5cuNjHf4tcv+ehqGSpL7H4E2SJG29IMivJLtFb8LbZrb6idwoyr33L/tOYd07ncts2o6y7dqt17Pt2u/eL5tNk82kyGYyRJk02UyaKJshm00TZfIhYzbTFjTm54iymVw4GWWJWufeqB1Emfzn5m4BDLKZ/D5/HGUgigiiDEGUi7iCKJvb6LgPyRJEUf7evYiwkAS+szDI9e90WtjV/6NGG+21TWSjgAjywWdAtjUczX9Dsvn/pGYJCoFn25YLRtu32dy1IMydDwIKwSjk58sHgIX52wLBtnb7oLX1uDVEDTqEiVE+fA3CtpA2IHcnaftAtnWuju1cEFsIUgt3oIaFkLR9MJv7jPZ3p7YLZvN3tbYPUnOBaetcuTtMg7B9yBqHMCQMwvzdqa0BcPuwdKPjcDPXNhnT/lz7PoaukvougzdJkqT3UrhjKQaUdWspYX7rdQrhZSYXJLYL/Dq2Mxu1s5s9nwsbs2QzabKZDNlshkxrGBllc/ts7nyUyeQCyiibCyyzGaLWPlGWKJttdy63Z+Pz2Wy785nC9bbwsl17o7+tcK7DFhWCziB/TJQForZzRPlruWiqtU1rHBVl8wFouz5kIWqNr7KEUVu0FRT6RB2OQzruc9ci2sdjHaOyiFiw5allmB8T60ywurnpDUp7tQwb3XH6DnehbroP8o/Sh/mQNJYLWts/Xk8sf8dp62P2IVEY7xgC5h+5L+xjsXyYmTvX+rg9YYwgFs/flZoLMYMwnr9bNHctDOOFx+qDWIJYPEkYTxDGk8TiueMgloBYAvLvD821ExCL5/ebO44bUkrbIYM3SZIklV6H8LII00FhxWFtO1EUEUWQiiKy2Vz4mMkHn9l8UJnJZomyUa4dZYiyWbKZbC4EzWbJRhnIRvl+GbJRhigT5dutIWc+WM3mQskomx+/cTtqDUdpC0nzfWidLx9qtvYjX1PreNq3o9y87YPU1vHBZkPUaDOhatv5XIDadj6Iotw12gLXQnDaLmzt0CYf0hIRFu4qjTreZUpE2O4O0/Z9wnbhaT7mKpxrDVxjZAmDwv2DxFqD2w7HuX3YoR0VAtX3Emt9mWb77oapm0gTIxPEc+/xDOJkgjjZwj5GNn8cBXGyYb4dJsjGkmTDMqJYkiheRhQrg3gZUawc4mWEiTKIlxMkygnjZYSJcmKJcsJkGWGygniinHhZBbFkBfFkGfFkBcmyysK4Yv12S32RwZskSZKkTgmCgCCAkABivfLeyz4pm43IRhGZfHCajSIy2Yhs1HYtmz+fjSJasrl+mY2uFY6zkMkfF8LXKNMWsGbS+eA0nQ9eM/nANVO4QzXKB7fZ/F2wUev5fBBLlG6767T1fDYq3PFa2GcyhTtioyhDkA9gg3aP6gft77bNZnLBZP6R/tyj862Pzef2YZTrmws6266F5K7l9rlwMh6liZMhHmRIkGsnyOTOkSYRtLbbtkT+2ubuHI2TId66onkPCiZTxEiRoIUELUGSNAlSQZJUkCATJkkHSdJhkkxYlgsBY+VkY0miWDlRPBf+ES8vhH9BojwX/iUriSXLiSXLiScriJdVEi+rIJmsJFFeQbK8krLySpKJODFX8FYvZfAmSZIkSduxMAwICfwffyUQ5QPIdH7LZCLS2WzbuUxE40bHheuZDNl0C5lUS+7dnekU2UyKTKqFKJMqbNlMiijdupJ4bvGgbLp1FfL2K403E6abCTLNhNkWwkxzbsu2EM+2EItaiOXbiaiFeJQiEbXk4rQoTZIWykiRJEU5LR2CwUQ+MKykadP3ZG6DRXyaoziNJGgmSUuQLIR+qaCMVJAL/NJhkmysLNeOVZBJVJKN9yNK9iNK9INkP4JkP4LyAYRlA4iV9yde3p9ExQASFQMoL0tQHo9RkYxRHo9Rlggpi4eukq2t5m+vJEmSJEldEAQB8VhAfDt5EjObjUhlszSms6RSKVItG0g3bSDVsoFMqolMcxPpVBPZVBOZlg1kU81EqSay6SaiVBNRqpkotYEo3QypJoJ0E0GmOb81EWZaiGWaiGWb24WAzbkAMGohEaUoo5myqGPwVxakKSPNADbkTrRfvbtINkRJ1lNOY1TOasppoJxGymgKKmgOKmgKK0nFKkjFKknHKknHK8gm+pGN9yOb7AfJ/rlwr6w/YVl/YmX9KC8royIZozK/VSTibe1kjMpk7tiAb/tm8CZJkiRJkgjDgLIwRlk8BuUJoLL7ismkSbc00tLUSEvTBlLNG0g1N5JubiTV3EQm1UimuYlsakM+BGwN/zZAqpGgpYEw3Ugs3Ug83UA8vYFEpoFkZgNl0QbKshsojzbk3z8IFUELFbRAUL/5evKvKSTV+T+hKUrkAryoLcirj8qpbQ32orL8+XJSYQXpeCXpeO5OvWwid7dekOwHZf2JJfsTlvenrKycimS8Q3D3buFeRSJG3FcDdCuDN0mSJEmS1LPE4sQrqohXVJUu/osiSDdDSwO0rMvvG0g3rSPVuI5U0zoyG9aRaV5HtqmBbPN6opb10NKQC/ZS6wlTjcTzAV8i00gy00iYf/62PEhRToodgnWdqycLtOS3d5CKYjRS1jHQi8ppoIw1+TCvkY7nU0E5UbyMbLycoPCuvfaLbFQQyy+wkSirIJ6soKIsQUUiF9yV5wO8ikSMimRIeaHdtk/GvGvvnRi8SZIkSZKkvicIIFGe2/rtUDgdz28VXZkzinLv3GteDy3rIdWYD/TWF4K91na2eT3ppvWkm9aRbVpP1LyeKN8nSDUQphryd+w1Es82A5AIMlTTSDWNuSW+t0Q6v3VCc5SgmXZblHvHXjMJ1pFgVYfrufOZsCy/wEZuZd22FXbbFtQ4fsYp7DBo0BYW3rv1qeDt2muv5eqrr6a2tpaJEyfyk5/8hAMOOKC7y5IkSZIkSduDIMiv4lrWIczbnBBI5rf3lElDqmGT8G7z7UailvVkmtaTbVpHNtX2KC7pZki3vX+vdRGOeLaZgPbv1UtR1v652s6GfBFtAV/zppdXNR4DBm/bp1tvvZVzzz2XG264gcmTJ3PNNdcwbdo0Fi1axNChQ7u7PEmSJEmSpM2LxSFWDeXVneoesIWBTxTlVspNt4VzbfsmSDVt9lom1ZR7715LE5nmDWRSG8i05N6117rgBukmyDQTpJuoHtC5+rcnQRRFRV4LpGeaPHky+++/Pz/96U8ByGazjBo1irPPPpsLLrigQ9/m5maam9ui2fr6ekaNGkVdXR1VVVXbtG5JkiRJkiT1LPX19VRXV79nVtQnlrZoaWlh7ty5TJ06tXAuDEOmTp3KnDlzNul/5ZVXUl1dXdhGjRq1LcuVJEmSJEnSdqBPBG+rVq0ik8lQU1PT4XxNTQ21tbWb9L/wwgupq6srbEuXLt1WpUqSJEmSJGk70Wfe8bYlysrKKCsr6+4yJEmSJEmS1Iv1iTvehgwZQiwWY/ny5R3OL1++nGHDhnVTVZIkSZIkSdqe9YngLZlMMmnSJGbPnl04l81mmT17NlOmTOnGyiRJkiRJkrS96jOPmp577rnMnDmT/fbbjwMOOIBrrrmGhoYGTjvttO4uTZIkSZIkSduhPhO8nXTSSaxcuZJLLrmE2tpa9t13X+67775NFlyQJEmSJEmSiiGIoijq7iJ6uvr6eqqrq6mrq6Oqqqq7y5EkSZIkSVI36mxW1Cfe8SZJkiRJkiRtawZvkiRJkiRJUgkYvEmSJEmSJEklYPAmSZIkSZIklYDBmyRJkiRJklQCBm+SJEmSJElSCRi8SZIkSZIkSSVg8CZJkiRJkiSVgMGbJEmSJEmSVAIGb5IkSZIkSVIJGLxJkiRJkiRJJWDwJkmSJEmSJJWAwZskSZIkSZJUAgZvkiRJkiRJUgkYvEmSJEmSJEklYPAmSZIkSZIklYDBmyRJkiRJklQC8e4uoDeIogiA+vr6bq5EkiRJkiRJ3a01I2rNjN6JwVsnrFu3DoBRo0Z1cyWSJEmSJEnqKdatW0d1dfU7Xg+i94rmRDabZdmyZQwYMIAgCLq7nKKor69n1KhRLF26lKqqqu4uR3pHflfVG/g9VW/hd1W9gd9T9RZ+V9Ub+D0tnSiKWLduHSNGjCAM3/lNbt7x1glhGDJy5MjuLqMkqqqq/A+fegW/q+oN/J6qt/C7qt7A76l6C7+r6g38npbGu93p1srFFSRJkiRJkqQSMHiTJEmSJEmSSsDgrY8qKyvj0ksvpaysrLtLkd6V31X1Bn5P1Vv4XVVv4PdUvYXfVfUGfk+7n4srSJIkSZIkSSXgHW+SJEmSJElSCRi8SZIkSZIkSSVg8CZJkiRJkiSVgMGbJEmSJEmSVAIGb33Utddey84770x5eTmTJ0/mqaee6u6SpILLLruMIAg6bHvssUd3lyXx2GOPcdxxxzFixAiCIOCOO+7ocD2KIi655BKGDx9ORUUFU6dO5eWXX+6eYtVnvdf39NRTT93kN3b69OndU6z6rCuvvJL999+fAQMGMHToUI4//ngWLVrUoU9TUxNnnnkmO+ywA/3792fGjBksX768mypWX9WZ7+phhx22ye/qGWec0U0Vqy+6/vrrmTBhAlVVVVRVVTFlyhTuvffewnV/T7uXwVsfdOutt3Luuedy6aWX8s9//pOJEycybdo0VqxY0d2lSQV77bUXb731VmH7+9//3t0lSTQ0NDBx4kSuvfbazV6/6qqr+PGPf8wNN9zAk08+Sb9+/Zg2bRpNTU3buFL1Ze/1PQWYPn16h9/Y3/72t9uwQgkeffRRzjzzTJ544gkefPBBUqkURx11FA0NDYU+X/3qV/nLX/7CbbfdxqOPPsqyZcs44YQTurFq9UWd+a4CfPGLX+zwu3rVVVd1U8Xqi0aOHMl3v/td5s6dyzPPPMMRRxzBxz72MZ5//nnA39PuFkRRFHV3Edq2Jk+ezP77789Pf/pTALLZLKNGjeLss8/mggsu6ObqpNwdb3fccQfz5s3r7lKkdxQEAbfffjvHH388kLvbbcSIEXzta1/jvPPOA6Curo6amhpuuukmTj755G6sVn3Vxt9TyN3xtnbt2k3uhJO608qVKxk6dCiPPvoohx56KHV1dey4447ccsstnHjiiQAsXLiQPffckzlz5nDggQd2c8Xqqzb+rkLujrd9992Xa665pnuLk9oZPHgwV199NSeeeKK/p93MO976mJaWFubOncvUqVML58IwZOrUqcyZM6cbK5M6evnllxkxYgS77LILp5xyCkuWLOnukqR39dprr1FbW9vh97W6uprJkyf7+6oe55FHHmHo0KGMGzeOL33pS7z99tvdXZL6uLq6OiD3PxQB5s6dSyqV6vCbuscee7DTTjv5m6putfF3tdVvfvMbhgwZwt57782FF15IY2Njd5Qnkclk+N3vfkdDQwNTpkzx97QHiHd3Adq2Vq1aRSaToaampsP5mpoaFi5c2E1VSR1NnjyZm266iXHjxvHWW2/x7W9/m0MOOYQFCxYwYMCA7i5P2qza2lqAzf6+tl6TeoLp06dzwgknMGbMGBYvXsxFF13E0UcfzZw5c4jFYt1dnvqgbDbLOeecw0EHHcTee+8N5H5Tk8kkAwcO7NDX31R1p819VwE+/elPM3r0aEaMGMFzzz3H+eefz6JFi/jTn/7UjdWqr5k/fz5TpkyhqamJ/v37c/vttzN+/HjmzZvn72k3M3iT1OMcffTRhfaECROYPHkyo0eP5ve//z2zZs3qxsokqfdr/9jzPvvsw4QJE9h111155JFHOPLII7uxMvVVZ555JgsWLPB9rurx3um7evrppxfa++yzD8OHD+fII49k8eLF7Lrrrtu6TPVR48aNY968edTV1fGHP/yBmTNn8uijj3Z3WcJHTfucIUOGEIvFNlnBZPny5QwbNqybqpLe3cCBA9l999155ZVXursU6R21/ob6+6reZpdddmHIkCH+xqpbnHXWWdx11108/PDDjBw5snB+2LBhtLS0sHbt2g79/U1Vd3mn7+rmTJ48GcDfVW1TyWSSsWPHMmnSJK688komTpzIj370I39PewCDtz4mmUwyadIkZs+eXTiXzWaZPXs2U6ZM6cbKpHe2fv16Fi9ezPDhw7u7FOkdjRkzhmHDhnX4fa2vr+fJJ5/091U92ptvvsnbb7/tb6y2qSiKOOuss7j99tt56KGHGDNmTIfrkyZNIpFIdPhNXbRoEUuWLPE3VdvUe31XN6d1gTB/V9Wdstkszc3N/p72AD5q2gede+65zJw5k/32248DDjiAa665hoaGBk477bTuLk0C4LzzzuO4445j9OjRLFu2jEsvvZRYLManPvWp7i5Nfdz69es7/H+vX3vtNebNm8fgwYPZaaedOOecc7jiiivYbbfdGDNmDBdffDEjRozosKKkVGrv9j0dPHgw3/72t5kxYwbDhg1j8eLFfOMb32Ds2LFMmzatG6tWX3PmmWdyyy238Oc//5kBAwYU3jNUXV1NRUUF1dXVzJo1i3PPPZfBgwdTVVXF2WefzZQpU1yBT9vUe31XFy9ezC233MIxxxzDDjvswHPPPcdXv/pVDj30UCZMmNDN1auvuPDCCzn66KPZaaedWLduHbfccguPPPII999/v7+nPUGkPuknP/lJtNNOO0XJZDI64IADoieeeKK7S5IKTjrppGj48OFRMpmM3ve+90UnnXRS9Morr3R3WVL08MMPR8Am28yZM6MoiqJsNhtdfPHFUU1NTVRWVhYdeeSR0aJFi7q3aPU57/Y9bWxsjI466qhoxx13jBKJRDR69Ojoi1/8YlRbW9vdZauP2dx3FIh+9atfFfps2LAh+vKXvxwNGjQoqqysjD7+8Y9Hb731VvcVrT7pvb6rS5YsiQ499NBo8ODBUVlZWTR27Njo61//elRXV9e9hatP+fznPx+NHj06SiaT0Y477hgdeeSR0QMPPFC47u9p9wqiKIq2ZdAnSZIkSZIk9QW+402SJEmSJEkqAYM3SZIkSZIkqQQM3iRJkiRJkqQSMHiTJEmSJEmSSsDgTZIkSZIkSSoBgzdJkiRJkiSpBAzeJEmSJEmSpBIweJMkSZIkSZJKwOBNkiRJJRcEAXfccUd3lyFJkrRNGbxJkiRt50499VSCINhkmz59eneXJkmStF2Ld3cBkiRJKr3p06fzq1/9qsO5srKybqpGkiSpb/CON0mSpD6grKyMYcOGddgGDRoE5B4Dvf766zn66KOpqKhgl1124Q9/+EOH8fPnz+eII46goqKCHXbYgdNPP53169d36PPLX/6Svfbai7KyMoYPH85ZZ53V4fqqVav4+Mc/TmVlJbvttht33nlnaf9oSZKkbmbwJkmSJC6++GJmzJjBs88+yymnnMLJJ5/Miy++CEBDQwPTpk1j0KBBPP3009x222389a9/7RCsXX/99Zx55pmcfvrpzJ8/nzvvvJOxY8d2+Ixvf/vbfPKTn+S5557jmGOO4ZRTTmH16tXb9O+UJEnaloIoiqLuLkKSJEmlc+qpp3LzzTdTXl7e4fxFF13ERRddRBAEnHHGGVx//fWFawceeCAf+MAHuO666/j5z3/O+eefz9KlS+nXrx8A99xzD8cddxzLli2jpqaG973vfZx22mlcccUVm60hCAK+9a1v8Z3vfAfIhXn9+/fn3nvv9V1zkiRpu+U73iRJkvqAww8/vEOwBjB48OBCe8qUKR2uTZkyhXnz5gHw4osvMnHixELoBnDQQQeRzWZZtGgRQRCwbNkyjjzyyHetYcKECYV2v379qKqqYsWKFV39kyRJkno8gzdJkqQ+oF+/fps8+lksFRUVneqXSCQ6HAdBQDabLUVJkiRJPYLveJMkSRJPPPHEJsd77rknAHvuuSfPPvssDQ0NheuPP/44YRgybtw4BgwYwM4778zs2bO3ac2SJEk9nXe8SZIk9QHNzc3U1tZ2OBePxxkyZAgAt912G/vttx8HH3wwv/nNb3jqqaf4xS9+AcApp5zCpZdeysyZM7nssstYuXIlZ599Np/97GepqakB4LLLLuOMM85g6NChHH300axbt47HH3+cs88+e9v+oZIkST2IwZskSVIfcN999zF8+PAO58aNG8fChQuB3Iqjv/vd7/jyl7/M8OHD+e1vf8v48eMBqKys5P777+crX/kK+++/P5WVlcyYMYMf/OAHhblmzpxJU1MTP/zhDznvvPMYMmQIJ5544rb7AyVJknogVzWVJEnq44Ig4Pbbb+f444/v7lIkSZK2K77jTZIkSZIkSSoBgzdJkiRJkiSpBHzHmyRJUh/nm0ckSZJKwzveJEmSJEmSpBIweJMkSZIkSZJKwOBNkiRJkiRJKgGDN0mSJEmSJKkEDN4kSZIkSZKkEjB4kyRJkiRJkkrA4E2SJEmSJEkqAYM3SZIkSZIkqQT+PxW3IhJkLXJCAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "## Plot a graph of model loss # show the graph of model loss in trainig and validation\n",
        "\n",
        "plt.figure(figsize=(15,8))\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss (MSE) on Training and Validation Data')\n",
        "plt.ylabel('Loss-Mean Squred Error')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Val Loss', 'Train Loss'], loc='upper right')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CD54f4wCIik2"
      },
      "source": [
        "### Predict Sales"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p75QVFqwBwmk",
        "outputId": "731bd68a-e49b-4b88-c716-a0f802669e9b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15/15 [==============================] - 0s 2ms/step\n",
            "[31.417734  21.220354   4.7196593 26.852045  16.403692  34.580494\n",
            " 20.758804  21.125383  18.414904  16.656893   6.99268   18.21966\n",
            "  4.8979464  9.0408535 21.9296    12.806722  18.01036   15.848582\n",
            "  7.797866  16.173466   9.119615   7.0110044  6.602976  14.834849\n",
            "  9.256564   3.559621   7.9958944 17.242098  13.51514   19.36989\n",
            "  9.369475  17.497568  21.569561  20.004793  11.4648075 22.048296\n",
            " 17.40079   23.607897  14.169772  10.9558325 11.268399  15.120288\n",
            "  3.8518872 14.260594   8.536372  14.463492   7.58078   17.40079\n",
            " 14.42262    7.5760717  6.713754  21.9296     8.604031  15.934314\n",
            "  4.695321   5.0222135 19.32795   20.942812   9.972568  13.3409605\n",
            " 15.139911   6.5433683 19.137207  15.174861   9.164914  19.908352\n",
            " 10.831012  14.373332   9.987815  14.764285   7.4007397 21.604433\n",
            "  7.204871  20.466913  12.563774  18.223497  23.103373  16.319998\n",
            " 17.497568  14.114753  20.655489   6.8951597 10.644683  15.720672\n",
            " 16.938118  17.671738  11.63856   10.355195   4.3273115  6.410767\n",
            " 21.544268  19.36989    7.4390297  5.310464   9.329084  10.222337\n",
            "  3.1649032 26.852045  28.403513   4.3525066  5.6985235 15.636931\n",
            "  7.946986   7.956064   9.057344  14.764285   6.0379877  5.98932\n",
            "  8.051751   5.288837   6.602976   7.841485   8.069265  22.08629\n",
            "  8.240805  20.151026   5.6985235 26.918314  17.077515   9.400619\n",
            " 17.497568  21.085949   6.8951597  7.4020967 13.199025   7.963542\n",
            "  7.993642   6.510568   4.359139  28.281649  13.444003   7.797866\n",
            " 10.337239   7.5432014 19.264431  15.37106    6.2916727 11.078197\n",
            " 10.293751  18.648272   6.510568  12.563774   7.7173758 19.32795\n",
            "  3.1649032  6.149497   9.1664915 30.761736   6.9206405  8.053128\n",
            " 15.848582  12.339154   4.120268  12.319783  22.321661  12.339154\n",
            " 12.563774  17.461306   7.723098  18.669308  20.151026  16.656893\n",
            " 20.967234   9.057344   6.8077383 16.582981   6.3122845  7.2171154\n",
            "  7.2173805 24.412577  17.391472   7.402831  13.671414  11.782079\n",
            "  7.5760717 16.656893  27.938532  22.181067   7.721855  25.166918\n",
            "  5.574973   8.428559  16.747652  27.272703   9.246051   9.256564\n",
            "  7.496188  14.328734   4.0990367 15.414613   7.7408857 13.918242\n",
            "  9.762905  15.139911  10.143068   5.6985235 14.463492  13.951335\n",
            "  4.120268  17.807077   4.0375853 17.08114   15.256427  10.089583\n",
            "  6.817773  15.174861   4.3273115  1.4163067  8.586685  12.37456\n",
            " 13.4068    17.382402  17.092617  20.655489  14.310363  11.287998\n",
            "  6.85508    6.676526   2.9108717  5.574973   8.051751  11.268399\n",
            " 15.256427   6.9206405 17.382402   6.676526  12.806722  16.582981\n",
            "  9.01859   11.44165    7.963542   6.817773  10.644683  11.462901\n",
            " 16.801722   6.562961   3.0752811 28.281649  25.166918  13.823487\n",
            " 15.761867   7.58078    7.834547  28.683146   7.834547   6.410767\n",
            "  4.3273115  7.5760717 14.125691   9.280607  18.525116   9.267183\n",
            "  5.539628  12.722218   5.1130857  6.072568   7.872463  16.801722\n",
            "  9.693255  13.396647  17.08114   20.32375    6.85508   21.945993\n",
            " 13.3409605 21.946344  19.00577   16.935167  18.621302   5.288837\n",
            " 12.339154  18.74473   20.60451    8.962937   6.562961   7.721855\n",
            "  8.148759  12.562656  14.761786  20.653154  17.077515   9.912691\n",
            "  7.9652596  6.8205976  7.872463  15.553493  19.458017   7.9652615\n",
            "  7.509945   9.246051  13.0507765 11.775274   9.762905  15.414613\n",
            " 15.202596  13.6534605  4.5267553  1.4720614  3.6611114 19.99608\n",
            " 21.373169  13.263423   2.6658518  6.475648   6.294458  28.6508\n",
            " 20.812492  11.350326   5.0215697 18.278322  21.058418   6.2225237\n",
            " 16.039553  14.328734  22.08629    3.559621  15.644687  18.669308\n",
            "  9.400615  28.937754  14.105758  17.908709   6.5618324 18.01036\n",
            " 12.319783   7.7173758 13.895797  14.463492  12.493129   6.0379877\n",
            "  6.3122845  7.4390297  8.570477  18.154806   9.649221  20.967234\n",
            " 16.493227  11.44165   21.946344  12.717574  11.573382  18.154806\n",
            "  6.979954   7.7173758 11.019221   6.294458  20.466913  19.199402\n",
            "  4.512447   9.119615  17.081125   4.037586  10.767676  16.29941\n",
            " 10.831012   9.604157  12.920837  11.951186  17.242098   6.6709585\n",
            "  4.8965936  8.358804   7.121706   6.5433683  3.7456455  8.358804\n",
            "  9.040542   6.17523   25.166918  16.598705  13.927377  15.720672\n",
            " 14.886226   4.695321  13.120873   5.539628   6.8205976  6.9206405\n",
            "  1.4163067 15.139911  11.070965   6.1766205 23.607897   9.652304\n",
            " 14.419147  11.183952   8.042495  18.851282  28.6508     9.008142\n",
            " 12.272616  11.947124   9.552407  18.21966    6.979954   8.536372\n",
            " 13.950852  23.607897   7.204871   9.329084  17.6393    27.938532\n",
            " 18.670609  18.223497  27.16662   22.212885  10.074108  22.104092\n",
            "  4.8979464  7.2171154  8.962937   7.5321503  9.2947035  3.908699\n",
            "  9.987815   2.6658518 31.417734  13.926873  28.6508     7.2173805\n",
            "  6.2862463 17.6393    27.938532  20.653154   9.552407   6.2020087\n",
            " 20.930025  11.350326  16.173466  15.37106    7.723098   7.496188\n",
            "  4.695321  15.80786   20.930025  20.653154  18.278322   7.4477644\n",
            "  6.3325458 15.636931  21.058418   8.132774  18.669308   5.164823\n",
            " 17.918097  16.231375   7.9652615 10.337241   6.517433  15.681098\n",
            " 10.401207   6.294458  19.99608   16.055796  11.331473  22.186472\n",
            " 18.71135   22.104092   7.7777843 17.382402  16.403692  11.019221\n",
            " 11.039078   9.677306  24.90838    7.0110044 22.104092   6.517433\n",
            " 22.321661  17.242098  21.569561  19.137207  19.36989   20.32375\n",
            " 14.485332  15.80786   19.137207  15.519931   3.559621  14.275462 ]\n"
          ]
        }
      ],
      "source": [
        "'''=== predict the SALES =='''\n",
        "\n",
        "# predict SALES using the test data\n",
        "test_predictions = model.predict(X_test).flatten()\n",
        "print(test_predictions)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-o7RZMLAImvP"
      },
      "source": [
        "### True and Predicted Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "if37LiYZCbD0",
        "outputId": "363defea-91b5-4ba5-9670-8ea312e80ad2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   True Value  Predicted Value\n",
              "0        26.2        31.417734\n",
              "1        19.0        21.220354\n",
              "2        12.8         4.719659\n",
              "3        20.8        26.852045\n",
              "4        16.9        16.403692\n",
              "5        23.8        34.580494"
            ],
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              "\n",
              "\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>True Value</th>\n",
              "      <th>Predicted Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>26.2</td>\n",
              "      <td>31.417734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>19.0</td>\n",
              "      <td>21.220354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12.8</td>\n",
              "      <td>4.719659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20.8</td>\n",
              "      <td>26.852045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16.9</td>\n",
              "      <td>16.403692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>23.8</td>\n",
              "      <td>34.580494</td>\n",
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              "  </tbody>\n",
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              "\n",
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              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-85eb0c33-58da-4cdf-894a-17c82c937a0e');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "# show the true value and predicted value in dataframe\n",
        "true_predicted = pd.DataFrame(list(zip(y_test, test_predictions)),\n",
        "                    columns=['True Value','Predicted Value'])\n",
        "true_predicted.head(6) # Show first six rows"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_CPfIujVIuUf"
      },
      "source": [
        "Visualize the preditction."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "XgkvEWW-BRJF",
        "outputId": "0689ad4d-3a2e-4dec-b726-0a23acafdade"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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kIiIiIiL+t3q1kT1avdp4nJgIr78OXboEd10mqNxORERERET859gxuOceaNvWCJCiouDFF2H9+hIRIIEySSIiIiIi4g9WK7z9Njz+OJw8aRzr3x+efRZq1Qru2rykIElERERERApnxQqjtG7dOuNxixZGaV2nTsFdl49UbiciIiIiIr45cgTuugsuu8wIkKKj4bXXYM2aEhsggTJJIiIiIiLirexsePNNGDcOUlKMY3fdZbT1jo0N7tr8QEGSiIiIiIiYt3SpMRD2zz+Nxy1bwhtvQLt2wV2XH6ncTkREREREPDt0yGjE0LmzESBVrQrTpsGqVaUqQAIFSSIiIiIi4k5WFrzwAlx4IXz0EVgsRovvbdvgvvsgNDTYK/Q7lduJiIiIiIhzS5YYpXWbNxuP27QxSusuvTS46wowZZJERERERCSvv/+GW2+FK680AqTq1eGdd4xW36U8QAIFSSIiIiIi4pCZaXSoa9wYPvsMQkKM+UfbtsGgQcbjMkDldiIiIiIiAj/+CMOGGQERQIcOxkDYiy8O6rKCoWyEgiIiIiIi4tzevXDjjdCjhxEg1awJH3wAy5aVyQAJFCSJiIiIiJRN6ekwaRI0aQJffWV0qXvwQdi6FQYONLrYlVEqtxMRERERKWu++w6GD4edO43HnTvDa69B8+bBXVcxoUySiIiIiEhZsXMn9OkD11xj/L52bZgzB376SQFSLgqSRERERERKu7Q0GDcOmjaFb7+FcuXg4Ydhyxbo169Ml9Y5E9Qg6cknn8RiseT5lZCQkPP19PR0hgwZQrVq1ahUqRJ9+/bl8OHDQVyxiIiIiEgJYrfD119DYqKx/ygjA7p1gz//hGefhaioYK+wWAp6Jqlp06YcOnQo59fy5ctzvjZixAjmzZvH3Llz+eWXXzh48CA33nhjEFcrIiIiIlJCbNsGvXrBDTcYHezq1IG5c41W37kSE1JQ0Bs3lCtXjri4uALHU1JSmDlzJnPmzKFr164AvPfeezRp0oSVK1fSrl27ol6qiIiIiEjxd/YsPPUUvPACZGVBWBg89BCMGQMVKwZ7dSVC0DNJ27dvp3bt2jRo0IDbb7+dffv2AbBmzRqysrLo1q1bzrkJCQnUrVuXFStWuLxeRkYGqampeX6JiIiIiJR6druRKUpIgGeeMQKknj1h0yaYPFkBkheCGiS1bduW999/nwULFjBt2jR2795Np06dOH36NMnJyYSFhVGlSpU831OzZk2Sk5NdXnPKlClER0fn/KpTp06AX4WIiIiISJBt3gzdu8Mtt8Dff0P9+sZepPnzoVGjYK+uxAlqud3VV1+d8/sWLVrQtm1b6tWrx2effUZkZKRP1xw9ejQjR47MeZyamqpASURERERKp9OnYeJEePllyM6G8HB47DF49FHw8X5aisGepNyqVKnChRdeyI4dO+jevTuZmZmcOnUqTzbp8OHDTvcwOYSHhxMeHl4EqxURERERCRK7HT7+2NhrdOiQcezaa+Gll6BBg+CurRQI+p6k3M6cOcPOnTupVasWrVq1onz58ixevDjn61u3bmXfvn20b98+iKsUEREREQmiP/+EK66A2283AqSGDeG77+CbbxQg+UlQM0kPPfQQffr0oV69ehw8eJDx48cTGhpKv379iI6OZtCgQYwcOZKYmBgqV67MsGHDaN++vTrbiYiIiEjZc+oUjB8Pb7wBVqtRTvf44zBqFEREBHt1pUpQg6S///6bfv36cfz4cWrUqEHHjh1ZuXIlNWrUAOCll14iJCSEvn37kpGRQY8ePXjzzTeDuWQRERERkaJls8GsWfDII3DkiHGsb1+jxXe9esFdWyllsdvt9mAvIpBSU1OJjo4mJSWFypUrB3s5IiIiIiLmrVsHQ4fCb78Zjxs3hldfhauuCu66SiizsUGx2pMkIiIiIiLAiRMwZAhceqkRIFWsCFOnwsaNCpCKQLHqbiciIiIiUqbZbPDuuzB6NBw7Zhy79VZ4/nk4//zgrq0MUZAkIiIiIlIcrF5tZI9WrzYeJybC669Dly7BXVcZpHI7EREREZFgOnYM7rkH2rY1AqSoKHjxRVi/XgFSkCiTJCIiIiISDFYrvP220cb75Enj2IABxt6jWrWCu7YyTkGSiIiIiEhRW7HCKK1bt8543KKFMf+oY8fgrksAlduJiIiIiBSdw4fhrrvgssuMACk6Gl57DdasUYBUjCiTJCIiIiISaNnZ8OabMG4cpKQYx+6+G6ZMgdjY4K5NClCQJCIiIiISSEuXGgNh//zTeNyypVFa165dcNclLqncTkREREQkEA4ehP79oXNnI0CKiYHp02HVKgVIxZyCJBERERERf8rKghdegMaN4aOPwGIxWnxv2wb33guhocFeoXigcjsREREREX9ZssQordu82Xjctq0xEPbSS4O7LvGKMkkiIiIiIoW1fz/ceitceaURIFWvDjNnwm+/KUAqgRQkiYiIiIj4KiMDnnkGEhLgs88gJMTIJG3bZnSvC9HtdkmkcjsREREREV/88AM88IAREAF06GCU1l18cVCXJYWn0FZERERExBt79sCNN0LPnkaAVLMmfPghLFumAKmUUJAkIiIiImJGejpMmgRNmsBXXxld6h58ELZuhQEDjC52Uiqo3E5ERERExJNvv4Xhw2HXLuNx585GaV2zZsFdlwSEMkkiIiIiIq7s3Al9+hi/du2C2rXh44/hp58UIJViCpJERERERPJLS4Nx46BpUyOLVK4cPPwwbNkCt92m0rpSTuV2IiIiIiIOdjt8/TWMGAF79xrHunWD114z2nxLmaAgSUREREQEjE51DzxgtPYGqFMHXnrJ6GSnzFGZonI7ERERESnbzp6F0aONPUY//ABhYfD447B5M/TtqwCpDFImSURERETKJrsd5s6FUaPg77+NY1dfDa+8Ao0aBXdtElQKkkRERESk7ElKMkrrFi82HtevDy+/DNdeq8yRqNxORERERMqQ06fhoYfgoouMACk8HMaPN4Km665TgCSAMkkiIiIiUhbY7TBnjtHG+9Ah49i11xqNGRo0CO7apNhRkCQiIiIipduff8LQobB0qfG4YUN49VXo1Su465JiS+V2IiIiIlI6nToFw4fDJZcYAVJkJDz1FGzapABJ3FImSURERERKF5sNZs2CRx6BI0eMY337wosvQt26wV2blAgKkkRERESk9Fi3DoYMgRUrjMeNG8Nrr0H37sFdl5QoKrcTERERkZLvxAn43//g0kuNAKliRZg6FTZuVIAkXlMmSURERERKLpsNZs6E0aPh+HHj2G23wfPPw3nnBXdtUmIpSBIRERGRkmn1aqO0bvVq43FiIrz+OnTpEtx1SYmncjsRERERKVmOHYPBg6FtWyNAiooymjKsX68ASfxCmSQRERERKRmsVnj7bXj8cTh50jg2YAA8+yzExQV3bVKqKEgSERERkeJvxQqjtG7dOuNxixbwxhvQsWNw1yWlksrtRERERKT4OnwY7rwTLrvMCJCio42W3mvWKECSgFEmSURERESKn+xsePNNGDcOUlKMY3ffDVOmQGxscNcmpZ6CJBEREREpXpYuhaFD4c8/jcctWxqlde3aBXddUmao3E5EREREioeDB6F/f+jc2QiQYmJg+nRYtUoBkhQpBUkiIiIiElxZWfDCC9C4MXz0EVgscO+9sG2b8d/Q0GCvUMoYlduJiIiISPAsXgzDhsHmzcbjtm2NgbCXXhrcdUmZpkySiIiIiBS9/fvhllugWzcjQKpeHWbOhN9+U4AkQacgSURERESKTkYGPPMMJCTA3LkQEmI0adi2zeheF6LbUwk+lduJiIiISNH44Qd44AEjIALo0MEorbv44qAuSyQ/heoiIiIiElh79sANN0DPnkaAVLMmfPghLFumAEmKJQVJIiIiIhIY6ekwaRI0aQJff210qRsxArZuhQEDjC52IsWQyu1ERERExP++/RaGD4ddu4zHnTsbpXXNmgV3XSImKJMkIiIiIv6zcyf06WP82rULateGjz+Gn35SgCQlhoIkERERESm8tDQYNw6aNjWySOXKwSOPwJYtcNttKq2TEkXldiIiIiLiO7vd2G80YgTs3Wsc69YNXnvNaPMtUgIpSBIRERER32zbBsOGwY8/Go/r1IGXXoIbb1TmSEo0lduJiIiIiHfOnoXRo409Rj/+CGFh8PjjsHkz9O2rAElKPGWSRERERMQcux3mzoVRo+Dvv41jV18Nr7wCjRoFd20ifqQgSUREREQ8S0oySuuWLDEe169vBEd9+ihzJKWOyu1ERERExLXUVHjoIbjoIiNAioiAJ580gqZrr1WAJKWSMkkiIiIiUpDdDnPmwMMPw6FDxrFrr4WXX4b4+KAuTSTQFCSJiIiISF4bN8LQobBsmfH4gguM0rpevYK7LpEionI7ERERETGcOgXDh0PLlkaAFBkJkyfDn38qQJIyRZkkERERkbLOZoMPP4RHH4UjR4xjffvCiy9C3brBXZtIEChIEhERESnL1q41SutWrDAeN24Mr70G3bsHd10iQaRyOxEREZGy6MQJ+N//4NJLjQCpYkV49lljP5ICJCnjlEkSERERKUtsNpg5E0aPhuPHjWP9+sFzz8F55wV3bSLFhIIkERERkbJi1SqjtG71auNx06bw+utwxRVBXZZIcaNyOxEREZHS7tgxGDwY2rUzAqSoKKMpw7p1CpBEnFAmSURERKS0slrhrbfgiSfg5Enj2IABxt6juLjgrk2kGFOQJCIiIlIarVgBQ4YY2SKAiy4ySus6dgzuukRKAJXbiYiIiJQmhw/DnXfCZZcZAVKVKkZw9McfCpBETFImSURERKQ0yM6GN96AceMgNdU4dvfdMGUKxMYGd20iJYyCJBEREZGSbulSo2vdn38aj1u1MgKmtm2Duy6REkrldiIiIiIl1cGDcPvt0LmzESDFxMD06fD77wqQRApBQZKIiIhISZOVBc8/D40bw5w5YLHAvffCtm3Gf0NDg71CkRKt2ARJzzzzDBaLhQcffDDnWHp6OkOGDKFatWpUqlSJvn37cvjw4eAtUkRERCTYFi82OtU9/DCcOWNkjFavNjJI1aoFe3UipUKxCJJWr17NW2+9RYsWLfIcHzFiBPPmzWPu3Ln88ssvHDx4kBtvvDFIqxQREREJov374ZZboFs32LwZqleHmTPht9+MPUgi4jdBD5LOnDnD7bffzowZM6hatWrO8ZSUFGbOnMmLL75I165dadWqFe+99x6//fYbK1euDOKKRURERIpQRobRoS4hAebOhZAQo0nDtm1G97qQoN/OiZQ6Qf9bNWTIEHr37k23bt3yHF+zZg1ZWVl5jickJFC3bl1WrFjh8noZGRmkpqbm+SUiIiJSIv3wAzRvDmPGQFqaMedo7Vp47TXI9eGyiPhXUFuAf/LJJ6xdu5bVq1cX+FpycjJhYWFUqVIlz/GaNWuSnJzs8ppTpkxhwoQJ/l6qiIiISNHZswdGjICvvzYe16wJzz0H/fsbTRpEJKCClknav38/w4cP56OPPiIiIsJv1x09ejQpKSk5v/bv3++3a4uIiIgEVHo6TJwITZoYAVJoqBEsbdsGAwYoQBIpIkHLJK1Zs4YjR47QsmXLnGNWq5WlS5fy+uuv88MPP5CZmcmpU6fyZJMOHz5MXFycy+uGh4cTHh4eyKWLiIiI+N+338Lw4bBrl/H4iivg9dehadOgLkukLApakHTllVfyp2Mq9D/uuusuEhISePTRR6lTpw7ly5dn8eLF9O3bF4CtW7eyb98+2rdvH4wli4iIiPjfzp1GcPTdd8bj2rXhhRfg1luVORIJkqAFSVFRUTRr1izPsYoVK1KtWrWc44MGDWLkyJHExMRQuXJlhg0bRvv27WnXrl0wliwiIiLiP2lp8Mwz8OyzRge7cuVg5EgYOxYqVQr26kTKtKA2bvDkpZdeIiQkhL59+5KRkUGPHj148803g70sEREREd/Z7cZ+oxEjYO9e41i3bkbHuoSEoC5NRAwWu91uD/YiAik1NZXo6GhSUlKoXLlysJcjIiIiZdm2bTBsGPz4o/G4Th146SW48UaV1okUAbOxQdDnJImIiIiUemfOwOjR0KyZESCFhcHjj8PmzdC3rwIkkWKmWJfbiYiIiJRodjvMnQujRsHffxvHrr4aXnkFGjUK7tpExCUFSSIiIiKBkJRklNYtWWI8jo+Hl1+GPn2UORIp5lRuJyIiIuJPqalG5uiii4wAKSICnnwS/voLrr1WAZJICaBMkoiIiIg/2O0wZw48/DAcOmQcu+46ozFDfHxw1yYiXlGQJCIiIlJYGzfC0KGwbJnx+IIL4NVXjf1HIlLiqNxORERExFenTsHw4dCypREgRUbC5MmwaZMCJJESTJkkEREREW/ZbPDhh/Doo3DkiHHsppvghRegbt3grk1ECk1BkoiIiIg31q41SutWrDAeN24Mr70G3bsHd10i4jcqtxMREREx48QJ+N//4NJLjQCpYkV49lljP5ICJJFSRZkkEREREXdsNpg5E0aPhuPHjWP9+sFzz8F55wV3bSISEAqSRERERFxZtcoorVu92njctCm8/jpccUVQlyUigaVyOxEREZH8jh6FwYOhXTsjQKpc2Zh3tG6dAiSRMkCZJBEREREHqxXeegueeAJOnjSODRhg7D2Kiwvu2kSkyChIEhEREQH47TcYMgTWrzceX3SRUVrXsWNQlyUiRU/ldiIiIlK2HT4Md94JHToYAVKVKkZw9McfCpBEyihlkkRERKRsys6GN96AceMgNdU4NmgQPP00xMYGd20iElQKkkRERKTsWbrUKK3btMl43KqVETC1bRvcdYlIsaByOxERESk7Dh6E22+Hzp2NACkmxmjU8PvvCpBEJIeCJBERESn9srLg+eehcWOYMwcsFrjvPti2De65B0JDg71CESlGVG4nIiIipdvixTBsGGzebDxu29YorWvVKrjrEpFiS5kkERERKZ3274dbboFu3YwAqUYNePddo9W3AiQRcUNBkoiIiJQuGRkwZQokJMDcuRASAkOHwtatcNddxmMRETdUbiciIiKlx4IF8MADsH278bhjR2Pm0UUXBXddIlKi6KMUERERKfn27IEbboCrrzYCpLg4mDXLaPWtAElEvKQgSUREREqu9HSYOBGaNIGvvza61I0caZTW9e9vdLETEfGSyu1ERESkZJo3Dx58EHbtMh5fcYVRWte0aTBXJSKlgDJJIiIiUrLs3AnXXAPXXmsESOedB598AkuWKEASEb9QkCQiIiIlQ1oajB0LiYnw3XdQvjw88ghs2QK33qrSOhHxG5XbiYiISPFmt8NXX8GIEbBvn3Gse3d49VWjzbeIiJ8pSBIREZHia+tWo6X3jz8aj+vWhZdeMjrZKXMkIgGicjsREREpfs6cgcceg+bNjQApLAwefxw2b4Ybb1SAJCIBpUySiIiIFB92O8ydC6NGwd9/G8d69YJXXoELLgju2kSkzFCQJCIieVhtdlbtPsGR0+nERkXQJj6G0BB9ai9FICkJhg0zutQBxMcbwdE11yhzJCJFSkGSiIjkWLDpEBPmJXEoJT3nWK3oCMb3SaRns1pBXJmUaqmpMGGC0YghOxsiIoxSu0cegcjIYK9ORMog7UkSERHACJDun702T4AEkJySzv2z17Jg06EgrUxKLbsdZs+Gxo3hxReNAOm664yM0vjxCpBEJGgUJImICFabnQnzkrA7+Zrj2IR5SVhtzs4Q8cHGjdC5MwwYAMnJxn6j+fPh66+NMjsRkSBSkCQiIqzafaJABik3O3AoJZ1Vu08U3aKkdDp1ymjpfcklsGyZkS2aPBk2bYKrrw726kREAO1JEhER4Mhp1wGSL+eJFGCzwYcfwqOPwpEjxrGbboIXXjBmH4mIFCMKkkREhNioCL+eJ5LH2rUwdCisWGE8TkgwmjR07x7cdYmIuKByOxERoU18DLWiI3DVZNmC0eWuTXxMUS5LSroTJ+D+++HSS40AqWJFePZZ2LBBAZKIFGsKkkREhNAQC+P7JAIUCJQcj8f3SdS8JDHHaoUZM+DCC2H6dKOLXb9+sHUrPPwwhIUFe4UiIm4VOkiyWq2sX7+ekydP+mM9IiISJD2b1WJa/5bERectqYuLjmBa/5aakyTmrFoF7drBPffA8ePQtCn89BPMmQPnnRfs1YmImOL1nqQHH3yQ5s2bM2jQIKxWK507d+a3336jQoUKfPvtt1xxxRUBWKaIiBSFns1q0T0xjlW7T3DkdDqxUUaJnTJI4tHRozBmDMycaWSOKlc2BsQOGQLlywd7dSIiXvE6SPr888/p378/APPmzWP37t1s2bKFWbNm8fjjj/Prr7/6fZEiIlJ0QkMstG9YLdjLkJLCajVK6p54wmjvDTBwIEydCnFxQV2aiIivvC63O3bsGHH//E9v/vz53HzzzVx44YXcfffd/Pnnn35foIiIiBRTv/1mNGUYOtQIkC66CJYvhw8+UIAkIiWa10FSzZo1SUpKwmq1smDBArr/050mLS2N0NBQvy9QREREipnDh+HOO6FDB1i/HqpUgddfhz/+MI6JiJRwXpfb3XXXXdxyyy3UqlULi8VCt27dAPj9999JSEjw+wJFRESkmMjOhjfegHHjIDXVODZoEEyZAjVqBHdtIiJ+5HWQ9OSTT9KsWTP279/PzTffTHh4OAChoaE89thjfl+giIiIFAO//GKU1W3aZDxu1coImNq2De66REQCwGK32+2+fnN6ejoREcV7+npqairR0dGkpKRQuXLlYC9HRESkZDl4EB56CD7+2HgcE2NkjgYNApXZi0gJYzY28HpPktVqZdKkSZx33nlUqlSJXbt2ATB27Fhmzpzp+4pFRESk+MjMhOefh8aNjQDJYoH77oNt24wZSAqQRKQU8zpImjx5Mu+//z7PPvssYbkmZjdr1ox33nnHr4sTERGRIFi82OhU9/DDcOaMUVK3ejVMmwbV1B5eREo/r4OkDz/8kLfffpvbb789Tze7iy66iC1btvh1cSIiIlKE9u+HW26Bbt1gyxajGcO77xqtvlu1CvbqRESKjNdB0oEDB7jgggsKHLfZbGRlZfllUSIiIlKEMjKMfUYJCTB3LoSEwLBhRmndXXcZj0VEyhCvu9slJiaybNky6tWrl+f4559/ziWXXOK3hYmIiEgRWLAAHngAtm83HnfsaMw8uuii4K5LRCSIvA6Sxo0bxx133MGBAwew2Wx8+eWXbN26lQ8//JBvv/02EGsUERERf9uzB0aMgK+/Nh7HxcFzz8HttxtNGkREyjCv8+fXXXcd8+bNY9GiRVSsWJFx48axefNm5s2bR/fu3QOxRhEREfGX9HSYOBGaNDECpNBQGDkStm6F/v0VIImIUMg5SSWB5iSJiIj8Y948ePBB+Gd8B126wGuvQdOmQV2WiEhRCdicJBERESlhduyAa66Ba681AqTzzoNPPjFafStAEhEpwOs9SSEhIVjcpOKtVmuhFiQiIiJ+kpZmdK179lljOGz58kZp3RNPQKVKwV6diEix5XWQ9NVXX+V5nJWVxbp16/jggw+YMGGC3xYmIiJlW2a2jVkr9rD3RBr1YiowoH19wsqpAMIUux2++spozLBvn3Gse3ejtK5x4+CuTUSkBPDbnqQ5c+bw6aef8s033/jjcn6jPUkiIiXPlPlJzFi2G1uuf6FCLDC4UzyjeyUGb2ElwdatRkvvH380HtetCy+9BDfcoKYMIlLmFfmepHbt2rF48WJ/XU5ERMqoKfOTeGtp3gAJwGaHt5buZsr8pOAsrLg7cwYeewyaNzcCpLAwo6xu82a48UYFSCIiXvBLkHTu3DleffVVzjvvPH9cTkREyqjMbBszlu12e86MZbvJzLYV0YpKALsdPv0UEhJg6lTIyoJeveCvv2DSJKhQIdgrFBEpcbzek1S1atU8jRvsdjunT5+mQoUKzJ4926+LExGRsmXWij0FMkj52ezGeYM6NSiaRRVnf/0Fw4bBTz8Zj+Pj4ZVXjE52yhyJiPjM6yDppZdeyhMkhYSEUKNGDdq2bUvVqlX9ujgRESl9rDY7q3af4MjpdGKjImgTH0NoiPHvyt4TaaauYfa8Uis1FSZMgFdfhexsiIiA0aPh4YchMjLYqxMRKfG8DpLuvPPOACxDRETKggWbDjFhXhKHUtJzjtWKjmB8n0R6NqtFvRhzpWFmzyt17Hb46CMjGEpONo5dfz28+KKRRRIREb8wFSRt3LjR9AVbtGjh82JERKT0WrDpEPfPXkv+arrklHTun72Waf1bMqB9fSbP3+y25C7EAgPa1w/kUounDRtg6FBYvtx4fMEFRkvvnj2Duy4RkVLIVJB08cUXY7FY8NQt3GKxaJisiIgUYLXZmTAvqUCABGAHLMCEeUl0T4xjcKd43lrqunnD4E7xZWte0qlTMG4cvPEG2GxGI4YnnjCGwoaHB3t1IiKlkqkgafdu952GRERE3Fm1+0SeErv87MChlHRW7T6RMwepzM9Jstnggw/g0Ufh6FHj2E03wQsvGLOPREQkYEwFSfXq1Qv0OkREpBQ7ctp1gOTsvNG9Ehl1VQKzVuxh74k06sVUYED7+mUng7R2LQwZAitXGo8TEozSum7dgrsuEZEywuvGDQ5JSUns27ePzMzMPMevvfbaQi9KRERKl9ioCK/PCysXUvbafJ84AY8/Dm+9ZTRpqFQJxo+HBx4whsOKiEiR8DpI2rVrFzfccAN//vlnnn1Kjrbg2pMkIiL5tYmPoVZ0BMkp6U73JVmAuGijHXiZZLXCzJkwZgwcP24c69cPnnsONKhdRKTIeV23MHz4cOLj4zly5AgVKlTgr7/+YunSpVx66aX8/PPPXl1r2rRptGjRgsqVK1O5cmXat2/P999/n/P19PR0hgwZQrVq1ahUqRJ9+/bl8OHD3i5ZRESCLDTEwvg+xl6i/CNOHY/H90nMmZdUpqxaBe3awb33GgFSs2bw888wZ44CJBGRIPE6SFqxYgUTJ06kevXqhISEEBISQseOHZkyZQoPPPCAV9c6//zzeeaZZ1izZg1//PEHXbt25brrruOvv/4CYMSIEcybN4+5c+fyyy+/cPDgQW688UZvlywiIsVAz2a1mNa/JXHReUvv4qIjmNa/JT2b1QrSyoLk6FH473+hbVv44w+oXBleftnYj9S5c7BXJyJSplnsnvp651O1alXWrl1LfHw8DRs25J133qFLly7s3LmT5s2bk5ZWuCnoMTExPPfcc9x0003UqFGDOXPmcNNNNwGwZcsWmjRpwooVK2jXrp2p66WmphIdHU1KSgqVK1cu1NpERKTwrDY7q3af4MjpdGKjjBK7MpVBslph+nSjjfepU8axO+6AZ56BuLigLk1EpLQzGxt4vSepWbNmbNiwgfj4eNq2bcuzzz5LWFgYb7/9Ng0a+L7B1mq1MnfuXM6ePUv79u1Zs2YNWVlZdMvVySchIYG6deu6DZIyMjLIyMjIeZyamurzmkRExP9CQyy0b1gt2MsIjl9/NQbCrl9vPL74Ynj9dejQIZirEhGRfLwut3viiSew2WwATJw4kd27d9OpUyfmz5/Pq6++6vUC/vzzTypVqkR4eDj33XcfX331FYmJiSQnJxMWFkaVKlXynF+zZk2Sk5NdXm/KlClER0fn/KpTp47XaxIREfGrw4eNbFHHjkaAVKWKMRz2jz8UIImIFENeZ5J69OiR8/sLLriALVu2cOLECapWrZrT4c4bjRs3Zv369aSkpPD5559zxx138Msvv3h9HYfRo0czcuTInMepqakKlEREJDiys41gaNw4cFQ2DBoEU6ZAjRrBXZuIiLjkdZA0e/ZsbrjhBipWrJhzLCbG95atYWFhXHDBBQC0atWK1atX88orr3DrrbeSmZnJqVOn8mSTDh8+TJybmu3w8HDCw8N9Xo+IiIhf/PKLUVq3aZPx+NJLjYCpTZvgrktERDzyutxuxIgR1KxZk//85z/Mnz/f73ORbDYbGRkZtGrVivLly7N48eKcr23dupV9+/bRvn17vz6niIiI3xw8CP/5D1xxhREgxcQYw2FXrlSAJCJSQnidSTp06BALFizg448/5pZbbqFChQrcfPPN3H777Vx22WVeXWv06NFcffXV1K1bl9OnTzNnzhx+/vlnfvjhB6Kjoxk0aBAjR44kJiaGypUrM2zYMNq3b2+6s52ISGlW5rvEFTeZmfDKKzBxIpw5AxaLMfvoqaegWhltVCEiUkJ5HSSVK1eOa665hmuuuYa0tDS++uor5syZQ5cuXTj//PPZuXOn6WsdOXKEgQMHcujQIaKjo2nRogU//PAD3bt3B+Cll14iJCSEvn37kpGRQY8ePXjzzTe9XbKISKmzYNMhJsxL4lBKes6xWtERjO+TWPbmDRUHixbBsGGwZYvxuF07o7SuZcvgrktERHzi9Zyk/I4dO8Ynn3zC9OnT2bx5s9/L7wpLc5KkpDqXaeXp+UnsOZ5G/WoVGNMrkciw0GAvS4qBBZsOcf/steT/n7cjh1QUg1mVxfrH/v0wciR8/rnxuEYNmDrV6GQX4nVFu4iIBFjA5iQBORmkjz76iMWLF1OnTh369evH545/JESkUAZ/uJqFSUdyHi/bDrNW7qN7YiwzBrYO4sok2Kw2OxPmJRUIkADsGIHShHlJdE+MC1jQoiwWkJEBL75olNKlpRkB0ZAhRqldvtEVIiJS8nj9Mddtt91GbGwsI0aMoEGDBvz888/s2LGDSZMmkZCQEIg1ipQp+QOk3BYmHWHwh6uLeEVSnKzafSJPcJKfHTiUks6q3ScC8vyOLFb+NSSnpHP/7LUs2HTI7fdbbXZW7DzON+sPsGLncay2QhUzBMeCBdC8OYwZYwRIHTvCunXw6qsKkERESgmvM0mhoaF89tln9OjRg9BQlf6I+NO5TKvLAMlhYdIRzmVaVXpXRh057TpA8uU8bxQ2ixXIDFRmto1ZK/aw90Qa9WIqMKB9fcLK+bncbc8eePBB+OYb43FcHDz3HNx+u9GkQURESg2vg6SPPvooEOsQEeDp+Ummz5t0ffMAr0aKo9ioCL+e5w1vsljtG+bt5uZqH5UjA1WYfVRT5icxY9lucielJs/fzOBO8YzulejTNfM4d84IhqZMgfR0CA2F4cNh/HjQXlcRkVJJu0pFipE9x9P8ep6UPm3iY6gVHYGrvIUFIzvTJt73Id+u+JrF8pSBAiMD5Uvp3ZT5Sby1NG+ABGCzw1tLdzPF5AcPLs2bB02bGgFRejp06QIbNsALLyhAEhEpxRQkiRQj9atV8Ot5UvqEhlgY38fIjuQPlByPx/dJDEjThioR5U2dlz+LFah9VJnZNmYs2+32nBnLdpOZbfPqugDs2AHXXAPXXgu7d8N558Enn8DixUbQJCIipZqCJJFiZIzJ0iCz50np1LNZLab1b0lcdN5gJC46ImDtvwd/uJo73nffNMRVFitQ+6hmrdhTIIOUn81unGdaWho88YQRCH33HZQvD48+asw/uvVW7T0SESkjfGoBLiKBERkWSvfEWLfNG7onxqppg9CzWS26J8YVyawidx0XHdxlsQK1j2rvCXNlp6bOs9vhq69gxAjYt884dtVVRse6xo29WpeIiJR8poKk1NRU0xfUwFaRwpkxsLXLm1LNSZLcQkMsBRok+JuZjosAsVFhTLiumdMslmMfVXJKutN9SRaMLJi3+6jqxZgrO/V43tatMGwYLFxoPK5bF156CW64QZkjEZEyylSQVKVKFSwm/6GwWq2FWpCIGIHSuUwrT89PYs/xNOpXq8CYXonKIEmRM9txsXtinMsyP8c+qvtnr8UCeQKlwuyjGtC+PpPnb3ZbchdiMc5z6swZYxjsiy9CVhaEhcEjj8Do0VBB+/5ERMoyU0HSTz/9lPP7PXv28Nhjj3HnnXfSvn17AFasWMEHH3zAlClTArNKkTIoMixUbb4l6Mx2UvRU0ubYR5V/TlJcIeYkhZULYXCneN5a6rp5w+BO8QXnJdnt8NlnMGoUHDhgHOvdG15+GS64wOt1iIhI6WMqSOrcuXPO7ydOnMiLL75Iv379co5de+21NG/enLfffps77rjD/6sUEZEiY7XZc/Y6RZgcyGqm42Ig9lE55iDln5MUYsH5nKS//jJK6xwf/sXHwyuvQJ8+Pq9BRERKH4vdbvdqMEWFChXYsGEDjRo1ynN827ZtXHzxxaSlFa/5LampqURHR5OSkqL9UiIiHizYdKhAtseMzRN7mioHzR2A+bPZRGa2jVkr9rD3RBr1YiowoH39vBmk1FSYMMFoxJCdDRERRlndww9DZGShn19EREoGs7GB193t6tSpw4wZM3j22WfzHH/nnXeoU6eO9ysVEZFiYcGmQ9w/e63T5grumO246CwAq1WIcrvcwsqFMKhTg4JfsNvho4+MYCg52Th2/fXGPqT4+EI9p4iIlF5eB0kvvfQSffv25fvvv6dt27YArFq1iu3bt/PFF1/4fYEiIsVFYbMgHrMdQWS12ZkwL8mnAMlMx0VXAVhySjr3z14bmPlOGzbA0KGwfLnxuFEjI5PUs6d/n0dEREodr8vtAPbv38+0adPYsmULAE2aNOG+++4rlpkklduJiD8UNgsyZX6S+X0zQbBi53H6zVjp8bzuTWJJz7Z51XHRarPTceoSlyV8jhbgyx/t6p85T6dOwbhx8MYbYLMZneqeeAJGjoTw8MJfX0RESqyAlduBUXL39NNP+7w4EZGSpLBZkCnzk5x2YLPZyTke7EDpyGlze5Cuuag21118nlfXXrX7hNs9TnbgUEo6q3afKNzcJ5sNPvgAHn0Ujh41jt18Mzz/vDH7SERExCSf6jyWLVtG//79ueyyyzjwT/vUWbNmsdxR0iAiUkq4K0NzHJswLwmri2E9mdk2Zixz3aIajM5smdm2wi20kGKjIvx6Xm5mAzCz5zm1Zg106AB3320ESAkJxnDYzz5TgCQiIl7zOkj64osv6NGjB5GRkaxdu5aMjAwAUlJSlF0SkVLHmyyIM7NW7HE77BSMjNKsFXt8X6QftImPoVZ0BK6K3SwY5YVt4mO8vnYgAzBOnID774fWrWHlSqhUCZ57ztiP1K2b99cTERHBhyDpqaeeYvr06cyYMYPy5cvnHO/QoQNr16716+JERIKtsFkQT0NWvT0vUEJDLIzvY5T85Q+UHI/H90n0ac9QQAIwqxXefhsuvBCmTze62P3nP7BlCzz0EISFeb1OERERB6+DpK1bt3L55ZcXOB4dHc2pU6f8sSYRkWKjsFmQejGeh6x6c14g9WxWi2n9WxIXnfe1xEVHFKr7nN8DsN9/h3bt4N574fhxaNYMfv7ZaPV9nnf7pURERJzxunFDXFwcO3bsoH79+nmOL1++nAYNnMyoEBEpwRxZkOSUdKf7khyd2VxlQQa0r8/k+ZvdltyFWIzzioOezWrRPTHO7wNfHQFY/g6Bcd7MSTp61BgAO3Om8bhyZZg4Ef73P8hV2SAiIlJYXgdJgwcPZvjw4bz77rtYLBYOHjzIihUreOihhxg7dmwg1igiEjSOLMj9s9digTyBkpksSFi5EAZ3infa3c5hcKf4YjMvCYzXXKgucy74HIBZrUZJ3RNPGO29Ae64A555BuLi/L5OERERr4Okxx57DJvNxpVXXklaWhqXX3454eHhPPTQQwwbNiwQaxQRCarCZkEc7b2L85ykouJ1APbrr8ZA2PXrjccXXwyvv250shMREQkQn4bJAmRmZrJjxw7OnDlDYmIilSpV8vfa/ELDZEXEX6w2e6HK0DKzbcxasYe9J9KoF1OBAe3rF6sMUrGSnGzMO/rwQ+NxlSowebKxDynU8wBbERERZ8zGBl4HSXfffTevvPIKUVFReY6fPXuWYcOG8e677/q24gBRkCQiUoJkZxuZovHjITUVLBYYNAiefhpq1Aj26kREpIQLWJAUGhrKoUOHiI2NzXP82LFjxMXFkZ2d7duKA0RBkohICfHLL0Zp3aZNxuNLL4U33oA2bYK7LhERKTXMxgam9ySlpqZit9ux2+2cPn2aiIh/W8RarVbmz59fIHASERHx6OBBY7bRxx8bj6tVgylT4O67VVonIiJBYTpIqlKlChaLBYvFwoUXXljg6xaLhQkTJvh1cSIiZcm5TCtPz09iz/E06lerwJheiUSGleIgITMTXnnFaON95oxRWnfffTBpkhEoiYiIBInpIOmnn37CbrfTtWtXvvjiC2Ji/p0JEhYWRr169ahdu3ZAFikiUtoN/nA1C5OO5Dxeth1mrdxH98RYZgxsHcSVBciiRTBsGGzZYjxu184orWvZMrjrEhERwYc9SXv37qVu3bpYLIUbLFhUtCdJRIq7/AFSfkUdKBW2i59b+/bBqFHw+efG4xo14NlnYeBACFGnPxERCSy/70lyWLJkCZUqVeLmm2/Oc3zu3LmkpaVxxx13eL9aEZEy6lym1W2ABLAw6QjnMq1FUnq3YNOhAvOgapmcB+VWRga88ILRxjstzQiIhg6FCROM9t4iIiLFiNcf202ZMoXq1asXOB4bG8vTTz/tl0WJiJQVT89P8ut5hbFg0yHun702T4AEkJySzv2z17Jg0yEfL7wAmjeHxx83AqROnWDdOmM/kgIkEREphrwOkvbt20d8fHyB4/Xq1WPfvn1+WZSISFmx53iaX8/zldVmZ8K8JJzVXzuOTZiXhNXmRYX27t1w/fVw9dWwfTvExcHs2Uar7xYt/LBqERGRwPA6SIqNjWXjxo0Fjm/YsIFq6kYkIuKV+tUq+PU8X63afaJABik3O3AoJZ1Vu094vti5c0YZXWIifPON0cZ75EjYuhVuv93oYiciIlKMeb0nqV+/fjzwwANERUVx+eWXA/DLL78wfPhwbrvtNr8vUMSTgG4yFwmwMb0SmbXScxZ+TK/EgK7jyGnXAZLp8+x2mDcPHnzQyCIBdOkCr70GTZsWfpEiIiJFxOsgadKkSezZs4crr7yScuWMb7fZbAwcOFB7kqTIBWyTuUgRiQwLpXtirMfudoFu2hAbFeH5JHfn7dgBw4fD/PnG4/POgxdfhJtvVuZIRERKHK9bgDts27aNDRs2EBkZSfPmzalXr56/1+YXagFeejk2mef/AXbcjk3r31KBkpQYrtqAF1X7b6vNTsepS0hOSXe6L8kCxEVHsPzRrnkztWlp8PTT8NxzxnDY8uWNFt+PPw6VKgV83SIiIt4wGxv4HCSVFAqSSifHDZ2rPRQub+hEirFzmVaenp/EnuNp1K9WgTG9Eouk7beD44MHIE+g5PSDB7sdvvzS2GvkaNpz1VXw6qvQuHGRrVlERMQbfp2TNHLkSCZNmkTFihUZOXKk23NffPFF71Yq4gNvNpm3b6iGIlIyRIaFMun65kF7/p7NajGtf8sCJaxx+UtYt26FYcNg4ULjcd268PLLRic7ldaJiEgpYCpIWrduHVlZWTm/d8WifxyliPhlk7mIFNCzWS26J8Y5b4Zy5gw89ZSx1ygrC8LC4JFHYPRoqBDY7nsiIiJFyVSQ9NNPPzn9vUiwFHqTeTEQ7NIqEVdCQyx5M7B2O3z6qbHX6MAB41jv3kb26IILgrJGERGRQPK6u51IcdAmPoZa0REeN5m3iY8p6qWZkn+T/rLtMGvlviLbpC9i2l9/GaV1jg/IGjSAV16Ba64J7rpEREQCyFSQdOONN5q+4JdffunzYkTMCg2xML5PIvfPXosF55vMx/dJLJZNG1x1MQNYmHSEwR+uVqAkwZeaCk8+aTRisFohIsIoq3vkEeP3IiIipViImZOio6NzflWuXJnFixfzxx9/5Hx9zZo1LF68mOjo6IAtVCQ/xybzuOi8N2xx0RHFtv33uUyr23k4YARK5zKtRbQikXzsdpg92+hQ99JLRoB0/fWweTOMG6cASUREygRTmaT33nsv5/ePPvoot9xyC9OnTyc01Ng/YbVa+d///qcW21Lk3G4yL4aenp9k+rxgdjmTMmrDBhg6FJYvNx43amRkknr2DO66REREipjXe5Leffddli9fnhMgAYSGhjJy5Eguu+wynnvuOb8uUMSTApvMi7E9x9P8el5pZ7XZS0wAXKKdOgVjx8Kbb4LNZnSqe+IJYwZSeHiwVyciIlLkvA6SsrOz2bJlC43zDQvcsmULNpvNbwsTKY3qV6vAsu3mzivrFmw6VGBeT63883qkcGw2eP99eOwxOHrUOHbzzfDCC1CnTlCXJiIiEkxeB0l33XUXgwYNYufOnbRp0waA33//nWeeeYa77rrL7wsUKU3G9Epk1sp9ps4ryxZsOsT9s9cW6FyYnJLO/bPXFts9ZyXKmjVGad3KlcbjJk3gtdfgyiuDuy4REZFiwOsg6fnnnycuLo4XXniBQ4cOAVCrVi0efvhhRo0a5fcFipQmkWGhdE+Mddu8oXtibJmel2S12ZkwL8lpa3c7RvfCCfOS6J4Yp9I7Xxw/bpTSvfWW0aShUiUYPx4eeMAYDisiIiJY7Ha7s3sRU1JTUwGKdcOG1NRUoqOjSUlJKdbrlLLFVRtwzUmCFTuP02/GSo/nfTy4XYnZi1YsWK0wc6bRxvvECePYf/4Dzz0HtWsHd20iIiJFxGxs4NMw2ezsbH7++Wd27tzJf/7zHwAOHjxI5cqVqVSpkm8rFilDZgxszblMK0/PT2LP8TTqV6vAmF6JZTqD5HDkdLrnk7w4T4DffzdK6xyjG5o1g9dfh86dg7suERGRYsrrIGnv3r307NmTffv2kZGRQffu3YmKimLq1KlkZGQwffr0QKxTpNSJDAtVm28nYqPMzeExe16ZdvSo0ZTh3XeNx5Urw8SJMGQIlPPpM7IilZltY9aKPew9kUa9mAoMaF+fsHKmxvuJiIgUitf/Sg4fPpxLL72UDRs2UK3av6UuN9xwA4MHD/br4qT0UUtn8aRNfAy1oiNITkl3ui/JgjEwuE18TFEvrVj+/Dpdk81q7Dl64gmjvTfAHXfA1KlQs2ZQ12vWlPlJzFi2G1uuH4LJ8zczuFM8o8t4YxMREQk8r4OkZcuW8dtvvxGWb4Nv/fr1OXDggN8WJqWPWjqXLb6WE4aGWBjfJ5H7Z6/FAnkCJUc4Mr5PYpEHJ8Xx59fZmq46uYPxP7zJeXu3AWC76GJC3nwDLrssKGv0xZT5Sby1dHeB4zY7OccVKImISCB5HSTZbDasVmuB43///TdRUVF+WZSUXK4+aVdL59LDTAlU/sYUy7bDrJX7TDem6NmsFtP6tywQAMQFKSjx9PP7xn8uoWrF8CLNMOVfU40zJ3nsl/fou2kJACnhFXnu8oF8cklPBp2qwuiArsZ/MrNtzFhWMEDKbcay3Yy6KkGldyIiEjBed7e79dZbiY6O5u233yYqKoqNGzdSo0YNrrvuOurWrct7770XqLX6RN3tio6rT9rH9m7CpO825zmem6N8avmjXYNeuiTuOSuBCrGQpwTKVec+B286+BWH8jarzU7HqUtc/vyC8R7kfk8CnWHKvaZy1mwGrv2OB5d/ROXMNGxY+LRFd57rfAcnKkTnfM+9l5eMMrWZy3Yx6bvNHs8b27sJgzo1KIIViYhIaRKw7nbPP/88PXv2JDExkfT0dP7zn/+wfft2qlevzscff1yoRUvJ5e6T9v/NWef2e+3AoZR0Vu0+oZbOxZiZEqgHuzV2GyABLEw6wrlMq+nSu2D/TKzafcJtgAR5AyQIfIbUsaa2+/5kwsLpJBzbC8CGuEaM634fG2o3LvA9JSX7svdEml/PExER8YXXQVKdOnXYsGEDn376KRs2bODMmTMMGjSI22+/ncjIyECsUYo5T8M/zVJL5+LLbAnUmfRsU9d7en5Siens58vPZaCH3qbu3M0r//cc123+BYATkZV59vKBfHrRVdgtzoMgmx1mrdgT9OyLp+xgvZgKpq5j9jwRERFfeBUkZWVlkZCQwLfffsvtt9/O7bffHqh1SQli5pN2M2IqhHk+SYJi1oo9BbIl+dns8Nuu46aut+d48c0C5N9zdWFN3/ZaBiRDmpkJr7xCtwkTCD17FhsWPrrkap7vNICUSM/rdGRfglXGaKb5xYD29Zk8f7Pbn7cQi3GeiIhIoHgVJJUvX570dH3aL3n5KwO0JTmVThfW8Mu1xL/MljaVDzVXylW/WvHMArjac1UhLJRzmVavMqMOfsuQLloEw4bBli2EAn/WacLorveyKe4C05eoF1MhaF36zDZvCSsXwuBO8U5LOx0Gd4ov9mWDIiJSsnn9r8yQIUOYOnUq2dnmymqk9PPXUM/9J8/55Trif2ZLm264pLap88YUwwYCk78z9lzlz2DY7JD2T4DkS66l0H8/9u2Dm26C7t1hyxaoUQPee49v3/zMqwApxAI1o8K5f/baAplfR6CyYNOhwq3VBTMluRPmJWH9580f3SuRey+PJ39yK8RSchpQiIhIyeb1nqTVq1ezePFifvzxR5o3b07FihXzfP3LL7/02+KkZPA0/NMs7TEovsyWQA3q2JC1+0557G5npmlDUfp2/QGPe64sFoitFM7h0xk5x/J3tctzPoUcepuRAS+8AJMnQ1oahITA0KEwYQIL/j7H27PXenW5QR3jmfz9FpeBSiD3UHkqyXVWmji6VyKjrkrw2G5eREQkELwOkqpUqULfvn0DsRYpoTwN/7Rj3GC6azavPQbFmzclUDMGtnbZBtyb9t9FZcGmQwz9ZL3H8+x2GNQhntBQS85Ne2zlCB742Oje6Neht99/Dw88ADt2GI87dYLXX4cWLf7Jyqzx6gOJ7omxdE2o6TYQDGSXSbMlh/nPCysXEvRGEyIiUjZ5HSQVtzlIUjx4Gv65bt9J7TEo4RwlTp7mJAHMGNiac5lWnp6fxJ7jadSvVoExvRKLXQbJUQZm1suLt5OW9e8w7VrREdxzeTz/t+FQoYbeOppFpGzeRt9ZL1Bv+aJ/LhSH7dnn+L19T46cySB253FsdrvXjVIWJR2hblVzmdpAdJk0W3Lor9JdERGRwjI9TNZms/Hcc8/xf//3f2RmZnLllVcyfvz4Yt/2W8Nki5a7rllmBpEGY13infzd30pyCdSKncfpN2Olz9/v+Al64z+XULViuE8/X1PmJ/Hhki3cs/IL7v/9cyKyM8kKCWXtdQNIfWQ0437anycoqhJZnlPnsrxeZ9WK5Tlx1vP3fTy4nd8zSWYG8oLx/4LHe2u/kYiIBI7Z2MB0kDRp0iSefPJJunXrRmRkJD/88AP9+vXj3Xff9duiA0FBUvESjBvsYHXzKumKQyYo0D8v36w/wHATpXbuOPYeLX+0q9eB95Tv/mLnu58ybvHb1E05DMCv9Vowvtt97Khet1DrciamYhgnz2Y6LdUrzOsw49v1BzyWNYZYYMukq0ts0C0iIsWf34OkRo0a8dBDD3HvvfcCsGjRInr37s25c+cICSm+/6ApSCrbXLUddtwCOtoOS17FYU+RvzKP7rKIy7YeZcB7q/yyXm8zMJlbtvHrNbfTZecfAByqVI2nuv6X7xI6Gpv4AuDuDvV579c9gPM9VIH8+zBz2S4mfbfZ43ljezfRPiQREQkYs7GB6T1J+/bto1evXjmPu3XrhsVi4eDBg5x//vmFW61IAHhqOxzIbl4lmasACWBh0hEGf7g64IHSlPlJTvew2ezkHDcTKHnKIm45fNpvaz5yOt1c5istDZ5+mtBnn6NLViaZIeV4p831vN7+VtLCAlu+3D0xjjbxMS73DgbyAwOzs7bMniciIhJIpoOk7OxsIiLybqotX748WVne1caLFBVf2g6XdecyrW7bd4MRKJ3LtAas9C4z2+axHfeMZbsZdVWC27IsM8NL95/03w35wqTDjPh0fZ7M1+T5m//NfNnt8OWXMHIk7NtHKLC0/iU82e1edlUL7AdNuduRh4ZY6J4Y5zK7FqgSR7Mt/jUKQEREigPTQZLdbufOO+8kPDw851h6ejr33XdfnllJmpMkxYWvbYfLsqfnm+v09vT8JCZd3zwga5i1Yo/beUxgZJRmrdjjsizLbBbx7g7xptYUFRHKmXSry708FcJC+XZjwUGsjsxXzP5d3PvFq7BwofGFunVZdM9j/De1TsBK63KvD/K2Iw8NsTj9YMBZiWOeQK8QzM7a0igAEREpDkx/PHjHHXcQGxtLdHR0zq/+/ftTu3btPMdEigu1HfbenuPmMitmz/OFP8qyzGYR1+877vF5Qiww5Z+AMH8445gDlrsteG4VMs/x2M/vcdf/bjACpPBwGDsWNm/m8kfvJSQAZZ4xFcvneRwXHWFqr5GjxDF/EOMI9KaYDKBdcczackejAEREpLgwnUnSfCQpadrEx1ArOoLklHS33bzaxMcU9dK8VlQtzOtXq8Cy7ebOCxR/lGWZzQ5+t8l9aSEYN+7XXHwe5cqFON3L06FhdT5f+3feb7Lb6bN5KWN+epdaZ4xAbG+7Lvw+fCybImtQb00yA9rX9zigFygwoNndeXHREfzycBfW7D3p1c+Kv0ocPfFm1paIiEgwme5uFwhTpkzhyy+/ZMuWLURGRnLZZZcxdepUGjdunHNOeno6o0aN4pNPPiEjI4MePXrw5ptvUrNmTVPPoe52ZduCTYe4b/Zal1+fXgK62xVlC/NzmVaajFvg8bzNE3sGdE9SwtjvPZZluWsVXdj5Rw6DOsYz9pp/b9ydBasT5v3Fhyv25pzT6OheJix6i8v2bQRgb5U4Jlx5Dz9d0CZPsOMIDMB10HBJ3aoF/uydKWx3uqLuPFeaZm2JiEjJ4vfudoHwyy+/MGTIEFq3bk12djZjxozhqquuIikpKWef04gRI/juu++YO3cu0dHRDB06lBtvvJFff/01mEsXKRJmmg/4M1CKDAule2Ks2+YN3RNjAzovyVGW5S7D4qksy1MW0aza0XlLMZ3t5XFktCplpPHg8o+4c808ytltpJcL4412N/N2275klAsrcG1HGdu9l8ezZdLVLoOG/E0WTp7NZNJ3/u1OV9Sd58LKhajNt4iIFGtBzSTld/ToUWJjY/nll1+4/PLLSUlJoUaNGsyZM4ebbroJgC1bttCkSRNWrFhBu3btPF5TmaSyy2qz03HqEpefwgd6eGZhBXP9pWFOkiPAhIIzgcz+T29g+3pMvK6Z23Mys6w8duMjjP7pXWqcPQXAD43aMenKwfwd7Tnj7csAVX+XX2qGkYiIlBUlIpOUX0pKCgAxMcYekTVr1pCVlUW3bt1yzklISKBu3boug6SMjAwyMjJyHqempgZ41VJcFfcW4J5udIO5/hkDW3Mu08rT85PYczyN+tUqMKZXYkAzSPmN7pXIqKsSfC7L6tmsFtP6t3Sxj6gan6894PEaHvdHbdhA2NChvLh8OQC7qtZmQrd7+aVBK1NrBM+d+pxx1Z3OV+o8JyIiklexCZJsNhsPPvggHTp0oFkz45Pb5ORkwsLCqFKlSp5za9asSXJystPrTJkyhQkTJgR6uVICFOcW4Gb2GQV7/ZFhoYVu813YvSeFLcvq2ayW05lAVpudL9cd8D0oOHXK6FL35ptgs0GFCvx08z3cX6Mz6aHlnX+PG8EeoOqPEkcREZHSpNgESUOGDGHTpk0s/+cTWV+NHj2akSNH5jxOTU2lTp06hV2elECBbgHua8mT2X1GZtdVvWK455OCIJAzd7zhLOsSGmLxLSiw2eD99+Gxx+DoUePYLbfA88/TpU4dNuYLCq02O09/v8XjGovDAFV1nhMREflXsQiShg4dyrfffsvSpUs5//x/J8/HxcWRmZnJqVOn8mSTDh8+TFxcnNNrhYeH5xl4K2XXzOW7PJ5Ty8cW4As2HWLc15s4ciYz51hspTAmXt/M7eZ5s0NOuyfGmW4+MGruBp681v+d7grDMXMnP0ezAiDoN91eBwVr1sDQobDyn655TZrAa6/BlVfmnJI/85WZbeOZBVtKTBlbYUscRURESoug/stnt9sZOnQoX331FUuWLCE+Pu+gwVatWlG+fHkWL16cc2zr1q3s27eP9u3bF/VypQQ5l2ll0WbPM3BG90jwesO7o6147gAJ4MiZTO6bvZYFmw65/F5v9hmFhlgY38e4UXe3wsOpRgbK3fMWJbMzdzKzbUW0ItdG90pky6SrGdu7CQPb12Ns7yZsmXR1ToBktdlZ/cd2dt80AHvr1kaAVKkSPP88rF+fJ0BypiQOUHUEehOva8agTg2K1dpERESKSlD/9RsyZAizZ89mzpw5REVFkZycTHJyMufOnQMgOjqaQYMGMXLkSH766SfWrFnDXXfdRfv27U11tpOy6+n5SabOW73vhFfXtdrsPPDJerfnDP9kPVYXqQNv9xk5mg/UrOw6O+p4pgnzklw+b1Gx2uxM+vYvt5kT+LdZQXHgKihYsOFvnr9xJBd0akX8F7Ox2O38cFFXfvpmKYwaBWEF23o7M7pXIvdeHk/+WDzEAvderjI2ERGR4iio5XbTpk0D4Iorrshz/L333uPOO+8E4KWXXiIkJIS+ffvmGSYr4s6e4+Y2wps9z2H5tqMeMyAZ2TaWbztK54TYAl/zZZ9Uz2a1iIooz+3v/O7y/GB36gPnzSjcCXazAndWzPmO2qMepGfyDgA216jP+O73sbpOM/jxINNi47wqbywtZWz+bj0uIiJSXAU1SDIzoikiIoI33niDN954owhWJKVF/WoVWLbd3HneeHuZ531OjvOcBUme9hk5Zh/l3yd17EyGk7MLKkynu8LcALtqRuFOMJoVeHyNR49ifeRR2r//HgCpYRV4sVN/ZrXsjTXEaH+ee9+YNwFCSR+g6iwIjqlYnhsuPo9u/+yhU8AkIiKlRbFo3CDib2N6JTJr5T5T53kjNT2rUOc59hndP3ttgaGmjtvL8X0SC9xsBrpTn5mW5K64a0bhSjCaFbh9jQk1YPp0ssY8QfnTxry2z5tdyTNX3MmxilXzXKc4ZO2Kmqsg+MTZLGb+uoeZv+4x/fMiIiJSEpSsWg8RkyLDQqlRyf2ekRqVwrwejnrR+VUKfZ5jn1FcdN6AJi46Iqf9d36ODJSrz+ktFK5T3/2z1xYok3O0JPfUEMJTMwpnirpZgbvX+M4zsznauDkMG0b50ylsqtmQG29/jod6jygQIOUWjPlawWA2CDb78yIiIlISKJMkpdK5TCtH83Wfy+/omUzOZVq9CpQe753I7N89Z6ge7+0+Q+VqyKmrciVfM1CeeNOS3NW1vQkWgjFzx9VrrHHmJI/9/C59//oJgJTwijx3+UDmXNwTW4jnnwlvs3aFHawbLGaDYLM/LyIiIiWBgiQplcx2t3t6fhKTrm9u+rph5UIIKxfitnlD+D/neOJsyKk7jgxU/pKxuEKUOXnTktzVWmMqmOvyNqBdXcZe07TAexPo4CH/ayxnzWbg2u94cPlHVM5Mw4aFTy66iucvH8iJCtEer+dq35g7xWWwri+8CYLN/LyU1GBRRETKFgVJUioFqrvdqt0nTHW3C9R+FW8zUJ5425I8P8fNvzuOoOLJa5sVWGdRBA+5195u30aeXPgWCcf2ArC+ViPGd7uPDbUbe3VNb7J2JWGwrju+7HPz9PNSEoNFEREpWxQkSakUqO52hQ0q/MHbDJQ7hWkI4ermPzd3pYBFFTzERkVQ8/QxHv/pXa7dvBSAE5GVmdr5Dj5r0R27xXwWo1rFMCbf0Mx01s7MYN23l+5m1FUJxTab4qkjozPe/LyUlGBRRETKluL5r7JIIZntWudtd7tAd5krar42hDBz8w9Qs3K402YUZoOH3Fk7q83Oip3H+Wb9AVbsPG5ucG5mJm2/mMmSd+7n2s1LsWFh1iW96DL4LT69qIdXAVJMxfKsGH2lV2WNs1bs8ThY1w4MfNf1DKxgc+yHM6MwPy8zlu32mKUVEREpKgqSpFSKDAule2LBOUW5dU+M9bq7XSC7zAVD7hvg/K/JXRbIzM0/wKCODZwGFWaDhzFfbgSM7nQdpy6h34yVDP9kPf1mrKTj1CXuO6ktWgQXXUTIo49SMfMca2oncO0dLzH2qv+REhnlefH/sPzz6+kbmnud7TE7MHflLs9lnLn5FDAWgmM/XK1o18F/YX9ebHbjPBERkeJAQZKUWjMGtnYZKHVPjGXGwNZeX9PXoCK3zGwbM5ftYtw3m5i5bFfQPz33pSW52Zv//Sedn2f2++dvSmb+xoPetSjftw9uugm6d4ctWyA2Ft57j6PfL+J442amnjc3d++DJ3Wqmi/nNBsg+BQw+kHPZrVY/mhXPh7cjrs71CemYt6GHf74eTF7noiISKBpT5KUajMGtuZcppWn5yex53ga9atVYEyvRK8zSLkVpstccd247m1DiHox5m7+XZ1n9vvTMq088c0mcy3KszLh+eexT56M5dw5bJYQfu91Gxe/+yqRsdXoCXRvVjvnNe45dpaXFm0v0FLdYVCH+nRLjCtUY4yEOPMZKzMBgquhro6A0ddgzizHfrj2DavxeO/EIvt5ERERKWoKkqTUiwwL9arNtxm+dJkr7hvXzTaEsNrsXFgzymVw4RBigQHt6zv92oD29Xnqu82mGgGcOJvl8muOltPb3v+MJlOegB07sAC/n9+U8d3vY0tsPLy4kq6Nq3Fe1YoFAuXGcVEFgt1ahWipXmDtae5ndeXmKUDwx0wrf/KmgciA9vWZPH+z25I7dz8vIiIiRU1BkoiPvLlJNLtxvbh2OXNk4/7Yc5I9x89yLstzieDgTvEuX0tYuRCuaRHHvI3JhVrX+aeSGbfkHZpsXwnAkYpVmdzlbr5JvAIs/wYKS7YeB44DsGw7zFq5L6fk0p8t1fMz28DDYiJA8MdMq2AJKxfC4E7xbrshuvt5ERERKWoKkkSKgDcb1wd1alA0izJp8IerWZh0xPT5IRZMlQ++fFtLlmz5gbOZVqdftwAxFcM4frZgNiY8K4P7fv+C+3//nIjsTLJCQnmv1bW82qEfZ8LNlWwtTDrC4A9XM2Nga1NBhS9lm45GH+6CG4D/dvQcIBSH9vPFmYbUioiIPylIEikC/ti4brXZA5bxcPVcT89P4s8Dqaa+JyzUwsM9ErjjMnM3p6EhFl645SKne2wcr2rSdc144ptNnHAESnY73XasYtzit6mbchiAX+u1YHy3+9hRva7JV/evhUlHOJdp9Rjs5A8U82ejXHE0+nD2Gh26J8byeG/PZZYluf18oDOpxXWvn4iIlFwKkkSKQGE3ri/YdCige2c8PZcZmVY7aZlWr25yzTTB+GPvCd79dQ/1Th7kyUVv0WXXGgAORlXnqa7/ZX7jDnlK67z19Pwkt3vW3GXScmejXHH1GitHlOPp65tzzcW1Ta3T01BXC8b7Vhzbzwcyk1rc9/qJiEjJpCBJpAiY3bj+n7b1Chwvyo5mrp7LrPd+283Qrhd4leHy1ASjR/0oqk6ZxT2rviDcmk1mSDneaXM9r7e/lbSwSB9X+q89x11n785lWj2WGprJRvnS6CO/3Fmp/E0zzLafD5ZAtQAv6Xv9RESk+NK/GiJFwLFx3R2bHbq+8HOeeTeeOpqB0dHMH8NE3T2XWafSsli1+4TX3+dognHdxefRvmE140bfbifrs7k07daOYSs+JdyazdL6l9Dz7td5tvOdOQGSBYir7HuJWf1qrrN8T89PMnUNM+c5fY1e8mWmVXEQqBbgGlIrIiKBokySSBFxlPzk3zuRW/7sUFF2NPP0XGb5pXHAli3s/s8g4tf9Rnng78qxTLryv/zQqH2e0jrH7568NpEv1v7tVYMJhzFuSrHcZZl8Oc8f/JGVKmqBagGuIbUiIhIoyiSJFKHRvRL5a0JPoiKcl2blzw4VZUezgyf9cyNZqMYBp09jffgRsps1J37db2SElueVy26j23/f5IcLLyuw9yh3BmXGwNa0OL+yV0/XPTHWbZmcuyyTL+f5iz+yUkXJTCbVlxbgGlIrIiKBokySSBFbv/8Up9Odt72GvNmh6pXCTV3THx3N1v99qtDXqFqhPK3qVfX+G+12+PRTTg8dTtRxIxu0qGFrJl55D/uqFiwhs1jgwzvbcFmj6jkBwoJNh/jzb3Od+ACPnenAyDLNWrnP47XcZaPE4CqTarZlvDMaUisiIoGiIEmkiJnN+ixKSua7Pw+5Pce/Hc0Kn404mZZF5+d+8q7r3qZNMGwY/PwzUcDeKnFMuPIellzQxuW32O2w7chpOjWuAZjbT1WhfAiX1KtKg+oVTc04AogMC6V7YqzbMj5P2Sj51+heiYy6KsFv84w0pFZERAJFQZL4VVHO8impzGZ9Zv66x+3X/d3RzF8lY6a77qWkwIQJ8OqrYLWSXi6MN9rdzNtt+5JRLszj8+TeZ2JmP1Valo2hXRp5vXdrxsDWLtuAm8lG5aa/H0Zg48+ByYHIUImIiChIEr8pylk+JZmZeTcWCx67dsX5+b01U7pkhh3jNUyYl0T3xLiCQYDdDrNnw8MPw2FjIGxS2yu555L/8Hd0TdPPsz35dM7vk1POmfoes+flN2Nga85lWnl6fhJ7jqdRv1oF09koB/39CBx/Z6hEREQUJIlfFOUsn5LEVebA3bwbO0Yc4cnzN11Eh0bVAWNeTGFvEM2ULpnlsuvehg0wZAj8+isAZ+s14PEug/m6puthrq78vucEmdk2wsqFcOJspqnvMXueM5FhoW6Hzrqjvx+B5+8MlYiIlG0KkqTQPM3ycZtVKMU8ZQ6m9W9Z4Otx0RFc3SyOdz2U2gEcO5sBwJT5SQVKjSbP3+xVqZEjmNtx5Iy5F2fSrzuOceR0OrVs6Vz67kuETJsGNhtUqMDWwcO5tlwbMsqV9+najvk3gzo1IMZkgwuz5/mT/n6IiIiUPAqSpNCKcpZPSWE2c+Bs3s2q3SdMBUmxURFMmZ/kNPNjs5Nz3FOg5CyY85c3lmzjpj8X8egvHxCSlmIcvOUWrM8+x51ztpNRyOd07EsyO0y2MENnfaW/HyIiIiWPgiQptKKc5VMSeJs5yH9j3CY+hioVynMqLcvp9R0d7S6uU4Xb31npdi0zlu1m1FUJLkvvXAVzrtzeug6jeyeyYf8p7v5gNRnZNpfnNkvewaQfp3HJoa0AbK9Whye73cuAJ+4mOjvML0GZY/6NY5+Xu2vWKmQXQF9LGvX3Q0REpORRkCSFZrZbmz9m+ZQEhc0cLExKdhkgOb5/fJ9E5vy+12OThdwlafmZaZudX4bVRqWIcrSOjyHTRYBU5VwqDy/9kH7rfyAEO2fCInm5Qz/eb3Ut1tBy7JqXxCM9GnvxrM7lnn+Te58XFNznBYXrAliYksai/vvhj/1pIiIiZZ2CJCk0M93a/DfLp+h527a5MJkDR+DiTpUK5dl/Io3P1x4w9Ty5W2XnZqZtdn7zNyUz9SY7s1bsKfBnHWKzctvGH3n4lw+pmm50nvsq8QqmXHEXR6L+DQYPpaQXqoGCw92X5b35d7fPqzAd5Apb0liUfz/8sT9NREREFCSJH3jq1gb+m+VT1BZsOsT4b/7i8OmMnGM1o8KZcF1TlzfdhckcmAlcTqVlMXn+FlPPAf+WpOXnS3lXWqaVVbtPFAi8LjmwhQmLptMieQcAm2vUZ3z3+1hVp5nT68RUCncbOJjx7m97CA215Ln5d7XPy9efvcxsGzOWue/256mksaj+fvhjf5qIiIgYVIMhfuH4FD8uOu+Nf1x0RIltb7xg0yHum702T4AEcPh0BvfNXsuCTYecfp8jc+DqlteC6/0x/t6XkrskLT9fy7uOnE7PCbxi0lKYOv8Vvpr9EC2Sd5AaVoEnr7yHa+58xWWABEYDhfF9jBt2X0MDx83/lPl5M2+OfV7XXXwe7RtWK1TwMWvFHtMlje4E+u+H2WDOVYmkiIiI5KVMkviNvz/FDyarzc7wT9a7PWf4J+tJmliwbbOZOUhXNzPep/zvj7/3bQ3uFO8yw+GpDMyV2KgIrk6owb5JzzJy6WyiM84CMLdZN6ZecQfHKlZ1+b25S8tCQyxOy+O85SmTUxiuShV9OS+Qfz+8CeY0S0hERMQzBUniV866tZVEy7cdddu5DSAj28bybUfpnBBb4Guu9sdYLMag2Hd/3cO7v+7JMzcJfA9c8gux4HEfirtgzpmcAOdAEqF9hzFhwwYANtVsyLhu97H2/CYevx/ylpY5AoeVO4+zYtcxft1xnHX7T5l6jQ6BvPl3Varo63me/n742nTBn8GciIiIKEgScWrGsl2mz3MWJEHezMGipGRm/lrw0/78c5O8DVzyaxwXxS2tzjd9c+0qmMvPAlQ/c5LPk74hdMznxsGqVfnk+nsZU709tpBQj8/lqoHCwqTkQmeTAnXzP6B9fSbP3+w2S+OupNEbhWm64O9gTkREpKzTniQRJ1LSXbfg9ua80BALbeJjmL8p2enXHffDE+YlYf3n7tjV/hUzbmp5HoM6NfCq9Kxns1osf7QrHw9uxyu3XcyIbhfmGbpazprN8D+/ZenMezlv3ufYLBay7/4vbNtG5ANDTQVIQ7s0ZPmjXQsESI45Ta4CpIbVK5p6Df64+bfa7KzYeZxv1h9gxc7jWG12wsqFMLhTvNvvc1fSaJaj6UL+YMzVvqv8BrSvj6eqPX8FcyIiImWBMkkiTlx0fjR/Hkg1dZ4nvsxNyr9/5XBKOk9/77mj3YWxUazYeZwjp9OpXikc7HDsbIbH/S/5y8CGdr2AVbtPYP1pCXXHPUrdQ0ZTgPW1GjG+231sqNGY7vN3c3cHcyVuHS6oUeC5Pc1psgBpWVaPGTV/3Pwv2HSoQDbLUQrpyOLkz/KYKWk0w2zThQtjo6hdtYLTP0dHMOesu52DP4I5ERGRskJBkgSFt7OHitrjvZsy+/f9ps7zxNe5SbkDl2/Wm5uJ9L85azmbaXX6tfz7n1yx2uysX/EXdSY9wfk/fAPAicjKTO18B5+16I7dYtxoL0w6wrbk026v5W4GkNngsU+LOOZtdJ6Jg8Lf/DuyWfkDsdylkKN7JTLqqoSADGk123Rh1OcbAdd/joEO5kRERMoSBUlS5Nx9al9cWoVHhoXSPTGWhUlHXJ7TPTGWyDDPpWaFmZvk7TVcBUhgBBy59z8588O6vWwfM5m7Fs+iYlY6VksIH118NS906k9KZFSB8/eeOOdxTa5mAJkNHrslxlG7SmRAbv7dZbPsGEHe419t4lymlbjoSO7sEO/3YN7b/VT597HlFshgTkREpCxRkCRFysyn9sUlUJoxsDWDP1ztNFDqnhjLjIGtTV3n5NlMQiy4zBa4y7Y4+KvrnR1j/1P3xIKty1e//QkXjH6YHif+BmBN7QTGXXU/f9Vs6PPzvXbbxX4Zuntdr/MCcvNvJpt1/GwmIz4zOvkFIpj3dj+VI3hz9ecYVi5Ebb5FREQKSUGSFBkzn9q7uvELlhkDW3Mu08rT85PYczyN+tUqMKZXoqkMEhhB4ZA5BYPC/FxlW3KXJd7Wug4vLdruU9e73PLvf2LfPuwjRtL6yy8AOFqhCs9ccRdfNuuSU1rnq/yDeHPzFPhZgJqVw7HZ7Xyz/gCxURF+z+R4O7zXVTBfmPJRMx308nO2j01ERET8R0GSFAmrzc77v+72uoFBcRAZFsqk65t7/X2eGhOAUTL2er9LnGYmnJUlVgwLJc1NSZ1ZySnnICMDnn8eJk/Gcu4c2ZYQPmx5DS93/A+pEZUK/RzgvpTMzNDd9Gwbt7/ze85xf2dyvB3e6yyYL2z5qJmmC654G+SJiIiIOSpUl4BbsOkQHacuYdJ3m02dX1pu/DyVcoFRgle1YniB465aY5/NtBYqi+RQYdEP0KwZPPEEnDvH7+c35Zo7X2Fit3v8FiCB51IyV+3OK4Ybn9+cSsvbYt2RyVmw6ZDpNThr7e3gyGZ5k5vKHcy7+nPydp2jeyVy7+XxHtt45+dtkCciIiLmKJMkAeVqD5I7xfnGz5uyKl+72pnJQPnq/FPJjF88g+47jOyMvVYtnux0Jx/Uvwws/i1xNNuaO//Q3S/X/c3JtGyn53pblukpy1OY4b3JKed49oetfisfzd10Yc/xNL5e/zen051nDc3sYxMRERHfKUiSgPH2Zr+43/h5W1blbVe7zGwbs1bs4XcTGShvhWdlcP/vn3Pf718QkZ2JrVw5QoYPZ3X/oXzwyV8+XbNetUj2Hnfd3c6b1tyhIRZSzmXy7q97PP68mC3LNNskxJHNyv9n68mJs5l+Lx91NF2w2uxUrxTGS4u2FzjHEW652scmIiIihacgSQLGTLmZQ3G/8fOlK5+jlMvVe5A7KJwyP6lAi2u/sNvptmMV4xa/Td2UwwD80eASLvlmFjRryiGT85dyq1A+hBWju5F0KJV3lu1kydaj2AvZmtuX7Jm7TJ23TUJyZ7OSU9OZ9O1fnDib5eS7//1zi6lUsEzS23U64ywYzy2umLXLFxERKY0UJEnAeHNzWJxv/HztyhcaYqHZeZXdBorj+yTy7ILNPm3ad6VSeDnOZGRT/8QBxi9+my671gBwMKo6k7v+lz6ThhHarDbgW2njgPb16PnK0jyvKyqiHC3rVuHyRjV8as3tTUDt4G7tZgfV5s7y5B7eG1k+hPtnr8051yF3MB8dGVbodebnqTx1RLcLGdr1gjw/Z8V9MLOIiEhJpCBJAsbszeHY3k18au1cVDeHvtxwA0yZn+R2GG23xFi6JtTkfx+t9cs6q0SW543bW9KuZgR7Rj1OnXenEWbNIjOkHDPa3MAXPQbyyE2t8gSi3sxfCrHAoI7xvL10d4Fzz6Rns3TbMfq1qevT7CJvAmozZZm+7gdzcFWClzuYt9rsHluYe1M+6imbZgE+Wb2PoV0vyDlWEgYzi4iIlEQKkiRgzMzBiYv2bfZNUd4c+nLDnZltY8Yy99mhxZuP8N6vhS+xc7xzz9zYjA4bfoERI2i4fz8Apzp15Y9RT9KyWSL3OQkiczcu8OTVWy9m8vdbAjLnytuMlruyTKvNzjE385nMPm/uEjxngbinFuae1pmft8F4SRrMLCIiUtKoBbjkkZltY+ayXYz7ZhMzl+0iM9vm87UcN5FAgRbLhdmD5K+2y2Z524ABYNaKPR6DH5sdFiYdLszSACPQ/PCyKHqOvANuugn274d69eCrr6jyyyK6XdeJ9g2ruXyfHVmTWtHOX2et6Aim929JtSjX+6sg7028t8y24q4VHeH25t9su3nLP9fylOVxlOBdd/F5Tt9DVy3M4zys0xlvgnFPJaBgBKxWv29yExERKRuUSZIczpoHPPXdZq5pEcfLt7X0qZTNTNmSN3zdH1QYZjNiuW+43Q1Rzcv7m1gLEFMxjCd6N6F2aDatZ79BSN+XITsbwsPh0UeNXxXczyjKLX/jghNnMoipGEZcdGRO9uQbk00efJlzZaYV94hujRjatZHLP1ez7eb93STEU8bJLG+CcV9LQEVERMQcBUkCGAGSs+YBdmDexmSWbPmBF265yKfyHX/dRILv+4MKw5eyKk9DVB26NIpl7b5TXpXc2YHjZzJouux7Lnz2STh40PjCNdfAyy9Dw4bmL5ZL7sYFzviSUfOGq4DaTBmlN93xAtEkxNN7Z4Y3wfi3Gw+aumZpGcwsIiJS1BQkian9M2czrYXa5+CPm0go/IZ8X3mbERvQvj6T52/2GPw8t2ibx3lD+V14dA8TFr3Fhfv+NA40aACvvGIESSb50vTCl4yat3wNqM12x/O1SUhR8CYYD3TAKiIiUtYpSBJT+2fAuGnzdymbt4ri5tBVAOHNDXxYuRAGd4o31dp77/Fz1KsWyb7j59xmQqIyzjJ8+RzuXDOPcnYbtvAIQh4fAw8/zLmQ8jz99Z/sOZ5G/WoVGNMrkciwUKfX8bXphb8bFbh7Hm8DarNBcfWo8GIZIDmYDcaLImAVEREpyxQkiRf7Z4K/zyHQN4eeAghvbuAdw1TNBkrvDLyU/374R8Ev2u3c8NdPjPn5XWqcPQXADxe2p8t3swm7oAGDP1ydp9X4su0wa+U+uifGMmNg6wKvrzAd0fy9x8xfzAbF1SuGs2Ln8WI9U8hMMF5UAauIiEhZpSBJTO+fcQjmPodA3hwGu6Xy+78VDKYSD+9iwsLptD6QBMDOmPOYcOU9LG3Qio8t0bybL0DKbWHSEQZ/uDonUPJX0wt/7jHzFzPBc3SF8oyau4Hk1OI/U8hMMF5cA1YREZHSQEGSmN4/4xDsfQ6BuDkMRNc8M3u9cks5l5Xz+8rpZxi1bBb9131PqN1GWvlwXrvsNmZeej2Z5coDcOBkmtthtWAESucyrUSGhfq16YW/9pj5i6fg2Q6cSssCsvJ8X0mfKVQcA1YREZHSQEGSmN4/U5z2Ofj75jAQXfPM7vVyaHF+NMdT0+nw67c8+ssHVE9LAeDbhE5M7nI3hyrXyHP+D38lm7ru0/OTmHR986A1vSgqroLnmpXDSc+2/RMk5RWotvFFqbgFrCIiIqWBgiQB/t0/8/bS3S7LlaB47XPw581hIAIIb/Z6AYyNO8fDTz5GlY1rAdherQ7ju93Lb/UvznOeI1jdf9Lc9fccN84rCx3RnAXPNrud29/53eX3lISZQt50I/Slc6GIiIjkpSBJcozulcioqxIY8+VG5m9KJi3TmvO10r7PIRABhNm9XlXOpfLYslmEP7uACLud7AoVeaXjf5jWojfZoXn/ijpudcf2bsJDn280df361Yx1lJWOaPmD50AOwS0K3nQj9LVzoYiIiOSlIEnyCCsXwvO3XMzUm8rWp9FmAoialcOx2e18s/6AqffE016vEJuV2zb+yMO/fEjV9NPGwdtvp9xzz/FgzTjKLdnOe7/u4VSuvUqOYDU6MixPEOvOmH+yhCW1I1phMyMlOYPmTTORYDceERERKU0sdrvdi10TJU9qairR0dGkpKRQuXLlYC9HijHHTSY43/hfpUL5PPtazHxCP+nbv5i5fE+B45cc2MKERdNpkbwDgM016nNk8nN0HnxTnvNcBQjfrD/A8E/We3xN5UIsvP6fS/KssSRlG/yxVqvNTsepSzxm0JY/2rVYBYiOdbvaK5d73YDpc4vTaxQRESlqZmMDBUkiuTi7Ka8YHsrZjIJZG8etpqtP6KfMT2LGst15MknVzp7ikV8+4NY/FwKQGlaBFzv1Z1bL3thCQrnn8vic/WG5ZWbbmLViD3tPpFEvpgIX1oxiwLurTL0mi5M1loR9K64yI57ed3fXAucZtOKYZVmx8zj9Zqz0eN7Hg9sBmD63fcNqJeLPX0REJBDMxgYqtxPJJffG/4VJyXy9/iAnzmY6PdddZ7Qp85PydAsMtVm5fd18Ri2bTXTGWQDmNuvG1Cvu4FjFqjnnOb4nd6DkLNiyABXDQknLtDrNjuSXf43FqSOasxt2wK8t2UviTKFANBM5cjq9RGUSRUREgkVBkkg+oSEWUs5l8t6vezwGIM46o+Wfj3Tp338xceF0Eo8YxzbVbMi47vex9rwmTq85Y9luRl2VQFi5kALBVu7nPWtyT1Jhu7d5yjoUJivh6ob9ttZ1TbVkf/L/NmGxWKgXU4EB7esTVi7E5feUtJlCgdhLtedYGi8v2qZ9SyIiIh4oSBLJx91gWVdyf5rvmI9U48wJRv/8Hjf+9RMApyIq8fzlA5lzUQ9sIaEur2WzG9cY0L6+x2G0FqByZPk8g2jNrNEsT1mHwmQlXJXTHUpJ56VF20ytb9bKfTm/nzx/M4M7OS9XdChOGTRPvO1GaKbxyMer9vl1YLKIiEhp5fpjV5Ey6rcdx9xmMZzJ/Wn+/iMpDFr9NUtm3MuNf/2EDQtzLupBl8FvMfuSXm4DJIe9J9JMDaO1A9e0iPN6jWY4gpj874Uj6zBlfpLbry/YdMjltX0JRD2x2Y1yxSnzk/x41eBxdCOEf/dOOeTvRmjm3H5t6pKcam5gsoiISFmnIEkklynzkxhosiECGDegtXLPFvr5Zx54+FbGLnmHqMxzrK/ViOsHvsCYnsM4WSHa9HXrxVQwPYzWYrFQKzqiwM2xyzWa4C6IcRybscz54GHHsQnzkrC6iPJW7T7hdSBq1oxlu8nMtgXk2kXNsZcqLjpvgBsXHVGgNM7TufWrVzT1nMV1XpSIiEhRUrmdyD9c7f/xZHyfREIPHYSHHoJPPiEGOBFZmamd7+CzFt2xW7z7LMKCMWNp1oo9ps6Pr1bR7/OPPAUxdsBdX0xP+6ACeSPuKFcc1KlBwJ6jKHmzl8rduSt2Hjf1fMVxXpSIiEhRU5AkQsFmC2bEVQ5nwtWN6DF/FkycCGfPQkgIu28awPVxPUmJjPJpLRXCQwkNsXgcRgsQYiGnYYE/u7f5K4hxdZ1A34ibzcKVFN7spXJ1rrd7nERERMoyBUlSrBXVPBcz+39yG9HtQoZadxParwds3WocbN8e3niD/zteiRSTjQecOZthzcnADO4U7za7NbhTfE5Ht8J0b8v/PlevFO7z+nNzFQy1iY+hSmR5TploOOGLejEVTJ+bfwaVpy55JZVj35I/M44iIiKllYIkKbaKcp6L2cxDhbBQ3uxUjSumj4YvvjAOxsbCs8/CgAEsSDrMy4vWFno9jgyMo1Nb/jlJIRacdnLzpXubs/c5rnI4VSqUJyUty2XWwWLBZWDpKSsRGmLhrg7xprvYecORXTPD2QwqM13yXCnuQ1pL4rwoERGRYLDY7e52FpR8ZqfqSvHiqj2043bTX/NcHDe1n67ex9frD7o9Nyw7i49SltH6o2lw7hyEhsLQoTBhAkRHY7XZ6Th1iV8aEgztcgEdLqiec5MdqGyHu/fZ7uT3jscA91wez9v/ZLmcfd3Tn5HVZqfVUws5lebfbNK9l5sLcDztQTN7HYeSNKS1uAdzIiIigWI2NlCQJMWOp2DDkaVY/mhXn2/sMrNtjPnyT+b/eYi0LM9DWa/YuZonF79N/ZNGW+vU1u2pOGM6oRe1yDlnxc7j9Jux0qf1uBLIm2wz73N0hfJElAvN0zraX3OSwAgs7ptd+MwbuM6uOZOZbSNh7Pce93ttmXS1qWC0qIJ6ERERKRyzsUFQy+2WLl3Kc889x5o1azh06BBfffUV119/fc7X7XY748ePZ8aMGZw6dYoOHTowbdo0GjVqFLxFS8CZ6azmrnOaJ1PmJ/H2st1uu7M51DmVzLjFM+i+43cADleKYXKXu/m/Jp2pNf8o40MP5dz8BqJjm2PmUCBuss28z6fSsvhoUEtCQixOsw6F2Qfl+P7pTsq/akaFceRMpsc/ozFXJ/D3qXNeZ9fM7EEz2yXPU7t0DWkVEREpeYIaJJ09e5aLLrqIu+++mxtvvLHA15999lleffVVPvjgA+Lj4xk7diw9evQgKSmJiAi1qS2tzAYbvgQlZtt8h2dlcP/vn3P/ys8Jt2aRFRLKu5dex2uX3caZcKMpwKGUdO6bvZbp/wQwgejYFsibbG/e5xtanu/y677sg8rNVaD17ILNHsvh7unc0KfnNLsHzcx5gQ7qRUREpOgFNUi6+uqrufrqq51+zW638/LLL/PEE09w3XXXAfDhhx9Ss2ZNvv76a2677baiXKoUIbPBhrdBiak233Y73Xf8zrjFM6iTchiA1Q0uZnSXe9hRva7Tbxn+yXqe6WsjtlI4cZUjOJzqvMWyrwJ1k232/Zv03WYiw0JdZrJy75eqU7UCCTWjOHEu06uskrNAy9umFd4w2/3OzHmBDOpFREQkOIptd7vdu3eTnJxMt27dco5FR0fTtm1bVqxY4TJIysjIICMjI+dxampqwNcq/hWoeS6eSqzqnzjA+MVv02XXGgDO1azFvsef4ua/Y41Wbi5kZNsY8el6AKpUKJ+T/fH3Zr9FScl+DZI8vc8OJ89muiz5c9YdLrfC7qka3SuRUVcl+L1phTczqDwJVFAvIiIiwVNsh4EkJycDULNmzTzHa9asmfM1Z6ZMmUJ0dHTOrzp16gR0neJ/jnku8O/Gd4fCzHNxVToVmZnOQ0s/5Id3h9Bl1xoyQ8rxRrub2bhwJUkdrnIbIOWX8k+ntugK5fMcrxRe+M8jZv66hwWbDhX6Og6532d37P/8euzLP/l1+zGs/0QWjtJFd4GGY09VYdYdVi6EQZ0aMPG6Zgzq1MAvXf3CyoUwuFO823Nyz6ByxxFsuvopsWAEixrSKiIiUnIU2yDJV6NHjyYlJSXn1/79+4O9JPGBY55LXHTeT9/joiN8bmJQoHTKbufqLctZ9M79DF3xGeHWbH6Jb0mPQW8w+9p7ubRpHY6eznB+MRccWaSIciF89N+2vHTrxYzt3YQB7Z2X6nlrwryknCDFE6vNzoqdx/lm/QFW7Dzu9Psc73NMxfJOrpDXqbQsbp/5Ox2nLuHb9Qc8ly7ybzbNm3W7Yub1eGN0r0TuvTye/LF2iMW79t9mgk0NaRURESlZim25XVxcHACHDx+mVq1/b4gPHz7MxRdf7PL7wsPDCQ8PD/TypAh42znN0+yX3CVWDY/v58mFb9Fp73oA/q4cy8QrB/Njo3ZgsTC9TyLPLticMwfIG3YgOTWDP/ac4JPV+/0yN8nB7N4kb1pz92xWi3NZ/5YMepKcks7QT8ydC/7ZUxWoGUT+Kufr2awW3RJjWZh0pMDXuiXGlon234Ga5SUiIhIMxTZIio+PJy4ujsWLF+cERampqfz+++/cf//9wV2cFBmzndPM3ESHlQthSKtYKj73DIP++IbyNisZoeWZ3vYmprXrS3r5CCqGhfLCLRexbt9JU13w3Hlp0fZCfb8rnhoAuJrZ466deFxl8/tlfM3f+Nq4wJfX4w1HOV9hTJmf5DRAAliYdIQp85MK1WiiuHO2N23y/M1OG2womBIRkZIgqEHSmTNn2LFjR87j3bt3s379emJiYqhbty4PPvggTz31FI0aNcppAV67du08s5RETN1EN42DTz5h1EMPwcGDACy8oA2Tug5mX9VaWIBrWtTildsuwWqz87+P/DPgNBDcNQDwdWaP2SYOheFL44KSMIPITNfEGct2M+qqhFIZDLhqq2+zk3PcESh5E0yJiIgEU1CDpD/++IMuXbrkPB45ciQAd9xxB++//z6PPPIIZ8+e5Z577uHUqVN07NiRBQsWaEaS5DBzEz1rxnf02PgRlp9/Nr7QoAFZL73MvqpNucLJp9nv/+q+GUGwmOnq5+vMHse+mvtnr/V7Zz5fuxFCyZhB5M/BtCWNNwHiCz9uMR1MiYiIBFtQg6QrrrgCu9313YXFYmHixIlMnDixCFclJYm7m+iojLM8uHwOd6yZh8Vug8hIGDMGHnqI8hERDHJxTbODRouS2a5+hZnZ42jikL9ssTAK040QSsYMIn8Opi1pzAaI7y/fXaazbSIiUvIU2z1JImY4vTm227nxryWM/vk9apw9BcDBrj2p/e50qFcPcN/koU7VyKJavmlxJpsUFHZmj6NZxspdxxny0VpOnctyeY0K5UNpcX40q/accHmjbHbd3q7T1/MCwZ+DaUsas4Hfj5uTy2y2TURESiYFSVKiVa+Yt5Nh4uFdTFg4ndYHkgDYGXMeE668h/unDKF2PaMcy12TB4CZy/d4vY6mtaP46+BpH1+FeyO6NWJo10amMjH+GMQbGmKhwwXVeaZvc+6fbezNcnattCwrK3efwAK0axDDhTWjqFO1Agk1ozhxLtNjN0IzAjVY2J/8OZi2pDEf+Jn7GSiN2TYRESmZVNcgRSoz28bMZbsY980mZi7bRWa2zedrLdh0iFFzNwBQOf0MExZOY94HD9L6QBJny0fwTOc7ufru19l+SYecm2hHk4f85WTJKencN3st981eS3Kq+dItyz8zdZ7o3dTn1+H2+sAnq83P+vLnIF5Xs6ryswMrd50gsnwIgy9vQKfGNbju4vNo37BaoZspBGqwsD/5czBtSTOgff0Cc6byC7FA98Sa7k/6R2nMtomISMlksbvbFFQKpKamEh0dTUpKCpUrV/bLNT3N4xHnnHW2CrHgU2crR7CD3cbNGxfx6C/vU+1cKgDzEjoxucsgDleuDpDTItpqs9Nx6hK/7beJKB/CurFXERkWmnPtQHWH+3hwO68aE/hzrpDVZue37ccY+N4qt68txAJbJl0dkGAgUHOS/MmfP98liavudg73Xh7PqKsSSBj7vcdsW6B+fkRERBzMxgYqt/NSSbhZK468aRPsiaOjXbND25m4cDqXHNoKwLZqdRnf/V5W1LsIgLjK4Tx5bdOcPxdPndK8dU3zWjnBsa/d4ZrVrsymg6kez/O2MYG3g3jdCQ2xsO3IaY+vKZB7Srx5PcH6EMNfg2lLGsffW08B4uBO8W6DqdKabRMRkZJJmSQvuJrH47j9KuxQy9IqM9vm10+RV/+xne3/fYDbNvxACHZOh0Xycof/8EGrPmSH/hv3P3hlIx7sfmHO42/WH2D4J+sL81Kcrjv3jaCzINqVFudXZv+Jc5xMc90cweGjQW3p0Kh6odfrq7Ffb2LWyr0ezxvYvh4Tr2tWBCtyzpcPMc5lWnl6fhJ7jqdRv1oFxvRKJDIstMB5hQ2+SvsQVTOvr6xm20REpPhQJsnPSsJQy+LKb3NkrFaYMYOLHhtN65RTAHzZtAtTrriLo5UKbtx/e9kuhl35b8ODQHRAy58J69msFjabnSe+2cSJs/8GP9ERIdSOrkC5ciFcdH4V2tSryvDPNpgvzQvij9SCTYf4ev3fps71ZU+Js5trwOuAwtRQ4XyB0uAPV7Mw6UjO42XbYdbKfXRPjGXGwNZ5rl2YDHJZGKIaVi7EYxaxrGbbRESk5FGQZFJJGGpZXJntWPXlur9d32StXAlDh8KaNYQBm2vUZ1z3+1hdx3XWIi3Tysqdx3MyMCfPZni7dNPeWrqb/13RiBW7jjFkzroCN+qp6TZS088wrX9LuifG0XHqEq/2Lh07E7i1O+MIXJZtP8rP246Z+h5fOrg5Cx4mfbe5QMmip4DC04cYAGO++pNzWTbiKhtZoPtm/5EnQMptYdIRBn+4mhkDW/sUfOV/jRqi+i8zwZSIiEiwKUgyqSQMtSyuzGYX/jp4minzk/LeMB45Ao89Bu+9ZzyOjsY2cSK3HmlIarbna67YdYwOjapjtdmZ9N1mU+vwZk9RbhdN/JGwciEes41REeW93ht17HQGVpu9SLKUzgIXM7zdU+Juw3/+p3YEFAdOpdM9sWaBcjcz+81OnM1ixKfrAYitVJ4jZ9yXOS5MOsKZ9OxCZZAzs20aoioiIlIC6V9lk/LP4ynseaWV1WZnxc7jfLP+ACt2Hsdqs5tqE+wwY9luoy14dja8/jo0bvxvgHTnnbB1KyEPPMBljeNMXc9xc2u2acMDXS8o0PK6gpP9Ka64a2nuyDb+usNcZia3Sd9tpuPUJSzYdMjpc/qrrbojcPEmQAr5pw26N9kQM8GDM99uPMTwT9bTb8bKPO+Htx9OeAqQHEZ8us50BtkZb0pNRUREpPhQJskssx/gl+HtSO72bXjqbOVgs8MHz87iv5++iGXjRuNgy5ZGwNS+fc55repVZcFfyR6vVyUyDDB/E/3qkh0M7hRP14SaORv0Nx1IYfJ8c1koMz708YbYWXmXP/e6+Bq4PNe3BX0vrePV95gJHjzJ/X4EYr8ZwL6T50yd5+rna9exM6a+3+x5IiIiUjSUSTLJ7J6Qot47Uly4GtJ66J8hrZfUrUpiLffdBWucOcGL377A4MfvxLJxI5mVq8C0abBqVZ4ACaB6pTBT63Kc581N9Ixlu/l562Guu/g8WtWrir8bQJ7JsPr0fY5VTJiXhNVmd5n1cZSmTZmf5NX1fQ1calf1vlmD2X1q7uR+P1rVq0qt6Ai/f0ZRt2qkqfNc/XwdSTX3/wOz54mIiEjRUJBkktmb7EB9ol2cuds07zDqsw3ccEltp18rZ81m0OqvWTLjXm786ydsWPj4oh60u+MNFnS8DkILlrvFRZu7eXWc1yY+hqgI84nTGct2M+nbv0gY+z1Pf7/F9PcFmqO867cdx0ztdfGm9M7bwMWCkSlsE1+ws6AnvnTBc8bxfqzZe5LxfRJz1uUvL916idvgy9N7EFvZ5P83TJ4nIiIiRUNBkklt4mMKdbNUmpnZ73M208rafScLHG+3byPfvf8AY5e8Q1TmOdbXupDrB77A6J7DOFkhOidrkltmto1NB1KoUN79XqHcfx6hIRZa1q1i+jXZ7DBzeeFLwrxV0eT+py/X/u3TXhd3+5e8CVwcfw/G90n0qZmEN/vUzDhyOp2ezWoxrX/LAnvKfNU9MZZKEeVcBl9m3oMG1Suaei6z54mIiEjR0J4kk0JDLIzvk8j9s9cW6H5W2BvGks7sfp8Fmw7n/D4u9Rhjfn6XazcvBeBEZGWmdr6Dz1p0x24xYndnbdXNdl6zUPDP4/JGNfjFZDvrYLmzQ33e+Gmnx/POZpor2cudHfK0f2lA+/pMnr/ZVGAYZ2JGkLvhomHlQkzvUzPDkcHt2awW3RPjWLX7BMkp55j03WZOns10muW0AOXLhTjNtuWek+QIvvLvt4uKKMcldaty4OQ5MrNtTrvTmXlPfWmdLiIiIoGlIMkLrm6WzNwwlmZmSwztQHlrFnf/8Q0P/PoJFbPSsVpC+Ojiq3mhU39SIqOcfp8jCHPXMjq3CmGhvHjLRQX+PLwJAoLlsobV+XLtAZJT0l3e2MdFR9C6flV+TDrs5Iy8HNkhs7N6PAUuXRNqMLhTwzztt50x01DC8V9nQa/ZNuyO9yN3Bjc0xJITVEeGhbr9YOPV2y6m84WxPD0/iT3H06hfrQJjeiUSmS+jlzv4emfZTpZsOUpqeja/bDvKL9uOumyWYSYY9LZ1uoiIiASegiQv5b5ZcnQ/83TDWNq1iY+hQvlQ0rLcZzc67l7HhEVv0fDE3wD8cV4Txne/j79qNnT7fdUrhXvVeS0t08q6fScLBElh5UK4skmsywGiwVatYhit68e4zFg6jO+TSNeEmkz5foupDIU3s3pcBS4hFkx3zPNmeOroXomMuiqhQMYJyDmWlpHN52sP+JTBNfvBxqTrm3t8XaEhFn7eepjFW46aem0O/nhPRUREpGhZ7P5u3VXMpKamEh0dTUpKCpUru++uJq65K50CGPnpOr5cd9Dp99ZOPcLYxe9w9bbfADhasQpTrriLr5p2ySmtc+ejQW3ZkpxqehgsGDegWyZdnWeNjg58wf6Bd5cliYooR8u6ValRKYzvNyUXKKuzAPf8M5PIU2bNMbto5rJdpt67sb2bMKhTA8Dzn7czVpud33YcY+C7q9y+x87+bDxx117eTAbXarMX+oONzGwbCWO/9xiYunpt5zKtHjNWIiIiElhmYwNlksQjM6VTjeMqA3mDpPDsTP676iuGrviMyOwMsi0h/NbzVoY2vp7T4RVNByvHzmZ43XnN0bTAcdNvpgNfUWhxfmWOns502eji9D8lXK7YKZix8JShMPve5T4vrFxIzntnhrMgxpX8fzZmFDaDm7sEz1feDIbN/9ryvz/LtsOizUfKdJmuiIhIcaZCeAFcdz0zO4vnwKm8Qzev2LmaH2YO4eFls4jMzuD3Os3ofderLLp3DM8O6uRVB7Iq4eX560CK168p903/yp3HTd3AB9qmA6ksHNGZmIrlC3Wd3O298+eC8z8227XO17bcrmZkuePLnCRHoHPdxefRvmG1Ii9x9SXYBNfvj2MY7oJNh/y2RhEREfEPZZLEZaborg71eO/XvW6/17GXxXGDXedUMuMWv033HasAOFwphsldBvF/TS4Hi4VbYirkZAVW7jrOkI/WcupcltNrO7qP3fHBap9eV51/hpwu2HSIx77406dr+JvNDlMXbObEWeev2Zvr3DFzFSt2Hy/wtfzZpkB2WPM1Q+evOUlFyZdg0937Y8f4GZ8wL4nuiXFlel+jiIhIcaNMUhnnLlM0c/le0+VFAy6uyYjlH7HonfvpvmMVWSGhvNXmRrr+dzr/l9gZLJY8N+KhIRY6XFCdZ/o2x4LzGTR28GoYan7vLNvJlPlJ3D/bdSAWDHuOe59FcWalkwApN0e2ydFhzR1fO6yZmZGVX0lteW1mtlP+1+bp/cnd5l5ERESKDwVJZZg3HeNcstuJ/P5bwlo0Y/ivHxNuzWJ5vYvoedfrTOlyN2fD//1U3dmNuKsBoDUqFa4cDeDw6UzeWro76PuQ8rP4aUWerpJ7mOzoXonce3l8gZv8EMu/DR58YXZGVm5XNoktkS2vfQk2zb4/vryPIiIiEjgqtyvDzGxEd6f+iQOMX/w2XXatMQ6cfz5f9R/JKHsjbLlyQ55aHTvblP/dxoPM/n2f74srpizAnwdSi+z5cu+PcdVuuzABi9kZWbltOpCK1WYvkeVl3rbzNvv++PI+ioiISOAoSCrDfNk8DxCZmc6QlZ8xeNWXhFuzsZcvj+Whh+Dxx7mhYkV6+9A+On/3sTd/3uHT2gKhZlQYR85kFmiI4ItrWsQxb2Nyoa/TrkEMK3d5LtHKv4/G2651nrSJj6FWdITL4bfOOMrLCtttLli8CTY9vT/OhuGKiIhI8ClIKsO83jxvt3P11l95YslMzjtttKne2aojDefMhAsvzDnNHzfi9atVYNn2Ql2i0K5KjOWuDg1oEx/Dsws2u51JZEb7BlU5dS67UNdwZCxGXZXgcWaPBbi1dd1CPZ8noSEWj8NvnSnp5WVmf8bdvT9mhuGKiIhIcJS8jQHiN2Y3og/qGE+j4/uZ9elYpn3zDOedPsr+6Jp8/sRrNFy9NE+A5C9jfNwj408X1ozKaTXtak+POzdcXJuB7evRPj6GEAus2HWSZduPmf5+C8Z7P7Z3Ewa2r8fY3k3YMulqRvdKNLU/xg60mPBDTpv2QHG1r8ydslRe5ur9iYuOYFr/lpqTJCIiUgwpk1SGOW603WVIhrSKZdTS97G//xKW7Gyyyofx5+330uzVKdwUVbEIV1s43mQ5HNo3qJ7n8eheiTzYrTEPfLyWhZuPePz+W1rX5eeth1nhQ+eyvi3PY8qNLdyWKf5/e/ceFXWZ/wH8PQPOgFwFkosiYHmBFVBRkHWpTcXrIc3dzbU0JY6Vi/1CllNaEiqt2MWOpm75s5XUTtlauuVmFpqSlopKZAjeWFz8JUheuCYgM9/fH99mmoG5fGdgmAHer3M4p+/3+8zMgzzL4b3P83weY/tjdGnOs9Jtbwvasu5lN5H6vumy7r1xeVlHD8MlIiKirsWZpF7OaNUzCNigLsFfn50FvPYaZK2tQFIS+pSWYHTum1DYOCCt6cTZj6fuD7NolgMAvPv2wbg2e2YOFFdiwrojZgOSDECglwtGBntbVT3wqfvDsO6RkZIKKiyfHoGzWVPMttM9fNZWnOQyjB9iuqw70HuXl9n7MFwiIiKSjjNJ1G4j+siaq5i17RXIv84XG9x7L7BhAzBjRpf1qTPOEnJTOGHdI9GYOiIQz00N1/t/8W/WN2HJriKjr107O1Lvj9gDxZVY/F6h2dko3SDw/knz50zp8nHrg5dnjsD0qCDpLwLw4SnzVQA15cCt3SumUguSZ0E0y8tW7SvROyMowMsFWUkRXF5GREREDo8hiQD8shE9yhdYuRHYuBFQqQBXV+CFF4CMDMCla/eQSC3cEBHogfNV9XphRAZgRlQgNvx5lPYP+bbV8wDA2VmOlZ+eQ1Vds/ZegKcSKx/6jd4f8iq1gFX7SiQt19MNAgu2FUh4xa99/ub5iXBVOEl+jYbUKoXWVjM8UFzZLvAEmgk8tlpeZklYIyIiIrIWQxIBggDs3Ak89xxw/bp4b/ZstLz6OnZeE/DfLy53ypk6lnhhegR2njA/Q/Lx4vFwksusOvtH6h/yBeW39AKCMZkzwrFwfBic5DKo1AIKK26bfY2GAOD9k/+1aqYnuJ+rpHYWVzOE8Rm0qtomLH6v0GThAUPBtCOsCWtERERE1mBI6u2KioAlS4BvvhGvhw4FNm5ETutAbH2nRG+G5m/7S00eCtsRd1pUWLO/BFdu/oxQ3754YXoEEiP6I6/E+P6fxIj+2pkXa5eRSflDXmq5aj8PpTZgFZTfQn2TZeW+zc30GJpFySupwj+OXTH73jKI1QwtYWoGTfjlPVftK0FiRIDNZ3M6EtaIiIiILMWQ1Fvdvg1kZgJvvQWo1YCbm3i9dClyDl42WPHOVpXSFu04pReGjl4Cdp6oQGJEf6NBKTGiP7Y+PrbT+mCK1HLVuu2sOQfI1EyPoVkU7759UPOz4SpybfVVOlkcZMzNoAnomoNhHSmsERERUe/A6nYORqUWcLzsJj4p+hHHy25CZcnOfynUauAfvxz+unmzeD1nDnD+PPD882iRO5utyNaZldLaBiRdmvulq6di/rhBSBjih/njBqF09dQuC0gAEBvmg0Avl3bV2jQ01ex0y1pbeg6QDMCjcSEGn2lmUdoGFqkBCQAam1UosLAUudSgZ+uDYS0Ja0RERESdgTNJDsTmey5OnwZSU4GCXwoKRESIRRomTNA22Xn8itmKbB2tlKZxp0Vlcjkd8GtQyp4V2aHPAqzf9O8klyErKQKL3ytsd96SsbLWmmBVVdskqeCDAGDCuiPtftaWFI0wx9IwY80Mmi04SlgjIiKi3oMzSQ7C2GyBZs/FgeJK69/85k3gqaeA2FgxIHl4AOvWifuRdAISYPtKabqknoXUGWcmHSiuxO9e+Qpzt57As7uKMHfrCfzula8k/7tqylq3PW8pwMvF4H4YTbAC2p8XZIyhn7XUohFSWBpmrJlBswVHCWtERETUe3AmyQHYbM+FSgVs3Qq8+CJw65elSPPmAa++CgQanpmSWgHNmkppbUk9C6mjZyZ11qZ/qdXwWlrV2mp7fxg9AMcu30RV3a9Bp+1slIahn3VnzI7IIIY5S8OMNTNotmBuVs7a74+IiIjIGIYkB2CTDfInTohL6woLxeuoKGDTJiAhweTL5seH4m/7S00uuZPLpFdK0w0MbctzD/SWVrpaajtDOjuAmquGl7O/BFuPlrc7tykpKgCTIgJwo74Z2Z+VGn1925+1n5vSbJ+ksDbMOMLBsI4S1oiIiKj3YEhyAJ2656K6Gli2DMjNFa+9vIDsbGDxYsDZ/I9b4SzHxHDTpbcnhveXdA6RocCgW0ZckLjTRmo7Q6QG0HVfnoevmxI+7koEeFp3SGnO/hKDVQEFAPvOViHI2xURQV6S3kv7s+7g3/2dsafNVgfDWtoHe4c1IiIi6j0YkhxAp+y5aG0Vy3lnZgK1teK95GQgJwfw95fcF5VaQPGPdSbbFP9YB5VaMPlHsrHAoFtG/McaaeFQajtDpAbQvx/5j961oXBhqvBDS6taUlXA7cmxkvqj+VnfaGiW1F6Xr5sCM0cGITEioNPCTGcfDGsNRwhrRERE1DswJDmADu+5OHpUPBD27FnxevRocWldfLzFfZFSKMDc0j+pgeHPY4Ml9SnU1/r9T9Zu5q9ss1/JXOVBqVUBz1fVWfSzltr/zBnh8PNQ9vjg4AhhjYiIiHo+VrdzALqV0IwxuOeishKYPx+4/34xIPXrJ84mFRRYFZCAzln6JzUwDOonLfy80IGDa81VaDNn1b4S7D97zWzlQanV/q7evmO06p2h/TVSK8wtHB+GmSMHIP5e3x4bkIiIiIi6CkOSg5g6IhCRAz0NPosc6Km/5+LuXeCNN4Bhw4D33gNkMuDJJ4GLF4GnnwacnKzuR2cs/ZMaGK7VNSExor/JNokR/eGqsP77saYUt4Zmv9KKT4qNFn4AxCAV3E9acYkQn74WlRM31X8WLSAiIiKyDYYkB7Foxymc/T/De4HO/l8dFu04JV4cPgyMGgX89a9Afb149tHJk8CWLYCfX4f70Rln41hSRnzr42ONBqXEiP7Y+vhYSe9lirFQItWtxrtGn2mC1PAAT5jLKbpVAaeOCMSx5yfgg0XjsOHPI/HBonE49vwEgwUILD2jiYiIiIg6RiYIgvWlw7qBuro6eHl5oba2Fp6ehmdq7O1OiwrhLx0w2Sag7gaO3dgP593/FG/4+QFr14rFGeSdm3U15woBhsstm/vDvKVVjeGZn5stI34+e5q2St6dFhXW7C/BlZs/I9S3L16YHmHxDJKpcuPAr4UXPjxVgX8VXbPovc3Z8OeRKLlWa7BYhcZT94tV/axlqnAEEREREZknNRuwcIMDWLO/xOizPqq7SDn1CZ75dhec7zaJgWjxYmD1asDHNodndrTcssJZjkUJYSYDw6KEML0A46pwQvasSKv7bK7cOPDrpv/KmjuSQ5KvmwI3G1vMtuvv4YKZ0wcAQLt+yGXQ64e1WLSAiIiIqGswJDmAKzcN7+FJKC/EyoNbcO+tHwEAF++NxNDd28XldjbW0XLLmkBgq8CgS0q5cc3nHSiuxMsmQmlb2TNHIPuzEsnV6JZPj8BfJw83OaNlDGeKiIiIiBwDQ5IDCPXti6OXfr0eUFuNFV+9g2kXvwUA/OTmjZzfJ8MtZSGyR0V1Wb86OnPRkcAgldRy43+dPBxfnb+Oxe8VSjqaVi4DNs0djelRgeLk3XuFkMHw8sO2hRMUznKkJAy26PswV2KciIiIiLoO9yQ5AM2eJGVrCxYV7EHq8d1wbW1Gq0yO7TFJWP+7R1GvdEPp6qkdqvTWE/3j6H+Q/Vmp2XbPTx2Kvx8pQ32TStL7/v3RUZgeFaS9tmWI0ewBa/s/RKl7wIiIiIhIGu5J6kZcFU5YevcSZm57FaE1lQCAk8Ej8FLi07hwTyiAjpfC7qmklht/5cBFSe183RT428Mj2oWSji4/NEalFrBqX4nREuMyiCXGEyMCuPSOiIiIqIswJNnbf/4DpKXh2X37AABV7j5Y82AKPg2/Xzz/CJ1XCrsnklpuXKoVM8KNztrYonBCQfmtdofU6tKUGC8ov8WiDURERERdhCHJXu7cEUt4v/IK0NwMODsDS5fC6/kX4HX0KhI6UAq7N5kfH4q/7S81WW7cEgFe0g6F7SzV9cYDkjXtiIiIiKjjGJK6miAAn34KpKUBV66I9yZOBDZuBMLD4Qoge5a3/frXzUgpNy5F2yp1XaW/h7QDbqW2IyIiIqKO69xTSMm0S5eA6dOBWbPEgBQcDOzeDeTlAeHh9u5dt7V8egSeuj8MHd2y07ZKXVeIDfNBoJcLjH2qDGKBiK4Ob0RERES9GavbdYXGRmDNGuD114GWFqBPHyAjA3jxRcDNzT596oFaWtV65cZVagFrPj9v9nUeLk547Y/Rdqsgp6luBxguMc7qdkRERESdQ2o2YEiyJUEAPv4YSE8Hrl4V702dCmzYAAwd2rV96YVaWtUYnvm5yf1KchlwbpX9S6vznCQiIiIi22MJcHsrLQX+53+AgwfF69BQYP164KGHtFXryLak7FdalBBm94AE2K7EOBERERFZjiGps9XXA6tXi4GotRVQKoFly4Dnnwdcu7ZyGon7lQBg69FyvRkluUwMSJrnjsAWJcaJiIiIyHJcbtdZBAHYtUvca3TtmngvKUkMS4MH2+5zHYxKLTjkbEjb/Urz40OhcGbdEiIiIqLehMvtulJxMbBkCZCfL17fe6+472jGDPv2qw1bBwVH3lejcJYjJaH3hFUiIiIish5nkjqithbIygI2bQJUKnE53QsviLNJLo51rk3O/hKbLjnTVGhrO5hYoY2IiIiIHIXUbMD1RtZQq4EdO4Bhw8QZI5UKmD1bLNawYoVDBqQtX5e3q/KmFoAtX5cjZ39Jh95fpRawal9Ju4AE/FrSetW+EqhMlZkjIiIiInIQDEmWKioCEhKABQuA69fFoPTFF2Kp75AQe/eunZZWNbYeNV7dDRCLGrS0qq3+jILyW3pL7NoSAFTWNqGg/JbVn0FERERE1FUYkqS6fVvcdxQTA3z7rXgI7Nq1wNmzwOTJ9u6dUTuPXzF5ThAgzijtPH7F6s+orjcekKxpR0RERERkTyzcYI5aDeTmimW8b9wQ782ZA7z+OjBwoH37JsF/b/3cqe0M6e8hbXmh1HZERERERPbEkGTK6dNAaipQUCBeR0SIRRoefNC+/bJAiE/fTm1nSGyYDwK9XFBV22RwX5IMQICXWA6ciIiIiMjRcbmdITduAE89BcTGigHJwwN44w1xP1I3CkgAMD8+FOaOKZLLxHbWcpLLkJUkVshr+1Ga66ykCIc4L4mIiIiIyByGJF0qFfD222Ixhv/9X/GA2HnzgAsXgKVLgT597N1Diymc5ViUEGayzaKEsA6flzR1RCDemjcaAV76S+oCvFxY/puIiIiIuhUut9M4flwszFBYKF5HRYlL6xIS7NuvTqA5B8mW5yQBYlBKjAhAQfktVNc3ob+HuMSOM0hERERE1J3wMNnqarEoQ26ueO3lBWRnA4sXA849K0O2tKqx8/gV/PfWzwjx6Yv58aEdnkEiIiIiIuoupB4m27NSgCVaW4G33gIyM4HaWvFecrJY1rt/f/v2zUYUznKkJAy2dzeIiIiIiBxa7wxJR4+KS+vOnhWvR48GNm8Gxo2zb7+IiIiIiMjuusVaq82bNyM0NBQuLi6Ii4tDgaYkt6UqK4H584H77xcDUr9+4mxSQQEDEhERERERAegGIenDDz9Eeno6srKyUFhYiOjoaEyZMgXV1dWWvdGmTWLVuvfeA2Qy4MkngYsXgaefBpycbNN5IiIiIiLqdhy+cENcXBzGjh2LTZs2AQDUajWCg4PxzDPPYNmyZWZfr92cBcATEM8+2rwZGDPGpv0mIiIiIiLH0iMKN7S0tODMmTNYvny59p5cLsekSZNw/Phxg69pbm5Gc3Oz9rr2l6IMdf36AatXi+ceyeVAXZ1tO09ERERERA6l7pcMYG6eyKFD0o0bN6BSqeDv769339/fH+fPnzf4mpycHKxatard/eDbt4FnnhG/iIiIiIio16qvr4eXl5fR5w4dkqyxfPlypKena69ramoQEhKCiooKk/8QRBp1dXUIDg7G1atXTU7DEmlwzJClOGbIUhwzZAmOF+MEQUB9fT2CgoJMtnPokOTn5wcnJydcv35d7/7169cREBBg8DVKpRJKpbLdfS8vLw4SsoinpyfHDFmEY4YsxTFDluKYIUtwvBgmZeLEoavbKRQKxMTE4NChQ9p7arUahw4dQnx8vB17RkREREREPZVDzyQBQHp6OhYsWIAxY8YgNjYW69evR2NjI5KTk+3dNSIiIiIi6oEcPiTNmTMHP/30E1566SVUVVVh5MiROHDgQLtiDsYolUpkZWUZXIJHZAjHDFmKY4YsxTFDluKYIUtwvHScw5+TRERERERE1JUcek8SERERERFRV2NIIiIiIiIi0sGQREREREREpIMhiYiIiIiISEePDkmbN29GaGgoXFxcEBcXh4KCAnt3iRzI119/jaSkJAQFBUEmk+Ff//qX3nNBEPDSSy8hMDAQrq6umDRpEi5dumSfzpLd5eTkYOzYsfDw8ED//v0xa9YsXLhwQa9NU1MTUlNT4evrC3d3d/zhD39odxg29R5vvfUWoqKitIc5xsfH4/PPP9c+53ghU9auXQuZTIa0tDTtPY4ZamvlypWQyWR6X8OHD9c+55ixXo8NSR9++CHS09ORlZWFwsJCREdHY8qUKaiurrZ318hBNDY2Ijo6Gps3bzb4/NVXX8Wbb76Jt99+GydPnoSbmxumTJmCpqamLu4pOYL8/HykpqbixIkTyMvLw927dzF58mQ0NjZq2yxduhT79u3D7t27kZ+fj2vXrmH27Nl27DXZ08CBA7F27VqcOXMGp0+fxoQJEzBz5kycO3cOAMcLGXfq1Cls2bIFUVFRevc5ZsiQ3/zmN6isrNR+HTt2TPuMY6YDhB4qNjZWSE1N1V6rVCohKChIyMnJsWOvyFEBEPbu3au9VqvVQkBAgPDaa69p79XU1AhKpVL44IMP7NBDcjTV1dUCACE/P18QBHF89OnTR9i9e7e2TWlpqQBAOH78uL26SQ6mX79+wjvvvMPxQkbV19cLQ4YMEfLy8oQHHnhAePbZZwVB4O8YMiwrK0uIjo42+IxjpmN65ExSS0sLzpw5g0mTJmnvyeVyTJo0CcePH7djz6i7KC8vR1VVld4Y8vLyQlxcHMcQAQBqa2sBAD4+PgCAM2fO4O7du3pjZvjw4Rg0aBDHDEGlUmHXrl1obGxEfHw8xwsZlZqaihkzZuiNDYC/Y8i4S5cuISgoCIMHD8Zjjz2GiooKABwzHeVs7w7Ywo0bN6BSqeDv769339/fH+fPn7dTr6g7qaqqAgCDY0jzjHovtVqNtLQ0jB8/HiNGjAAgjhmFQgFvb2+9thwzvdsPP/yA+Ph4NDU1wd3dHXv37kVERASKioo4XqidXbt2obCwEKdOnWr3jL9jyJC4uDi8++67GDZsGCorK7Fq1SokJCSguLiYY6aDemRIIiKypdTUVBQXF+ut+yYyZNiwYSgqKkJtbS0++ugjLFiwAPn5+fbuFjmgq1ev4tlnn0VeXh5cXFzs3R3qJqZNm6b976ioKMTFxSEkJAT//Oc/4erqaseedX89crmdn58fnJyc2lXvuH79OgICAuzUK+pONOOEY4jaWrJkCf7973/j8OHDGDhwoPZ+QEAAWlpaUFNTo9eeY6Z3UygUuO+++xATE4OcnBxER0djw4YNHC/UzpkzZ1BdXY3Ro0fD2dkZzs7OyM/Px5tvvglnZ2f4+/tzzJBZ3t7eGDp0KC5fvszfMx3UI0OSQqFATEwMDh06pL2nVqtx6NAhxMfH27Fn1F2EhYUhICBAbwzV1dXh5MmTHEO9lCAIWLJkCfbu3YuvvvoKYWFhes9jYmLQp08fvTFz4cIFVFRUcMyQllqtRnNzM8cLtTNx4kT88MMPKCoq0n6NGTMGjz32mPa/OWbInIaGBpSVlSEwMJC/Zzqoxy63S09Px4IFCzBmzBjExsZi/fr1aGxsRHJysr27Rg6ioaEBly9f1l6Xl5ejqKgIPj4+GDRoENLS0vDyyy9jyJAhCAsLQ2ZmJoKCgjBr1iz7dZrsJjU1Fe+//z4++eQTeHh4aNdze3l5wdXVFV5eXkhJSUF6ejp8fHzg6emJZ555BvHx8Rg3bpyde0/2sHz5ckybNg2DBg1CfX093n//fRw5cgRffPEFxwu14+Hhod3jqOHm5gZfX1/tfY4ZaisjIwNJSUkICQnBtWvXkJWVBScnJ8ydO5e/ZzrK3uX1bGnjxo3CoEGDBIVCIcTGxgonTpywd5fIgRw+fFgA0O5rwYIFgiCIZcAzMzMFf39/QalUChMnThQuXLhg306T3RgaKwCE3NxcbZs7d+4If/nLX4R+/foJffv2FR5++GGhsrLSfp0mu3riiSeEkJAQQaFQCPfcc48wceJE4csvv9Q+53ghc3RLgAsCxwy1N2fOHCEwMFBQKBTCgAEDhDlz5giXL1/WPueYsZ5MEATBTvmMiIiIiIjI4fTIPUlERERERETWYkgiIiIiIiLSwZBERERERESkgyGJiIiIiIhIB0MSERERERGRDoYkIiIiIiIiHQxJREREREREOhiSiIiIiIiIdDAkERERGREaGor169fbuxtERNTFGJKIiKjTyGQyk18rV67skn5ERkbi6aefNvhs586dUCqVuHHjRpf0hYiIuh+GJCIi6jSVlZXar/Xr18PT01PvXkZGhratIAhobW21ST9SUlKwa9cu3Llzp92z3NxcPPTQQ/Dz87PJZxMRUffHkERERJ0mICBA++Xl5QWZTKa9Pn/+PDw8PPD5558jJiYGSqUSx44dw8KFCzFr1iy990lLS8Pvf/977bVarUZOTg7CwsLg6uqK6OhofPTRR0b7MW/ePNy5cwcff/yx3v3y8nIcOXIEKSkpKCsrw8yZM+Hv7w93d3eMHTsWBw8eNPqeV65cgUwmQ1FRkfZeTU0NZDIZjhw5or1XXFyMadOmwd3dHf7+/pg/f77erNVHH32EyMhIuLq6wtfXF5MmTUJjY6Ppf1giIupSDElERNSlli1bhrVr16K0tBRRUVGSXpOTk4MdO3bg7bffxrlz57B06VLMmzcP+fn5Btv7+flh5syZ2LZtm979d999FwMHDsTkyZPR0NCA6dOn49ChQ/juu+8wdepUJCUloaKiwurvraamBhMmTMCoUaNw+vRpHDhwANevX8cjjzwCQJxpmzt3Lp544gmUlpbiyJEjmD17NgRBsPoziYio8znbuwNERNS7rF69GomJiZLbNzc3Y82aNTh48CDi4+MBAIMHD8axY8ewZcsWPPDAAwZfl5KSgmnTpqG8vBxhYWEQBAHbt2/HggULIJfLER0djejoaG377Oxs7N27F59++imWLFli1fe2adMmjBo1CmvWrNHe27ZtG4KDg3Hx4kU0NDSgtbUVs2fPRkhICABx/xQRETkWziQREVGXGjNmjEXtL1++jJ9//hmJiYlwd3fXfu3YsQNlZWVGX5eYmIiBAwciNzcXAHDo0CFUVFQgOTkZANDQ0ICMjAyEh4fD29sb7u7uKC0t7dBM0vfff4/Dhw/r9XP48OEAgLKyMkRHR2PixImIjIzEn/70J2zduhW3b9+2+vOIiMg2OJNERERdys3NTe9aLpe3W2529+5d7X83NDQAAD777DMMGDBAr51SqTT6OXK5HAsXLsT27duxcuVK5Obm4sEHH8TgwYMBABkZGcjLy8Prr7+O++67D66urvjjH/+IlpYWo+8HQK+vuv3U9DUpKQmvvPJKu9cHBgbCyckJeXl5+Pbbb/Hll19i48aNePHFF3Hy5EmEhYUZ/V6IiKhrcSaJiIjs6p577kFlZaXePd3iCBEREVAqlaioqMB9992n9xUcHGzyvZOTk3H16lXs2bMHe/fuRUpKivbZN998g4ULF+Lhhx9GZGQkAgICcOXKFZP9BKDXV91+AsDo0aNx7tw5hIaGtuurJhzKZDKMHz8eq1atwnfffQeFQoG9e/ea/D6IiKhrMSQREZFdTZgwAadPn8aOHTtw6dIlZGVlobi4WPvcw8MDGRkZWLp0KbZv346ysjIUFhZi48aN2L59u8n3DgsLw4QJE/Dkk09CqVRi9uzZ2mdDhgzBnj17UFRUhO+//x6PPvoo1Gq10fdydXXFuHHjtEUn8vPzsWLFCr02qampuHXrFubOnYtTp06hrKwMX3zxBZKTk6FSqXDy5EmsWbMGp0+fRkVFBfbs2YOffvoJ4eHhVv7rERGRLTAkERGRXU2ZMgWZmZl47rnnMHbsWNTX1+Pxxx/Xa5OdnY3MzEzk5OQgPDwcU6dOxWeffSZpiVpKSgpu376NRx99FC4uLtr7b7zxBvr164ff/va3SEpKwpQpUzB69GiT77Vt2za0trYiJiYGaWlpePnll/WeBwUF4ZtvvoFKpcLkyZMRGRmJtLQ0eHt7Qy6Xw9PTE19//TWmT5+OoUOHYsWKFVi3bh2mTZtmwb8YERHZmkxg3VEiIiIiIiItziQRERERERHpYEgiIiIiIiLSwZBERERERESkgyGJiIiIiIhIB0MSERERERGRDoYkIiIiIiIiHQxJREREREREOhiSiIiIiIiIdDAkERERERER6WBIIiIiIiIi0sGQREREREREpOP/ATTN+W7vSfrTAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# visualize the prediction uisng diagonal line\n",
        "y = test_predictions #y-axis\n",
        "x = y_test #x-axis\n",
        "fig, ax = plt.subplots(figsize=(10,6)) # create figure\n",
        "ax.scatter(x,y) #scatter plots for x,y\n",
        "ax.set(xlim=(0,55), ylim=(0, 55)) #set limit\n",
        "ax.plot(ax.get_xlim(), ax.get_ylim(), color ='red') # draw 45 degree diagonal in figure\n",
        "plt.xlabel('True Values')\n",
        "plt.ylabel('Predicted values')\n",
        "plt.title('Evaluation Result')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H5wOpuX0I4P4"
      },
      "source": [
        "Show the accuracy of Linear Regression on the dataset. The linear regression graph is created by train data and the model line is shown by the blue line which is created using test data and predicted data as we can see most of the red dots are on the line, thus we can say that model has produced the best-fit line."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "Wj4mwhHlBRL1",
        "outputId": "c82c3ff0-4bc6-4a82-c869-5e2f3aaaf78a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='sales'>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Accuracy of linear regression on the dataset\n",
        "\n",
        "\n",
        "plt.figure(figsize=(10,5))\n",
        "sns.regplot(x=y_test,y=test_predictions,scatter_kws={'color':'red'})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3aehFxs9fuXe"
      },
      "source": [
        "### Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s5UPnfGmdIK_"
      },
      "source": [
        "Step 6 - Predict on the Test Data and Compute Evaluation Metrics\n",
        "The first line of code predicts on the train data, while the second line prints the RMSE value on the train data. The same is repeated in the third and fourth lines of code which predicts and prints the RMSE value on test data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P7m2LWkrcE20",
        "outputId": "ba3a55c6-3d5d-4120-ac7d-cb62554ab790"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "23/23 [==============================] - 0s 2ms/step\n",
            "5.427096996448226\n",
            "15/15 [==============================] - 0s 9ms/step\n",
            "5.575046908767078\n"
          ]
        }
      ],
      "source": [
        "pred_train= model.predict(X_train)\n",
        "print(np.sqrt(mean_squared_error(y_train,pred_train)))\n",
        "\n",
        "pred= model.predict(X_test)\n",
        "print(np.sqrt(mean_squared_error(y_test,pred)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yAxJHqm8dqDP"
      },
      "source": [
        "Evaluation of the Model Performance\n",
        "The output above shows that the RMSE, which is our evaluation metric, was 3.784 thousand for train data and 3.750 thousand for test data. Ideally, the lower the RMSE value, the better the model performance. However, in contrast to accuracy, it is not straightforward to interpret RMSE as we would have to look at the unit which in our case is in thousands."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M3v8MWIMgWge"
      },
      "source": [
        "## Regression Evaluation Metrics\n",
        "\n",
        "\n",
        "Here are three common evaluation metrics for regression problems:\n",
        "\n",
        "**Mean Absolute Error** (MAE) is the mean of the absolute value of the errors:\n",
        "\n",
        "$$\\frac 1n\\sum_{i=1}^n|y_i-\\hat{y}_i|$$\n",
        "\n",
        "**Mean Squared Error** (MSE) is the mean of the squared errors:\n",
        "\n",
        "$$\\frac 1n\\sum_{i=1}^n(y_i-\\hat{y}_i)^2$$\n",
        "\n",
        "**Root Mean Squared Error** (RMSE) is the square root of the mean of the squared errors:\n",
        "\n",
        "$$\\sqrt{\\frac 1n\\sum_{i=1}^n(y_i-\\hat{y}_i)^2}$$\n",
        "\n",
        "Comparing these metrics:\n",
        "\n",
        "- **MAE** is the easiest to understand, because it's the average error.\n",
        "- **MSE** is more popular than MAE, because MSE \"punishes\" larger errors, which tends to be useful in the real world.\n",
        "- **RMSE** is even more popular than MSE, because RMSE is interpretable in the \"y\" units.\n",
        "\n",
        "All of these are **loss functions**, because you want to minimize them."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "environment": {
      "kernel": "python3",
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