{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DGPlYumZnO1t"
      },
      "source": [
        "# Manual Hyperparameter Tuning a Neural Network\n",
        "\n",
        "## Introduction\n",
        "In Challenge 4, we built a simple neural network using Keras. In this challenge, we will manually experiment with hyperarameters.\n",
        "\n",
        "This notebook contains the same workflow. Step 2 is the section that shows the simple 3-layer neural network from chapter 4. Section three is for you to change the same neural network by adding additional layers, neurons, and changing the number of epochs - then running the plot graph cell to see the results.\n",
        "\n",
        "NOTE: Be sure to re-run the begining cells before working on Section 3.\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": null,
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "coiKjFmcnaHU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cedce0f6-3453-4c38-dff9-e8fef008a80b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": null,
      "metadata": {
        "id": "BTNIhkVYv9HZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "9552f187-a289-4a6b-c0e0-4603444613ff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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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              "\n",
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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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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              "  </tbody>\n",
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              "      </button>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      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",
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              "\n",
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              "          const element = document.querySelector('#df-96a7cde0-69d8-41f1-8bc6-74045fa78ffc');\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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              "    </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": null,
      "metadata": {
        "id": "IzyfOhGaEzlL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "602eca8d-be0f-4bd1-bdfb-47d06e501e12"
      },
      "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": null,
      "metadata": {
        "id": "P7gHdfMdBk71",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae57db9f-3c06-4d22-f27e-440158f6c325"
      },
      "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": "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": null,
      "metadata": {
        "id": "i8bsyyVPm13-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "outputId": "07dffc4d-ab3b-4d68-a1a9-e15142b1c2ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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9c74b2eGnv3cWuY9f+A04NLLHpktvS6N6WHWHX8QNAMDBm4cLn+/JJ3Dw1mHs9f8ZAKCtoQ0AyBfyZfrl5wsQi+Ra/ZnkkJOTh4CgZ3Do1gR/nrkD4J/z360Jftp1odTHEYtFkGgWpFQaGmrQ1FRH/r+Syry8fOkEDpEilDqJr127dpH/lkdlL51JT09HZGSk9PGLFy8QEhICAwMDWFpaKjEyKi33X69g/5LBuBXyAjcevMDMYZ2ho6WJvaduAwD2uwxGVHwqFmw9h6zsXNx/KjuD+yqt4D4L/27X05HgC4cW+G7TmYoZCBXJ/dKP2D98J249v4Mbkbcw0+5b6GhqSxPu/cM9EJX6EgtOFywTu+nvrbj0rRec7abjdIgnhrUejLY122DSsWkAgKSMJCRlyK5IlJOXg5i0WDyMfyRtq2lYA8baxqhlVANqIjW0siwoTXyc8ATp2ekVMfRKyd33J+wfuQO3Iu/gRmQAZtpNKTjf/v+c75E7EJUSjQWnlgIANl3ahkvTz8L5k2k4fd8Lw9oMQtuarTHpyHvOd2osHsYVnG+/iBtIzniF/aN2YJnnKmTmvMFE269Q16Q2Tt/3rLjBE9y3emH/lgm4FRiBG7fDMfPrHtDRlmDvL1cAAPu3TkBUdDIWLP8dADBvZh/cCozAk/B4SCTq6OPYEl8OscU3swt+XtLS3sD3SijWun6BzDfZePY8EXadrTB6aCc4Lz6stHH+13EmvpT++uuvIttFIhG0tLTQoEED1K3LZZSKExwcjNGj3y5d5ubmBgAYMGAAVq1apaywqAyOet9DVUMdLJvkCAsTPQQ+jIbTzL2I++di11rmhoVmYUpjWI+WEImAX88FKTpkKoOjgb+jqq4pljktgoW+OQKj7sJpZ3/EvS74+ryWUQ2ZGVS/CH+M+Hksfui9BCs/XYpH8U/Qf+8w3I8p29fmy5wW46v2b28CFji74Jsd+y1OuPTkbwWMjIpy9M4fBee7z8KC8/3iLpy2D0RcWjwAoJZRTeS/Uz7qF+GPEQfG4Yc+S7DyM5eC8717OO5Hl37VocT0RDhtH4AVny7BhamnoaGmjvvRofh81zDcfcl7Q1SkoyduoqqpHpbN6w8LMwMEBj+H05ANiIsvuNi1VnVj5Oe//X3X0ZZg65ovUcPSCJlvshH6KAajJnvg6Imb0j7DJm6H2+LBOLRjEowNdfDsRSIWrvgD2/f6VvTwKo3KWBMvEooqbH8PsVhcZE38/9tEIhG6dOmCEydOwMjISGHB0n+HqMMCZYdAFanzpvf3of+OPJaDVCo/f6HsCKgCCYl7lB1CkX578EKhxxtcyruwK5Nc77Tnz59Hu3btcP78eaSkpCAlJQXnz59Hhw4dcOrUKVy+fBmJiYmYPXu2ouMlIiIiIpLBdeJLacaMGdi5cyc6dXq7XJqDgwO0tLQwadIk3L9/Hxs3bsS4ceMUFigRERERUVEqYzmNXDPxT548gb6+fqF2fX19PH36FADQsGFDJCQkfFh0RERERERUiFxJvI2NDebMmYP4+HhpW3x8PL7//nu0a9cOAPDo0SPUrFlTMVESERERERUjTxAUuqkCucppdu/ejc8//xw1atSQJurPnz9HvXr18OeffwIAXr9+jUWLFikuUiIiIiKiIqhKHbsiyZXEW1lZ4cGDBzh37hwePnwobevRowfE4oLJ/f79+yssSCIiIiIiekuuJB4oWGbSyckJTk5OioyHiIiIiKhMVKUERpFKncRv3rwZkyZNgpaWFjZv3lxi3+nTp39wYEREREREpcFymhJs2LABI0eOhJaWFjZs2FBsP5FIxCSeiIiIiKgclTqJDw8PL/LfRERERETKxHKaEjg7O5eqn0gkwvr16+UOiIiIiIioLFhOU4I7d+7IPL59+zZyc3NhZWUFAHj48CHU1NRgY2Oj2AiJiIiIiEhGqZP4ixcvSv/t7u4OPT097N+/H0ZGRgCA5ORkjB07Fl27dlV8lERERERExeBMfCmtX78e586dkybwAGBkZIQffvgBPXv2xHfffaewAImIiIiISlIZa+LF8uyUmpqK+Pj4Qu3x8fFIS0v74KCIiIiIiKh4cs3EDxgwAGPHjsX69evRvn17AIC/vz/mzJmDgQMHKjRAIiIiIqKSsJymlLZv347Zs2djxIgRyMnJKTiQujrGjx+PtWvXKjRAIiIiIqKSVMZyGrmSeG1tbWzduhVr167FkydPAAD169eHjo6OQoMjIiIiIqLC5Eri/09HRwctW7ZUVCxERERERGXGmXgiIiIiIhVTGWvi5VqdhoiIiIiIlIcz8URERESk0lhOQ0RERESkYlhOQ0REREREHz3OxBMRERGRSmM5DRERERGRimE5DRERERERffQ4E09EREREKo3lNEREREREKoblNEREREREVCZbtmxBnTp1oKWlhQ4dOuDGjRsl9t+4cSOsrKxQpUoV1KxZE7NmzcKbN2/K9JqciSciIiIilabMcpojR47A2dkZ27dvR4cOHbBx40b06tULYWFhMDMzK9T/l19+wbx587Bnzx506tQJDx8+xFdffQWRSAR3d/dSvy5n4omIiIhIpeXlCwrdysLd3R0TJ07E2LFj0bRpU2zfvh3a2trYs2dPkf2vXbuGzp07Y8SIEahTpw569uyJ4cOHv3f2/t+YxBMRERERvSMrKwupqakyW1ZWVqF+2dnZCAgIgKOjo7RNLBbD0dERfn5+RR67U6dOCAgIkCbtT58+xZkzZ9CnT58yxfjRlNOIOixQdghUgQT/lcoOgSqQaOoWZYdAROVkhMfXyg6BSOHlNG5ubnB1dZVpc3FxwdKlS2XaEhISkJeXB3Nzc5l2c3NzhIaGFnnsESNGICEhAV26dIEgCMjNzcXkyZOxYEHZcmHOxBMRERGRSlN0Oc38+fORkpIis82fP18hsfr6+mLlypXYunUrbt++jT/++AOnT5/G8uXLy3Scj2YmnoiIiIjoYyCRSCCRSN7bz9TUFGpqaoiNjZVpj42NhYWFRZH7LF68GF9++SUmTJgAAGjRogXS09MxadIkLFy4EGJx6ebYORNPRERERCotTxAUupWWpqYmbGxs4OPjI23Lz8+Hj48PbG1ti9wnIyOjUKKupqYGABDK8NqciSciIiIilabMmz05OztjzJgxaNu2Ldq3b4+NGzciPT0dY8eOBQCMHj0a1atXh5ubGwCgb9++cHd3R+vWrdGhQwc8fvwYixcvRt++faXJfGkwiSciIiIiktPQoUMRHx+PJUuWICYmBtbW1vD09JRe7BoZGSkz875o0SKIRCIsWrQIUVFRqFq1Kvr27YsVK1aU6XVFQlnm7csRV6epXLg6TeUimmqg7BCIqJyM6H5O2SFQBTo0sIOyQyjSyD/8FXq8j3Wc7+JMPBERERGpNGWW0ygLL2wlIiIiIlIxnIknIiIiIpWm6Js9qQLOxBMRERERqRjOxBMRERGRSquMNfFM4omIiIhIpVXGJJ7lNEREREREKoYz8URERESk0irjha1M4omIiIhIpbGchoiIiIiIPnqciSciIiIilcZyGiIiIiIiFVMZk3iW0xARERERqRjOxBMRERGRSquMF7YyiSciIiIilcZyGiIiIiIi+uhxJp6IiIiIVBrLaYiIiIiIVAzLaYiIiIiI6KPHmXgiIiIiUmkspyEiIiIiUjGVMYlnOQ0RERERkYrhTDwRERERqbTKeGErk3giIiIiUmkspynB4MGD4enpCaESftIhIiIiIvqYlDqJT05OxqeffopatWphyZIlePr0aXnGRURERERUKnmCoNBNFZS6nMbHxwfPnj3D3r17ceDAAaxYsQJ2dnaYMGECBg0aBIlEUp5xqoQpgztizsiusDDRRdCjGExbfxI3H7x4735De7TE4R+G4cSlBxjw/c/SdsF/ZZH95/x4Fut+/lthcVP5unnzJnbv3o3g4GDEx8djy5YtcHR0VHZY9B5Tuk3AHIfpsNA3R1BUMKYdm4Obz24X239w6/5Y/ulC1DGphUfxTzD3hAvOPjgvfX7vqK34quNImX08H3ij99ZBMm19mvXEkt5z0dKyGd7kZuHSoysY4CG7Hykez3fl1qOeOT5tWA0GWhqITMnA/qAIPE1OL7Lvwq5N0LSqfqH2OzHJWHftIQBAoibGsOY10dbSGLqa6ohPz4LXkxj4hMeV6zgqM1VJvBWpTDXxtWvXxtKlS7F06VJcuHABe/bswcSJEzF16lQMHz4c48aNg42NTXnF+lEb4tgC7jP6YPLqE/C//wIzh3WC16axsBrijvhi3ggAoHY1Q6yb3huX74QXes6it2wS37tTI+xeOBC/XwhWePxUfjIyMmBlZYVBgwZh6tSpyg6HSmFIm4FwH7ASk4/Mgn/ELcz8ZAq8vj0Oq2U2iH+dUKi/bd32+PWr3Zj/lytOBXtiRNsvcGLSL2izuhvuR4dI+529fx5jf54ifZyVmy1znIHW/eAxfDMWnFyGCw8vQV2sjubVmpbfQAkAz3dl17G6MUa2qIU9geF4kpQOpwYWmNe5MWafD0JqVm6h/huvP4S6+G0hg66mOtwcWuDGiyRp26iWtdG0qj623nyM+IwstDAzwFjrukh+k43b0a8qYlhUCci9xGT37t3x888/IyYmBm5ubjh8+DA6dOigyNhUivPwLvD48yb2nbqNkPA4TF71JzLeZGNc3+I/1IjFIhxyHQqXnd54GpVU6PnYpNcy2+fdmuJiQDjCXyaX51BIwezs7DBr1iz06NFD2aFQKTl3/xYe1/Zj3/VDCIkJw+TDM5GRnYFxtl8W2X+G/TfwDPHGOp/NCI19iCWnV+D28yBMtZsk0y8rNwuxaXHS7VXmK+lzamI1bBq0CnNOLMaOK3vwKO4JQmLCcOzO8fIcKoHnu7Lr3bAaLkbE4fKzBESlZWLPnXBk5eXDrnbVIvun5+QhJStHurUwM0B2Xj783/k73tBYF39HxiMkIQ0JGdm4GBGPyJQM1DfSrahhVTp5+YJCN1XwQevEh4eHY926dVi5ciVSUlIqbYmAhroabBpbwvvGY2mbIAjwvvkEti1qFbvfkvHdEZf8GntOBrz3NcyMdfFpZyvs/uuWQmImoqJpqGnApqY1vMN8pW2CIMA7zBe2ddsVuY9t3XbwDvWVafMK8YFtHdn+9g27INbtMUIX38LWoe4w1jGSPtemZivUMKqOfCEft+f+jZcrwnDmm9/QrFoThY2NCuP5rtzURCLUNdRBcFyqtE0AEByXgobGeqU6hn2dqvB7kYisvHxp26Ok12hTzQhGWhoAgKam+rDQ1cK92BSFxk9vMYkvhTdv3uDnn39G9+7d0bBhQxw4cADjx49HeHg4PD09yyPGj56poTbU1dUQm/Rapj026TUsinkT6NyqNsb3a4uJK0s36zKmT2ukpWfhD9/7HxwvERXPVNcE6mrqiE2TrV2NTY2Hhb55kftY6JsX7p8m298zxAejD06Gw4/9MPdPF9g16Iyz3/wOsajgbbieaV0AwNI+8/CD11p8tn0okjNfwXfGaRhpG4HKB8935aYnUYeaWISUrByZ9tSsHBj8k4CXpJ6RDmoaaONihOzPw/6gCESlZuKnPm2wv387fN/ZCvuCIhCamKbQ+KlyK3VN/I0bN7Bnzx4cOXIEb968wYABA+Dp6QkHBweIRKIyvWhWVhaysrJkG/NzAXHlWLZeV1sTB5d+gYkrjyMxJaNU+4zr2xaHvIKQlV24Po+IPn5HAn6X/jv45QPcjbqPp65BsG/YFRceXoL4n/fRFV7r8UfgXwCAsT9PwYvlIfiidX/svLpXKXGTfHi+Kwf7OlURmZJR6CLYnvXN0cBYF+uuhSEhIwuNTfXxVas6SM7Mxv341GKORh+CF7aWoGPHjmjVqhWWL1+OkSNHwshI/pkCNzc3uLq6yjZadgFqdJX7mMqU8CoDubl5MDeWrXUzN9ZFTFLhT931q5ugrqUxTq57W28pFhe8oedcXQ6rIRtkauS7WNdB4zpVMXTRr+U0AiL6v4TXicjNy4W5nplMu7l+VcSkxha5T0xqbOH+esX3B4DwxAjEpyWgQdV6uPDwEqJTCvo+iA6V9snOzcbTxAjUMq4h73DoPXi+K7e0rFzk5QswkMjOuutLNJDyJqeYvQpI1MSwrWGC3/61Cp2GWIShzWpiw/VHCIx5BQB4npqJ2oba+LRRNSbx5URVSmAUqdTlNJ999hmuXr2KqVOnflACDwDz589HSkqKzAZL2w86pjLl5OYhIPQlHNo1kLaJRCI4tKsPv3uRhfqHPotH8+GbYP3lT9Ltr79DcTEgHNZf/oTn/6qZG9/XBrdCXuDuo5hyHwtRZZeTl4OA54FwsLKTtolEIjg0soNf+M0i9/ELvynTHwB6NP4EfhFF9weA6oaWMNExRnRqwe91wPNAvMl5AyvzhtI+6mJ11DGuhWdJzz9kSFQCnu/KLU8QEP4qHc3M3i4ZKQLQ3MwAj4qYhHtXh+rGUBeLcfV5oky7ulgMdbG40M0x8wUBYpStcoGoJKWeiT99+jRev34NbW3tD35RiURSeF15FS+lcf/1CvYvGYxbIS9w48ELzBzWGTpamth7qmCd4f0ugxEVn4oFW88hKzsX95/Kzti8SssEgELtejoSfOHQAt9tOlMxAyGFS09PR2Tk2w9zL168QEhICAwMDGBpaanEyKg47he2YP+X23Ar8g5uRARg5idToCPRwd7rBfdx2P/ldkSlRGPBXwXfKG7y3YZLM8/AuftUnL7vhWE2g9C2VmtM+nUGAEBHUwcufebh98A/EZMah/qmdbGm/zI8TngKrxAfAEDamzRsv7IHrn3m43lyFJ4lRWKOY8H+x26fqPj/hEqE57tyO/soGl+3rY/w5HQ8SX4NpwYWkKiJcelZPABgsk09JL/JwZH7sh+u7OpURcDLZLz+V5lrZm4eHsSnYnjzWsjOi0BCRjaamOqha62q+PnuswobV2XDcpoS/PsTJck66n0PVQ11sGySIyxM9BD4MBpOM/ci7p+LXWuZGyJfjq96hvVoCZEI+PVckKJDpgoSHByM0aNHSx+7ubkBAAYMGIBVq1YpKywqwdHbf6CqrgmWfboAFnrmCIy6B6ctAxGXVvBHvZZxDeQLb1ei8Au/gRH7JuCHzxZhZd8leBT/BP13jpCuGZ4n5KFl9WYY02E4DKsY4GVKNM6FXsTiUz8g+521w+ccX4zc/DwcHL0DVTS04P8sAN0395VZmpAUj+e7crselQQ9iQYGN60BA4kGnqVkYPXVUOka8SbaEvz7r3c1XS00NtWH25WQwgcE8NONxxjavCamtGsAXU11JGRk4ej957zZUzmqjOU0IqGU2blYLEZsbCyqVi163dQPDqTDgnI5Ln2cirsbLf03iaYaKDsEIionI7qfU3YIVIEODfw47wlksvpPhR4vce7nCj1eeShTDUujRo3euxJNUlLhmxYREREREZUXltO8h6urKwwMOKNGRERERB+PylhOU6YkftiwYTAzM3t/RyIiIiIiKjelTuLLekMnIiIiIqKKwHKaEnB1GiIiIiL6GDGJL0F+fv77OxERERERUblT7TssEREREVGlxwtbiYiIiIhUTGUspxErOwAiIiIiIiobzsQTERERkWpjOQ0RERERkYphOQ0REREREX3sOBNPRERERKqN5TRERERERCqG5TRERERERPSx40w8EREREak2ltMQEREREamYSpjEs5yGiIiIiEjFcCaeiIiIiFRbJbywlUk8EREREak2ltMQEREREdHHjjPxRERERKTaWE5DRERERKRiKmESz3IaIiIiIiIVw5l4IiIiIlJtlfDCVibxRERERKTaWE5DREREREQfO87EExEREZFqYzkNEREREZGKYTkNERERERGVxZYtW1CnTh1oaWmhQ4cOuHHjRon9X716hW+//RbVqlWDRCJBo0aNcObMmTK9JmfiiYiIiEi1KbGc5siRI3B2dsb27dvRoUMHbNy4Eb169UJYWBjMzMwK9c/OzkaPHj1gZmaG3377DdWrV8ezZ89gaGhYptdlEk9EREREqk2JSby7uzsmTpyIsWPHAgC2b9+O06dPY8+ePZg3b16h/nv27EFSUhKuXbsGDQ0NAECdOnXK/LospyEiIiIiekdWVhZSU1NltqysrEL9srOzERAQAEdHR2mbWCyGo6Mj/Pz8ijz2X3/9BVtbW3z77bcwNzdH8+bNsXLlSuTl5ZUpxo9nJr7zJmVHQBVINHWLskOgCiT8lKLsEKgCibRGKjsEqkC/pN5UdghUgQ4N7KDsEIqm4Atb3dzc4OrqKtPm4uKCpUuXyrQlJCQgLy8P5ubmMu3m5uYIDQ0t8thPnz7FhQsXMHLkSJw5cwaPHz/GlClTkJOTAxcXl1LH+PEk8URERERE8lBwOc38+fPh7Ows0yaRSBRy7Pz8fJiZmWHnzp1QU1ODjY0NoqKisHbtWibxRERERETykkgkpUraTU1NoaamhtjYWJn22NhYWFhYFLlPtWrVoKGhATU1NWlbkyZNEBMTg+zsbGhqapYqRtbEExEREZFqEwTFbqWkqakJGxsb+Pj4SNvy8/Ph4+MDW1vbIvfp3LkzHj9+jPz8fGnbw4cPUa1atVIn8ACTeCIiIiJSdUpK4gHA2dkZHh4e2L9/P0JCQvDNN98gPT1dulrN6NGjMX/+fGn/b775BklJSZgxYwYePnyI06dPY+XKlfj222/L9LospyEiIiIiktPQoUMRHx+PJUuWICYmBtbW1vD09JRe7BoZGQmx+O28ec2aNeHl5YVZs2ahZcuWqF69OmbMmIG5c+eW6XVFgvBx3KdW5Kyj7BCoImXz82NlwtVpKheuTlPJDCm6ZID+m4QDU5UdQpFEY7cq9HjC3ikKPV55YCZFRERERKpNiTd7UhbWxBMRERERqRjOxBMRERGRavs4qsMrFJN4IiIiIlJtLKchIiIiIqKPHWfiiYiIiEi1sZyGiIiIiEjFVMIknuU0REREREQqhjPxRERERKTaKuGFrUziiYiIiEi1sZyGiIiIiIg+dpyJJyIiIiLVxnIaIiIiIiIVw3IaIiIiIiL62HEmnoiIiIhUG8tpiIiIiIhUDMtpiIiIiIjoY8eZeCIiIiJSbSynKb0nT55g48aNCAkJAQA0bdoUM2bMQP369RUWHBERERHRe7GcpnS8vLzQtGlT3LhxAy1btkTLli3h7++PZs2a4fz584qOkYiIiIiI3iHXTPy8efMwa9YsrFq1qlD73Llz0aNHD4UER0RERET0XpWwnEaumfiQkBCMHz++UPu4cePw4MGDDw6KiIiIiKjUBEGxmwqQK4mvWrUqAgMDC7UHBgbCzMzsQ2MiIiIiIqISyFVOM3HiREyaNAlPnz5Fp06dAABXr17F6tWr4ezsrNAAiYiIiIhKVAnLaeRK4hcvXgw9PT2sX78e8+fPBwBYWlpi6dKlmD59ukIDJCIiIiIqEZP40hGJRJg1axZmzZqFtLQ0AICenp5CAyMiIiIioqJ98B1b9fT0Km0CP6XzJIQveoDM1Ym4PsMX7WrZlNh/cKsBCJl7G5mrE3F3zg30btKr2L7bBm+C4J6OGd2+lWlf4DgHV6f5IH1VPJJXRClkHFQ6U7pNQLjrXWRuiMX12T5oV7tNif0Ht+6PkEU3kbkhFncXXEPvprKrNu0dtRXCTyky29kpvxc6Tp9mPXF9tg8y3GOQtOYZjk88pNBxkWLdvHkTkydPRpcuXWBlZQVvb29lh0RymPJ1D4SHbUTmq724ftkV7drWK7bvgM/b4ubV5UiO2YnXibtxx38lRo3oItNnr8fXEN4cktnO/vV9eQ+DSmmKQwuErx+NzF2Tcd1lMNrVK/76vjFdGkM4MFVmy9w1WaaPjkQDP37ZDc83foWMXZNx320Evv6kWXkPo3KrhBe2lnomvk2bNvDx8YGRkRFat24NkUhUbN/bt28rJLiP2RDrQXD/fBUmH5sB/8ibmNntW3hN+hNWq1oj/nV8of62dTrg11H7MP+MC07dP4sRbYbgxNjDaOPeGfdjZFf06d+iLzrWbo+olJeFjqOppoljQcfh9+wGxncYXW7jI1lD2gyE+4CVmHxkFvwjbmHmJ1Pg9e1xWC2zQfzrhEL9beu2x69f7cb8v1xxKtgTI9p+gROTfkGb1d1wPzpE2u/s/fMY+/MU6eOs3GyZ4wy07geP4Zux4OQyXHh4CepidTSv1rT8BkofLCMjA1ZWVhg0aBCmTp2q7HBIDkMGd4T7mpGYPG0P/G88wcxpTvA6OQ9WLWcjPj61UP+k5HSsWP0nQsNeIjsnF5/1bo29OychLi4F57zvSfud9QrC2Ek7pI+zsnIqZDxUsiEdGsB9RBdM3ucL/ycxmNnLGl5z+sHq+0OIT8sscp+UjCxYzX07oSL8K+lzH9EF3ZtWx6jt5xGRkIqezWth6xg7vHyVjpN3IspzOJUXy2mK9/nnn0MikQAA+vfvX17xqAxnu2nwuL4X+24eBABM/m06Pm3qhHHtR2P1hfWF+s/oOgWeoeex7uJGAMASz+XoYdUdU7t8jW9+myHtZ2lQDT8OWI9eOz7H6YmFZ2WXeq0AAIxpN6ocRkXFce7+LTyu7ce+6wVv2pMPz8SnzXpinO2XWH1+Q6H+M+y/gWeIN9b5bAYALDm9Aj0af4KpdpPwzeFZ0n5ZuVmITYsr8jXVxGrYNGgV5pxYjD1+B6XtITFhihwaKZidnR3s7OyUHQZ9AOfpveGx5yL2HbgMAJg8dQ8+dbLGuDF2WL3uZKH+ly6HyDzevMULY0Z1RZfOVjJJfFZWDmJjU8o3eCozZydrePjex76/C87j5H0X8Wmr2hhn1wSrTxU9KSkIQGxKRrHH7NTQAvuvhOJSaME35h6+9/H1J83Qvp45k3hSmFIn8S4uLkX+uzLSUNOATY3WcPNZJ20TBAHeDy/Ctk77IvexrdMB7pd+lGnzCvVG/xZ9pY9FIhEOjtiNtRc34kFsyL8PQUqioaYBm5rWcDvnLm0TBAHeYb6wrduuyH1s67aD+4UtMm1eIT7o3/JTmTb7hl0Q6/YYyRmvcOHhZSw6tRxJ6ckAgDY1W6GGUXXkC/m4PfdvWOibIfDFPcw5sVhmNp+IFEdDQw02berCbe1f0jZBEOB9MRi2HRqW6hjdP2kGq0bVMHfRYZl2+25NEBu5Fcmv0nHB9wEWLT2GpKTXCo2fykZDTQybOmZwOxkgbRMEwPvBC9g2sCh2P10tDUS4j4ZYJMLtZ/FYcOw6HkQlSZ+/9igG/VrXxZ7LIXiZnA77JtXRyMIQs365Uq7jqdRUpARGkeS6sLWyM9UxgbqaeqEZ1Ni0ODQ2a1TkPhZ65kX2t9Azlz6e2/075ObnYvPfWxUfNMnNVLeY850aj8bmxZxv/aLOdzws9N+eb88QH/wRdBLhic9Q37QuVvZdgrPf/A7b9Y7IF/JRz7QuAGBpn3lw/mMhIhIj8Z3DVPjOOI1Gy2yQnJGs4JESkampHtTV1RAbJztjHhubisaNLIvdT1+/CqKe/gSJRB15efmYMmMfvH2Cpc97ngvCHyduIjwiHvXrmWHlsqE4++f3sLVzQX4lLAP4WJjqVYG6mhixqbJlM7EpGWhczbDIfcJiXmHcLh/cfZ4IgyqamN2nNa4tHoRm839BVHI6AGDawUvYOa47ojaNRU5uHvIFYOKeC/g7rHCZLCkIk/jiGRkZlVgH/66kpKQSn8/KykJWVpZsY64AqJfu+P9FbWpYY0bXKWjj3knZoVAFORLwtlwq+OUD3I26j6euQbBv2BUXHl6C+J/ftxVe6/FHYMGs4Nifp+DF8hB80bo/dl7dq5S4iaiwtLQ3sG6/ALq6WnD4pBncV4/E0/A4aanNkWPXpX2D7z/H3eBIPA3ZCHu7prhw8b6ywiY5XH8cg+uPY6SPrz2OQciqEfi6e3Ms+d0fADCtRyt0rG+Ovu6n8CwxDd2sLLFldEFNvM/9F8oKnf5jSp3Eb9y4UfrvxMRE/PDDD+jVqxdsbW0BAH5+fvDy8sLixYvfeyw3Nze4urrKNnZUB2w1SxuOUiWkJyI3LxfmerJXr5vrmSEmLbbIfWLSYkvs37VeZ5jpVkXk4rf1zupq6ljfzw0zu32Luj/wYkZlSXhdzPnWr4qY1GLOd2pR57v4/gAQnhiB+LQENKhaDxceXkJ0SkHfB9Gh0j7Zudl4mhiBWsY15B0OEZUgISENubl5MDczkGk3N9dHTAn17IIg4MnTgt/ZoLvP0KSxJebP6VeoXv7/wsPjER+figb1zZnEK1FCWiZy8/Jhrl9Fpt3cQBsxJdS8vys3Lx93niWgwT8/M1oaalj5RUcM2HQGZ4KeAQDuPU+EdS1TzO7dmkl8eamE32iVeonJMWPGSLerV69i2bJl+PXXXzF9+nRMnz4dv/76K5YtW4ZLly6991jz589HSkqKzIZ2Gh80kIqUk5eDgBd34NDQXtomEong0NAefhE3itzHL8Jfpj8A9GjUHX4RBZ/aD976FS3XdYD1elvpFpXyEmsvbkSvHZ+X11CoFHLychDwPBAOVm8vVhSJRHBoZAe/8JtF7uMXflOmPwD0aPwJ/CKK7g8A1Q0tYaJjjOjUghmegOeBeJPzBlbmb+tw1cXqqGNcC8+Snn/IkIioGDk5eQi4HQ6Hd5YDFIlEcLBvDj//R6U+jlgsgkRS/DxZ9erGMDHRRXT0qw8Jlz5QTl4+AiLi4NCsprRNJAIcmtaA3zuz7SURi0RoUcME0f8k/RpqYmiqqyH/X+UdefmC9BtWKgdcYrJ0vLy8sHr16kLtTk5OmDdv3nv3l0gk0pVu3kaiWj/Y7pd+xP7hO3Hr+R3ciLyFmXbfQkdTG3tvFKwisn+4B6JSX2LB6YKLgDf9vRWXvvWCs910nA7xxLDWg9G2ZhtMOjYNAJCUkYSkDNkypJy8HMSkxeJh/Ns/HDUNa8BY2xi1jGpATaSGVpYtAQCPE54gPTu9IoZeKblf2IL9X27Drcg7uBERgJmfTIGORAd7r/8MANj/5XZEpURjwV8F3zBt8t2GSzPPwLn7VJy+74VhNoPQtlZrTPq1YCUiHU0duPSZh98D/0RMahzqm9bFmv7L8DjhKbxCfAAAaW/SsP3KHrj2mY/nyVF4lhSJOY4F+x+7faLi/xOoVNLT0xEZGSl9/OLFC4SEhMDAwACWlsXXVNPHw33zWezf9TVu3Q7HjZsFS0zq6Eiw90DBJNX+3ZMR9TIZCxYfAQDMm9MPt24/xZOnsZBoaqCPkzW+HNEF30wvKHnT0ZHAZeFA/H7iJmJiX6F+PXOsWTEcj5/Ewuv8XaWNkwq4ewZi/0RH3AqPw42nsZjZsxV0JOrY+8+3KPsnOSIqOR0LjvkBABZ/3g7Xn8TgcWwKDLUlmNOnNWqb6mGXb8E3KmlvcuAbEoW1wzojMzsPzxJSYde4OkZ3aQxnXthKCiRXEm9iYoI///wT3333nUz7n3/+CRMTE4UE9rE7Gvg7quqaYpnTIljomyMw6i6cdvZH3OuCixlrGdVAvpAv7e8X4Y8RP4/FD72XYOWnS/Eo/gn67x1WaI3491nmtBhftX+7vGTg7II3FfstTrj05G8FjIyKcvT2H6iqa4Jlny6AhZ45AqPuwWnLQMSlFdwToJbxv853+A2M2DcBP3y2CCv7Lik43ztHSFeVyRPy0LJ6M4zpMByGVQzwMiUa50IvYvGpH5D9zlrxc44vRm5+Hg6O3oEqGlrwfxaA7pv74lXmqwodP5VecHAwRo9+ew8HNzc3AMCAAQOwatUqZYVFZXD0t+uoaqqHZUsGw8LcAIFBz+DUbzXi4grWiK9V00TmYlQdHQm2bhqLGtWNkZmZjdCwlxg1dhuO/lZQB5+Xl4+WLWphzKiuMDTUwcvoZJzzvofFrseQnZ2rlDHSW0f9H6OqXhUsG9geFgY6CIyMh9Pak4j752LXWiZ6MrPqRjoSeIz7BBYGOkhOf4OAiHh0Wv4bQl6+XWxg2FYvuH1hi0OTe8BYVwvPEtKw8Lfr2H4huNDrk4JUwnIakfDvOxSUwr59+zBhwgT07t0bHTp0AAD4+/vD09MTHh4e+Oqrr8oeiLNOmfchFZbNhZEqE+Enro1dmYi0Rio7BKpIQ2yVHQFVIOHAx3kTO1Hn91+TWRbC1eUKPV55kCuT+uqrr9CkSRNs3rwZf/zxBwCgSZMmuHLlijSpJyIiIiKi8iH3dGiHDh1w6NCh93ckIiIiIipPKnIxqiJ9cE3DmzdvkJ2dLdOmr6//oYclIiIiIiqdSlgTX+olJt+VkZGBqVOnwszMDDo6OjAyMpLZiIiIiIio/MiVxM+ZMwcXLlzAtm3bIJFIsGvXLri6usLS0hIHDhxQdIxERERERMXjOvGlc/LkSRw4cAD29vYYO3YsunbtigYNGqB27do4dOgQRo7kygREREREVEFYTlM6SUlJqFevHoCC+vekpIKbFHXp0gWXL19WXHRERERERFSIXEl8vXr1EB4eDgBo3Lgxjh49CqBght7Q0FBhwRERERERvVclLKeRK4kfO3YsgoKCAADz5s3Dli1boKWlhVmzZmHOnDkKDZCIiIiIqET5gmI3FVDmmvicnBycOnUK27dvBwA4OjoiNDQUAQEBaNCgAVq2bKnwIImIiIiI6K0yJ/EaGhq4e/euTFvt2rVRu3ZthQVFRERERFRqKlICo0hyldOMGjUKu3fvVnQsRERERERlVwlr4uVaYjI3Nxd79uyBt7c3bGxsoKOjI/O8u7u7QoIjIiIiIqLC5Erig4OD0aZNGwDAw4cPZZ4TiUQfHhURERERUWmpyMWoiiRXEn/x4kVFx0FEREREJJ9KmMTLVRNPRERERETKI9dMPBERERHRR0NFLkZVJCbxRERERKTaKmESz3IaIiIiIiIVw5l4IiIiIlJtlfDCVibxRERERKTaWE5DREREREQfO87EExEREZFqYzkNEREREZGKqYRJPMtpiIiIiIhUDGfiiYiIiEi1VcILW5nEExEREZFqq4RJPMtpiIiIiIhUDGfiiYiIiEi18cJWIiIiIiIVIwiK3cpoy5YtqFOnDrS0tNChQwfcuHGjVPsdPnwYIpEI/fv3L/NrMoknIiIiIpLTkSNH4OzsDBcXF9y+fRutWrVCr169EBcXV+J+ERERmD17Nrp27SrX6zKJJyIiIiLVli8odisDd3d3TJw4EWPHjkXTpk2xfft2aGtrY8+ePcXuk5eXh5EjR8LV1RX16tWTa8hM4omIiIhItSm4nCYrKwupqakyW1ZWVqGXzc7ORkBAABwdHaVtYrEYjo6O8PPzKzbcZcuWwczMDOPHj5d7yB/Pha15/DxB9F8l0hqp7BCoAglvDik7BKpAoqadlR0CVaipyg6gQri5ucHV1VWmzcXFBUuXLpVpS0hIQF5eHszNzWXazc3NERoaWuSxr1y5gt27dyMwMPCDYvx4kngiIiIiInkoeHWa+fPnw9nZWaZNIpF88HHT0tLw5ZdfwsPDA6amph90LCbxRERERKTaFHyzJ4lEUqqk3dTUFGpqaoiNjZVpj42NhYWFRaH+T548QUREBPr27Stty8/PBwCoq6sjLCwM9evXL1WMZa5hyc3NxYEDBwoFS0RERERUmWhqasLGxgY+Pj7Stvz8fPj4+MDW1rZQ/8aNG+PevXsIDAyUbv369cMnn3yCwMBA1KxZs9SvXeaZeHV1dUyePBkhISFl3ZWIiIiISPGUeLMnZ2dnjBkzBm3btkX79u2xceNGpKenY+zYsQCA0aNHo3r16nBzc4OWlhaaN28us7+hoSEAFGp/H7nKadq3b4/AwEDUrl1bnt2JiIiIiBRHiUn80KFDER8fjyVLliAmJgbW1tbw9PSUXuwaGRkJsVjxC7iIBKHsRURHjx7F/PnzMWvWLNjY2EBHR0fm+ZYtW5Y9kBl6Zd6HVBhXI6pcdn2m7AioAnF1msqFq9NULsKDq8oOoUgiU/mXaiyKkLBboccrD3LNxA8bNgwAMH36dGmbSCSCIAgQiUTIy8tTTHRERERERO+j4AtbVYFcSXx4eLii4yAiIiIiko8Sy2mURa4knrXwRERERETKI3dh8sGDB9G5c2dYWlri2bNnAICNGzfizz//VFhwRERERETvJQiK3VSAXEn8tm3b4OzsjD59+uDVq1fSGnhDQ0Ns3LhRkfEREREREZWMSXzp/Pjjj/Dw8MDChQuhpqYmbW/bti3u3bunsOCIiIiIiKgwuS9sbd26daF2iUSC9PT0Dw6KiIiIiKjUKuGFrXLNxNetWxeBgYGF2j09PdGkSZMPjYmIiIiIqPTyBcVuKkCumXhnZ2d8++23ePPmDQRBwI0bN/Drr7/Czc0Nu3btUnSMRERERET0DrmS+AkTJqBKlSpYtGgRMjIyMGLECFhaWmLTpk3SG0EREREREVUIFbkYVZHkSuIBYOTIkRg5ciQyMjLw+vVrmJmZKTIuIiIiIqLSUZESGEWSO4kHgLi4OISFhQEARCIRqlatqpCgiIiIiIioeHJd2JqWloYvv/wSlpaWsLOzg52dHSwtLTFq1CikpKQoOkYiIiIiouJxnfjSmTBhAvz9/XH69Gm8evUKr169wqlTp3Dr1i18/fXXio6RiIiIiKh4XJ2mdE6dOgUvLy906dJF2tarVy94eHjAyclJYcEREREREVFhciXxJiYmMDAwKNRuYGAAIyOjDw6KiIiIiKjUVKQERpHkKqdZtGgRnJ2dERMTI22LiYnBnDlzsHjxYoUFR0RERET0XiynKZ1t27bh8ePHqFWrFmrVqgUAiIyMhEQiQXx8PHbs2CHte/v2bcVESkREREREAORM4vv376/gMIiIiIiI5FQJy2nkSuJdXFwUHQcRERERkXxElS+Jl6smnoiIiIiIlEeumfi8vDxs2LABR48eRWRkJLKzs2WeT0pKUkhwRERERETvJeZMfKm4urrC3d0dQ4cORUpKCpydnTFw4ECIxWIsXbpUwSESEREREZVALCh2UwFyJfGHDh2Ch4cHvvvuO6irq2P48OHYtWsXlixZguvXrys6RiIiIiIieodcSXxMTAxatGgBANDV1UVKSgoA4LPPPsPp06cVFx0RERER0fuIBMVuKkCuJL5GjRqIjo4GANSvXx/nzp0DANy8eRMSiURx0RERERERvQ/LaUpnwIAB8PHxAQBMmzYNixcvRsOGDTF69GiMGzdOoQF+zKZ0mYjwJcHIXBeP67MuoF0tmxL7D7buj5AFAchcF4+7c6+jd9OexfbdNmQjhE1pmGE3Raa9YdUGODHhMOJXRCBldRT+nnEO9g26KmQ8VLIp3SYg3PUuMjfE4vpsH7Sr3abE/oNb90fIopvI3BCLuwuuoXfTHjLP7x21FcJPKTLb2Sm/FzpOn2Y9cX22DzLcY5C05hmOTzyk0HFR6Uz5ugfCwzYi89VeXL/sinZt6xXbd8DnbXHz6nIkx+zE68TduOO/EqNGdJHps9fjawhvDslsZ//6vryHQQp28+ZNTJ48GV26dIGVlRW8vb2VHRLJYcrwgQg//xsy71zA9cM70a5Fk2L7ThjcF5cPbkWS31kk+Z3F+d0bC/V3+XYcQk79gte3vKV92rdsWt7DoEpGrtVpVq1aJf330KFDUatWLfj5+aFhw4bo27evwoL7mA1pPRDuA9ww+ehM+EfcxEz7b+H1zXFYrWiD+NcJhfrb1umAX0fvxfxTS3HqvidG2HyBE+N/RZt1XXA/OkSmb/+WfdGxdjtEvXpZ6DinJh3Do/gn6L7lU2TmvMFMuyk4NekY6i9vidi0uHIbb2U3pM1AuA9YiclHZsE/4hZmfjIFXt8eh9Uym6LPd932+PWr3Zj/lytOBXtiRNsvcGLSL2izupvM+T57/zzG/vz2g1pWruxKTwOt+8Fj+GYsOLkMFx5egrpYHc2r8Q9BRRsyuCPc14zE5Gl74H/jCWZOc4LXyXmwajkb8fGphfonJadjxeo/ERr2Etk5ufisd2vs3TkJcXEpOOd9T9rvrFcQxk56e4frrKycChkPKU5GRgasrKwwaNAgTJ06VdnhkByGODnAfe40THZdC/+7DzDzyyHw2ukOq0+HIz7pVaH+9u3b4NfT53EtMBhvsrIwd8IonPPYgGb9RuFlXMHfg4cRzzF1hTuePn+JKloSzBo9FOc8NqCB01AkJBc+JimAipTAKJJIED6OW1yJZugpO4QyuT7rAm5G3sa032cDAEQiEZ4vDcWPf+/Aam/3Qv0Pj9kHHYkO+u78QtrmN+sCAqPu4pujM6VtlgbV4O98Eb229cfpSb9h46Wt2HRpKwDARMcECSsj0HVTL1x5eg0AoCvRRdqaaDhu6Qufh77lN2BFy1OtWxRcn+2Dm89uY9qxOQD+Od/LH+DHSzux+vyGQv0Pj90LHYk2+m4fKm3z+84bgVH38M3hWQAKZuINqxhggMfIIl9TTayGCNd7cDnjhj1+B8thVBVo12fKjuCDXL/sipsBTzFt1n4A/5z/x5vx47ZzWL3uZKmOEeD3A057BmKJ628ACmbiDQ20MWBI4Z8fVSe8qZzfFllZWWHLli1wdHRUdigVStS0s7JD+CDXD+/EzXuhmLai4G+3SCTC8wvH8eOh37B618/v3V8sFiP5uiem/uCOg395FtlHT0cbqTfPw2HcdFy4HqDQ+Cua8OCqskMokkjnc4UeT0j/U6HHKw9yZ1JhYWGYOnUqHBwc4ODggKlTpyIsLEyRsX20NNQ0YFOzNbzfSZoFQYD3Q1/Y1mlf5D62ddvDO+yiTJtXqLdMf5FIhIOjPLD2wiY8iAktdIzE9ESExj7E6HbDoa2pDTWxGr7uNA6xaXEIeB6okLFRYQXn2xreYb7SNkEQ4B3mC9u67Yrcx7ZuO3iH+sq0eYX4wLaObH/7hl0Q6/YYoYtvYetQdxjrGEmfa1OzFWoYVUe+kI/bc//GyxVhOPPNb2hWrfiveUnxNDTUYNOmLrwvBEvbBEGA98Vg2HZoWKpjdP+kGawaVcPlK7K/1/bdmiA2citC767F1s1jYWysq9DYiahkGhrqsGlqBe/rN6VtgiDA2+8WbK2bl+oY2lpa0FBXR1JK4W/l/v8ak4Z8jlepaQgKfayQuIkAOctpfv/9dwwbNgxt27aFra0tAOD69eto3rw5Dh8+jEGDBik0yI+NqY4J1NXUC5WvxKbFobFZ0X/ULfTMi+xvoW8ufTzXwRm5+bnYfGlbsa/tuKUvTkz4FWmro5Ev5CPudTyctg3Aq8xX8g+ISmSqW8z5To1HY/NGRe5joV/U+Y6XOd+eIT74I+gkwhOfob5pXazsuwRnv/kdtusdkS/ko55pXQDA0j7z4PzHQkQkRuI7h6nwnXEajZbZIDkjWcEjpaKYmupBXV0NsXEpMu2xsalo3Miy2P309asg6ulPkEjUkZeXjykz9sHb5+0HAc9zQfjjxE2ER8Sjfj0zrFw2FGf//B62di7Iz/8oviAl+s8zNTSEuro6YhNkb1IZm5iExvVqleoYq7/7Bi/jEuDtd0um/VO7Tji83hXaWlqIjk9EjwkzkfgqpZij0AdTkYtRFUmuJP7777/H/PnzsWzZMpl2FxcXfP/99+9N4rOyspCVlSXbmCsA6iJ5wvlPaFPDGjPsvkGbtV1K7LflC3fEvY5H1829kJmTiQm2Y3By0lG0W2+HmNTYCoqWFOFIwNuLWINfPsDdqPt46hoE+4ZdceHhJYhFBb8PK7zW44/AvwAAY3+eghfLQ/BF6/7YeXWvUuKm0klLewPr9gugq6sFh0+awX31SDwNj8OlywXXRBw59vaeGsH3n+NucCSehmyEvV1TXLh4X1lhE1EZzJ0wCsP6OMJ+zFRk/evu9Rdv3Ib1wK9gamiIiV/0xVH35egwbGKRdfakAJUwiZernCY6OhqjR48u1D5q1Cjp0pMlcXNzg4GBgcyGW9nv3e9jkZCeiNy8XJjrmcm0m+uZIaaYi0tj0mKL7v9P4t21fieY6VZF5NIQ5LgnI8c9GXVMamN9/5UIX1Iwe9e9kR0+a+aEYfvG4lr4ddx5EYRvjzkjMycTY9oXXVdNHy7hdTHnW79qsR+cYlKLOt/F9weA8MQIxKcloEHVglVPolMK+j6IfluCkZ2bjaeJEahlXEOusVDZJSSkITc3D+ZmBjLt5ub6iIktflZNEAQ8eRqLoLvP4L7pDH47fgPz5/Qrtn94eDzi41PRoL55sX2ISLESXr1Cbm4uzE2NZdrNTYwR86/Z+X/7buxwzJswCj0nzMK9h08KPZ+R+QZPIqPgf/c+Jixehdy8PIwfVDkW/6CKIVcSb29vj7///rtQ+5UrV9C16/uXO5w/fz5SUlJkNrTVlCcUpcjJy0HA8ztwaGQnbROJRHBoZAe/iBtF7uMXfgMOjexl2npYdZf2P3jzMFqu6QjrtZ2kW9Srl1h7YRN6bR8AANDW0AYA5Av5MsfJzxcgFqnWhaKqpOB8B8LBqojzHX6zyH38wm/K9AeAHo0/gV9E0f0BoLqhJUx0jBGdGgMACHgeiDc5b2Bl/rZES12sjjrGtfAs6fmHDInKICcnDwG3w+HwSTNpm0gkgoN9c/j5Pyr1ccRiESSS4r/8rF7dGCYmuoiOfvUh4RJRGeTk5CLgQRgcOraVtolEIjh0tIFfYHCx+80ZNwKLJ38Fp0nfIeB+4WvYiiIWiSHR1PjgmKkYlfBmT3KV0/Tr1w9z585FQEAAOnbsCKCgJv7YsWNwdXXFX3/9JdP33yQSSeGbQqlYKY2770/YP3IHbkXewY3IAMy0mwIdTW3s9S9YRWT/yB2ISonGglNLAQCbLm3Dpeln4fzJNJy+74VhbQahbc3WmHRkGgAgKSMJSRmyn/pz8nIQkxqLh3EFiYJfxA0kZ7zC/lE7sMxzFTJz3mCi7Veoa1Ibp+8XfUU8KYb7hS3Y/+W2gvMdEYCZn0yBjkQHe68XrFyw/8vtBef7L1cAwCbfbbg08wycu08tON82g9C2VmtM+nUGAEBHUwcufebh98A/EZMah/qmdbGm/zI8TngKr5CCezCkvUnD9it74NpnPp4nR+FZUiTmOBbsf+z2iYr/T6jE3Defxf5dX+PW7XDcuFmwxKSOjgR7D1wCAOzfPRlRL5OxYPERAMC8Of1w6/ZTPHkaC4mmBvo4WePLEV3wzfSCEigdHQlcFg7E7yduIib2FerXM8eaFcPx+EksvM7fVdo4qezS09MRGRkpffzixQuEhITAwMAAlpbFXzNBHw/3fUew320hbgWH4sa9B5g5egh0qmhh7/GCO9Dvd1uEqLgELNiwHQDw/fiRWDZtAkbMcUXEy2jpLP7rjEykZ2RCu4oWFn49Bn9duILohASYGhri2xEDUd3cFMe8LhYbB32gSlhOI1cSP2VKwbrWW7duxdatW4t8Dij4NJuXl/cB4X28jt75A1V1TbGsz0JY6Jsj8MVdOG0fiLi0eABALaOayH9n9U6/CH+MODAOP/RZgpWfueBR/BP03z280BrxJUlMT4TT9gFY8ekSXJh6Ghpq6rgfHYrPdw3D3ZfFzxjQhzt6+w9U1TXBsk8XwELPHIFR9+C05Z3zbVxD5hsSv/AbGLFvAn74bBFW9l1ScL53jpCe7zwhDy2rN8OYDsNhWMUAL1OicS70Ihaf+gHZ76wVP+f4YuTm5+Hg6B2ooqEF/2cB6L65Ly9krmBHf7uOqqZ6WLZkMCzMDRAY9AxO/VYjLq5gNYpaNU1kLkbV0ZFg66axqFHdGJmZ2QgNe4lRY7fh6G8FdfB5eflo2aIWxozqCkNDHbyMTsY573tY7HoM2dm5ShkjySc4OFimvNTNzQ1AwU0R372nCn28jnr6oKqxIZZNmwALU2MEhj6C09ffIS6xYPGAWtXMZX6/vxk2ABJNTfy+aYXMcZZu2Q3XLXuQl5ePxnVrY8ym3jA1MkDiq1TcDA5B1y+n4MHj8AodG/23cZ14Ug4VWyeePpCKrxNPZVNZ14mvrFR9nXgqm492nXiTPgo9npB4RqHHKw9yzcQX5dWrVzA0NFTU4YiIiIiISqcSltPINR26evVqHDlyRPr4iy++gLGxMapXr46goCCFBUdERERERIXJlcRv374dNWvWBACcP38e3t7e8PT0RO/evTFnzhyFBkhEREREVCKuTlM6MTEx0iT+1KlTGDJkCHr27Ik6deqgQ4cOCg2QiIiIiIhkyTUTb2RkhOfPC9ap9vT0hKOjI4CCm5v8V1ejISIiIqKPFGfiS2fgwIEYMWIEGjZsiMTERPTu3RsAcOfOHTRo0EChARIRERERlagSXtgqVxK/YcMG1KlTB8+fP8eaNWugq6sLAIiOjpZZJ56IiIiIiBRPriReQ0MDs2fPLtQ+a9asDw6IiIiIiKhMVKQERpHkSuJr1aoFe3t72NnZwd7eHvXr11d0XEREREREpVMJy2nkurB15cqV0NLSwurVq9GwYUPUrFkTo0aNgoeHBx49eqToGImIiIiI6B1yzcSPGjUKo0aNAlBQB3/p0iWcOnUKU6ZMQX5+PleoISIiIqKKUwln4uVK4gEgIyMDV65cga+vLy5evIg7d+6gefPmsLe3V2B4RERERETvwZr40unUqRPu3LmDJk2awN7eHvPmzUO3bt1gZGSk6PiIiIiIiOhf5EriQ0NDoaOjg8aNG6Nx48Zo0qQJE3giIiIiUo5KWE4j14WtiYmJuHDhAjp27AgvLy907twZ1atXx4gRI+Dh4aHoGImIiIiIilcJ79gqEgThgyIVBAEBAQH46aefcOjQIbkvbBXN0PuQMEjV5Mn1+ZFU1a7PlB0BVSDhzSFlh0AVSNS0s7JDoAokPLiq7BCKJKpnr9DjCU99FXq88iBXOc3t27fh6+sLX19fXLlyBWlpaWjRogWmTZsGOzs7RcdIRERERFS8SlhOI1cS3759e7Ru3Rp2dnaYOHEiunXrBgMDA0XHRkRERET0fipSAqNIciXxSUlJ0NfXV3QsRERERERUCnIVJuvr6+PVq1fYtWsX5s+fj6SkJAAFZTZRUVEKDZCIiIiIqERiQbGbCpBrJv7u3btwcHCAoaEhIiIiMHHiRBgbG+OPP/5AZGQkDhw4oOg4iYiIiIiKVgnLaeSaiXd2dsbYsWPx6NEjaGlpSdv79OmDy5cvKyw4IiIiIiIqTK6Z+Js3b2LHjh2F2qtXr46YmJgPDoqIiIiIqNRUpARGkeRK4iUSCVJTUwu1P3z4EFWrVv3goIiIiIiISo3lNKXTr18/LFu2DDk5OQAAkUiEyMhIzJ07F4MGDVJogEREREREJEuuJH79+vV4/fo1zMzMkJmZCTs7OzRo0AC6urpYsWKFomMkIiIiIioeV6cpHQMDA5w/fx5Xr15FUFAQXr9+jTZt2sDR0VHR8RERERERlawSltPIlcQDgI+PD3x8fBAXF4f8/HyEhobil19+AQDs2bNHYQESEREREZEsuZJ4V1dXLFu2DG3btkW1atUgEokUHRcRERERUemoSAmMIsmVxG/fvh379u3Dl19+qeh4iIiIiIjKRsnlNFu2bMHatWsRExODVq1a4ccff0T79u2L7Ovh4YEDBw4gODgYAGBjY4OVK1cW2784cl3Ymp2djU6dOsmzKxERERHRf8aRI0fg7OwMFxcX3L59G61atUKvXr0QFxdXZH9fX18MHz4cFy9ehJ+fH2rWrImePXsiKiqqTK8rVxI/YcIEaf07EREREZFSiQTFbmXg7u6OiRMnYuzYsWjatCm2b98ObW3tYq8RPXToEKZMmQJra2s0btwYu3btQn5+Pnx8fMr0unKV07x58wY7d+6Et7c3WrZsCQ0NjUKDISIiIiKqEAquic/KykJWVpZMm0QigUQikWnLzs5GQEAA5s+f/zYUsRiOjo7w8/Mr1WtlZGQgJycHxsbGZYpRriT+7t27sLa2BgBpPc//yX2R689fyLcfqaQRHl8rOwSqQL+k3lR2CFSBRE07KzsEqkDCg6vKDoFI4dzc3ODq6irT5uLigqVLl8q0JSQkIC8vD+bm5jLt5ubmCA0NLdVrzZ07F5aWlmVeql2uJP7ixYvy7EZEREREpHgKnomfP38+nJ2dZdr+PQuvCKtWrcLhw4fh6+sLLS2tMu0r9zrxREREREQfBQWvTlNU6UxRTE1NoaamhtjYWJn22NhYWFhYlLjvunXrsGrVKml5elnJdWErEREREVFlp6mpCRsbG5mLUv9/kaqtrW2x+61ZswbLly+Hp6cn2rZtK9drcyaeiIiIiFSbEm/25OzsjDFjxqBt27Zo3749Nm7ciPT0dIwdOxYAMHr0aFSvXh1ubm4AgNWrV2PJkiX45ZdfUKdOHcTExAAAdHV1oaurW+rXZRJPRERERKpNiTd7Gjp0KOLj47FkyRLExMTA2toanp6e0otdIyMjIRa/LX7Ztm0bsrOzMXjwYJnjFHXhbEmYxBMRERERfYCpU6di6tSpRT7n6+sr8zgiIkIhr8kknoiIiIhUmxLLaZSFSTwRERERqTYlltMoC1enISIiIiJSMZyJJyIiIiLVxnIaIiIiIiIVw3IaIiIiIiL62HEmnoiIiIhUG8tpiIiIiIhUDMtpiIiIiIjoY8eZeCIiIiJSbSynISIiIiJSMSynISIiIiKijx1n4omIiIhItbGchoiIiIhIxbCchoiIiIiIPnaciSciIiIi1cZyGiIiIiIiFcNymtJ5/vw5Xrx4IX1848YNzJw5Ezt37lRYYEREREREVDS5kvgRI0bg4sWLAICYmBj06NEDN27cwMKFC7Fs2TKFBkhEREREVCKxoNhNBciVxAcHB6N9+/YAgKNHj6J58+a4du0aDh06hH379ikyPiIiIiKikokExW4qQK4kPicnBxKJBADg7e2Nfv36AQAaN26M6OhoxUVHRERERESFyJXEN2vWDNu3b8fff/+N8+fPw8nJCQDw8uVLmJiYKDRAIiIiIqISsZymdFavXo0dO3bA3t4ew4cPR6tWrQAAf/31l7TMhoiIiIioQlTCchq5lpi0t7dHQkICUlNTYWRkJG2fNGkStLW1FRYcEREREREVJvcdWwVBQEBAAHbs2IG0tDQAgKamJpN4IiIiIqpYlbCcRq6Z+GfPnsHJyQmRkZHIyspCjx49oKenh9WrVyMrKwvbt29XdJxEREREREVTkRIYRZIriZ8xYwbatm2LoKAgmQtZBwwYgIkTJyosOFUzZXx3zJnqBAszAwTdf45p8w7h5u3wIvsO+KwNFsz6DA3qmkFDXQ2PnsZi/VYv/HzUT9pHR0eCVUsGo3+f1jAx0kV4ZAI27/TGjn2+FTQiKkmPeub4tGE1GGhpIDIlA/uDIvA0Ob3Ivgu7NkHTqvqF2u/EJGPdtYcAAImaGMOa10RbS2PoaqojPj0LXk9i4BMeV67joNKZ4tACc/q0hoWBNoKeJ2Dawcu4+bToczOmS2Psm+Qo0/YmOxdVJryd4NCRaGDVEFv0t6kHE10thMenYvO5IOy4eL9cx0GlM2X4QMwZNwIWpsYICnuMaSs24Oa9kCL7ThjcF6M/743mDeoCAAIehGHBxh0y/V2+HYdhvR1R08IM2Tk5CHgQhoWbduLG3QcVMh5SjJs3b2L37t0IDg5GfHw8tmzZAkdHx/fvSFQO5Eri//77b1y7dg2ampoy7XXq1EFUVJRCAlM1Q/q3g/vyoZg8+yD8A55i5tc94HXMGVYdFiA+Ia1Q/6TkdKxwP4XQR9HIzs7FZz1bYe+P4xAXn4pz//wRd18+DN27NsaoyR6IiExAz0+aY+vaUXgZ8wonPQMreIT0ro7VjTGyRS3sCQzHk6R0ODWwwLzOjTH7fBBSs3IL9d94/SHUxW+r13Q11eHm0AI3XiRJ20a1rI2mVfWx9eZjxGdkoYWZAcZa10Xym2zcjn5VEcOiYgzp0ADuI7pg8j5f+D+Jwcxe1vCa0w9W3x9CfFpmkfukZGTBau4h6WNBkJ0lch/RBd2bVseo7ecRkZCKns1rYesYO7x8lY6TdyLKczj0HkOcHOA+dxomu66F/90HmPnlEHjtdIfVp8MRn/SqUH/79m3w6+nzuBYYjDdZWZg7YRTOeWxAs36j8DIuAQDwMOI5pq5wx9PnL1FFS4JZo4finMcGNHAaioTkwsekj1NGRgasrKwwaNAgTJ06Vdnh0LtUpARGkeSqic/Pz0deXl6h9hcvXkBPT++Dg1JFzlN6wePgZez75QpCwl5i8ncHkJGZjXEjuxbZ/9LVMJw4fRuhD6PxNCIem3d64+79F+jSsZG0T6f29bH/8DVcuhqGZ88T4XHgEoKCn6N9m7oVNSwqRu+G1XAxIg6XnyUgKi0Te+6EIysvH3a1qxbZPz0nDylZOdKthZkBsvPy4R/1NolvaKyLvyPjEZKQhoSMbFyMiEdkSgbqG+lW1LCoGM5O1vDwvY99f4cg5GUyJu+7iIysXIyza1LsPoIAxKZkSLe4VNlkv1NDC+y/EopLoVF4lpAGD9/7CIpMQPt65uU9HHoP56+GwuPYSew7fgYhTyIw2XUtMt5kYdzAz4rsP+p7V2w7fBxBoY8QFh6JCYtXQSwWw6FjW2mfX0+fh4/fLYS/eIkHj8PhvHozDPR00dKqfkUNixTAzs4Os2bNQo8ePZQdCv1bJVydRq4kvmfPnti4caP0sUgkwuvXr+Hi4oI+ffooKjaVoaGhBptWteF96e3XooIgwPvSA9i2K90bdPduTWDVwAKX/cKkbdduPEG/3tawrGYIALDv0hiNGlhIZ+pJOdREItQ11EFwXKq0TQAQHJeChsal+xBrX6cq/F4kIisvX9r2KOk12lQzgpGWBgCgqak+LHS1cC82RaHxU9loqIlhU8cM3vefS9sEAfB+8AK2DSyK3U9XSwMR7qMRuWEMTszsg6bVjWWev/YoBv1a14WlkQ4AwL5JdTSyMMS54OdFHY4qiIaGOmyaWsH7+k1pmyAI8Pa7BVvr5qU6hraWFjTU1ZGUklrk8xoa6pg05HO8Sk1DUOhjhcRNRJWPXOU069evR69evdC0aVO8efMGI0aMwKNHj2Bqaopff/1V0TF+9ExN9KCurobYONk37Ni4VDRuWK3Y/fT1qiAqeD0kEnXk5QmYMucgvH3ffhCYNu8Qdm4Yg6hgd+Tk5CI/X8DEWfvxt9/DchsLvZ+eRB1qYhFSsnJk2lOzcmCpV+W9+9cz0kFNA23svP1Upn1/UATGt66Ln/q0QW5+PgQB2HUnHKGJhcuxqOKY6lWBupoYsf+aSY9NyUDjfz5g/1tYzCuM2+WDu88TYVBFE7P7tMa1xYPQbP4viPrnuolpBy9h57juiNo0Fjm5ecgXgIl7LuDvsJflPSQqgamhIdTV1RGbkCTTHpuYhMb1apXqGKu/+wYv4xLg7XdLpv1Tu044vN4V2lpaiI5PRI8JM5H4ih/SiRSiEpbTyJXE16hRA0FBQTh8+DDu3r2L169fY/z48Rg5ciSqVHl/EpOVlYWsrCzZRiEPEKnJE47KSnv9Btb2S6GrI4FDt6Zw/2EYnj6Lx6WrBbPx0yY6oGPb+ug7YhOePU9Et06NsGVNQU28zyVeDKWq7OtURWRKRqGLYHvWN0cDY12suxaGhIwsNDbVx1et6iA5Mxv344ue0aOP0/XHMbj+OEb6+NrjGISsGoGvuzfHkt/9AQDTerRCx/rm6Ot+Cs8S09DNyhJbRhfUxPvcf6Gs0OkDzZ0wCsP6OMJ+zFRkZWfLPHfxxm1YD/wKpoaGmPhFXxx1X44OwyYWWWdPRGWkIiUwiiRXEg8A6urqGDVqlFz7urm5wdXVVbZRyxrQbi1vOEqVkJiG3Nw8mJvJrj5ibqaPmLjiZ1kEQcCTf1YeCQp+jiaNqmH+zE9x6WoYtLQ0sHLRIAwY/RPOnL8LALj34AWsm9fC7G97MYlXorSsXOTlCzCQaMi060s0kPImp5i9CkjUxLCtYYLfHsgmaRpiEYY2q4kN1x8hMOYVAOB5aiZqG2rj00bVmMQrUUJaJnLz8mGuLztBYW6gjZiUjFIdIzcvH3eeJaCBmQEAQEtDDSu/6IgBm87gTNAzAMC954mwrmWK2b1bM4lXooRXr5CbmwtzU9nyJ3MTY8T8a3b+374bOxzzJoyC4/iZuPfwSaHnMzLf4ElkFJ5ERsH/7n08PHsY4wf1xSqPgwodAxFVDqVO4v/6669SH7Rfv34lPj9//nw4OzvLtBnUmVbq439scnLyEBD0DA7dmuDPM3cAFFwn4NCtCX7adaHUxxGLRZBoFpwSDQ01aGqqIz9f9pNlXl4+xGKR4oKnMssTBIS/SkczM30ERCcDAEQAmpsZ4NyTmBL37VDdGOpiMa4+T5RpVxeLoS4WF1rBJF8QIAbPtzLl5OUjICIODs1q4s9/lowViQCHpjXwk/fdUh1DLBKhRQ0TnLlbkLBrqImhqa6G/H+d77x8AWIRz7cy5eTkIuBBGBw6tsWfPn8D+Of9vKMNfvrl92L3mzNuBBZ+PQa9Jjoj4H5oqV5LLBJDoqnx/o5E9H4spyle//79S9VPJBIVuXLNuyQSCSQSyb92VO1SGvetXti/ZQJuBUbgxu1wzPy6B3S0Jdj7yxUAwP6tExAVnYwFywv+CMyb2Qe3AiPwJDweEok6+ji2xJdDbPHN7IIZmbS0N/C9Eoq1rl8g8002nj1PhF1nK4we2gnOiw8rbZxU4OyjaHzdtj7Ck9PxJPk1nBpYQKImxqVn8QCAyTb1kPwmB0fuy16kaFenKgJeJuN1tuwylJm5eXgQn4rhzWshOy8CCRnZaGKqh661quLnfxI/Uh53z0Dsn+iIW+FxuPE0FjN7toKORB17LxesA75/kiOiktOx4FjBfR4Wf94O15/E4HFsCgy1JZjTpzVqm+phl2/BRelpb3LgGxKFtcM6IzM7D88SUmHXuDpGd2kM53/eM0h53PcdwX63hbgVHIob9x5g5ugh0Kmihb3HTwMA9rstQlRcAhZsKFj3//vxI7Fs2gSMmOOKiJfR0ln81xmZSM/IhHYVLSz8egz+unAF0QkJMDU0xLcjBqK6uSmOeV1U2jip7NLT0xEZGSl9/OLFC4SEhMDAwACWlpZKjIxYTlOC/Pz893eqxI6euImqpnpYNq8/LMwMEBj8HE5DNiDunzKIWtWNZf4PdbQl2LrmS9SwNELmm2yEPorBqMkeOHri7YoIwyZuh9viwTi0YxKMDXXw7EUiFq74A9v3+lb08OhfrkclQU+igcFNa8BAooFnKRlYfTVUuka8ibYE/347qaarhcam+nC7UvQNY3668RhDm9fElHYNoKupjoSMLBy9/5w3e/oIHPV/jKp6VbBsYHtYGOggMDIeTmtPSpeNrGWiJzOrbqQjgce4T2BhoIPk9DcIiIhHp+W/IeRlsrTPsK1ecPvCFocm94CxrhaeJaRh4W/Xsf1CcIWPj2Qd9fRBVWNDLJs2ARamxggMfQSnr79DXGLB+atVzVzmW9Jvhg2ARFMTv29aIXOcpVt2w3XLHuTl5aNx3doYs6k3TI0MkPgqFTeDQ9D1yyl48LjoGwLSxyk4OBijR4+WPnZzcwNQcLPLVatWKSssqqREwr+/v1cSkck4ZYdAFWiEx9fKDoEq0C/vfDilSuBW5VulrDITHlxVdghEEH2l2HtsCPtiFXq88iD3ha3p6em4dOkSIiMjkf2vK/CnT5/+wYEREREREZUKy2lK586dO+jTpw8yMjKQnp4OY2NjJCQkQFtbG2ZmZkziiYiIiIjKkVx3bJ01axb69u2L5ORkVKlSBdevX8ezZ89gY2ODdevWKTpGIiIiIqLiiQXFbipAriQ+MDAQ3333HcRiMdTU1JCVlYWaNWtizZo1WLBggaJjJCIiIiIqnkhQ7KYC5EriNTQ0IBYX7GpmZiZdbsnAwADPnz8vaVciIiIiIvpActXEt27dGjdv3kTDhg1hZ2eHJUuWICEhAQcPHkTz5s0VHSMRERERUfFUpARGkeSaiV+5ciWqVasGAFixYgWMjIzwzTffICEhATt27FBogEREREREJaqE5TRyzcQ3a9ZMent4MzMzbN++HcePH0fTpk1hbW2tyPiIiIiIiOhf5JqJ//zzz3HgwAEAwKtXr9CxY0e4u7ujf//+2LZtm0IDJCIiIiIqEVenKZ3bt2+ja9euAIDffvsN5ubmePbsGQ4cOIDNmzcrNEAiIiIiohJVwnIauZL4jIwM6OnpAQDOnTuHgQMHQiwWo2PHjnj27JlCAyQiIiIiIllyJfENGjTAiRMn8Pz5c3h5eaFnz54AgLi4OOjr6ys0QCIiIiKiErGcpnSWLFmC2bNno06dOujQoQNsbW0BFMzKt27dWqEBEhERERGVqBKW08i1Os3gwYPRpUsXREdHo1WrVtJ2BwcHDBgwQGHBERERERFRYXIl8QBgYWEBCwsLmbb27dt/cEBERERERGWiIiUwiiR3Ek9ERERE9FFQkRIYRZKrJp6IiIiIiJSHM/FEREREpNpYTkNEREREpGJYTkNERERERB87zsQTERERkWpjOQ0RERERkYphOQ0REREREX3sOBNPRERERKqN5TRERERERCqG5TRERERERPSx40w8EREREak2ltMQEREREakYltMQEREREdHHjkk8EREREak2saDYrYy2bNmCOnXqQEtLCx06dMCNGzdK7H/s2DE0btwYWlpaaNGiBc6cOVP2IZd5DyIiIiKij4lIUOxWBkeOHIGzszNcXFxw+/ZttGrVCr169UJcXFyR/a9du4bhw4dj/PjxuHPnDvr374/+/fsjODi4TK/LJJ6IiIiISE7u7u6YOHEixo4di6ZNm2L79u3Q1tbGnj17iuy/adMmODk5Yc6cOWjSpAmWL1+ONm3a4KeffirT6zKJJyIiIiLVpuBymqysLKSmpspsWVlZhV42OzsbAQEBcHR0fBuKWAxHR0f4+fkVGaqfn59MfwDo1atXsf2L89GsTiMkFv1p5b8sKysLbm5umD9/PiQSibLDoXJWmc/3oYEdlB1ChavM5xuYquwAKlzlPt+VD8/3x0dwUezqNEuXLoWrq6tMm4uLC5YuXSrTlpCQgLy8PJibm8u0m5ubIzQ0tMhjx8TEFNk/JiamTDFyJl6JsrKy4OrqWuQnO/rv4fmuXHi+Kxee78qF5/u/b/78+UhJSZHZ5s+fr+ywZHw0M/FERERERB8DiURSqm9ZTE1NoaamhtjYWJn22NhYWFhYFLmPhYVFmfoXhzPxRERERERy0NTUhI2NDXx8fKRt+fn58PHxga2tbZH72NrayvQHgPPnzxfbvziciSciIiIikpOzszPGjBmDtm3bon379ti4cSPS09MxduxYAMDo0aNRvXp1uLm5AQBmzJgBOzs7rF+/Hp9++ikOHz6MW7duYefOnWV6XSbxSiSRSODi4sKLYioJnu/Khee7cuH5rlx4vuldQ4cORXx8PJYsWYKYmBhYW1vD09NTevFqZGQkxOK3xS+dOnXCL7/8gkWLFmHBggVo2LAhTpw4gebNm5fpdUWCICj2cl4iIiIiIipXrIknIiIiIlIxTOKJiIiIiFQMk3giIiIiIhXDJF4B7O3tMXPmTABAnTp1sHHjxlLvGxERAZFIhMDAwA+KQVHHISLF2rdvHwwNDaWPly5dCmtra6XFQ0QfTiQS4cSJE8oOgyo5JvEKdvPmTUyaNKnU/WvWrIno6GjpFcm+vr4QiUR49epVOUVI5UUkEpW49e3bFyKRCNevXy9yfwcHBwwcOLCCo6aKNnv27ELrAxMREZUVl5hUsKpVq5apv5qaWpnv0EUfp+joaOm/jxw5giVLliAsLEzapquriy5dumDPnj3o2LGjzL4RERG4ePEiTp48WWHxUtlkZ2dDU1Pzg4+jq6sLXV1dBURE9H45OTnQ0NBQdhhEVA44E19G6enpGD16NHR1dVGtWjWsX79e5vl/l9OEhoaiS5cu0NLSQtOmTeHt7S3zNdy7ZTARERH45JNPAABGRkYQiUT46quvAACenp7o0qULDA0NYWJigs8++wxPnjypiCFTKVlYWEg3AwMDiEQimTZdXV2MHz8eR44cQUZGhsy++/btQ7Vq1eDk5KSk6Onf7O3tMXXqVMycOROmpqbo1asX3N3d0aJFC+jo6KBmzZqYMmUKXr9+LbPfvn37UKtWLWhra2PAgAFITEyUef7f5TT5+flYtmwZatSoAYlEIl1fmEpmb2+P6dOn4/vvv4exsTEsLCywdOlS6fOvXr3ChAkTULVqVejr66N79+4ICgoCAKSkpEBNTQ23bt0CUHAOjI2NZT5c//zzz6hZsyaAgg9wU6dORbVq1aClpYXatWtLb9oCFHwLt23bNvTu3RtVqlRBvXr18Ntvv8nEO3fuXDRq1Aja2tqoV68eFi9ejJycHOnz//+52LFjB2rWrAltbW0MGTIEKSkpMsfZtWsXmjRpAi0tLTRu3Bhbt26VPvf/vydHjhyBnZ0dtLS0cOjQoQ/8n/7v+u2339CiRQtUqVIFJiYmcHR0RHp6Om7evIkePXrA1NQUBgYGsLOzw+3bt0s81vPnzzFkyBAYGhrC2NgYn3/+OSIiIqTP+/r6on379tDR0YGhoSE6d+6MZ8+elfMI6b+OSXwZzZkzB5cuXcKff/6Jc+fOwdfXt9hf7ry8PPTv3x/a2trw9/fHzp07sXDhwmKPXbNmTfz+++8AgLCwMERHR2PTpk0ACj48ODs749atW/Dx8YFYLMaAAQOQn5+v+EFSuRk5ciSysrJk/sALgoD9+/fjq6++gpqamhKjo3/bv38/NDU1cfXqVWzfvh1isRibN2/G/fv3sX//fly4cAHff/+9tL+/vz/Gjx+PqVOnIjAwEJ988gl++OGHEl9j06ZNWL9+PdatW4e7d++iV69e6NevHx49elTew1N5+/fvh46ODvz9/bFmzRosW7YM58+fBwB88cUXiIuLw9mzZxEQEIA2bdrAwcEBSUlJMDAwgLW1NXx9fQEA9+7dg0gkwp07d6Qfyi5dugQ7OzsAwObNm/HXX3/h6NGjCAsLw6FDh1CnTh2ZWBYvXoxBgwYhKCgII0eOxLBhwxASEiJ9Xk9PD/v27cODBw+wadMmeHh4YMOGDTLHePz4MY4ePYqTJ0/C09MTd+7cwZQpU6TPHzp0CEuWLMGKFSsQEhKClStXYvHixdi/f7/McebNm4cZM2YgJCQEvXr1Usj/9X9NdHQ0hg8fjnHjxiEkJAS+vr4YOHAgBEFAWloaxowZgytXruD69eto2LAh+vTpg7S0tCKPlZOTg169ekFPTw9///03rl69Cl1dXTg5OSE7Oxu5ubno378/7OzscPfuXfj5+WHSpEkQiUQVPGr6zxGo1NLS0gRNTU3h6NGj0rbExEShSpUqwowZMwRBEITatWsLGzZsEARBEM6ePSuoq6sL0dHR0v7nz58XAAjHjx8XBEEQwsPDBQDCnTt3BEEQhIsXLwoAhOTk5BJjiY+PFwAI9+7dK/I4pFx79+4VDAwMinxu2LBhgp2dnfSxj4+PAEB49OhRxQRHpWJnZye0bt26xD7Hjh0TTExMpI+HDx8u9OnTR6bP0KFDZX4WXFxchFatWkkfW1paCitWrJDZp127dsKUKVPkD74SsLOzE7p06SLT1q5dO2Hu3LnC33//Lejr6wtv3ryReb5+/frCjh07BEEQBGdnZ+HTTz8VBEEQNm7cKAwdOlRo1aqVcPbsWUEQBKFBgwbCzp07BUEQhGnTpgndu3cX8vPzi4wFgDB58mSZtg4dOgjffPNNsfGvXbtWsLGxkT52cXER1NTUhBcvXkjbzp49K4jFYunfkPr16wu//PKLzHGWL18u2NraCoLw9u/Axo0bi31dKhAQECAAECIiIt7bNy8vT9DT0xNOnjwpbXv37/jBgwcFKysrmZ+PrKwsoUqVKoKXl5eQmJgoABB8fX0VPg6q3DgTXwZPnjxBdnY2OnToIG0zNjaGlZVVkf3DwsJQs2ZNmZr39u3by/Xajx49wvDhw1GvXj3o6+tLZ4EiIyPlOh4pz7hx43D58mVpOdSePXtgZ2eHBg0aKDky+jcbGxuZx97e3nBwcED16tWhp6eHL7/8EomJidLyqJCQEJn3BwCwtbUt9vipqal4+fIlOnfuLNPeuXNnmVlcKlrLli1lHlerVg1xcXEICgrC69evYWJiIr0GQVdXF+Hh4dLfOzs7O1y5cgV5eXm4dOkS7O3tYW9vD19fX7x8+RKPHz+Gvb09AOCrr75CYGAgrKysMH36dJw7d65QLP8+z7a2tjLn8MiRI+jcubO0tG7RokWF3r9r1aqF6tWryxwjPz8fYWFhSE9Px5MnTzB+/HiZMf3www+FSivbtm1b9v/MSqZVq1ZwcHBAixYt8MUXX8DDwwPJyckAgNjYWEycOBENGzaEgYEB9PX18fr162L/3gYFBeHx48fQ09OTnhdjY2O8efMGT548gbGxMb766iv06tULffv2xaZNm2SuoSKSF5N4FdG3b18kJSXBw8MD/v7+8Pf3B1BQq0mqxcHBAbVq1cK+ffuQmpqKP/74A+PHj1d2WFQEHR0d6b8jIiLw2WefoWXLlvj9998REBCALVu2AODvobL8+4JNkUiE/Px8vH79GtWqVUNgYKDMFhYWhjlz5gAAunXrhrS0NNy+fRuXL1+WSeIvXboES0tLNGzYEADQpk0bhIeHY/ny5cjMzMSQIUMwePDgUsfp5+eHkSNHok+fPjh16hTu3LmDhQsXlunn5v9lPh4eHjJjCg4OLrTi1bs/t1Q0NTU1nD9/HmfPnkXTpk3x448/wsrKCuHh4RgzZgwCAwOxadMmXLt2DYGBgTAxMSn2fL1+/Ro2NjaFft4ePnyIESNGAAD27t0LPz8/dOrUCUeOHEGjRo2KXamMqLS4Ok0Z1K9fHxoaGvD390etWrUAAMnJyXj48KG0dvJdVlZWeP78OWJjY2Fubg6gYAnKkvx/9Yu8vDxpW2JiIsLCwuDh4YGuXbsCAK5cuaKQMVHFE4vFGDt2LHbv3o3q1atDU1OzTAkBKUdAQADy8/Oxfv16iMUF8x9Hjx6V6dOkSRPpB+z/K+kPtb6+PiwtLXH16lWZ95CrV6/K/a0dFSTdMTExUFdXL1S7/n+GhoZo2bIlfvrpJ2hoaKBx48YwMzPD0KFDcerUqULv6fr6+hg6dCiGDh2KwYMHw8nJCUlJSTA2NgZQcJ5Hjx4t7X/9+nW0bt0aAHDt2jXUrl1b5pqooi5qjIyMxMuXL2FpaSk9hlgshpWVFczNzWFpaYmnT59i5MiRH/T/QwVEIhE6d+6Mzp07Y8mSJahduzaOHz+Oq1evYuvWrejTpw+AgotWExISij1OmzZtcOTIEZiZmUFfX7/Yfq1bt0br1q0xf/582Nra4pdffim0UhlRWTCJL4P/ry4yZ84cmJiYwMzMDAsXLpT+Qf+3Hj16oH79+hgzZgzWrFmDtLQ0LFq0CACKvaCldu3aEIlEOHXqFPr06YMqVarAyMgIJiYm2LlzJ6pVq4bIyEjMmzev3MZJ5W/s2LFYtmwZFixYgOHDh6NKlSrKDoneo0GDBsjJycGPP/6Ivn37Si92fdf06dPRuXNnrFu3Dp9//jm8vLzeu9LMnDlz4OLigvr168Pa2hp79+5FYGAgVxX5AI6OjrC1tUX//v2xZs0aNGrUCC9fvsTp06cxYMAAabmJvb09fvzxR+mHaGNjYzRp0gRHjhyRfssCAO7u7qhWrRpat24NsViMY8eOwcLCQuYmXseOHUPbtm3RpUsXHDp0CDdu3MDu3bsBAA0bNkRkZCQOHz6Mdu3a4fTp0zh+/HihuLW0tDBmzBisW7cOqampmD59OoYMGSItyXR1dcX06dNhYGAAJycnZGVl4datW0hOToazs3N5/Xf+J/n7+8PHxwc9e/aEmZkZ/P39ER8fjyZNmqBhw4Y4ePAg2rZti9TUVMyZM6fE9+iRI0di7dq1+Pzzz6UrTT179gx//PEHvv/+e+Tk5GDnzp3o168fLC0tERYWhkePHsl86COSi7KL8lVNWlqaMGrUKEFbW1swNzcX1qxZI9jZ2RV5YasgCEJISIjQuXNnQVNTU2jcuLFw8uRJAYDg6ekpCELRF6QuW7ZMsLCwEEQikTBmzBhBEAouiG3SpIkgkUiEli1bCr6+viVeIEvKVdKFrf/Xs2dPAYBw48aNigmKyuTd3+v/c3d3F6pVqyZUqVJF6NWrl3DgwIFCF6Lv3r1bqFGjhlClShWhb9++wrp160q8sDUvL09YunSpUL16dUFDQ0Pm4koqXlHn5/PPP5e+Z6ampgrTpk0TLC0tBQ0NDaFmzZrCyJEjhcjISGn/48ePCwCEbdu2SdtmzJghABBCQ0OlbTt37hSsra0FHR0dQV9fX3BwcBBu374tfR6AsGXLFqFHjx6CRCIR6tSpIxw5ckQmtjlz5ggmJiaCrq6uMHToUGHDhg1F/lxs3bpVsLS0FLS0tITBgwcLSUlJMsc5dOiQYG1tLWhqagpGRkZCt27dhD/++EMQBP4dKIsHDx4IvXr1EqpWrSpIJBKhUaNGwo8//igIgiDcvn1baNu2raClpSU0bNhQOHbsWKG/7e/+/RUEQYiOjhZGjx4tmJqaChKJRKhXr54wceJEISUlRYiJiRH69+8vVKtWTdDU1BRq164tLFmyRMjLy6vgUdN/jUgQBEFJnx8qpatXr6JLly54/Pgx6tevr+xwiIjoA4lEIhw/fhz9+/eX+xhLly7FiRMnEBgYqLC4iOi/jeU05ez48ePQ1dVFw4YN8fjxY8yYMQOdO3dmAk9EREREcmMSX87S0tIwd+5cREZGwtTUFI6OjoXu8kpEREREVBYspyEiIiIiUjFcJ56IiIiISMUwiSciIiIiUjFM4omIiIiIVAyTeCIiIiIiFcMknoiIiIhIxTCJJyIiIiJSMUziiYiIiIhUDJN4IiIiIiIVwySeiIiIiEjF/A/YS0zGt8bYCwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "## 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": null,
      "metadata": {
        "id": "8UdKVi-2tNKI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "outputId": "3e2ef7c2-f47a-4a4b-a801-984ab6bf54ad"
      },
      "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",
      "source": [
        "advertising_df.corr()"
      ],
      "metadata": {
        "id": "X1tn4RQYA7o-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "74fda621-e9c3-42c0-b997-be9f9ff9fa3e"
      },
      "execution_count": null,
      "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-24ee76e9-3389-4d35-9e19-9029076915ad\">\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-24ee76e9-3389-4d35-9e19-9029076915ad')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
              "    <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-8273e5fb-65a2-4721-9ea5-195168bb3525\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8273e5fb-65a2-4721-9ea5-195168bb3525')\"\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-8273e5fb-65a2-4721-9ea5-195168bb3525 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-24ee76e9-3389-4d35-9e19-9029076915ad 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-24ee76e9-3389-4d35-9e19-9029076915ad');\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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WKFX9SCauZp4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 842
        },
        "outputId": "5fe67aee-7e12-407e-b217-905cfd94f96b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-11-6c77a4103e7b>: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": 11
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "3f7bN--N5TYF"
      },
      "source": [
        "Rather than plot them separately, an efficient way to view the linear relationsips between variables is to use a \"for loop\" that plots all of the features at once.\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": null,
      "metadata": {
        "id": "otYqhhMapHhY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "7e656391-19ee-4052-ac30-5e4f49bf5d23"
      },
      "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": null,
      "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": null,
      "metadata": {
        "id": "hVOPQyd2HtxP"
      },
      "outputs": [],
      "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",
        "\n"
      ]
    },
    {
      "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 ratio\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": null,
      "metadata": {
        "id": "K4p_3UGEcqPb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20c8e9f7-8a74-430a-95ef-d4f38b3f9eb0"
      },
      "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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J10hkM7BAhbh"
      },
      "source": [
        "#### Build and Train the Network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K-_Vage0th27",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c7a0a34-38d7-4e69-f09e-d331cc572a68"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/32\n",
            "23/23 [==============================] - 2s 14ms/step - loss: 6650.7524 - mse: 6650.7524 - val_loss: 5988.0024 - val_mse: 5988.0024\n",
            "Epoch 2/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 5201.2021 - mse: 5201.2021 - val_loss: 4691.7046 - val_mse: 4691.7046\n",
            "Epoch 3/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 4098.2744 - mse: 4098.2744 - val_loss: 3693.1416 - val_mse: 3693.1416\n",
            "Epoch 4/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 3249.0540 - mse: 3249.0540 - val_loss: 2955.8945 - val_mse: 2955.8945\n",
            "Epoch 5/32\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 2620.8323 - mse: 2620.8323 - val_loss: 2360.3127 - val_mse: 2360.3127\n",
            "Epoch 6/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 2110.0891 - mse: 2110.0891 - val_loss: 1924.2250 - val_mse: 1924.2250\n",
            "Epoch 7/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 1727.6505 - mse: 1727.6505 - val_loss: 1567.8118 - val_mse: 1567.8118\n",
            "Epoch 8/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 1413.7540 - mse: 1413.7540 - val_loss: 1282.6582 - val_mse: 1282.6582\n",
            "Epoch 9/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 1130.8163 - mse: 1130.8163 - val_loss: 934.6696 - val_mse: 934.6696\n",
            "Epoch 10/32\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 713.6685 - mse: 713.6685 - val_loss: 451.1403 - val_mse: 451.1403\n",
            "Epoch 11/32\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 347.5288 - mse: 347.5288 - val_loss: 211.8526 - val_mse: 211.8526\n",
            "Epoch 12/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 201.1693 - mse: 201.1693 - val_loss: 135.8934 - val_mse: 135.8934\n",
            "Epoch 13/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 150.9529 - mse: 150.9529 - val_loss: 112.7614 - val_mse: 112.7614\n",
            "Epoch 14/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 135.2691 - mse: 135.2691 - val_loss: 104.1701 - val_mse: 104.1701\n",
            "Epoch 15/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 127.9212 - mse: 127.9212 - val_loss: 100.5317 - val_mse: 100.5317\n",
            "Epoch 16/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 124.3473 - mse: 124.3473 - val_loss: 97.4547 - val_mse: 97.4547\n",
            "Epoch 17/32\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 121.0918 - mse: 121.0918 - val_loss: 94.7039 - val_mse: 94.7039\n",
            "Epoch 18/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 118.3253 - mse: 118.3253 - val_loss: 91.4572 - val_mse: 91.4572\n",
            "Epoch 19/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 115.4136 - mse: 115.4136 - val_loss: 88.4836 - val_mse: 88.4836\n",
            "Epoch 20/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 112.5515 - mse: 112.5515 - val_loss: 84.9482 - val_mse: 84.9482\n",
            "Epoch 21/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 108.7553 - mse: 108.7553 - val_loss: 80.1010 - val_mse: 80.1010\n",
            "Epoch 22/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 103.7544 - mse: 103.7544 - val_loss: 75.1391 - val_mse: 75.1391\n",
            "Epoch 23/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 98.0247 - mse: 98.0247 - val_loss: 68.1211 - val_mse: 68.1211\n",
            "Epoch 24/32\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 91.0498 - mse: 91.0498 - val_loss: 61.5812 - val_mse: 61.5812\n",
            "Epoch 25/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 84.9844 - mse: 84.9844 - val_loss: 55.5421 - val_mse: 55.5421\n",
            "Epoch 26/32\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 79.5662 - mse: 79.5662 - val_loss: 50.0055 - val_mse: 50.0055\n",
            "Epoch 27/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 73.8904 - mse: 73.8904 - val_loss: 45.9566 - val_mse: 45.9566\n",
            "Epoch 28/32\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 69.5305 - mse: 69.5305 - val_loss: 42.5203 - val_mse: 42.5203\n",
            "Epoch 29/32\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 65.9228 - mse: 65.9228 - val_loss: 39.2514 - val_mse: 39.2514\n",
            "Epoch 30/32\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 62.9458 - mse: 62.9458 - val_loss: 36.5935 - val_mse: 36.5935\n",
            "Epoch 31/32\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 60.0667 - mse: 60.0667 - val_loss: 34.5339 - val_mse: 34.5339\n",
            "Epoch 32/32\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 57.6828 - mse: 57.6828 - val_loss: 32.6905 - val_mse: 32.6905\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": "RHBzoonKnFfj"
      },
      "source": [
        "### Visualization"
      ]
    },
    {
      "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": null,
      "metadata": {
        "id": "jB1VUt-bmTPH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 603
        },
        "outputId": "914c6b4c-8ea1-44f5-fb39-09b02c68ad8f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": 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\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",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Step 3:  Tune the Neural Network Hyperparameters"
      ],
      "metadata": {
        "id": "qja3d7BkyaKP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CHALLENGE: Manual Hyperparameter Tuning"
      ],
      "metadata": {
        "id": "fOikBhQYwBhG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Play with:\n",
        "1. Adding additional layers\n",
        "2. Adding additional neurons\n",
        "3. Change the number of epochs.\n",
        "\n",
        "NOTE: After each change, run the cell to plot the graph and review the loss curves."
      ],
      "metadata": {
        "id": "TH4MgbsVyRhB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Build Model\n",
        "model = Sequential()\n",
        "model.add(Dense(4,input_dim=4, activation='relu'))\n",
        "model.add(Dense(4,activation='relu'))\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 = 100)"
      ],
      "metadata": {
        "id": "C-urqQicwAdn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6154f40b-d365-439f-ce46-b311ebde64cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "23/23 [==============================] - 1s 7ms/step - loss: 222.9089 - mse: 222.9089 - val_loss: 232.9150 - val_mse: 232.9150\n",
            "Epoch 2/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 217.7270 - mse: 217.7270 - val_loss: 230.9861 - val_mse: 230.9861\n",
            "Epoch 3/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 216.5080 - mse: 216.5080 - val_loss: 230.1003 - val_mse: 230.1003\n",
            "Epoch 4/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 215.7948 - mse: 215.7948 - val_loss: 229.3728 - val_mse: 229.3728\n",
            "Epoch 5/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 215.1493 - mse: 215.1493 - val_loss: 228.6784 - val_mse: 228.6784\n",
            "Epoch 6/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 214.5063 - mse: 214.5063 - val_loss: 227.9842 - val_mse: 227.9842\n",
            "Epoch 7/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 213.8602 - mse: 213.8602 - val_loss: 227.3055 - val_mse: 227.3055\n",
            "Epoch 8/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 213.2240 - mse: 213.2240 - val_loss: 226.6180 - val_mse: 226.6180\n",
            "Epoch 9/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 212.5853 - mse: 212.5853 - val_loss: 225.9292 - val_mse: 225.9292\n",
            "Epoch 10/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 211.9453 - mse: 211.9453 - val_loss: 225.2510 - val_mse: 225.2510\n",
            "Epoch 11/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 211.3065 - mse: 211.3065 - val_loss: 224.5838 - val_mse: 224.5838\n",
            "Epoch 12/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 210.6759 - mse: 210.6759 - val_loss: 223.9090 - val_mse: 223.9090\n",
            "Epoch 13/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 210.0448 - mse: 210.0448 - val_loss: 223.2464 - val_mse: 223.2464\n",
            "Epoch 14/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 209.4207 - mse: 209.4207 - val_loss: 222.5831 - val_mse: 222.5831\n",
            "Epoch 15/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 208.8009 - mse: 208.8009 - val_loss: 221.9264 - val_mse: 221.9264\n",
            "Epoch 16/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 208.1840 - mse: 208.1840 - val_loss: 221.2717 - val_mse: 221.2717\n",
            "Epoch 17/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 207.5709 - mse: 207.5709 - val_loss: 220.6362 - val_mse: 220.6362\n",
            "Epoch 18/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 206.9594 - mse: 206.9594 - val_loss: 219.9914 - val_mse: 219.9914\n",
            "Epoch 19/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 206.3526 - mse: 206.3526 - val_loss: 219.3492 - val_mse: 219.3492\n",
            "Epoch 20/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 205.7463 - mse: 205.7463 - val_loss: 218.7045 - val_mse: 218.7045\n",
            "Epoch 21/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 205.1362 - mse: 205.1362 - val_loss: 218.0719 - val_mse: 218.0719\n",
            "Epoch 22/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 204.5319 - mse: 204.5319 - val_loss: 217.4383 - val_mse: 217.4383\n",
            "Epoch 23/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 203.9277 - mse: 203.9277 - val_loss: 216.8025 - val_mse: 216.8025\n",
            "Epoch 24/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 203.3272 - mse: 203.3272 - val_loss: 216.1661 - val_mse: 216.1661\n",
            "Epoch 25/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 202.7254 - mse: 202.7254 - val_loss: 215.5279 - val_mse: 215.5279\n",
            "Epoch 26/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 202.1256 - mse: 202.1256 - val_loss: 214.8989 - val_mse: 214.8989\n",
            "Epoch 27/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 201.5293 - mse: 201.5293 - val_loss: 214.2793 - val_mse: 214.2793\n",
            "Epoch 28/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 200.9327 - mse: 200.9327 - val_loss: 213.6704 - val_mse: 213.6704\n",
            "Epoch 29/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 200.3420 - mse: 200.3420 - val_loss: 213.0609 - val_mse: 213.0609\n",
            "Epoch 30/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 199.7499 - mse: 199.7499 - val_loss: 212.4538 - val_mse: 212.4538\n",
            "Epoch 31/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 199.1616 - mse: 199.1616 - val_loss: 211.8382 - val_mse: 211.8382\n",
            "Epoch 32/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 198.5699 - mse: 198.5699 - val_loss: 211.2287 - val_mse: 211.2287\n",
            "Epoch 33/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 197.9802 - mse: 197.9802 - val_loss: 210.6209 - val_mse: 210.6209\n",
            "Epoch 34/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 197.3944 - mse: 197.3944 - val_loss: 210.0125 - val_mse: 210.0125\n",
            "Epoch 35/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 196.8070 - mse: 196.8070 - val_loss: 209.4087 - val_mse: 209.4087\n",
            "Epoch 36/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 196.2240 - mse: 196.2240 - val_loss: 208.8039 - val_mse: 208.8039\n",
            "Epoch 37/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 195.6402 - mse: 195.6402 - val_loss: 208.2001 - val_mse: 208.2001\n",
            "Epoch 38/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 195.0570 - mse: 195.0570 - val_loss: 207.6033 - val_mse: 207.6033\n",
            "Epoch 39/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 194.4794 - mse: 194.4794 - val_loss: 206.9985 - val_mse: 206.9985\n",
            "Epoch 40/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 193.8997 - mse: 193.8997 - val_loss: 206.4010 - val_mse: 206.4010\n",
            "Epoch 41/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 193.3234 - mse: 193.3234 - val_loss: 205.8082 - val_mse: 205.8082\n",
            "Epoch 42/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 192.7478 - mse: 192.7478 - val_loss: 205.2156 - val_mse: 205.2156\n",
            "Epoch 43/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 192.1752 - mse: 192.1752 - val_loss: 204.6196 - val_mse: 204.6196\n",
            "Epoch 44/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 191.5994 - mse: 191.5994 - val_loss: 204.0249 - val_mse: 204.0249\n",
            "Epoch 45/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 191.0287 - mse: 191.0287 - val_loss: 203.4328 - val_mse: 203.4328\n",
            "Epoch 46/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 190.4607 - mse: 190.4607 - val_loss: 202.8428 - val_mse: 202.8428\n",
            "Epoch 47/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 189.8924 - mse: 189.8924 - val_loss: 202.2557 - val_mse: 202.2557\n",
            "Epoch 48/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 189.3240 - mse: 189.3240 - val_loss: 201.6680 - val_mse: 201.6680\n",
            "Epoch 49/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 188.7582 - mse: 188.7582 - val_loss: 201.0821 - val_mse: 201.0821\n",
            "Epoch 50/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 188.1916 - mse: 188.1916 - val_loss: 200.5043 - val_mse: 200.5043\n",
            "Epoch 51/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 187.6300 - mse: 187.6300 - val_loss: 199.9217 - val_mse: 199.9217\n",
            "Epoch 52/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 187.0671 - mse: 187.0671 - val_loss: 199.3408 - val_mse: 199.3408\n",
            "Epoch 53/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 186.5064 - mse: 186.5064 - val_loss: 198.7588 - val_mse: 198.7588\n",
            "Epoch 54/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 185.9435 - mse: 185.9435 - val_loss: 198.1837 - val_mse: 198.1837\n",
            "Epoch 55/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 185.3886 - mse: 185.3886 - val_loss: 197.6037 - val_mse: 197.6037\n",
            "Epoch 56/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 184.8290 - mse: 184.8290 - val_loss: 197.0315 - val_mse: 197.0315\n",
            "Epoch 57/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 184.2738 - mse: 184.2738 - val_loss: 196.4588 - val_mse: 196.4588\n",
            "Epoch 58/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 183.7199 - mse: 183.7199 - val_loss: 195.8877 - val_mse: 195.8877\n",
            "Epoch 59/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 183.1677 - mse: 183.1677 - val_loss: 195.3158 - val_mse: 195.3158\n",
            "Epoch 60/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 182.6160 - mse: 182.6160 - val_loss: 194.7484 - val_mse: 194.7484\n",
            "Epoch 61/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 182.0681 - mse: 182.0681 - val_loss: 194.1796 - val_mse: 194.1796\n",
            "Epoch 62/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 181.5181 - mse: 181.5181 - val_loss: 193.6155 - val_mse: 193.6155\n",
            "Epoch 63/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 180.9698 - mse: 180.9698 - val_loss: 193.0527 - val_mse: 193.0527\n",
            "Epoch 64/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 180.4272 - mse: 180.4272 - val_loss: 192.4797 - val_mse: 192.4797\n",
            "Epoch 65/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 179.8758 - mse: 179.8758 - val_loss: 191.9213 - val_mse: 191.9213\n",
            "Epoch 66/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 179.3345 - mse: 179.3345 - val_loss: 191.3596 - val_mse: 191.3596\n",
            "Epoch 67/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 178.7927 - mse: 178.7927 - val_loss: 190.8004 - val_mse: 190.8004\n",
            "Epoch 68/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 178.2518 - mse: 178.2518 - val_loss: 190.2433 - val_mse: 190.2433\n",
            "Epoch 69/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 177.7128 - mse: 177.7128 - val_loss: 189.6847 - val_mse: 189.6847\n",
            "Epoch 70/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 177.1739 - mse: 177.1739 - val_loss: 189.1308 - val_mse: 189.1308\n",
            "Epoch 71/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 176.6364 - mse: 176.6364 - val_loss: 188.5711 - val_mse: 188.5711\n",
            "Epoch 72/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 176.0986 - mse: 176.0986 - val_loss: 188.0179 - val_mse: 188.0179\n",
            "Epoch 73/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 175.5635 - mse: 175.5635 - val_loss: 187.4632 - val_mse: 187.4632\n",
            "Epoch 74/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 175.0290 - mse: 175.0290 - val_loss: 186.9152 - val_mse: 186.9152\n",
            "Epoch 75/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 174.4980 - mse: 174.4980 - val_loss: 186.3618 - val_mse: 186.3618\n",
            "Epoch 76/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 173.9626 - mse: 173.9626 - val_loss: 185.8201 - val_mse: 185.8201\n",
            "Epoch 77/100\n",
            "23/23 [==============================] - 0s 2ms/step - loss: 173.4380 - mse: 173.4380 - val_loss: 185.2661 - val_mse: 185.2661\n",
            "Epoch 78/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 172.9059 - mse: 172.9059 - val_loss: 184.7188 - val_mse: 184.7188\n",
            "Epoch 79/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 172.3777 - mse: 172.3777 - val_loss: 184.1725 - val_mse: 184.1725\n",
            "Epoch 80/100\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 171.8518 - mse: 171.8518 - val_loss: 183.6257 - val_mse: 183.6257\n",
            "Epoch 81/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 171.3239 - mse: 171.3239 - val_loss: 183.0883 - val_mse: 183.0883\n",
            "Epoch 82/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 170.8016 - mse: 170.8016 - val_loss: 182.5476 - val_mse: 182.5476\n",
            "Epoch 83/100\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 170.2809 - mse: 170.2809 - val_loss: 182.0017 - val_mse: 182.0017\n",
            "Epoch 84/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 169.7561 - mse: 169.7561 - val_loss: 181.4631 - val_mse: 181.4631\n",
            "Epoch 85/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 169.2353 - mse: 169.2353 - val_loss: 180.9269 - val_mse: 180.9269\n",
            "Epoch 86/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 168.7156 - mse: 168.7156 - val_loss: 180.3911 - val_mse: 180.3911\n",
            "Epoch 87/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 168.1975 - mse: 168.1975 - val_loss: 179.8550 - val_mse: 179.8550\n",
            "Epoch 88/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 167.6810 - mse: 167.6810 - val_loss: 179.3188 - val_mse: 179.3188\n",
            "Epoch 89/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 167.1638 - mse: 167.1638 - val_loss: 178.7871 - val_mse: 178.7871\n",
            "Epoch 90/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 166.6505 - mse: 166.6505 - val_loss: 178.2514 - val_mse: 178.2514\n",
            "Epoch 91/100\n",
            "23/23 [==============================] - 0s 5ms/step - loss: 166.1347 - mse: 166.1347 - val_loss: 177.7215 - val_mse: 177.7215\n",
            "Epoch 92/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 165.6232 - mse: 165.6232 - val_loss: 177.1871 - val_mse: 177.1871\n",
            "Epoch 93/100\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 165.1070 - mse: 165.1070 - val_loss: 176.6647 - val_mse: 176.6647\n",
            "Epoch 94/100\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 164.6004 - mse: 164.6004 - val_loss: 176.1339 - val_mse: 176.1339\n",
            "Epoch 95/100\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 164.0913 - mse: 164.0913 - val_loss: 175.6023 - val_mse: 175.6023\n",
            "Epoch 96/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 163.5806 - mse: 163.5806 - val_loss: 175.0818 - val_mse: 175.0818\n",
            "Epoch 97/100\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 163.0746 - mse: 163.0746 - val_loss: 174.5613 - val_mse: 174.5613\n",
            "Epoch 98/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 162.5714 - mse: 162.5714 - val_loss: 174.0334 - val_mse: 174.0334\n",
            "Epoch 99/100\n",
            "23/23 [==============================] - 0s 3ms/step - loss: 162.0644 - mse: 162.0644 - val_loss: 173.5100 - val_mse: 173.5100\n",
            "Epoch 100/100\n",
            "23/23 [==============================] - 0s 4ms/step - loss: 161.5586 - mse: 161.5586 - val_loss: 172.9902 - val_mse: 172.9902\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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()"
      ],
      "metadata": {
        "id": "GLCPzbzYxNll",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "2ee9ca0d-ca5f-41cd-e109-40bcaf765a67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The following model adds a learning rate and batch size. It also shows four nodes per layer."
      ],
      "metadata": {
        "id": "OdxCPd228jWO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Build Model (Building a four layer network - with two hidden layers)\n",
        "model = Sequential()\n",
        "model.add(Dense(4,input_dim=4, activation='relu'))\n",
        "model.add(Dense(4,activation='relu'))                                        # You don't have to specify input size.Just define the hidden layers\n",
        "model.add(Dense(4,activation='relu'))\n",
        "model.add(Dense(1))\n",
        "\n",
        "\n",
        "# Compile Model\n",
        "opt = keras.optimizers.Adam(learning_rate=.001)\n",
        "model.compile(optimizer=opt, 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, batch_size=32, verbose=0)\n",
        "                                                                               # Train the model, iterating on the data in batches of 32 samples\n",
        "\n",
        "#Epoch - #number of epochs to train 32\n",
        "#Batch size - amount of data each iteration in an epoch sees"
      ],
      "metadata": {
        "id": "iunArNuM8fMj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "fSSf_JsE81XD",
        "outputId": "3e0ec982-5538-45b6-ac22-9fdba0f737fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": 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          "metadata": {}
        }
      ]
    }
  ],
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    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "environment": {
      "kernel": "python3",
      "name": "tf2-gpu.2-6.m87",
      "type": "gcloud",
      "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-6:m87"
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    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
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