ML_PROJECT_COVID19.ipynb

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    "colab": {
      "name": " ML_PROJECT_COVID19.ipynb",
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      "toc_visible": true,
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AbedMHroub/Corona-virus-COVID19-predictions-project/blob/master/ML_PROJECT_COVID19.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eLrVi4d_7Yvc",
        "colab_type": "text"
      },
      "source": [
        "---\n",
        " Machine learning Project \n",
        "\n",
        "---\n",
        "# ***Corona virus (COVID19) predictions project***\n",
        "\n",
        ">*Today, the world is facing a widespread spread of the corona virus and an increase in the number of infections and deaths due to this disease, wishing safety and health for all.*\n",
        "\n",
        "**Aim :** Predict the number of infected people and the number of deaths of coronavirus.\n",
        "\n",
        "**DataSet :** We used data for the **United States** to train our model and we got data for **95 days** that includes the date of the day and the number of people with the disease and for the data of the dead we got data for **60 days** and also includes the date of the day and the number of deaths due to disease.\n",
        "\n",
        "**We used 3 types of different model :**\n",
        "\n",
        "1. *Linear regression with log values*\n",
        "2. *Support Vector Regression (SVR)*\n",
        "3. *Artificial Neutral Network (MLPRegressor)* \n",
        "\n",
        ">*For more information in addition to the data set, you can visit the following link and view the **README** file [https://github.com/AbedMHroub/Corona-virus-COVID19-predictions-project](https://github.com/AbedMHroub/Corona-virus-COVID19-predictions-project)*\n",
        "---\n",
        "**Team members : Abed Al-fattah Hroub & Mohammad Zyada**\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wi6PePOgqWL0",
        "colab_type": "text"
      },
      "source": [
        "# **Import Libraries** \n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "10aRGtTtqXKj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "from sklearn.model_selection import train_test_split\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6JLhdjzyLNs0",
        "colab_type": "text"
      },
      "source": [
        "# **Infected**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ViIWNV6p3cqQ",
        "colab_type": "text"
      },
      "source": [
        "## **Preparing Infected data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l7EsMFV3JUdv",
        "colab_type": "code",
        "outputId": "4a3f40e7-1b14-4514-b09f-b0810d636c38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        }
      },
      "source": [
        "path='https://raw.githubusercontent.com/AbedMHroub/Corona-virus-COVID19-predictions-project/master/dataset/Infected.csv'\n",
        "ds = pd.read_csv(path, index_col=0)\n",
        "ds.head(13)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>num_of_date</th>\n",
              "      <th>num_of_patients</th>\n",
              "      <th>Date_test</th>\n",
              "      <th>num_of_date_test</th>\n",
              "      <th>num_of_patients_test</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-21</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-02-05</td>\n",
              "      <td>16.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-22</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-02-16</td>\n",
              "      <td>27.0</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-23</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-02-22</td>\n",
              "      <td>33.0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-24</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-02-27</td>\n",
              "      <td>38.0</td>\n",
              "      <td>58.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-25</th>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-03-03</td>\n",
              "      <td>43.0</td>\n",
              "      <td>118.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-26</th>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>2020-03-05</td>\n",
              "      <td>45.0</td>\n",
              "      <td>217.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-27</th>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1663.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-28</th>\n",
              "      <td>8</td>\n",
              "      <td>5</td>\n",
              "      <td>2020-03-23</td>\n",
              "      <td>63.0</td>\n",
              "      <td>43847.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-29</th>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>2020-03-30</td>\n",
              "      <td>70.0</td>\n",
              "      <td>161807.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-30</th>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>2020-04-05</td>\n",
              "      <td>76.0</td>\n",
              "      <td>312237.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "      <td>2020-04-10</td>\n",
              "      <td>80.0</td>\n",
              "      <td>460252.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>12</td>\n",
              "      <td>8</td>\n",
              "      <td>2020-04-19</td>\n",
              "      <td>89.0</td>\n",
              "      <td>657996.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-02</th>\n",
              "      <td>13</td>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            num_of_date  ...  num_of_patients_test\n",
              "Date                     ...                      \n",
              "2020-01-21            1  ...                  11.0\n",
              "2020-01-22            2  ...                  13.0\n",
              "2020-01-23            3  ...                  15.0\n",
              "2020-01-24            4  ...                  58.0\n",
              "2020-01-25            5  ...                 118.0\n",
              "2020-01-26            6  ...                 217.0\n",
              "2020-01-27            7  ...                1663.0\n",
              "2020-01-28            8  ...               43847.0\n",
              "2020-01-29            9  ...              161807.0\n",
              "2020-01-30           10  ...              312237.0\n",
              "2020-01-31           11  ...              460252.0\n",
              "2020-02-01           12  ...              657996.0\n",
              "2020-02-02           13  ...                   NaN\n",
              "\n",
              "[13 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 242
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqWDmrXuLMPu",
        "colab_type": "code",
        "cellView": "code",
        "colab": {}
      },
      "source": [
        "x = ds['num_of_date']\n",
        "y = ds['num_of_patients']\n",
        "x_test_patient = ds['num_of_date_test'][:12]\n",
        "y_test_patient = ds['num_of_patients_test'][:12]\n",
        "x_prediction =[[95],[96],[97],[98],[99],[100],[101]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPHyTv283eTV",
        "colab_type": "text"
      },
      "source": [
        "## **linear_regression**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-IgwuyLhpfC",
        "colab_type": "text"
      },
      "source": [
        "### Code & Validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6i49PcWdEZK",
        "colab_type": "code",
        "outputId": "7a268fdf-64ec-4ea0-909f-db5a5e360515",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "linear_regression=LinearRegression()\n",
        "\n",
        "yy=np.log10(y)\n",
        "\n",
        "scores = []\n",
        "######################################################################################################################\n",
        "Linear_Regression = LinearRegression()\n",
        "######################################################################################################################\n",
        "cv = KFold(n_splits=10, random_state=1, shuffle=True)\n",
        "for train_index, test_index in cv.split(x):\n",
        "    X_train, X_test, y_train, y_test , yy_train, yy_test= x[train_index], x[test_index], y[train_index], y[test_index], yy[train_index], yy[test_index]\n",
        "    Linear_Regression.fit(X_train.values.reshape(-1,1), yy_train)\n",
        "    scores.append(Linear_Regression.score(X_test.values.reshape(-1,1), yy_test))\n",
        "    \n",
        "print(\"Average score for Linear Regression:\",sum(scores)/len(scores))    \n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average score for Linear Regression: 0.947731350509466\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c-HR1_nCk5f4",
        "colab_type": "text"
      },
      "source": [
        "**After the validation we evaluate the Linear Model:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R-UCKGm_HFcw",
        "colab_type": "text"
      },
      "source": [
        "### Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LsuV_EsWkcr5",
        "colab_type": "code",
        "outputId": "821bead4-e872-447a-ec4d-ff4067a2599e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "Linear_Regression.fit(x.values.reshape(-1,1), yy)\n",
        "y_test_patient_log=np.log10(y_test_patient)\n",
        "evaluation_1 =Linear_Regression.predict(x_test_patient.values.reshape(-1,1))\n",
        "score=Linear_Regression.score(x_test_patient.values.reshape(-1,1), y_test_patient_log)   \n",
        "print(\"Final Evaluation Score for Linear Regression :\",score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Final Evaluation Score for Linear Regression : 0.9303292029481322\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YmMkniQa7U7r",
        "colab_type": "text"
      },
      "source": [
        "### Prediction for new days"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gOTY4y8V7ttX",
        "colab_type": "code",
        "outputId": "a4fe4015-2b44-4058-f521-756104b1f493",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "print('Evaluation for expecting 6 days in future in Linear_Regression:')\n",
        "for predict in x_prediction:\n",
        "  print('day',predict,'=',int(10**Linear_Regression.predict([predict])))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation for expecting 6 days in future in Linear_Regression:\n",
            "day [95] = 2750230\n",
            "day [96] = 3242796\n",
            "day [97] = 3823580\n",
            "day [98] = 4508382\n",
            "day [99] = 5315832\n",
            "day [100] = 6267896\n",
            "day [101] = 7390475\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FiGhZcFIh3nX",
        "colab_type": "text"
      },
      "source": [
        "### plot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OPxUIiDLQhpO",
        "colab_type": "text"
      },
      "source": [
        "Here the data are displayed in logarithmic representation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEkDAdyTdp8v",
        "colab_type": "code",
        "outputId": "b9ecb906-39d1-42c5-d0d8-354822c92dbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "predicted1 = Linear_Regression.predict(x.values.reshape(-1,1))\n",
        "predicted11 = 10**Linear_Regression.predict(x.values.reshape(-1,1))\n",
        "plt.plot(x, yy, 'o-',label='data set')\n",
        "plt.plot(x, predicted1,c='#ff0000',label='linear_regression')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value in log ')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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582YnQlPKZ3IncduHZQtHElOZvHyP18ndfd74GfXw48dtS9/p06BePVi2DK691tdvJaCVihJNo0aNkABpoK98yxjDzp07tUQTIgpTesmPV/PGFyyARx6Bgwdtc7AXXoAKFYp8zkBWqks0ERER/PXXX5x77rma5IOMMYa//vqLiIgIf4eiHJTfLJj8VKtYnsOJed9AVODNQIcPw8CBdvGNZs3sQhzFnNBQmgV8gq9Zsyb79u3j8OHD/g5FOSAiIoKaNWv6OwzlkIJmweSW3w1EkeFhvNylWf6J3Rib1B97zPZtHznS9m4vV85H76R0CvgEHx4enn33pVKqdChKKSa/G4gKnOnyxx/w6KN2oes2bWDGDNvaVwV+gldKBb7cfWBOnU4nLcP763u5Sy9e3UCUmQlvvQVDhkBGBrz+OgwYAGFhnl8XQjTBK6WKxVMfmIIUWHrJz+7ddj3UFSugfXuYNs0uo6dycGzBD6VUaChsH5isqRI1oiILn9zT02HcOLjsMts6fMYM+PprTe750BG8UqpYCrMCUbFa4m7ZAj17wrp18I9/wOTJUN1zS99QpwleKeW13HefPnDlRZQrW4bUAvrAFLkUA5CaCi+9BC+/DOecAx99BHfeeUZzMHUmTfBKKa/kdffpy1/tJEygbBkhPfPvi6rhZYSKEWWL37jrf/+zo/bt26FbN7smxLnn+uotBT1N8Eopjwqa8litcgRDOzbyflqjN06dgmeegTfegJo14csvoVOnoh8vRGmCV0rly5vGX38eT/FtX/QlS+wMmV9/hb59bWmmcmXfHDvEOJrgRWQvcBLIANLz65eglAoc7nX2MiJkFNCvqnpUpG9OnJAATz5pZ8bUr2+nQF59tW+OHaJKYgR/nTHmSAmcRylVTLlH7AUld/dujsU7cZwdrR86BEOHwogREOmj/3GEMC3RKKWyFWZOe7GmPGb580979+ncuRAdbbtAtmxZ9OOpHJxO8AZYLCIGeNMYM83h8ymliqAwvWOKNeUxizHw7rswaJC9oDpqlO3dHh5e9GOqMzid4NsaY+JF5DzgaxHZaYxZ6b6DiPQGegPUrl3b4XCUUrl5cyE1TIRMY3wzQ+b336FPH1i4EK680tbcdT0ARzia4I0x8a6/D4nIJ8DlwMpc+0wDpoFd8MPJeJRSZyqoLOOTETvY5mBTp9o2vsbAhAm27q7NwRzjWC8aETlLRCpl/QzcBGxz6nxKqcKJ2xhP7OilHssyReoXk5effrLL5fXrZ0ftW7dq58cS4OQI/nzgE9cqTGWB/xpjFjp4PqWUl7wpy9SIiuS7odcX70Tp6fDqq/Dcc3ZWzKxZ8OCD2maghDiW4I0xvwDRTh1fKVV43l5M9cn0x02bbJuBDRugSxeYNAkuuKB4x1SFou2ClQoRWaP2gpJ7scsyKSkwfDi0agXx8TBvHsyfr8ndD3QevFIhwps57sUuy3z3HTz8MOzcaUsxr71mO0Aqv9ARvFIhoqC+7cUqyyQm2gWv27WDpCQ7BXL2bE3ufqYJXqkQUTky/5uIilWWWbwYmjaF//zHzpLZtg06dChGpMpXtESjVAiYumIPx5PTKCPg1ra9eHPcjx6FJ56wI/WGDWHlSmjb1mcxq+LTEbxSQcwYw7hFuxj91U5uja7O2Dsvo0ZUJEIxR+3z50PjxrbdwNNP2xkzmtwDjo7glQoy7u1+K5QP41RqBve0rsVLtzcjrIxwR8taRT/4wYO2DPPxx9C8OXz1lf1bBaQCE7yIbMU2DXN3HFgHjDLG/OVEYEqpwst9A9Op1AzCyght6p5DWJli3FxkDLzzDjz+uL2I+vLLtjyjzcECmjclmq+AL4D7XX8WYJP7QWC2Y5EppQotr6mQGZmGcYt/KvpB9+6Fjh2he3do0gQ2b7Y92zW5BzxvSjQ3GGNauD3eKiIbjDEtRKSbU4EppbxX0B2qBU2RzFNmpr37dNgw21pg4kTbHKyMXrorLbxJ8GEicrkx5nsAEWkNZHUISncsMqWUV7zpK1PoZfV27rRtBlavtqP3qVPhoouKGakqad4k+IeBmSJSERDgBNDT1SHyZSeDU0oVzJt2v17fwJSWBmPHwgsvQMWKtu7erZs2ByulCkzwxpgfgGYiUsX1+Ljb5o+cCkwp5R1P5ZdCLau3YQP06GFr7F272pLM+ef7MFJV0ryZRVMFeA642vV4BTAyV6JXSpWwrLp7fqvkeN1XJjnZjtjHjYNq1ewUyNtv92msyj+8uVoyEzgJ3OX6cwKY5WRQSinPCuoM6XVZ5ttvISYGxoyxzcG2b9fkHkS8qcHXM8bc4fb4BRHZ5FRASqmCeaq7e1WWOXnSTnWcPBnq1IGvv4YbbnAmWOU33iT4ZBFpa4xZBSAisUAR5lwppXwlv7q7QMFlmYUL7aLXf/wBgwbBiy/aC6oq6HiT4B8F3nbV4gU4CnR3MiilVN4Kqrt7nA75118weLCdGXPppbZ3+5VXOhKnCgzezKLZBESLSGXX4xOOR6WUOkNB893zrbsbY1dV6t/fdoB89lm74lL58g5HrPwt3wQvIoPzeR4AY8xrDsWklMpDkeruBw7Yu0/j4qBlS9u7PVqXSg4VnkbwlUosCqVUgQpVdzcGZs2yJZnUVHjlFdsorKw2kA0l+f5rG2Ne8MUJRCQM25ws3hhziy+OqVQourBKBPuPp5zx/Bl1919/hd694Ztv4Oqr4a23oEGDEopSBZKS6Bo0ENhRAudRKqi1a1D1jOdy1N0zMuCNN+zyeWvX2imQy5Zpcg9hjiZ4EakJ3AxMd/I8SgU7YwwbfkugepUIakRFnLki0/btdkWlQYPg2mvhxx/h0Ue182OIc7ogNx54Cg/1fBHpDfQGqF27tsPhKFW65G4DfH+bWrx0+2V/73D6tJ3HPmoUVKoE770H992nzcEU4F0vmvLAHUAd9/2NMSMLeN0twCFjzHoRuTa//Ywx04BpAK1atcpveq9SISevaZHzN8TTus65dtS+bp1tDrZ1K9xzD0yYYHvJKOXize9vnwL/wPZ+P+X2pyCxwG0ishf4ALheRN4rYpxKhZy8pkWmpGXyxudb4KmnoE0be/PSp5/C++9rcldn8KZEU9MY07GwBzbGDAOGAbhG8E8aY3QFKKUK4Gl1pja/b2X0wglw7AD06mV7t1ep4ocoVWngTYJfLSLNjDFbHY9GqRCX392qFVOTGLp8Ft02fUX8ORfCkiVwvRetgFVI8ybBtwW6i8ivQCr2vgpjjLnM88v+ZoxZDiwvSoBKhZK8yjLX7fmBlxZN4vzEo8xq04Wqr42hxlWX+ClCVZp4k+A7OR6FUgrIebfqOUnHGbFkGp23r2BX1do81+15bu7VmVu9WZ1JKTz3oqnsaix2sgTjUSok5egSaQy37ljJ89+8SaXUJF6PvY+4jg+w4pkO/g5TlTKeRvD/BW4B1gMGW5rJYoCLHYxLqZDhXnc//+QRRi2ezI0/f8+mCxvwVKfH+KN6PV6+uam/w1SlkKdeNLe4/q5bcuEoFXrGLtpF8ul07tm8iKeXzSQ8M4MXr+vJrFa3ceE5FXnZ20WzlcpFW8sp5SdZZZmwX3/hvwsnctXvW1hd+zKGdhzA72df6N3qTEp5oAleKT+I2xjP8HmbuGfNJzz57XuklQljaIf+fBDdIbvNgMfVmZTygiZ4pfxg3tsLmfPRGGIO7ObrSy7nmZv68melv7tF5rs6k1KF4FWCF5G2QH1jzCwRqQZUNMb86mxoSgWh06fh3/9m5sSXOFm+AgNuHcKCS6/O0Rws39WZlCokb5qNPQe0AhoCs4Bw4D1srxmllJdWvLOAWk89xsV/7uXLxtfyQvteHKuQs81AjahIrbsrn/FmBH870BzYAGCM2S8iupyfUt5KSuLnPo/Tds50/qx4Dg/d+RzL6rU+Yzctyyhf8ybBnzbGGBExACJylsMxKRU0Vk37iLrDBnHJ0QO8F9OJ0dc+RGL5Ctnbw0TINIbqWpZRDvAmwX8kIm8CUSLSC+gBvOVsWEqVcsePs/ehvrT95L/8evaF3HPvv/lf7TPbN2Uaw6+jb/ZDgCoUFJjgjTHjRORG4AS2Dj/CGPO145EpVVp99hk8+ii1Dhxk6uVdGN/2PlLCI/LcVadCKid5NYvGldA1qSvlyaFD8Nhj8OGH0KwZt98whC0X1s93d625K6cVuKKTiJwUkROuPykikiEiJ0oiOKVKBWNgzhxo3Bg++cSukbpuHYcaNsv3JTkWzFbKId6UaLJnzIiIYJfvu8LJoJQqNf74Ax59FL74Aq64AqZPhyZNOHbqNLYnX06R4WGa2FWJ8WZN1mzGigO0b6kKbZmZMHUqNGkCy5bB+PGwahU0acKJlDQenPU9R5PS6HttPWpERSLoqF2VPG9udOri9rAM9qanFMciUirQ7d5t10NdsQLat4dp04g7Xp6xY1ewPyGZ8LAypGVkMqN7K65vdD5PdWzk74hViPLmIuutbj+nA3uxZRqlQkt6Orz+OowYAeXLw4wZ8NBDxG3an2Md1dMZmYSHCSeS0/0csAp13tTgHyqJQJQKaJs3Q8+esH49dO4MkyZB9epA3uuopmUYxi7apeUY5VeeluybSF5XiVyMMY95OrCIRAArgfKu88wzxjxXxDiV8o/UVBg1CkaPhnPOgY8+gjvvzNEczH0dVXf5Pa9USfE0gl9XzGOnAtcbYxJFJBxYJSJfGWP+V8zjKlUy1qyxo/YdO+Cf/7TlmXPPBf5erMNTEtebmJS/eVqy7+3iHNgYY4BE18Nw1598fyNQKmCcOgXDh8OECVCzJnz5JXTqlL3ZfQ3V/OhNTCoQeDOLphrwL6AxkH2/tTGmwJ6mIhKGXbT7EmCSMWZtHvv0BnoD1K5d2+vAlXLEN9/YGTJ790LfvrY0Uyln89S8au6gjcNU4PFmFs0c4EPgZuAR4EHgsDcHN8ZkADEiEgV8IiJNjTHbcu0zDZgG0KpVKx3hK/84dgyefBJmzoQGDWDlSmjXLs9d8yvLaOMwFWi8udHpXGPMDCDNGLPCGNMDKNSKBMaYBGAZ0LEIMSrlrE8+sW0G3n4bhg6FTZvyTe6Qf21da+4q0HiT4NNcfx8QkZtFpDlwTkEvEpFqrpE7IhIJ3AjsLHKkSvnan3/CXXdBly5wwQXw/ffw8ssQ6TlRD+nQkDKS8zmtuatA5E2JZpSIVAGeACYClYHHvXjdhcDbrjp8GeAjY8znRY5UKV8xBt59FwYNshdUX3oJhgyB8HCPL8uaORPvKtFEhoeRkpahNXcVsLxJ8GuNMceB48B13h7YGLMFu9SfUoHj99+hTx9YuBCuusrejdqo4FYCec2cMRhevztGE7sKWN6UaL4TkcUi0lNEznY8IqWckJlp7z5t0gS+/dZOgfz22wKTe9zGeGJHL2XQh5vOmDmTkpbJ2EW7nIxaqWIpMMEbYxoAzwBNgPUi8rmIdHM8MqV8ZdcuuOYa6N8frrwStm2DAQOgjOevf9aoPd7DzUx6t6oKZF61CzbGfG+MGQxcDhwFinUTlFIlIj3dzmOPjrZJfdYsWLQI6tTx6uX5zXd3pzNnVCDz5kanysDtwD1APeATbKJXKnBt2mTbDGzYYGfJTJpkZ8p4IffF1PzozBkV6Ly5yLoZiANGGmPWOByPUsWTkmKbg40ZY/vGzJsHd9zh9cu9aUMAdvEOnTmjAp03Cf5iV18ZpQLbd9/Bww/Dzp3w4IPw2mu2A2QhFFSW0SX3VGnizUVWTe4qsCUmwmOP2btPk5NtnX327EInd/B80VSX3FOljTcjeKUC1+LF0Lu3nd/er5+9E7VixSIf7vzKERw8ceaKlDWiIvluaKE6dCjld95cZI01xnxX0HNKlaijR+GJJ+xIvWFDO6c9NrZIh3Lv7V42dw8C9GKqKr28mSY50cvnlCoZ8+fb5mDvvgtPP21nzBQjuWfNdTdAWqZBgLMrhCNoWUaVbp6W7LsSuAqoJiKD3TZVBsKcDkypMxw8aG9Wmj8fmje37QZiYop1yLwuqhqgQrmybBxxU7GOrZS/eRrBlwMqYv8nUMntzwngTudDU8rFGFuKufRS+Pxz+Pe/Ye3aYid30PVUVXDztGTfCmCFiMw2xvxWgjEp9be9exUCrBcAABbLSURBVO1F1K+/hrZtYfp0W3P3kepRkXne0KR3qKpg4E0NfnpWX3cAETlbRBY5GJNSkJFhG4I1bWoXv540CVas8GlyB2hXv+oZz+lFVRUsvJkmWdW1IhMAxphjInKegzGpULdjh71hafVq6NgR3nwTHFivd8mOP5m7fh8Nz6/IyZR0DhxP0d7uKqh4k+AzRaS2MeZ3ABG5CHsdSinfSkuDsWPhhRfsXPZ33oFu3UDOnLpYXBt+P0a//26g8YWVeb/3FVQsr7eEqODjzbd6OLBKRFYAArQDejsalQo9GzdCjx52yuNdd8HEiXCeb39RdJ/vjsA5FcKZ2b21JncVtLxpVbAQaAF8CHwAtDTGaA1e+UZysl3ounVrOw3yk0/gww8dSe7u892NgcTUDL77+YhPz6NUIMk3wYtII9ffLYDawH7Xn9qu55Qqnm+/tVMdx4yB7t1h+3bo3NmRU+U13z01XVdkUsHN0++mTwC9gFfz2GYAbcyhiubECRg2DCZPtotvfP013HCDo6fMr7e7zndXwczTPPherr+9XmhbqQJ99ZVd9HrfPhg0yPZuP+ssx05njGHmd3vz3a7z3VUw89SqoIunFxpjPva0XURqAe8A52NH/NOMMW8UJUgVBP76Cx5/3PaPadzYToG84gpHTuV+MTWyXBhJpzNoVqMyuw8lkpKWmb2fzndXwc5TieZW19/nYXvSLHU9vg5YDXhM8EA68IQxZoOIVMIu2P21MWZ7cQJWpYwxMHeu7SFz7Bg8+ywMHw7lyztyutwrMiWdzqBsGaHHVXWRMpKd+HW+uwoFnko0DwGIyGKgsTHmgOvxhcDsgg7s2v+A6+eTIrIDqAFogg8V+/dD377w6afQsiV88w1cdpmjp8zrYmp6pmHc1z/x3dDrNaGrkOJNq4JaWcnd5U/srBqviUgdoDmwNo9tvUVknYisO3z4cGEOqwKVMTBjhi3FLFoEr7wC//uf48kdtHmYUu68ucNjiav3zPuux3cD33h7AhGpCMwHBhljTuTeboyZBkwDaNWqld4hW9r98gv06gVLl8LVV9vmYPXrO3pK95p7fl8gvZiqQlGBCd4Y019Ebgeudj01zRjziTcHF5FwbHKfU9BFWVXKZWTYu0+HD4ewMJgyxXaBLOPNL4mFl5XU4xOSETz3ztCLqSpUeXuP9gbgpDHmGxGpICKVjDEnPb1ARASYAewwxrxW3EBVAPvxR+jZ0/Zov/lmmDoVatb0+WnyS+p5JfcwETKN0YupKqR5syZrL2zvmXOAetgLpVOB9gW8NBb4J7BVRDa5nnvaGPNl0cNVAeX0aXsX6osvQuXK8N57cN99xWoO5l5ucU/OuWfHFFTLyzSGX0ffXOQ4lAoG3ozg+wGX47pAaozZ7U27YGPMKmxzMhWMfvjBjtq3boV77rG926tV8/rl7om8SmQ4InAsKS3HyDw+IZmhH28hPiGZmat+PWN2jCdac1fKuwSfaow5La5RmYiURdsFh66kJHj+eXj1VbjgAjsF8rbbCnWI3KPxhOS07G25v1gpaYXvF6M1d6Usb66ArRCRp4FIEbkRmAsscDYsFZCWL7dTHceOtaP37dsLndwh77nqBalasZzH7Vm/KtaIiuTlLs205q4U3o3g/wU8DGwF+gBfAtOdDEoFmOPH4amnYNo0qFfPToG8rugtigo7J72GqxbvPuoHsss5NfRCqlJ58pjgRSQM+NEY0wh4q2RCUgHl88/hkUfgwAF44gkYORIqVCjWIfNb6DovWeWWrOStrQaU8p7HBG+MyRCRXe5L9qkQcfgwDBwI779vF77++GO4/HKfHHpIh4YMmbeZtIy8L+XkNzLv3LyGJnSlCsGbEs3ZwI8i8j1wKutJY0zhi68q8BkDH3wAjz1mSzMvvGBXXCrnuQZeGJ2b1+DTTftYtusIAtmzaBKS0nRkrpQPeZPgn3U8ChUY9u2DRx+1ZZnLL7f9ZJo2deRUp05nElMrirh+sY4cXynluR98BPAIcAn2AusMY0x6SQWmSlBmpu0ZM2QIpKXZKZADB9qWAw7IyDRsiz9O15a+v9tVKfU3TyP4t4E04FugE9AYGFgSQakS9PPPtjnY8uV2Zsxbb9mZMg7acziRpNMZXFYzytHzKBXqPCX4xsaYZgAiMgP4vmRCUiUiPR3Gj7cLcJQrZxN7z57FajPgrc1/JAAQXUsTvFJO8pTgs28vNMakSwn8h69KyNatNpn/8IO9UWnyZKhRchc1t+w7TqXyZbm4qnNrsSqlPCf4aBHJ6t8u2DtZT7h+NsaYyo5Hp3wrNRX+/W/75+yz7WyZu+4qkVG7uy37EmhaowplyuigQSkneVqyz5krbMo/1q61o/Yff4Ru3eD116Fq1RIPIzU9g+0HTtCjbd0SP7dSocaZ1RhU4Dh1CgYPhiuvtPPaP/8c3n3XL8kdYOeBk6RlGKL1AqtSjvN2wQ9VGi1damfI/PKLbTcwZozt2+5HW/bZC6yX1azi1ziUCgU6gg9GCQk2sbdvb+eyr1hhl9ArgeQetzGe2NFLqTv0C2JHLyVuY3yObS9/tROAu99ck2ObUsr3dAQfbD77zN6NevCg7QD5/PMQWTKLX+Tu8x6fkMywj7dmb8+5LSV7m7YlUMoZmuCDxaFDtn/Mhx/anu2ffgqtWmVvzr0U3nWNqrFs5+EcKyrl7gWT16pLWfvk9fpjSWlnhJWclsGTczcDkJ5pztg2dtEuTfBKOUSMCZzFmVq1amXWrVvn7zBKF2NgzhzbWuDkSXY8PJBHqt/A74np+S6FV5DI8DDuaFmD+evjC70wR2EJ6NqpShWDiKw3xrTKa5uO4EuIp9Gwe/fE/PbL6+fIg/sZt3QqV+1ay7ZalzLo9lHsqVwbk2hbBnlaCs+T5LQM3vuf77pD13Ctj5pXD3hdO1Up5ziW4EVkJnALcMgY40xLwlLC0xqknurU7vu5/3w8KZX7Ni1k6PJZhJlMXmjfi7db3EJmmcC7dcF9fdTcKzLp2qlKOcvJEfxs4D/AOw6eo1QoaA3S5LQMnnDVqTMyPY+16xyNZ8zCibT5YxurLopmaMcB7Iu6wKfxAoSJkFHM8l1eS+npikxKlRzHErwxZqWI1HHq+KWJN2uQFpTYwzIz6PlDHINXzeF0WDhDOj3G3GY3OtJmoLg1+MjwsDwXvtYVmZQqWX6vwYtIb6A3QO3atf0cTfHkN1PFm3Gwpzr1pYd+YcxXE7js4M8sqn8Fz974KIcqnVvo+NyXwvNmFk2ri84p1CwaXZFJqcDi6Cwa1wj+c29r8KV5Fk3uOnthZI14IWedulx6Gv1Xf8Cja+eREFGJ5258hC8bxhY4as9K5FGaeJUKejqLxkFZo/a8Rt75KSjxjl20i/N/3MDYhROpd+R35je9ngn/9ygnzqqM5JpRk3s0rYlcKZVFE3wxFGXULsCm527Kd3vn+lXo/PZnMGcC1KoFX33FHR07cocP4lVKhRbHetGIyPvAGqChiOwTkZ5OnctfCpodkxeP876//hqaNYM33oC+fWHbNujYsZhRKqVClZOzaO516tiBwpvZMe7ynfd97Bg88QTMmgUNGsDKldCunY+iVEqFqpAv0Xhz52h+s0Y8XZ7OPVMl39r4J5/Y0frhwzBsGIwYARERjr5npVRoCOkE7+kO09x3m7rfuu++Lbf85oCf4eBBGDAA5s2DmBj44gto0aKI70Qppc4U0v3gi1JD96RGVGTByd0YeOcdaNwYFiyw66N+/70md6WUz4X0CL6wNXRPBPhu6PWed/rtN+jTBxYtgquughkzoFEjn8WglFLuQnoE78tOhh6PlZkJ//kPNGkCq1bBxInw7bea3JVSjgrpBD+kQ0PKlil+LxePXRF37YKrr7b19rZt4ccfoX9/KBPSH71SqgSEdJbp3LwGNaIiCA8TBHuH6dkVws/4uUZUJN2uqE2NqMg8t+VZd09Lg9GjIToatm+H2bPhq6/gootK+m0qpUJUSNfgjyel8cexZPpfX5/BNzbw3YE3boSePe3fd9xhyzMX+L6lr1JKeRLSI/hVPx8h08A1Dar65oApKfD009C6Nezfb6dAzpunyV0p5RchPYJf+dNhKkWUJbpmVPEP9t13dtS+axd07w6vvgrnnFP84yqlVBGF7AjeGMPK3Ydpe0lVyoYV42NITLQXUNu1syP4RYtsywFN7kopPwvZBP/zoUQOHE+hXf1qRT/IokV26uOkSTbJb9sGN+XfKVIppUpSyJRocvecSU3PBOCNJT9RoVxY4fqnHz0KgwfD22/bueyrVtkbl5RSKoCERIL31HPmzxOpDPt4K4B3SX7+fOjXD44cgeHD4ZlntDmYUioghUSJpqCeM8lpGYxdtMvzQQ4csFMe77wTqleHdetg1ChN7kqpgBUSCd6bnjP57mOMvWjauLHt+Pjyy7Y5WEyMj6NUSinfCokEf2GVgkfZefaS2bsXOnSAHj3sSktbtsDQoVA2JCpbSqlSLiQS/I2Nz/e4/YxeMhkZMGGCnSGzZg1MngzLl9vVlpRSqpQIiaHoH8eSqRJZlrPKl+VAQsoZqzXlWGlpxw57w9KaNdCpE0ydCrVr+/cNKKVUEQR9gj98MpUVPx2mV7uLGdrJQ3vetDR45RUYORIqVoR334X77wcpfrdJpZTyB0cTvIh0BN4AwoDpxpjRvj5HQWuqHkuyUyKrRHp4q+vX21H75s1w1122PHO+57KOUkoFOsdq8CISBkwCOgGNgXtFpLEvz5E1vz0+IRmDnd9+LCktx89ZJiz5mbiN8TkPkJxsL5q2aQOHDtkFsD/8UJO7UiooOHmR9XLgZ2PML8aY08AHwD98eYLCrKl6xlz3lSttr/YxY2xzsO3boXNnX4anlFJ+5WSCrwH84fZ4n+u5HESkt4isE5F1hw8fLtQJCrum6v6EZDhxwt6Jes01kJ4O33wD06dDlA86SiqlVADx+zRJY8w0Y0wrY0yratUK1/irsGuqdvlzCzRtClOmwOOPw9at0L59oY6hlFKlhZMJPh6o5fa4pus5nxnSoSGR4WEF7heVfII3vnydV2c/DZUqwerV8NprcNZZvgxHKaUCipOzaH4A6otIXWxivwe4z5cnyJq7nu8sGgyxG5bx4pI3qZKaCCNG2BWXypf3ZRhKKRWQHEvwxph0EekPLMJOk5xpjPnR1+fp3LxG3l0g9++Hvn3hs0+hVSuYMQMuu8zXp1dKqYDl6Dx4Y8yXwJdOniOPk9pk/uSTkJoK48bBwIHaP0YpFXKCK+v98gv06gVLl9pZMtOnwyWX+DsqpZTyC7/PovGJjAx4/XU7Q+aHH2z/mKVLNbkrpUJa6R/BHztmm4KtXQu33GKnQNas6e+olFLK70r/CD4qCurVgzlz4LPPNLkrpZRL6R/Bi9jkrpRSKofSP4JXSimVJ03wSikVpDTBK6VUkNIEr5RSQUoTvFJKBSlN8EopFaQ0wSulVJDSBK+UUkFKjDH+jiGbiBwGfivES6oCRxwKpzQI9fcP+hmAfgYQ2p/BRcaYPJfDC6gEX1giss4Y08rfcfhLqL9/0M8A9DMA/QzyoyUapZQKUprglVIqSJX2BD/N3wH4Wai/f9DPAPQzAP0M8lSqa/BKKaXyV9pH8EoppfKhCV4ppYJUqUzwItJRRHaJyM8iMtTf8ZQEEaklIstEZLuI/CgiA13PnyMiX4vIbtffZ/s7VieJSJiIbBSRz12P64rIWtd34UMRKefvGJ0kIlEiMk9EdorIDhG5MgS/A4+7/hvYJiLvi0hEqH0PvFXqEryIhAGTgE5AY+BeEWns36hKRDrwhDGmMXAF0M/1vocCS4wx9YElrsfBbCCww+3xGOB1Y8wlwDGgp1+iKjlvAAuNMY2AaOxnETLfARGpATwGtDLGNAXCgHsIve+BV0pdggcuB342xvxijDkNfAD8w88xOc4Yc8AYs8H180nsf9g1sO/9bddubwOd/ROh80SkJnAzMN31WIDrgXmuXYL9/VcBrgZmABhjThtjEgih74BLWSBSRMoCFYADhND3oDBKY4KvAfzh9nif67mQISJ1gObAWuB8Y8wB16aDwPl+CqskjAeeAjJdj88FEowx6a7Hwf5dqAscBma5ylTTReQsQug7YIyJB8YBv2MT+3FgPaH1PfBaaUzwIU1EKgLzgUHGmBPu24yd8xqU815F5BbgkDFmvb9j8aOyQAtgijGmOXCKXOWYYP4OALiuL/wD+z+76sBZQEe/BhXASmOCjwdquT2u6Xou6IlIODa5zzHGfOx6+k8RudC1/ULgkL/ic1gscJuI7MWW5a7H1qOjXL+qQ/B/F/YB+4wxa12P52ETfqh8BwBuAH41xhw2xqQBH2O/G6H0PfBaaUzwPwD1XVfNy2EvsHzm55gc56o3zwB2GGNec9v0GfCg6+cHgU9LOraSYIwZZoypaYypg/03X2qMuR9YBtzp2i1o3z+AMeYg8IeINHQ91R7YToh8B1x+B64QkQqu/yayPoOQ+R4URqm8k1VE/g9bjw0DZhpjXvJzSI4TkbbAt8BW/q5BP42tw38E1Ma2Wr7LGHPUL0GWEBG5FnjSGHOLiFyMHdGfA2wEuhljUv0Zn5NEJAZ7kbkc8AvwEHagFjLfARF5AbgbO7NsI/AwtuYeMt8Db5XKBK+UUqpgpbFEo5RSygua4JVSKkhpgldKqSClCV4ppYKUJnillApSmuBVwBERIyKvuj1+UkSe99GxZ4vInQXvWezzdHV1e1yW6/k6IpLsajWwQ0S+F5HuTsejQpMmeBWIUoEuIlLV34G4c7tT0hs9gV7GmOvy2LbHGNPcGHMp9qatQSLykE+CVMqNJngViNKxa2w+nntD7hG4iCS6/r5WRFaIyKci8ouIjBaR+10j5K0iUs/tMDeIyDoR+cnV4yarz/xYEflBRLaISB+3434rIp9h75jMHc+9ruNvE5ExrudGAG2BGSIy1tMbNcb8AgzGtsBFRC4XkTWuEf7qrLtWRWSl6yanrPOuEpFobz5MFboKMyJRqiRNAraIyCuFeE00cClwFHuX53RjzOViF0cZAAxy7VcH23a6HrBMRC4BHgCOG2Nai0h54DsRWezavwXQ1Bjzq/vJRKQ6tg95S2wP8sUi0tkYM1JErsfebbvOi7g3AI1cP+8E2hlj0kXkBuDfwB3YNhXdsaP9BkCEMWZzIT4bFYJ0BK8CkqtT5ju4RrZe+sHVNz8V2ANkJeit2KSe5SNjTKYxZjf2fwSNgJuAB0RkE7b9w7lAfdf+3+dO7i6tgeWuxlfpwBxsv/bCErefqwBzRWQb8DrQxPX8XOAWV8O5HsDsIpxHhRgdwatANh47up3l9lw6roGJiJTB9mTJ4t57JNPtcSY5v+u5+3MYbJIdYIxZ5L7B1ffmVNHC91pz/l6l6kVgmTHmdlff/+UAxpgkEfka2yr3LuxvDUp5pCN4FbBcDbM+Iufya3v5O7ndBoQX4dBdRaSMqy5/MbALWAQ86hohIyINXItpePI9cI2IVHUtJXkvsKIwgbiS+DhgouupKvzd6rZ7rt2nAxOwv6kcK8x5VGjSEbwKdK8C/d0evwV8KiKbgYUUbXT9OzY5VwYeMcakiMh0bBlng6sN7WEKWPbNGHNA7KLvy7C/AXxhjPGmTW09EdkIRAAngQnGmNmuba8Ab4vIM8AXuc63XkROkPM3GqXypd0klSolXBd1lwONjDGZBeyulJZolCoNROQB7MXf4Zrclbd0BK+UUkFKR/BKKRWkNMErpVSQ0gSvlFJBShO8UkoFKU3wSikVpP4f4q/tMmLrlmoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M4CQfLJVQiQJ",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Here the data is displayed by exponential representation (linear_regression Curve Was Converted)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "av8Ve4nQQY6Z",
        "colab_type": "code",
        "outputId": "c9530e3a-cf12-44c9-8d77-31cfb62539ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        }
      },
      "source": [
        "plt.plot(x[:79], predicted11[:79],label='linear_regression')\n",
        "plt.scatter(x_test_patient, 10**evaluation_1 ,s=60, c='#ff0000',label='Predict values')\n",
        "plt.scatter(x_test_patient, y_test_patient ,s=50, c='#003300',label='Original test values')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value ')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMqLmWxE3db3",
        "colab_type": "text"
      },
      "source": [
        "## **SVR**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O1gNgjmDhkP5",
        "colab_type": "text"
      },
      "source": [
        "### Code & Validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvQHc9qvc9ui",
        "colab_type": "code",
        "outputId": "489df1ea-fde5-4e9d-d323-95d5ec2dbe23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "scores_1 = []\n",
        "scores_2 = []\n",
        "scores_3 = []\n",
        "scores_4 = []\n",
        "scores_5 = []\n",
        "######################################################################################################################\n",
        "SVR_Regressor_1 = SVR(kernel='rbf')\n",
        "SVR_Regressor_2 = SVR(kernel='poly',degree=5)\n",
        "SVR_Regressor_3 = SVR(kernel='linear')\n",
        "SVR_Regressor_4 = SVR(kernel='poly',degree=2)\n",
        "SVR_Regressor_5 = SVR(kernel='poly',degree=7)\n",
        "######################################################################################################################\n",
        "cv = KFold(n_splits=10, random_state=1, shuffle=True)\n",
        "for train_index, test_index in cv.split(x):\n",
        "    X_train, X_test, y_train, y_test ,yy_train, yy_test= x[train_index], x[test_index], y[train_index], y[test_index], yy[train_index], yy[test_index]\n",
        "    #\n",
        "    SVR_Regressor_1.fit(X_train.values.reshape(-1,1), y_train)\n",
        "    scores_1.append(SVR_Regressor_1.score(X_test.values.reshape(-1,1), y_test))\n",
        "    #\n",
        "    SVR_Regressor_2.fit(X_train.values.reshape(-1,1), y_train)\n",
        "    scores_2.append(SVR_Regressor_2.score(X_test.values.reshape(-1,1), y_test))\n",
        "    #\n",
        "    SVR_Regressor_3.fit(X_train.values.reshape(-1,1), y_train)\n",
        "    scores_3.append(SVR_Regressor_3.score(X_test.values.reshape(-1,1), y_test))\n",
        "    #\n",
        "    SVR_Regressor_4.fit(X_train.values.reshape(-1,1), y_train)\n",
        "    scores_4.append(SVR_Regressor_4.score(X_test.values.reshape(-1,1), y_test))\n",
        "    #\n",
        "    SVR_Regressor_5.fit(X_train.values.reshape(-1,1), y_train)\n",
        "    scores_5.append(SVR_Regressor_5.score(X_test.values.reshape(-1,1), y_test))\n",
        "\n",
        "print(\"Average score for SVR_Regressor_1:\",sum(scores_1)/10,\n",
        "      \"\\nAverage score for SVR_Regressor_2:\",sum(scores_2)/10,\n",
        "      \"\\nAverage score for SVR_Regressor_3:\",sum(scores_3)/10,\n",
        "      \"\\nAverage score for SVR_Regressor_4:\",sum(scores_4)/10,\n",
        "      \"\\nAverage score for SVR_Regressor_5:\",sum(scores_5)/10)   \n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average score for SVR_Regressor_1: -0.3947486705063331 \n",
            "Average score for SVR_Regressor_2: 0.9540088594110667 \n",
            "Average score for SVR_Regressor_3: -0.1608372934452422 \n",
            "Average score for SVR_Regressor_4: -0.387433213590172 \n",
            "Average score for SVR_Regressor_5: 0.9103206959724819\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TF9rMSSNmSJ-",
        "colab_type": "text"
      },
      "source": [
        "**After the validation we chosed best parameter for SVR (SVR_Regressor_2) to evaluate:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_ynBqRWH9fQ",
        "colab_type": "text"
      },
      "source": [
        "### Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D72kK0YfmT8o",
        "colab_type": "code",
        "outputId": "ca15e969-135b-4c0e-c867-2f3b90134fdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "SVR_Regressor_2.fit(x.values.reshape(-1,1), y)\n",
        "y_test_patient_log=np.log10(y_test_patient)\n",
        "evaluation_2 =SVR_Regressor_2.predict(x_test_patient.values.reshape(-1,1))\n",
        "score=SVR_Regressor_2.score(x_test_patient.values.reshape(-1,1), y_test_patient)   \n",
        "print(\"Final Evaluation Score for SVR Regression :\",score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Final Evaluation Score for SVR Regression : 0.9587420012661088\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "84dnU1SRS75j",
        "colab_type": "text"
      },
      "source": [
        "### Prediction for new days"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLvUFTUvTFYX",
        "colab_type": "code",
        "outputId": "05e04fac-518f-4450-a47c-8712c9ccecde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "\n",
        "print('Evaluation for expecting 6 days in future in SVR_Regression:')\n",
        "for predict in x_prediction:\n",
        "  print('day',predict,'=',int(SVR_Regressor_2.predict([predict])))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation for expecting 6 days in future in SVR_Regression:\n",
            "day [95] = 834622\n",
            "day [96] = 879540\n",
            "day [97] = 926369\n",
            "day [98] = 975169\n",
            "day [99] = 1026003\n",
            "day [100] = 1078932\n",
            "day [101] = 1134022\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eGhaFCkph2AY",
        "colab_type": "text"
      },
      "source": [
        "### plot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JPXaC5FrR_ja",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "Here the data is displayed by exponential representation\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qrz6ge5reG6X",
        "colab_type": "code",
        "outputId": "80f1cefd-02e0-4b04-ef19-42e3b1cb6abd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "predicted2 = (SVR_Regressor_2.predict(x.values.reshape(-1,1)))\n",
        "predicted22= np.log10(predicted2[23:])\n",
        "plt.plot(x,y, 'o-',label='data set')\n",
        "plt.plot(x, predicted2,c='#ff0000',label='SVR_Regressor_2')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value in log')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3gIwXk2mR8hI",
        "colab_type": "code",
        "outputId": "15dbad53-3186-4002-952e-4e84b6bd5d29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plt.plot(x, predicted2,label='SVR_Regressor_2')\n",
        "plt.scatter(x_test_patient, evaluation_2 ,s=60, c='#ff0000',label='Predict values')\n",
        "plt.scatter(x_test_patient, y_test_patient ,s=50, c='#003300',label='Original test values')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value in Log')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oPGx5rY-fRPG",
        "colab_type": "text"
      },
      "source": [
        "## **MLPRegressor**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPn27AX6hPia",
        "colab_type": "text"
      },
      "source": [
        "### Code & Validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ER04UD8dfVWM",
        "colab_type": "code",
        "outputId": "886e3740-bc84-4309-c2a8-c99cdbbcd8e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "\n",
        "scores_1 = []\n",
        "scores_2 = []\n",
        "scores_3 = []\n",
        "scores_4 = []\n",
        "scores_5 = []\n",
        "######################################################################################################################\n",
        "MLP_Regressor_1 = MLPRegressor(hidden_layer_sizes=(4), activation='tanh', solver='lbfgs' ,learning_rate_init=0.01, max_iter=1000,random_state=120, validation_fraction=0.1)\n",
        "MLP_Regressor_2 = MLPRegressor(hidden_layer_sizes=(5), activation='tanh', solver='lbfgs' ,learning_rate_init=0.01, max_iter=1000,random_state=120, validation_fraction=0.1)\n",
        "MLP_Regressor_3 = MLPRegressor(hidden_layer_sizes=(1), activation='tanh', solver='lbfgs' ,learning_rate_init=0.3, max_iter=1000,random_state=120, validation_fraction=0.2)\n",
        "MLP_Regressor_4 = MLPRegressor(hidden_layer_sizes=(5), activation='relu', solver='lbfgs' ,learning_rate_init=0.01, max_iter=1000,random_state=1, validation_fraction=0.1)\n",
        "MLP_Regressor_5 = MLPRegressor(hidden_layer_sizes=(5), activation='tanh', solver='sgd' ,learning_rate_init=0.01, max_iter=1000,random_state=1, validation_fraction=0.1)\n",
        "######################################################################################################################\n",
        "cv = KFold(n_splits=10, random_state=1, shuffle=True)\n",
        "for train_index, test_index in cv.split(x):\n",
        "    X_train, X_test, y_train, y_test ,yy_train, yy_test= x[train_index], x[test_index], y[train_index], y[test_index], yy[train_index], yy[test_index]\n",
        "    #\n",
        "    MLP_Regressor_1.fit(X_train.values.reshape(-1,1), yy_train)\n",
        "    scores_1.append(MLP_Regressor_1.score(X_test.values.reshape(-1,1), yy_test))\n",
        "    #\n",
        "    MLP_Regressor_2.fit(X_train.values.reshape(-1,1), yy_train)\n",
        "    scores_2.append(MLP_Regressor_2.score(X_test.values.reshape(-1,1), yy_test))\n",
        "    #\n",
        "    MLP_Regressor_3.fit(X_train.values.reshape(-1,1), yy_train)\n",
        "    scores_3.append(MLP_Regressor_3.score(X_test.values.reshape(-1,1), yy_test))\n",
        "     #\n",
        "    MLP_Regressor_4.fit(X_train.values.reshape(-1,1), yy_train)\n",
        "    scores_4.append(MLP_Regressor_4.score(X_test.values.reshape(-1,1), yy_test))\n",
        "    #\n",
        "    MLP_Regressor_5.fit(X_train.values.reshape(-1,1), yy_train)\n",
        "    scores_5.append(MLP_Regressor_5.score(X_test.values.reshape(-1,1), yy_test))\n",
        "\n",
        "print(\"Average score for MLP_Regressor_1:\",sum(scores_1)/10,\"\\nAverage score for MLP_Regressor_2:\",sum(scores_2)/10,\"\\nAverage score for MLP_Regressor_3:\",sum(scores_3)/10\n",
        "      ,\"\\nAverage score for MLP_Regressor_4:\",sum(scores_4)/10,\"\\nAverage score for MLP_Regressor_5:\",sum(scores_5)/10)    \n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average score for MLP_Regressor_1: 0.9973259107392481 \n",
            "Average score for MLP_Regressor_2: 0.9979228277995988 \n",
            "Average score for MLP_Regressor_3: 0.9910101918732378 \n",
            "Average score for MLP_Regressor_4: -0.22137070515497576 \n",
            "Average score for MLP_Regressor_5: -0.2732449480660675\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3rO754e_OUb2",
        "colab_type": "text"
      },
      "source": [
        "**After the validation we chosed best parameter for MLP (MLP_Regressor_2) to evaluate:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jZeuLeooIGCA",
        "colab_type": "text"
      },
      "source": [
        "### Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bTsH5GY8-mnV",
        "colab_type": "code",
        "outputId": "0cb52b1f-5e4d-4bc4-b6fb-d0139a0face9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "MLP_Regressor = MLPRegressor(hidden_layer_sizes=(5), activation='tanh', solver='lbfgs' ,learning_rate_init=0.01, max_iter=1000,random_state=120, validation_fraction=0.1)\n",
        "MLP_Regressor.fit(x.values.reshape(-1,1), yy)\n",
        "y_test_patient_log=np.log10(y_test_patient)\n",
        "evaluation_3 =MLP_Regressor.predict(x_test_patient.values.reshape(-1,1))\n",
        "score=MLP_Regressor.score(x_test_patient.values.reshape(-1,1), y_test_patient_log)   \n",
        "print(\"Final Evaluation Score for MLP_Regressor :\",score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Final Evaluation Score for MLP_Regressor : 0.9984346234582061\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJ2leCIVAFvX",
        "colab_type": "text"
      },
      "source": [
        "### Prediction for new days"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oqNSoUbuAHYT",
        "colab_type": "code",
        "outputId": "46f048bb-1bea-412c-9b0b-12d214f167d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "print('Evaluation for expecting 6 days in future in MLP_Regressor:')\n",
        "for predict in x_prediction:\n",
        "  print('day',predict,'=',int(10**MLP_Regressor.predict([predict])))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation for expecting 6 days in future in MLP_Regressor:\n",
            "day [95] = 781790\n",
            "day [96] = 801797\n",
            "day [97] = 822086\n",
            "day [98] = 842747\n",
            "day [99] = 863865\n",
            "day [100] = 885524\n",
            "day [101] = 907799\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iiEiqrNlhVeI",
        "colab_type": "text"
      },
      "source": [
        "### plot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "isrfGkFJS3K7",
        "colab_type": "text"
      },
      "source": [
        "Here the data is displayed by exponential"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bfbFJCNKfZ1I",
        "colab_type": "code",
        "outputId": "032c0611-c2b7-4374-8dfd-2cc7edc073dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "predicted3 =10**MLP_Regressor.predict(x.values.reshape(-1,1))\n",
        "predicted33=MLP_Regressor.predict(x.values.reshape(-1,1))\n",
        "plt.plot(x, y, 'o-',label='data set')\n",
        "plt.plot(x, predicted3,c='#ff0000',label='MLP_regression')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value ')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sZlVpy5rSoXd",
        "colab_type": "code",
        "outputId": "680fd202-1933-4981-df09-c8ab6b061783",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plt.plot(x, predicted3,label='MLP_regression')\n",
        "plt.scatter(x_test_patient, 10**evaluation_3 ,s=50, c='#ff0000',label='Predict values')\n",
        "plt.scatter(x_test_patient, y_test_patient,s=40, c='#003300',label='Original test values')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value in Log')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wnT3iNvbrKjv",
        "colab_type": "text"
      },
      "source": [
        "## **Conclusion**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UWQvtNs8EHy",
        "colab_type": "code",
        "outputId": "5b5ee880-9c23-44f0-a201-23f686b7df8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "print(\"\\n\\t   The Three models in exponantial case \")\n",
        "plt.plot(x[:76], 10**predicted1[:76],label='linear_regression')\n",
        "plt.plot(x, predicted2,label='SVR')\n",
        "plt.plot(x, predicted3,label='MLPRegressor (THE BEST)')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t   The Three models in exponantial case \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NnlszY9VTk0F",
        "colab_type": "code",
        "outputId": "de29f0eb-62fe-48be-cc93-6f30efcdf6d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "print(\"\\n\\t   The Three models in Log case \")\n",
        "plt.plot(x, predicted1,label='linear_regression')\n",
        "plt.plot(x[23:], predicted22,label='SVR')\n",
        "plt.plot(x, predicted33,label='MLPRegressor (THE BEST)')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value')\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t   The Three models in Log case \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8CU-HXDGU8Xb",
        "colab_type": "code",
        "outputId": "80dbd335-9eaa-4a40-9972-ca79e58826bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "print(\"\\n\\t   The Best model (MLP Model) \")\n",
        "plt.plot(x, predicted3,label='MLPRegressor (THE BEST)')\n",
        "plt.plot(x_prediction, 10**MLP_Regressor.predict(x_prediction),label='new day prediction')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t   The Best model (MLP Model) \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_thfnlLkU82p",
        "colab_type": "code",
        "outputId": "ebba9423-8acd-4f33-8f19-dfc73a00ac67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "print(\"\\n\\t The Best model (MLP Model) in Log\")\n",
        "plt.plot(x, np.log10(predicted3),label='MLPRegressor (THE BEST)')\n",
        "plt.plot(x_prediction, MLP_Regressor.predict(x_prediction), label='new day prediction')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value in log')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t The Best model (MLP Model) in Log\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RSV942JgmtAu",
        "colab_type": "text"
      },
      "source": [
        "# **Deaths**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZrhRwWKHLdsJ",
        "colab_type": "text"
      },
      "source": [
        "## **Preparing deaths data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bbRCqEUQdoDi",
        "colab_type": "code",
        "outputId": "e5718afa-4fc3-43f4-f8ba-9b079435b832",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "path2='https://raw.githubusercontent.com/AbedMHroub/Corona-virus-COVID19-predictions-project/master/dataset/Deaths_us.csv'\n",
        "ds_death = pd.read_csv(path2, index_col=0)\n",
        "ds_death.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>num_of_date</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Date_test</th>\n",
              "      <th>num_of_date_test</th>\n",
              "      <th>Deaths_test</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-03-02</th>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>2020-03-18</td>\n",
              "      <td>17.0</td>\n",
              "      <td>171.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-03</th>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>2020-03-24</td>\n",
              "      <td>23.0</td>\n",
              "      <td>957.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-04</th>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>31.0</td>\n",
              "      <td>6394.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-05</th>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>2020-04-11</td>\n",
              "      <td>41.0</td>\n",
              "      <td>23843.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-06</th>\n",
              "      <td>5</td>\n",
              "      <td>14</td>\n",
              "      <td>2020-04-22</td>\n",
              "      <td>52.0</td>\n",
              "      <td>47894.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-07</th>\n",
              "      <td>6</td>\n",
              "      <td>17</td>\n",
              "      <td>2020-04-28</td>\n",
              "      <td>58.0</td>\n",
              "      <td>59266.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-08</th>\n",
              "      <td>7</td>\n",
              "      <td>21</td>\n",
              "      <td>2020-04-05</td>\n",
              "      <td>35.0</td>\n",
              "      <td>11793.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-09</th>\n",
              "      <td>8</td>\n",
              "      <td>22</td>\n",
              "      <td>2020-04-16</td>\n",
              "      <td>46.0</td>\n",
              "      <td>34619.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-10</th>\n",
              "      <td>9</td>\n",
              "      <td>30</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-11</th>\n",
              "      <td>10</td>\n",
              "      <td>36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            num_of_date  Deaths   Date_test  num_of_date_test  Deaths_test\n",
              "Date                                                                      \n",
              "2020-03-02            1       6  2020-03-18              17.0        171.0\n",
              "2020-03-03            2       9  2020-03-24              23.0        957.0\n",
              "2020-03-04            3      11  2020-04-01              31.0       6394.0\n",
              "2020-03-05            4      12  2020-04-11              41.0      23843.0\n",
              "2020-03-06            5      14  2020-04-22              52.0      47894.0\n",
              "2020-03-07            6      17  2020-04-28              58.0      59266.0\n",
              "2020-03-08            7      21  2020-04-05              35.0      11793.0\n",
              "2020-03-09            8      22  2020-04-16              46.0      34619.0\n",
              "2020-03-10            9      30         NaN               NaN          NaN\n",
              "2020-03-11           10      36         NaN               NaN          NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gQQ-8gU3mzHy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_death = ds_death['num_of_date']\n",
        "y_death = ds_death['Deaths']\n",
        "x_test_deaths = ds_death['num_of_date_test'][:8]\n",
        "y_test_deaths = ds_death['Deaths_test'][:8]\n",
        "x_prediction2 =[[60],[61],[62],[63],[64],[65],[67]]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQ2R_NaYmz-y",
        "colab_type": "text"
      },
      "source": [
        "## **linear_regression**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pOsd1xkUnoQg",
        "colab_type": "text"
      },
      "source": [
        "### Code & Validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-X5VAyZ8nmjl",
        "colab_type": "code",
        "outputId": "d0943b30-a88e-4873-bf6e-d8bf8be1c66a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "linear_regression=LinearRegression()\n",
        "\n",
        "y_log_D = np.log10(y_death)\n",
        "\n",
        "scores = []\n",
        "######################################################################################################################\n",
        "Linear_Regression = LinearRegression()\n",
        "######################################################################################################################\n",
        "cv = KFold(n_splits=10, random_state=1, shuffle=True)\n",
        "for train_index, test_index in cv.split(x_death):\n",
        "    X_train_D, X_test_D, y_log_train_D, y_log_test_D= x_death[train_index], x_death[test_index], y_log_D[train_index], y_log_D[test_index]\n",
        "    \n",
        "    Linear_Regression.fit(X_train_D.values.reshape(-1,1), y_log_train_D)\n",
        "    scores.append(Linear_Regression.score(X_test_D.values.reshape(-1,1), y_log_test_D))\n",
        "    \n",
        "print(\"Average score for Linear Regression:\",sum(scores)/10)    \n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average score for Linear Regression: 0.8742874590701348\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ie2HmoSnhztq",
        "colab_type": "text"
      },
      "source": [
        "**After the validation we evaluate the Linear Model:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NECy8G3hIJ2r",
        "colab_type": "text"
      },
      "source": [
        "### Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMjYHnoph3A1",
        "colab_type": "code",
        "outputId": "6c802dbb-1e74-4ef8-d3df-ee428dbb0172",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "Linear_Regression.fit(x_death.values.reshape(-1,1), y_log_D)\n",
        "y_test_deaths_log=np.log10(y_test_deaths)\n",
        "evaluation_4 =Linear_Regression.predict(x_test_deaths.values.reshape(-1,1))\n",
        "score=Linear_Regression.score(x_test_deaths.values.reshape(-1,1), y_test_deaths_log)   \n",
        "print(\"Final Evaluation Score for Linear Regression :\",score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Final Evaluation Score for Linear Regression : 0.8300935990327268\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z5ywtEvrrhlk",
        "colab_type": "text"
      },
      "source": [
        "### Prediction for new days"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMvndMKDrgVz",
        "colab_type": "code",
        "outputId": "e0646da7-e297-421f-bef2-06fce110aae7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "print('Evaluation for expecting 6 days in future in Linear_Regression:')\n",
        "for predict in x_prediction2:\n",
        "  print('day',predict,'=',int(10**Linear_Regression.predict([predict])))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation for expecting 6 days in future in Linear_Regression:\n",
            "day [60] = 314958\n",
            "day [61] = 374098\n",
            "day [62] = 444343\n",
            "day [63] = 527777\n",
            "day [64] = 626878\n",
            "day [65] = 744587\n",
            "day [67] = 1050462\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Nm5HYLlofyr",
        "colab_type": "text"
      },
      "source": [
        "### plot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w0XyrG22bUEu",
        "colab_type": "text"
      },
      "source": [
        "Here the data are displayed in logarithmic representation\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tkgs4rvfolag",
        "colab_type": "code",
        "outputId": "c568035d-4a73-42ef-da53-7a0f08ce225e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "predicted4 =(Linear_Regression.predict(x_death.values.reshape(-1,1)))\n",
        "plt.plot(x_death, y_log_D, 'o-',label='data set')\n",
        "plt.plot(x_death, predicted4,c='#ff0000',label='linear_regression')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value in Log')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "flzt0SGGbV6V",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Here the data are displayed in logarithmic representation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DtiEhKm0XCUp",
        "colab_type": "code",
        "outputId": "9a8888ee-1fa9-4c5f-9169-88dbf3306b42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plt.plot(x_death[:58], 10**(predicted4[:58]),label='linear_regression')\n",
        "plt.scatter(x_test_deaths, 10**evaluation_4 ,s=60, c='#ff0000',label='Predict values')\n",
        "plt.scatter(x_test_deaths, y_test_deaths ,s=50, c='#003300',label='Original values')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value in Log')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vUxUr9ugo1je",
        "colab_type": "text"
      },
      "source": [
        "## **SVR**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7T8DNnTvo4WL",
        "colab_type": "text"
      },
      "source": [
        "### Code & Validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tp7hMdGOomNO",
        "colab_type": "code",
        "outputId": "e237045e-a410-4894-a846-11b913215dcd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "from sklearn.svm import SVR\n",
        "\n",
        "\n",
        "scores_1 = []\n",
        "scores_2 = []\n",
        "scores_3 = []\n",
        "######################################################################################################################\n",
        "SVR_Regressor_1 = SVR(kernel='rbf')\n",
        "SVR_Regressor_2 = SVR(kernel='poly',degree=4)\n",
        "SVR_Regressor_3 = SVR(kernel='linear')\n",
        "######################################################################################################################\n",
        "cv = KFold(n_splits=8, random_state=1, shuffle=True)\n",
        "for train_index, test_index in cv.split(x_death):\n",
        "    X_train_D, X_test_D, y_train_D, y_test_D = x_death[train_index], x_death[test_index], y_death[train_index], y_death[test_index]\n",
        "    #\n",
        "    SVR_Regressor_1.fit(X_train_D.values.reshape(-1,1), y_train_D)\n",
        "    scores_1.append(SVR_Regressor_1.score(X_test_D.values.reshape(-1,1), y_test_D))\n",
        "    #\n",
        "    SVR_Regressor_2.fit(X_train_D.values.reshape(-1,1), y_train_D)\n",
        "    scores_2.append(SVR_Regressor_2.score(X_test_D.values.reshape(-1,1), y_test_D))\n",
        "    #\n",
        "    SVR_Regressor_3.fit(X_train_D.values.reshape(-1,1), y_train_D)\n",
        "    scores_3.append(SVR_Regressor_3.score(X_test_D.values.reshape(-1,1), y_test_D))\n",
        "\n",
        "print(\"Average score for SVR_Regressor_1:\",sum(scores_1)/8,\"\\nAverage score for SVR_Regressor_2:\",sum(scores_2)/8,\"\\nAverage score for SVR_Regressor_3:\",sum(scores_3)/8)    \n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average score for SVR_Regressor_1: -0.5473117937312791 \n",
            "Average score for SVR_Regressor_2: 0.919539558478903 \n",
            "Average score for SVR_Regressor_3: 0.3446979640411154\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8veNi-ZehRbG",
        "colab_type": "text"
      },
      "source": [
        "**After the validation we chosed best parameter for SVR (SVR_Regressor_2) to evaluate:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "usgo14_hIOKx",
        "colab_type": "text"
      },
      "source": [
        "### Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "14vJQnrvguQH",
        "colab_type": "code",
        "outputId": "7bc57f92-ed4a-43a6-db0b-7495f6e5d09f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "SVR_Regressor_2.fit(x_death.values.reshape(-1,1), y_death)\n",
        "y_test_deaths_log=np.log10(y_test_deaths)\n",
        "evaluation_5 =SVR_Regressor_2.predict(x_test_deaths.values.reshape(-1,1))\n",
        "score=SVR_Regressor_2.score(x_test_deaths.values.reshape(-1,1), y_test_deaths)   \n",
        "print(\"Final Evaluation Score for SVR Regression :\",score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Final Evaluation Score for SVR Regression : 0.9342479063269598\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s1rXWf62QRvx",
        "colab_type": "text"
      },
      "source": [
        "### Prediction for new days"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWZo98BYxjVR",
        "colab_type": "code",
        "outputId": "e8a80c9e-3d5d-43ca-ef4e-c1f4e12d5822",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "print('Evaluation for expecting 6 days in future in SVR_Regression:')\n",
        "for predict in x_prediction2:\n",
        "  print('day',predict,'=',int(SVR_Regressor_2.predict([predict])))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation for expecting 6 days in future in SVR_Regression:\n",
            "day [60] = 73019\n",
            "day [61] = 78009\n",
            "day [62] = 83251\n",
            "day [63] = 88753\n",
            "day [64] = 94523\n",
            "day [65] = 100570\n",
            "day [67] = 113530\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2BpuTh5apL5z",
        "colab_type": "text"
      },
      "source": [
        "### plot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pSaZKu5Qbfxr",
        "colab_type": "text"
      },
      "source": [
        "Here the data are displayed in logarithmic representation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AoVOLf73pOHP",
        "colab_type": "code",
        "outputId": "269ceee0-548e-4570-9512-1a64b63f8631",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "predicted5 = (SVR_Regressor_2.predict(x_death.values.reshape(-1,1)))\n",
        "plt.plot(x_death, y_death, 'o-',label='data set')\n",
        "plt.plot(x_death, predicted5,c='#ff0000',label='SVR_Regressor_2')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value in Log')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fAfj4JCvYzq3",
        "colab_type": "code",
        "outputId": "34faa1bd-8a2a-4b5b-aa91-0b75e3372b0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plt.plot(x_death, predicted5,label='SVR_Regressor_2')\n",
        "plt.scatter(x_test_deaths, evaluation_5 ,s=60, c='#ff0000',label='Predict values')\n",
        "plt.scatter(x_test_deaths, y_test_deaths ,s=50, c='#003300',label='Original test values')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value in Log')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MEi_ZrqCpYpk",
        "colab_type": "text"
      },
      "source": [
        "## **MLPRegressor**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ULWy7g1pe_p",
        "colab_type": "text"
      },
      "source": [
        "### Code & Validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mgkIzE5YpoHU",
        "colab_type": "code",
        "outputId": "26352998-9d01-42da-c74b-74aed0d6ed95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.neural_network import MLPRegressor\n",
        "\n",
        "scores_1 = []\n",
        "scores_2 = []\n",
        "scores_3 = []\n",
        "scores_4 = []\n",
        "scores_5 = []\n",
        "\n",
        "######################################################################################################################\n",
        "MLP_Regressor_1 = MLPRegressor(hidden_layer_sizes=(4), activation='tanh', solver='lbfgs' ,learning_rate_init=0.01, max_iter=1000,random_state=93, validation_fraction=0.1)\n",
        "MLP_Regressor_2 = MLPRegressor(hidden_layer_sizes=(5), activation='tanh', solver='lbfgs' ,learning_rate_init=0.01, max_iter=1000,random_state=93, validation_fraction=0.1)\n",
        "MLP_Regressor_3 = MLPRegressor(hidden_layer_sizes=(6), activation='tanh', solver='lbfgs' ,learning_rate_init=0.01, max_iter=1000,random_state=93, validation_fraction=0.1)\n",
        "MLP_Regressor_4 = MLPRegressor(hidden_layer_sizes=(4), activation='relu', solver='lbfgs' ,learning_rate_init=0.01, max_iter=1000,random_state=93, validation_fraction=0.1)\n",
        "MLP_Regressor_5 = MLPRegressor(hidden_layer_sizes=(4), activation='tanh', solver='sgd' ,learning_rate_init=0.01, max_iter=1000,random_state=93, validation_fraction=0.1)\n",
        "######################################################################################################################\n",
        "cv = KFold(n_splits=8, random_state=1, shuffle=True)\n",
        "for train_index, test_index in cv.split(x_death):\n",
        "    X_train_D, X_test_D, y_log_train_D, y_log_test_D = x_death[train_index], x_death[test_index], y_log_D[train_index], y_log_D[test_index]\n",
        "    #\n",
        "    MLP_Regressor_1.fit(X_train_D.values.reshape(-1,1), y_log_train_D)\n",
        "    scores_1.append(MLP_Regressor_1.score(X_test_D.values.reshape(-1,1), y_log_test_D))\n",
        "    #\n",
        "    MLP_Regressor_2.fit(X_train_D.values.reshape(-1,1), y_log_train_D)\n",
        "    scores_2.append(MLP_Regressor_2.score(X_test_D.values.reshape(-1,1), y_log_test_D))\n",
        "    #\n",
        "    MLP_Regressor_3.fit(X_train_D.values.reshape(-1,1), y_log_train_D)\n",
        "    scores_3.append(MLP_Regressor_3.score(X_test_D.values.reshape(-1,1), y_log_test_D))\n",
        "    #\n",
        "    MLP_Regressor_4.fit(X_train_D.values.reshape(-1,1), y_log_train_D)\n",
        "    scores_4.append(MLP_Regressor_4.score(X_test_D.values.reshape(-1,1), y_log_test_D))\n",
        "    #\n",
        "    MLP_Regressor_5.fit(X_train_D.values.reshape(-1,1), y_log_train_D)\n",
        "    scores_5.append(MLP_Regressor_5.score(X_test_D.values.reshape(-1,1), y_log_test_D))\n",
        "\n",
        "print(  \"Average score for MLP_Regressor_1:\",sum(scores_1)/8,\n",
        "      \"\\nAverage score for MLP_Regressor_2:\",sum(scores_2)/8,\n",
        "      \"\\nAverage score for MLP_Regressor_3:\",sum(scores_3)/8,\n",
        "      \"\\nAverage score for MLP_Regressor_4:\",sum(scores_4)/8,\n",
        "      \"\\nAverage score for MLP_Regressor_5:\",sum(scores_5)/8)    \n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average score for MLP_Regressor_1: 0.9993729870547378 \n",
            "Average score for MLP_Regressor_2: 0.9996863587179058 \n",
            "Average score for MLP_Regressor_3: 0.9995091187119902 \n",
            "Average score for MLP_Regressor_4: 0.9687048772810132 \n",
            "Average score for MLP_Regressor_5: -0.41477176691560547\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wjVdrPCMe9A8",
        "colab_type": "text"
      },
      "source": [
        "**After the validation we chosed best parameter for MLP (MLP_Regressor_2) to evaluate:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0fb39-KxIRsW",
        "colab_type": "text"
      },
      "source": [
        "### Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kBj5BtNaeAx5",
        "colab_type": "code",
        "outputId": "df5b9009-3654-4cf4-977b-b04504839e38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "MLP_Regressor_D = MLPRegressor(hidden_layer_sizes=(5), activation='tanh', solver='lbfgs' ,learning_rate_init=0.01,\n",
        "                               max_iter=1000,random_state=93, validation_fraction=0.1)\n",
        "MLP_Regressor_D.fit(x_death.values.reshape(-1,1), y_log_D)\n",
        "y_test_deaths_log=np.log10(y_test_deaths)\n",
        "evaluation_6 =MLP_Regressor_D.predict(x_test_deaths.values.reshape(-1,1))\n",
        "score=MLP_Regressor_D.score(x_test_deaths.values.reshape(-1,1), y_test_deaths_log)   \n",
        "print(\"Final Evaluation Score for MLP Regression :\",score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Final Evaluation Score for MLP Regression : 0.9998623441881356\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GiVNLRWvSO8Y",
        "colab_type": "text"
      },
      "source": [
        "### Prediction for new days"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I9wyQTvpSQLn",
        "colab_type": "code",
        "outputId": "2402717e-7ccc-40e5-8149-bc2226b92e51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "print('Evaluation for expecting 6 days in future in MLP_Regression:')\n",
        "for predict in x_prediction2:\n",
        "  print('day',predict,'=',int(10**MLP_Regressor_D.predict([predict])))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation for expecting 6 days in future in MLP_Regression:\n",
            "day [60] = 63629\n",
            "day [61] = 65503\n",
            "day [62] = 67337\n",
            "day [63] = 69130\n",
            "day [64] = 70878\n",
            "day [65] = 72581\n",
            "day [67] = 75843\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z7sIVawoptRV",
        "colab_type": "text"
      },
      "source": [
        "### plot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HpujqmbJbhwC",
        "colab_type": "text"
      },
      "source": [
        "Here the data are displayed in logarithmic representation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckTpDz92pvp8",
        "colab_type": "code",
        "outputId": "59f23223-4be8-459d-93be-87d2908b36e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "predicted6 = MLP_Regressor_D.predict(x_death.values.reshape(-1,1))\n",
        "plt.plot(x_death, y_death, 'o-',label='data set')\n",
        "plt.plot(x_death, 10**predicted6,c='#ff0000',label='MLP_Regressor_D')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value in log')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KRbEl0QiY-LT",
        "colab_type": "code",
        "outputId": "8eab0988-1d50-44f7-a764-de68b6f51b1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "plt.plot(x_death, 10**predicted6,label='MLP_Regressor_D')\n",
        "plt.scatter(x_test_deaths, 10**evaluation_6 ,s=60, c='#ff0000',label='Predict values')\n",
        "plt.scatter(x_test_deaths, y_test_deaths ,s=50, c='#003300',label='Original test values')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value in Log')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b421ydtrrYaM",
        "colab_type": "text"
      },
      "source": [
        "## **Conclusion**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tlc13DtVsiop",
        "colab_type": "code",
        "outputId": "c6d31ff0-f648-4e48-c6f6-f29a976ee9d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "print(\"\\n\\t   The Three models in exponantial case \")\n",
        "plt.plot(x_death[:45], 10**predicted4[:45],label='linear_regression')\n",
        "plt.plot(x_death, predicted5,label='SVR')\n",
        "plt.plot(x_death, 10**predicted6,label='MLPRegressor (THE BEST)')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value')\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t   The Three models in exponantial case \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sU+QNZwPDqe1YlEIFUh8BlWO0hkOLoOzjULvLfafiTHFM+mcSe/z38H6r9xn4+MBUKsleVg0iSqmSSqmNSqmzSikfpVQbpVRppdQOpdQF47WUkVcppb5WSvkqpU4ppZolqcfVyH9BKeWaJL25Uuq0UeZrpXJgV3orcXBwwMvLi6ioKAB27NhB5cqVLSqbsCS8i4sLn376abL07FoqXmud+GVqbXFxccnSPv30U958802GDRuGp6cnnp6eVKpUid27d+Pp6cmsWZYtWZOQ39PTk6+//jrFPBMmTMDT0xNvb29Onz7N33//nXhu9uzZieUT5vd8+OGHdO/enZMnT+Lt7c2sWbNo3LhxYr6nn346sdzOnTtxdHSkYsWK7N+/PxO/HZGTzl4PzzvzQ/wOQ6CneVhvkq+7eFM8U/dNZceVHbzb4l0G1RuUY02y9p3IV8BWrXU9oAngA0wG3LXWdQB34z3Ak0Ad42cksAhAKVUamAa0AloC0xICj5FnRJJyD/Wfdr179+aPP/4AYO3atRleVLFjx474+vomS0+6VHxQUBDPP/88LVq0oEWLFolfYkFBQXTv3p2GDRvy2muvUb16dW7dusXly5d5/PHHeeWVV2jUqBF+fn7Mnj2bFi1a4OTkxLRp0wCIjIykT58+NGnShEaNGiXuhTJ58uTEmfgTJ04EzBMlu3TpgpOTE127duXq1auA+a/8UaNG0apVK9599937PkN4eDinTp2iSZMmGfqdZFVMTAzR0dGUKlUqzXyBgYH3LUvj5OSUbt39+vVjzZo1WW6jsJ7QqFgC7kTlnf6QQ4vMo7GavJCYZNImph2Yxl///sX4ZuN5ucHLOdokq81YV0qVADoCQwG01jFAjFLqGaCTkW0VsAeYBDwDrNZaa+CQcRdT0ci7Q2sdYtS7A+illNoDFNdaHzLSVwP9gL+y0u7Pj3zO2ZCzWakimXql6zGp5aR0873wwgtMnz6dvn37curUKV599VX++ecfi6+zZcsWGjdunCw96VLx48aNY8KECbRv356rV6/Ss2dPfHx8+Pjjj+nSpQtTpkxh69atLFu2LLH8hQsXWLVqFa1bt2b79u1cuHCBI0eOoLXm6aefZu/evQQFBVGpUqXEIBgaGkpwcDCbNm3i7NmzKKW4c+cOAG+88Qaurq64urqyfPly3nzzTTZv3gyAv78/Bw4cwNb2/kcHHh4eNGrUyKLfw/r169m3b1/i+wcDa+fOnRPrd3V1ZcKECcnqmDt3Lj/88ANXrlzhySefxNnZOfHcO++8k3hn17BhQ9asWcOYMWMYNGgQ33zzDd26dWPYsGFUqlQpzXa6uLjw/vvvW/SZRO44l5dmqof6g8/v0MYNCpr7Z7TWzDw0k18v/opbEzeGNx6e482y5rInNYEgYIVSqglwDBgHlNdaBxp5rgPljePKgF+S8v5GWlrp/imkJ6OUGon57oZq1apl/hNZmZOTE5cvX2bt2rX07t3b4nIJX4pOTk73Pbbq3LkzISEhFC1alBkzZgCwc+dOvL29E/OEhYURERHBvn372LRpEwC9evW67y/v6tWr07p1awC2b9/O9u3badq0KWBe9+vChQt06NCBt99+m0mTJtG3b186dOhAXFwc9vb2DB8+nL59+yb27xw8eJBffvkFgJdffvm+u44BAwYkCyBg/kvf0dGyFVQTvswTdOrU6b7zu3fvpmzZsmnWMWHCBCZOnEhsbCz9+/dn3bp1vPCC+a+/2bNn07///UMme/bsyaVLl9i6dSt//fUXTZs2xcvLK802lytXjmvXrln0mUTuSBiZVT8v3IkcWQpoaDkSMAeQL45+wU/nf2J4o+GMapI7q/haM4gUAJoBb2itDyulvuK/R1cAaK21UkpbsQ0J11kCLAFwcXFJ83qW3DFY09NPP83EiRPZs2cPwcHBFpVJ7UsxpaXiTSYThw4dwt7e3uI2OTj8NypFa82UKVN4/fXk49OPHz/On3/+yfvvv0/Xrl358MMPOXLkCO7u7mzcuJFvvvmGXbt2WXytpAoXLkx0dLTFbc4udnZ29OrVi7179yYGkdSULl2aF198kRdffJG+ffuyd+9enn8+9Qle0dHRFC5cOLubLLKRT2A4JYvYUb54Li93EnPXvOR7vT5Qshpaa+Yen8sPPj8wpP4QxjUbR251CVuzT8Qf8NdaJ+zvuhFzULlhPKbCeL1pnA8AqiYpX8VISyu9SgrpD7VXX32VadOmpfhYKjMeXCq+R48e9y3W6OnpCUC7du3YsGEDYL7buH37dor19ezZk+XLlxMREQFAQEAAN2/e5Nq1axQpUoQhQ4bwzjvvcPz4cSIiIggNDaV3797MnTuXkydPAtC2bVvWrVsHwJo1a+jQoUO6n6N+/fop9vdYm9aa/fv3U7t27TTz7dq1i7t37wLm/puLFy+me9d7/vx5ix/RidzhExhGvQrFcu0LOtGJ7yHqNrQxjzxceHIhK7xWMOjxQbzb4t1cbZ/V7kS01teVUn5Kqce11ueAroC38eMKzDJefzWK/AaMVUqtw9yJHqq1DlRKbQM+TdKZ3gOYorUOUUqFKaVaA4eBV4CHfinbKlWqpDjsFMzDfBP6DgAOHTpkUZ1Jl4r/+uuvGTNmDE5OTsTFxdGxY0cWL17MtGnTGDx4MN9//z1t2rShQoUKFCtWLDFYJOjRowc+Pj60adMGMO/K+MMPP+Dr68s777yDjY0NdnZ2LFq0iPDwcJ555hmio6PRWidumjV//nyGDRvG7NmzcXR0ZMWKFel+hnr16hEaGkp4eDjFimXt+XTSPhEnJydWr16dLE9Cn0hsbCxOTk64ubklnkvaJwJw5MgRjh07xtixYylQoAAmk4nXXnuNFi1apNmO3bt306dPnyx9FmE9JpPm3PVwBrWomn5ma4qPhQPzoVobqNaapaeWsvjkYp597Fmmtpqa+wFOa221H8AZ8ABOAZuBUkAZzKOyLgA7gdJGXgUsAC4CpwGXJPW8CvgaP8OSpLsAXkaZbwCVXpuaN2+uH+Tt7Z0sLb+Jjo7WsbGxWmutDxw4oJs0aZLLLUpuzpw5eunSpbndjGzToUMHHRISkixd/nvMG/4NitDVJ23R649czd2GeK7Velpxrc9t1Su9VupGKxvpSXsn6bj4uBxrAuChU/lOtep+IlprT+OL/kFdU8irgTGp1LMcWJ5CugcgzwOywdWrVxk4cCAmk4mCBQuydOnS3G5SMqNHj+ann37K7WZki6CgIN566610hw6L3JO43ElujswymWDfPCjXgHXxIXzp8SXdq3dnZruZ2Kax/HtOkk2pBAB16tThxIkTud2MNNnb2/Pyyzk7Bt5aHB0dE4ddi7zJJzAcGwV1yuViELmwDYJ82PzEWD458imdqnbi846fU8Am73x1y7InBvONkBC5S/47zDvOXg+jRlkHChfMpb/4tYZ/5rDNsRrT/LbQpmIb/vfE/7Czscud9qRCggjmv3CDg4Plf2CRq7TWBAcHZ2j4tbCes9fDc3d+yNWD7A0+xeSiNjRxbMK8zvMoaFsw99qTirxzT5SLqlSpgr+/P0FBQbndFJHP2dvb37d8isgdkffiuBJ8l/7Ncu/f4ug/n/BWeUfqlKrDgq4LKGJXJNfakhYJIpgnlNWsWTO3myGEyCN8AhM61XPnTuT02c2Mjb1M5UKlWNxjCcUK5oFlV1Ihj7OEEOIBnn7mdd6aVE1561lrunD7AqMOf0Qpk2ZJz+WUti+d423ICAkiQgjxgBN+d6hcsjDliuVs/5RfuB8jtw3HPi6GpRV7Ur5M3Ry9fmZIEBFCiAd4Xr2Dc9WSOXrNm3dvMmL7CGJjwvk26DZV27+bfqE8QIKIEEIkERR+j4A7UTkaRO5E3+H1Ha9zOyqExdcCecxpCBSvmGPXzwoJIkIIkcTJxP6QnAkikbGRuLm7cTXsKvOL1KNRbDy0G58j184OEkSEECIJT7872NooGle2fqf6vfh7jNs1Du9gb2a7TKKl15/Q9GUoYdnW2HmBBBEhhEjipP8dHi9fzOoz1eNMcUzaO4nD1w8zvd10ulw0VuVun3ynzbxMgogQQhhMJo2n3x2cq1n3UZbWmhmHZuB+1Z3JLSfzdNnm5j1Dmg6Bkrm89HwGSRARQgjDpVuRhEfH4VzFukFk3vF5/HLhF0Y6jeSl+i/B/nmgTQ/dXQhIEBFCiEQJkwyteSey0msly72WM7DuQMY6j4WwQDi2CpxfhFLVrXZda5EgIoQQhpN+dyhaqAC1HYtapf7Nvpv537H/0bNGz/92Jdz/FZjioMPbVrmmtUkQEUIIg6ffHZyqlMDWJvu3nN19dTcfHfiINhXb8Fn7z8ybSoVfh2MroMlgKFUj26+ZEySICCEEEB0bj09gmFXmhxy7cYx39r5DgzINmNd5Hna2xp4g++aa91Dv8Fa2XzOnWDWIKKUuK6VOK6U8lVIeRlpppdQOpdQF47WUka6UUl8rpXyVUqeUUs2S1ONq5L+glHJNkt7cqN/XKJvLO9YLIR5WZ66FEmfS2T5T/fzt87zh/gYVHSrev6T7HT/wWA5NX4IytbP1mjkpJ+5EOmutnbXWCXutTwbctdZ1AHfjPcCTQB3jZySwCMxBB5gGtAJaAtMSAo+RZ0SScr2s/3GEEI8iT79QAJpmYxAJiAhg1I5RFLYrzLfdv6WUfan/Tv79ufn1iUnZdr3ckBuPs54BVhnHq4B+SdJXa7NDQEmlVEWgJ7BDax2itb4N7AB6GeeKa60PafOWhKuT1CWEEBni6XeHSiXsKVc8e1buDYkOYdSOUUTHR7O422IqFa3038lbF8DzR2jxGpR4uDchs3YQ0cB2pdQxpdRII6281jrQOL4OlDeOKwN+Scr6G2lppfunkC6EEBnm6Xc724b23o29y5idYwiMDGRB1wXUKVXn/gy7P4UC9tD+4e0LSWDtnQ3ba60DlFLlgB1KqbNJT2qttVLK6hubGwFsJEC1atWsfTkhxEMmOOIefiFRDGmV9XkasfGxTNgzAZ8QH+Z1nkfTck3vzxB4Cs78Ah0mQlHHLF8vt1n1TkRrHWC83gQ2Ye7TuGE8isJ4vWlkDwCSzvevYqSllV4lhfSU2rFEa+2itXZxdHz4/9GEENnrpL8xyTCL/SFaa6YdmMaBaweY1mYanap2Sp5p9ydgXwLavpGla+UVVgsiSikHpVSxhGOgB+AF/AYkjLByBX41jn8DXjFGabUGQo3HXtuAHkqpUkaHeg9gm3EuTCnV2hiV9UqSuoQQwmKeV42Ve6tkbeXeecfn8ful3xnrPJZn6zybPMPVw3B+K7QbB4VzdtMra7Hm46zywCZj1G0B4Eet9Val1FFgg1JqOHAFGGjk/xPoDfgCd4FhAFrrEKXUDOCokW+61jrEOHYDVgKFgb+MHyGEyJATfneoW74YRQpm/itxjc8alnstZ9DjgxjpNDJ5Bq3BfTo4OEKrUVlobd5itSCitb4ENEkhPRjomkK6BsakUtdyYHkK6R5Aoyw3VgiRb5lMmpN+d+jjlPmdBLde3srnRz6na7WuTGk5hRSnrF3aDVf2Qa/PoaBDFlqct1j0OEsp1V4pNcw4dlRK1bRus4QQImdcDo4kLDou0/0hR68fZeo/U3Eu58ysDrPMy5k8yGSCHdOgRFVwGZbFFuct6d6JKKWmAS7A48AKwA74AWhn3aYJIYT1Hb+a0KleKp2cyZ2/fZ5xu8ZRtVhV5neZj32BVOaYnN4A10/Bc0uhQKGsNDfPseRO5FngaSASQGt9DShmzUYJIUROOXgxmFJF7KhTLmMr9wZGBDJ6x2gK2xVmcbfFlCiUSqd8bBS4z4CKTaBR/2xocd5iSZ9ITNL5HMZIKyGEeOhprTl0KZjWtcpgk4GVe0PvhTJq5yii4qJY+eRKKhZNoz/l8GII84dnF4HNo7fmrSWfaINS6lvMy5CMAHYCS63bLCGEsD6/kCgC7kTRpnYZi8tEx0Xzxq438Av346suX1G3VN3UM0fegn/mQN1eULNjNrQ470n3TkRr/aVSqjsQhrlf5EOt9Q6rt0wIIazs4KVbALSpZVkQiTfFM2nvJDxvejL7idm0qNAi7QJ/fwExEdDt46w2Nc+yaIivETQkcIYN9m8AACAASURBVAghHikHLwZTtmghHrOgP0RrzWdHPmOX3y4mt5xMzxo90y4QfBE8lkEzVyhXL5tanPdYMjorHPNCigAFMY/OitRaF7dmw4QQwpq01hy8FEzrWqVTntfxgGVey1h/bj3DGg3jpfovpX+BndPAthB0mpINrc27LHmclTgSy1he5BmgtTUbJYQQ1vbvrUhuhN2zqD/k94u/89Xxr+hTqw/jm41Pv/Krh8Dnd+j8HhQrn37+h1iGhgoYe31sxrzHhxBCPLQOXgoG0u8POXjtIB/u/5BWFVoxo+0MbFQ6X5smE2ydAkUrQJsUF+F4pFjyOOu5JG9tME88jLZai4QQIgccvBhM+eKFqFk29VkL50LOMWHPBGqWrMncznP/2xs9LSdWw7Xj5omFj9DyJqmxpGP9qSTHccBlzI+0hBDioWSeHxJC+8fKpNofEhgRiNtONxzsHFjYdSHFClowx/puCOz8CKq3g8YDsrfReZQlfSKP1kIvQoh8z/dmBLciUu8PCYsJw83djbtxd1n15CoqOFSwrGL3jyE6DHp/CRZ01j8KUg0iSqn5/DcqKxmt9ZtWaZEQQljZf/0hZZOdi4mPYfzu8VwOu8y33b5NezJhUgHH4Ngqcz9I+QbZ2dw8La07EY8ca4UQQuSggxeDqVyyMFVLF74v3aRNfLD/A45eP8pnHT6jZcWWllVoioc/3oai5eCJSVZocd6VahDRWq/KyYYIIUROMJnM62V1qVc+WX/I18e/5s9//2Rcs3H0rdXX8kqPr4ZrJ+C578A+f02hs2R0liMwCWgAJK5zrLXuYsV2CSGEVZy7Ec7tu7HJ+kM2nNvAMq9lDKg7gOGNhlteYWSwuS+kento/Oit0pseS+aJrAF8gJrAx5hHZx1Nq4AQQuRVBy8a/SFJgsjffn/zyeFP6FilI1NbTbVoBnuihM70PvmnMz0pS4JIGa31MiBWa/231vpVwOK7EKWUrVLqhFJqi/G+plLqsFLKVym1XilV0EgvZLz3Nc7XSFLHFCP9nFKqZ5L0Xkaar1JqsqVtEkLkXwcvBVOtdBEqlzT3h5y5dYZ39r5DvdL1mN1xNgVsMrBruL+H+VFW69FQrr6VWpy3WRJEYo3XQKVUH6VUU6B0Bq4xDvOdTILPgbla68eA20DCfeNw4LaRPtfIh1KqAfAC0BDoBSw0ApMtsAB4EvOjtsFGXiGESFG8SXP4UnDiLPWAiADGuI+htH1pFnRdQBG7IpZXltCZXqwCdMq/f8NaEkRmKqVKAG8DE4HvgAmWVK6UqgL0McokrL3VBdhoZFkF9DOOnzHeY5zvmmStrnVa63ta638BX6Cl8eOrtb6ktY4B1iGTIIUQafAJDCMsOo42tcsQei+U0TtHE2OKYWHXhZQtnHy4b5qOrYRAT+gxEwrl381eLblvO6y1DgVCgc4ZrH8e8C7/badbBrijtY4z3vsDlY3jyoAfgNY6TikVauSvDBxKUmfSMn4PpLfKYPuEEPnIgYvm/UOa1yjG+N3j8A/3Z0n3JdQqWStjFUUGg/t0qNEBGj1vhZY+PCy5E9mvlNqulBqulLJ4J3ulVF/gptb6WOablz2UUiOVUh5KKY+goKDcbo4QIpfsOnuTxys4MP/0TDxueDCz3UxcKrhkvCL3j8ybTfWenS8705NKN4horesC72PukzimlNqilBpiQd3tgKeVUpcxP2rqAnyFeZvdhDugKkCAcRwAVAUwzpcAgpOmP1AmtfSUPsMSrbWL1trF0dHRgqYLIR41oXdjOXr5NqWr7OKvf/9iXLNx9K7VO+MV+R3N953pSVm0FLzW+ojW+i3M/RAh/Nd3kVaZKVrrKlrrGpg7xndprV8CdgMJg6ldgV+N49+M9xjnd2mttZH+gjF6qyZQBziCeZhxHWO0V0HjGr9Z8nmEEPnPnvM3sSl+GK/ITfSv2z9jc0ESmOLhz7ehWMV8NzM9NZZMNiwOPIv5S7o2sAlzMMmsScA6pdRM4ASwzEhfBnyvlPLFHKheANBan1FKbQC8Ma8iPEZrHW+0bSywDbAFlmutz2ShXUKIR9j6Mzuwr7CZ9pXa816r9zI2FyTBsRUQeBL6L8/XnelJKfMf+2lkUOpfYDOwQWt9MEdaZUUuLi7aw0OWBRMiP/EK8mbwllcoXqAC219Yj4NdJvb5CL8BC1pAxSbwym/5qi9EKXVMa51i55Elo7Nq6fQijRBC5FHXI68zeucYTPH2jHOelbkAojX88RbERkOfOfkqgKTHko51CSBCiIdSeEw4bu5uRMRGEnftVfo2rJe5is5sgrNboPNUKFsnexv5kMvA/H4hhHh4xJpieXvP2/x7518Kh7xOk6qNKFIwE195kbfgz3egUlNoMzb7G/qQS/dORCnVzpI0IYTIK7TWzDg4g4OBB3m94btcu16VbvXLZ66yvyZBdCg8swBs5e/uB1kyxHe+hWlCCJEnLD29lE2+m3jd6XV0uHkwadf65TJe0dk/wGsjdHwHyjfM5lY+GtLaHrcN0BZwVEq9leRUccxDaoUQIs/ZcmkL80/M56laTzHGeQwDFh+kYaXiVCxROP3CSUXdhi1vQflG0N6i5QLzpbTuRAoCRTEHmmJJfsL4b7KgEELkGUevH+WD/R/QokILPm77MSGRMRy7epuumXmUte19iAyCZ76BAgWzv7GPiLS2x/0b+FsptVJrfSUH2ySEEBl26c4lxu0eR9ViVZnbaS52tnbsPueP1tA9o0Hk/Dbw/AHav2XuUBepsqRP5DulVMmEN0qpUkqpbVZskxBCZMitqFu4ubthZ2PHwq4LKVGoBADuPjcoX7wQjSpnYN/zyFvw61go1zBf7xNiKUuCSFmt9Z2EN1rr20AmeqiEECL7RcVF8eauNwmOCmZB1wVUKVYFgHtx8ew9H0SXeuUtX+JEa/h9HETfgeeWQIFCVmz5o8GSIGJSSlVLeKOUqg7IBEQhRK6LN8Uz5Z8peN3y4vOOn9OobKPEc4cuhRAZE0+3jIzK8lxjnlTY5QOo0Cj9/MKiyYbvAfuUUn8DCugAjLRqq4QQwgJfenyJ+1V3JrecTJdqXe47t9P7BvZ2NrR7zMIdC0P+Nc8JqdFBJhVmQLpBRGu9VSnVDGhtJI3XWt+ybrOEECJta3zW8IPPDwypP4SX6r9037no2Hh+P3WNrvXLY29nwYwEUzxsGgXKBvotBBuLdskQpPE4SylVz3htBlQDrhk/1Yw0IYTIFbuu7uLzI5/TuWpnJrpMTHZ+u/cN7tyN5YUWVVMonYL988DvEPT+EkpWSz+/SJTWncjbwAjgfymc05h3KhRCiBzldcuLSXsn0bBMQ2Z1mIWtTfI7jfVHr1K5ZGHa1bbgUdY1T9j9KTToB04DrdDiR1ta80RGGK+dc645QgiRuoCIAMa6j6VM4TLM7zqfInZFkuXxC7nLft9gJnSri41NOqOyosNg4zBwKAd958oS75mQ1rInz6VVUGv9S/Y3RwghUhZ6LxS3nW7EmGJY3nU5ZQunfJexwcMPpWCAS5W0K9QatoyH25dh6B9QpHT2NzofSOtx1lPGaznMa2jtMt53Bg4AEkSEEDkiJj6G8bvHczX8Kku6L6FWyVop5os3aX7y8KdjHUcqlUxnrazjq8HrZ+jyPlRva4VW5w9pPc4aBqCU2g400FoHGu8rAitzpHVCiHxPa82HBz7E44YHn3X4jBYVWqSad+/5IK6HRTPtqQZpV3rD2zyct1Yn89ImItMsGcdWNSGAGG5gHq2VJqWUvVLqiFLqpFLqjFLqYyO9plLqsFLKVym1XilV0EgvZLz3Nc7XSFLXFCP9nFKqZ5L0Xkaar1JK1icQ4hE0/8R8/rj0B280fYO+tfqmmXfd0auUcSiY9oKLMZHmfpBCxeDZJZBCx7ywnCVBxF0ptU0pNVQpNRT4A9hpQbl7QBetdRPAGeillGoNfA7M1Vo/BtwGhhv5hwO3jfS5Rj6UUg2AF4CGQC9goVLKVillCywAngQaAIONvEKIR8TP539m6emlPFfnOUY0HpFm3qDwe7j73OS5ZpUpWCCNr7a/3oWgc+ZlTYplcqMqkciSPdbHAouBJsbPEq31GxaU01rrCOOtnfGTMDR4o5G+CuhnHD9jvMc431WZF7x5Blintb6ntf4X8AVaGj++WutLWusYYJ2RVwjxCNgfsJ8Zh2bQtlJb3m/9frrrX/1y3J84k2ZQWnNDTq6HEz9Ah7ehtgw8zQ6W7vV4HAjXWu9UShVRShXTWoenV8i4WzgGPIb5ruEicEdrHWdk8QcqG8eVAT8ArXWcUioUKGOkH0pSbdIyfg+kt0qlHSMxlmqpVk0mEgmR150NOctbe96idsna/O+J/2FnY5dmfq0164/64VK9FI+VK5ZypuunzYsrVm8HnaZYodX5kyV7rI/AfGfwrZFUGdhsSeVa63ittTNQBfOdQ71MtjNLtNZLtNYuWmsXR0fH3GiCEMJC1yOvM2bnGIoVLMbCrgspWrBoumU8rtzm0q1IBqZ2F3I3BNa9BIVLwoCVsld6NrKkT2QM0A7zjoZorS+QwaXgjaXkdwNtgJJKqYR/wSpAgHEcAFQFMM6XAIKTpj9QJrV0IcRDKjwmnNE7R3M37i4Luy2kvINlfRbrjvhRtFAB+jSumPykKR5+GQFh12Dg91BUdrLITpYEkXtGnwOQ+AWf7lLwSinHhM2slFKFge6AD+ZgkrC9rivwq3H8m/Ee4/wurbU20l8wRm/VBOoAR4CjQB1jtFdBzJ3vv1nweYQQeVBsfCwT9kzgcuhl5nSaQ91SdS0qFxYdy5+nA3mqSUUcCqVwh7FnFvjuhN5fQNXUhweLzLHknu5vpdRUoLBSqjvgBvxuQbmKwCqjX8QG2KC13qKU8gbWKaVmAieAZUb+ZcD3SilfIARzUEBrfUYptQHwBuKAMVrreACl1FhgG2ALLNdan7HoUwsh8hStNR8d/IjDgYeZ2W4mbSq1sbjs7yevERUbz6AWKfR3nv0T9n4BTYdA82HZ2GKRQJn/2E8jg3lIxGtAD8z7iWwDvtPpFcyjXFxctIeHR243QwiRxDcnvuHbU98yxnkMo5qMylDZp7/ZR0ycib/Gdbh/BNctX1jaGcrUhmFbwc4+m1udfyiljmmtXVI6l+adiHEXcUZrXQ9Yao3GCSHyt43nN/LtqW959rFned3p9QyV9b4Wxin/UD7s2+D+AHI3BNYOApsCMHC1BBArSrNPxHhsdC7p9rhCCJFd9vrvZeahmbSr3I4P2nxg+V7ohg0efhS0teHZppX/S4y7Zx6JdecqvLBG9gexMkv6REoBZ5RSR4DIhESt9dNWa5UQ4pF35tYZJv49kbql6jLniTnpzgV5UHRsPJtOBNCzUQVKORQ0J5pMsNkNrh6A55fJwoo5wJIg8oHVWyGEyFf8wv1wc3ejtH1pFnZbmOK+IOnZduY6oVGxDHJJMtJ/90zw2ghdp0Hj/qkXFtkmrf1E7IFRmGebnwaWJZlpLoQQmXIn+g5uO92IM8WxsNvCVPcFSc/6o35UKVWYtrXLmBOOrYJ//gfNXKH9hGxssUhLWn0iqwAXzAHkSVLeJlcIISwWFRfFmF1juBZxjfld5lOrRMr7gqTnavBdDlwMZqBLVfPuhb7usGUC1O4Kff4nOxTmoLQeZzXQWjcGUEotwzzBTwghMiXOFMc7f7+D1y0v5jwxh2blm2W6rg0eftgo6N+8inlNrA2uUK4BDFwFthnrWxFZk9adSGzCgTzGEkJkhdaa6Qen87f/37zX6j26Vu+a6bri4k38dMyPJ+o6UsnmNqwZaN4b5MX15leRo9K6E2milAozjhXmGethxrHWWhe3euuEEI+E+Sfms8l3E6OajGLg4wOzVNfeC0HcCLvHzCdLmQPIvTB4dSuUqJx+YZHt0toeV7b7EkJk2RqfNSw9vZT+dfvj1sQty/WtO+JHeQdbunpNgpve8NIGqNA4G1oqMkPWQxZCWM3Wy1v5/MjndK7amfdavZfhyYQPuhIcifvZG2ysvAGbi+7w1FfwWLdsaq3IDEtW8RVCiAw7EniEqf9MpWm5pnzR8QsK2GT9b9av3X0Zbfs7TYM2Q/u3oPnQrDdUZIkEESFEtjsXco5xu8dRvXh1vu7yNfYFsr521aWgCAqdXMVE27XQ6HnoIvOg8wIJIkKIbOUf7s+onaNwsHNgUbdFlChUIlvqPbBpITMLLOdezW7QbzHYyNdXXiD/CkKIbBMSHcKonaOIiY/h2+7fUsGhQrbUG3hwHYMDPuVqieYUevEHKFAwW+oVWSdBRAiRLe7G3mXMzjFcj7zOgq4LqF2ydvZUfH4b5ba54Uldig3dAHaFs6dekS0kiAghsiw2Ppa3/n4L7xBvZnecjXM55+yp+NIeTOtfxstUnb0uCylTukz21CuyjQzxFUJkSbwpnin7prA/YD8ft/2YztU6Z0/F/+6FtYMJtK3M6NipbOnilD31imxltTsRpVRVpdRupZS3UuqMUmqckV5aKbVDKXXBeC1lpCul1NdKKV+l1CmlVLMkdbka+S8opVyTpDdXSp02ynytsjoIXQiRIVprZh6eybbL25joMpHn6jyXPRX7usOaAdwrVpV+4e/wbLvGlHaQfpC8yJqPs+KAt7XWDYDWwBilVANgMuCuta4DuBvvwbxScB3jZySwCMxBB5gGtAJaAtMSAo+RZ0SScr2s+HmEEA/46vhXbDy/kRGNR+Da0DX9ApY4vw3WvgBl6/B+yVlEFSzDiA6ZW+1XWJ/VgojWOlBrfdw4Dgd8gMrAM5iXmcd47WccPwOs1maHgJJKqYpAT2CH1jpEa30b2AH0Ms4V11of0lprYHWSuoQQVrbcaznLvJYxsO5A3mj6RvZU6vO7eWvb8g3x6vYDP3lH8Wq7GpQsIncheVWOdKwrpWoATYHDQHmtdaBx6jpQ3jiuDPglKeZvpKWV7p9CekrXH6mU8lBKeQQFBWXpswgh4OfzPzP32FyerPEkU1tNzfJyJgB4/WJe0r2SM/FDNjN1qz/lihViREe5C8nLrB5ElFJFgZ+B8VrrsKTnjDsIbe02aK2XaK1dtNYujo6O1r6cEI+0Py/9yccHP6Zd5XZ80v4TbG2yYa3WEz/Az8Ohait4eRPrT4dxyj+U9/rUp5i97A+Sl1k1iCil7DAHkDVa61+M5BvGoyiM15tGegCQZLNkqhhpaaVXSSFdCGEl7lfdmbpvKs3KN2Nup7nYZXUDKK3NW9r+OgZqdYIhGwmJK8QX287SqmZpnm5SKTuaLazImqOzFLAM8NFaz0ly6jcgoQfOFfg1Sforxiit1kCo8dhrG9BDKVXK6FDvAWwzzoUppVob13olSV1CiGy2P2A/7/z9Dg3LNGRB1wUULpDFSX8mE2ydAu7TofEAGLweCjowe9tZwqPjmNGvUfY8JhNWZc15Iu2Al4HTSilPI20qMAvYoJQaDlwBEnao+RPoDfgCd4FhAFrrEKXUDOCokW+61jrEOHYDVgKFgb+MHyFENjt6/Sjjdo+jdsnaLOy2EAc7h6xVGBcDm0eD10Zo7QY9PgEbGzz97rDuqB+vta9J3fKyS+HDQJm7JfIPFxcX7eHhkdvNEOKhcTLoJCO3j6SiQ0WW91pOafvSWavwXgRseBku7oJuH0G78aAU8SbNswv3cz00ml0TO1G0kMyFziuUUse01i4pnZN/JSFEqs4En2H0ztGUKVyGpT2WZj2A3L4Ma1+EoLPwzAJoOiTx1LqjVznlH8pXLzhLAHmIyL+UECJF3sHejNw+kmJ2xfiux3c4FsniyMZ/95qH8GoTDNkItbskngqOuMcXW8/RupZ0pj9sZAFGIUQyPsE+jNg+Agc7B5b3Wk6loln4YtcaDi+B1f3AwRFG7LovgGitmfzLaaJi4pn+jHSmP2zkTkQIcZ9zIecYsWMEReyKsLzncioXTXEOr2XiYuDPt+H4aqj7JDy3BOyL35dl7RE/dnjf4P0+9aUz/SEkQUQIkehcyDle2/4a9rb2LO+5nCrFqqRfKDURN2H9y+B3CDpMhM7vJduN0PdmBNO3nKFDnbK82q5mFlsvcoMEESEEYNyBbB9BQduCrOi5gqrFqqZfKDXXTpjXwLobAv1XQKPkq/vei4tn3LoTFLaz5csBTbCxkcdYDyPpExFCcObWGV7d9up/AaR4FgLI6Y2wvBegYPi2FAMIwJzt5zlzLYzPn3eifHH7zF9P5Cq5ExEinztx8wRuO90oUagE3/X4LvOPsEzxsGsG7JsL1drAwO+haMojuvb73uLbvZd4sVU1ejTMnn3YRe6QICJEPnYk8Ahjd42lfJHyLO2xlAoOmfxCjwiCzaPAdyc0HwpPzoYCKS/ffjsyhrc2eFLb0YEP+jTIfONFniBBRIh8al/APsbvHk/VYlVZ2mMpZQuXzVxFF3fDptch6g70mQMthqeaNSomnpHfexASGcMy1xYULpgNKwCLXCVBRIh8aMeVHUzaO4naJWuzpPsSStmXSr/Qg+JjYfcnsG8elK0LQ36BCo1SzR4bb2Lsj8fxuHKb+YOb0qhyiSx8ApFXSBARIp/5wfsHvjj6BU6OTizouoAShTLxZX77MmwcDgEe0MwVen0GBVNflNFk0kzaeAr3szeZ2a8RfZ1kVvqjQoKIEPmESZuY4zGHVd6r6FqtK7M6zMK+QAZHRWkNJ76HrVNB2aQ6fPf+IppP/vThlxMBvN29LkNaV8/CpxB5jQQRIfKBmPgY3tv3Hlsvb2VwvcFMajEp4zsShl2D394E3x1Qo4N5AcVS6QeEhXsusmzfvwxtW4OxXR7L5CcQeZUEESEecaH3Qhm/ezweNzx4q/lbDG04NGPrU2kNp9bDX++alzF58gtoMSLZ7POU/Hj4KrO3naOfcyU+7NtA1sV6BEkQEeIRFhgRyOido7kSfoVZHWbRp1afjFUQdg3+mAjn/jDvf95vEZSpbVHRP08H8v7m03R+3JHZMiP9kSVBRIhH1LmQc7jtdONu3F2+7fYtLSu2tLywKR6OfgfuM8AUC91nQJsxYOEjsP2+txi/zpOm1Uqx8KXm2NnK4hiPKgkiQjyCDgUeYvzu8TjYObDqyVXULVXX8sLXPGHLePP6V7W7Qp//QWnLF0c86XeHkas9qFnWgeUyF+SRZ7U/D5RSy5VSN5VSXknSSiuldiilLhivpYx0pZT6Winlq5Q6pZRqlqSMq5H/glLKNUl6c6XUaaPM10oetgoBwO8Xf2f0jtFUdKjImt5rLA8g0WHmUVdLO5sfY/VfDkN+zlAA8b0ZwdAVRyjlUJDVw1tSoohdJj+FeFhY8x5zJdDrgbTJgLvWug7gbrwHeBKoY/yMBBaBOegA04BWQEtgWkLgMfKMSFLuwWsJka9orfn25LdM3TeVZuWbsfrJ1ZYtY2KKh2OrYH4zOLQQmg+DMUeg0fOQgb/Nrt2J4pVlh7G1UfwwvJUsqphPWO1xltZ6r1KqxgPJzwCdjONVwB5gkpG+WmutgUNKqZJKqYpG3h1a6xAApdQOoJdSag9QXGt9yEhfDfQD/rLW5xEiL4uIieD9/e/jftWdvrX68nHbjylom/LaVfe5vB+2Tobrp6Bqa3hxA1Ruln65B1y4Ec7wVR6ER8ex7vXW1Cib+sRD8WjJ6T6R8lrrQOP4OlDeOK4M+CXJ52+kpZXun0J6ipRSIzHf4VCtWrUsNF+IvOdS6CXG7x7P1bCrvNviXYbUH5L+UNqQS7DzY/DeDMWrwPPLMnznkWDPuZu88eMJCtnZ8v1rrWhYSZYzyU9yrWNda62VUjqHrrUEWALg4uKSI9cUIie4X3XnvX3vUci2EEt7LKVFhRZpF7h9GfbOBs+1YFsQOk2Ftm9AwSIZvrbWmpUHLjNjizePVyjOMlcXKpUsnLkPIh5aOR1EbiilKmqtA43HVTeN9AAg6S44VYy0AP57/JWQvsdIr5JCfiHyhThTHAs9F7L09FIalWnE3M5z0+7/uOMH/3wJJ34AZQstR0L78VAsc0u/x8ab+PDXM6w9cpUeDcozd5AzDoVksGd+lNP/6r8BrsAs4/XXJOljlVLrMHeihxqBZhvwaZLO9B7AFK11iFIqTCnVGjgMvALMz8kPIkRuCYgIYPLeyXgGefJ8neeZ0moKhWwLpZw5NAD2zTF3nCsFLq9C+wlQPPMLIAaGRjF+nSeH/w3BrVNtJvZ4XCYS5mNWCyJKqbWY7yLKKqX8MY+ymgVsUEoNB64AA43sfwK9AV/gLjAMwAgWM4CjRr7pCZ3sgBvmEWCFMXeoS6e6eORt/Xcr0w9OR6P5vMPn9K7VO+WM4dfhnzlwbCVoEzR7GTq8DSUyuWuh4Y9TgUzddJqYOBNzBjbhuWZZq088/JR5QFT+4eLioj08PHK7GUJkyN3Yu8w6MotNvptwcnTi8w6fp7yNbfgN2P8VeCwz7/fR9CXoMNGihRLTEh4dy7TfzvDL8QCaVC3JvEHO1JQRWPmGUuqY1tolpXPyEFOIPO5I4BGmH5rO1bCrjGg8gtHOo7GzeWAS3w1vOLQATm0AUxw0GQwdJ0LpWlm+vsflEMav9+TanSje7FqHN7o8JsuYiEQSRITIo25F3eJLjy/549IfVClahe96fHf/+ldag6+7OXhc3AUFCkOzV6C1m8WLJKYlJDKG2dvOse7oVaqWKsJPo9rSvHomdkAUjzQJIkLkMfGmeDae38hXx78iKj6KkU4jGdF4xH8bSEXdhpPrwGMF3DoHRStA1w/NM82LlM6G62t+PHyFL7efJ+JeHK+2q8mE7nUpKqOvRArkvwoh8hDvYG9mHprJ6VunaVmhJe+1fo9aJWqZ7zr8PcBjOXj9DHHRULk59FtsniRYwILZ6RbwuBzCh7+ewTswjLa1y/DR0w2pW75YttQtHk0SRP7f3p1HyVWWeRz//mpPr+mksy8kISsQCUFBRTHIllDT+QAAEBxJREFUKqKII4zLCOKCelxHOXNAZ3WOo+KgonKco4g6c1xAZhgZ8RgwgGIUSCBAlg5mxe6k051Od3pfannmj3ubVLdJ6C46VKr7+ZxTp+5971t13ye5XU/d99Z9X+dOAl0DXXz76W/z0+0/ZXJyMl96/Zd488I3o4798IdvwTN3QdNmiJcH1zteeT3MOnPM9r+5oZ3b1v2J39Q1M6s6xe3vXs3lK2f6JFLuRXkSca6IzIy1z6/lliduoaW3hWuWXcMnl7+Xql0Pw+/eAnt/DxjMPgve/DVYeTWkqsZs//nJoyoV4zMXL+WDr19IWcI/GtzI+JHiXJHsaNvBrRtvZf3+9ayoWcZtp7yNlbsfgwfODiaCmroY1twMK98xJhfKB+VyxmO7D3Hn+j38pq6Z6klxPnvxUq47bwFVKR+63Y2OJxHnXmY723bynWe+wwPPP0BFNMVNyYW8c8sfiT71YHCR/NwPB4lj1qqCBkQ8lsb2Xu7Z2MDdT9ZT39rL5LI4N16ylOteu4BKTx6uQJ5EnHuZ7Dq8i/946pusrX+ISUT4UGcf17U2UB1vgtOvgldcA6ecN+IpaEdiIJNjXV0Td22s53d/OkjO4LWnTuXGS5Zx6ekzScV91kH30ngSce4EslyODXU/5yd1/8VDXc8zyXJ8sKOTa9MJJi+5DC66HBZfCLFjjH1VoB1Nndy1oZ57N+3jUPcAM6tSfOyCxVx99jzmTx39iL3OHYsnEefGWucBenf+hvufu4cfd+9gZyzC5GyWD2ZTvHf+ZdRcdFXw89zI2N71ve9wLw/VNXHvpn089efDxCLiohUz+OtXzeP8pdOI+iCJ7gTwJOLcS9XeAHvXY3sfZXv9ev4v18p9FeW0R6MsS1TwhdlreNPqj5CqGflc5SORzRlP1x/moe1NrKtrZvuBTgAWT6/g85ev4KrVc6itGNszHOeG8yTi3GhkBuDAZmh4Aho2QP0GGrv2cX9FOb+srGRXZZQYk7lg+tm8e9VHOXvmK8fsXotczniuqZPHdh/isd2HeHxPK4d70kQj4pWn1PC5y5dz4YoZLKot9/s73MvGk4hzx5IZgIPbofGZ8PE0ND6LZfvZE4/x25qZPFJbxaYpczDgrGmr+IdT38KlCy6lOvnSp4ht7uhjy/52tu7r4Nl97Wzc20pbTxqA+VPKuOS0GZy3uJY1S6dTXea/rnLF4UnEOTPoaoKmrdBcB83bwuVtkB0AoCdZydMzlvDo8tfwSLaNhv42AJZVz+Gjp1zIFYuuYF7lvOPt5Ti7Nxraetm6v52t+zvYsq+dLfs7ONjZ/0KdhbXlXLRiBq9eNJVzF01hbo1fHHcnB08ibuLo64DWXXBoF7TuDp93waGdwaCGg8qn0z59GZvOvJIn4xGeHGhhW/tustZCoq+Dc2edy/Xz1nD+3POPPyXtMNmcsf9wL3tautl9sIs9Ld3saO5i6/4O2nuDM4xoRCyZXsH5S6ZxxpwqTp9dzYpZlX4fhztpeRJx40cuG8zo114fXOxu2zs0WXQfHFq/cjY2dRGHll1KXcUUtsdFXbqduvbdNHQ9D23PE4/EWVm7kvef8X5Wz1jN6umrKYsf/Swgnc3R3NnPgfY+Gtt7aWjrpb61h4a2Xhraeqhv62Ugkzuy+2SMRdPKuXzlLM6YU8UZs6tZNrPS791wJcWTiDv55XLQ2xokga7moOupszFIGJ2N0NEIHfuhYx9YduhrK2aSnrqIxlPfQH3FZBoSKRqUoz7TRUN3Iw1dDXS3/xnag+rzKuexYuoK3r7k7ayavool1afRNxClrWeAtp4B1m07TEtXEy1d/bR0DtDS1U9TZx8H2vs51N3P8IlCa8rizK0pY+mMyhcuei+sLWfRtApqKxJ+AdyVvJKfHlfSZcBtQBS4w8y+fLz6Pj1ukZgFw5f3dUB/R/jcPnS9txV6WqHnUNC91BMmjp6WYJ7w/LcDuhIVdFTNoL1iKocnTebQpCoOJZK0RKK0kKUx3U1DdyMtvU3kOPL6mOJUx2dSEZ1BmaaTZDqxzBwYmE1PX5z23jSdfRnaegboGRiWlELRiJhSnqC2Isn0yiQzq1LMrA4f4fLcmkneDeXGhXE7Pa6kKHA7cDHQAGyQdJ+ZbStuy04yZsGHcC4TdPnkMlguSy6bxrJpcpk0lstgmTSWS2OZASw7gKUHsEw/lu0nl+4jl+4nl+klm+knO9BDLtNDNt1LLtMLmT6y6R6ymR4s00Mu00cuE2zLZbvJZXvJWpYBiTQiLUhLpCUGBAMSfYrSHU3RFU3RFU3QHYvTWTOTrimz6ZboVY4+svQpTZo+kAFp4AD0HYC+MNxcDMtWYOkqcukZ5AZWkEtPwQamBs+ZStoIbvSToDwRo3pSnMoUVE2CeVPKqEzFqClLUFMWp6Y8QU1ZgsllcWorkkwN1yN+855zpZ1EgHOAnWa2G0DSz4ArgTFPIld+byVphp61HfMc7iifLTbs+VjsKLWGv8YAU7AbG1Yrfz/GkTqmoeVHHsIULA9+V8+F5bnwNVnARtrtEgES4eMF5eFjZCwXBYuARZDFUS5FxFJESRIlxSSVUaUKkpEKUtFyUpFKymJVVMVrqE5MoTJRQVkyRioepSIZpTwZozwRozwZoywRpSIZLJcno0yKR71LybmXoNSTyBygPm+9ATh3eCVJNwA3AMyfP7+gHU23KrL8ZdfGUT9+DHSULUere7R6+TUlhclAQ7bJBrfpSBlhXYng01x5dSJBuYJyJEQ0WFcUKQJEUSR4JhJ7YVskEgfFUSSOIjGIxIlEE0SiSSKROFFFiEaiRBQhHokRj8aIRWLEIlHikRjJaJJkLE4iGicVS5KMxknFEiRjSVKxYDkVS1KZnER5PEUyFvMPdudKRKknkRExs+8C34Xgmkgh7/G9G9aPaZucc248GNsR4F5++4D8O7zmhmXOOedeBqWeRDYASyQtlJQA3gncV+Q2OefchFHS3VlmlpH0cWAtwU987zSzrUVulnPOTRglnUQAzOxXwK+K3Q7nnJuISr07yznnXBF5EnHOOVcwTyLOOecK5knEOedcwUp+AMbRknQQeH4EVWuBlhPcnJfLeIoFxlc84ykW8HhOZi8lllPMbNrRNky4JDJSkjYea9TKUjOeYoHxFc94igU8npPZiYrFu7Occ84VzJOIc865gnkSObbvFrsBY2g8xQLjK57xFAt4PCezExKLXxNxzjlXMD8Tcc45VzBPIs455wrmSWQYSZdJek7STkk3Fbs9oyXpTknNkrbklU2R9KCkHeFzTTHbOFKS5kl6WNI2SVslfSosL9V4UpKekPRMGM+/hOULJT0eHnN3hdMalARJUUmbJP0yXC/lWPZK2izpaUkbw7KSPNYAJE2WdI+k7ZLqJL3mRMTjSSSPpChwO/Am4DTgXZJOK26rRu2HwGXDym4C1pnZEmBduF4KMsBnzew04NXAx8L/j1KNpx94o5mdCawCLpP0auArwNfNbDHQBnygiG0crU8BdXnrpRwLwAVmtirvfopSPdYAbgN+bWbLgTMJ/p/GPh4z80f4AF4DrM1bvxm4udjtKiCOBcCWvPXngFnh8izguWK3scC4fgFcPB7iAcqAp4BzCe4ijoXlQ47Bk/lBMJPoOuCNwC8BlWosYXv3ArXDykryWAOqgT2EP546kfH4mchQc4D6vPWGsKzUzTCzxnD5ADCjmI0phKQFwFnA45RwPGH3z9NAM/AgsAs4bGaZsEopHXPfAP4OyIXrUyndWAAMeEDSk5JuCMtK9VhbCBwEfhB2N94hqZwTEI8nkQnGgq8gJfW7bkkVwH8DnzazjvxtpRaPmWXNbBXBt/hzgOVFblJBJF0BNJvZk8Vuyxh6nZmtJujO/pik8/M3ltixFgNWA98xs7OAboZ1XY1VPJ5EhtoHzMtbnxuWlbomSbMAwufmIrdnxCTFCRLIj83sf8Liko1nkJkdBh4m6PKZLGlwltFSOebOA94qaS/wM4IurdsozVgAMLN94XMzcC9Bki/VY60BaDCzx8P1ewiSypjH40lkqA3AkvAXJgngncB9RW7TWLgPuC5cvo7g2sJJT5KA7wN1Zva1vE2lGs80SZPD5UkE13fqCJLJO8JqJRGPmd1sZnPNbAHB38lDZvYeSjAWAEnlkioHl4FLgC2U6LFmZgeAeknLwqILgW2cgHj8jvVhJF1O0NcbBe40sy8WuUmjIumnwBqCYZ+bgH8C/he4G5hPMAz+NWbWWqw2jpSk1wGPAps50u/+OYLrIqUYzyuAHxEcWxHgbjP7gqRFBN/mpwCbgL8xs/7itXR0JK0BbjSzK0o1lrDd94arMeAnZvZFSVMpwWMNQNIq4A4gAewGric87hjDeDyJOOecK5h3ZznnnCuYJxHnnHMF8yTinHOuYJ5EnHPOFcyTiHPOuYJ5EnETkiSTdGve+o2S/nmM3vuHkt7x4jVf8n6uDkdnfXhY+QJJveFwF3XhyMHvO9HtcROTJxE3UfUDb5dUW+yG5Mu723skPgB8yMwuOMq2XWZ2lpmtILgZ8NOSrh+TRjqXx5OIm6gyBHNO/+3wDcPPJCR1hc9rJP1W0i8k7Zb0ZUnvCb/pb5Z0at7bXCRpo6Q/heNMDQ6++FVJGyQ9K+nDee/7qKT7CO4qHt6ed4Xvv0XSV8KyfwReB3xf0lePF6iZ7QY+A3wyfO05kv4Ynqn8YfCuZkm/C29QG9zv7yWdOZJ/TDdxjeZbj3Pjze3As5JuGcVrzgRWAK0EdwHfYWbnKJgw6xPAp8N6CwjGXjoVeFjSYuBaoN3MXiUpCayX9EBYfzVwhpntyd+ZpNkEc3ScTTA/xwOS3hbe6f5GgjvFN46g3U9xZLDH7cDrzSwj6SLg34C/Ihhi5n0EZy1LgZSZPTOKfxs3AfmZiJuwwhGB/5PwG/oIbTCzxnAoj13AYBLYTJA4Bt1tZjkz20GQbJYTjMd0bTgU/OMEQ6cvCes/MTyBhF4FPGJmB8Mh1n8MnH+Uei9GecvVwM8VzH75deD0sPznwBXhoJfvJ5jgzLnj8jMRN9F9g+Bb+g/yyjKEX7AkRQjGHhqUPw5ULm89x9C/p+HjCRnBB/knzGxt/oZw7Knuwpo/YmdxZAbCfwUeNrOrwnlaHgEwsx5JDwJXAtcQnP04d1x+JuImtHDwubsZOo3rXo58gL4ViBfw1ldLioTXSRYRzCi3Fvho+E0fSUvDEWOP5wngDZJqFUzf/C7gt6NpSJgo/h34VlhUzZEh2t83rPodwDcJzrjaRrMfNzH5mYhzcCvw8bz17wG/kPQM8GsKO0v4M0ECqAI+YmZ9ku4g6PJ6Khzm/iDwtuO9iZk1SrqJYIh1Afeb2UiG7z5V0iYgBXQC3zSzH4bbbgF+JOnvgfuH7e9JSR0MPTNz7ph8FF/n3AvCC/mPAMvNLPci1Z3z7iznXEDStQQX/D/vCcSNlJ+JOOecK5ifiTjnnCuYJxHnnHMF8yTinHOuYJ5EnHPOFcyTiHPOuYL9P3hCzCsls/NuAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hnWYgXNK-EJx",
        "colab_type": "code",
        "outputId": "c0c7127f-f3f9-48b2-b00e-aa005c1eca48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "print(\"\\n\\t   The Three models in Log case \")\n",
        "plt.plot(x_death, predicted4,label='linear_regression')\n",
        "plt.plot(x_death, np.log10(predicted5),label='SVR')\n",
        "plt.plot(x_death, predicted6,label='MLPRegressor (THE BEST)')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t   The Three models in Log case \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S5QiC_U_Zt0c",
        "colab_type": "code",
        "outputId": "daa3f27e-bd24-42b8-e1ef-9be25852f413",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "print(\"\\n\\t   The Best model (MLP Model) in exponantial case \")\n",
        "plt.plot(x_death, 10**predicted6,label='MLPRegressor (THE BEST)')\n",
        "plt.plot(x_prediction2, 10**MLP_Regressor_D.predict(x_prediction2),label='new day prediction ')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value')\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t   The Best model (MLP Model) in exponantial case \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KfKWV4vSaQGN",
        "colab_type": "code",
        "outputId": "619ab5d5-7017-4008-cd71-c76963c2cb22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "print(\"\\n\\t The Best model (MLP Model) in Log\")\n",
        "plt.plot(x_death, predicted6,label='MLPRegressor (THE BEST)')\n",
        "plt.plot(x_prediction2, MLP_Regressor_D.predict(x_prediction2), label='new day prediction')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value in log')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t The Best model (MLP Model) in Log\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}