ML_PROJECT_COVID19.ipynb

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      "name": " ML_PROJECT_COVID19.ipynb",
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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/Day-16-Singleton/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": "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 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.neural_network import MLPRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.svm import SVR"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ViIWNV6p3cqQ",
        "colab_type": "text"
      },
      "source": [
        "# **Preparing patients data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CqWDmrXuLMPu",
        "colab_type": "code",
        "cellView": "code",
        "outputId": "5e70202b-c300-43aa-92bb-e373f6a165de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "ds = pd.read_csv('us_covid19.csv')\n",
        "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]]\n",
        "ds.head()"
      ],
      "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>Date</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",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-21</td>\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>1</th>\n",
              "      <td>2020-01-22</td>\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>2</th>\n",
              "      <td>2020-01-23</td>\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>3</th>\n",
              "      <td>2020-01-24</td>\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>4</th>\n",
              "      <td>2020-01-25</td>\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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date  num_of_date  ...  num_of_date_test num_of_patients_test\n",
              "0  2020-01-21            1  ...              16.0                 11.0\n",
              "1  2020-01-22            2  ...              27.0                 13.0\n",
              "2  2020-01-23            3  ...              33.0                 15.0\n",
              "3  2020-01-24            4  ...              38.0                 58.0\n",
              "4  2020-01-25            5  ...              43.0                118.0\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u5pupQkOxF1U",
        "colab_type": "code",
        "outputId": "f0ac4ea1-7317-46c8-f74a-94886054abb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        }
      },
      "source": [
        "x,y_test_patient"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0      1\n",
              " 1      2\n",
              " 2      3\n",
              " 3      4\n",
              " 4      5\n",
              "       ..\n",
              " 78    91\n",
              " 79    92\n",
              " 80    93\n",
              " 81    94\n",
              " 82    95\n",
              " Name: num_of_date, Length: 83, dtype: int64, 0         11.0\n",
              " 1         13.0\n",
              " 2         15.0\n",
              " 3         58.0\n",
              " 4        118.0\n",
              " 5        217.0\n",
              " 6       1663.0\n",
              " 7      43847.0\n",
              " 8     161807.0\n",
              " 9     312237.0\n",
              " 10    460252.0\n",
              " 11    657996.0\n",
              " Name: num_of_patients_test, dtype: float64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 239
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPHyTv283eTV",
        "colab_type": "text"
      },
      "source": [
        "## **linear_regression**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-IgwuyLhpfC",
        "colab_type": "text"
      },
      "source": [
        "### Code & Validatio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6i49PcWdEZK",
        "colab_type": "code",
        "outputId": "32149996-2a58-4445-9c1f-39ab0772b369",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\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": "4b18a3cd-0609-437c-e80b-3ef1a9c4b7a5",
        "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",
        "score"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9303292029481322"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 241
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YmMkniQa7U7r",
        "colab_type": "text"
      },
      "source": [
        "### **Prediction for new days**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gOTY4y8V7ttX",
        "colab_type": "code",
        "outputId": "ea33e258-6679-46f2-ec08-08b89edf09f3",
        "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": "code",
      "metadata": {
        "id": "xEkDAdyTdp8v",
        "colab_type": "code",
        "outputId": "06ea1482-3327-4df0-cee3-72dd1b72d2f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 885
        }
      },
      "source": [
        "predicted1 = 10**Linear_Regression.predict(x.values.reshape(-1,1)[:76])\n",
        "predicted11 = 10**Linear_Regression.predict(x.values.reshape(-1,1))\n",
        "plt.plot(x, y, 'o-',label='data set')\n",
        "plt.plot(x[:76], 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()\n",
        "plt.plot(x, predicted1,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 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"
          }
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-243-f15064976a95>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Predict value in Log'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredicted1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'linear_regression'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test_patient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mevaluation_1\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'#ff0000'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Predict values'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test_patient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test_patient\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'#003300'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Original values'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2761\u001b[0m     return gca().plot(\n\u001b[1;32m   2762\u001b[0m         *args, scalex=scalex, scaley=scaley, **({\"data\": data} if data\n\u001b[0;32m-> 2763\u001b[0;31m         is not None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2764\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2765\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1644\u001b[0m         \"\"\"\n\u001b[1;32m   1645\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1646\u001b[0;31m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1647\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1648\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    214\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    341\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 342\u001b[0;31m             raise ValueError(f\"x and y must have same first dimension, but \"\n\u001b[0m\u001b[1;32m    343\u001b[0m                              f\"have shapes {x.shape} and {y.shape}\")\n\u001b[1;32m    344\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (83,) and (76,)"
          ]
        },
        {
          "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": "7e1fd34f-6618-4ffb-f4d9-51887559ee60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "from sklearn.svm import SVR\n",
        "SVR_model = SVR(kernel='rbf')\n",
        "\n",
        "\n",
        "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_1) to evaluate:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_ynBqRWH9fQ",
        "colab_type": "text"
      },
      "source": [
        "### **Evaluation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D72kK0YfmT8o",
        "colab_type": "code",
        "outputId": "8eb7fdd4-2276-4f14-af5b-2d96ae7fb785",
        "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",
        "score"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9587420012661088"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 253
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "84dnU1SRS75j",
        "colab_type": "text"
      },
      "source": [
        "### Prediction for new days"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLvUFTUvTFYX",
        "colab_type": "code",
        "outputId": "d47942b3-8814-40f2-d3cc-49af22c9da7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "\n",
        "print('Evaluation for expecting 6 days in future in Linear_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 Linear_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": "code",
      "metadata": {
        "id": "qrz6ge5reG6X",
        "colab_type": "code",
        "outputId": "87dc1804-7752-4a4c-9f29-24e2495fc257",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "source": [
        "predicted2 = SVR_Regressor_2.predict(x.values.reshape(-1,1))\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()\n",
        "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 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"
          }
        },
        {
          "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": "NiwXQGGdeLmL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "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": "cb3e8cd8-6c51-4b11-f30c-9b30496d18c0",
        "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",
        "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": "6e99c379-7ed4-4cab-ba51-c3b629d26fab",
        "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",
        "score"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9984346234582061"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 248
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJ2leCIVAFvX",
        "colab_type": "text"
      },
      "source": [
        "### Prediction for new days"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oqNSoUbuAHYT",
        "colab_type": "code",
        "outputId": "9a4b7150-f2e2-4e6e-efd1-d9b740b63c8d",
        "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**MLP_Regressor.predict([predict])))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation for expecting 6 days in future in Linear_Regression:\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": "code",
      "metadata": {
        "id": "bfbFJCNKfZ1I",
        "colab_type": "code",
        "outputId": "2fb60fce-7376-4f0d-fd46-51617c0d7e46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "source": [
        "predicted3 = 10**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='linear_regression')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value in Log')\n",
        "plt.show()\n",
        "plt.plot(x, predicted3,label='linear_regression')\n",
        "plt.scatter(x_test_patient, 10**evaluation_3 ,s=60, c='#ff0000',label='Predict values')\n",
        "plt.scatter(x_test_patient, y_test_patient,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"
          }
        },
        {
          "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": "FB6PTyCEfgxV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wnT3iNvbrKjv",
        "colab_type": "text"
      },
      "source": [
        "## **Conclusion**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UWQvtNs8EHy",
        "colab_type": "code",
        "outputId": "1bd4b995-2e8a-42c9-ecc7-164a452e32b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(\"\\n\\t   The Three models in exponantial case \")\n",
        "plt.plot(x[:76], predicted1,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()\n",
        "##########################################################\n",
        "print(\"\\n\\t   The Three models in Log case \")\n",
        "plt.plot(x, np.log10(predicted11),label='linear_regression')\n",
        "plt.plot(x, np.log10(predicted2),label='SVR')\n",
        "plt.plot(x, np.log10(predicted3),label='MLPRegressor (THE BEST)')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value')\n",
        "plt.show()\n",
        "########################################################\n",
        "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')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value')\n",
        "plt.show()\n",
        "######################################################\n",
        "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')\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 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"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\t   The Three models in Log case \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: RuntimeWarning: invalid value encountered in log10\n",
            "  if sys.path[0] == '':\n"
          ],
          "name": "stderr"
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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": "HATRwyLMHHpL",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "print(\"\\n\\t   The Three models in Log case \")\n",
        "plt.plot(X_train, predicted1,label='linear_regression')\n",
        "plt.plot(X_train, predicted2,label='SVR')\n",
        "plt.plot(X_train, predicted3,label='MLPRegressor (THE BEST)')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value')\n",
        "plt.show()\n",
        "print(\"\\n\\t\\t The Best model (MLP Model)\")\n",
        "plt.plot(X_train, y_train, 'o-',label='data set')\n",
        "plt.plot(x_evaluation, 10**MLP_Regressor.predict(x_evaluation), 'o-',label='data set')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value')\n",
        "plt.show()\n",
        "print(\"\\n\\t The Best model (MLP Model) in Log\")\n",
        "plt.plot(X_train, predicted3,label='data set')\n",
        "plt.plot(x_evaluation, MLP_Regressor.predict(x_evaluation), label='data set')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value in log')\n",
        "plt.show()\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RSV942JgmtAu",
        "colab_type": "text"
      },
      "source": [
        "# Deaths"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gQQ-8gU3mzHy",
        "colab_type": "code",
        "outputId": "6e788f5c-dc6f-48fe-fb70-704d0fc12047",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "ds_death=pd.read_csv('Deaths_us.csv')\n",
        "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",
        "ds_death.head()\n",
        "\n",
        "#x_test=dtest_death['num_of_date']\n",
        "#y_test=dtest_death['Deaths']\n",
        "#x=x.values.reshape(-1,1)\n",
        "#x_test=x_test.values.reshape(-1,1)\n",
        "#y_test_new=np.log10(y_test)\n",
        "#new_y=np.log10(y)"
      ],
      "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>Date</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",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-03-02</td>\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>1</th>\n",
              "      <td>2020-03-03</td>\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>2</th>\n",
              "      <td>2020-03-04</td>\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>3</th>\n",
              "      <td>2020-03-05</td>\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>4</th>\n",
              "      <td>2020-03-06</td>\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",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date  num_of_date  Deaths   Date_test  num_of_date_test  Deaths_test\n",
              "0  2020-03-02            1       6  2020-03-18              17.0        171.0\n",
              "1  2020-03-03            2       9  2020-03-24              23.0        957.0\n",
              "2  2020-03-04            3      11  2020-04-01              31.0       6394.0\n",
              "3  2020-03-05            4      12  2020-04-11              41.0      23843.0\n",
              "4  2020-03-06            5      14  2020-04-22              52.0      47894.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-NIRsoSbquS9",
        "colab_type": "code",
        "outputId": "7281a598-1732-4ce8-ce56-d1b8bb1c5c51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "y_death , y_test_deaths"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0         6\n",
              " 1         9\n",
              " 2        11\n",
              " 3        12\n",
              " 4        14\n",
              " 5        17\n",
              " 6        21\n",
              " 7        22\n",
              " 8        30\n",
              " 9        36\n",
              " 10       40\n",
              " 11       47\n",
              " 12       54\n",
              " 13       73\n",
              " 14       95\n",
              " 15      121\n",
              " 16      239\n",
              " 17      309\n",
              " 18      374\n",
              " 19      509\n",
              " 20      689\n",
              " 21     1260\n",
              " 22     1614\n",
              " 23     2110\n",
              " 24     2754\n",
              " 25     3251\n",
              " 26     3948\n",
              " 27     5027\n",
              " 28     7438\n",
              " 29     8694\n",
              " 30    10231\n",
              " 31    13128\n",
              " 32    15347\n",
              " 33    17503\n",
              " 34    19604\n",
              " 35    21830\n",
              " 36    25558\n",
              " 37    27272\n",
              " 38    29825\n",
              " 39    32443\n",
              " 40    37147\n",
              " 41    39014\n",
              " 42    40575\n",
              " 43    42853\n",
              " 44    45536\n",
              " 45    50236\n",
              " 46    52191\n",
              " 47    54256\n",
              " 48    55412\n",
              " 49    56796\n",
              " 50    61655\n",
              " 51    63856\n",
              " Name: Deaths, dtype: int64, 0      171.0\n",
              " 1      957.0\n",
              " 2     6394.0\n",
              " 3    23843.0\n",
              " 4    47894.0\n",
              " 5    59266.0\n",
              " 6    11793.0\n",
              " 7    34619.0\n",
              " Name: Deaths_test, dtype: float64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 262
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQ2R_NaYmz-y",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "## 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": "276e1462-2268-4386-df1e-40a21af2fadd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "linear_regression=LinearRegression()\n",
        "\n",
        "yy=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_train_D, y_test_D ,yy_train_D, yy_test_D= x_death[train_index], x_death[test_index], y_death[train_index], y_death[test_index], yy[train_index], yy[test_index]\n",
        "    \n",
        "    Linear_Regression.fit(X_train_D.values.reshape(-1,1), yy_train_D)\n",
        "    scores.append(Linear_Regression.score(X_test_D.values.reshape(-1,1), yy_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": "code",
      "metadata": {
        "id": "-Pc2u_NDNZpi",
        "colab_type": "code",
        "outputId": "2b9afc1b-2d37-4d66-ccbb-ae321bdb591b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "plt.plot(x_death, y_death)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "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": "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": "290262ee-d450-456b-8c5f-5a77254d5c95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "Linear_Regression.fit(x_death.values.reshape(-1,1), yy)\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",
        "score"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8300935990327268"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z5ywtEvrrhlk",
        "colab_type": "text"
      },
      "source": [
        "### Prediction for new days"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMvndMKDrgVz",
        "colab_type": "code",
        "outputId": "28b4bb4d-dd3f-4f85-c32b-c450a450c17b",
        "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": "code",
      "metadata": {
        "id": "Tkgs4rvfolag",
        "colab_type": "code",
        "outputId": "2472395f-1fab-4194-913f-d4fcd96167c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "source": [
        "predicted4 = Linear_Regression.predict(x_death.values.reshape(-1,1))\n",
        "plt.plot(x_death, yy, '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()\n",
        "plt.plot(x_death, predicted4,label='linear_regression')\n",
        "plt.scatter(x_test_deaths, evaluation_4 ,s=60, c='#ff0000',label='Predict values')\n",
        "plt.scatter(x_test_deaths, y_test_deaths_log ,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"
          }
        },
        {
          "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": "b7639da2-9ace-4ce5-dc1c-e34b028d8f30",
        "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 ,yy_train_D, yy_test_D= x_death[train_index], x_death[test_index], y_death[train_index], y_death[test_index], yy[train_index], yy[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_1) to evaluate:**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "usgo14_hIOKx",
        "colab_type": "text"
      },
      "source": [
        "### **Evaluation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "14vJQnrvguQH",
        "colab_type": "code",
        "outputId": "7c2ecadd-4b24-44d6-dce3-403dcbdce547",
        "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",
        "score"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9342479063269598"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 272
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s1rXWf62QRvx",
        "colab_type": "text"
      },
      "source": [
        "### Prediction for new days"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWZo98BYxjVR",
        "colab_type": "code",
        "outputId": "ef0db439-e95f-4141-8112-5b21e1e23584",
        "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(SVR_Regressor_2.predict([predict])))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation for expecting 6 days in future in Linear_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": "code",
      "metadata": {
        "id": "AoVOLf73pOHP",
        "colab_type": "code",
        "outputId": "1a384748-4ca6-4c24-a6df-b7ae87b88e09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "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()\n",
        "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 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"
          }
        },
        {
          "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": "b3f49aab-1206-45d9-fdb5-3108351f07e5",
        "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_train_D, y_test_D ,yy_train_D, yy_test_D= x_death[train_index], x_death[test_index], y_death[train_index], y_death[test_index], yy[train_index], yy[test_index]\n",
        "    #\n",
        "    MLP_Regressor_1.fit(X_train_D.values.reshape(-1,1), yy_train_D)\n",
        "    scores_1.append(MLP_Regressor_1.score(X_test_D.values.reshape(-1,1), yy_test_D))\n",
        "    #\n",
        "    MLP_Regressor_2.fit(X_train_D.values.reshape(-1,1), yy_train_D)\n",
        "    scores_2.append(MLP_Regressor_2.score(X_test_D.values.reshape(-1,1), yy_test_D))\n",
        "    #\n",
        "    MLP_Regressor_3.fit(X_train_D.values.reshape(-1,1), yy_train_D)\n",
        "    scores_3.append(MLP_Regressor_3.score(X_test_D.values.reshape(-1,1), yy_test_D))\n",
        "    #\n",
        "    MLP_Regressor_4.fit(X_train_D.values.reshape(-1,1), yy_train_D)\n",
        "    scores_4.append(MLP_Regressor_4.score(X_test_D.values.reshape(-1,1), yy_test_D))\n",
        "    #\n",
        "    MLP_Regressor_5.fit(X_train_D.values.reshape(-1,1), yy_train_D)\n",
        "    scores_5.append(MLP_Regressor_5.score(X_test_D.values.reshape(-1,1), yy_test_D))\n",
        "\n",
        "print(\"Average score for MLP_Regressor_1:\",sum(scores_1)/8,\"\\nAverage score for MLP_Regressor_2:\",sum(scores_2)/8,\"\\nAverage score for MLP_Regressor_3:\",sum(scores_3)/8\n",
        "      ,\"\\nAverage score for MLP_Regressor_4:\",sum(scores_4)/8,\"\\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": "5ca41465-6c0f-4d3e-dbf4-c8ce3dbbc11f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "MLP_Regressor_D = MLPRegressor(hidden_layer_sizes=(5), activation='tanh', solver='lbfgs' \n",
        "                               ,learning_rate_init=0.01, max_iter=1000000,random_state=93, validation_fraction=0.1)\n",
        "MLP_Regressor_D.fit(x_death.values.reshape(-1,1), yy)\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",
        "score"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9998623441881356"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 275
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GiVNLRWvSO8Y",
        "colab_type": "text"
      },
      "source": [
        "### Prediction for new days"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I9wyQTvpSQLn",
        "colab_type": "code",
        "outputId": "226c2098-7c9b-4e1e-a4a7-4d23131726c8",
        "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**MLP_Regressor_D.predict([predict])))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation for expecting 6 days in future in Linear_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": "code",
      "metadata": {
        "id": "ckTpDz92pvp8",
        "colab_type": "code",
        "outputId": "395912c5-2082-447e-b017-2f8373767217",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        }
      },
      "source": [
        "predicted6 = 10**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, predicted6,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()\n",
        "plt.plot(x_death, predicted6,label='SVR_Regressor_2')\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 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"
          }
        },
        {
          "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": "a9d29a19-666b-4ff5-f64c-398f3caec6df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(\"\\n\\t   The Three models in exponantial case \")\n",
        "plt.plot(x_death, 10**predicted4,label='linear_regression')\n",
        "plt.plot(x_death, 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()\n",
        "##########################################################\n",
        "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, np.log10(predicted6),label='MLPRegressor (THE BEST)')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value')\n",
        "plt.show()\n",
        "########################################################\n",
        "print(\"\\n\\t   The Best model (MLP Model) \")\n",
        "plt.plot(x_death, predicted6,label='MLPRegressor (THE BEST)')\n",
        "plt.plot(x_prediction2, 10**MLP_Regressor_D.predict(x_prediction2),label='new day')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value')\n",
        "plt.show()\n",
        "######################################################\n",
        "print(\"\\n\\t The Best model (MLP Model) in Log\")\n",
        "plt.plot(x_death, np.log10(predicted6),label='MLPRegressor (THE BEST)')\n",
        "plt.plot(x_prediction2, MLP_Regressor_D.predict(x_prediction2), label='new day')\n",
        "plt.legend()\n",
        "plt.xlabel('Number of Day')\n",
        "plt.ylabel('Predict value in log')\n",
        "plt.show()\n",
        "\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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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "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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sLCywsLDAxsaGDRs2MGTIEPbulYu+SqOohHS+2nGZQ1fuUte+Aj++05jGTlZa7TM7L5uY1Bii4kOIvnmCmLuXuJUSRUxeBjF6uiTr6oIpYKre5UlPRw87E0sczBxobmqHvak9DmYO2JnaYWdih62pLab6plqNGWSC14ipqSlBQUFkZGRgbGzMgQMHqFKlikbnHj58GBsbG7788kvmzZuXn7Aevj5jxgzmzJnDL7/88lIxCiEQQqCjo/0iSbm5uejpPf6tM2/ePKZOnUrDhg0ZPnw4oP4P7uF1Aqxevfq5bT96/LNMnDiRyZMno1Kp8PLy4ujRo3To0AGAb7/9lv79+z92/PTp0+nSpQsTJkwAIDAwEDc3NwICAgAYNmwYvXr1euw8e3t7Tp48SevWrZ8bs1Q8cvJU/Hr8BksPhaKjKEztWZdhrZzRK6LCYGk5aUSlRHEz+SY3U26qP068QXTSDe5kJyIeOdZQCBz0DKhiXh136zpUqeyOfYUqOJg6YG9qT0XjiugoJV+wTCZ4DfXo0YPdu3fTv39/NmzYgLe3N8ePH9f4/KeNRkFdTvjh63FxcYwePZqbN28CsGTJElq3bk1cXByDBg3i1q1btGzZkgMHDuDn50dqairdunWjefPm+Pn5sWfPHjZv3szmzZvJysqiX79+zJw5k7S0NN566y2io6PJy8tj2rRpDBw4kClTprBjxw709PTo2rUrCxcuJCIighEjRnDv3j0qVarE77//jpOTE8OGDcPIyIgLFy7QunXrx2rrpKSkEBgYSMOGxbsqNjs7m8zMTKysCh69xcbG0rVr1/zP3d3dn9t23759Wb9+vUzwpcT5iAR8tl4i9E4qXevZ8lXv+jhYGhe6nVxVLlEpUUQkRRCZHElEcgQRyeqP72U8XvTWRuhQNSuTZjnZOOYJHCs4U9XekyounbCp3hFF37CoLk9rylSCn39uPlcSrhRpm67Wrnze7PPnHvf2228za9YsevXqRWBgICNGjChUgt+1axdubm5PvP5oOeEJEyYwceJE2rRpw82bN+nWrRshISHMnDmTjh078sUXX7B3715WrVqVf35YWBhr1qyhRYsW7N+/n7CwMM6dO4cQgt69e3Ps2DHi4uJwcHBg9+7dACQlJREfH8/WrVu5cuUKiqKQmJgIwPjx4xk6dChDhw7lt99+46OPPmLbtm0AREdHc+rUqfwplId8fX1p0KCBRl+HTZs2ceLEifzPr1279tj7HTp0yG9/6NChTJw48Yk2Fi9ezLp164iMjKR79+54eHjkv/fpp5/mb7BSv3591q9fz7hx4xg4cCDLly+nc+fODB8+HAcHhwLj9PT0ZOrUqRpdk6Q999Oy+ebfK2zyjaKKpTG/vOtJl3q2zz0vMzeTyORIwpPCuZ54nfCkcG4k3SAiOYJcVW7+cdZGVlQzqkwb/YpUy1JwigvHKTOVqjl5mNo3BJd26j9VW4BB2as0WaYSfElyd3cnIiKCDRs20KNHD43Pe5iw3N3dH9vZ6WnlhA8ePEhwcHD+McnJyaSmpnLixAm2bt0KwGuvvfbYiLVatWq0aNECgP3797N//34aNWoEqOvohIWF0bZtWyZNmsTnn39Or169aNu2Lbm5uRgZGfHee+/Rq1cvevXqBah3sPrnn38AGDJkCJ999ll+XwMGDHgiuYN6hKxpHaGHifah9u3bP/Z+YaZocnJy6N+/Pxs3buTtt98Gnj5F061bN8LDw9m7dy///vsvjRo1IigoqMCYK1euzK1btzS6JqnoCSH42z+GeXtCSMrI4QMvFyZ0roWJweMpKzsvm/CkcMLuhxGWGEZ4YjjhSeFEp0QjHkyq6Cg6OJo54mLhgpejFy6GFamedJdqt4OxuHECUi+qG7OuAXXehOrtwLkNmJT94oBlKsFrMtLWpt69ezN58mSOHDlCfHy8Ruc8K2E9rZywSqXizJkzGBlp/ji1qen/36gRQvDFF1/wwQcfPHGcv78/e/bsYerUqXTq1Inp06dz7tw5Dh06xF9//cXy5cv577//NO7rUcbGxmRmZmocc1HR19fntdde49ixY/kJ/lmsra0ZNGgQgwYNolevXhw7dow333zzmcdnZmZibFz4KQDp5V27m4LP1iDO3kigSTUr5vZrQB1bc+6m38X37lWuJlzl6v2rhN0PIzI5kjyRB4C+jj7OFs7Uq1iPXi69cLF0wcXChWrGthjG+MP1Q3B2C9y9rO7IpCK4tH/wpwNYVi2hK9aeMpXgS9qIESOwtLTEzc2NI0eOvHR7/1tOuGvXrixbtoxPP/0UUJcm9vDwoHXr1mzevJnPP/+c/fv3c//+/ae2161bN6ZNm8bgwYMxMzMjJiYGfX19cnNzsba25p133sHS0pJff/2V1NRU0tPT6dGjB61bt8bFxQWAVq1asXHjRoYMGcL69etp27btc6+jbt26fPfddy/99SgsIQQnT57M/43lWf777z9atGiBiYkJKSkpXL9+/bmlqUNDQzWedpKKRmZOHsv/u8aKY2EYmyQxpJMKS8srLAz8jdCEUO5n/f/3fRWzKtS2qk3nap2pZVWLWpa1cKrghL6OPggB90Lh+n9w4lf1evTcDNDRB6cW0PkrqNERbN2gGBYllCSZ4AvB0dGRjz766KnvrV69On+uGuDMmTMatfloOeHvv/+ecePG4e7uTm5uLl5eXvz888/MmDEDb29v/vjjD1q2bImdnR3m5uakpqY+1lbXrl0JCQmhZcuWgHo3qnXr1nHt2jU+/fRTdHR00NfX56effiIlJYU+ffqQmZmJECL/pumyZcsYPnw43377bf5N1udxdXUlKSmJlJQUzM3NNbruZ3l0Dt7d3Z21a9c+cczDOficnBzc3d0ZO3Zs/nuPzsEDnDt3Dj8/Pz788EP09PRQqVSMHDmSpk2bFhjH4cOH6dmz50tdi/R8QghiUmPYfOkUfwacJI0bmNaOJY9Mtt0C/dv61LKqRQenDtSxqoOrtSu1rWpjZvA/T6lmJELIbvUo/fphSIpSv16xJjR+F2p2gmqtwbAUPN1ajBQhxPOPKiaenp7C1/fxvblDQkKoW7duCUVUOmRlZaGrq4uenh6nT59mzJgx+Uv8SovFixdjbm7OyJEjSzqUIuHl5cX27dufWKEjvx9fTlpOGoFxgQTEBRAYF0hg3CWSs5PUbwo9qleoTXMHd+pVrEfdinWpYVEDfd2nPLwkBNwOhLADcO2QumCXyANDC3Dxghqd1KN0q7L9fIkmFEXxE0J4Pu09OYIvA27evMlbb72FSqXCwMDgpdfMa8OYMWPYsmVLSYdRJOLi4vjkk0+eu/xSer7Y1Fgu3L1AQFwAAXcDuHr/KiqhQkGhooETyffqkJPhyFtuLfm8U3tMDQq4/5SZBOFHIHQ/XDsAqXfUr9s3hDYToVYXqOIJujKtPSRH8JJUCPL78dnyVHlcvX9VndDvBnDh7gXupKuTsLGeMe6V3GlUuREWSi02HFO4HJNN21o2zO7TAGebp9zAfziXHrpXPVK/eRpUuerKizU6Qq2u6pG6+fOXTZZnZX4EL4QolxviSmVLaRoMlQYqoSL0fijnb5/n3O1z+N3xIyU7BQA7UzsaV26MR2UPPCp7UNuqNunZgkX7Q/nudATWpoZ8792I193tH//Zzs1S3xQN3adO7ImR6tdtG0Cr8eqk7thMjtI1VOq/SkZGRsTHx1OxYkWZ5KUSI4QgPj6+UEtYy6OY1BhO3zrN6VunOXf7HIlZ6gfknMyd6FqtK03tmtLEtgl2pnb55wgh2HPpNjN3XiYuNYshLaoxqWsdLIwfzK2n3XuQ0P9V3yDNTgU9I/XyxdYToHY3sNBOMa7yrtQneEdHR6Kjo4mLiyvpUKRXnJGREY6Or1aiycjN4Pzt8xyPPs7p2NNEJqtH1JWNK+Pl6EUL+xY0tWv6WEJ/1M34dKZtD+JoaBz1HSrwy7ueNKxqCXGh4Lcbrv4LUecAAeYO4DYA6nQH57Zl8snR0qbUJ3h9fX2qV69e0mFI0isjKjmKYzHHOB5znPOx58lWZWOsZ4ynrSdv13mblg4tcbFwKfA36uxcFb8cD+f7Q2Ho6ShM7+nKu05x6F1ZBNt2Q/yDEhX2DaH9FKj9mvpj+Vt6kSr1CV6SJO0SQhB6P5SDNw9yMPIg1xLVyde5gjNv1XkLL0cvmtg2wUDXQKP2zoTHM3VbEDfv3uej6rcYYROMydn9cOgO6OipR+fNR0OdHmChWVVW6cXIBC9JryAhBJfuXcpP6lEpUegoOjSu3JjPm36Ol6MXThUKftr3f8WnZrFopy8pl/bwhfEF2pkFoBebBvFmULMzuPZSL2U0ttTSVUn/SyZ4SXpF5KnyuHD3Qn5Sv5N+Bz1Fj+b2zRnRYAQdqnagonHFQrerSonDb/86si5tZ4YIxMAgD2FYCcW1vzqpV/cC/Vf75nRJkQleksoxIQQX4y6yK3wXByIPkJCZgKGuIa0cWjGh8QS8HL2wMLQofMPJsRCyk/SL/2B06yxNUXFH147UBiOxbvIGimNTrWwiLRWOTPCSVA5FJEWwK3wXu8N3E50ajZGuEe2qtqNztc54VfHCRP8FVqgkxUDIDri8DRF1FgXBLVGFw0o/qrV9my4dOqOU8+JdZY1M8JJUTqRmp7I3Yi9bw7YSeC8QHUWH5nbNGeMxhk5OnV5sD9DkWxC8HS5vVdd7AVIt6rBRbyAb0xrTqHELvuhRF2tTzW7ASsVLJnhJKsOEEATEBfBP2D/si9hHRm4GNS1rMqnJJHq49KCySeXCN5py50FS/0ddHgDA1o2UVlNYGOXKmjADalU2Y+6gBjR3KfycvVR8ZIKXpDIoNTuV7de3s/nqZsKTwjHRM6FH9R68UesN3GzcCv/Ud8Z9CNkJl7bAjeOAgMr1oIMPua59WB2qz+IDoeQJwWev1WJkGxcM9OR0TGknE7wklSHX7l9j49WN7Ly+k/TcdNxs3JjVahbdnLsVfl49O11d7+XSXxC2H1Q56m3r2n0G9d+Ayq5cuHmfLzcGERKbTPs6lZjdpwFVreUTpmWFTPCSVMqphIpj0cf4I/gPzt0+h4GOAd2rd8fb1Zv6NvUL11hejrrk7qUtcGW3uu6LuT00ex/c+oNDI1AUkjJy+HbbJdafvUllc0N+HNyY7g3sZD2oMkYmeEkqpbLysth5fSdrLq8hIjkCe1N7JjaZSL+a/bAyKkSteiHUN0gDN0PwNkiPV5fcbfCGuvZLtdb5SxqFEOwIiGH2rhAS0rIY1sqZT7rUxtzoKZtuSKWeTPCSVMokZSWx4coGNlzZQEJmAnWt67LAawFdqnVBT6cQP7Lx1yFwk/rP/QjQM1YX8nIboN7CTs/wscNv3Etj+vYgjofdw93Rgt+HNcXN8QXWyEulhkzwklRKpGSnsC54HWuD15Kak4qXoxfD6g/D09ZT86mR9AT16peLGyH6PKCASztoNwXq9gLDJ/fMzcrN4+cj4fxw5BqGujrM7F2fd1pUQ1dHTseUdTLBS1IJS8tJY33IelZfXk1KdgqdnTozuuFo6ljX0ayBvFz1ZtMBf8LVPZCXrV4B03mmerReQEGvU9fuMXVbEOH30ujlbs+0XvWwrSDLCpQXMsFLUglJz0ln49WN/B70O4lZibR3bM9Yj7HUrajhloB3r0DAOvXceuodMKkIniPAYxDYuRdYejcuJYt5e0LYeiEGJ2sT1oxoRrvalYroyqTSQusJXlEUXcAXiBFC9NJ2f5JU2mXmZrLp6iZ+C/qNhMwE2lRpwziPcTSwaaDByUkQ9A9cWAcxvuryu7Vfg4be6u3s9Ap+olSlEmw4f5P5/14hIyeP8R1rMq5DTYz0Zd2Y8qg4RvATgBCgQjH0JUmlVlZeFn+F/sWvl37lXsY9Wti3YJzHODwqexR8ohAQeQr816qfMM3NgEp1oetccB8IZpqNvINvJeOz7RIXbibSwsWaOX3dqFnZrAiuTCqttJrgFUVxBHoCc4FPtNmXJJVWQgj2Re5jse9ibqXdwtPWk2+9vsXTzrPgE9PuqefV/ddCfBgYVoCGb0OjIVClsca7H6Vl5bLkYCi/nYzAwlif7wY05I3GVeSa9leAtkfwS4DPgCdv3T+gKEgCEewAACAASURBVMr7wPsATk6F22BAkkq7y/cuM//8fC7cvUAdqzqsbLWSFvYtnp1cVSq4cRT8focre9RPl1ZtAW0mQv2+YFC4gmH7L9/mqx2XuZWUydtNqzKluyuWJrIw2KtCawleUZRewF0hhJ+iKO2fdZwQYiWwEsDT01NoKx5JKk530++y1H8pO67vwNrImpmtZtKnRh90n1UjPT1BPVr3/Q0SroOxlfrp0sbvQmXXQvcffT+dr3YEczDkDnVszfnLuxGeztYveVVSWaPNEXxroLeiKD0AI6CCoijrhBDvaLFPSSpRuapc1oes58eAH8lR5fBeg/cY6TYSM4OnzHULATF+cH6Veu16biZUbQ7tPod6fV5oF6ScPBW/nbjBkoNhAHzR3ZURbaqjrysLg72KtJbghRBfAF8APBjBT5bJXSrPAu4GMPvMbELvh9K2Slu+aP4FVc2rPnlgdpq6Fsz5VXA7EPRN1atgmr4Hdm4v3L9fZAI+W4O4cjuFznUr81Xv+jhaycJgrzKNEryiKG2AWkKI3xVFqQSYCSFuaDc0SSobEjMTWeK/hL/D/sbWxJbF7RfTyanTk/PscVfh/K/qp0yzktUPI/VYqF4JY/Tii8wS07OZv/cKG85F4WBhxMohTeha3+4lr0oqD56b4BVFmQF4AnWA3wF9YB3qKRiNCCGOAEdeKEJJKsUORh5k9pnZJGUlMbTeUMZ6jH28bK8qT12K9+wKCD8Mugbq6RfP98CphcYrYZ5GCMHWCzHM3R1CYkYOo9pW5+POtTE1lM8vSmqafCf0AxoB/gBCiFuKojxzVYwkvQqSspL4+tzX7A7fTV3ruqzssvLx0gIZiRCwHs6tVBf6MneAjlOh8TCN160X5HpcKlO3BnE6PB6Pqpas7deA+g6yMJj0OE0SfLYQQiiKIgAURXmBjR0lqfw4GXOS6aemE58Rz5iGYxjlPgp9nQfldOOvw9mf4cJ6yElTL3HsNAPqvg66L19yNzMnjx8PX+Pno+EY6eswt18DvJs6oSMLg0lPoUmC36woygrAUlGUUcAI4BfthiVJpU96Tjrf+X7H5tDN1LCowfcdv6d+xfrq1TARJ+D0j+piXzp60OBNaDFavYFGETkeFsfUbUFExqfT18MBn571qGRu+PwTpVfWcxO8EGKhoihdgGTU8/DThRAHtB6ZJJUi/nf88TnhQ0xqDEPrDWV84/EYoqve7u7U9xB7EYytoe0kaDoSKtgXWd93kzOZvTuEnRdvUd3GlPUjm9O6pk2RtS+VXxrdjXmQ0GVSl145WXlZLL+wnDWX1+Bg5sBv3X7D07oe+K2FU8sgMRIq1oJeS9SrYQyKbllinkrw59lIFuy9SlauigmdajGmfQ1ZGEzSmCaraFKAh0+YGqBeRZMmhJDFw6RyLTg+GJ8TPlxLvMaA2gOYXH8EJv7r4dxb6m3vHJtCt3lQpwfoFO2DREExSfhsvcTF6CTa1LRhVp/6uFSShcGkwtFkiiZ/xYyiXtjbB2ihzaAkqSSphIrVl1ezzH8Z1kbW/NRiJm3Cz8KyppCTri7P23oCOLV8qWWOT5Oalcui/aGsPnUDa1NDlr7tQe+GDrIwmPRCCrVgVgghgG0P1sZP0U5IklRyEjMT8Tnpw7HoY3SxbcaMVBUWG0eqE7nbW9BqPNjWK/J+hRDsDbrNzJ3B3EnJZFAzJz57zRULY7nZtfTiNJmieeORT3VQP/SUqbWIJKmEXIy7yOSjk4lPv8cXOnZ4n/kLxcAMWoxR/7Fw1Eq/UQnpTN8exOGrcdSzr8BP7zSmkZOVVvqSXi2ajOBff+TjXCAC9TSNJJULQgjWBq9lid8ibIUOf8REU1/3PnTwgWaj1JUdtSA7V8WvJ8L5/lAYOorC1J51GdbKGT1ZGEwqIprMwQ8vjkAkqSSkZacydf9oDsZfpHNaOjPTFSq0m6ouJWCovZua524kMHXbJULvpPJafTumv14PB0tjrfUnvZqemeAVRVnG/6+eeYIQ4iOtRCRJxUEIIoP/YsL5eUSQw+Q0Fe82noziOQz0tZdoE9Ky+ebfEDb7RlPF0phVQz3pVNdWa/1Jr7aCRvC+xRaFJBWnG8c5dmQ6U8RddBWFFdX60bytzwvVX9eUEIK//KKZtyeElMxcRrerwUedamJiIAuDSdrzzO8uIcSa4gxEkrTu5lnEf7P45X4gy60sqGNcmSXdVlHFykWr3YbdScFnWxDnbiTgWc2KOf0a4GonHyORtE+TVTSVgM+Beqh3ZgJACNFRi3FJUtGJDYT/5pBxbT8+dg4csLakR7VufNVmNsZ62puOycjOY9l/Yaw8Fo6ZkR7z33RjQJOqsjCYVGw0+f1wPbAJ6AmMBoYCcdoMSpKKxL1rcHguXP6HOBNLxtfxIDgrgUlNPmFo/aFafXjo8NW7TN8eRFRCBm82duTLHq5UNJOFwaTipUmCryiEWKUoygQhxFHgqKIo57UdmCS9sJTbcOQb8F8LekZcaf4eH6ZcJDknhaUdltLBqYPWur6dlMmsXZfZc+k2NSqZsmFUC1rWqKi1/iSpIJok+JwHf8cqitITuAXI7dml0iczWV3Z8fQPkJcDTUdyuGYrPj8/jwoGFVjbfS2u1q5a6TpPJVhzKoLv9l8lVyWY3LU2o7xcMNSThcGkkqNJgp+jKIoFMAlYBlQAJmo1KkkqjNxs8Psdjs5XFwFr8Caigw9/3D7JwtPTqVexHss6LqOSycvvpPQ0gdGJfLn1EkExyXjVrsTsPvWpVlHuiyOVPE0S/FkhRBKQBGjvd1tJKiwh1Bts7J8GCdehuhd0nonKwYNvz3/LupB1dKnWhblt5mrlZmpyZg7f7bvK2jOR2JgZsnxQI3q62cvCYFKpoUmCP6koSgTqG63/CCHuazckSdLArQDYPxUijoNNHRi0BWp1IUuVzRdHJ3Mg8gBD6g1hsudkdJSiffRfCMGuwFhm7QrmXmoW77aoxqRudahgJAuDSaWLJqUKaiuK0gx4G/BRFCUY2CiEWKf16CTpf6XchkOzIOBPMLGGnt+pN7LW1SMpK4mP/vsI/7v+TPaczND6Q4u8+8j4NKZtv8yx0DgaVKnAqqGeuDtaFnk/klQUNN3R6RxwTlGUecAiYA0gE7xUfHKz4MxPcOxbyMuG1h+pt8czsgAgNjWW0QdHE5USxQKvBXSv3r1Iu8/KzWPl0XCWH76Gvq4OX71ejyEtndGVa9qlUkyTB50qAP1Qj+BrAFuBZlqOS5L+X+g+2DsFEsLVuyd1nQMVa+S/HXY/jNEHRpORm8GKLitoate0SLs/df0eU7cFER6XRk83e6a/Xg/bCtorayBJRUWTEfxFYBswSwhxWsvxSNL/i7+uTuxh+9X7nr7zN9Ts/NghF+MuMvbgWAx1DVndfTW1rWoXWff3UrOYtyeEf/xjqGptzO/Dm9KhTuUia1+StE2TBO/yYCcnSSoeuVlw8nv1dIyuAXSdC83eBz2Dxw47desUHx/+GBtjG1Z0WUFV86pF0r1KJdjsG8XX/14hPTuXcR1q8GGHWhgbyDXtUtmiyU1Wmdyl4hNxEnZ9DPdCoV5feO0bqGD/xGH7I/bz+fHPcbFwYUWXFdgY2xRJ91duJ+OzNQi/yPs0q27N3L4NqGVr/vwTJakUkrVKpdIhLR4OTIeAdWDpBIP/glpdnnroX6F/MfvMbNxt3FneaTkWhhYv3X16di5LD4Wx6vgNzI30WDigIW82riLXtEtlmiY3WVsLIU4+7zVJeiFCqJc87p8KWcnQZiJ4fQYGJk89fM3lNSz0XUjrKq1Z1G4RJvpPP64wDoXcYfr2y8QkZjDQsypTurtiZWrw/BMlqZTTZAS/DGiswWuSVDhxobBrIkSegKrNodcSsK331EOFEKwMXMnygOV0qdaF+W3no6/7cg8W3UrMYObOy+y7fIfatmZsGd2Sps6yzJJUfhS0ZV9LoBVQSVGUTx55qwIg7zZJLy4nE45/BycWq0fqry+FRu+CztOfOBVCsNR/KauCVvG6y+vMaj0LPZ0Xn13MzVOx+lQEiw6EohKCz19zZWTb6ujLza6lcqagnxIDwOzBMY/eZUoG+mszKKkcu3kWto+F+GvgPlC9Qsbs2UXAhBDMPz+f9SHr6V+7P9NaTHup0gP+N+/jszWIkNhkOrpWZmbv+lS1fvlpHkkqjQrasu9h7ffVQojIYoxJKo+y09Wbb5z+ASwcYchWqFHwpmB5qjxmn5nN32F/807dd/is6WcvfNMzKT2H+fuusOHcTWzNjfj5ncZ0q28nb6JK5Zomv+f+qijKACFEIoCiKFaoa9F0025oUrkReRq2j1NXfPQcAV1mgWHBSw/zVHlMPzWdHdd3MMptFOMbjX+hZCyEYHvALebsDiYhLZsRraszsUttzAzlAjKp/NPku9zmYXIHEELcVxTluY/zKYpiBBwDDB/085cQYsYLRyqVPTmZ6sJgZ34Ey6rw7g5waffc0x5N7mMbjmWMx5gX6j48LpVp24M4eS2eho4WrB7ejAZVXn5JpSSVFZokeJWiKE5CiJsAiqJUAzR5+CkL6CiESFUURR84oSjKv0KIMy8Rr1RWxF6Ef96HuCvg+d6DUbvZc0/LU+Ux7eQ0dobvZJzHOEY3HF3orjNz8vjpyHV+OnIdQ30dZvdtwKBmTrIwmPTK0STB+6BOzkcBBWgLvP+8kx48AZv64FP9B3/kU7HlnSoPTi6Bw1+DScWn1o95lkeT+4ceH/JBww8K3f2JsHtM2x7EjXtp9G7owNSedaksC4NJryhNShXsVRSlMdDiwUsfCyHuadK4oii6gB9QE/hBCHH2Kce8z4P/MJycnDSNWyqNEm7A1tEQdUZdZqDXYnXNdg3kqfKYenIqu8J3Mb7ReN53f+4Y4jFxKVnM2R3M9oBbOFc04Y/3mtG2lna26JOkskJ5VqkZRVFchRBXHiT3Jwgh/DXuRFEsUZcZHi+ECHrWcZ6ensLX11fTZqXS5OIm2P0JKLrQcyG4DQANb4rmqfLwOenD7vDdfNToI0a5j9K4W5VK8Oe5myzYe4XMHBWj29dgbPsaGOnLRzWkV4OiKH5CCM+nvVfQCH4SMAr47invCaDgNW6PHixEoqIoh4HXgGcmeKkMykqBPZ/CxQ3g1AreWKm+oaqhXFUuPid82HNjDxMaT2Ck20iNzw2+lcyXWy8REJVIqxoVmd23ATUqPX+eX5JeFQWtgx/14O8X2mhbUZRKQM6D5G4MdAHmv1CUUul06wL89R7cvwHtv4C2k0FX8+WHuapcvjzxJf/e+LdQyT01K5clB0L5/VQElsb6LB7YkL4esjCYJP2vgkoVvFHQiUKIf57Ttj2w5sE8vA6wWQixq/AhSqWOEOqljwdmgFllGLoLnFsXqolcVS5fHv+SfyP+ZWKTiYxoMEKDbgX7Lt9h5s7LxCZlMqi5E593c8XCRG52LUlPU9Bw6/UHf1dGXZPmvwefdwBOAQUmeCFEINDoZQOUSpmMRPVDS1d2QZ2e0Ge5xjdSH3o0uX/S5BOGNxj+3HOi76czY/tlDl25i6udOcsHNaZJNasXvQpJeiUUNEUzHEBRlP1APSFE7IPP7YHVxRKdVLrEBsLmdyEpCrp9DS3GaHwj9aFHk/ukJpMY1mBYgcfn5KlYdeIGSw+GoSjg06Muw1o7y8JgkqQBTSZMqz5M7g/cAeR6xleN/1rYPVm9tn3YHnBqXugmHi6F1DS5+0Um4LM1iCu3U+haz5YZvetTxdL4BS9Akl49miT4Q4qi7AM2PPh8IHBQeyFJpUpOhjqxB6wDl/bwxq8FVn98loflB3aH72ZC4wkFJvfE9Gy++fcKG89H4WBhxC/vetKlnu0LX4Ikvao0edDpQ0VR+gFeD15aKYTYqt2wpFIhORY2DoJb/updltpPAZ3Cry9XCRUzT89kx/UdfOjx4TNXywgh+Mc/hrl7QkjKyOEDLxc+6lQLU1kYTJJeiKY/Of5AihDioKIoJoqimAshUrQZmFTCbl2ADYMgMwne/hNce75QMyqhYtbpWWy9tpXRDUc/s/zAtbupTN12iTPhCTR2smRuPzfq2ld4mSuQpFeeJnuyjkJdSsAaqAFUAX4GOmk3NKnEXN4KW8eAqQ28tw/s3F6oGSEE887O4++wvxnlNoqxDcc+cUxmTh4/HL7Gz0evY2Kgx9dvuDHQsyo6sjCYJL00TUbw44BmwFkAIUSYJuWCpTJIpYJjC+DI11C1BQxc90Lz7fD/OzFturqJ4fWHP7We+9HQOKZtC+JmQjpvNKrClz3rYmNmWBRXIkkSmiX4LCFE9sMfTkVR9JBVIcuf7HTYNgaCt0HDQfD6EtB7sWQrhOA73+9YH7Ked+q+w8QmEx9L7neTM5m5K5jdgbG42Jjy56jmtKphU1RXIknSA5ok+KOKonwJGCuK0gUYC+zUblhSsUqKgY3e6nXuXWZDq/GFXt/+0MMNstcEr8Hb1fuxbfbyVIJ1ZyJZuO8qWXkqPulSmw/auWCoJwuDSZI2aJLgPwdGApeAD4A9wK/aDEoqRtF+6pUy2WkwaBPUfrmdGH8I+IFVQasYUHsAXzT7Ij+5B8Uk8eXWSwRGJ9G2lg2z+jSguo1pUVyBJEnPUGCCf1BH5rIQwhX4pXhCkopN4BZ12QFzO3h3G1Su+1LN/XzxZ1YEruCNWm8wtcVUFEUhJTOH7/aHsvZ0BNamhnzv3YjX3e1lYTBJKgYFJnghRJ6iKFcf3bJPKgeEgCPfwNFvoFpreOsPMK34Uk3+EvgLPwT8QO8avZnRcgYKCnsuxTJz52XupmTxTvNqTO5WBwtjWRhMkoqLJlM0VsBlRVHOAWkPXxRC9NZaVJL25OXCnkngtxo8BkOvJaBn8FJN/hb0G99f+J6eLj2Z1WoW0QmZTN8RxJGrcdSzr8CKIZ54VLUsmvglSdKYJgl+mtajkIpHToa6fvvV3dB2EnSc9sI3Ux9ac3kNi/0W0925O9Obz+Lnozf4/lAYejoK03rVY2jLaujJwmCSVCIKqgdvBIxGvZ/qJWCVECK3uAKTiljGfdjgDTfPQPcF0LzwG1r/r3XB61jou5Cu1brS13ESvZef5trdVLo3sGP66/Wwt5CFwSSpJBU0gl8D5ADHge5APWBCcQQlFbGkGFj3JiRch/6/QYMC93LRyIYrG5h/fj5eVToi7g5i0F5fqlga89swTzq6ysJgklQaFJTg6wkh3AAURVkFnCuekKQiFX8d1vZVj+AH/wUu7V66yY1XNjLv7Dxqm7fgxKlupGXeYUz7GnzUsRbGBnJNuySVFgUl+JyHHwghcuWytjLo7hVY2wfysmHYTnB4+Q22Nl7ZyNyzczHLc8fvXC+aOqsLg9W2NS+CgCVJKkoFJfiGiqIkP/hYQf0ka/KDj4UQQpb6K81iA+GPvqCjB8N2g229l27yj8sbWOA7j7zUumTED2bBm270b+IoC4NJUilV0JZ98nftsiraF9a9AQbmMHQHVKzx0k3OOvoLWyK+JzelLj0qf47PMDesTV9ueaUkSdold1IobyJOwJ8DwbSSOrlbvtzuirFJGYzevpxwsRaD7Ab89Noi2tS0L6JgJUnSJpngy5OIE7Cuvzqpv7sdKrx4Is7NU7HmdCSLzvyGTqVtVDNuysa3f8TM0KgIA5YkSZtkgi8vIk/B+rfAqhoM3fXCddwBAqIS8dl6idDMXRjZ7qGFrRc/dFmMga6ckpGkskQm+PLg5llYPwAsqsDQnS+c3JMzc/h271XWnY3EyuEoRrb/0q1aN772+hp9HVlDRpLKGpngy7poX/VDTOZ2D5J74TfbEkKwMzCW2buCiU/NxNPjHFcy/+V1l9eZ1XoWejry20SSyiL5k1uWxfjDH/3Ue6cO3alO8oUUcS+NaduDOB52DzfHCnRqdY7dN7fyZq03md5yOjqKrCMjSWWVTPBl1Z3L6uRubAXDdkEFh0KdnpWbx4qj4Sw/fA1DXR1m9q5LtM5GNl7dgLerN1OaTZHJXZLKOJngy6KEcHVy1zdRj9wtHAt1+qnr95i6LYjwuDR6udvj09OVFZcX8PfVvxlWfxifNPlEbsghSeWATPBlTXKsurZMXjYM36teNaOhe6lZzNsdwj8XYnCyNmHNiGa0rmnF9JPT2Rm+k/fd3+dDjw9lcpekckIm+LIkPUH9hGp6PLy7Ayq7anSaSiXYeD6Kb/4NISMnj/EdazKuQ010dVVMOT6FfRH7GN9oPO+7v6/lC5AkqTjJBF9WZKepn1CNvwaDt4BjE41OC4lNxmfrJfxvJtK8ujVz+7lRs7IZ2XnZTDoymcNRh5nsOZmh9Ydq+QIkSSpuMsGXBbnZsOkdiPGFt9aCS/vnnpKWlcvSQ2GsOnEDC2N9Fg5oyJuNq6AoCuk56Uw8MpFTt07xZfMv8Xb11volSJJU/GSCL+2EgB3j4fp/0Hs51H39uafsv3ybr3Zc5lZSJt7NqvJZN1esHhQGS8pKYuyhsQTdC2JWq1n0q9VP21cgSVIJ0VqCVxSlKrAWsAUEsFIIsVRb/ZVbR76GwI3QwQcaDynw0JjEDL7acZkDwXeoY2vOX96N8HS2zn//TtodRh8czc3kmyxqv4hOTp20Hb0kSSVImyP4XGCSEMJfURRzwE9RlANCiGAt9lm++P8BR+dDo3fA69NnHpaTp+L3kzdYfCAMgCndXXmvTXX0H9nsOjI5kg8OfEBiViI/df6JZvbNtB6+JEklS2sJXggRC8Q++DhFUZQQoAogE7wmrh2CXR+DSwfotQSesXTRL/I+PlsvceV2Cp3rVuar3vVxtDJ57JiQ+BBGHxyNEIJV3VZRv2L94rgCSZJKWLHMwSuK4gw0As4WR39l3u1LsHkoVHJV31TVfbLQV1J6Dt/svcKGczextzBixZAmdK1n+8Qa9tO3TjPxyETMDcxZ0WUFLhYuxXUVkiSVMK0neEVRzIC/gY+FEMlPef994H0AJ6eX25yiXEi+pS77a2gOgzaD0eM7Iwoh2BYQw5xdISRm5PBem+pM7FIbM8Mn/yl3Xt/J9JPTqW5ZnR87/YidaeFr1UiSVHZpNcEriqKPOrmvF0L887RjhBArgZUAnp6eQpvxlHrZ6bDBG7KSYcQ+dfnfR1yPS2Xq1iBOh8fTsKola/s1oL6DxRPNCCFYFbSKpf5LaWbXjCUdlmBuIDfFlqRXjTZX0SjAKiBECLFIW/2UG0LA9rEQexG8N4Jdg/y3MnPy+PHIdX4+ch1DfR3m9G2AdzMndJ+y2XWeKo9vzn3Dxqsb6e7cnTlt5siNOiTpFaXNEXxrYAhwSVGUgAevfSmE2KPFPsuuY9/C5a3QeSbUeS3/5eNhcUzbFkREfDp9PBzw6VmXyuZP3zYvPSedL45/wX9R/zG8/nA+bvKxrAgpSa8wba6iOQHIqlWaCN4Oh+eC+9vQegIAd1MymbMrhB0Xb1HdxpR17zWnTS2bZzZxN/0u4/8bT0h8CFOaTWFw3cHFFb0kSaWUfJK1pMVehK2jwbEpvL6UPAF/nolgwb6rZOWo+LhzLUa3q4GRvu4zm7iacJVxh8aRnJ3M9x2/p33V9sUXvyRJpZZM8CUp9S5sGKTetGPgeoLuZuGzzY+LUYm0rlmR2X0a4FLJrMAmjkYd5dNjn1LBoAJru6/F1VqzCpOSJJV/MsGXlNxs2PwupMeTPmQ3C4/Es/rUDaxNDVgy0IM+Hg4F1mUXQrA+ZD3f+n6Lq7Uryzsup5LJi222LUlS+SQTfEnZPxVunuZis4V8sD6ROym3GdTMic+6uWJh8uSDTY/Kzstmzpk5bL22lU5OnZjXZh4m+iYFniNJ0qtHJviScHEjnFvBvgpv8sExB+raG/DjO41p7GT13FPj0uP4+MjHBMYF8oH7B4z1GCtXykiS9FQywReznOgAlO0f4SfqMTnxTXx61GV4a2f0dJ+fpAPjApl4eCIpOSksar+ILtW6FEPEkiSVVTLBFyP/K9dx2DQAlcqULc6z2NevDQ6Wxhqdu/3admaenkllk8qs67KO2la1tRytJEllnUzwxeB+Wjbz91ymR+B4rHUTCOi8gYVtNRt9Z+dls+D8AjZd3URzu+YsbLcQSyNLLUcsSVJ5IBO8Fgkh+Msvmnl7Qng/Zx1eupfI6rGUZs26anT+rdRbTDoyiaD4IIbVH8aExhPQ05H/ZJIkaUZmCy0Ju5OCz7Ygzt1IYLTtVcYkbYfGQzFsNkyj80/EnGDK8SnkqfJY0n4JnarJ3ZckSSocmeCLWEZ2Hsv+C2PlsXBMDfVY3s2CnmcWg70HdF/w3PPzVHmsDFzJTxd/oqZVTRa3X0y1CtWKIXJJksobmeCL0OGrd5m+PYiohAzeaFwFny7VqLixFyg66o079J9eJOyhu+l3+fL4l5y9fZbeNXoztcVUjPU0uwkrSZL0v2SCLwJ3kjOZufMyey7dxqWSKRtGtaClizVsGwt3LsPgLWBV8Cj8WPQxpp6YSkZuBjNbzaRfzX4FPskqSZL0PDLBv4Q8leCP0xEs3B9KTp6KyV1rM8rLBUM9XfBbDRf/hHafQ61nr5jJycthif8S1gavpbZVbb71+hYXS7mtniRJL08m+BcUGJ2Iz9YgLsUk0baWDXP6NqBaRVP1mzH+sOdTqNFRneCfITI5ks+OfUZwfDBv13mbyU0nY6hrWExXIElSeScTfCElZ+bw3b6rrD0TiY2ZIcu8G9HL3f7/p1PSE9QbZptWhjd+BZ0ny/wKIdgSuoWFvgvR19GXq2QkSdKK/2vv3sOrqM8Ejn9fSEgEknCnQIAgcqfcCbACBbQsKNa6BR5RFwRWLHZdrbooUmgLyDZqtSIIIlTUh229tAhCy0UElaJAuF+CECIgGkhACLcQOMm7f8ywHlIu52AOJzN5P89znpz5zZw57wuTN7/zOzO/sQIfIlVl8bZsJn6wk9xTBQzt2pDH/7UZifFBPupTEwAADZ1JREFUE4MVFcJ7I+DUIRi+BCpV/6f95J7JZcKaCaz+ejXd6nRj4s0T7WbYxpiIsAIfgv1HTzNhwQ4+3p1L63qJzB7WiTbJl7ia9KPJkLUS7pgKyR3/afXy/cuZ+NlE8gP5jE0dy93N77aJwowxEWMF/goKAoW89kkWL3+USWz5cvz6jpYM7ZZyyZtdk/EBrH4BOgyDjsMuWnX87HHS1qexKGsRraq3YkqPKdyYZF+kGmMiywr8ZXyedZRx87exN/c0t/+wDuMHtOQHSZc5jz13N8wfDfU6wm3PXbTqw/0fMvnzyeQV5DG67WgeaPMAseWuPN+7McaUBCvwxRw9VcCUv+3iLxsPklz1Bl6/vzO9m9e6/AsKTsLb90FMnHMxU4xzFszR/KNMWTuFZfuX0aJaC1798as0q9bsOmVhjDFW4P9fUZHyTvpX/M/fd3HmXICHejXm4T5NuKHC5W92TVGRczHT0T0wdAEkJTtfxn65mLR1aZw+f5pHOjzCsFbDrNdujLnurMADXxw6ybj520jff4zUlGo8c1drmtROuPoLVz4DGQuh72Ro1JMDJw4w+fPJfJb9GW1qtGHizRNpXKVx5BMwxphLKNMF/sy5AFNXZDL70ywS4mN4bmAbBnZMDm2KgI1vwafPQ4dhnEsdxetbXmXW1lnElo/l6S5PM7jpYMpf4hx4Y4y5XspsgV+RcZgJC3bw9fF8BndKZmz/FlStVCG0F2etgkWPQuM+pHe6h0mLBpOVl0Xfhn15MvVJalW8wpi9McZcJ2WuwH9zPJ/ffrCDpTsO06RWZd55sBupjaqFvoOcXfD2UA7XvIkXkhvxt+UPUK9yPV655RV6JPeIXODGGBOmMlPgA4VFzF2zjxeW76ZIlTH9mvEf3W+kQkwYFxqdyuHcvEG8lViJVxOKKDz4MQ+2eZCRPxxp0/oaY0qdMlHgNx04xtPzt5ORfYLezWoy8c7W1K9WMbydFJzk0z/fRVpCIftjY+hVtytjOo+hfkL9yARtjDHfk68LfF7+eZ5dsov/XXeAWglxzLi3A/1a/yDsedYzszfw/JJR/CP2HCnxtZnRfRLd63WPUNTGGFMyfFngVZWFW75h0qIMvj1dwPB/acRjfZtSOS68dI/kH2H6+uf5a9ZiKlHEEw0GMKTnJCqUD/HLWGOMiSLfFfgvj5xm/PvbWZ15hLbJScwd3pnW9ZLC2kd+IJ95GfN4besszp3PZ8ipfH5+64tUaX5HhKI2xpiS55sCXxAoZMaqvbyyai9x5csx6c5W3NOl4aUnBruMQFGA+Znzmbl5Jjn5OfQ+pzx27CQpd78NDbpGMHpjjCl5vijwazKP8Kv3t5N15DR3tK3L+NtbUCvxyje4DlakRSzfv5xpm6ax78Q+2iU2Ji37IJ3OnoN/XwB120UwemOMiQzPF/i8M+cZ9dYGqleuwJsjUunZtGbIr1VV1nyzhqmbprLz6E5uSmrEywnt+dGWBUiVhjB8AdRqHsHojTEmciJW4EXkj8AAIEdVW0fqfZIqxvLGiFRa1U0kPja0qQFUlbWH1jJ903Q2526mbqW6PNPkHm5fO4/yeQehy2i4ZTxUqBSpsI0xJuIi2YOfC0wD3ozgewDQsWHVkLddf2g90zdPZ8PhDdSuWJvxbR/mrqwNxC77HdRoCiOWQoMuEYzWGGOuj4gVeFX9RERSIrX/cKUfSmfGlhmsO7SOmvHVGVvnFn72zR7i3h8DUg56PAE9/xtiQx+7N8aY0izqY/AiMgoYBdCgQYMS33969jpmbnyJtUe2UiOmImNi6jJozybiA5ugWmPo9RS0GQzV7BZ6xhh/iXqBV9VZwCyATp066TXt5O9PwblTEDjrPM6fZXMgj2mFh1kbo9QIFPJk3gkGnvyK+Io1oeMIaDMI6naAMK9qNcYYr4h6gS8RuxZBUSHExFEQG8+0uELeKH+W6jExPJnYkoHJvYmv2dzppSfUsaJujCkT/FHgf7kdgJ1HdzJu9Tgyj2cyuOlgHu/0OBVjw5xUzBhjfCKSp0n+CegF1BCRg8CvVXVOJN4rUBRgzrY5zNwyk6rxVW1udmOMIbJn0QyJ1L6D5RXk8dCHD7H1yFb6p/RnXNdxJMWFN/eMMcb4keeHaBIrJJKckMx9Le+jf6P+0Q7HGGNKDc8XeBEhrWdatMMwxphSJ4z71RljjPESK/DGGONTVuCNMcanrMAbY4xPWYE3xhifsgJvjDE+ZQXeGGN8ygq8Mcb4lKhe2wy9kSAiucD+EDatARyJcDjXi59yAX/l46dcwPIpzb5PLg1V9ZI3oy5VBT5UIpKuqp2iHUdJ8FMu4K98/JQLWD6lWaRysSEaY4zxKSvwxhjjU14t8LOiHUAJ8lMu4K98/JQLWD6lWURy8eQYvDHGmKvzag/eGGPMVViBN8YYn/JUgReRfiLyhYhkishT0Y4nXCLyRxHJEZHtQW3VRGS5iOxxf1aNZoyhEpH6IrJSRHaKyA4RecRt92o+8SKyTkS2uPn81m1vJCJr3WPubRGpEO1YQyUi5UVkk4gscpe9nMs+EdkmIptFJN1t8+SxBiAiVUTkPRHZJSIZItItEvl4psCLSHlgOtAfaAkMEZGW0Y0qbHOBfsXangJWqGoTYIW77AUB4HFVbQl0BX7h/n94NZ8CoI+qtgXaAf1EpCuQBryoqjcBx4CRUYwxXI8AGUHLXs4FoLeqtgs6X9yrxxrAS8ASVW0OtMX5fyr5fFTVEw+gG7A0aHksMDbacV1DHinA9qDlL4A67vM6wBfRjvEa81oA/NgP+QAVgY1AF5yrC2Pc9ouOwdL8AJLdItEHWASIV3Nx490H1CjW5sljDUgCvsQ9ySWS+XimBw/UA74KWj7otnldbVXNdp8fAmpHM5hrISIpQHtgLR7Oxx3S2AzkAMuBvcBxVQ24m3jpmPsDMAYocper491cABRYJiIbRGSU2+bVY60RkAu87g6hzRaRSkQgHy8VeN9T50+3p85bFZHKwF+AR1X1RPA6r+WjqoWq2g6n95sKNI9ySNdERAYAOaq6IdqxlKDuqtoBZ4j2FyLSM3ilx461GKADMENV2wOnKTYcU1L5eKnAfw3UD1pOdtu87rCI1AFwf+ZEOZ6QiUgsTnGfp6p/dZs9m88FqnocWIkzjFFFRGLcVV455m4GfiIi+4A/4wzTvIQ3cwFAVb92f+YA83H+AHv1WDsIHFTVte7yezgFv8Tz8VKBXw80cc8EqADcDSyMckwlYSEwzH0+DGcsu9QTEQHmABmq+kLQKq/mU1NEqrjPb8D5PiEDp9APdDfzRD6qOlZVk1U1Bef35CNVvRcP5gIgIpVEJOHCc6AvsB2PHmuqegj4SkSauU23ADuJRD7R/sIhzC8nbgN244yNjot2PNcQ/5+AbOA8zl/xkThjoyuAPcCHQLVoxxliLt1xPkJuBTa7j9s8nE8bYJObz3Zggtt+I7AOyATeBeKiHWuYefUCFnk5FzfuLe5jx4Xffa8ea27s7YB093h7H6gaiXxsqgJjjPEpLw3RGGOMCYMVeGOM8Skr8MYY41NW4I0xxqeswBtjjE9ZgTeljoioiPw+aPkJEflNCe17rogMvPqW3/t9BrmzBK4s1p4iIvnuJeoZ7gyW90c6HlM2WYE3pVEB8G8iUiPagQQLugo0FCOBB1S19yXW7VXV9qraAudCpEdFZHiJBGlMECvwpjQK4Nyj8pfFVxTvgYvIKfdnLxH5WEQWiEiWiPxORO51e8jbRKRx0G5uFZF0EdntzttyYaKx50RkvYhsFZEHg/b7qYgsxLnasHg8Q9z9bxeRNLdtAs6FYHNE5LkrJaqqWcBjwH+5r00Vkc/cHv6aC1c7isgnItIu6H1Xi0jbUP4xTdkVTo/EmOtpOrBVRJ4N4zVtgRbAt0AWMFtVU8W5GcnDwKPudik4c5k0BlaKyE3AUCBPVTuLSBzwDxFZ5m7fAWitql8Gv5mI1MWZY70jzvzqy0Tkp6o6UUT6AE+oanoIcW/ku4nNdgE9VDUgIrcCU4Cf4UwLcT9Ob78pEK+qW8L4tzFlkPXgTamkzsyUb+L2bEO0XlWzVbUAZzqLCwV6G05Rv+AdVS1S1T04fwia48xvMtSdLngtzmXjTdzt1xUv7q7OwCpVzVVnGt55QM9LbHc1EvQ8CXhXnLt+vQi0ctvfBQa4E7yNwLl5jDFXZD14U5r9Aad3+3pQWwC3YyIi5YDg284VBD0vClou4uJjvfj8HIpTZB9W1aXBK0SkF850rpHUnu/uvDQJWKmqd7nz7K8CUNUzIrIcuBMYjPOpwZgrsh68KbVU9VvgHS6+tdw+vituPwFir2HXg0SknDsufyPOnXSWAqPdHjIi0tSdufBK1gE/EpEa7i0lhwAfhxOIW8SfB152m5L4bhrf+4ttPhuYivNJ5Vg472PKJuvBm9Lu98B/Bi2/BiwQkS3AEq6td30ApzgnAj9X1bMiMhtnGGejOxVyLvDTK+1EVbPFufn7SpxPAItVNZQpXhuLyCYgHjgJTFXVue66Z4E3RORXwOJi77dBRE5w8ScaYy7LZpM0xiPcL3VXAc1VtegqmxtjQzTGeIGIDMX58necFXcTKuvBG2OMT1kP3hhjfMoKvDHG+JQVeGOM8Skr8MYY41NW4I0xxqf+D7izSzGdG0mDAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "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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4cd74WKqaN/R8586d+fbbbwkLCyMqKop27drx5ptv0qNHj9OO06NHD9555x0A1q9fz9q1awHPJF+VKlXivPPOY8+ePXzyyW8nfPXq1aNevXo888wzefPLG2P8aF+SJ6lIWEgnFbDEEvRmzJjBhAkTaNu2La1ateLDDz2XpMqXL0+DBg3yZnbs3r07hw8fzncel3vuuYcjR47QokULnnjiCTp27AhA27Ztad++Pc2bN+fWW2/l0ksvPWm/2267jQYNGthIu8b424FNnqSCepq/QqT315nYsPnYMOVnct9999G+fXtGjBgR6FDKFPs8ljEHt8MkN0xLkN78aMPmm2LRsWNHKlWqxH/+859Ah2JM6ZW2y3Omkp7mGfsrCJPK2bDEYvKVmJgY6BCMKd2OpHruUzmaCnd8ENRjfxWVXWNxylqToAlO9jksI44dgGnXeZrBbp0NDQqexjrUWGLBM4HU/v377T+1CShVZf/+/URFRQU6FONPGYdhxo2w72cY/A40urTwfUKMNYUBMTExpKSkYANUmkCLiooiJiam8IomNGUeh5mDYedquGUaXHRFoCPyC0ssQGRkJI0bNw50GMaY0iw7E2bHw5Zv4PfjofnVgY7IbyyxGGOMv+Vkw/ujIGkBDHgBYm8ufJ8QZtdYjDHGjzQnh5WvxMMP78NVT0Nc6R/Q1RKLMcb40XPz15KRuonvYobDpfcHOpwSYU1hxhjjJ2989QuvfbODo11e48mBsYXvUEpYYjHGGD+YtWIb//zkR65pW4+xA9siYRLokEqMNYUZY0wx+2TdLv4ydx2XXRzNf25qS1gZSipgicUYY4rV0uR9PDBrNe0bVueN2ztSLqLsfc2WvXdsjDF+sn7HIUZNTeDC6EpMjO9EhXLhgQ4pIPyaWESkmojMEZEfRWSjiHQTkRoislBEktxzdVdXRGSciCSLyFoR6eB1nHhXP0lE4r3KO4rIOrfPOBEpW+ebxpigsXX/UYZOWkG1iuWYMrwz51WMDHRIAePvM5aXgE9VtTnQFtgIPAYsUtWmwCK3DtAPaOoeo4DXAUSkBjAW6AJ0BsbmJiNXZ6TXfn39/H6MMeY0+45kED9xBVk5ypThnalTtWyP9+a3xCIi5wE9gAkAqnpCVQ8CA4EprtoU4Dq3PBCYqh7LgGoiUhfoAyxU1QOq+iuwEOjrtlVV1WXqGT1yqtexjDGmRBzNyGLYpJXsTktn4tBONKldOdAhBZw/z1gaA6nAJBH5XkTeFpFKQB1V3eXq7AbquOX6wHav/VNcWUHlKfmUG2NMiTiRlcPd0xPZsCuNV2/tQIeG1QvfqQzwZ2KJADoAr6tqe+AovzV7AeDONPw+Vr2IjBKRBBFJsBGMjTHFISdH+fN/1/J10j7+3+/b0KtFncJ3KiP8mVhSgBRVXe7W5+BJNHtcMxbuea/bvgNo4LV/jCsrqDwmn/LTqOp4VY1T1bjo6OhzelPGGAPwr09/ZO73O3i0TzNujmtQ+A5liN8Si6ruBraLSDNX1AvYAMwDcnt2xQMfuuV5wBDXO6wrcMg1mS0AeotIdXfRvjewwG1LE5GurjfYEK9jGWOM37z99SbeXLKJId0uYPTlFwU6nKDj7yFd/gDMEJFywCZgGJ5kNltERgBbgdzxo+cD/YFk4Jiri6oeEJGngZWu3lOqesAtjwYmAxWAT9zDGGP85sPVO3jm4430b3M+Y69phd3lcDopa9PxxsXFaUJCQqDDMMaEoG+S9jFs8go6NKzOlOGdiYosGzdAikiiqsb5Wt/uvDfGGB9s2JnG3dMTuSi6MuOHxJWZpHI2LLEYY0whdh48zrDJK6gSFcHkYZ05r0LZvaveF5ZYjDGmAIeOZzJ00gqOZWQzaVgnzj+vbN9V7wubj8UYY84gIyubu6clsnnfUSYP60zz86sGOqSQYInFGGPyoar8ec5avtu0n+dvbsulTWoFOqSQYU1hxhiTj/989jMfrN7JI70v5vcdYgrfweSxxGKMMaeYvXI7ryxOZlCnBtzbs0mgwwk5lliMMcbL10mp/GXuOro3rcXT17W2GyDPgiUWY4xxftp9mNHTV9GkdmVeu60DkeH2FXk27K9mjDHAnrR0hk1aQcXy4Uwc2okqUXavytmyXmHGmDLv2IksRkxZycHjmcy+qxv1qlUIdEghzc5YjDFlWnaOcv/M1WzYmcYrt7andf3zAh1SyLPEYowp056dv5HPN+5h7DWtuKK5TdZVHCyxGGPKrGnLtjLhm80MvaQR8Zc0CnQ4pYYlFmNMmfTVz6k8Oe8Hrmhem78NaBnocEoVSyzGmDLnp92HuXfGKi6uU4Vxg9sTHmb3qhQnSyzGmDIl9XAGwyevpGK5cCYOjaNyeescW9z8mlhEZIuIrBOR1SKS4MpqiMhCEUlyz9VduYjIOBFJFpG1ItLB6zjxrn6SiMR7lXd0x092+9rPDmPMGaVnZjNqWgL7j2YwIb4Tdc+zbsX+UBJnLD1VtZ3XtJaPAYtUtSmwyK0D9AOausco4HXwJCJgLNAF6AyMzU1Grs5Ir/36+v/tGGNCkary6Jy1fL/tIC/e0o42Mdat2F8C0RQ2EJjilqcA13mVT1WPZUA1EakL9AEWquoBVf0VWAj0dduqquoyVVVgqtexjDHmJC8tSuJ/a3byp77N6Nu6bqDDKdX8nVgU+ExEEkVklCuro6q73PJuILfjeH1gu9e+Ka6soPKUfMqNMeYkH67ewYufJ3FDhxjuueyiQIdT6vl01UpEfgc0VdVJIhINVFbVzT7s+jtV3SEitYGFIvKj90ZVVRHRooddNC6pjQJo2LChv1/OGBNEvt/2K4/OWUvnRjV49vc2WnFJKPSMRUTGAn8GxriiSGC6LwdX1R3ueS8wF881kj2uGQv3vNdV3wE08No9xpUVVB6TT3l+cYxX1ThVjYuOjvYldGNMKbDz4HFGTk3k/KpRvHFHR8pHhAc6pDLBl6aw64FrgaMAqroTqFLYTiJSSUSq5C4DvYH1wDwgt2dXPPChW54HDHG9w7oCh1yT2QKgt4hUdxftewML3LY0EenqeoMN8TqWMaaMO3Yii5FTE0jPzObt+DhqVCoX6JDKDF+awk54N1m5JOGLOsBcd9oZAbyjqp+KyEpgtoiMALYCN7v684H+QDJwDBgGoKoHRORpYKWr95SqHnDLo4HJQAXgE/cwxpRxOTnKw7PXsGFXGhPjO3FxnUJ/C5ti5EtimS0ib+LppTUSGA68VdhOqroJaJtP+X6gVz7lCtx7hmNNBCbmU54AtC4sFmNM2fLi5z/zyfrd/LV/C3o2rx3ocMqcQhOLqv5bRK4C0oBmwBOqutDvkRljzFn435qdjPsimZs6xnBn98aBDqdM8qlXmEsklkyMMUFtzfaDPPLeGjo1qs4z11sPsEApNLGIyGE896MAlMPTK+yoqlb1Z2DGGFMUuw+lM3JqArUql+eN260HWCD50hSWd9XL9b4aCHT1Z1DGGFMUx09kM3JqAkczsvjv6EuoWbl8oEMq04p0570bbuUDPMOsGGNMwKkqj8xZw/qdh3hpUHuan2+NKYHmS1PY771Ww4A4IN1vERljTBGMW5TMx2t38Vi/5lzZ0qYWDga+XLy/xms5C9iCpznMGGMCav66Xbzw+c/8vkN97upxYaDDMY4v11iGlUQgxhhTFD/sPMTDs9fQvmE1nr2+jfUACyJnTCwi8jK/9QY7jare75eIjDGmEPuOZDBqaiLVKkby5h0diYq0HmDBpKAzloQSi8IYY3x0IiuHe6Ynsu9IBnPuvoTaVaICHZI5xRkTi6pOOdM2Y4wJBFXliQ/Xs3LLr4wb3N5mgQxSvvQKi8YzbH5LIO+ngape4ce4jDHmNFO/28qsldu5t+dFXNu2XqDDMWfgy30sM4CNQGPg73h6ha0saAdjjCluS5P38dRHG7iyRR0evqpZoMMxBfAlsdRU1QlApqp+parDATtbMcaUmG37j3HvO6u4KLoSLw5qR1iY9QALZr7cx5LpnneJyNXATqCG/0IyxpjfHMnwTNilCm8NiaNyeZ/GzjUB5Mu/0DMich7wMPAyUBV40K9RGWMMngm7Hnp3NUl7DzN1eBcuqOnrPIMmkHxJLMtV9RBwCOjp53iMMSbPS4uS+GzDHv42oCW/a1or0OEYH/lyjWWpiHwmIiPcnPPGGON3n67fxUuLkrixYwzDL20U6HBMERSaWFT1YuBxoBWQKCIficjtvr6AiISLyPci8pFbbywiy0UkWUTeFZFyrry8W0922xt5HWOMK/9JRPp4lfd1Zcki8pjP79oYE9R+3J3GQ7PX0K5BNf5hE3aFHJ+GzVfVFar6ENAZOAAU5ebJB/B0V871L+AFVW0C/AqMcOUjgF9d+QuuHiLSEhiEJ7H1BV5zySoceBXoh+cem8GurjEmhP169AQjpyZQuXwEb95hE3aFokITi4hUFZF4EfkE+BbYhSfBFEpEYoCrgbfduuDpqjzHVZkCXOeWB/JbwpoD9PKaWGyWqmao6mYg2b1+ZyBZVTep6glgFjbqsjEhLSs7h/tmrmLPoQzevKMjdaracC2hyJeL92uAD4CnVPW7Ih7/ReBPQO4slDWBg6qa5dZTgPpuuT6wHUBVs0TkkKtfH1jmdUzvfbafUt4lvyBEZBQwCqBhw4ZFfAvGmJLy7PwfWZq8n/+7MZb2De2SbqjyJbFcqKpnHOX4TERkALBXVRNF5PIiR1aMVHU8MB4gLi6uyO/FGON/cxJTmLh0M0MvacTNcQ0CHY45B77Mx3K2X8SXAteKSH88Y4xVBV4CqolIhDtriQF2uPo7gAZAiohEAOcB+73Kc3nvc6ZyY0wIWb39IH+Zu45uF9bkr1e3CHQ45hwVac77olDVMaoao6qN8Fx8/0JVbwMWAze6avHAh255nlvHbf/CJbV5wCDXa6wx0BRYgWe8sqaul1k59xrz/PV+jDH+sTctnbumJVC7Snleva0DkeF++1oyJcSXi/eX+lJWBH8GHhKRZDzXUCa48glATVf+EPAYgKr+AMwGNgCfAveqarY747kPWICn19lsV9cYEyIysrK5e3oiacezGH9HHDUqlQt0SKYYSGEtXSKySlU7FFYWKuLi4jQhweYwMybQVJUx769j1srtvHJrewbE2jD4wUpEElU1ztf6BU1N3A24BIgWkYe8NlUFrGO5MeacTF++jVkrtzP68ossqZQyBV28LwdUdnWqeJWn8ds1EmOMKbLlm/bz93k/0LNZNA/3trlVSpuCpib+CvhKRCar6tYSjMkYU4rtPHic0TNW0bBGRV4a3J5wm1ul1PGl+8XbIlItd0VEqovIAj/GZIwppdIzsxk1LYGMrBzGD4mjalRkoEMyfuBLYqmlqgdzV1T1V6C2/0IyxpRGuRfrf9iZxou3tKNJ7cqBDsn4iS+JJUdE8sZBEZELALt73RhTJBO+2czc73fw0JUXc2XLOoEOx/iRL0O6/BX4RkS+AgTojht3yxhjfPFN0j6enb+Rvq3O596eTQIdjvEzX4Z0+VREOgBdXdEfVXWff8MyxpQW2/Yf476Zq2hSuzL/vrktYXaxvtQ7Y1OYiDR3zx2AhsBO92joyowxpkDHTmQxaloCOTnKW0PiqFzel0YSE+oK+ld+GBgJ/CefbYpnXhVjjMmXqvLoe2v5ec9hJg3rzAU1KwU6JFNCCrqPZaR77lly4RhjSovXvvyFj9ftYky/5lx2cXSgwzElqKAhXX5f0I6q+n7xh2OMKQ0W/7iXf3/2E9e2rceoHhcGOhxTwgpqCrvGPdfGM2bYF269J54pii2xGGNOsyn1CPfP+p4W51flXzfE4plh3JQlBTWFDQMQkc+Alqq6y63XBSaXSHTGmJByOD2TUdMSiQwPY/yQjlQoZ+PVlkW+dNFokJtUnD14eokZY0yenBzlwXdXs3nfUaaN6ExM9YqBDskEiC+JZZEbG2ymW78F+Nx/IRljQtGLn//M5xv38uQ1LbnkolqBDscEkC83SN4nItcDPVzReFWd69+wjDGh5NP1uxj3RTI3dYwh/pJGgQ7HBJivk0uvAj5W1QeBBSJSpbAdRCRKRFaIyBoR+UFE/u7KG4vIchFJFpF33Xz1uDnt33Xly8Hp21AAABe+SURBVEWkkdexxrjyn0Skj1d5X1eWLCKPFeF9G2OKyU+7D/PQ7DW0a1CNZ65vbRfrjU9z3o8E5gBvuqL6wAc+HDsDuEJV2wLtgL4i0hX4F/CCqjYBfgVGuPojgF9d+QuuHiLSEhgEtAL6Aq+JSLiIhAOvAv2AlsBgV9cYU0IOHjvByKkJVC4fwZt3dKR8hF2sN76dsdwLXIpn5khUNQkfhs1XjyNuNdI9cu/Yn+PKpwDXueWBbh23vZd4fvoMBGapaoaqbgaSgc7ukayqm1T1BDDL1TXGlICs7Bz+MPN7dh9K5407OlKnalSgQzJBwpfEkuG+uAEQkQh8HDbfnVmsBvYCC4FfgIOqmuWqpOA5A8I9bwdw2w8BNb3LT9nnTOX5xTFKRBJEJCE1NdWX0I0xhfjnJz/yddI+nrmuNR0aVg90OCaI+JJYvhKRvwAVROQq4D3gf74cXFWzVbUdEIPnDKP5WUd6DlR1vKrGqWpcdLQNLWHMufpvYgpvf7OZoZc04uZODQIdjgkyviSWPwOpwDrgLmA+8HhRXsTNQLkY6AZUc2c94Ek4O9zyDqAB5J0VnQfs9y4/ZZ8zlRtj/Gj19oOMmbuObhfW5K9Xtwh0OCYIFZhY3AXyjar6lqrepKo3uuVCm8JEJFpEqrnlCsBVwEY8CeZGVy0e+NAtz3PruO1fuNeZBwxyvcYaA02BFcBKoKnrZVYOzwX+eT6/c2NMke1NS+euaQnUrlKeV2/rQGS4rx1LTVlS4H0sqprtuvM2VNVtRTx2XWCKS05hwGxV/UhENgCzROQZ4Htggqs/AZgmIsnAATyJAlX9QURmAxuALOBeVc0GEJH7gAVAODBRVX8oYozGGB9lZGVz1/RE0o5n8f7oS6hRqVygQzJBSgo7+RCRJUB7PGcJR3PLVfVa/4bmH3FxcZqQkBDoMIwJKarKo3PWMicxhddu60D/NnUDHZIpQSKSqKpxvtb3ZUiXv51DPMaYUmDi0i3MSUzh/l5NLamYQhU0H0sUcDfQBM+F+wle3YSNMWXEkp9T+cfHG+jTqg5/7NU00OGYEFDQlbcpQByepNKP/KcoNsaUYpv3HeW+d1ZxcZ0qPH9zO8LCbLgWU7iCmsJaqmobABGZgOcaizGmjEhLz+TOKSuJCA/jrSFxVCrvS8u5MQWfsWTmLlgTmDFlS3aO8sDM79m6/xiv3daBBjVsbhXju4J+grQVkTS3LHjuvE9zy6qqVf0enTEmIJ6dv5HFP6XyzHWt6XphzUCHY0JMQVMT2zClxpRBM1dsY4IbruX2rhcEOhwTguy2WWNMnu9+2c/fPlhPj4ujedyGazFnyRKLMQaALfuOcs+MRBrVqsQrt7YnwoZrMWfJPjnGGA4dz2TElJUIMCE+jqpRkYEOyYQw6z9oTBmXlZ3Dfe+sYtuBY0wf0YULalYKdEgmxFliMaaMe/qjDXydtI9/3dCGLtYDzBQDawozpgyb9t0Wpny3lZHdG3NLp4aBDseUEpZYjCmjvknax5P/20Cv5rV5rJ/1ADPFxxKLMWXQL6lHGD0jkSbRlXlpcHvCbQwwU4wssRhTxvx69AQjJq8kMjyMt+PjqGxjgJliZp8oY8qQjKxs7pqWyM5D6cwc2cXGADN+4bczFhFpICKLRWSDiPwgIg+48hoislBEktxzdVcuIjJORJJFZK2IdPA6VryrnyQi8V7lHUVkndtnnIjY+bwxZ5CTozz63lpWbDnAf25qS8cLagQ6JFNK+bMpLAt4WFVbAl2Be0WkJfAYsEhVmwKL3Dp45nxp6h6jgNfBk4iAsUAXoDMwNjcZuTojvfbr68f3Y0xIe37hz8xbs5NH+zTjmrb1Ah2OKcX8llhUdZeqrnLLh4GNQH1gIJ5JxHDP17nlgcBU9VgGVBORukAfYKGqHlDVX4GFQF+3raqqLlNVBaZ6HcsY42X2yu28sjiZW+IaMPryiwIdjinlSuTivYg0AtoDy4E6qrrLbdoN1HHL9YHtXruluLKCylPyKc/v9UeJSIKIJKSmpp7TezEm1CxN3sdf5q6je9NaPHN9a6zF2Pib3xOLiFQG/gv8UVXTvLe5Mw31dwyqOl5V41Q1Ljo62t8vZ0zQ2LAzjbunJXJRdGVeva0DkTawpCkBfv2UiUgknqQyQ1Xfd8V7XDMW7nmvK98BNPDaPcaVFVQek0+5MQbYcfA4wyavoFL5CCYN62QDS5oS489eYQJMADaq6vNem+YBuT274oEPvcqHuN5hXYFDrslsAdBbRKq7i/a9gQVuW5qIdHWvNcTrWMaUaQePnSB+4gqOnchm8vBO1KtWIdAhmTLEn/exXArcAawTkdWu7C/AP4HZIjIC2Arc7LbNB/oDycAxYBiAqh4QkaeBla7eU6p6wC2PBiYDFYBP3MOYMi09M5tRUxPZtv8YU4Z3pvn5Nou4KVniucxRdsTFxWlCQkKgwzDGL7JzlD/MXMX8dbt5eXB761ZsioWIJKpqnK/17UqeMaWEqvLkvB+Yv243j1/dwpKKCRhLLMaUEi9+nsS0ZVsZ1eNC7ux+YaDDMWWYJRZjSoHJSzfz0qIkbo6LYUy/5oEOx5RxlliMCXEfrt7Bk//bQO+WdXj2+jZ2A6QJOEssxoSwxT/t5eHZa+h6YQ3GDW5PhN0AaYKAfQqNCVHLNu3nnumJNK9bhbeGxBEVGR7okIwBLLEYE5ISt/7KiMkraVC9IlOGdaaK3VVvgoglFmNCzPodhxg6aQXRVcoz484u1KxcPtAhGXMSSyzGhJAfd6dx+4TlVI2KZMbIrtSuGhXokIw5jSUWY0JE8t4j3P72cspHhDFzZFfq2/hfJkhZYjEmBGzclcag8d8BMOPOrjSsaXPVm+BlicWYILd6+0EGjV9GRFgY797VjSa1Kwc6JGMK5M/RjY0x52j5pv2MmJJAjUrlmHFnFxrUsDMVE/zsjMWYIPXVz6nET1pBnarlmX1XN0sqJmTYGYsxQWjBD7v5wzvf06R2ZaaN6Gxdik1IscRiTJD5cPUOHpq9htiY85g8tDPnVbSbH01oscRiTBCZuWIbf5m7ji6Na/B2fCcql7f/oib0+HPO+4kisldE1nuV1RCRhSKS5J6ru3IRkXEikiwia0Wkg9c+8a5+kojEe5V3FJF1bp9xYkO6mhA34ZvNjHl/HZdfHM3kYZ0tqZiQ5c+L95OBvqeUPQYsUtWmwCK3DtAPaOoeo4DXwZOIgLFAF6AzMDY3Gbk6I732O/W1jAkJqspLnyfx9Ecb6Nf6fN68wwaUNKHNb4lFVZcAB04pHghMcctTgOu8yqeqxzKgmojUBfoAC1X1gKr+CiwE+rptVVV1maoqMNXrWMaEjBNZOTzy3lpe+PxnbugQw8uD21MuwjprmtBW0ufadVR1l1veDdRxy/WB7V71UlxZQeUp+ZTnS0RG4TkTomHDhucQvjHF5+CxE9w1LZHlmw/w4JUXc3+vJjZJlykVAtaIq6oqIlpCrzUeGA8QFxdXIq9pTEE27zvK8Mkr2fHrcV4a1I6B7c74u8iYkFPS59x7XDMW7nmvK98BNPCqF+PKCiqPyafcmKD3bfI+rn9tKYeOZ/LOyC6WVEypU9KJZR6Q27MrHvjQq3yI6x3WFTjkmswWAL1FpLq7aN8bWOC2pYlIV9cbbIjXsYwJSqrK61/+wu0TllOrcnnmjr6EuEY1Ah2WMcXOb01hIjITuByoJSIpeHp3/ROYLSIjgK3Aza76fKA/kAwcA4YBqOoBEXkaWOnqPaWquR0CRuPpeVYB+MQ9jAlKaemZPPreGhb8sIerY+vyfzfEUsm6E5tSSjydqsqOuLg4TUhICHQYpgz5afdh7p6eyLYDxxjTrzkjftfYLtKbkCIiiaoa52t9+8lkjJ9k5yiTlm7m35/9RJWoSGaO7Ernxtb0ZUo/SyzG+EHy3iP8ac4aVm07SK/mtfl/v29j0wibMsMSizHFKCs7h7e/2czzC3+mQmQ4L9zSluva1bemL1OmWGIxppj8vOcwj763hjUph+jdsg7PXN+a2lXsLMWUPZZYjDlHWdk5vLlkEy99nkSl8uGMG9yea2Lr2lmKKbMssRhzDjbuSuPROWtYvyONq9vU5e8DW1HLJuUyZZwlFmPOwv4jGbz8RTIzlm+lalQkr93Wgf5t6gY6LGOCgiUWY4rg2IksJn6zmTe+2sSxE1nc0qkBj/ZpTo1K5QIdmjFBwxKLMT5Iz8zmvcQUXl6UxN7DGfRuWYc/9W1Gk9pVAh2aMUHHEosxBTh0LJPpy7cyaelm9h05QdwF1Xnttg42xpcxBbDEYkw+th84xpRvtzBzxTaOnsjmsoujufuyi+h6YQ3r7WVMISyxGOOkZ2az4IfdzE7YztLk/YSHCdfE1uWuyy6iRd2qgQ7PmJBhicWUadk5SsKWA8xft4sPVu/k0PFMYqpX4OGrLubGuBjqnlch0CEaE3IssZgy50RWDt9t2s+n63fx2Q972H/0BOUiwujT6nwGdWpAtwtrEhZmzV3GnC1LLKbUy85RNuxMY+kv+/j2l/2s3HyA45nZVCoXTs/mtenb+nx6Nqtt86MYU0zsf5IpdfampbMm5RBrUw6yJuUQa7Yf5NDxTACa1q7MzXEx9Lg4mkub1CIqMjzA0RpT+lhiMSHp+Ilstuw/ypZ9R9nsnrfsO8amfUfZdyQDgPAwoWntyvRrfT5dL6zJJRfVtKHrjSkBIZ9YRKQv8BIQDrytqv8McEjmHOTkKIfTszh4/AT7jpwg9XAGqYfTST2cwZ60DLYe8CSQ3WnpJ+1Xq3J5GteqSM9m0TQ7vwrtGlSjVb3zqFDOzkiMKWkhnVhEJBx4FbgKSAFWisg8Vd0Q2MhCl6qSo5CjSnaOe6iSk/PbelaOkpWtZOXkkJmtZGbnkJGVw4msHDKzPc8nvJ4zsnI4fiKL4ydyOJaZRfqJbI5kZHMkI5MjGVkcycjmcHomB49lcvDYCXLymS07TKBm5fI0rFGRS5vUolHNijSqVYnGtSpxQc2KVImKLPk/ljEmXyGdWIDOQLKqbgIQkVnAQKDYE8uAl78mPTPnpDLV078BTyvR08tP3e/kbblletL66cueGqon19W8eoq6BJFbluMq5JbluCTinUzyeUvFqlxEGBUiw6lcPsLziIqgWoVIYqpXoHrFSKpVKEe1ipFUq1iOmpXKEV2lPLWrlqdGxXJEhIf5NzhjTLEI9cRSH9jutZ4CdDm1koiMAkYBNGzY8KxeqEl0ZTKz8/nWzadX6qlFuXdqy0llZ97ntPriXU/y9hV3nJPKxNUSz698yVv2VAiT37Z5ln9bD3frYSKe9XAhXITwME9ZeJgQES5EhAnhYWFEhAmR4WFEhgvlIsIoFxFG+YgwyoWHUy7it/LyEeFULBdOVGQ44daN15hSL9QTi09UdTwwHiAuLu6sfpO/OKh9scZkjDGlVai3LewAGnitx7gyY4wxARLqiWUl0FREGotIOWAQMC/AMRljTJkW0k1hqpolIvcBC/B0N56oqj8EOCxjjCnTQjqxAKjqfGB+oOMwxhjjEepNYcYYY4KMJRZjjDHFyhKLMcaYYmWJxRhjTLGS/IYlKc1EJBXY6kPVWsA+P4fjDxZ3yQvV2C3ukhXKcVdS1WhfdyhzicVXIpKgqnGBjqOoLO6SF6qxW9wlqyzFbU1hxhhjipUlFmOMMcXKEsuZjQ90AGfJ4i55oRq7xV2yykzcdo3FGGNMsbIzFmOMMcXKEosxxphiZYklHyLSV0R+EpFkEXks0PGciYhMFJG9IrLeq6yGiCwUkST3XD2QMeZHRBqIyGIR2SAiP4jIA648qGMXkSgRWSEia1zcf3fljUVkufu8vOumcAg6IhIuIt+LyEduPejjFpEtIrJORFaLSIIrC+rPCYCIVBOROSLyo4hsFJFuwR63iDRzf+fcR5qI/PFs4rbEcgoRCQdeBfoBLYHBItIysFGd0WSg7ylljwGLVLUpsMitB5ss4GFVbQl0Be51f+Ngjz0DuEJV2wLtgL4i0hX4F/CCqjYBfgVGBDDGgjwAbPRaD5W4e6pqO697KYL9cwLwEvCpqjYH2uL5uwd13Kr6k/s7twM6AseAuZxN3KpqD68H0A1Y4LU+BhgT6LgKiLcRsN5r/SegrluuC/wU6Bh9eA8fAleFUuxARWAV0AXP3dQR+X1+guWBZ3bVRcAVwEeAhEjcW4Bap5QF9ecEOA/YjOscFSpxnxJrb2Dp2cZtZyynqw9s91pPcWWhoo6q7nLLu4E6gQymMCLSCGgPLCcEYnfNSauBvcBC4BfgoKpmuSrB+nl5EfgTkOPWaxIacSvwmYgkisgoVxbsn5PGQCowyTU9vi0ilQj+uL0NAma65SLHbYmlFFPPT4yg7U8uIpWB/wJ/VNU0723BGruqZqunqSAG6Aw0D3BIhRKRAcBeVU0MdCxn4Xeq2gFP0/S9ItLDe2OQfk4igA7A66raHjjKKc1HQRo3AO5a27XAe6du8zVuSyyn2wE08FqPcWWhYo+I1AVwz3sDHE++RCQST1KZoarvu+KQiB1AVQ8Ci/E0IVUTkdzZWIPx83IpcK2IbAFm4WkOe4ngjxtV3eGe9+Jp7+9M8H9OUoAUVV3u1ufgSTTBHneufsAqVd3j1osctyWW060EmroeM+XwnBLOC3BMRTEPiHfL8XiuXwQVERFgArBRVZ/32hTUsYtItIhUc8sV8FwX2ognwdzoqgVd3Ko6RlVjVLURns/zF6p6G0Eet4hUEpEquct42v3XE+SfE1XdDWwXkWauqBewgSCP28tgfmsGg7OJO9AXiYLxAfQHfsbTfv7XQMdTQJwzgV1AJp5fSSPwtJ0vApKAz4EagY4zn7h/h+d0ei2w2j36B3vsQCzwvYt7PfCEK78QWAEk42k+KB/oWAt4D5cDH4VC3C6+Ne7xQ+7/xWD/nLgY2wEJ7rPyAVA9ROKuBOwHzvMqK3LcNqSLMcaYYmVNYcYYY4qVJRZjjDHFyhKLMcaYYmWJxRhjTLGyxGKMMaZYWWIxZZKIqIj8x2v9ERF5spiOPVlEbiy85jm/zk1u5NzFp5Q3EpHjbjiRjW5E5qH+jseYXJZYTFmVAfxeRGoFOhBvXnfC+2IEMFJVe+az7RdVba+qLfDcFPlHERlWLEEaUwhLLKasysIzl/eDp2449YxDRI6458tF5CsR+VBENonIP0XkNndGsE5ELvI6zJUikiAiP7uxunIHsHxORFaKyFoRucvruF+LyDw8d2ifGs9gd/z1IvIvV/YEnhtNJ4jIcwW9UVXdBDwE3O/27Swi37kzmm9z7xAXkSUi0s7rdb8Rkba+/DGN8VaUX0fGlDavAmtF5P+KsE9boAVwANgEvK2qncUzWdkfgD+6eo3wjGt1EbBYRJoAQ4BDqtpJRMoDS0XkM1e/A9BaVTd7v5iI1MMzb0pHPHOmfCYi16nqUyJyBfCIqib4EPcqfhsw80egu6pmiciVwLPADXiG2RmK5+zmYiBKVdcU4W9jDGBnLKYMU8+IylNxv+R9tFJVd6lqBp4hf3ITwzo8ySTXbFXNUdUkPAmoOZ6xroa4YfeX4xkqo6mrv+LUpOJ0Ar5U1VT1DHE/A+iRT73CiNfyecB74pl59AWglSt/DxjgBggdjmciOWOKzM5YTFn3Ip5f85O8yrJwP7pEJAzwnrI3w2s5x2s9h5P/P506VpLi+XL/g6ou8N4gIpfjGVrdn9rz2+yRTwOLVfV6Nx/OlwCqekxEFgIDgZvxnCUZU2R2xmLKNFU9AMzm5Gl5t/Dbl+q1QORZHPomEQlz110uxDML3wLgHndGgIhc7EbtLcgK4DIRqeWmzR4MfFWUQFzy+Dfwsis6j9+GyB96SvW3gXF4zsx+LcrrGJPLzliMgf8A93mtvwV8KCJrgE85u7OJbXiSQlXgblVNF5G38TSXrXJTB6QC1xV0EFXdJSKP4RniXoCPVdWX4dYvEpHvgSjgMDBOVSe7bf8HTBGRx4GPT3m9RBFJ4+QzOGOKxEY3NsbkcZ0FvgSaq2pOIdWNyZc1hRljABCRIXg6FfzVkoo5F3bGYowxpljZGYsxxphiZYnFGGNMsbLEYowxplhZYjHGGFOsLLEYY4wpVv8f+Ugpn+vQoecAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "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": "code",
      "metadata": {
        "id": "hnWYgXNK-EJx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}