Covid_19_India_ARIMA_Forecast.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Covid 19 India ARIMA Forecast.ipynb",
      "provenance": [],
      "mount_file_id": "15hZ5y3U9YckWhrMLI65oUwaaN2hY3xoe",
      "authorship_tag": "ABX9TyOjVtzZ2QtU0Yr9zNRYd4wB",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Durgaprasad-77/Covid-19-India-ARIMA-Forecast/blob/main/Covid_19_India_ARIMA_Forecast.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i63NeYRl738a"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pylab as plt\n",
        "%matplotlib inline \n",
        "from matplotlib.pylab import rcParams\n",
        "rcParams['figure.figsize'] = 10 , 6"
      ],
      "execution_count": 239,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oVtZ-QuS8o1v",
        "outputId": "045777af-f1e7-4f6b-cc18-e786cc69c66e"
      },
      "source": [
        "pip install gspread"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: gspread in /usr/local/lib/python3.7/dist-packages (3.0.1)\n",
            "Requirement already satisfied: requests>=2.2.1 in /usr/local/lib/python3.7/dist-packages (from gspread) (2.23.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.2.1->gspread) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.2.1->gspread) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.2.1->gspread) (2020.12.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.2.1->gspread) (2.10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eqeQD0e-87d4",
        "outputId": "e3df9ab0-3cd8-4d25-c425-d182865cfd7c"
      },
      "source": [
        "pip install PyOpenSSL"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting PyOpenSSL\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b2/5e/06351ede29fd4899782ad335c2e02f1f862a887c20a3541f17c3fa1a3525/pyOpenSSL-20.0.1-py2.py3-none-any.whl (54kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 3.3MB/s \n",
            "\u001b[?25hCollecting cryptography>=3.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b2/26/7af637e6a7e87258b963f1731c5982fb31cd507f0d90d91836e446955d02/cryptography-3.4.7-cp36-abi3-manylinux2014_x86_64.whl (3.2MB)\n",
            "\u001b[K     |████████████████████████████████| 3.2MB 11.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: six>=1.5.2 in /usr/local/lib/python3.7/dist-packages (from PyOpenSSL) (1.15.0)\n",
            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.7/dist-packages (from cryptography>=3.2->PyOpenSSL) (1.14.5)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.12->cryptography>=3.2->PyOpenSSL) (2.20)\n",
            "Installing collected packages: cryptography, PyOpenSSL\n",
            "Successfully installed PyOpenSSL-20.0.1 cryptography-3.4.7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ibIbRbnc9bRw",
        "outputId": "d1557020-c3e3-41e0-8cd7-bd8f26a08011"
      },
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "\n",
        "import gspread\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "gc = gspread.authorize(GoogleCredentials.get_application_default())\n",
        "\n",
        "worksheet = gc.open('Covid 19 Statewise Time Series').sheet1\n",
        "\n",
        "# get_all_values gives a list of rows.\n",
        "rows = worksheet.get_all_values()\n",
        "print(rows)\n",
        "\n",
        "# Convert to a DataFrame and render.\n",
        "import pandas as pd\n",
        "data = pd.DataFrame.from_records(rows[1:],columns=rows[0])"
      ],
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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              "         Date  Date_YMD  ... Daily Deceased Total Deceased\n",
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              "..        ...       ...  ...            ...            ...\n",
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              "434  20210408  20210408  ...            802         167103\n",
              "435  20210409  20210409  ...            773         167876\n",
              "436  20210410  20210410  ...            838         168714\n",
              "\n",
              "[437 rows x 8 columns]"
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        "data = data.drop(['Date_YMD','Total Confirmed','Daily Recovered','Total Recovered','Daily Deceased','Total Deceased'],axis = 1)"
      ],
      "execution_count": 242,
      "outputs": []
    },
    {
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              "         Date Daily Confirmed\n",
              "0    20200130               1\n",
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              "3    20200202               1\n",
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              "..        ...             ...\n",
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              "435  20210409          144945\n",
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              "\n",
              "[437 rows x 2 columns]"
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-04-06</th>\n",
              "      <td>115312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-04-07</th>\n",
              "      <td>126276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-04-08</th>\n",
              "      <td>131878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-04-09</th>\n",
              "      <td>144945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-04-10</th>\n",
              "      <td>152565</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>437 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           Daily Confirmed\n",
              "Date                      \n",
              "2020-01-30               1\n",
              "2020-01-31               0\n",
              "2020-02-01               0\n",
              "2020-02-02               1\n",
              "2020-02-03               1\n",
              "...                    ...\n",
              "2021-04-06          115312\n",
              "2021-04-07          126276\n",
              "2021-04-08          131878\n",
              "2021-04-09          144945\n",
              "2021-04-10          152565\n",
              "\n",
              "[437 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NUYUMgzrKgB1"
      },
      "source": [
        "data = datac.reset_index()\n"
      ],
      "execution_count": 246,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "W63JO70SMrEK",
        "outputId": "1a311b82-dffa-45ad-f497-afb73f62e514"
      },
      "source": [
        "data"
      ],
      "execution_count": 247,
      "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>Daily Confirmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-30</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-02-01</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-02-02</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-02-03</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>432</th>\n",
              "      <td>2021-04-06</td>\n",
              "      <td>115312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>433</th>\n",
              "      <td>2021-04-07</td>\n",
              "      <td>126276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>434</th>\n",
              "      <td>2021-04-08</td>\n",
              "      <td>131878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>435</th>\n",
              "      <td>2021-04-09</td>\n",
              "      <td>144945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>436</th>\n",
              "      <td>2021-04-10</td>\n",
              "      <td>152565</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>437 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date Daily Confirmed\n",
              "0   2020-01-30               1\n",
              "1   2020-01-31               0\n",
              "2   2020-02-01               0\n",
              "3   2020-02-02               1\n",
              "4   2020-02-03               1\n",
              "..         ...             ...\n",
              "432 2021-04-06          115312\n",
              "433 2021-04-07          126276\n",
              "434 2021-04-08          131878\n",
              "435 2021-04-09          144945\n",
              "436 2021-04-10          152565\n",
              "\n",
              "[437 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GT9Smn3BLzZJ",
        "outputId": "2de59fbe-1718-4d21-a2e2-9178be3b3021"
      },
      "source": [
        "pip install pmdarima"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pmdarima\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e4/a8/bdf15174e35d072e145d16388b1d3bc7605b752610170cb022a290411427/pmdarima-1.8.0-cp37-cp37m-manylinux1_x86_64.whl (1.5MB)\n",
            "\u001b[K     |████████████████████████████████| 1.5MB 10.6MB/s \n",
            "\u001b[?25hCollecting Cython<0.29.18,>=0.29\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/dc/6c/198da24bf0a1d5b5ee6119e891c5300bb1b0b8904832fac9ff62d4e381c9/Cython-0.29.17-cp37-cp37m-manylinux1_x86_64.whl (2.1MB)\n",
            "\u001b[K     |████████████████████████████████| 2.1MB 39.0MB/s \n",
            "\u001b[?25hCollecting statsmodels!=0.12.0,>=0.11\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/da/69/8eef30a6237c54f3c0b524140e2975f4b1eea3489b45eb3339574fc8acee/statsmodels-0.12.2-cp37-cp37m-manylinux1_x86_64.whl (9.5MB)\n",
            "\u001b[K     |████████████████████████████████| 9.5MB 39.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (54.2.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.0.1)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.4.1)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.1.5)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.19.5)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (0.22.2.post1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.24.3)\n",
            "Requirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.7/dist-packages (from statsmodels!=0.12.0,>=0.11->pmdarima) (0.5.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima) (2018.9)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy>=0.5->statsmodels!=0.12.0,>=0.11->pmdarima) (1.15.0)\n",
            "Installing collected packages: Cython, statsmodels, pmdarima\n",
            "  Found existing installation: Cython 0.29.22\n",
            "    Uninstalling Cython-0.29.22:\n",
            "      Successfully uninstalled Cython-0.29.22\n",
            "  Found existing installation: statsmodels 0.10.2\n",
            "    Uninstalling statsmodels-0.10.2:\n",
            "      Successfully uninstalled statsmodels-0.10.2\n",
            "Successfully installed Cython-0.29.17 pmdarima-1.8.0 statsmodels-0.12.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uF0EtjtZN56C"
      },
      "source": [
        "data=datac.astype(float)"
      ],
      "execution_count": 248,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "SS4w-WETMNmp",
        "outputId": "e9239d05-e734-4b7c-ad33-72c3f099c016"
      },
      "source": [
        "data['Daily Confirmed'].plot(figsize=(12,5))"
      ],
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f014f27d190>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 249
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Zw5nzfeeeN4DHnPF07yEiIrPgx1uaOffzv072hh3vG081ctN/vMiv9hxPeV1EZK7r8scz2OfXJwLsU8tgT3qXtfaWNJeuTjHXAh9P8zr3APekGN8GbEwx3p3qPURE5PT7/nMH+eIjrwHw8K5WzqornjDnYGcAa+Ev7tvBA39SwNlLSk73MkVEpqXbyWCfW1+KMae/RERERBaI3+7v4EuPvsb1G2u5cFkZT72eejN5c3eQK9dWkeN2cf+2I6d5lSIi05coEakryWNtTRGLS/NP6XUUYIuISFrtA8P85f07WFtTxL/fdB5vX1/Da8cHON4/NGbe8EiU1v4hzqsvY9PyMrYcVC22iJx5uv1hvB4Xhbke7v/Ym/jUdWtP6XUUYIuISFq/ea2dvuAId77/PPK9bq5aVw3A0+Oy2C29QayF5ZUFXLqigqYOP52DoVQvKSIyZ3X6Q1QV5mKMoSQ/h7ycGdrkKCIiC1dju58Cr5u1NUUArK4uZHFpPo/sbh3TUeRwVxCAZRU+Ll1RAcBLh5TFFpEzS7c/TEWhd9qvowBbRETSauwYZHV1IS7n1AVjDH/45uW80NTN3c8fSs473B0AYHlFARsXFePzutlysHtW1iwicqq6AyEqC3On/ToKsEVEJK0D7X5WVReNGfvjtzSweUMt//LY68mWfc3dQUrycygt8OJxu9i0vJznm7roD47MxrJFRE5J12CYCp8y2CIiMkP6gmE6B0OsqSkcM26M4bPvWEc0Ztl9rB+IZ7CXVRQk57zrnDoOdQW45F9+w9Ovpz2IV0RkzvCHInQMDrPoFDuHjKYAW0REkp7e38HRnng9dWOHH4A1NUUT5iW+Qk0cK9zcHWRZhS95/cZN9Tzy55dTmJvDf7/SMtPLFhGZtu3NvcQsXLS8fNqvpQBbREQAeHDHMT7yny8na6sPtA8CsKq6cMLcAq+bvBwXPYEw4UiMlt4gy0dlsAE2LCrhvPpSXm8bnPnFi4hM08uHenC7TPKY9OlQgC0iIuw51s/fPrALiJeGwIkOIqkOWjDGUOHLpcsfoq1/mJiF+vKCCfPW1xVxsNPP8Eh0Zj+AiMg0vXS4J75J+xRPbxxNAbaIiPCTrUfwul0sLS9gcDgCTOwgMl65z0tPIEzbwDAAtcV5E+acVVdMzJ7IhouIzEWhSJQdR/uyUh4CCrBFRBY8ay3PHujkzSsrqCvJYzAUD7Cbu4Msr/Slva+i0Eu3P0x7IsAuSR1gA8luIyIic9Huln7CkRgXNSjAFhGRLDjcHeRY3xBvWVNFUZ4nmcHuHxqhrCB9u6pEBjsRYNcUTQywl5YX4PO6ee145gx2IBThS4/sU1s/EZkV25t7Adi0rCwrr6cAW0RkgXu+MX7s+VtWVVKUl4M/NEIsZvGHIhTn56S9r7LwRA12Xo6L4vyJdYsul2FtbRH7JslgP7L7ON977hBP7W+f3ocRETkFh7uDVPi8VGThkBlQgC0isuA929hFfXk+yyoKkhnswVAEa6E4L/1mn3Kfl1AkxuHuADXFeRiTulb7rLpiXjs+MOZo9fF+vTceWDc5rQFFRE6nlt4gS8qm3/86QQG2iMgCFo7EePGNbi5fVYUxhsJcD/7hCAND8VKNTBnsxGln+1oHUpaHJKyuLmRwOEK30zMb4r/MOpzSkqFwlOeb4ln0NzoCE+5/8Y1uguHIyX84EZEpOtY7xGIF2CIikg0vHuzGH4pwzVnVABTl5RCJ2WRddUmmALswHmC39g9Tk2KDY0K585Vrov2ftZYPfn8r1/z7MzzX2MmzjZ0Mj8SoLPTS1Dk2g90xMMwHvr+FH77YfOofUkQkA2stx/qGWFI2sdXoqZp+oz8RETlj/WpPGz6vm8tWVQJQ6JSEHOsbAqA4L1MG+0StYk1R+rrFsoL4a/Q6Gxjf6PTT3B3E53XzobtfosDrpjjPw++dv5gf/O4wI9EYOe54/qepw4+18R3+IiIzodMfIhSJZbVERAG2iMgCFY1ZntjXxpXrqsnLcQMnaq4TAXamDHa570SHkZoUPbATSvPj83qdEpHf7o+Xg/zPxy/jt/s7eKW5j0tWlFOUl8NI1HKkJ8jKqvjpkW90xUtG9rYqwBaRmdHSG/95pwBbRESmbXtzL13+MJs31ibHCp0TzI45v3BSdQZJSJSIABlLREqdDHafk8F+5kAnq6oLWVNTxJqaouS8HUf7gHjWOhFgH3RKRg53BxkcHqEoQ0ZdRORUnAiws1ciohpsEZEFyFrL9547SK7HxRVrq5PjiQA2WSKSIYNd4PWQ72S+M5aIOJnu3mCYYDjC1oM9XLGmasK8lVXxQ23eGFWHfbAzQKI5yWS9tEVETkVLbxCAxaXa5CgiItPw4y3NPLGvnU9duzaZtYaxGWyXgUJv5i86E2UiqU5xTPB53eS4Db3BEbYe6iEcjfG2tRMD7KK8HGqKc8e06jvUFUgeXbxPZSIiMgNaeoco93nx5WavsEMBtojIAjMwPMIXH3mNt62p4rbLG8ZcKxpVg12Ul4PLlbq3dUKlUyZSnaFNnzGG0gIv/UNhDnXGa6o3LCpJOXdVdSFvOHNCkSgtvUEubSinstDL3lYdty4i2Xesdyir9degAFtEZMHZ3dJPKBLjtssbJgTQiQA7GI5mrL9OKPd5Kc7zkO91Z5xXmp9Db2CEtoFhvB5XsrPIeDXFeXQNhgBo7g4Ss7CiqpD1i0oUYIvIjMj2ITMwzQDbGPNXxpi9xpg9xpifGmPyjDENxpitxpgmY8z9xhivMzfXed7kXF8+6nU+64zvN8ZcN2p8szPWZIz5zHTWKiIicbuclndnL56YRR5dLpKpg0jC29ZUcf3GuknnlRV46Q2GaesfpjbDqY+l+d5kv+zEBscVVT7OW1LC/vZBjvYEJ30vEZGpSvTAzmb9NUwjwDbGLAb+HNhkrd0IuIGbgX8F7rTWrgJ6gducW24Dep3xO515GGPWO/dtADYD3zbGuI0xbuBbwPXAeuAWZ66IiEzD7mN91JfnJzcfjuZxu5IbFzP1wE74w8sa+Nf3nTPpvNKCHPqC8Qx2pnrtsoIcAuEo4UgsWSrSUOnjA5csw+0yfOvppknfS0RkqvqCIwyPxKgrmSMBtsMD5BtjPEABcBy4CnjAuX4v8F7n8Q3Oc5zrV5t4CuMG4D5rbchaewhoAi52/jRZaw9aa8PAfc5cERGZhl0t/ZyzuDTt9USZyFQy2FM1PoOdTrKl31CYI91BKgtzKcrLobYkjw9cvJQHtrcoiy0iWdPmnFqb6S/+p+KUA2xr7THgq8AR4oF1P7Ad6LPWRpxpLcBi5/Fi4Khzb8SZXzF6fNw96cYnMMbcbozZZozZ1tnZeaofSURkXnr69Q4e2XUciB/20tI7xMYU5SEJidMcp5LBnqpS39Qy2KUF8ax6f3CETn+I6lHt//7kbSsBuO/lI1lbl4gsbO1OgF1TnL7V6KmYTolIGfGMcgOwCPARL/E47ay1d1lrN1lrN1VVTWz9JCKyUD22+zh//MNtfPXX+wHYfSxef33OkvQBdqIX9lQ2OU5Vab6XcDRGOBKbUga7NzhCtz9E5agAu7Ykj7Pqinn1SF/Ke4PhCD988TCxmM3aukVkfusYiG+qztQJ6VRMp0TkGuCQtbbTWjsC/By4DCh1SkYAlgDHnMfHgHoA53oJ0D16fNw96cZFRGQK9rb288mfvkrM2mRnjkSAvTFNmzw4cVx6dktETrxW5hrseAa7Lximyx+mclyd+Hn1pew82kc0RRD92O42/vHBvexoSR2Ai4iMl8hgV8+VDDbx0pBLjTEFTi311cA+4Gngfc6cW4EHnccPOc9xrj9lrbXO+M1Ol5EGYDXwEvAysNrpSuIlvhHyoWmsV0RkQfnq4/vx5Xr46FtWMBiKMDwSpbF9kMWl+ZSkaZMHJzqJZDrF8WQlSj8g3oovnURQ3xccoWtcBhviAXYgHB1zGE1Cc3d8U2SrcwqliMhk2geHKfd5yfVkbjV6sk75+z9r7VZjzAPAK0AEeBW4C3gEuM8Y80Vn7G7nlruBHxljmoAe4gEz1tq9xpifEQ/OI8DHrbVRAGPMJ4DHiXcoucdau/dU1ysishCMRGO8fKiHnmCYp/d38unN65LZ4y5/iPaB0KSbeYpmoAZ7dAa7LlMG28lYt/QNEYrEqBifwV4a35y542gva2uLxlxrdjY/HutVgC0iU9M+MHavR7ZMq8DOWnsHcMe44YPEO4CMnzsM3Jjmdb4EfCnF+KPAo9NZo4jIQuEPRfizn7zCswfim70rC3O59c3L+F1TNwDd/jAdg8MTAtPxCnPjwXBWS0ScQNkYqMrwy8zndeNxGd5wMtSVhWPnNlT4KMnPYcfRPt5/0dIx15q74wG2MtgiMlXtA8MZv1U7VdnbwSIiIrPqr+7fwQtNXXzuXespK8hhdXURBV5Pssyiyx+iYyDEW1Zn3gyezGBndZNjPFivLMwlx52+OjFxrHqiBKSicGwG2+UynFtfmnKj45FEBrtvOFvLFpF5rn1gmHWTJB1OhY5KFxGZJ3Yc7eP3z1/MbZc38PsXLOFsp1NIpROkHukJMhiKTJqtmYk+2Ika7EzlISfm5nCoK15PPT6DDXDh0jL2tw9yz/OHiG/lgcHhEXoC8RMgjymDLSJTEI1ZOgdDM5LBVoAtIjIPjERjdPlD1KU47jcRpO5rHQCYtN5wZXUhRbkeqrP4S8frceHzuqf0i6ysIIdwNAakDrA/cvlyrl5XzRce3sd/vnAYOFEeUlmYO6ZE5PW2AfY4nVNEREbr9oeIWbL6sy5BAbaIyDzQORjCWlL2mM7LcVOY62GvE2BPFuResaaKnXdcm9VNjgBXnVXDW9dMflZBSf6JspDyFMe5F+fl8L0Pb6K2OI99x+OfKVEe8qaVFfQPjeAPRWhsH+TG77zI3z6wK0ufQETmk3anB3bNXNvkKCIic0N78rjf1L8oKgu9NHYMApOfWGaMwZjsrg/gm7ecP6V5iY4jJfk5eD2p80DxWu0c+odGgBMZ7DetqOCXO1t57fgAf3X/DgZDEQ51+YnFLC7XDHwoETljnTjFURlsERFJIXlYQprTyCoKcxmJxuuVZ+Lr0GxKnOY4foPjeMX5OQw4AfaRngDlPi9rawsB+PpvGmnpHeL3L1jM8EiMDuegHRGRhPZBBdgiIpJBW38ig536F0Vio2Nejit5UuNcldgQmar+erSS/LEZ7KXlBSxyatCfb+rivPpSfv/8JQDJTZMiIgnt/cO4zImfj9mkAFtEZB5oHwyR4zaUF6T+RZEIVmuK8zAzUf+RRYkM9mS/9IrzTmSwW3qHqC8voLooD49TCnLjpiUsqygA4HC3AmwRGas3OEJJfg6eDK1DT5UCbBGReaC9f5jqory0dcYViQA7TQnJXFLm/CWhwjd5BntgOAJATyBMZaEXt8tQW5JHrsfFu85ZxKLSfLxulwJsEZkgEIrgy52Zb/Tm9veEIiIywcuHe1hR6UsGzRCvJcy0ebHKyQZXT7LBcS4YfShNJsX5HvyhCMFwBH8okjxW/dr1tXg9rmQf7/ryfA6rRERExvGHIhTOUICtDLaIyBnk56+0cON3X+SuZw+OGW/rH05bfw0ngtV0myDnkkQN9mSbHBMB9OGueAeRcifj/Y/vXs9nrl+XnNdQ6UvOERFJCIRnLoOtAFtEZA778ZZm3vmN53j5cA8/evFwsqfz0d6xAWP7QChj8Jw4Ln2yFn1zwcpqH//fBUt42yQ9s5MBtlP+Ue5L3bd7eYWP5p4AsZjN7kJF5IzmD0VVIiIishA9tKOVva0D3PjdFwF488oKhkaiHOsbTs7xh+IlEpky2PVlBXhchpVVhTO+5unK9bj52k3nTjovcRBOokNIeZqa7WWVPoZHYrQPDlNXMvGkSxFZGLYe7ObCZWXJTY2BUIRFGX5uTocy2CIic1Q4EmNnSx83bVrCx69cyfc+vImf/PElrKoq5Pio48BPHJaQPjtdW5LHc5++kqvPqp7xdZ8uJQXjA+zUJSUNFb4x8wBeaOrizicOzPAKRWSu2Nc6wPvv2sJvXmtPjs3kJkcF2CIip1kwHOHHW5qJTlKysLe1n1AkxhVrq/nb69bx9vU1GGOoK82n08BuKB8AACAASURBVB8iHIkBUz+NrK4kf8636DsZiQz24UkC7EWl8X8vHQMnDpu5+/lDfP3JxmQfbRGZ3/YdHwBOHI8O8QBbmxxFROaJh3cd5x9+sYenXu/IOG97cy8AFy4rGzO+uDQPa08E1slDZub4CY3ZNroG22VOdB8ZL7Fpsi8YBiAWs2w73APArpa+07BSEZltje2DAHQH4j8HrLUEwlF8ue4ZeT8F2CIip1lThx+Ax/YcB+I11Km8cqSXJWX5EzLTiTri405gfbRnCGNInmK4UBTnxzNPXf4wZQXetD3AEydX9jnZ6qZOf7J/9o4jCrBFFoL9ToDd6wTYoUiMaMyqRERE5EzTFwzz7IHOCeOJTMpv9rXz4I5jnPf5X/PEvvYxc6y1bG/unZC9hhMlD8f743XYR3qC1BbnkZczM5mYuSo/x02OOx5UpysPAfC4XRTleegLxgPsbYfj3wwU53l49agCbJGFoLE9ntjocb7JSiQ2VCIiInKG+e4zB/nwPS9xtGdsS72mTj8VPi8DwxE+9X93EolZPv/LvQyPRJNzOgdDtA+EOK++dMLrJjLYrX2JDHaQ+rKCGfwkc5MxJlkmUpYhwIb48euJeuttzT1UFnq5dkMtO472Ya3a94nMZ4PDIxxzNoYnMtgBJ8D2eRVgi4icUX73RhcAvx6VnQ6GI7T0DnHzxfX4vG6shTvevZ6W3iG+N+rwmMRGnMUpyj58uR6K8zy0Or8wjvYGqS9feAE2nNjoWDFZgJ3vTdZgbzsc/2bggqVl9ATCHOnRITQi81mjU5aX63HRExibwVYNtojIGaQ/OMKeY/0APLGvLTl+sDOAtbBxUQmfecdZ/P+/dzYfuayBy1dV8osdx5LzuvzxADtxQMx4i0rzOd4/xPBIlLaBYZYu1ADbyWBnKhGBeAa7b2gkGVBfuKws+e3Ay07JiIjMTwfa4mV5FywtozeYyGDHvzFUDbaIyBlk66FuYhYubijnpUM9ya8lGzviP+hX1xTyoUuXcdNF9QCcs6SE5u4gI9F4671EgF1VmDrArivJo7VvmGN9Q1gLSysW1gbHhKkG2CX5OfQHR5JZ/2UVPtbUFLKi0seXH3uNll5lsUXmI2ste1r7yc9xc86SEnoCYaeDSCKDrQBbRGTOa+rwc8/zh3i2sZNcj4tPb15LzMKTTku+pg4/HpdhmXP4ScLqmkIiMUuzc+x3lz8ekFcUpuvtHM9gJ8obFmoGu+QkMtj9QyNjeoZ73C7u+vAmQpEYH7r7JX65s3XS3uQicubo9oe44Vsv8OMtR9i0vIyKQi8jUYs/FEnWYM/JTY7GmFJjzAPGmNeNMa8ZY95kjCk3xjxhjGl0/lnmzDXGmG8YY5qMMbuMMReMep1bnfmNxphbR41faIzZ7dzzDTOfTkgQkXnprmff4AsP7+PHW45w0fJyLlhaRllBDtub432XG9v9LK/0keMe++N3VVURcKKFX7c/RIHXTUGaDTiLSvPpDY6w1ylDWYibHOFEC75JA+x8L31DI7Q5AXa1U3qzqrqQuz60iZi1fPKnr3LXqDp4ETmz/XJnK7ta+vncu9bzrQ9eQLkv/v/73sDIiU2OczHABr4O/Mpauw44F3gN+AzwpLV2NfCk8xzgemC18+d24DsAxphy4A7gEuBi4I5EUO7M+eio+zZPc70iIjNqf7ufxaX5lPu8vPOcOowxLCrNTx4G09TpZ1VV4YT7VlbHM9qJALvLH6IyTXkIwFtXVwHw/ecPketxUZWmVnu+O5kMdjRmOdQZ/4Zg9L+vN62s4Om/uYI1NYW8dKh75hYrIqfVk693sKLKx22XN1Ccl0O5L/7zoicYxu/UYBfOtS4ixpgS4K3A3QDW2rC1tg+4AbjXmXYv8F7n8Q3AD23cFqDUGFMHXAc8Ya3tsdb2Ak8Am51rxdbaLTbeQ+mHo15LRGTOicUsje2DvH19Ddv/4Rpuduqra4rzaB8IYa3lWO8Q9eUT66ULvB4Wl+Ynd7t3+cNpy0MAzl5SwptXVtAXHGFpecG8OgL9ZEw1wE7M298+SGWhd8I3CC6XYX1dMfudzVAicmbzhyJsPdjD1euqk2NlzqmuvYHwqAz23Osi0gB0Av9pjHnVGPN9Y4wPqLHWHnfmtAE1zuPFwNFR97c4Y5nGW1KMi4jMSUd7gwTDUdbVFmGMSQa98QB7mN7gCKFILO2Ji6uqC6ecwQb42NtWAgu3/hrin93rcbGoJPMmz8Rx6QfaB6kqSn2k/NraYlr7h+l3DqQRkTPX841dhKMxrlpXkxxL/EW8xwmwcz0uPO6Z2Y44nVf1ABcA37HWng8EOFEOAoCTeZ7xHSPGmNuNMduMMds6OyeemiYicjoksp9raovGjNcW59EdCHPY2cBYlyYYXFVdyBudfmIxS5c/TGWGDDbAW1dXsnlDLVefVZNx3nx23YZanv/0lVM6aAbi/cVrilP/xWWd879b4khlETlzPfV6O0V5HjYtP3EabtnoADscmbH6a5hegN0CtFhrtzrPHyAecLc75R04/+xwrh8D6kfdv8QZyzS+JMX4BNbau6y1m6y1m6qqqqbxkURETl0ywK4ZG2AnArqdzrHcqQ6PgXiAPTwSo6V3iJ7A5BlsYwzf/dCFfOCSpdNd+hnL5TJUp8lIj5YoEQGoSZvBdgLstoHsLE5EZs225l4uaagYUw5WlOvB4zL0BMMEQtEZKw+BaQTY1to24KgxZq0zdDWwD3gISHQCuRV40Hn8EPBhp5vIpUC/U0ryOHCtMabM2dx4LfC4c23AGHOp0z3kw6NeS0RkztnfPkh9ef6Etk81JfGA7tUj8QC7rjR1gLe6Or75MdFDe7IAW6audFSAXZ0mg11XkkdRnofXVYctckYLRaI0dweT30olGGMo83npDYTxhyIzdkw6xMs8puOTwE+MMV7gIPAR4kH7z4wxtwHNwE3O3EeBdwBNQNCZi7W2xxjzz8DLzrwvWGt7nMd/BvwAyAcec/6IiMyKv7p/BwB3vv+8lNf3tw2ydlz2GuIlIgCvHOnF63GlPdZ7/aJiPC7Dr/bET37MtMlRTk7xmAA79V9wjDGsqy3SRkeRM9zBzgDRmGV1zcSOTeUF3mQN9kz1wIZpBtjW2h3AphSXrk4x1wIfT/M69wD3pBjfBmyczhpFRLKhrX+YB3ccwwJ//fY11I/bWBiKRDnUFeDaDRProWucgK6ld4jlFek7fhR4PZxbX8qzjfG9JMpgZ09ejpv8HDdDI1FqMrQ0XFtbxIM7WrHWLtjOLCJnugPtqcv1AMp8OfQGw4QjseTm55mgkxxFRKbgv19pIWbBAPe9fGTC9YOdASIxy9ra4gnXygpy8HriP27TbXBMuHRFOSPR+N5wBdjZldjomC6DDbC6uojB4UjyJE1rLbfctYVPP7DrtKxRRKavsd2P22VYUeWbcK3c56XLHy8RmckMtgJsEZFJWGt5YHsLFzeUc9W6Gu5/uYVwJDZmTqKsIFWJiDEmudExXYu+hEtXVCQfT9ZFRE5OYqNjui4icOIAmu5ACICn93fw4sFufrb9aDIrJiJz24H2QZZVFJDrmbiJccOiEg51BWjtG56bmxxFRBaK7c29HOoKcOOFS/jgpUvp8od4Yl/7mDn72wfJcRsaKidmTOBE54pFaTY4Jly4rAyPy+BxmTGdL2T6SgtyMCbzNwOJ+vhufxhrLV//TSOLS/PxeT1848nG07VUEZmGxg4/a6onJjsANm+sBWBoJErBDG5yVIAtIjKJ/7uthQKvm3ecXcdbV1expCyfn2xtHjNnf9sgKyoLk6Ug4yU6iUyWwU7UYVcUelUDnGWl+V4qfBNPcRytwgm+u/whthzsYWdLP5+4ahV/+OblPLL7OJ//5V46BoZP15JF5CQNj0Rp7g6wJsUGR4CVVYXJa3N2k6OIyHwXDEd4eFcr7zy7LnkowS0XL+Urj+/njU4/K6viP6j3tw1y4bKytK+T6CRSVzJ5z+a/fvsaWvuGsrB6Ge3mi+t508qKjHMSZTnd/jDdTh32tetryM1xc7x/mB+92Mze1gF+9rE3zfh6ReTkHewMELOwOkW5XsLmjXUcaG+cswfNiIjMe4/tbiMQjnLjphPnYd20qR6Py/BfW+ObHQeHRzjWN5Q8qCSVRN1vukNmRrtsVeWY95PsuGJtNbe+eXnGOcV5OXhchu5AiLaBYbweF+U+L4W5Hr5207n86RUr2Xa4h4FhHacuMhc1dfqB+MFd6bzj7HiZSLlv5srwFGCLiGTwwPYWllcUcNGo43arinK5bmMtD2xvYXgkmtz8lmqDY8K162v5g0uXpq3RlrnB5TLxLgODYdr6h6ktzhtTqnPZqkpiFra80T2LqxSRdA51BjCGjD9r19UW88CfvIn3nLt4xtahAFtEJI2OwWG2HOrmhvMWT6iH/uAlS+kfGuGRXcfZ3xbPmGTKYC+v9PHF956NJ0P9r8wNFYW58Qy2E2CPdsHSMvJz3LzQ1DVLqxORTA51+VlUkk9eTuYOIZuWl5PvnbkuIqrBFhFJ41d72rAW3nlO3YRrb1pRwYpKHz988TBFeTkU5npYUjZ5+YfMfZWF8T65PYEw59WXjrnm9bi4ZEU5zynAFpmTDnUFUva/Pt2UShERSeORXcdZVV2Y8jQwYwwfuGQpO1v6eeGNLv7m2jXq+jFPVPi8dPnjNdi1KTalXr6qkoOdgZQbUbv8IeIHF2fWGwjzb796neGRaFbWLCLxMwsOdgVYXqEAW0Rk1nUOhrj2zmf46uP7k2Mdg8O8dLiHd549MXudcOOmeq45q4bv/sGFfOSyhtOxVDkNKgpzOdY3RDgSm1AiAiQ7kbx0qGfM+MDwCJd9+Sm+/KvXJ32Ph3a28u3fvsHje9s43BXgnd94jqM9wex8AJEFqjsQZnA4Mif2uijAFpEFLRyJ8Wc/2c6Bdj//5+km7neOQf/13nashXdkCLBL8nP4/q2buG5D7elarpwGFYVeEknoVBnstTVF5Oe42XG0b8z44a4AoUiM7z17kO3NvRnfY6dz78O7jvOD3x1mb+sAvx53eJGInJxDXQEAGlQiIiIye36zr50bvvUCLx/u5c73n8tbVlfyD7/YQ/vAME++1s7S8oK0hxXI/FXpO3HSY6oA2+N2cfbikgkB9hEnA52f4+Yv73+VPcf6075H4t5nDnTy81daANh6MHVnkljMMhKNndyHEFmADnXGA+wVymCLiMyOlt4gH/3RNkKRKN+45Xx+7/wlfOGGjYxELT96sZkX3ujm6rOqVVe9AFU4h80AKUtEAM5bWsq+1gHCkROBb3N3PMD+jw9tIjQS473feoEfbWmecG9/cISDXQGuWldNOBJjYDjCiiofLx3uIRabWL/99Scbuebfn0l5TUROONgVIMdtpnTewExTgC0iC9Kju49jLdz7kYt5z7mLgHjf1EsayvnuM28QjsR4+1k1s7xKmQ2VznHpLhPveZ7KefWlhKMxXjs+kBw72hOkstDL5asrefwv38pb11TxuV/s4VtPN425d2dLPHv9R5c1UFeSx/KKAv70bSvpC47Q2OGf8F6/2tNGc3eQAx2D2fqIIvPSoS4/S8sL5kQ71NlfgYjILHhkdxtnLy6hvrxgzPj7L6onErMU5Xm4qKF8llYnsymRwa4szCUnzS/qc532faPLRI70BFnq/PdU5vPyHx+6kBvOW8RXHt/PY7uPJ+ftONqHMXBOfQn/8aEL+fYHL+TSFfGNkw/vauX7zx0kGI4A8c22+52DjH7XpMNtREaz1vLubz7PfzzzBhCvwW6onBtlfQqwRWTBOdoTZOfRvpT9ra/fWEdJfg5XratOG1zJ/Fbh1GDXpai/TlhUkkdVUW5ysyKMDbABctwuvvK+czl3SQmf/u9dtPTGS0h2HO1jZVUhxXk5nLOklPWLillSls+ikjy++VQTX3zkNX69N77hMRFU5+W4eDFNjfZ4+9sG+egPt7H5fz/L3//PbtVvy7z12vFBdh/r58WD3URjlsPdwTnRAxsUYIvIAvSrPW0AKVvw5XvdPPjxy/j8ezac7mXJHJHvdePzuqlJU38N8T7o5ywuYW9rvEQkHInR2jc0JsCG+ME037zlAkKRGN9/7hAQD4DPXlwy4fX+6PIGrt8Y70jTPjAMwPNNXZQW5PCecxexxQkiMvldUxfv+87v2N7cS0Whl59sPcKf//RVBdkyLz31evwvooe64n3pw5HYnGjRBwqwRWQB2tHSx7KKggnlIQnLK32UFnhTXpOF4T3nLeKa9Zlr8BeV5tM+GA+EW/uGiFlS/je1tKKAhkofR3uCRGOWtoHhlJuw/vgtK/jOH1xIgddNx2D8wJrnG7u4bGUll62qZHA4wpOvtdPlD6Vd0z/9ci+VRbk8/MnL+ckfX8rn3rWex/a08Yn/emXMhkxrLTuP9hGK6KAbOXM9+XoHAC29QxxwSqkUYIuIzJJDnYE50cZJ5q5/+f1zuGlTfcY5VUW59AVHCEWiyRZ94zPYCYtK82ntH6ZjcJhozFJXmj47Xl2US/vAMK39w7QNDHPpinLetKICY+D2H23niq/8Fn8oMuG+aMxyqCvAdRtqWeQE8Ldd3sAd717P43vb+dwv9gCwr3WAd3zjeW741gv800P7pvTvQ2Su6fKH2HG0jxWVPqIxy7MHOoG50aIPFGCLyAJjreVwd4Dlc+SHsJy5qp0OI13+cDLAXpbmiOZFpXkc7x+itS+e8V5Ukr6NWHVxHh2DoeTJjg2VhVQX5/GjP7qET127Bn8owtNO5m604/1DjEQtyyrGBvkfuayB9563iCedr9O/88wbtPQGuWpdNT996Qjbm3smvJbIXPf06x1YCx+5PH6K7pOvd+DzutN2/jndFGCLyILSMRgiGI7OmSyHnLkSv8g7B0Mc6Qni9biSQfd4dSX59AVHkifNpTrAJqG6KJfOwRAtvUMALCmLB+OXr67kT69YRWWhN7mPYLREH+7xATbA6poiuvxh/KEIzd0Bzqsv5Zu3nM+ikjz+/n/2TFrbLTLXPL63jUUlebzb2aze0jtEQ5VvzpxdoABbRBaUg85JX3OllZOcuaqL4kFyx8AwR7qD1Jfl43Kl/uW+yCkJeeVI/Aj1jBnsojzaB4Zp6Q1iDGPKSdwuw7Ubanl6fwfDI2Prp08E2BP/8pgIuo90BznUFWB5hQ9froe/f+d6Xm8b5MEdx6b6sUVm3eDwCM82drF5Yx2lBV7KffE9M3Pp57oCbBFZUBIZxOWVqWtlRaYqkcHucDLY6eqvIZ7BBniluZcCr5vifE/audXFuQTDUfa3DVJTlEeuxz3m+vUbawmGozzj1JwmNHcH8Hpc1KXofrKsPB5072rpY3A4kgy4r99Yy4ZFxdz5mwM8vKuVbz3dhLXKZsvc9tTrHYQjMa4/O951J/GN5FzZ4AhZCLCNMW5jzKvGmIed5w3GmK3GmCZjzP3GGK8znus8b3KuLx/1Gp91xvcbY64bNb7ZGWsyxnxmumsVETnsBCGZMogiU1FR6MUYkvXS6eqv4UTGen/7IHUleRm/xq4pjgfurxzpTZaHjHbpigryclxsPTi2dvpwd4Cl5QUps+hLnYD62cZ4UJ5Yq8tl+NS1aznaM8Qn/utVvvL4fna29Gf62CKz7ld72qgqyuXCpWXAicB6LpX+ZSOD/RfAa6Oe/ytwp7V2FdAL3OaM3wb0OuN3OvMwxqwHbgY2AJuBbztBuxv4FnA9sB64xZkrInJSRmfkDnYGaKjwpf0qX2Sqctwuygu8NLYPMhiKpG37CFBTEg+arSXZ4SOdROlJ+0CIxSkC7By3i+qiPLoDY9v1NXcHWZZmDSX5OZQW5PB8YxcAy0fVaV+xtorPXr+Of3vfOXjdLh7e2ZpxfSKzqbF9kKde72Dzhtrkz/GGqnmWwTbGLAHeCXzfeW6Aq4AHnCn3Au91Ht/gPMe5frUz/wbgPmttyFp7CGgCLnb+NFlrD1prw8B9zlwRkSk71BVg/T8+zlbnFLxDXX6Vh0jWVBXlsq05XledqUQk1+OmsjAeZNdmOMAGGLNRMlUGG+LZ825/OPncWhsPsDNk0ZeVFzAwHMGYsf26jTF87G0ruWlTPW9dU8XDu44T06ZHmYMGh0f42I+3U5SXwyeuWpUcv25DLTect4h1dUWzuLqxppvB/t/A3wGJ7vUVQJ+1NtGgswVY7DxeDBwFcK73O/OT4+PuSTc+gTHmdmPMNmPMts7OzlRTRGSB+sWrxxgaifLo7uNEojGO9ATn1EYYObNVOR0/IHOADbDY2axYN8UMNsCSstSvWeHLHXPgTOdgiKGRaMoOIglLneC7tjiPvBx3yjnvPreOtoFhtjubMUXmku89e5DDXQH+zwfOH3PS6sqqQr5+8/kT9ivMplMOsI0x7wI6rLXbs7ieU2Ktvctau8lau6mqqmq2lyMic4S1lkd2HwfgucYudh/rZyRqWVOjAFuyY3QwXF+eOXBObHRclKFFH0BxvodcT/zXc7oMdmWhl+7AiQx2c0/6Fn0JifKRTHOuOasGr8fFE/vaM65RZDY819TF+UvLuHRFxWwvZVLTyWBfBrzHGHOYePnGVcDXgVJjTGJ79BIg0fvnGFAP4FwvAbpHj4+7J924iMiUHGj309ThZ01NIQe7Anzt1wfIz3Hz9kmOwBaZqkQnkaqiXAq86TuDwIl2e5NlsI0xVDsbHdNmsAu99ATCyVKOo5OcJAknNjouz1BG4sv10FDhS3bbEZkrguEIu1v6uaShfLaXMiWnHGBbaz9rrV1irV1OfJPiU9baDwJPA+9zpt0KPOg8fsh5jnP9KRvfefQQcLPTZaQBWA28BLwMrHa6knid93joVNcrIgvPI7tacRn4wg0bAXi+qYv3nLuIorycWV6ZzBeJeunJykPgRCeRukky2PHXjc9ZlOZI9QpfLtGYpX9oBIh3MgHGfG0+XiKDvTRDBhtgcVk+x5xDbkTmiu3NvURilkvOgOw1QOa/bp+aTwP3GWO+CLwK3O2M3w38yBjTBPQQD5ix1u41xvwM2AdEgI9ba6MAxphPAI8DbuAea+3eGViviMxD1loe2tnKJQ0VXNJQzuLSfI71DfGBS5bO9tJkHqk6iQB788ZajvUNTamVWG1JHnUlE3tgJ1QUxg/W6A6EKPN56RgIUeB148tN/2v9rEXFrK0p4rKVlRnfe3FpPtubJ9ZgD4Wj7DveT19whLesrsLr0VEacvpsPdiD22W4cFnZbC9lSrISYFtrfwv81nl8kHgHkPFzhoEb09z/JeBLKcYfBR7NxhpFZGF55Ugfh7uDfPzKVRhjuHHTEnYc7eOcJSWzvTSZRxIZ7Ewt+hLqywv4p/dsmNLr/s3b14ypsR4v0ZGkyx9mVTV0+kNpj2lPKM7L4fG/euuk772oNJ/+oRH8oQh/ff8OCnM9fOb6dbz/ri3J0pFzl5TwzVsumDQbLpItWw91s3FxCYUZ/hI5l5wZqxQRmaIj3fHjpX/+Sgt5OS6uP7sOgL+8Zs0sr0zmo/ryAlwG1tVmtz3YiqpCVmTYs5/MYDut+joGhpPZ9OlK9N4+3BXg6f0djEQtv97Xzkg0xtdvPo+Ytfzjg3v52I+389hfvCUr7ymSyfBIlJ1H+/nIZctneylTpgBbROYNay0fumcrx/uGcbsMmzfUnjHZDjkzLSrN56m/uWJKJSLZVOGLB9OJw2Y6/aGsBfmLnU2YzzZ2MhK1vGlFBa8e7eWbt5zPtRviR1P3B0f4p1/uo6ljkFXVc6f3sMxPR3uChKMxzqornu2lTJkKqERk3tjZ0k9zd5D68nyGRqLcdFH95DeJTNPyytN/MmhZQQ7GxEtEADoHQmNaBk5HojXgU691APD5Gzaw+5+uSwbXANefXYcx8Miutqy8p0gmRxJdcs6gkiSldkRk3nh4Zys5bsPP//QyQtFo1gIOkbnG43ZRVuCl2x9iKBxlMBTJWolIVWEuXreLV470kuM2NFT6yHGPzcfVFOdx0fJyHtndyl9cszor7yuSTnO30+f9NH9TNB3KYIvIvBCLWR7edZy3ramipCBHwbXMe5XOcemJkySzFWC7XIa60jxiFlZVF00IrhPeeXYdB9r9NLYPZuV9RdI50hOkMNdDuc8720uZMgXYIjIvbD/SS9vAMO8+d9FsL0XktKjw5dIdCNHpHwayF2DDiZ7dZ2Wo675ibXwX5is6Vl1mWHN3gKXlBRhzekuxpkMBtojMC7/c2Uqux8XVZ+mURlkYKpwMdsdAPIM9WZu+k5HoJLKuLn2Avbg0H4/L0NwdZHgkyofu3sqXHtlH+8Bw1tYhAtDcE2TZGVR/DQqwRWQeiERjPLr7OFefVa2uIbJgVBbm0uUP0enPbokInOgksq42fdcGj9vFkrJ8mnuCvN42yHONXXzvuUO8+5vPMzwSzdpaZGGLxiwtPUNn1AZHUIAtIvPA1kM9dPnDvOsclYfIwlHh8zIwHOFY7xAuc6J1XzacWx8/0GPj4swHMy2t8HGkO5isw/78ezbQMRjioZ2tyTnW2qytSxae9oFhwtHYaW+FOV0KsEXkjPfwrlZ8XjdXrq2e7aWInDara+LlG7/c2UpFYS7uLLYKvHJtNTv+8e2TbipbWp5Pc3eApk4/XreLD16ylLU1RfzghcM0dwf4X/+zmw13PM53n3kja2uTheVEBxHfLK/k5CjAFpEzUiIr1uUP8cudx7l2Qy35Xvcsr0rk9LnmrGqWlOXT2j9MVWH2stcAxhg8abqHjLas3MfAcIRth3tpqPThcbv48JuXse/4AFd/7Rn+e3sLBV43/729Javrk4XjSE8AQDXYIiIzrS8Y5uqvPcNf3vcqdz5xgKGRKB+/ctVsL0vktPK4Xdx2eQMA1cXZDbCnKlEX+8qRXlbVFALwe+cvZuPiYm44bzHP/t2VfOLKVTR2+DnY6Z+VNcqZrbk7iMdlqRV/7gAAIABJREFUqCs5s1qvKsAWkTOKtZa//8UeDncH+MWOVn6y9Qjvv6ieVdWFs700kdPu/RfVU+7zztoBHImsorWwqir+/8ECr4eHP/kWvnbTuf+vvTuPj6q+9z/++kz2PWQhhEAISwwGAsgOooJWxaXubV3qeq3VVqtXr62199b2tra2Xnt/Wu3ivlaqV627VBE3KLvsa1gDZCEJZN/n+/tjJjFIAoghkwnv5+PBY2a+53vOfM54Mn7me74LafGRbStA/nNtcUBilOC2vqiq7e5IMNFwexEJCo3NXq55ahE7ymvZubeOO8/MIS4ylL8t3MFtp2klOTk2RYeH8u6tJxEToNlz2g886+xHbv/EKPIyEpi9pogbTxnaXaFJL+CcY3nBPk4dHnzja5Rgi0hQeO3znczfXMYZuWlcPHYAN54ylBCPcdWUrECHJhJQafGBu3UeHR5KalwEe6oayE7r/C7S6blp/OH9jeytaaRPu4GTzjkWbClnXn4p9U0t5A1I4Juj+uPpwgGbErx27q2jvKaRMQMTAx3KV6YEW0R6vOYWL3/+aDN5GQn89cpxQbWal0hvNygpmrLqBgandD7LQ2uCtK6oknGD+jAvv5RTh6fxry1lXP7YQkI8RqjHaGj2UlXfzHcnD+qu8KUHevzTLZRUNbRNE6kEW0Skiy3bsZeXFhewrayWv3x3rJJrkR5mzMBEmr2OiNDOZ/EZ7l9yfUNRFdtKa7n7tVV8cPvJbVOwfXD7KWQmRXPBI/N4ev42rpiUqb/1Y9T8/FJ+/fY6AE4clkxkmKft+gkmSrBFpMfKL6nmkj/Px2PGOXnpnJHbL9AhiciX3H328bQcYjGZ1LgI+kSHsaGoisYWLwAF5XUUVtTjMRjQJ4oQj3HN1CzueHkF8/LLmJad0h3hSw9R19jC++uK+e076xicEkNJZT3z8suYkNUn6AY4gmYREZEe7O+Ld+AxY95dp/LIFWPVL1OkB/J4jLBDJEBmRk6/ODYUV7F8xz4Adu2ro6iijtS4iLb9zx2dTnJMOM/8a1vbvqt3VXDWg5/yj893Ha1TkABr8Toue2wBP3rxc7zO8dClJ/CdCZkAjB4QfN1DQC3YItIDFJTX8rN/rMbrdZwxIo2rpmTR2OzllWW7OD03LaCDuESkawzvF8+Li3bQ0Oxrwd61z9eC3S8hqq1ORGgIZ4zoxzurCnHOsXjbXq55ahG1jS3c9epKcvvHc1xa8HUXkIN7YeF2lhfs494LR3LphExCPEZSbDhvrdwdlDOIgFqwRSTAmlq83Pzi5yzbvpeiynp+/voa5ueXMntNEeU1jXxnwsBAhygiXSCnX1xbcg2w259gp3/pB/Tx6XFU1DVRUtXA459uITYilLdumUZsRBg3Pb+Uyvqm7g5djhLnHPPyS7l/9gamDUvh8om+5BogIzGKRT/7BlOHBWdXISXYIhIwXq/jv99cy4qCfdx/ySjevHkaWcnR/GjWcv7j5RUMTonhpOzUQIcpIl0gxz9QzWOQl5HA7n11FFXUk564f4Ld2kK9vqiKVbsqmDwkmZEZCfzxshPYXlbLTc8vpbFdoi7B66evruKKxxcSHR7Cry4Y2asGtirBFpGAqG9q4YbnlvDcgu1cP20wZ+WlExUewu8uHsW+2kamDUvh79+f3NaaISLBrTVxHt4vnuy+sWwsrqa6ofmAJbBz/PXmby6lsKKePP9UbVOGJnPfxaOYl1/Gk/O2dvgedY0tvL58F17vwQddSuB5vY43V+zm7Lx+fHznjINO8xiMjjjBNrOBZjbXzNaa2Rozu9VfnmRm75vZJv9jH3+5mdlDZpZvZivNbGy7Y13tr7/JzK5uVz7OzFb593nIetNPG5Fj3EtLCvhgXQn3fDOXn51zfFv5pCHJLP2v03n86vH0jVPfa5HeIjYilHGD+vCN4/vSPzGKijpfV4/2fbAB+sSE0zcuom1QY96AhLZtl4wbwIj+8Xy8YU+H7/HLN9dw66zlfLyx4+3Sc2wvr6WmsYVTjkslMqzzKR6D1ddpwW4G7nDO5QKTgR+aWS5wFzDHOZcNzPG/BjgLyPb/uwH4M/gScuAeYBIwEbinNSn31/leu/1mfo14RaQHeXnJTnLT47n2xMEH3BZMiArrVbcKRcTnlZumcvsZOfRP/CKp7p9w4A/pnH5xFFc2ADCif/x+26YMSWbpjr3UN7XsVz53QwmzFhcA8O7qwsOKxx1iekE5etbsrgBgRP+EQ9QMTkecYDvnCp1zy/zPq4B1QAZwPvCMv9ozwAX+5+cDzzqfBUCimaUDZwLvO+fKnXN7gfeBmf5t8c65Bc73F/Bsu2OJSBB4ffkulmwrP6B8fVElq3ZV8K3xAwIQlYgEWv92/a77dZBgt3YnGZIaQ1xk2H7bJg9JprHZy7Ide7n9peW8uWI3AL95ex3D+sZydl4/3l9bzL7aRp6Zv43axuYOY3jwg02c+sDHNLWoP3cgrN1dSajHyE6LDXQoR0WXTNNnZlnACcBCIM051/rTsQhI8z/PAAra7bbTX3aw8p0dlHf0/jfgaxUnMzPzyE9ERLrMioJ93DprOQDnjkrngW+Pblvp7ZWlOwkLMc4f0+GftIj0chn+FmwzOpyGs7Ufdmv/6/YmDE7CDO59ex1rdldSUtnASdkpbCqp5s4zcxiaGss7q4q48E/z2VpaQ3VDMz+cMWy/YzS3eHl+4Xb2VDUwZ10xM0emH4WzlINZs7uSYX1jD7oCaDD72oMczSwWeAW4zTlX2X6bv+X5qN9/cc496pwb75wbn5qqGQdEeoIH52wiMTqMm2cM462Vhdz37nrAd1vw+QU7OD03jaSY8ABHKSKBkO5PsFNjIzpcpKZ1xpGOEuyEqDBG9I9nzW5fyrG8YB+fF/gWrzlhYCKnHJdKVFgIW0trSE+I5MnPtlLXuH93kk/zS9lT1UCox3hh4Q7AN/B67oYSyqobuu5ED8PCLWUs27G3W9+zu20vq2FdYWVbv3vwJdi9tXsIfM0WbDMLw5dcv+Cce9VfXGxm6c65Qn83jxJ/+S6g/YS2A/xlu4DpXyr/yF8+oIP6ItKDfbaplM937OXD9SXceWYOP5wxjOqGZp6at436Ji+fbNxDQlQYvzhvRKBDFZEAiY0IJSEq7IAZRFrlZSTw07OGc/HYjruRTRmSzOpdlXxzdH/eXLGb/1u6EzMYOSCBqPAQ7jwzB69z5GUk8J1HF/Dy0gKumpLVtv8rS3eSGB3GdycN4uG5+dzx0greX1tEZX0zF52QwR++M+ZonHaH7nljDVHhIbz2gxO77T27y6biKv7zH6tZuNXXVTAqLITPfjKDFq+jtLrhgP71vckRJ9j+GT2eANY55/7QbtMbwNXAff7H19uV32xms/ANaKzwJ+Gzgd+0G9h4BvBT51y5mVWa2WR8XU+uAv54pPGKyNH33uoibnx+KQBDUmK4emoWAD89ezgF5bW8vnwXEaEenr52omYIETnG5abHk5kU3eE2j8f4/ilDO933+pOGMLxfPGMH9eHNFbt5b3URQ1Njiff3175u2mDAN4hx/KA+/PHDfM4fk0FCVBil1Q38c20xl04YyJVTBvHop1t4Z1UhM0f2o7S6gdlriqhvaumWmS2cc+woryXEDOdcrxjc3eJ1NLV4CfEYt7z4OcWV9fxk5nCiwjz84s21fLJpT9t/JyXYHTsRuBJYZWbL/WV340usXzKzfwO2A9/2b3sHOBvIB2qBawH8ifSvgMX+ev/tnGsdFfUD4GkgCnjX/09EeqDiynp++upKRmbE8+x1k0iICmubwzoiNIQnrpkA+OY+9Whua5Fj3lPXTuBI88m0+EguHjcA5xxJMeGU1zQyZmDiAfXMjF+cN4LzH5nHb99Zx30Xj+J/Zm/A63VcNSWLtPhI5tx+Cn1iwomNCGVefilXPL6QD9eXcHbeF/2yl2wrJy4yrK3rSlcpr2mk1t99paSqocP+6D2B1+tYtK2cUQMSiA73pY6ts7h8+YfI//xzA8/M38bkIcmsL6risavGc3puGl6v4+G5+cxdv4ewEA9xEaGMGnDgf7Pe4ogTbOfcZ0BnfxqndVDfAT/s5FhPAk92UL4EGHmkMYpI92hu8XLrrM+pa2rh/33nhIP2rVZyLSJwYGJ2JMyMEwYmMmd9CaM7SLABRmYkcP1Jg/nrx1uob2rh9RW7ue7EwQzr65u9YmC7VvTJQ5JJjYvgjeW72xLs+ZtLueqJRcRHhfHebSd16d23gr11bc83FVf32AT7jRW7ue3vy4kKC+H49DgcsGZXJQOTonjthye2tUjXN7Xwt4U7CPEYH64vYeaIfpye65vrwuMxTjmuLx+sK6apxcv5Y/oTFd47BziCVnIUka/o5SUF/OXjzbS0Wynt97M3sGBLOfdekNf2Py0Rke4wdpCvh+mYg7SG/vs3juOyiZm8u7qI5JgIfnRadof1QjzGOXnpfLihhJqGZgrKa7nxuaUMTIqmpqGZH//fyv2++76ugvLatuf5JVVddtyu9v7aYlJiw7l4XAbR4aFEhHr41vgBbC+r5fa/L29bOXP2miIq6pr48xXjePnGKTzw7dH7HWd6TioVdU3UNrZwybjePU1rl0zTJyLHhjnrivnxKytxzjfy/eHLx7K1tIZHP9nCdydncnEv/8IUkZ7n0gkDiQ4PYWRG5/15I8NC+O1Fedw1cziNLV4SosI6rXt6bhpPz9/Goq3lrNxZQVVDM2/dchIfbSzh56+v4ewHP+WKyZkMTY1lypDk/e7KNbd4eWjOJk7J6cu4QX3ayjvrX12w15dgR4WFkL+n+khO/6hravENTj87L51fX5C337bsvrH84s21vLu6iHNGpTNrUQEDk6KYOjS5w7uVJ2en4jHISo5hbGafA7b3JkqwReSwrN1dyW2zlpObHs8l4wbw32+t5Q/vb6S0uoHYiFB+PHN4oEMUkWNQcmwE1544+LDqJkR3nli3GjeoD+GhHubll7Jsx15GZSSQmRzNlZMHkRobwe/eW8/PX18DwDVTs/abEemJz7by0If5/OXjLfx4Zg4nZPbhsU+2sGLnPt64eRqpcRH7vVdBeR19osMYkhrLpuKemWAv3lZOVUMzM4b3PWDblVOyeHDOJuasL2bUgAT+taWMO04/rtOugAnRYdxxRg7HpcX1igGdB6MEW0QOaXnBPq56YiGxkaE8etV4MhKj2FhczdPztwFw7dSstj54IiLBLDIshAlZffjn2mJ27q1tW6TGzDgrL50zR/SjuKqev368hafnbyMrOZprThzMjrJa/veDjczISaWpxfHrt9f5j+ehucXxh/c38NuLRu33Xjv31jIwKZphqbHMWV/c7efayjnHO6uKKKyoY3tZLW+vKuR7Jw3hpulDmbu+hPAQD9OyUw7YL8RjnJSdyicb9zA01dc98IITDr6A2JcX/emtlGCLSKecc7y0pICfv76GvvER/O36yW0rsN15Zg7vrCqkuqGZa6cdXuuRiEgwmDo0hfvzNwBw8nH7L2Dn8RjpCVH8/Nxc8kuq+eOH+Vw9NYs/fZSPx4zfXJRHWlwk64uq2LynmrGD+vDEp1t5ev5WrpycRW67qekKymsZ0T+BYX1j+fuSAnaU1ZLRJ6ptBqbuUFBey3+9vpqPNuwBICLUQ1R4CO+sKuSm6UP5cH0Jk4YkERvRcco4PSeVN1bs5tFPtjA2M3G/QaPHMg1yFJFOvb2qkJ+8sooJWUm89oMT9/viTIoJ58FLx3DvBSPbkm4Rkd7gxGG+1tq4iNAOp/8DX6J97qh0ymoa2VJaw7zNpZyUnUJ6QhQej5HbP55vju5PRmIUt56WTUxEKE/N2wrAh+uLWbp9L7v21TEgKYrsNF/r78n3z+WyRxfQ3OLd771eX76La59axNLth17xcV9tI9UNzYesV9fYwveeXcLJ98/lX5vL+NX5I1j5izNY9YszuXpKFmt2V7BqZwWb99RwagfdQ1qdlO37AVJR18R5o/sf8n2PFWrBFhH2VDWwcuc+ZuT0bes755zj4Q/zye4byzPXTeywRWV6TudfuiIiwSovI4HE6DCmDEnucCn3VhMGJwHw+ue7KCiv47pO+oInRIcxaXAyS7fvpcXruHXWchqbvTS1OAb2iWbasBTuvXAk20preOzTrTy3YHtbv/K6xhZ+9dY6SqsbmLthD5dNHMi/n34c8/PLSIoJZ9KQJCJCfdPdNTZ7Of+ReeSkxfHoVeP5eOMe3ltdRGOzl4vGZrT9cAC4543VfLCumJtnDOOyiZn0b9dQMmVoMg/O2cTv3lsPcNAEOzUugryMBNbsruCcUUqwWynBFjmGNTS3MH9zGXe+vJLS6gamDk3mgW+PJj0hirkbSlhfVMUD3xrdrbcrRUQCLcRj/O36yaTEdj6nP/hWrE2JDecp/3iUyUOSO607blAfPlhXzCcb91BV39y2yM7ApGhCQzxcMWkQzjk2FFfzwD83ck5eOn3jI3lx0Q5Kqxt46toJLNhSxqOfbOHFRQVtx81IjOLd204iPjKMWYt3sL2slsKKemoamrnrlZVU1DURFuLhlWU7+fb4Afz+ktG8sWI3Ly3Zyc0zhnHHGTkHxDpmYCLhoR4+yy9laGoMg5JjDvo5/Oi0bPJLqg8YxHksUxcRkV7M63W8t7qQTzbuaSvbVlrDKffP5fj/eo/cn8/m2qcWEx8Zyk9mDmdFwT5ueHYpNQ3N/O/7m+ifEMl5Y9QiISLHntz+8fQ9xMIvZsb4QUlU1TeTGB1GTlrnKz2Oz/JNS/fHDzcB8MjlYxmbmciojIT9jvfL80ZQ3dDMy0t30tDcwl8+3sykwUnMyOnLT886nheun8QPZwzllZum8ruL89i1r46PNuyhtrGZh+bkkxIbQWOzl4c+3ERhRT2/uTCPhXefxqUTBvLy0p0UVtTx5482M7xfHLd9o+P5wCPDQhib6esac7DW61an56Zx0/TOl7Y/FqkFW6SX2rynmh88v4wNxVXERoSy8O7TcMANzy2hoq6J707OJCI0hJx+cZw6vC8xEaEMTonmxueXccb/fsKufXU8cvnYg94eFRE51k0YnMR7a4qYNDjpoCvV5mUkEB7iYdmOfeSkxXF2Xvp+y7G3GpwSw+iBicxeU0RmUjQlVQ38/pIvZh+ZOjSFqUN9XT3GDEzk/tkbeH9tMSWV9ZRWN/C3703i+88u5fFPtxIR6uEbuWlEhoVw4ylDmbW4gN+8s551hZX86vwRhB7k+33ykGQWbCnn1OFpX+PTOXYpwRbpBZxzvLiogPfXFrGnuoGTs1N5eelOnHPcelo2D87ZxJsrdrNwazmb99Tw7HUT9+uL12rmyHQuGpvBq8t28Z/nHM85ow788hcRkS9M8vfDnnKQ7iHgaxUemRHPsh37mDrs4HXPGtmP+95dzyNz88lIjOLk7NQO64V4jNOGp/HOqkIWbS1j8pAkpg5N4eTjUnl7VSFn5Ka1zf6RlRLD2MxE3lyxm/BQD+eNPvh0epdPyiQ81MNE//nJV6OmKZEg5PU6Kuub2p7/6q113P3aKnaU1xIVFsKfP96Mc/Di9yZz2zeyyUmL4/7ZG3jt813ccuqwDpPrVr+9KI9XfzCV608a0l2nIyIStEZmJPDE1eO5dGLmIeu2ru544tDOv4MBzhzRD4D1RVV8Z8LAg7aMn56bRlVDM8WVDfxgum+O6dZuHed+adDhhWN9q+2ekZt2yEV3+sZF8oPpwzQG5wipBVskSDjn2FRSzbz8Up5bsJ2de+t45tqJfLi+mCfnbeXaE7P4r3Ny8XiM3fvqCAvxtA04uXxSJve8sYbh/eLavoA7ExEa0uuXsBUR6UqnHX943SjOH5PBusIqpgw9eAv24JQYhveLY2NxFZeMG3DQutOyU4gKC2FIagwn+ReDOW9Mf8JDPcwc2W+/uueN6s8by3epAaUbmHMu0DF0qfHjx7slS5YEOgyRLtXY7OX2l5bz1spCwNeXr6axmV1762ho9nLVlEH88rwRnS49W1XfxF2vrOLmU4dxfHp8h3VERKTn+HB9MZtLavjeyYdOhj/bVEq/hEiG9Y3thsikPTNb6pwbf0C5EmyRnqGirokFW8qYOjSZOP+y44u3lfPCgu1sKa1h5c4KfnRaNhePzSAzKZrdFfV8+y//Ii8jgUeuGKvbeCIiIt2sswRbXUREAqi4sp6XFheQv6eaOetKqG5oJjMpmgcvHUN6QhTfe3YJzvkm8r//klF8a/zAtn0zEqP46M7phHqs05ZrERER6X5KsEW6idfruP2l5TS1OL45Op0FW8qZtXgHDc1e+idEcdrxfTl1eF9+9+56LvzTfPrFR9LQ5OXtH01jSGrHt/00hZ6IiEjPowRb5ChrbPYSFmK8vLSAfyzfTUSoh7dXFRIe4uHsvH7cfnoOmcnRbfVnDO/LXz/ezLP/2s4vzx/RaXItIiIiPZP6YIscJUu2lfP79zawbMdeMpOjKatuJCctjieuGc/KnRWMGpDQ1te6I845df0QERHpwdQHW6SLOOf4zTvr2F5Wy70X5rVNhef1OlbvrqCwop4QM26d9TmJ0eFcMzWLRdvKKdxXz70XjiQuMuyg81C3UnItIiISnJRgi7RTXFnPL95Yw9DUWK49MYs/f7SZ6oZmJg5OIjo8lKSYcBZvK+exT7fiMVi2Yy9jBiZSVd/M+qIqKuqa2o6VlRzNS9+fQt/4SJxzNDR7iQwLCeDZiYiISHdQFxERoLK+iX98vosHP9hEVX0zjS1eQj2G1zliIkKpqm/er/5ZI/txy6nZ3PfeesqqG4gI9ZDTL46Jg5MYlBzDjrJaThyW0ta6LSIiIr2PuoiIfElTi5cnP9vK35cUsK20Bq+DMQMTuf+SUeSXVPPKsp3ccmo2I/rHs7W0hsYWLyVVDeytaeSskelEhYfw7HUTOzy2VkIUERE5dvX4BNvMZgIPAiHA4865+wIckgSQ1+tYW1jJyp0VZCZFMyYzkYVbyogMC2Fgn2jeXlVIi9fL4JRYXl22k7KaRsZm9iE5NpzNJdV8uKGEtLhI0hMj2bynmoLyOqYMSebcUf05bXhfRg9MBCA7LY6z8tLb3jc7LQ6AEQE5axEREQkmPTrBNrMQ4BHgdGAnsNjM3nDOrQ1sZPJ11Te1sK6wkn21TTS1eAkP9ZAcE8G/tpTy4qICkmPCGdE/nuy0OEoq69lb20RqXARvrNhNfkn1QY9tBs5BSmwEWcnRPL9wO43NXuIjQznt+DT21jayp6qB4/rG8bOzc5k5sl83nbWIiIgcC3p0gg1MBPKdc1sAzGwWcD6gBPsoc87hdV881jQ0s6+uCY9BiMfwmFFW3UhJVT3FlQ3sqWpgT3U9pVWNVDU0UdPQQk1DM/XNLRhG/8RIvA721TYCsK2slsZmb4fvPXFwEl6v4/+W7qSmsQWP0dYPeni/OH5/ySgmZiWxtrCS1bsqmDo0haYWL5v3VHN6bhqxEaFsKK5ibGYfIsNC2gYYhnqMUC3MIiIiIkdZjx7kaGaXADOdc9f7X18JTHLO3dzZPjEZx7njb3rkoMc9nFM+nE/lcD67wztO970XDrzO4fA9et2BZUd6SfSJDiMlNoKEqDCiI0KJCQ8hKiyEFufYubeOEI/RJzoMw8joE8XEwUn0jYsgLMRDQ7OXPVUN9EuIZIy/m4bX69hdUUdKbASRYSFU1TcRGxGq6etERESkR+jVgxzN7AbgBoDE/kM4c8Shb/kfTo5mHLrS4R3nMOp0UdJ4OIfxmOEx3yP+R2tXjv/R8D8aRIeHkhjtWxSlucXhdY6kmHD6xkfSNy6CVH+i3JU8HmNAny9WODzYoiwiIiIiPUVPT7B3AQPbvR7gL9uPc+5R4FHwTdN374V53ROdiIiIiMiX9PQOqYuBbDMbbGbhwKXAGwGOSURERESkUz26Bds512xmNwOz8U3T96Rzbk2AwxIRERER6VSPTrABnHPvAO8EOg4RERERkcPR07uIiIiIiIgEFSXYIiIiIiJdSAm2iIiIiEgXUoItIiIiItKFlGCLiIiIiHQhJdgiIiIiIl1ICbaIiIiISBcy51ygY+hSZlYFbPC/TAAqAhjOkQjGmFulAKWBDuIIBPNnDsEbf7BeL62C9XOH4I1d10xgBGvcrYL5ugmmzz6YYu3IkV4nOc65uC8X9viFZo7ABufceAAze9Q5d0OgA/oqgjHmVma2pPWzDybB/JlD8MYfrNdLq2D93CF4Y9c1ExjBGnerYL5ugumzD6ZYO3Kk14mZLemovLd3EXkz0AEcgWCMOdgF+2ce7PEHq2D+3IM59mAWrJ97sMbdGwTTZx9MsR51vbGLSND+Ug12+uzlq9D1Il+Vrhk5Erpu5HB8nRbsjvbrjS3YjwY6gGOYPnv5KnS9yFela0aOhK4bORxHep10uF+va8EWEREREQmk3tiCLSIiIiISMEqwRURERES6kBJs+crM7AIzc2Y2PNCxSHAws+pDbP/IzDQISTCzAWb2upltMrPNZvagmYUfpP5tZhbdnTFKz3Oo7xgR6N78RQm2HInLgM/8j4fNzEKOTjgi0huYmQGvAv9wzmUDxwGxwL0H2e02QAm2iByObstflGDLV2JmscA04N+AS/1l083sEzN728w2mNlfzMzj31ZtZg+Y2QpgSuAil0DzXydvtXv9sJldE8CQpOc5Fah3zj0F4JxrAf4duM7MYszsf8xstZmtNLNbzOxHQH9grpnNDWDc0gOYWayZzTGzZWa2yszO95dnmdk6M3vMzNaY2T/NLCrQ8Ur36u78RQm2fFXnA+855zYCZWY2zl8+EbgFyAWGAhf5y2OAhc650c65z7o9WhEJJiOApe0LnHOVwA7geiALGOOcGwW84Jx7CNgNzHDOzejmWKXnqQcudM6NBWYAD/jvigBkA48450YA+4CLAxSjBE635i9KsOWrugyY5X8+iy9usyxyzm2uF1RqAAAFGUlEQVTxtzi9iO9XIkAL8Er3higivdB04K/OuWYA51x5YMORHsiA35jZSuADIANI82/b6pxb7n++FN+PNTm2dGv+EnqkO8qxx8yS8N3CzTMzB4QADnjb/9he6+t6/0Ur0sz+P+ojAxWI9FhrgUvaF5hZPJAJbAtEQBJUrgBSgXHOuSYz28YX3zMN7eq1AOoicgwJRP6iFmz5Ki4BnnPODXLOZTnnBgJbgZOAiWY22N936Tv4BhGItLcdyDWzCDNLBE4LdEDS48wBos3sKmgbWPQA8DQwG/i+mYX6tyX596kC4ro/VOmBEoASf3I9AxgU6ICkx+j2/EUJtnwVlwGvfansFX/5YuBhYB2+i/bL9eQY5U+IGpxzBcBLwGr/4+cBDUx6HOdbWvhC4FtmtgnYiK9f7d3A4/j6Yq/0Dzq63L/bo8B7GuR47Gr9jgFeAMab2SrgKmB9QAOTnqTb8xctlS5fm5lNB/7DOXduoGORnsfMRgOPOecmBjoWEel99B0jR+po5i9qwRaRo8bMbsQ3aOQ/Ax2LiPQ++o6Rnkot2CIiIiIiXUgt2CIiIhI0zGygmc01s7X+hWNu9Zcnmdn7ZrbJ/9jHX36Ff3GiVWY239+lpPVYT5pZiZmtDtT5SO+kBFtERESCSTNwh3MuF5gM/NDMcoG7gDnOuWx8M9Lc5a+/FTjFOZcH/ArfwNhWTwMzuytwOXYowRYREZGg4ZwrdM4t8z+vwjf7Qwa+lfqe8Vd7BrjAX2e+c26vv3wBMKDdsT4BtGiRdDkl2CIiIhKUzCwLOAFYCKQ55wr9m4r4YhXH9v4NeLdbgpNjmlZyFBERkaBjZrH45jK+zTlXaWZt25xzzr9iX/v6M/Al2NMQOcrUgi0iIiJBxczC8CXXLzjnXvUXF5tZun97OlDSrv4ofIsVne+cK+vueOXYowRbREREgob5mqqfANY55/7QbtMbwNX+51cDr/vrZwKvAlc65zZ2Z6xy7NI82CIiIhI0zGwa8CmwCvD6i+/G1w/7JSAT2A582zlXbmaPAxf7ywCanXPj/cd6EZgOpADFwD3OuSe66VSkF1OCLSIiIiLShdRFRERERESkCynBFhERERHpQkqwRURERES6kBJsEREREZEupARbRERERKQLKcEWEemlzKzFzJab2RozW2Fmd5jZQb/3zSzLzC7vrhhFRHojJdgiIr1XnXNujHNuBHA6cBZwzyH2yQKUYIuIfA2aB1tEpJcys2rnXGy710OAxfgW1RgEPAfE+Dff7Jybb2YLgOOBrcAzwEPAffgW44gAHnHO/bXbTkJEJAgpwRYR6aW+nGD7y/YBOUAV4HXO1ZtZNvCic268mU0H/sM5d66//g1AX+fcr80sApgHfMs5t7VbT0ZEJIiEBjoAEREJiDDgYTMbA7QAx3VS7wxglJld4n+dAGTja+EWEZEOKMEWETlG+LuItAAl+PpiFwOj8Y3Hqe9sN+AW59zsbglSRKQX0CBHEZFjgJmlAn8BHna+voEJQKFzzgtcCYT4q1YBce12nQ3cZGZh/uMcZ2YxiIhIp9SCLSLSe0WZ2XJ83UGa8Q1q/IN/25+AV8zsKuA9oMZfvhJoMbMVwNPAg/hmFllmZgbsAS7orhMQEQlGGuQoIiIiItKF1EVERERERKQLKcEWEREREelCSrBFRERERLqQEmwRERERkS6kBFtEREREpAspwRYRERER6UJKsEVEREREupASbBERERGRLvT/AVoU6dEkkUdkAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x7zVeeVBOMhK"
      },
      "source": [
        "from statsmodels.tsa.stattools import adfuller\n",
        "def adf_test(dataset):\n",
        "     dftest = adfuller(dataset, autolag = 'AIC')\n",
        "     print(\"1. ADF : \",dftest[0])\n",
        "     print(\"2. P-Value : \", dftest[1])\n",
        "     print(\"3. Num Of Lags : \", dftest[2])\n",
        "     print(\"4. Num Of Observations Used For ADF Regression:\",      \n",
        "dftest[3])\n",
        "     print(\"5. Critical Values :\")\n",
        "     for key, val in dftest[4].items():\n",
        "         print(\"\\t\",key, \": \", val)\n",
        "\n"
      ],
      "execution_count": 250,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w2zA3ylGPOaC",
        "outputId": "cb78abde-f512-4611-8be7-4f4b69ffaf7e"
      },
      "source": [
        "adf_test(data['Daily Confirmed'])"
      ],
      "execution_count": 251,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1. ADF :  -0.25432207232867104\n",
            "2. P-Value :  0.9317291702322467\n",
            "3. Num Of Lags :  17\n",
            "4. Num Of Observations Used For ADF Regression: 419\n",
            "5. Critical Values :\n",
            "\t 1% :  -3.4460536145702383\n",
            "\t 5% :  -2.868462751982839\n",
            "\t 10% :  -2.570457599182051\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2vsyqV9JPn3i"
      },
      "source": [
        "from pmdarima import auto_arima\n",
        "import warnings \n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 252,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qx1r5VzCP7yK",
        "outputId": "f12f8211-aded-4720-f60e-6f1a6280d09c"
      },
      "source": [
        "stepwise_fit = auto_arima(data['Daily Confirmed'], trace=True,\n",
        "suppress_warnings=True)"
      ],
      "execution_count": 253,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Performing stepwise search to minimize aic\n",
            " ARIMA(2,2,2)(0,0,0)[0]             : AIC=8457.008, Time=0.31 sec\n",
            " ARIMA(0,2,0)(0,0,0)[0]             : AIC=8790.176, Time=0.02 sec\n",
            " ARIMA(1,2,0)(0,0,0)[0]             : AIC=8698.364, Time=0.05 sec\n",
            " ARIMA(0,2,1)(0,0,0)[0]             : AIC=8475.072, Time=0.21 sec\n",
            " ARIMA(1,2,2)(0,0,0)[0]             : AIC=8468.096, Time=0.43 sec\n",
            " ARIMA(2,2,1)(0,0,0)[0]             : AIC=8462.780, Time=0.16 sec\n",
            " ARIMA(3,2,2)(0,0,0)[0]             : AIC=inf, Time=1.07 sec\n",
            " ARIMA(2,2,3)(0,0,0)[0]             : AIC=8403.115, Time=1.39 sec\n",
            " ARIMA(1,2,3)(0,0,0)[0]             : AIC=8408.522, Time=0.91 sec\n",
            " ARIMA(3,2,3)(0,0,0)[0]             : AIC=8387.226, Time=1.08 sec\n",
            " ARIMA(4,2,3)(0,0,0)[0]             : AIC=8389.089, Time=1.37 sec\n",
            " ARIMA(3,2,4)(0,0,0)[0]             : AIC=inf, Time=1.68 sec\n",
            " ARIMA(2,2,4)(0,0,0)[0]             : AIC=8420.211, Time=0.64 sec\n",
            " ARIMA(4,2,2)(0,0,0)[0]             : AIC=inf, Time=1.07 sec\n",
            " ARIMA(4,2,4)(0,0,0)[0]             : AIC=8389.692, Time=2.00 sec\n",
            " ARIMA(3,2,3)(0,0,0)[0] intercept   : AIC=8402.361, Time=1.39 sec\n",
            "\n",
            "Best model:  ARIMA(3,2,3)(0,0,0)[0]          \n",
            "Total fit time: 13.808 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "zNInrzYZQWNK",
        "outputId": "5bf1071a-69a2-4652-f1e6-6b8d1e570dd2"
      },
      "source": [
        "stepwise_fit.summary()"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>SARIMAX Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>437</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>           <td>SARIMAX(3, 2, 3)</td> <th>  Log Likelihood     </th> <td>-4186.613</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>            <td>Sun, 11 Apr 2021</td> <th>  AIC                </th> <td>8387.226</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                <td>04:47:23</td>     <th>  BIC                </th> <td>8415.753</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>8398.485</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                      <td> - 437</td>      <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L1</th>  <td>   -0.3780</td> <td>    0.069</td> <td>   -5.514</td> <td> 0.000</td> <td>   -0.512</td> <td>   -0.244</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L2</th>  <td>   -0.9602</td> <td>    0.038</td> <td>  -25.138</td> <td> 0.000</td> <td>   -1.035</td> <td>   -0.885</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L3</th>  <td>    0.0477</td> <td>    0.067</td> <td>    0.708</td> <td> 0.479</td> <td>   -0.084</td> <td>    0.180</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1</th>  <td>   -0.6237</td> <td>    0.040</td> <td>  -15.698</td> <td> 0.000</td> <td>   -0.702</td> <td>   -0.546</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L2</th>  <td>    0.6282</td> <td>    0.032</td> <td>   19.847</td> <td> 0.000</td> <td>    0.566</td> <td>    0.690</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L3</th>  <td>   -0.8684</td> <td>    0.035</td> <td>  -24.592</td> <td> 0.000</td> <td>   -0.938</td> <td>   -0.799</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sigma2</th> <td> 1.855e+07</td> <td> 1.41e-09</td> <td> 1.32e+16</td> <td> 0.000</td> <td> 1.86e+07</td> <td> 1.86e+07</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Ljung-Box (L1) (Q):</th>     <td>0.01</td>  <th>  Jarque-Bera (JB):  </th> <td>216.11</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Q):</th>                <td>0.92</td>  <th>  Prob(JB):          </th>  <td>0.00</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Heteroskedasticity (H):</th> <td>70.96</td> <th>  Skew:              </th>  <td>0.01</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(H) (two-sided):</th>    <td>0.00</td>  <th>  Kurtosis:          </th>  <td>6.45</td> \n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step).<br/>[2] Covariance matrix is singular or near-singular, with condition number 3.31e+31. Standard errors may be unstable."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                               SARIMAX Results                                \n",
              "==============================================================================\n",
              "Dep. Variable:                      y   No. Observations:                  437\n",
              "Model:               SARIMAX(3, 2, 3)   Log Likelihood               -4186.613\n",
              "Date:                Sun, 11 Apr 2021   AIC                           8387.226\n",
              "Time:                        04:47:23   BIC                           8415.753\n",
              "Sample:                             0   HQIC                          8398.485\n",
              "                                - 437                                         \n",
              "Covariance Type:                  opg                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "ar.L1         -0.3780      0.069     -5.514      0.000      -0.512      -0.244\n",
              "ar.L2         -0.9602      0.038    -25.138      0.000      -1.035      -0.885\n",
              "ar.L3          0.0477      0.067      0.708      0.479      -0.084       0.180\n",
              "ma.L1         -0.6237      0.040    -15.698      0.000      -0.702      -0.546\n",
              "ma.L2          0.6282      0.032     19.847      0.000       0.566       0.690\n",
              "ma.L3         -0.8684      0.035    -24.592      0.000      -0.938      -0.799\n",
              "sigma2      1.855e+07   1.41e-09   1.32e+16      0.000    1.86e+07    1.86e+07\n",
              "===================================================================================\n",
              "Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):               216.11\n",
              "Prob(Q):                              0.92   Prob(JB):                         0.00\n",
              "Heteroskedasticity (H):              70.96   Skew:                             0.01\n",
              "Prob(H) (two-sided):                  0.00   Kurtosis:                         6.45\n",
              "===================================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
              "[2] Covariance matrix is singular or near-singular, with condition number 3.31e+31. Standard errors may be unstable.\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hJQHbIPzRAib",
        "outputId": "c7d8bed4-642e-471d-940b-7548fbb01f60"
      },
      "source": [
        "print(data.shape)\n",
        "train=data.iloc[:-30]\n",
        "test=data.iloc[-30:]\n",
        "print(train.shape,test.shape)"
      ],
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(437, 1)\n",
            "(407, 1) (30, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "q4WdqaKGQiPq",
        "outputId": "1f87d1c4-bb4a-47dc-f2e1-e377c2df4988"
      },
      "source": [
        "from statsmodels.tsa.arima_model import ARIMA\n",
        "model=ARIMA(train['Daily Confirmed'],order=(3,2,3))\n",
        "model=model.fit()\n",
        "model.summary()"
      ],
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>ARIMA Model Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th> <td>D2.Daily Confirmed</td> <th>  No. Observations:  </th>    <td>405</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>           <td>ARIMA(3, 2, 3)</td>   <th>  Log Likelihood     </th> <td>-3839.609</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>              <td>css-mle</td>      <th>  S.D. of innovations</th> <td>3113.828</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>           <td>Sun, 11 Apr 2021</td>  <th>  AIC                </th> <td>7695.219</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>               <td>06:18:25</td>      <th>  BIC                </th> <td>7727.250</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>            <td>02-01-2020</td>     <th>  HQIC               </th> <td>7707.897</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                  <td>- 03-11-2021</td>    <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "              <td></td>                <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>                    <td>   -1.1833</td> <td>    1.191</td> <td>   -0.994</td> <td> 0.320</td> <td>   -3.517</td> <td>    1.150</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L1.D2.Daily Confirmed</th> <td>   -0.6400</td> <td>    0.049</td> <td>  -12.964</td> <td> 0.000</td> <td>   -0.737</td> <td>   -0.543</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L2.D2.Daily Confirmed</th> <td>   -1.0613</td> <td>    0.025</td> <td>  -43.108</td> <td> 0.000</td> <td>   -1.110</td> <td>   -1.013</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L3.D2.Daily Confirmed</th> <td>   -0.1601</td> <td>    0.049</td> <td>   -3.254</td> <td> 0.001</td> <td>   -0.257</td> <td>   -0.064</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1.D2.Daily Confirmed</th> <td>   -0.4186</td> <td>    0.018</td> <td>  -23.700</td> <td> 0.000</td> <td>   -0.453</td> <td>   -0.384</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L2.D2.Daily Confirmed</th> <td>    0.4183</td> <td>    0.017</td> <td>   24.038</td> <td> 0.000</td> <td>    0.384</td> <td>    0.452</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L3.D2.Daily Confirmed</th> <td>   -0.9997</td> <td>    0.025</td> <td>  -40.161</td> <td> 0.000</td> <td>   -1.048</td> <td>   -0.951</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<caption>Roots</caption>\n",
              "<tr>\n",
              "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.1</th> <td>          -0.2426</td> <td>          -0.9787j</td> <td>           1.0083</td> <td>          -0.2887</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.2</th> <td>          -0.2426</td> <td>          +0.9787j</td> <td>           1.0083</td> <td>           0.2887</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AR.3</th> <td>          -6.1438</td> <td>          -0.0000j</td> <td>           6.1438</td> <td>          -0.5000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.1</th> <td>           1.0000</td> <td>          -0.0000j</td> <td>           1.0000</td> <td>          -0.0000</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.2</th> <td>          -0.2908</td> <td>          -0.9570j</td> <td>           1.0002</td> <td>          -0.2970</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>MA.3</th> <td>          -0.2908</td> <td>          +0.9570j</td> <td>           1.0002</td> <td>           0.2970</td>\n",
              "</tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                             ARIMA Model Results                              \n",
              "==============================================================================\n",
              "Dep. Variable:     D2.Daily Confirmed   No. Observations:                  405\n",
              "Model:                 ARIMA(3, 2, 3)   Log Likelihood               -3839.609\n",
              "Method:                       css-mle   S.D. of innovations           3113.828\n",
              "Date:                Sun, 11 Apr 2021   AIC                           7695.219\n",
              "Time:                        06:18:25   BIC                           7727.250\n",
              "Sample:                    02-01-2020   HQIC                          7707.897\n",
              "                         - 03-11-2021                                         \n",
              "============================================================================================\n",
              "                               coef    std err          z      P>|z|      [0.025      0.975]\n",
              "--------------------------------------------------------------------------------------------\n",
              "const                       -1.1833      1.191     -0.994      0.320      -3.517       1.150\n",
              "ar.L1.D2.Daily Confirmed    -0.6400      0.049    -12.964      0.000      -0.737      -0.543\n",
              "ar.L2.D2.Daily Confirmed    -1.0613      0.025    -43.108      0.000      -1.110      -1.013\n",
              "ar.L3.D2.Daily Confirmed    -0.1601      0.049     -3.254      0.001      -0.257      -0.064\n",
              "ma.L1.D2.Daily Confirmed    -0.4186      0.018    -23.700      0.000      -0.453      -0.384\n",
              "ma.L2.D2.Daily Confirmed     0.4183      0.017     24.038      0.000       0.384       0.452\n",
              "ma.L3.D2.Daily Confirmed    -0.9997      0.025    -40.161      0.000      -1.048      -0.951\n",
              "                                    Roots                                    \n",
              "=============================================================================\n",
              "                  Real          Imaginary           Modulus         Frequency\n",
              "-----------------------------------------------------------------------------\n",
              "AR.1           -0.2426           -0.9787j            1.0083           -0.2887\n",
              "AR.2           -0.2426           +0.9787j            1.0083            0.2887\n",
              "AR.3           -6.1438           -0.0000j            6.1438           -0.5000\n",
              "MA.1            1.0000           -0.0000j            1.0000           -0.0000\n",
              "MA.2           -0.2908           -0.9570j            1.0002           -0.2970\n",
              "MA.3           -0.2908           +0.9570j            1.0002            0.2970\n",
              "-----------------------------------------------------------------------------\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "z7loiNroRSYq",
        "outputId": "b54856e0-a07e-46b5-8ba4-39dabbe80b89"
      },
      "source": [
        "start=len(train)\n",
        "end=len(train)+len(test)-1\n",
        "pred=model.predict(start=start,end=end,typ='levels').rename('ARIMA Predictions')\n",
        "pred.plot(legend=True)\n",
        "test['Daily Confirmed'].plot(legend=True)"
      ],
      "execution_count": 256,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f013e1b7610>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 256
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7jiK7ufjR2uL",
        "outputId": "97695528-9cae-4c25-ecab-d91e21134bcf"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt\n",
        "test['Daily Confirmed'].mean()\n",
        "rmse=sqrt(mean_squared_error(pred,test['Daily Confirmed']))\n",
        "print(rmse)"
      ],
      "execution_count": 257,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "61696.120959626445\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "x8Hyta1_Sdzi",
        "outputId": "daf1b5ca-c0b7-479f-f570-104d1df539e8"
      },
      "source": [
        "\n",
        "model2=ARIMA(data['Daily Confirmed'],order=(3,2,3))\n",
        "model2=model2.fit()\n",
        "data.tail()"
      ],
      "execution_count": 258,
      "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>Daily Confirmed</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-04-06</th>\n",
              "      <td>115312.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-04-07</th>\n",
              "      <td>126276.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-04-08</th>\n",
              "      <td>131878.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-04-09</th>\n",
              "      <td>144945.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-04-10</th>\n",
              "      <td>152565.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Daily Confirmed\n",
              "Date                       \n",
              "2021-04-06         115312.0\n",
              "2021-04-07         126276.0\n",
              "2021-04-08         131878.0\n",
              "2021-04-09         144945.0\n",
              "2021-04-10         152565.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yiZqthjkSHii",
        "outputId": "140b0595-dec0-467c-d592-7d83264450ec"
      },
      "source": [
        "index_future_dates=pd.date_range(start='2021-04-10',end='2021-5-10')\n",
        "#print(index_future_dates)\n",
        "predd=model2.predict(start=len(data),end=len(data)+30,typ='levels').rename('Covid19 India ARIMA Predictions')\n",
        "#print(comp_pred)\n",
        "predd.index=index_future_dates\n",
        "print(predd)"
      ],
      "execution_count": 321,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-04-10    157940.366719\n",
            "2021-04-11    159556.007770\n",
            "2021-04-12    165406.672498\n",
            "2021-04-13    173188.350915\n",
            "2021-04-14    176023.689009\n",
            "2021-04-15    179100.456650\n",
            "2021-04-16    186952.947741\n",
            "2021-04-17    192562.607468\n",
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            "2021-05-07    293701.452831\n",
            "2021-05-08    299831.412156\n",
            "2021-05-09    302586.373877\n",
            "2021-05-10    308019.653732\n",
            "Freq: D, Name: Covid19 India ARIMA Predictions, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "An_U3eCnZyYj"
      },
      "source": [
        "final = pd.concat([predd, data], ignore_index=True, sort=False)"
      ],
      "execution_count": 322,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n1k7iNDeaDdr"
      },
      "source": [
        "pret = predd.to_frame()"
      ],
      "execution_count": 323,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iBl7JJAhadNr"
      },
      "source": [
        "final = pd.concat([predd, data], ignore_index=True, sort=False)"
      ],
      "execution_count": 324,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pFlcyi71c0HE"
      },
      "source": [
        "pret.index.name = 'Date'"
      ],
      "execution_count": 325,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
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        "outputId": "a5f7b1f3-f2ec-4965-8e88-631f941caa58"
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      "source": [
        "pret.head()"
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      "execution_count": 326,
      "outputs": [
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              "            Covid19 India ARIMA Predictions\n",
              "Date                                       \n",
              "2021-04-10                    157940.366719\n",
              "2021-04-11                    159556.007770\n",
              "2021-04-12                    165406.672498\n",
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        "pret.columns = ['Daily Confirmed']"
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      "execution_count": 327,
      "outputs": []
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            "text/plain": [
              "            Daily Confirmed\n",
              "Date                       \n",
              "2021-04-10    157940.366719\n",
              "2021-04-11    159556.007770\n",
              "2021-04-12    165406.672498\n",
              "2021-04-13    173188.350915\n",
              "2021-04-14    176023.689009\n",
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              "2021-04-16    186952.947741\n",
              "2021-04-17    192562.607468\n",
              "2021-04-18    194472.544207\n",
              "2021-04-19    200185.807423\n",
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              "2021-05-08    299831.412156\n",
              "2021-05-09    302586.373877\n",
              "2021-05-10    308019.653732"
            ]
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          "execution_count": 328
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      "cell_type": "code",
      "metadata": {
        "id": "tVfEm9NueD4U"
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      "source": [
        "final = pd.concat([data, pret], ignore_index=False, sort=False)\n"
      ],
      "execution_count": 329,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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        "outputId": "277e0f3d-dc12-48ec-ff75-c4078d327c26"
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      "source": [
        "final.tail()"
      ],
      "execution_count": 330,
      "outputs": [
        {
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          "data": {
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            "text/plain": [
              "            Daily Confirmed\n",
              "Date                       \n",
              "2021-05-06    286334.861675\n",
              "2021-05-07    293701.452831\n",
              "2021-05-08    299831.412156\n",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "2UZChDNBb9ve"
      },
      "source": [
        "dataf = pd.concat([data, pret]).sort_values('Date').reset_index(drop=False)"
      ],
      "execution_count": 331,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RcITmm67ekAE",
        "outputId": "5e86f4d3-13c9-4387-ba4e-b9c4ab638ea2"
      },
      "source": [
        "finalld.info(\n",
        "    \n",
        ")"
      ],
      "execution_count": 332,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 468 entries, 0 to 467\n",
            "Data columns (total 2 columns):\n",
            " #   Column           Non-Null Count  Dtype         \n",
            "---  ------           --------------  -----         \n",
            " 0   Date             468 non-null    datetime64[ns]\n",
            " 1   Daily Confirmed  468 non-null    float64       \n",
            "dtypes: datetime64[ns](1), float64(1)\n",
            "memory usage: 7.4 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_KINXQzfubc"
      },
      "source": [
        "finalls[\"Date\"] = finalld[\"Date\"].astype(int)"
      ],
      "execution_count": 333,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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        "id": "qdBGdUHYf86k",
        "outputId": "c8589f30-81f5-4b95-f5d7-385ddb089557"
      },
      "source": [
        "dataf"
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      "execution_count": 334,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>3</th>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>2021-05-06</td>\n",
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              "    <tr>\n",
              "      <th>464</th>\n",
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              "    <tr>\n",
              "      <th>465</th>\n",
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              "    <tr>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>467</th>\n",
              "      <td>2021-05-10</td>\n",
              "      <td>308019.653732</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>468 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date  Daily Confirmed\n",
              "0   2020-01-30         1.000000\n",
              "1   2020-01-31         0.000000\n",
              "2   2020-02-01         0.000000\n",
              "3   2020-02-02         1.000000\n",
              "4   2020-02-03         1.000000\n",
              "..         ...              ...\n",
              "463 2021-05-06    286334.861675\n",
              "464 2021-05-07    293701.452831\n",
              "465 2021-05-08    299831.412156\n",
              "466 2021-05-09    302586.373877\n",
              "467 2021-05-10    308019.653732\n",
              "\n",
              "[468 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 334
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EzHQnQc2fHCD"
      },
      "source": [
        "P = pd.DataFrame(dataf)"
      ],
      "execution_count": 335,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Um23QVflg-cl"
      },
      "source": [
        "P.Date = pd.to_datetime(P.Date)"
      ],
      "execution_count": 336,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "rXrEe0grhKVe",
        "outputId": "d56a64f2-2bd7-4978-e476-0c7df632801a"
      },
      "source": [
        "P"
      ],
      "execution_count": 337,
      "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>Daily Confirmed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-30</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-31</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-02-01</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-02-02</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-02-03</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>463</th>\n",
              "      <td>2021-05-06</td>\n",
              "      <td>286334.861675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>464</th>\n",
              "      <td>2021-05-07</td>\n",
              "      <td>293701.452831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>465</th>\n",
              "      <td>2021-05-08</td>\n",
              "      <td>299831.412156</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466</th>\n",
              "      <td>2021-05-09</td>\n",
              "      <td>302586.373877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>467</th>\n",
              "      <td>2021-05-10</td>\n",
              "      <td>308019.653732</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>468 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date  Daily Confirmed\n",
              "0   2020-01-30         1.000000\n",
              "1   2020-01-31         0.000000\n",
              "2   2020-02-01         0.000000\n",
              "3   2020-02-02         1.000000\n",
              "4   2020-02-03         1.000000\n",
              "..         ...              ...\n",
              "463 2021-05-06    286334.861675\n",
              "464 2021-05-07    293701.452831\n",
              "465 2021-05-08    299831.412156\n",
              "466 2021-05-09    302586.373877\n",
              "467 2021-05-10    308019.653732\n",
              "\n",
              "[468 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 337
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "1_MzcPqMi4ut",
        "outputId": "2f973ce5-ecd1-4baa-d978-7c62ba596c2d"
      },
      "source": [
        "\n",
        "import seaborn as sns\n",
        "sns.lineplot(data=P, x=\"Date\", y=\"Daily Confirmed\")"
      ],
      "execution_count": 338,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f011d503b50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 338
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YhDS8Kcykbg0"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}