Covid_19_Outbreak_Prediction_using_ML.ipynb

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  "metadata": {
    "colab": {
      "name": "Covid-19 Outbreak Prediction using ML.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/reenarao00/Data-Science./blob/main/Covid_19_Outbreak_Prediction_using_ML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importing the Dependencies"
      ],
      "metadata": {
        "id": "j8yrVjpnb7rr"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0Pffkxc5Mp8"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import datetime as dt\n",
        "from datetime import timedelta\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.svm import SVR\n",
        "from statsmodels.tsa.api import Holt\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data collection and Processing"
      ],
      "metadata": {
        "id": "pgIzb2P4cQFl"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_B0aAhNh6VKk",
        "outputId": "d05e3fc9-fa06-46cd-caa6-906d94ad4d52",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        }
      },
      "source": [
        "covid = pd.read_csv(\"covid_19_data.csv\")\n",
        "covid.head(10)"
      ],
      "execution_count": null,
      "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>SNo</th>\n",
              "      <th>ObservationDate</th>\n",
              "      <th>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>Last Update</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>01/22/2020</td>\n",
              "      <td>Anhui</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>01/22/2020</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>01/22/2020</td>\n",
              "      <td>Chongqing</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>01/22/2020</td>\n",
              "      <td>Fujian</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>01/22/2020</td>\n",
              "      <td>Gansu</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>01/22/2020</td>\n",
              "      <td>Guangdong</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>01/22/2020</td>\n",
              "      <td>Guangxi</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>01/22/2020</td>\n",
              "      <td>Guizhou</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>01/22/2020</td>\n",
              "      <td>Hainan</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>01/22/2020</td>\n",
              "      <td>Hebei</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   SNo ObservationDate Province/State  ... Confirmed Deaths  Recovered\n",
              "0    1      01/22/2020          Anhui  ...       1.0    0.0        0.0\n",
              "1    2      01/22/2020        Beijing  ...      14.0    0.0        0.0\n",
              "2    3      01/22/2020      Chongqing  ...       6.0    0.0        0.0\n",
              "3    4      01/22/2020         Fujian  ...       1.0    0.0        0.0\n",
              "4    5      01/22/2020          Gansu  ...       0.0    0.0        0.0\n",
              "5    6      01/22/2020      Guangdong  ...      26.0    0.0        0.0\n",
              "6    7      01/22/2020        Guangxi  ...       2.0    0.0        0.0\n",
              "7    8      01/22/2020        Guizhou  ...       1.0    0.0        0.0\n",
              "8    9      01/22/2020         Hainan  ...       4.0    0.0        0.0\n",
              "9   10      01/22/2020          Hebei  ...       1.0    0.0        0.0\n",
              "\n",
              "[10 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cHHaAVo98ksl",
        "outputId": "7ecc92e1-469e-4ee5-8772-29a2d90af68f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "source": [
        "print(\"Size/Shape of the dataset\",covid.shape)\n",
        "print(\"Checking for null values\",covid.isnull().sum())\n",
        "print(\"Checking Data-type\",covid.dtypes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Size/Shape of the dataset (18327, 8)\n",
            "Checking for null values SNo                   0\n",
            "ObservationDate       0\n",
            "Province/State     9277\n",
            "Country/Region        0\n",
            "Last Update           0\n",
            "Confirmed             0\n",
            "Deaths                0\n",
            "Recovered             0\n",
            "dtype: int64\n",
            "Checking Data-type SNo                  int64\n",
            "ObservationDate     object\n",
            "Province/State      object\n",
            "Country/Region      object\n",
            "Last Update         object\n",
            "Confirmed          float64\n",
            "Deaths             float64\n",
            "Recovered          float64\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_rnPaCMq9j14"
      },
      "source": [
        "#Dropping the column\n",
        "covid.drop([\"SNo\"],1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ugvWmzo-CKH",
        "outputId": "2645e7e6-7595-489d-ab9e-5f7c00ddeebf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "source": [
        "covid.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ObservationDate       0\n",
              "Province/State     9277\n",
              "Country/Region        0\n",
              "Last Update           0\n",
              "Confirmed             0\n",
              "Deaths                0\n",
              "Recovered             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zqTvvoU9-Ouf"
      },
      "source": [
        "covid[\"ObservationDate\"] = pd.to_datetime(covid[\"ObservationDate\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMwOFbap-uVw"
      },
      "source": [
        "#Grouping differnent types of cases as per the date\n",
        "datewise = covid.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sD_c8lcj_1FY",
        "outputId": "68d53054-02bf-425e-f89b-5f56070f016b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "print(\"Basic Information\")\n",
        "print(\"Total number of Confirmed cases around the world\",datewise[\"Confirmed\"].iloc[-1])\n",
        "print(\"Total number of Recovered cases around the world\",datewise[\"Recovered\"].iloc[-1])\n",
        "print(\"Total number of Death cases around the world\",datewise[\"Deaths\"].iloc[-1])\n",
        "print(\"Total number of Active cases around the world\",(datewise[\"Confirmed\"].iloc[-1]-datewise[\"Recovered\"].iloc[-1]-datewise[\"Deaths\"].iloc[-1]))\n",
        "print(\"Total number of Closed cases around the world\",(datewise[\"Recovered\"].iloc[-1]+datewise[\"Deaths\"].iloc[-1]))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Basic Information\n",
            "Total number of Confirmed cases around the world 2811193.0\n",
            "Total number of Recovered cases around the world 793601.0\n",
            "Total number of Death cases around the world 197159.0\n",
            "Total number of Active cases around the world 1820433.0\n",
            "Total number of Closed cases around the world 990760.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ozKjnxdaA5lN",
        "outputId": "f5e3a590-05ab-4408-a8c2-e869491a8ff7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        }
      },
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "sns.barplot(x=datewise.index.date,y=datewise[\"Confirmed\"]-datewise[\"Recovered\"]-datewise[\"Deaths\"])\n",
        "plt.title(\"Distributions plot for Active Cases\")\n",
        "plt.xticks(rotation=90)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
              "        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
              "        51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
              "        68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
              "        85, 86, 87, 88, 89, 90, 91, 92, 93]),\n",
              " <a list of 94 Text major ticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jDBqNIyaB4mG",
        "outputId": "d7759769-3489-4874-8d39-0b9799de2cbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        }
      },
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "sns.barplot(x=datewise.index.date,y=datewise[\"Recovered\"]+datewise[\"Deaths\"])\n",
        "plt.title(\"Distribution plot for Closed Cases\")\n",
        "plt.xticks(rotation=90)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
              "        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
              "        51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
              "        68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
              "        85, 86, 87, 88, 89, 90, 91, 92, 93]),\n",
              " <a list of 94 Text major ticklabel objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOH0PDBOCQO_",
        "outputId": "e7b9fa99-7323-4b48-8fbf-664a4d3dbcdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "source": [
        "datewise[\"WeekofYear\"] = datewise.index.weekofyear\n",
        "week_num = []\n",
        "weekwise_confirmed = []\n",
        "weekwise_recovered = []\n",
        "weekwise_deaths = []\n",
        "w = 1\n",
        "for i in list(datewise[\"WeekofYear\"].unique()):\n",
        "    weekwise_confirmed.append(datewise[datewise[\"WeekofYear\"]==i][\"Confirmed\"].iloc[-1])\n",
        "    weekwise_recovered.append(datewise[datewise[\"WeekofYear\"]==i][\"Recovered\"].iloc[-1])\n",
        "    weekwise_deaths.append(datewise[datewise[\"WeekofYear\"]==i][\"Deaths\"].iloc[-1])\n",
        "    week_num.append(w)\n",
        "    w=w+1\n",
        "plt.figure(figsize=(8,5))\n",
        "plt.plot(week_num,weekwise_confirmed,linewidth=3)\n",
        "plt.plot(week_num,weekwise_recovered,linewidth =3)\n",
        "plt.plot(week_num,weekwise_deaths,linewidth = 3)\n",
        "plt.xlabel(\"WeekNumber\")\n",
        "plt.ylabel(\"Number of cases\")\n",
        "plt.title(\"Weekly Progress of different types of cases\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Weekly Progress of different types of cases')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8998/Spb3HzeAX5w2GbPsXkrWESVxERGRdsxbXcW3751KfWMCgPEDS7n1jL0oLOg6qbPrRCIiItJFNA0rur4mDkB5WSF3n70PfUtiEUfWkpK4iIhIitbDihbF8rjjG3szsn9JxJG1pSQuIiIS6mhY0d1H9os4svYpiYuIiISiHFa0M5TERUREgPsjHla0M5TERUSkx3tx9iquinhY0c5QEhcRkR6t9bCiuw6PZljRzlASFxGRHqu9YUXvPCuaYUU7Q0lcRER6pK42rGhnKImLiEiP0xWHFe2MjCVxMxtpZi+a2Uwzm2FmF7WzziFmtsHMPggfV2UqHhEREei6w4p2RiYb/RuAi939PTPrDbxrZs+6+8xW673q7sdnMA4REZGkrjqsaGdk7Ejc3Ze7+3vhfCUwCxieqe2JiIhszuMfLuuyw4p2RlbOiZvZGGAP4K12Fu9vZh+a2b/MbFI24hERkZ7nnQXr+NGDHybL+43r36WGFe2MjPehN7My4GHg++5e0Wrxe8Bod68ys2OBx4A2P4nM7FzgXIBRo0ZlOGIREelu2htW9LYzpnSpYUU7I6PRm1mMIIE/4O6PtF7u7hXuXhXOPwXEzKxNzwJ3v93dp7j7lIEDB2YyZBER6WZyZVjRzshk73QD7gRmuftNHawzJFwPM9snjGdtpmISEZGeJZeGFe2MTDanHwicCUwzsw/CusuBUQDufivwReB8M2sANgKnu7tnMCYREekhcm1Y0c7IWBJ399eATfYWcPdbgFsyFYOIiPRcNz6dW8OKdkZun9EXERFpx/1vLuS2l3NrWNHOUBIXEZFupe2wooNyYljRzlASFxGRbmPOqioubDOs6B45MaxoZyiJi4hIt5BIOJc8/BHVOTqsaGcoiYuISLfwwNuLeHfhZ0AwKtntX98rp4YV7QwlcRERyXnLN2zkxn/NTpbPP2Q8k4b1jTCi7FASFxGRnNY0tGhVXQMA4waWcsGhEyKOKjuUxEVEJKc9NW0Fz81alSz/4tTJFMXyI4woe5TERUQkZ62vqefqx5svJ/vavqPYZ2z/CCPKLiVxERHJWT97ahZrquoBGNynF5ccs2PEEWWXkriIiOSk1+es4cGpS5Lla0/ahT5FuT8y2ZZQEhcRkZyzsb6Ryx+dliwfs8uQbndf9HQoiYuISM757fOfJIcX7V1UwE9PnBRxRNFQEhcRkZwyfekG7nh1frL8k2N3YlCf7n1Tl44oiYuISM5oaExwycMf0RjeHH2/cf358t4jI44qOkriIiKSM/78+nxmLKsAoLAgj5+fOhmz7jm4STqUxEVEJCcsXFvNTc9+kix//4iJjC0vjTCi6CmJi4hIl+fuXP7oNGrjCQB2GtqHbx88LuKooqckLiIiXd5D7y7h9TlrAcgzuPG0XYnlK4XpHRARkS5tdWUd1z85K1k+56CxTB7RL8KIug4lcRER6dJ++s8ZbNgYB2Bk/2J+cOT2EUfUdSiJi4hIl/XczJU88dHyZPlnp+xKSWFBhBF1LUriIiLSJVXWxrnyH80jlJ2653AOnjgwwoi6HiVxERHpkn759Mcs31ALwIDSQq48bueII+p6lMRFRKTLmbpgHfe9uTBZvuqEndmutDDCiLomJXEREelS6hoaufSRaXhwZ1UO3WEgJ+42LNqguiglcRER6VL++NJc5qyqAqCkMJ/rT9m1R99adVOUxEVEpMv4dGUlf3hxTrL846N2YHi/4ggj6tqUxEVEpEtIJJxLHv6IeGPQjr7HqH6cuf+YaIPq4pTERUSkS7j/rYW8t2g9ALF848bTJpOfp2b0TVESFxGRyC1bv5Eb/zU7WT7/kAlsP7h3hBHlBiVxERGJlLtz5WPTqa5vBGD8wFIuOHR8xFHlBiVxERGJ1BMfLef52auS5RtPm0yvgvwII8odSuIiIhKZz6rruebxGcnymfuNZsqY/hFGlFuUxEVEJDI3PDWLtdX1AAzpU8SPj94h4ohyS8aSuJmNNLMXzWymmc0ws4vaWcfM7HdmNsfMPjKzPTMVj4iIdC2vfbqGh95dkixfd/Iu9C6KRRhR7snkeG4NwMXu/p6Z9QbeNbNn3X1myjrHABPDx77AH8OpiIh0YxvrG7n80WnJ8nG7DuXInQdHGFFuytiRuLsvd/f3wvlKYBYwvNVqJwH3euBNoJ+ZDc1UTCIi0jX89rlPWLSuBoA+RQVcfaJGKOuMrJwTN7MxwB7AW60WDQcWp5SX0DbRi4hINzJ96Qb+9Oq8ZPmK43ZmUO+iCCPKXRlP4mZWBjwMfN/dKzr5Guea2VQzm7p69eptG6CIiGRNvDHBjx/6iEQ4QtkB4wfwpSkjog0qh2U0iZtZjCCBP+Duj7SzylJgZEp5RFjXgrvf7u5T3H3KwIEDMxOsiIhk3J2vzWfm8uB4rldBHj/TCGVbJZO90w24E5jl7jd1sNrjwNfDXur7ARvcfXmmYhIRkegsWFPNb579JFn+wZHbM6a8NMKIcl8me6cfCJwJTDOzD8K6y4FRAO5+K/AUcCwwB6gBzs5gPCIiEhF357JHplHXkABg0rA+fOugsRFHlfsylsTd/TVgk20k7u7ABZmKQUREuoa/T13Cf+atBSA/LxihrCBf9xvbWnoHRUQko1ZV1nL9k823CPnWQWPZZXjfCCPqPpTERUQko376+EwqahsAGNW/hO8fsX3EEXUfSuIiIpIxz85cyZPTmvsr//zUXSku1Ahl24qSuIiIZERlbZwrH5ueLH9xrxEcOKE8woi6HyVxERHJiP/598esqKgFoLyskJ8cu1PEEXU/SuIiIrLNTV2wjvveXJgsX33CJLYrLYwwou5ps0nczA40s9Jw/gwzu8nMRmc+NBERyUV1DY1c8vBHyfLhOw7i+Mka2yoT0jkS/yNQY2a7ARcDc4F7MxqViIjkrP99cS5zV1cDUFqYz3Un76Jbq2ZIOkm8Ibwpy0nALe7+B6B3ZsMSEZFcNG91FX98aW6y/OOjd2RYv+III+re0rljW6WZXUZwC9WDzSwPiGU2LBERyTXuzhWPTae+Mbi16u4j+3Hmfjr7mknpHIl/GagDvunuKwhGGvtlRqMSEZGc848PlvHG3ODWqnkGN5yyC3l5akbPpM0m8TBxPwz0CqvWAI9mMigREcktG2riLW6tevaBY5k0TLdWzbR0eqd/G3gIuC2sGg48lsmgREQkt/zP07NZU1UPwNC+RfzgSN1aNRvSaU6/gGBY0QoAd/8UGJTJoEREJHe8t+gz/vL2omT56hMmUdYrkyNdS5N0knidu9c3FcysAPDMhSQiIrmioTHB5Y9Mw8OscPiOgzhq0uBog+pB0kniL5vZ5UCxmR0J/B34Z2bDEhGRXHD3GwuYvaISgKJYHtecOEnXhGdROkn8UmA1MA04D3gKuCKTQYmISNe3bP1Gbnr2k2T5osO3Z2T/kggj6nk2e9LC3RPAn4A/mVl/YER48xcREenBfvrPGdTUNwKw/eAyvnXw2Igj6nnS6Z3+kpn1CRP4uwTJ/DeZD01ERLqq52au5OkZK5PlG07ZlVi+xtTKtnTe8b7uXgGcCtzr7vsCh2c2LBER6apq6hu4+vEZyfKXp4xk7zH9I4yo50oniReY2VDgv4AnMhyPiIh0cTc//ylL128EoH9pIZces2PEEfVc6STxa4GngTnu/o6ZjQM+zWxYIiLSFc1eUcGdr85Pli8/dieNEx6hdDq2/Z3gsrKm8jzgtEwGJSIiXU8i4fzk0ek0JIK+zfuO7c9pew6POKqebbNJ3MyKgHOASUBRU727fzODcYmISBfz4NTFvLvwMwBi+cYNp2ic8Kil05x+HzAEOAp4mWAUs8pMBiUiIl3L2qo6fv6v2cnyuZ8bx4RBvSOMSCC9JD7B3a8Eqt39HuA4YN/MhiUiIl3Jz56azYaNcQBG9S/hu4dNjDgigfSSeDycrjezXYC+aAAUEZEe4z9z1/Lwe0uS5WtPmkRRLD/CiKRJOsPM3G5m2xHcavVxoAy4KqNRiYhIl1DX0MgVj01Llo/bdSiH7KDjuK4ind7pd4SzrwDjMhuOiIh0JX96ZR5zV1cDUNargKtO2DniiCRVOrdd/ZmZ9Uspb2dm12c2LBERidrCtdX8/oU5yfKPvrA9g/sUbeIZkm3pnBM/xt3XNxXc/TPg2MyFJCIiUXN3rvrHDOoaEgDsOrwvZ+4/JtqgpI10kni+mfVqKphZMdBrE+uLiEiOe3Lacl7+ZDUAZnDDKbuQn6drwruadDq2PQA8b2Z3heWzgXsyF5KIiESpojbOtf+cmSx/fb/RTB7RbxPPkKik07HtRjP7EDgirLrO3Z/ObFgiIhKVm575hFWVdQAM6t2Li4/aIeKIpCPpHInj7v8G/p3hWEREJGIfLVnPvf9ZkCxfdcLO9CmKRRaPbFrGRnA3sz+b2Sozm97B8kPMbIOZfRA+dO25iEiEGsMBTsLxTfjc9gM5bteh0QYlm5TWkXgn3Q3cAty7iXVedffjMxiDiIik6b7/LGDa0g0AFBbkcd1JkzTASRfX4ZG4mT0fTm/szAu7+yvAuk7GJSIiWbSyopZfPfNJsvzdQycwekBphBFJOjZ1JD7UzA4ATjSzvwEtfo65+3vbYPv7h53mlgE/cvcZ2+A1RURkC137xEyq6hoAGD+wlHM/rxt05oJNJfGrgCsJhh69qdUyBw7bym2/B4x29yozOxZ4DGh3WBwzOxc4F2DUqFFbuVkREUn10serePKj5cny9SfvSq8CDXCSCzpsTnf3h9z9GOB/3P3QVo+tTeC4e4W7V4XzTwExMyvvYN3b3X2Ku08ZOHDg1m5aRERCtfFGrvpHcyPoqXsOZ//xAyKMSLZEOteJX2dmJwKfC6tecvcntnbDZjYEWOnubmb7EPygWLu1rysiIum75YU5LFpXA0Df4hiXH7tTxBHJlthsEjeznwP7ENy5DeAiMzvA3S/fzPP+ChwClJvZEuBqIAbg7rcCXwTON7MGYCNwurt7Z3dERES2zJxVldz2ytxk+bJjdqS8THfVziXpXGJ2HLC7uycAzOwe4H1gk0nc3b+ymeW3EFyCJiIiWeYeXBMebwyOnfYavR3/NWVkxFHJlkr3Zi+pN83tm4lAREQkex5+bylvzQ+uAs7PM244ZRfyNMBJzknnSPznwPtm9iLBZWafAy7NaFQiIpIxn1XX87OnZiXL3zpoLDsO6RNhRNJZ6XRs+6uZvQTsHVZd4u4rMhqViIhkzI3/ns266noAhvcr5qIj2r26V3JAugOgLAcez3AsIiKSYVMXrONv7yxOln964iRKCjN5B27JpIwNgCIiIl1LvDHBTx5tHpPqCzsP5oidB0cYkWwtJXERkR7iztfm8/HKSgBKCvO55sRJEUckW2uTSdzM8s1sdraCERGRzFi8robfPtc8wMkPj9yeYf2KI4xItoVNJnF3bwQ+NjPdsFxEJEe5O9c8PoPaeAKAnYb24awDxkQblGwT6fRm2A6YYWZvA9VNle5+YsaiEhGRbeaZmSt5fvYqAMzghlN2oSBfZ1O7g3SS+JUZj0JERDKiqq6Bax5vHuDkK/uMYs9R20UYkWxL6Vwn/rKZjQYmuvtzZlYCaIw6EZEc8NtnP2H5hloAyssKueSoHSOOSLalzbanmNm3gYeA28Kq4QRjf4uISBc2Y9kG7npjQbJ8xXE707ckFl1Ass2lc1LkAuBAoALA3T8FBmUyKBER2TqJRDDASWMiGODkgPEDOGn3YRFHJdtaOkm8zt3rmwpmVgBoyFARkS7sL28v4oPF6wEozM/jupN3wUwDnHQ36STxl83scqDYzI4E/g78M7NhiYhIZ62sqOXGfzXf4uP8Q8YzfmBZhBFJpqSTxC8FVgPTgPOAp4ArMhmUiIh0jrtz5WPTqaxrAGBseSnnHzI+4qgkU9LpnZ4ws3uAtwia0T92dzWni4h0Qf+avoJnZq5Mln9+6q4UxXRBUXe12SRuZscBtwJzCcYTH2tm57n7vzIdnIiIpG9DTZyr/tHymvD9xg2IMCLJtHRu9vJr4FB3nwNgZuOBJwElcRGRLuSGp2aypqoOgMF9enHZsbomvLtL55x4ZVMCD80DKjMUj4iIdMLrc9bw4NQlyfJ1J+1CnwRsRfUAACAASURBVCJdE97ddXgkbmanhrNTzewp4EGCc+JfAt7JQmwiIpKGjfWNXPbItGT5uF2H8oVJQyKMSLJlU83pJ6TMrwQ+H86vBjR+nYhIF/Gb5z5h0boaAPoWx7j6xJ0jjkiypcMk7u5nZzMQERHZch8tWc8dr85Lln9y3E4M6l0UYUSSTen0Th8LfBcYk7q+hiIVEYlWvDHBjx/6iPDOqhw4YQBf2mtEtEFJVqXTO/0x4E6Cu7QlMhuOiIik6/ZX5jF7RdDPuCiWx89Pmaxbq/Yw6STxWnf/XcYjERGRtM1dXcXNz3+aLF985A6MGlASYUQShXSS+M1mdjXwDFDXVOnu72UsKhER6VAi4Vz28DTqG4LG0ckj+nL2gWOiDUoikU4S3xU4EziM5uZ0D8siIpJlf31nEW8vWAdAQZ7xi1MnU5Cfzm0/pLtJJ4l/CRiXOhypiIhEY8WGWn7xVPMIZed9fhw7D+sTYUQSpXR+uk0H+mU6EBER2TR354qUEcrGDSzlu4dNjDgqiVI6R+L9gNlm9g4tz4nrEjMRkSx6ctpynpvVPELZL06drBHKerh0kvjVGY9CREQ2aX1NPdc83jxC2df2HcU+Y/tHGJF0BemMJ/5yNgIREZGOXf/kLNZUBV2ThvQp4tJjNEKZpHfHtkqC3ugAhUAMqHZ39aQQEcmCVz9dzUPvpoxQdvIu9NYIZUJ6R+K9m+YtuBXQScB+mQxKREQCNfUNXP5oyghlk4dy5M6DI4xIupIturDQA48BR21uXTP7s5mtMrPpHSw3M/udmc0xs4/MbM8tiUVEpCe46ZlPWLxuIxCMUHbNCZMijki6knSa009NKeYBU4DaNF77buAW4N4Olh8DTAwf+wJ/DKciIgJ8uHg9f359frJ85fE7M7B3rwgjkq4mnd7pqeOKNwALCJrUN8ndXzGzMZtY5STgXnd34E0z62dmQ919eRoxiYh0a/UNCS55uHmEsoMnlnPansOjDUq6nHTOiWdqXPHhwOKU8pKwTklcRHq821+ZmxyhrDiWz89O2VUjlEkbHSZxM7tqE89zd78uA/F0FMu5wLkAo0aNytZmRUQiMWdVFb97fk6yfPEXtmdkf41QJm1tqmNbdTsPgHOAS7bBtpcCI1PKI8K6Ntz9dnef4u5TBg4cuA02LSLSNSUSzmWPfER9YzDe1G4j+nL2gWMjjkq6qg6PxN39103zZtYbuAg4G/gb8OuOnrcFHgcuNLO/EXRo26Dz4SLS0z3w9iLeWfAZEI5Qdtpk8vPUjC7t2+Q5cTPrD/wQ+BpwD7Cnu3+Wzgub2V+BQ4ByM1tCcPvWGIC73wo8BRwLzAFqCH4giIj0WMs3bOTGfzWPUHb+IePZaajuqyUd29Q58V8CpwK3A7u6e9WWvLC7f2Uzyx24YEteU0Sku3J3rnh0OlXhCGXjB5Zy4WETIo5KurpNnRO/GBgGXAEsM7OK8FFpZhXZCU9EpGd44qPlPD97VbL8i9Mm06tAI5TJpm3qnPgW3c1NREQ657PqliOUnbnfaPYeoxHKZPOUqEVEInbdkzNZWx2MUDa0bxE/PnqHiCOSXKEkLiISoZc/Wc0j7zVfXXu9RiiTLaAkLiISkeq6Bi5/pHmEshN2G8bhO2mEMkmfkriISER+/cwnLF0fjFDWryTG1SfsHHFEkmuUxEVEIvD+os+4643mEcquOn5nyss0QplsGSVxEZEsq29IcOnD0/CUEcpO2UMjlMmWUxIXEcmyW1+ey8crgxHKSgo1Qpl0npK4iEgWzVlVyS0vNI9Q9qMv7KARyqTTlMRFRLIkkXAueXhacoSy3Uf24xsHjIk2KNn2KldAQ11WNqUkLiKSJfe/tZB3FwZjSMXyjRs1Qln38tlCeOKH8NvJ8MFfsrLJTY5iJiIi28bS9a1HKJvADkN6RxiRbDOrP4bXfgMfPQjeGNS9/lvY40zIz2yaVRIXEcmwYISyaVTXB1/wEwaVccGh4yOOSrbasvfh1Ztg1j8Bb7msuD9UrYS+mb3qQElcRCTDHv9wGS9+vBoAM7jxtF01QlkuW/gGvPIrmPt822VjDoaDL4ZxhwR/7AxTEhcRyaB11fX89J8zk+Wv7zeavUZrhLKc4w5znoNXfw2L/tN2+fZHB8l75D5ZDUtJXEQkg657YibrwhHKhvUt4r+P3jHiiGSLJBIw6/Egea/4qOUyy4NJp8BBP4Ahu0YSnpK4iEiGvPTxKh59v3mEshtO2ZWyXvrazQmNcZj296DD2ppPWi7Li8FupwfJe0C0fRv0aRIRyYCl6zdy6cPNI5SdtPswDt1xUIQRSVriG+H9++H1m2HD4pbLCophr2/AAd+FviOiia8VJXERkW1sTVUdZ97xFisqagHYriTGVcdrhLIura4S3rkT/vMHqF7VclmvPrDPt2Hf86FsYDTxdUBJXERkG6qsjXPWXW8zb001AIX5efz+K3syQCOUdU016+CtW4NH7YaWy0oGwH7fgb2/BcX9oolvM5TERUS2kdp4I9+6ZyrTl1YAkGdw8+m7c9DE8ogjkzYqlsN/boGpd0G8uuWyPsPhgO/Bnl+Hwq59X3slcRGRbSDemODCv7zHW/PXJet+fuquHLPr0AijkjbWzQ/Od3/wADTWt1zWf1zQWW3y6VBQGE18W0hJXERkKyUSziUPfcRzs5rPpV52zI58ee9REUYlLayaFfQ0n/ZQ861RmwyaBAf/EHY+OeO3Sd3WcitaEZEuxt257smZPJJyKdn5h4znvM/rtqpdwtJ3g1ujzn6i7bLhU+BzPwpu1JKj47kriYuIbIXfvzCHu15fkCx/ZZ9R/PioHaILSIK7qy14LbhBy7wX2y4fd0hwd7UxB+ds8m6iJC4i0kn3vLGAm55tvhHIcbsO5fqTd8FyPDHktMXvwLNXtn9r1B2OC5rNR0zJflwZoiQuItIJj72/lKsfn5EsHzyxnJu+vJvGB4/KhiXw3DXBXdZSWR7sclrQYW3wpEhCyyQlcRGRLfTC7JVc/PcPk+U9RvXjtjP30shkUaivDnqbv/47aNjYXJ8Xg92/Cgd9P+h13k0piYuIbIG356/j/PvfozERjB+9w+De3HXW3pQU6us0qxIJmPZgcPRdubzlsp1OhCOvhf5jIwktm/SpExFJ0/SlGzjn7neoa0gAMLJ/Mfeesw/9SnLjmuJuY9Gb8O9LYdn7LeuHTIajfw5jDoomrggoiYuIpGH+mmrOuuttKusaABjYuxf3n7Mvg/sURRxZD/LZQnjuapjxaMv6ssFw2JVB83lezzqloSQuIrIZyzds5Iw73mJNVXCHrz5FBdz7zX0YPaA04sh6iLrK4EYtb9wCjXXN9fm94IALg05rvXpHF1+ElMRFRDZhXXU9Z975NkvXB52mimJ53HX23uw0tE/EkfUAiQR8+Bd4/lqoWtly2aRT4YhrYLvRUUTWZSiJi4h0oKqugbPveps5q6oAiOUbt56xF3uN7h9xZD3Agtfg35fBio9a1g/bMzjvPWq/aOLqYvIy+eJmdrSZfWxmc8zs0naWn2Vmq83sg/DxrUzGIyKSrtp4I+feO5UPlwTDU5rBTf+1O4fsMCjiyLq5dfPh/86Eu49rmcB7D4VTboNvPa8EniJjR+Jmlg/8ATgSWAK8Y2aPu/vMVqv+n7tfmKk4RES2VENjgov+9j5vzF2brLv+5F04YbdhEUbVzdVWwKu/gjf/2HJ0sYJiOPB7cOBFUKg+CK1lsjl9H2COu88DMLO/AScBrZO4iEiX4e5c/ug0np7RfA72v4/aga/t27PPvWZMohHevw9euB6qV7dctut/wRFXQ98R0cSWAzKZxIcDi1PKS4B921nvNDP7HPAJ8AN3X9zOOiIiGefu/OypWTw4dUmy7tsHj+U7h2hEsoyY9zI8fTmsnN6yfsTecPQvutU9zjMl6o5t/wT+6u51ZnYecA9wWOuVzOxc4FyAUaM0Pq+IZMb/vjSXP706P1n+0l4juPzYnTSgyba2di48cyV8/GTL+j4j4MifBvc613uelkwm8aXAyJTyiLAuyd3XphTvAP6nvRdy99uB2wGmTJni2zZMERF44K2F/PLpj5PloyYN5uen7qoEvi1tXA+v/BLeug0S8eb6WElwrff+F0JhSXTx5aBMJvF3gIlmNpYgeZ8OfDV1BTMb6u5NN709EZiVwXhERNr1zw+XccVjzU26B4wfwM2n70FBfkYv4Ok5Ghvg3bvgxZ/BxnUtl+32VTj8SuijToOdkbEk7u4NZnYh8DSQD/zZ3WeY2bXAVHd/HPiemZ0INADrgLMyFY+ISHte/mQ1P3zwAzxs49ttRF9u//oUimI96/adGTPn+eC89+rZLetH7Q9H/QyG7xlNXN2EuedW6/SUKVN86tSpUYchIt3AuwvXccYdb7Mx3gjAhEFlPHje/vQv1YAmW231J/DMFfDp0y3r+46CL1wLO5+s895bwMzedfc2Pf2i7tgmIhKJWcsrOPuud5IJfHi/Yu47Zx8l8K21/EN44/cw/RHwxub6wjI4+Iew3wUQ06Ax24qSuIj0OAvXVvP1P79NRW0wIll5WSH3nbMPQ/sWRxxZjnIPms3f+B3Mf7nVQoM9zghGGes9OJLwujMlcRHpUVZW1HLGnW+xujIYDat3rwLuPnsfxg0siziyHNRQD9MfCo68V7VzH6+xn4cvXAdDd8t+bD2EkriI9Bjra+r5+p1vs3hdMCJZr4I87jxrb3YZ3jfiyHJM7QZ4925481aoXNZymeXBpFOCy8XUaS3jlMRFpEeoqW/g7Lvf4eOVlQAU5Bl/PGNP9hmrEcnStmFJcG/zd++B+sqWy2KlsOfXYb/ze/zwoNmkJC4i3V5dQyPn3fcu7y9an6z71Zd247AddY42LSumhZ3VHoZEQ8tlZYNh3/Ngr7OhRD+Isk1JXES6tcaE88P/+5BXP12TrPvpiZM4eY/hEUaVA9xh3ovw+u+CaWvl28MB34XJX4aCXtmPTwAlcRHpxtbX1HPtEzN5ctryZN0PjtiebxwwJrqgurrGeHB52Bu/h5XT2i4ffVCQvCd+AfJ0R7uoKYmLSLezqqKWO16bz/1vLqSmvvla5bMOGMP3Dp8QYWRdWG0FvHdPcM67YmnLZZYHO50IB3wPRuwVTXzSLiVxEek2Fq+r4daX5/L3d5dQ35BosezUPYdz1fE7a0CT1iqWhZ3V7oa6ipbLYiXBNd77fQf6j40kPNk0JXERyXmfrqzkjy/N5R8fLqMx0fJW0jsM7s13Dh3PCZOHkZenBJ60ckbQZD7t7207q5UOhH3Og73PUWe1Lk5JXERy1kdL1vOHF+fw9IyVbZbtPrIfFx46gcN2HKTk3cQ9uKPaG7+HOc+1XT5gIhxwIUw+XbdGzRFK4iKSU9ydt+av4w8vzmnR47zJgRMGcMEhE9h//AA1nTdpjMOMx4Lboq74qO3yUfsH57u3P1qd1XKMkriI5AR356WPV/OHF+cwdeFnbZYfsdNgvnPoePYctV0E0XVRdZXw3r3BOe8Ni1stNNjphCB5j9w7kvBk6ymJi0iX1phw/jV9OX94cS6zlrfseJVncPzkYXzn0PHsOKRPRBF2MfGNMPdFmP0kzPon1G1oubygGPb4WtBZbcD4aGKUbUZJXES6pPqGBI99sJRbX5rLvDXVLZbF8o0v7jWC8z43njHlpRFF2IXUrINPnobZT8DcFyBe03adkgFhZ7VvQemA7McoGaEkLiJdSm28kb+9vYjbX5nHsg21LZYVxfL46j6j+fbnxmrY0PWLYPZTQeJe+EbLsbtT9R8XDEay+1ch1sPfs25ISVxEuoSK2jj3v7mQP782nzVV9S2W9S4q4KwDxnDWAWMYUNZDb/HpHlwWNvvJIHG310GtSfn2sONxsOPxMGxPdVbrxpTERSRS66rruev1+dz9xgIqa1terzygtJBzDh7LGfuNpk9RLKIII9TYAIvfak7c6xd2vO6IvYPEvcNxMHD77MUokVISF5FILN+wkT+9Mp+/vr2IjfGWTcHD+hZx7ufG8eW9R1FcmB9RhBGprwkGHJn9JHzyb6hZ2/56+YUw9vOw47Gww7HQe0h245SkmngNa2vXsnZj+KhdS99efTlqzFEZ37aSuIhk1YI11dz2ylweencJ8caWd1cbW17K+Z8fz8l7DKewoAc1AdesCxL27CdhzvPQsLH99Xr1CQYe2fE4mHAEFKlHfia4OzUNNcmEnJqc12xc07K+di0b2/l77TloTyVxEek+Zq+o4H9fnMsTHy2j1Z1R2WloHy44dDzH7DKU/J5yd7XPFsLHTwWJe1Md08qGhOe3j4MxB0NBYXbj7Cbcnep4dTL5rtm4pkUibp2waxtrN/+im7Cudt02inzTlMRFZJvbsDHOnFVVzF1VxaerKpmxrII35rZtFt5zVD8uPGwCh+4wqPvfXc0dVk5P6ZjWzjCfTcp3SOmYtoc6pqWhsr6SRRWLWFixkEWVi1hRvaJNgq5rrMvItmN5MQYUD6C8qJwBxQMYUDyA4WXZGa9eSVxEOsXdWVtdz6crq5izuoo5KyuZs7qKT1dWsapy01+WB08s54JDJ7Dv2P7dO3nX18Cy91I6pi3qeN0R+zQfcZdPzF6MOaSqvoqFlQuTyXpx5eIgaVcs4rO6tnfx2xq98nsxoChIyE3T/kX9KS8ub1E3oHgAvWO9I/scK4mLyCa5O8s31DJnVRWfrqpizqoq5qyq5NNVVayviW/Rax01aTDfOWQCu43sl6FoI9BQB58tgLVzYe0cWDc3nJ8Llcs6fl6yY9pxsMMx6pgWqqqvYlHlohZH1YsqFrGoctFWN1EX5Re1ScCtE/WAogGUF5dTGivNiR+YSuIiAgS3N13yWQ2frmyZrOeurqaqrmHzL5CiMD+PcQNLmTCojImDejNhUBmTR/RlZP+SDEWfYY0NsGFRc3JeFybstXODe5J7YvOvAeqYFqqOVwdJOjyqbkrSCysWdjpRF+YVMqrPKEb2HsnoPqMZXja8zVFzSUFJTiTmLaEkLtLD1DckWLi2Opmom6ZzV1dR35BmMgqVFOYzYVBZ8tGUsEduV0xBfo6dx00koGJpyyPppmT92UJIbFmrAwCWD9uNgXGH9LiOaTXxmmRibkrSTUfXa2s7uGxuM2J5MUb2HsmoPqMY3Xs0o/qMSs4PLh1MnuXYZ24bUBIXyWF1DY1U1jZQsTEeTGvjLcqVtXEqwvoNNXEWrK1m4doaGlp3D9+MvsUxJg4qY+LgMsYPLGPi4CBZD+1TlFtjdbtD1aqWR9Jr58C6ecGjoTM9kg36joQB42DABOg/PhhYZMAE6DcK8rvnTWpq4jUsr17O0qqlLK1ayrKqZS2m6+vWd+p1Y3kxRvQe0Zyke4eJus9oBpcMJj+vh903YDOUxEUikkg4lXVBou0wEde1rK9oSswbg2ndFh45b87A3r2YmDyqLmN8eHRdXlbYNZshG+qhrgJqNzRPayvazleuCBP3PKiv7Ny2eg8NE3SrZL3dWIgVbdv96gJqG2pZVr0sSMqVS1laHSTopiS9NeenC/IKGFE2gtF9Riebv5sS9ZCSIUrUW0BJXHocd6e+MUF9Q4J4o1PfEMzXNzZS39C8rKO6eNN8Y4K61HLK8rp26uobE9TFE1SFibmqvgHfsgPibWZ4v+Jkop44OGwOH9ibviVZPGpMNIbJNjUJt07AG1ol5lbrdnRTlM4qGZCSoFOSdf9x0Kts224rYnWNdSyvWs6yqmUsqVrSnKCrl7K0cmmnm7ybNCXqFkfT4dH1kNIhFOQp/WwLehclKxoTzmc19aypqmNtVT2VtXHqG52GxqYE1zwfb/RwmqChMUigDWFd6ny76yUSxBvC+nC+IdGcsBsSiTZ3CctlBXlGn+IYvYsK6FPUctq7KEaf4nAalof1K2L8wDJKe23hv35DPcSrg0um6qub5+NN5dRpTfvL6ypbJuPOHhFvrV59gwTdP2zyHjC+OWkXbxdNTBlQE69h9cbVLY6eU5u7V29cvVWvX2AFDCkdwvDewxleNpxhpcMYVjaM4WVBeWDJwB55jjrblMSl0zbWNwZJubqeNZV1rK2uY01Vc6JOna6rqY/sqLMrK+tV0DIBt07IxW0Tc99kYo5RFMtr2czdGA+SZX0V1FVB/fqW5aXVsGBLEnBYTmxZ7/Sssfygh3dR36Dnd+q0qE/zfEn/4Gi6/3goLYeueGogTU3JeXXN6pbTVnVV8aqt2k6+5QdJumw4w8paJujhZcMZWDxQzd5dgJK4JCUSzoaNcdZUBcl4bXVdmJzrm+tSptX1HdwmMgfE8o1Yfh6FBXkUNk1T58NpLGVZr3bqWq/fNB8Lp70Kmutj+UFdU1IuKyogn0RKwm2abkgphwm4KnV5ZTitbrlOXRVk6I5UGdOrT/tJt8P5piQdlmMlOZ2QUzUl51U1q1izcU2H061Nzk3yLI8hJUPaJOim+UElg9TknQP0F+qmauONVGyMsyF8VNSG8zVxNmxsYMPGeLJ5uykpr6uu3+Jey1uiX0mM8rJeDCgtpG9xLJnoYvlGQZjgCvKMWEEesbwgycYKgrrCgjwK8oJ1U+dj+UFyLGhKyinzqcub5gvyjVhe3tb1qHZvbh6uqwzO29aFibWprroiZXnqo6Jl0o7XbLs3OJPyCiBWCoUlQeIsLGlVLm01TVleWBrOl7ZMyr16Qzc/kmsaSKPNUXM7R8/V8eptuu1YXozy4nKGlg4NEnTvoMm7KVEPLh1MLK979pzvSZTEuyh3p6a+sVXyTU3KDS2SdLI+nG7rXsvtieVbkJTLCsPk3IvypvlW0/6lhcSivG440QjxjVBXGybgqpaJtb1kmzzCbac+3Zt7ZJvlBx2wCnuH01IoLAsSZmFTeXMJuLRtMu4h1zY3aUw0Ut1QTXV9NVXxKqrj1VTHm+er6qtaluNVVMWrqInXBHUpz2vsaGCTTorlxRhYPJCBJQMZVDKI8uLy5mnxIMpLgmnfXn275hUFsk1lNImb2dHAzUA+cIe7/6LV8l7AvcBewFrgy+6+IJMxZYq7E290NtY3UhNvCKb1jdTGg2nq/MZ4IxvrG5JJuiI8Mk5NwhW18Ug6YPUuKqC8LEjGA0qbk3Bzcm6u61NU0LkvCffg3G3DRojXdmJaGyTkdqc17a/bmRt1ZIWFSbas1bR3SgJOTcqbKRcUdZvm5dbcnYZEA/FEvPnRGG9bF9anlusa61ok1hbJuFWyropXtTu0ZKbF8mItknJToh5YPLDFvJKzpMpYEjezfOAPwJHAEuAdM3vc3WemrHYO8Jm7TzCz04EbgS9nKqZU8XgDH8yfQ208SKq19Q3UNjSysb6RuoYEtfUNbGxIUBtvoLapLh6sUxsm5NqGRmrjCeriDdTGG2l0p/W/luGtyqnzCcDJwzGcPBKYOcU4pZbACprqnTxLhOslmuuS5QR5EJQtQR4JYgYlhUZpYR4lMaMklkdJLI/iQqM4ZhQXGKUxoyzmlMagpMApzk9QQCOeiOONDdAYxz0OtQ1QHceXx/FEsHxVY5xViQY80YAn4sFtKRNxPNEAiQa8MawP1w+WN+DeAI2NePi+ePiGNL1LrX+2NNdby7K1v36b1zLwwjygV5vXSv1jpL5Oe7G02U5+LzxWDOEjmC8JpgVNdUXBtKAkuI44VgwFvYL1CnpBQTGeX5iyL9489VZ13rwsqKvHG9ZCw1qoaV43db3U56UuT5AIpp4g4Ynm+nA+tT45Hz4vOd/qee29Zuv5xkRjWgm3qdx63Yau2rluM3rl96K8uLxlUm41HVQyiD6FfZScZYtl8kh8H2COu88DMLO/AScBqUn8JOCacP4h4BYzM/fM92PeWF/PN//zpS17Un746JWJiLKoIXxs3XC5LTW9N+kv6CYagarg0fS+SrdQGiulNFZKWayMslhZMF9YlqxrWp5cp9Wypmmsm96xTbqGTCbx4cDilPISYN+O1nH3BjPbAAwA1qSuZGbnAucCjBo1apsEVxzrzolFpHsqsAJi+TEK8gqI5cWaH/nBtKP6wrxCSgvbJtjUxJuamEtiJbrGWXJCTnRsc/fbgdsBpkyZsk2O0vPyjNJE285J7TVm2Sbn0nlex3Vt17FWTe6t6q2jeFLmrW1ty+XNtc1lw8zC9cL5sL6pTLjcwi+3YBrWh/PWwTyWl1wvub0W20+JMTlpWd9mvVb7094+tn6Njl675fvSdv0teY61fFLb57SzP62bUVPr2tuHjt6Tjl6rad08y8MIpk1Jqmk+td7MyCNl3vLIIy/5GWmvflOv07S8MK8wmVhTE21Tcm4vATc98vPylVhFWslkEl8KjEwpjwjr2ltniZkVAH0JOrhlXH5BIW+ePSMbmxIREcmITP6sfQeYaGZjzawQOB14vNU6jwPfCOe/CLyQjfPhIiIi3UHGjsTDc9wXAk8T9Gz6s7vPMLNrganu/jhwJ3Cfmc0B1hEkehEREUlDRs+Ju/tTwFOt6q5Kma8FtrCLuIiIiEBmm9NFREQkg5TERUREcpSSuIiISI5SEhcREclRSuIiIiI5SklcREQkRymJi4iI5CjLtRukmdlqYGHUcWxD5bQa8KWH6Kn7DT1337XfPU9P3fdM7Pdodx/YujLnknh3Y2ZT3X1K1HFkW0/db+i5+6797nl66r5nc7/VnC4iIpKjlMRFRERylJJ49G6POoCI9NT9hp6779rvnqen7nvW9lvnxEVERHKUjsRFRERylJJ4BMxspJm9aGYzzWyGmV0UdUzZZGb5Zva+mT0RdSzZZGb9zOwhM5ttZrPMbP+oY8oWM/tB+FmfbmZ/NbOiqGPKBDP7s5mtMrPpKXX9zexZM/s0nG4XZYyZ0sG+/zL8vH9kZo+aWb8oY8yE9vY7ZdnFZuZmVp6p7SuJR6MBuNjddwb2Ay4ws50jjimbLgJmRR1EBG4G/u3uOwK70UPePyCDMAAABhJJREFUAzMbDnwPmOLuuwD5wOnRRpUxdwNHt6q7FHje3ScCz4fl7uhu2u77s8Au7j4Z+AS4LNtBZcHdtN1vzGwk8AVgUSY3riQeAXdf7u7vhfOVBF/mw6ONKjvMbARwHHBH1LFkk5n1BT4H3Ang7vXuvj7aqLKqACg2swKgBFgWcTwZ4e6vAOtaVZ8E3BPO3wOcnNWgsqS9fXf3Z9y9ISy+CYzIemAZ1sHfHOA3wI+BjHY8UxKPmJmNAfYA3oo2kqz5LcEHOxF1IFk2FlgN3BWeSrjDzEqjDiob3H0p8CuCI5LlwAZ3fybaqLJqsLsvD+dXAIOjDCZC3wT+FXUQ2WBmJwFL3f3DTG9LSTxCZlYGPAx8390roo4n08zseGCVu78bdSwRKAD2BP7o7nsA1XTfZtUWwnPAJxH8kBkGlJrZGdFGFQ0PLgfqcZcEmdlPCE4jPhB1LJlmZiXA5cBV2dieknhEzCxGkMAfcPdHoo4nSw4ETjSzBcDfgMPM7P5oQ8qaJcASd29qcXmIIKn3BEcA8919tbvHgUeAAyKOKZtWmtlQgHC6KuJ4ssrMzgKOB77mPeOa5vEEP1g/DL/rRgDvmdmQTGxMSTwCZmYE50ZnuftNUceTLe5+mbuPcPcxBB2bXnD3HnFE5u4rgMVmtkNYdTgwM8KQsmkRsJ+ZlYSf/cPpIZ36Qo8D3wjnvwH8I8JYssrMjiY4fXaiu9dEHU82uPs0dx/k7mPC77olwJ7hd8A2pyQejQOBMwmORD8IH8dGHZRk3HeBB8zsI2B34GcRx5MVYevDQ/+/vfsJsbIK4zj+/WlgEkYNKUSoQYuSwRRGRKcWtZigjRmOtLBFIbQLwoIIC6EmMESFkKiMqJULqUVkTE7FRDlTalKNthE00AqKJLWF/8bHxTkXz1yu4B3GpjPz+6ze97zvPO9zZ/E+c85czgMcAkZI750puZOXpF3AMHCvpJOS1gObgR5JR0mrEpsnM8cb5RqffQcwBxjI77m3JzXJG+Aan/u/e/70WN0wMzObejwTNzMzq5SLuJmZWaVcxM3MzCrlIm5mZlYpF3EzM7NKuYibVUDSdknPFeefS3qvON8qaUObMT+Q1NtifFDSweJ8maTBcabeHPspSTsmIpaZuYib1WIfeZczSTOAO4DO4no3MDSBz5sn6dEJjDchJM2c7BzM/k9cxM3qMAQ0+o93AoeBs5JulzQLWASEpK8l/ZBn6o2tPu+R1J/Hv5F0X3NwSa/lmXmjSG4BNra4b8xMWtKnkh7Kx//m/tFHJH0haXme1R+TtKoIMz+PH5W0qYj1pKT9eVOQdxq55LhbJf1U/A7MDBdxsypExO/AJUkLSLPuYVLnu5XAMtI2ptuB3ojoAt4HXs8//i7wbB5/AXirjC1pCzAXeDoiRvPwMHBB0sNtpHkLaSvdTuAs0Af0AI8Drxb3LQfWAPcDa/Ny/SLgCeCBiFgKjALrirjfR8SSiPi2jXzMprybJjsBM7tuQ6QC3g1sI/Wg7wZOA78Bj5C2twSYCfyRO+V1A7vzOMCsIuYrpAL5TIvn9QEvAy9eZ34XgP58PAKcj4iLkkaAu4v7BiLibwBJHwMPkjpcdQEHcp6zudooZJTULMjMmriIm9Wj8X/xxaTl9BPA88AZYBC4KyLGLDdLuhX4J89uWzkAdEnqiIhT5YWI+EpSH7CiGL7E2BW8m4vji0WXqsvA+RznsqTyXdO813MAAj6MiJda5HiuWCEws4KX083qMURq6XgqIkZz0b2NtKS+C5graSWkVreSOnOf+uOS1uZxSVpSxOwnNeTYI2lOi2f2kbpQNfwKLJU0Q9J80tJ4u3okdUiaDawm/XHyJdAraV7Os0PSwnHENptWXMTN6jFC+lb6d01jpyPiT6AXeCN/AexHrvbsXgesz+NHgMfKoBGxG9gJfJILa3ntM+CvYmgfcJzURvVNUmeydu0nLY//DHwUEQcj4hfS0v3e3OVtALhzHLHNphV3MTMzM6uUZ+JmZmaVchE3MzOrlIu4mZlZpVzEzczMKuUibmZmVikXcTMzs0q5iJuZmVXKRdzMzKxSVwAHWdPiRFNJZwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eOU60zEKETQV",
        "outputId": "6fa3e61f-ce97-44f5-9538-252588cd10e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "fig,(ax1,ax2) = plt.subplots(1,2,figsize=(12,4))\n",
        "sns.barplot(x= week_num,y=pd.Series(weekwise_confirmed).diff().fillna(0),ax=ax1)\n",
        "sns.barplot(x= week_num,y=pd.Series(weekwise_deaths).diff().fillna(0),ax=ax2)\n",
        "ax1.set_xlabel(\"Week Number\")\n",
        "ax2.set_xlabel(\"Week Number\")\n",
        "ax1.set_ylabel(\"Numberof Confirmed cases\")\n",
        "ax2.set_ylabel(\"Numberof Death cases\")\n",
        "ax1.set_title(\"Weekly increase in number of Confirmed cases\")\n",
        "ax2.set_title(\"Weekly increase in number of Death Cases\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2xofznbZFp-X",
        "outputId": "d30c5b35-a770-4235-9e0a-202897b572c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        }
      },
      "source": [
        "print(\"Average increase in number of Confirmed cases everyday:\",np.round(datewise[\"Confirmed\"].diff().fillna(0).mean()))\n",
        "print(\"Average increase in number of Recovered cases everyday:\",np.round(datewise[\"Recovered\"].diff().fillna(0).mean()))\n",
        "print(\"Average increase in number of Death cases everyday:\",np.round(datewise[\"Deaths\"].diff().fillna(0).mean()))\n",
        "\n",
        "plt.figure(figsize=(15,6))\n",
        "plt.plot(datewise[\"Confirmed\"].diff().fillna(0),label=\"Daily increase in confirmed cases\",linewidth=3)\n",
        "plt.plot(datewise[\"Recovered\"].diff().fillna(0),label=\"Daily increase in recovered cases\",linewidth=3)\n",
        "plt.plot(datewise[\"Deaths\"].diff().fillna(0),label=\"Daily increase in death cases\",linewidth=3)\n",
        "plt.xlabel(\"Timestamp\")\n",
        "plt.ylabel(\"Daily increase\")\n",
        "plt.title(\"Daily increase\")\n",
        "plt.legend()\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average increase in number of Confirmed cases everyday: 29900.0\n",
            "Average increase in number of Recovered cases everyday: 8442.0\n",
            "Average increase in number of Death cases everyday: 2097.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZ3OXFn0HBKl"
      },
      "source": [
        "#Country wise analysis\n",
        "#Calculating Country wise Mortality rate\n",
        "countrywise= covid[covid[\"ObservationDate\"]==covid[\"ObservationDate\"].max()].groupby([\"Country/Region\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"}).sort_values([\"Confirmed\"],ascending=False)\n",
        "countrywise[\"Mortality\"]=(countrywise[\"Deaths\"]/countrywise[\"Recovered\"])*100\n",
        "countrywise[\"Recovered\"]=(countrywise[\"Recovered\"]/countrywise[\"Confirmed\"])*100"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ksTfgDoNIIFQ",
        "outputId": "bd2d4281-0c6f-4c2e-cb71-5801e50a85bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        }
      },
      "source": [
        "fig,(ax1,ax2)=plt.subplots(1,2,figsize=(25,10))\n",
        "top_15confirmed = countrywise.sort_values([\"Confirmed\"],ascending=False).head(15)\n",
        "top_15deaths = countrywise.sort_values([\"Deaths\"],ascending=False).head(15)\n",
        "sns.barplot(x=top_15confirmed[\"Confirmed\"],y=top_15confirmed.index,ax=ax1)\n",
        "ax1.set_title(\"Top 15 countries as per number of confirmed cases\")\n",
        "sns.barplot(x=top_15deaths[\"Deaths\"],y=top_15deaths.index,ax=ax2)\n",
        "ax1.set_title(\"Top 15 countries as per number of death cases\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Top 15 countries as per number of death cases')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zrVntjFaKCN7",
        "outputId": "f1b64dd4-2893-4a70-8e0e-934aaf340d95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "#Data Anlaysis for India\n",
        "india_data = covid[covid[\"Country/Region\"]==\"India\"]\n",
        "datewise_india = india_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
        "print(datewise_india.iloc[-1])\n",
        "print(\"Total Active Cases\",datewise_india[\"Confirmed\"].iloc[-1]-datewise_india[\"Recovered\"].iloc[-1]-datewise_india[\"Deaths\"].iloc[-1])\n",
        "print(\"Total Closed Cases\",datewise_india[\"Recovered\"].iloc[-1]+datewise_india[\"Deaths\"].iloc[-1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confirmed    24530.0\n",
            "Recovered     5498.0\n",
            "Deaths         780.0\n",
            "Name: 2020-04-24 00:00:00, dtype: float64\n",
            "Total Active Cases 18252.0\n",
            "Total Closed Cases 6278.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PBvvKaqBLI3R",
        "outputId": "fd322fb4-c5c7-4dd6-9d44-37559c5f1bdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "#Data Anlaysis for US\n",
        "us_data = covid[covid[\"Country/Region\"]==\"US\"]\n",
        "datewise_us = us_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
        "print(datewise_us.iloc[-1])\n",
        "print(\"Total Active Cases\",datewise_us[\"Confirmed\"].iloc[-1]-datewise_us[\"Recovered\"].iloc[-1]-datewise_us[\"Deaths\"].iloc[-1])\n",
        "print(\"Total Closed Cases\",datewise_us[\"Recovered\"].iloc[-1]+datewise_us[\"Deaths\"].iloc[-1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confirmed    905333.0\n",
            "Recovered     99079.0\n",
            "Deaths        51949.0\n",
            "Name: 2020-04-24 00:00:00, dtype: float64\n",
            "Total Active Cases 754305.0\n",
            "Total Closed Cases 151028.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "24HqY4g_Lj7M",
        "outputId": "cffdb2be-d8e3-4f24-d0a5-8f2d4e8e2b4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "source": [
        "datewise_india[\"WeekofYear\"] = datewise_india.index.weekofyear\n",
        "week_num_india = []\n",
        "india_weekwise_confirmed = []\n",
        "india_weekwise_recovered = []\n",
        "india_weekwise_deaths = []\n",
        "w = 1\n",
        "for i in list(datewise_india[\"WeekofYear\"].unique()):\n",
        "    india_weekwise_confirmed.append(datewise_india[datewise_india[\"WeekofYear\"]==i][\"Confirmed\"].iloc[-1])\n",
        "    india_weekwise_recovered.append(datewise_india[datewise_india[\"WeekofYear\"]==i][\"Recovered\"].iloc[-1])\n",
        "    india_weekwise_deaths.append(datewise_india[datewise_india[\"WeekofYear\"]==i][\"Deaths\"].iloc[-1])\n",
        "    week_num_india.append(w)\n",
        "    w=w+1\n",
        "plt.figure(figsize=(8,5))\n",
        "plt.plot(week_num_india,india_weekwise_confirmed,linewidth=3)\n",
        "plt.plot(week_num_india,india_weekwise_recovered,linewidth =3)\n",
        "plt.plot(week_num_india,india_weekwise_deaths,linewidth = 3)\n",
        "plt.xlabel(\"WeekNumber\")\n",
        "plt.ylabel(\"Number of cases\")\n",
        "plt.title(\"Weekly Progress of different types of cases\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Weekly Progress of different types of cases')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xIQfC2KtMdz4",
        "outputId": "a2b96a80-6a58-4c37-b03d-7d0ec0bec5c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "max_ind = datewise_india[\"Confirmed\"].max()\n",
        "china_data = covid[covid[\"Country/Region\"]==\"Mainland China\"]\n",
        "Italy_data = covid[covid[\"Country/Region\"]==\"Italy\"]\n",
        "US_data = covid[covid[\"Country/Region\"]==\"US\"]\n",
        "spain_data = covid[covid[\"Country/Region\"]==\"Spain\"]\n",
        "datewise_china = china_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
        "datewise_Italy = Italy_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
        "datewise_US=US_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
        "datewise_Spain=spain_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
        "print(\"It took\",datewise_india[datewise_india[\"Confirmed\"]>0].shape[0],\"days in India to reach\",max_ind,\"Confirmed Cases\")\n",
        "print(\"It took\",datewise_Italy[(datewise_Italy[\"Confirmed\"]>0)&(datewise_Italy[\"Confirmed\"]<=max_ind)].shape[0],\"days in Italy to reach number of Confirmed Cases\")\n",
        "print(\"It took\",datewise_US[(datewise_US[\"Confirmed\"]>0)&(datewise_US[\"Confirmed\"]<=max_ind)].shape[0],\"days in US to reach number of Confirmed Cases\")\n",
        "print(\"It took\",datewise_Spain[(datewise_Spain[\"Confirmed\"]>0)&(datewise_Spain[\"Confirmed\"]<=max_ind)].shape[0],\"days in Spain to reach number of Confirmed Cases\")\n",
        "print(\"It took\",datewise_china[(datewise_china[\"Confirmed\"]>0)&(datewise_china[\"Confirmed\"]<=max_ind)].shape[0],\"days in China to reach number of Confirmed Cases\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "It took 86 days in India to reach 24530.0 Confirmed Cases\n",
            "It took 44 days in Italy to reach number of Confirmed Cases\n",
            "It took 59 days in US to reach number of Confirmed Cases\n",
            "It took 49 days in Spain to reach number of Confirmed Cases\n",
            "It took 14 days in China to reach number of Confirmed Cases\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvJTmn_IQQW-"
      },
      "source": [
        "datewise[\"Days Since\"]=datewise.index-datewise.index[0]\n",
        "datewise[\"Days Since\"] = datewise[\"Days Since\"].dt.days\n",
        "train_ml = datewise.iloc[:int(datewise.shape[0]*0.95)]\n",
        "valid_ml = datewise.iloc[:int(datewise.shape[0]*0.95):]\n",
        "model_scores=[]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqEkxiSTSFYM",
        "outputId": "d9735999-6df0-4848-9779-5dbc768b6bc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        }
      },
      "source": [
        "lin_reg = LinearRegression(normalize=True)\n",
        "svm = SVR(C=1,degree=5,kernel='poly',epsilon=0.001)\n",
        "lin_reg.fit(np.array(train_ml[\"Days Since\"]).reshape(-1,1),np.array(train_ml[\"Confirmed\"]).reshape(-1,1))\n",
        "svm.fit(np.array(train_ml[\"Days Since\"]).reshape(-1,1),np.array(train_ml[\"Confirmed\"]).reshape(-1,1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1, cache_size=200, coef0=0.0, degree=5, epsilon=0.001, gamma='scale',\n",
              "    kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-vdSWf-vSs-i"
      },
      "source": [
        "prediction_valid_lin_reg = lin_reg.predict(np.array(valid_ml[\"Days Since\"]).reshape(-1,1))\n",
        "prediction_valid_svm = svm.predict(np.array(valid_ml[\"Days Since\"]).reshape(-1,1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H5Ru-3q_THpI",
        "outputId": "e1d7d9d3-4043-46c7-c752-c384f49dd415",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "new_date = []\n",
        "new_prediction_lr=[]\n",
        "new_prediction_svm=[]\n",
        "for i in range(1,18):\n",
        "  new_date.append(datewise.index[-1]+timedelta(days=i))\n",
        "  new_prediction_lr.append(lin_reg.predict(np.array(datewise[\"Days Since\"].max()+i).reshape(-1,1))[0][0])\n",
        "  new_prediction_svm.append(svm.predict(np.array(datewise[\"Days Since\"].max()+i).reshape(-1,1))[0])\n",
        "pd.set_option(\"display.float_format\",lambda x: '%.f' % x)\n",
        "model_predictions=pd.DataFrame(zip(new_date,new_prediction_lr,new_prediction_svm),columns = [\"Dates\",\"LR\",\"SVR\"])\n",
        "model_predictions.head(5)"
      ],
      "execution_count": null,
      "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>Dates</th>\n",
              "      <th>LR</th>\n",
              "      <th>SVR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-04-25</td>\n",
              "      <td>1560529</td>\n",
              "      <td>3322586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-04-26</td>\n",
              "      <td>1582219</td>\n",
              "      <td>3500761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-04-27</td>\n",
              "      <td>1603909</td>\n",
              "      <td>3686599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-04-28</td>\n",
              "      <td>1625599</td>\n",
              "      <td>3880344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-04-29</td>\n",
              "      <td>1647289</td>\n",
              "      <td>4082245</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Dates      LR     SVR\n",
              "0 2020-04-25 1560529 3322586\n",
              "1 2020-04-26 1582219 3500761\n",
              "2 2020-04-27 1603909 3686599\n",
              "3 2020-04-28 1625599 3880344\n",
              "4 2020-04-29 1647289 4082245"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X6Pt_-i5UZU_"
      },
      "source": [
        "model_train=datewise.iloc[:int(datewise.shape[0]*0.85)]\n",
        "valid=datewise.iloc[int(datewise.shape[0]*0.85):]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qd34fQ4IVzLR"
      },
      "source": [
        "holt=Holt(np.asarray(model_train[\"Confirmed\"])).fit(smoothing_level=1.4,smoothing_slope=0.2)\n",
        "y_pred = valid.copy()\n",
        "y_pred[\"Holt\"]=holt.forecast(len(valid))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ddhHqG1wWUXW",
        "outputId": "6d0440c7-33f8-4b3f-f3f5-a8e1c1dfd02a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "holt_new_date=[]\n",
        "holt_new_prediction=[]\n",
        "for i in range(1,18):\n",
        "   holt_new_date.append(datewise.index[-1]+timedelta(days=i))\n",
        "   holt_new_prediction.append(holt.forecast((len(valid)+i))[-1])\n",
        "\n",
        "model_predictions[\"Holts Linear Model Prediction\"]=holt_new_prediction\n",
        "model_predictions.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dates</th>\n",
              "      <th>LR</th>\n",
              "      <th>SVR</th>\n",
              "      <th>Holts Linear Model Prediction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-04-25</td>\n",
              "      <td>1560529</td>\n",
              "      <td>3322586</td>\n",
              "      <td>2855246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-04-26</td>\n",
              "      <td>1582219</td>\n",
              "      <td>3500761</td>\n",
              "      <td>2933902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-04-27</td>\n",
              "      <td>1603909</td>\n",
              "      <td>3686599</td>\n",
              "      <td>3012558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-04-28</td>\n",
              "      <td>1625599</td>\n",
              "      <td>3880344</td>\n",
              "      <td>3091214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-04-29</td>\n",
              "      <td>1647289</td>\n",
              "      <td>4082245</td>\n",
              "      <td>3169870</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Dates      LR     SVR  Holts Linear Model Prediction\n",
              "0 2020-04-25 1560529 3322586                        2855246\n",
              "1 2020-04-26 1582219 3500761                        2933902\n",
              "2 2020-04-27 1603909 3686599                        3012558\n",
              "3 2020-04-28 1625599 3880344                        3091214\n",
              "4 2020-04-29 1647289 4082245                        3169870"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DqPWG2EeW2r9"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}