Covid19_cases_prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Covid19_cases_prediction.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMmglM/vRAGdnu/+bLSMcBQ",
      "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/vickydaiya/Widhya-Internship/blob/main/Covid19_cases_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ILOp1q5byDU3"
      },
      "source": [
        "### **Downloading the data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "J5PDjUdUxsAP",
        "outputId": "34ebd9df-0cac-41e8-d917-1c38b721b3dc"
      },
      "source": [
        "import pandas as pd\r\n",
        "\r\n",
        "url = \"https://raw.githubusercontent.com/WidhyaOrg/datasets/master/covid19.csv\"\r\n",
        "data = pd.read_csv(url)\r\n",
        "data.head()"
      ],
      "execution_count": 1,
      "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>Date</th>\n",
              "      <th>State/UnionTerritory</th>\n",
              "      <th>ConfirmedIndianNational</th>\n",
              "      <th>ConfirmedForeignNational</th>\n",
              "      <th>Cured</th>\n",
              "      <th>Deaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>30/01/20</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>31/01/20</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>01/02/20</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>02/02/20</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>03/02/20</td>\n",
              "      <td>Kerala</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Sno      Date State/UnionTerritory  ...  ConfirmedForeignNational  Cured  Deaths\n",
              "0    1  30/01/20               Kerala  ...                         0      0       0\n",
              "1    2  31/01/20               Kerala  ...                         0      0       0\n",
              "2    3  01/02/20               Kerala  ...                         0      0       0\n",
              "3    4  02/02/20               Kerala  ...                         0      0       0\n",
              "4    5  03/02/20               Kerala  ...                         0      0       0\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D48T7Fk8-Ryo"
      },
      "source": [
        "### **Checking for NA values**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ycSmEr2UzZne",
        "outputId": "fac7cf8d-a0e6-498e-ac27-1eb2dec4ea05"
      },
      "source": [
        "for cols in data.columns:\r\n",
        "  print(cols,\": \",data[cols].isna().sum())"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sno :  0\n",
            "Date :  0\n",
            "State/UnionTerritory :  0\n",
            "ConfirmedIndianNational :  0\n",
            "ConfirmedForeignNational :  0\n",
            "Cured :  0\n",
            "Deaths :  0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LpI8dpR3-Va6"
      },
      "source": [
        "### **Grouping data by date**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vg-GTbGP0FOP"
      },
      "source": [
        "data_by_dates = data.groupby(by = \"Date\",sort = False)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w-Jl_dQ4-YaL"
      },
      "source": [
        "### **Total number of cases (ConfirmedIndianNational +\tConfirmedForeignNational + Cured + Deaths)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JCjRmptN9CVr"
      },
      "source": [
        "total_cases = data_by_dates.sum().drop('Sno',axis=1).sum(axis=1)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sp3son_Y_ArN"
      },
      "source": [
        "### **Plotting total cases vs date**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "4vobkw-8_Dkw",
        "outputId": "767b544a-472f-4cd0-c163-1df571aeb008"
      },
      "source": [
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "plt.style.use('dark_background')\r\n",
        "total_cases.plot()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc111f57dd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IiEBoaCiEECguLsazzz4LAMjNzcWWLVuQm5uL+vp6vPDCC2hsbLT4IIiIzCVk0n1w798P+h32PT3TxOZP9WFjY2OzVov5y5/Em999I2TOcslr6ah1+zRJIqKewk3piRERk3D0611orG+QupxuY8ATEf1b2MPT4axQ2P3ZM00Y8ERE/zbuFw+jNPcEzhXY9r1WO4sBT0QEQD10MPxHhjjM0TvAgCciAnDr6L3hZj2O7UyRuhSzYcATUY8nk8sx9pFZyPvhIK5evCR1OWbDgCeiHi/4/vHwUPV3mHPfmzDgiahHUw8djGmxz+DKhYvI2/+j1OWYFe/JSkQ9jpNMhpERkzDpycehnTAON6/fwI5Va+z2ph5tYcATUY/Ru68bJjwWhfujH0N/v4G4eK4cX6/+EOnbku3+ujOtYcATUY8xf81KDB0/FoX6Y/jqz+/j+HcH0Nhg/79YbQsDnoh6BKWvD4aOH4tv136M1P/9u9TlWAW/ZCWiHuGehx4EAGTsSpW4EuthwBNRjzBqWgTOGYtQfbqk45UdBAOeiBxe335eGHzvGGTt2Sd1KVbFgCcihzdyymTI5HJkp30ndSlWxYAnIoc3aloEqs+UOsxVIjuLAU9EDq23e19ow8chK7VnTc8ADHgicnAjHpwIZ4UC2anfSV2K1THgicihjXooApcqKlGSkyd1KVbHgCcih+Xi2hvDJ05Adup3EEJIXY7VMeCJyGENmzgBLq69e+T0DMCAJyIHNnpaBK5cuIhThiypS5FEhwGfkJCAiooKZGdnm5Z5eXkhJSUFBQUFSElJgVKpND22Zs0aGI1GZGZmIiwszDJVExF1QK5QIOSBicjZu9+hLyjWng4Dfv369Zg1a1azZXFxcUhLS0NwcDDS0tIQFxcHAIiMjIRWq4VWq8WiRYuwbt06y1RNRNQB7YRxcHXv2+N+3HS7DgP+hx9+wIULF5oti4qKQmJiIgAgMTERs2fPNi3fsGEDACA9PR1KpRJqtdrcNRMRdWj0QxG4VnsFxkNHpC5FMl2ag/f19UV5eTkAoLy8HL6+vgAAjUaDkpL/XMintLQUGo3GDGUSEXWeTC7HyCmTkfv9AYe7S9PdMMv14Lty+lFsbCwWLVoEAFCpVOYog4gIABB07xj07efVY8+eadKlI/iKigrT1ItarUZlZSUAoKysDP7+/qb1/Pz8UFZW1uo24uPjodPpoNPpUF1d3ZUyiIhaFTrzIdRdu478g4ekLkVSXQr45ORkxMTEAABiYmKwY8cO0/J58+YBAMLDw1FTU2OayiEisoaQyfdjwpwoGL7dg5vXb0hdjuREe23Tpk3i7Nmzoq6uTpSUlIgFCxaIfv36idTUVFFQUCD27NkjvLy8TOuvXbtWFBYWiqysLDF27Nh2t93U9Hp9p9ZjY2Nja68NCB4i/ngoVSzd/Hfh4tpb8nos3TqRnXZRJBsbG1u7zV3VX7yWsl28nrpDeHirJK/HGq2j7ORNt4nI7il698KCD1ahj6cHPoxZjMtV/F4PMNNZNEREUnFycsKTf3wDfiOG4+9LlqMsv0DqkmwGr0VDRHZt1kvPYsyMqfjqvQ+Q+/0BqcuxKQx4IrJb4Y/9AtNiY/Cvf/wT+zckSV2OzeEUDRHZpRnPLcDMF2KRf+AQvvzTn6UuxyYx4InIrsgVCsz93f9g3COR0O/YiX+8+TYa63vm1SI7woAnIrvRx9MDz6x5B0PGhuHbD/4XqR+vl7okm8aAJyK7oBrkh199+B68Bqrx+W/fgOHbPVKXZPMY8ERk8wLG3IOFa/8MCIGPfvVSj71D091iwBORTXP18MAzq9/Gtcu1+Pi5/8L5klKpS7IbDHgismmz45bCTanEJ88vZLjfJZ4HT0Q2a2TEJIx7JBJpnyTyF6pdwIAnIpvk6uGBOW8sx9kTRp4t00WcoiEimzR7edPUzLIefdu97uARPBHZnBEPTsK4X3BqprsY8ERkU1w9PPD4Ck7NmAOnaIjIpnBqxnwY8EQkOSeZDL6DAxHywP0Y94tIpKxL4NSMGTDgicjqFL17Ydj9EzBo1AgMGjUC/veEoLebGwCgOCObUzNmwoAnIquSOzvj2Y/fR1DYaDTcrEfZiQIcSf4WZ7JzcSb7OKpPl0AIIXWZDoEBT0RW9chvXkJQ2GhsWfEnHP16N+rr6qQuyWEx4InIasIip2PyU3Px/YYvkP7lV1KX4/B4miQRWYV66GA8/ub/4OTRDHy9+kOpy+kRunUEf+rUKdTW1qKhoQH19fXQ6XTw8vLC5s2bERgYiOLiYsydOxeXLl0yV71EZId6ufVBzF/+hBtXr2LDb17jHZispNtH8FOmTEFYWBh0Oh0AIC4uDmlpaQgODkZaWhri4uK6XSQR2bfoP7yG/v4abPjNa6itPi91OT2G2adooqKikJiYCABITEzE7Nmzzb0LIrIjEc88hdHTp+Dr1R/i1LFMqcvpUboV8EIIpKSk4MiRI4iNjQUA+Pr6ory8HABQXl4OX1/f7ldJRHZp2P3h+NnSxchM2Yv9G5KkLqfH6dYc/KRJk3D27Fl4e3tjz549yM/Pb7FOW+ezxsbGYtGiRQAAlUrVnTKIyMY4OTlh6q/mYdYLsag4WYzNr/9R6pJ6pG4dwZ89exYAUFVVhe3bt2P8+PGoqKiAWq0GAKjValRWVrbaNz4+HjqdDjqdDtXV1d0pg4hsSN9+Xohd9xc8vOQ5ZOxOwwf/fxFu/PST1GX1SF0O+D59+qBv376mf8+YMQM5OTlITk5GTEwMACAmJgY7duwwT6VEZPMGjw3Fy1sSMXhcGLa8+TY2Ll/BcJdQl6dofH19sX379lsbcXbGpk2bsHv3buj1emzZsgULFy7E6dOnMXfuXLMVS0S26fYpmeozpYh//mWcKyiUuqwer8sBf+rUKYSGhrZYfuHCBUybNq1bRRGR/Rg6fix+tvR5DBo1Asd2pmDr71byqN1G8FIFRNQlmuHBePjXizF80gRcPFeOTf/zOxz9epfUZdFtGPBEdFf6+Q1E5EvP4t6HZ+DqpRokv/s+DiZt40XDbBADnog61MutD0Y8MBGjpkVg5JTJaKxvQOrH67Hv75/j+pWrUpdHbWDAE1Gr3JSeGBkxGaOmRyB4gg7OLi64XFWNHzd/iX2ffo7LVTy92dYx4Il6KCcnJ3gHDoJaOwRKXx94+nrD08cbSl8fePh4w2uAL2RyOc6XnsWBL7YiO/V7nM7M5s047AgDnqiHcPNSYtCokQgYPfLWrfLuGQFXD3fT43XXrqOmsgo1FZUozsjCkeRSHP/uB5Tl8d6o9ooBT9QDjJ4xFU+/+wfIZDI01NejvPAkMnan4UzWcZTmncDFcxW4dvmy1GWSmTHgiXqA6c/OR+XJYmz9wyqU5Z1A3bXrUpdEVsA7OhE5uMFjQzEweCj2f5aEU8cyGe49CAOeyMFN+uXj+KnmMo7tTJG6FLIyBjyRA/P09cY9Ux9A+pdf4eb1G1KXQ1bGgCdyYPfNfRROMhl+3LxN6lJIAgx4Igfl7OKCCY9FIff7A7hQdk7qckgCDHgiBzVmxlS49++Hg19slboUkggDnshBTfrl46g8dRoF/9JLXQpJhAFP5ID87xmBQaNG4ACP3ns0BjyRA5r0yzm4fuUqjuzYKXUpJCEGPJGD6dvfC6GzpuFI8k7eWamHY8ATOZgJj0XBWaHg9Awx4IkcicxZjvvn/j+cOHgIVcVnpC6HJMaAJ3IQgWNG4el334KnrzcOfMEfNhGvJklk1+QKBUJnTcPkpx6H/8gQXLtci9SP1yNv/0GpSyMbwIAnsjPOLi7QhAQjZPL9mDAnCu79+6Hi35cCPvrVLtRduyZ1iWQjLBbwM2fOxJo1ayCXy/HJJ59g5cqVltpVtzn36gU3pQec4CR1KUQtuPRxhf/IEAz6952YBg7TwlmhQGNjI/K+P4gDX/yDP2aiVlkk4GUyGT788ENMnz4dpaWl0Ov1SE5ORl5eniV21y5XD3d4+njD09fn3/eb9IaH7637TjYtd1N6Wr0uort1/epVlOTkYf+GL3A6KxenM7NRe/6C1GWRDbNIwI8fPx6FhYU4deoUACApKQlRUVFmD/hh94fjF/+9pNXH5AoFPLxV6NXHtdnyxsZGXLlwETWVVbhw9hxOGbJQU1GFKxcvQjTyZsJkexpu3kRZfgEqThZDNDZKXQ7ZEYsEvEajQUlJien/paWlCA8PN/t+rl+9ioqTxa0+1lBfj8uV1bduIlxZhZryStRUVuFyVTUa6uvNXgsRka2R7EvW2NhYLFq0CACgUqm6tI3TmTnYsOxVc5ZFROQwLHIefFlZGfz9/U3/9/PzQ1lZWbN14uPjodPpoNPpUF1dbYkyiIh6NIsEvF6vh1arRWBgIBQKBaKjo5GcnGyJXRERURssMkXT0NCAF198Ebt374ZcLsenn36K3NxcS+yKiIjaYLE5+G+//RbffvutpTZPREQd4LVoiIgcFAOeiMhBMeCJiBwUA56IyEE5AZD89/mVlZU4ffp0l/qqVKoedR49x+u4etJYAY7XHAICAuDj49PuOsKem16vl7wGjpfj5Vg5XlscL6doiIgcFAOeiMhByQG8KXUR3XXs2DGpS7Aqjtdx9aSxAhyvpdnEl6xERGR+nKIhInJQDHgiIgdl1YDv1asX0tPTkZGRgZycHLz55psAgMDAQBw6dAhGoxFJSUlQKBSmPmq1Grt37wYAzJs3DwUFBSgoKMC8efNM67z11ls4c+YMamtrW+yzqf+YMWPw448/IicnB5mZmZg7d65pnfb23x0zZ85Efn4+jEYjli9fDgCYMmUKjh49iuzsbKxfvx5yudy0vrOzM44ePdpmXwD4/PPPkZ+fj+zsbCQkJMDZ2blFfz8/P+zduxfHjx9HTk4Oliz5z20Nvby8kJKSgoKCAqSkpECpVJplrACQkJCAiooKZGdnm5aNHj0aP/74I7KyspCcnAx3d/dmfY4cOQIXFxfce++9yMrKgtFoxJo1a0yPr1q1Cnl5ecjMzMSXX34JT0/PFv09PDzw9ddfIy8vDzk5OXj77bdNj7u4uCApKQlGoxGHDh1CQECAWcba1nPsqONt7bVt773UmffyJ598goyMDGRmZuIf//gH3NzcTI9J+blt67W11/Fa9bxMNzc3AUA4OzuLQ4cOifDwcLF582bxxBNPCABi3bp14rnnnjOt/8wzz4iXX35ZeHl5iaKiIuHl5SWUSqUoKioSSqVSABDh4eFCrVaL2traFvtr6q/VasXQoUMFADFgwABx9uxZ4enpKQC0u/+uNplMJgoLC0VQUJBQKBQiIyNDhISEiDNnzgitVisAiN/97ndiwYIFpj4RERHi/fffb7MvABEZGWlaf9OmTc1qbeqvVqtFWFiYACD69u0rTpw4Yeq/cuVKsXz5cgFALF++XLzzzjtme20nT54swsLCRHZ2tmnZ4cOHxQMPPCAAiPnz54vf//73pscCAwPFjh07BACRnp4uwsPDBQCxc+dOMWvWLAFATJ8+XcjlcgFAvPPOO83qberv6uoqIiIiBAChUCjE/v37Tf0XL14s1q1bJwCIJ554QiQlJZllrG09x4463tZe2/beS515L7u7u5vWf++990zbkvJz295ra6fjNW+Ad7a5urqKo0ePivHjx4uqqirTm3rChAli165dpvWSkpJESEiIiI6OFh999JFp+UcffSSio6ObbbO1gG/qf+fyjIwM0xPZ3v672u7cTlxcnHjttddEYWGhadmkSZPEN998Y/r/O++8IyIjI1vtGxcX12IfS5cuFW+99VaL/neu989//lNMmzZNABD5+flCrVab3sj5+flmfV0DAgKahcClS5dM//bz8xPHjx83/f+5554TixcvFmq1WuTl5ZmW3/laN7XZs2eLzz//vEX/O9f761//Kn71q18JAGLXrl1iwoQJAoCQy+WiqqrKIu/npufYkcd752vb3nvpbt/Lf/vb38Rvf/tb0/+l+ty299ra43itPgcvk8lgMBhQWVmJPXv2oKioCJcuXUJDQwOAWzfo1mg0pnWHDRuGvLy8Vm/k3bRee/tq6n87nU4HFxcXFBUVoX///m3uvztaq1etVsPZ2Rljx44FAMyZM1wnRHUAAAdiSURBVKfZrQ2nTJmC7777rlNjdXZ2xtNPP41du3a16H+7gIAAhIWFIT09HQDg6+uL8vJyAEB5eTl8fX27Pdb2HD9+HFFRUQCAxx9/vNl4Z82ahV27dkGj0aC0tNS0vK3XYMGCBc3uMdDU/3aenp545JFHkJaWBqD569DQ0ICamhr079/ffANE8+e4J4y3SXvvpc6+lz/99FOUl5dj+PDh+OCDDwBI+7m90+2vrT2O1+oB39jYiLCwMPj5+WH8+PEYPnx4m+uGh4ebgqkrWuuvVqvx2WefYf78+RBCdHnbXSGEQHR0NFavXo309HTU1taaXrCBAwfiwoULuHbtWqe29be//Q379+/HgQMH2uzv5uaGbdu2YenSpa1+P9FUkyUtWLAAzz//PI4cOQJ3d3fU1dUBABQKBfz8/HDq1KlObeeVV15BfX09Nm7c2GZ/uVyOL774Au+//36nt9tddz7Hjj7e9jS9l+7mvbxgwQIMHDgQeXl5eOKJJwDYzue2o8+PPYxXsrNoampqsG/fPtx3331QKpWmLxtvv0F3ZGSk6YilMzfyvtPt/QHA3d0d33zzDV599VXTE3r+/Pk2998dbdV76NAhPPDAAwgPD8f+/ftRUFAA4NbRWdOXyR2N9Y033oC3tzdefvll07Lb+wO3jvC3bduGjRs3Yvv27ablFRUVUKvVAG69iSorK7s91vacOHECM2fOxLhx4/DFF1+gqKgIADB58mTTH6eysjL4+fmZ+tw53piYGPz85z/HU089ZVp2e/8mH3/8cYsvLW9/LuVyOTw9PXH+/HmzjK2159iRx3untt5Ld/NeBm4d9CUlJeGxxx4DIO3ntklrr629jtci81atNZVKZfrCoHfv3mL//v3iZz/7mdiyZUuzLw+a5hkPHjwo+vbtKwAILy8vcfLkSaFUKoVSqRQnT54UXl5ezbZ/5xz87f0VCoVITU0Vv/71r1vU1db+u9PkcrkoKioSgYGBpi9bRowYIby9vQUA4eLiIlJTU8WUKVNMNQwbNqzdvgDEwoULxcGDB0Xv3r1bjKGpPwCRmJgoVq9e3aKuVatWNfuiaOXKlWZ9je+cp20ar5OTk0hMTBTz58831TFz5kzTend+6dj0XcLMmTPF8ePHhUqlajGO2/v/4Q9/EFu3bhVOTk7N1nv++eebfem4efNms421tefYkcd752vb1nups+/lIUOGmLb17rvvinfffVcA0n5u23tt7XS85vtwd9RGjRoljh07JjIzM0V2drZ4/fXXBQARFBQk0tPThdFoFFu2bBEuLi5CpVKJtLS0Zv3nz58vjEajMBqN4plnnjEtX7lypSgpKRENDQ2ipKRErFixokX/p556StTV1QmDwWBqY8aMaXP/5hhvZGSkOHHihCgsLBSvvPKK6U2Sm5sr8vPzTS+iTCYTx44d67AvAHHz5k1RWFhoGsPrr7/eov/EiROFEEJkZmaa1msKkH79+onU1FRRUFAg9uzZ0+KPZHfapk2bxNmzZ0VdXZ0oKSkRCxYsEEuWLBEnTpwQJ06cEG+//bZp3cOHDzf7IzV27FiRnZ0tCgsLxQcffGBabjQaxZkzZ0zjaAqv2/trNBohhBC5ubmm9RYuXCgAiF69eoktW7YIo9Eo0tPTRVBQkFnG2tZz7Kjjbe21be291Nn3spOTkzhw4IDIysoS2dnZ4vPPPxfu7u428blt67W10/GaL8DN2Z566qlmpxFZu78128SJE00fZCn6W7tpNBqxc+dOyfpzvJZr3X0v2tPn1h7Gy2vREBE5KF6qgIjIQTHgiYgcFAOeiMhBMeCJiBwUA556pPr6ehgMBuTk5CAjIwMvv/wynJyc2u0TEBCAJ5980koVEnUfA556pGvXriEsLAz33HMPpk+fjsjISKxYsaLdPoGBgfjlL39ppQqJzEPyc0nZ2Kzd7vzVc1BQkKiurhbArV9s7t+/Xxw9elQcPXpU3HfffQKA+Ne//iUuXbokDAaDWLp0qZDJZGLVqlXi8OHDIjMzUyxatEjycbGx3dEkL4CNzeqttUtLX7x4Ufj4+AhXV1fRq1cvAUAMHTpU6PV6AUA8+OCD4quvvjKtHxsbK1599VUB3Lr0hF6vF4GBgZKPjY2tqf3ndkBEBODWlRvXrl2L0NBQNDQ0IDg4uNX1ZsyYgdGjR2POnDkAbl26V6vVori42IrVErWNAU8EICgoCA0NDaisrMSKFStQUVGBMWPGQCaT4fr16632cXJywksvvYSUlBQrV0vUOfySlXo8lUqFjz76CGvXrgVw60j83LlzEELg6aefNt33tra2ttk9Vnfv3o3FixebHtdqtejTp4/1B0DUBh7BU4/k6uoKg8EAhUKB+vp6fPbZZ/jLX/4C4NbNVLZt24Z58+Zh165duHLlCgAgKysLDQ0NyMjIwPr167FmzRoEBgbi2LFjcHJyQlVVFWbPni3lsIia4cXGiIgcFKdoiIgcFAOeiMhBMeCJiBwUA56IyEEx4ImIHBQDnojIQTHgiYgc1P8BSD3QQniSbGgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mj5tFw8RH5Uu"
      },
      "source": [
        "### **Computing value of r**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9JiuvTCoWc7U"
      },
      "source": [
        "As we saw above, the plot number of cases vs time looks like an exponential curve. Hence, we will fit an exponential model which predict's the number of cases P_t on a day as P_t = P_o * (e^r*t). P_o is the number of cases on a particular day and t is number of days between the day for which P_t is to be calculated and the day for which we considered P_o "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "61t2PVxmYUl0"
      },
      "source": [
        "Example: if we want to predict cases for 15-03-20 using number of cases from 05-03-20. Then, P_t is cases on 15th, P_o is cases on 5th and t is 10 (15-5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iS50GxonYX-F"
      },
      "source": [
        "r is the rate of increase in number of cases in a day. We have 18 (we are considering a subset of this data) such values (r from 04-03-20 to 20-03-20). We will take the average of this value as r for our model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8yLnSfeoA4Lq",
        "outputId": "0ccff32b-fa8d-4b29-8d16-103c695af176"
      },
      "source": [
        "total_cases_subset = total_cases['04/03/20':'21/03/20']\r\n",
        "\r\n",
        "r_sum = 0.0\r\n",
        "\r\n",
        "for i in range(len(total_cases_subset)-1):\r\n",
        "  r_sum = r_sum + (total_cases_subset[i+1] - total_cases_subset[i])/total_cases_subset[i]\r\n",
        "\r\n",
        "r_avg = r_sum/(len(total_cases_subset) - 1)\r\n",
        "print('Value of r for our model: ',r_avg)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Value of r for our model:  0.14814803793984233\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lsxPCxNBH7sg"
      },
      "source": [
        "### **predicted total cases after 26 days from 04-03-20 (predicted total cases on 30-03-20)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T1t_QAvnH9yD",
        "outputId": "bc8a3c9f-2aa8-40d2-cf02-25d6a86f4323"
      },
      "source": [
        "import math\r\n",
        "\r\n",
        "print('Predicted number of cases: ',round(total_cases_subset['04/03/20'] * math.exp(r_avg * 26)))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicted number of cases:  1459\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}