Linear_regression_covid.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a8338a7c",
   "metadata": {},
   "source": [
    "# Author : Shaik Imranuddin\n",
    "\n",
    "## Prediction using Linear Regression\n",
    "\n",
    "I have tried to predict the number of Covid-19 cases in India based on the dataset containing previous cases information. \n",
    "Here I have made a Linear Regression model to predict the Covid-19 cases.\n",
    "\n",
    "## Technical Stack  : Sikit Learn, Numpy Array, Pandas, Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "080a05a0",
   "metadata": {},
   "source": [
    "## Importing the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c29934e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "589ab96c",
   "metadata": {},
   "source": [
    "## Reading the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b821cebe",
   "metadata": {},
   "outputs": [],
   "source": [
    "std=pd.read_csv('state_wise_daily.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a53e635e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Date_YMD</th>\n",
       "      <th>Status</th>\n",
       "      <th>TT</th>\n",
       "      <th>AN</th>\n",
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       "      <th>0</th>\n",
       "      <td>14-Mar-20</td>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>Confirmed</td>\n",
       "      <td>81</td>\n",
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       "      <th>3</th>\n",
       "      <td>15-Mar-20</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>Confirmed</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>15-Mar-20</td>\n",
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       "<p>5 rows × 42 columns</p>\n",
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       "        Date    Date_YMD     Status  TT  AN  AP  AR  AS  BR  CH  ...  PB  RJ  \\\n",
       "0  14-Mar-20  2020-03-14  Confirmed  81   0   1   0   0   0   0  ...   1   3   \n",
       "1  14-Mar-20  2020-03-14  Recovered   9   0   0   0   0   0   0  ...   0   1   \n",
       "2  14-Mar-20  2020-03-14   Deceased   2   0   0   0   0   0   0  ...   0   0   \n",
       "3  15-Mar-20  2020-03-15  Confirmed  27   0   0   0   0   0   0  ...   0   1   \n",
       "4  15-Mar-20  2020-03-15  Recovered   4   0   0   0   0   0   0  ...   0   2   \n",
       "\n",
       "   SK  TN  TG  TR  UP  UT  WB  UN  \n",
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       "1   0   0   0   0   4   0   0   0  \n",
       "2   0   0   0   0   0   0   0   0  \n",
       "3   0   0   2   0   1   0   0   0  \n",
       "4   0   0   1   0   0   0   0   0  \n",
       "\n",
       "[5 rows x 42 columns]"
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   "source": [
    "std.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b1082706",
   "metadata": {},
   "outputs": [
    {
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       "      <td>41671.131403</td>\n",
       "      <td>10.460282</td>\n",
       "      <td>2502.314774</td>\n",
       "      <td>40.440980</td>\n",
       "      <td>603.252413</td>\n",
       "      <td>1050.308834</td>\n",
       "      <td>89.268745</td>\n",
       "      <td>1435.974016</td>\n",
       "      <td>15.101707</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>840.044543</td>\n",
       "      <td>1387.775798</td>\n",
       "      <td>21.610245</td>\n",
       "      <td>3100.342242</td>\n",
       "      <td>853.563474</td>\n",
       "      <td>76.263549</td>\n",
       "      <td>2505.765405</td>\n",
       "      <td>478.025984</td>\n",
       "      <td>2074.104677</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>std</th>\n",
       "      <td>78308.933326</td>\n",
       "      <td>21.395078</td>\n",
       "      <td>4835.213995</td>\n",
       "      <td>80.742073</td>\n",
       "      <td>1211.537768</td>\n",
       "      <td>2513.030627</td>\n",
       "      <td>173.327767</td>\n",
       "      <td>3219.209735</td>\n",
       "      <td>40.263167</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1638.785505</td>\n",
       "      <td>3368.976748</td>\n",
       "      <td>52.945810</td>\n",
       "      <td>6364.960630</td>\n",
       "      <td>1511.367737</td>\n",
       "      <td>161.426828</td>\n",
       "      <td>6149.023963</td>\n",
       "      <td>1245.182537</td>\n",
       "      <td>4206.739805</td>\n",
       "      <td>108.884981</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-374.000000</td>\n",
       "      <td>-19.000000</td>\n",
       "      <td>-1295.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>553.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>41.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>33.500000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>50%</th>\n",
       "      <td>11796.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>179.000000</td>\n",
       "      <td>196.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>567.000000</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>247.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48201.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2215.500000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>476.000000</td>\n",
       "      <td>841.000000</td>\n",
       "      <td>87.500000</td>\n",
       "      <td>1377.500000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>713.500000</td>\n",
       "      <td>1342.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>3696.500000</td>\n",
       "      <td>1271.000000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>2071.000000</td>\n",
       "      <td>390.500000</td>\n",
       "      <td>2950.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>422391.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>24819.000000</td>\n",
       "      <td>497.000000</td>\n",
       "      <td>6573.000000</td>\n",
       "      <td>15853.000000</td>\n",
       "      <td>963.000000</td>\n",
       "      <td>18746.000000</td>\n",
       "      <td>359.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9059.000000</td>\n",
       "      <td>29459.000000</td>\n",
       "      <td>420.000000</td>\n",
       "      <td>36184.000000</td>\n",
       "      <td>17183.000000</td>\n",
       "      <td>1066.000000</td>\n",
       "      <td>40852.000000</td>\n",
       "      <td>9642.000000</td>\n",
       "      <td>20846.000000</td>\n",
       "      <td>1043.000000</td>\n",
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      "text/plain": [
       "                  TT           AN            AP           AR           AS  \\\n",
       "count    1347.000000  1347.000000   1347.000000  1347.000000  1347.000000   \n",
       "mean    41671.131403    10.460282   2502.314774    40.440980   603.252413   \n",
       "std     78308.933326    21.395078   4835.213995    80.742073  1211.537768   \n",
       "min         0.000000     0.000000      0.000000     0.000000    -1.000000   \n",
       "25%       553.500000     0.000000     14.000000     0.000000     2.000000   \n",
       "50%     11796.000000     1.000000    106.000000     1.000000    29.000000   \n",
       "75%     48201.000000    12.000000   2215.500000    37.000000   476.000000   \n",
       "max    422391.000000   149.000000  24819.000000   497.000000  6573.000000   \n",
       "\n",
       "                 BR           CH            CT           DN      DD  ...  \\\n",
       "count   1347.000000  1347.000000   1347.000000  1347.000000  1347.0  ...   \n",
       "mean    1050.308834    89.268745   1435.974016    15.101707     0.0  ...   \n",
       "std     2513.030627   173.327767   3219.209735    40.263167     0.0  ...   \n",
       "min        0.000000     0.000000      0.000000     0.000000     0.0  ...   \n",
       "25%        5.000000     1.000000      7.000000     0.000000     0.0  ...   \n",
       "50%       96.000000    10.000000    147.000000     0.000000     0.0  ...   \n",
       "75%      841.000000    87.500000   1377.500000    11.000000     0.0  ...   \n",
       "max    15853.000000   963.000000  18746.000000   359.000000     0.0  ...   \n",
       "\n",
       "                PB            RJ           SK            TN            TG  \\\n",
       "count  1347.000000   1347.000000  1347.000000   1347.000000   1347.000000   \n",
       "mean    840.044543   1387.775798    21.610245   3100.342242    853.563474   \n",
       "std    1638.785505   3368.976748    52.945810   6364.960630   1511.367737   \n",
       "min      -3.000000      0.000000    -1.000000      0.000000      0.000000   \n",
       "25%      15.000000     12.000000     0.000000     41.500000      7.000000   \n",
       "50%     179.000000    196.000000     2.000000    567.000000    157.000000   \n",
       "75%     713.500000   1342.000000    19.000000   3696.500000   1271.000000   \n",
       "max    9059.000000  29459.000000   420.000000  36184.000000  17183.000000   \n",
       "\n",
       "                TR            UP           UT            WB           UN  \n",
       "count  1347.000000   1347.000000  1347.000000   1347.000000  1347.000000  \n",
       "mean     76.263549   2505.765405   478.025984   2074.104677     0.000000  \n",
       "std     161.426828   6149.023963  1245.182537   4206.739805   108.884981  \n",
       "min      -2.000000     -1.000000  -374.000000    -19.000000 -1295.000000  \n",
       "25%       0.000000     27.000000     3.000000     33.500000     0.000000  \n",
       "50%       3.000000    247.000000    54.000000    249.000000     0.000000  \n",
       "75%      64.500000   2071.000000   390.500000   2950.000000     0.000000  \n",
       "max    1066.000000  40852.000000  9642.000000  20846.000000  1043.000000  \n",
       "\n",
       "[8 rows x 39 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using describe() to see mean,max,min,count,std,percentiles of the given dataset\n",
    "\n",
    "std.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eb31c004",
   "metadata": {},
   "outputs": [],
   "source": [
    "std['Confirmed']=std['TT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bd6df6a8",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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      "text/plain": [
       "        Date    Date_YMD     Status  TT  AN  AP  AR  AS  BR  CH  ...  RJ  SK  \\\n",
       "0  14-Mar-20  2020-03-14  Confirmed  81   0   1   0   0   0   0  ...   3   0   \n",
       "1  14-Mar-20  2020-03-14  Recovered   9   0   0   0   0   0   0  ...   1   0   \n",
       "2  14-Mar-20  2020-03-14   Deceased   2   0   0   0   0   0   0  ...   0   0   \n",
       "3  15-Mar-20  2020-03-15  Confirmed  27   0   0   0   0   0   0  ...   1   0   \n",
       "4  15-Mar-20  2020-03-15  Recovered   4   0   0   0   0   0   0  ...   2   0   \n",
       "\n",
       "   TN  TG  TR  UP  UT  WB  UN  Confirmed  \n",
       "0   1   1   0  12   0   0   0         81  \n",
       "1   0   0   0   4   0   0   0          9  \n",
       "2   0   0   0   0   0   0   0          2  \n",
       "3   0   2   0   1   0   0   0         27  \n",
       "4   0   1   0   0   0   0   0          4  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "56c5ae7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "std=std[std.Status=='Confirmed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7d1221a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "std=std.drop(columns='Status', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dfc06ea3",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "         Date    Date_YMD  TT  AN  AP  AR  AS  BR  CH  CT  ...  RJ  SK  TN  \\\n",
       "0   14-Mar-20  2020-03-14  81   0   1   0   0   0   0   0  ...   3   0   1   \n",
       "3   15-Mar-20  2020-03-15  27   0   0   0   0   0   0   0  ...   1   0   0   \n",
       "6   16-Mar-20  2020-03-16  15   0   0   0   0   0   0   0  ...   0   0   0   \n",
       "9   17-Mar-20  2020-03-17  11   0   0   0   0   0   0   0  ...   0   0   0   \n",
       "12  18-Mar-20  2020-03-18  37   0   0   0   0   0   0   0  ...   3   0   1   \n",
       "\n",
       "    TG  TR  UP  UT  WB  UN  Confirmed  \n",
       "0    1   0  12   0   0   0         81  \n",
       "3    2   0   1   0   0   0         27  \n",
       "6    1   0   0   1   0   0         15  \n",
       "9    1   0   2   0   1   0         11  \n",
       "12   8   0   2   1   0   0         37  \n",
       "\n",
       "[5 rows x 42 columns]"
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ea1ac215",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns=['Date','Date_YMD', 'Confirmed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0f1b7405",
   "metadata": {},
   "outputs": [],
   "source": [
    "std=std[columns]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0ef2e8d9",
   "metadata": {},
   "outputs": [
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       "         Date    Date_YMD  Confirmed\n",
       "0   14-Mar-20  2020-03-14         81\n",
       "3   15-Mar-20  2020-03-15         27\n",
       "6   16-Mar-20  2020-03-16         15\n",
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  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fd388864",
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01</th>\n",
       "      <td>133152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-02</th>\n",
       "      <td>134044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-03</th>\n",
       "      <td>132424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-04</th>\n",
       "      <td>120454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-05</th>\n",
       "      <td>114486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>449 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Confirmed\n",
       "Date_YMD             \n",
       "2020-03-14         81\n",
       "2020-03-15         27\n",
       "2020-03-16         15\n",
       "2020-03-17         11\n",
       "2020-03-18         37\n",
       "...               ...\n",
       "2021-06-01     133152\n",
       "2021-06-02     134044\n",
       "2021-06-03     132424\n",
       "2021-06-04     120454\n",
       "2021-06-05     114486\n",
       "\n",
       "[449 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std.groupby('Date_YMD').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8d7a357d",
   "metadata": {},
   "outputs": [],
   "source": [
    "std['Date']=pd.to_datetime(std['Date'])\n",
    "std['Date']=std['Date'].map(dt.datetime.toordinal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "320fb9a5",
   "metadata": {},
   "outputs": [
    {
     "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>Date_YMD</th>\n",
       "      <th>Confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1332</th>\n",
       "      <td>737942</td>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>133152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>737943</td>\n",
       "      <td>2021-06-02</td>\n",
       "      <td>134044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>737944</td>\n",
       "      <td>2021-06-03</td>\n",
       "      <td>132424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1341</th>\n",
       "      <td>737945</td>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>120454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1344</th>\n",
       "      <td>737946</td>\n",
       "      <td>2021-06-05</td>\n",
       "      <td>114486</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Date_YMD  Confirmed\n",
       "1332  737942  2021-06-01     133152\n",
       "1335  737943  2021-06-02     134044\n",
       "1338  737944  2021-06-03     132424\n",
       "1341  737945  2021-06-04     120454\n",
       "1344  737946  2021-06-05     114486"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0308424a",
   "metadata": {},
   "source": [
    "## Visualization of Input Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "76509ad7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the distribution of confirmed cases\n",
    "\n",
    "std.plot(x='Date_YMD', y='Confirmed', style='.')  \n",
    "plt.title('Dates vs Confirmed cases')  \n",
    "plt.xlabel('Date')  \n",
    "plt.ylabel('Cases')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b8f5afd",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "This step involved division of data into \"attributes\" (inputs) and \"labels\" (outputs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f85c768e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = std.iloc[:, :-2].values   # Date\n",
    "Y = std.iloc[:, 2].values     # Confirmed cases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db4ff68f",
   "metadata": {},
   "source": [
    "## Training the model\n",
    "Splitting the data into training and testing sets and\n",
    "\n",
    "Training the algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6a55263b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training completed.\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0) \n",
    "\n",
    "regsr = LinearRegression()  \n",
    "regsr.fit(X_train.reshape(-1,1), Y_train) \n",
    "\n",
    "print(\"Training completed.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "200fa1a7",
   "metadata": {},
   "source": [
    "## Plotting the Regression Line\n",
    "\n",
    "Now since our model is trained, let visualize the best-fit line of regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "23b127cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the regression line\n",
    "regline = regsr.coef_*X+regsr.intercept_\n",
    "\n",
    "# Plotting on the training dataset\n",
    "plt.scatter(X, Y)\n",
    "plt.plot(X, regline, color='red');\n",
    "plt.title('Date vs Confirmed cases on Training data')  \n",
    "plt.xlabel('Date')  \n",
    "plt.ylabel('Cases')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae609521",
   "metadata": {},
   "source": [
    "## Making Predictions\n",
    "Now that we have trained our algorithm, it is time to make some predictions to test the model.\n",
    "\n",
    "We will use our testing dataset for this predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3c6f159c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[737622]\n",
      " [737552]\n",
      " [737872]\n",
      " [737840]\n",
      " [737728]\n",
      " [737632]\n",
      " [737510]\n",
      " [737830]\n",
      " [737769]\n",
      " [737574]\n",
      " [737876]\n",
      " [737655]\n",
      " [737818]\n",
      " [737662]\n",
      " [737846]\n",
      " [737519]\n",
      " [737616]\n",
      " [737499]\n",
      " [737508]\n",
      " [737750]\n",
      " [737862]\n",
      " [737677]\n",
      " [737916]\n",
      " [737611]\n",
      " [737806]\n",
      " [737885]\n",
      " [737842]\n",
      " [737718]\n",
      " [737722]\n",
      " [737711]\n",
      " [737940]\n",
      " [737873]\n",
      " [737791]\n",
      " [737535]\n",
      " [737729]\n",
      " [737734]\n",
      " [737891]\n",
      " [737708]\n",
      " [737864]\n",
      " [737801]\n",
      " [737658]\n",
      " [737717]\n",
      " [737884]\n",
      " [737703]\n",
      " [737903]\n",
      " [737883]\n",
      " [737598]\n",
      " [737640]\n",
      " [737847]\n",
      " [737569]\n",
      " [737692]\n",
      " [737829]\n",
      " [737669]\n",
      " [737504]\n",
      " [737605]\n",
      " [737576]\n",
      " [737861]\n",
      " [737513]\n",
      " [737558]\n",
      " [737898]\n",
      " [737642]\n",
      " [737879]\n",
      " [737759]\n",
      " [737554]\n",
      " [737686]\n",
      " [737600]\n",
      " [737594]\n",
      " [737886]\n",
      " [737946]\n",
      " [737909]\n",
      " [737588]\n",
      " [737908]\n",
      " [737910]\n",
      " [737633]\n",
      " [737557]\n",
      " [737850]\n",
      " [737668]\n",
      " [737932]\n",
      " [737563]\n",
      " [737639]\n",
      " [737912]\n",
      " [737935]\n",
      " [737652]\n",
      " [737943]\n",
      " [737799]\n",
      " [737673]\n",
      " [737656]\n",
      " [737694]\n",
      " [737739]\n",
      " [737745]]\n"
     ]
    }
   ],
   "source": [
    "# Test dataset\n",
    "\n",
    "print(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "00f21b7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 22960.17351049  -5069.90584552 123067.59978193 110253.84921914\n",
      "  65405.72224957  26964.47056133 -21887.95345908 106249.55216831\n",
      "  81823.3401581    3739.54766637 124669.31860226  36174.3537783\n",
      " 101444.39570731  38977.36171389 112656.4274497  -18284.08611333\n",
      "  20557.59527999 -26292.68021506 -22688.81286931  74215.17576146\n",
      " 119063.30273104  44983.8072902  140686.50680566  18555.44675452\n",
      "  96639.23924625 128273.18594801 111054.70862931  61401.42519873\n",
      "  63003.14401907  58598.41726309 150296.81972772 123468.02948701\n",
      "  90632.79366994 -11877.21083194  65806.15195465  67808.30048007\n",
      " 130675.76417851  57397.12814784 119864.1621412   94637.09072083\n",
      "  37375.64289355  61000.99549365 127872.75624293  55394.97962242\n",
      " 135480.92063957 127472.32653785  13349.86058843  30167.90820205\n",
      " 113056.85715479   1737.39914095  50990.25286651 105849.12246323\n",
      "  41780.36964953 -24290.53168964  16152.86852401   4540.40707654\n",
      " 118662.87302595 -20686.66434383  -2667.32761502 133478.77211416\n",
      "  30968.76761222 125870.60771751  77819.04310721  -4269.04643536\n",
      "  48587.67463595  14150.7199986   11748.1417681  128673.6156531\n",
      " 152699.39795828 137883.49887007   9345.5635376  137483.06916499\n",
      " 138283.92857516  27364.90026641  -3067.75732011 114258.14627004\n",
      "  41379.93994445 147093.38208705   -665.17908955  29767.47849697\n",
      " 139084.78798532 148294.6712023   34973.06466305 151498.10884297\n",
      "  93836.23131067  43382.08846986  36574.78348339  51791.11227667\n",
      "  69810.44900548  72213.02723604]\n"
     ]
    }
   ],
   "source": [
    "# Model Prediction \n",
    "\n",
    "Y_pred = regsr.predict(X_test)\n",
    "print(Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "11d1c740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting on the testing dataset\n",
    "\n",
    "plt.scatter(X_test, Y_test)\n",
    "plt.plot(X_test, Y_pred, color='red');\n",
    "plt.title('Date vs Confirmed cases on Testing data')  \n",
    "plt.xlabel('Date')  \n",
    "plt.ylabel('Cases')   \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bdca7b5",
   "metadata": {},
   "source": [
    "## Comparing Actual result to the Predicted Model result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e4948466",
   "metadata": {},
   "outputs": [
    {
     "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35468</td>\n",
       "      <td>22960.173510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3344</td>\n",
       "      <td>-5069.905846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47239</td>\n",
       "      <td>123067.599782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13916</td>\n",
       "      <td>110253.849219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48117</td>\n",
       "      <td>65405.722250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>90600</td>\n",
       "      <td>43382.088470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>69196</td>\n",
       "      <td>36574.783483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>88759</td>\n",
       "      <td>51791.112277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>44724</td>\n",
       "      <td>69810.449005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>28609</td>\n",
       "      <td>72213.027236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual      Predicted\n",
       "0    35468   22960.173510\n",
       "1     3344   -5069.905846\n",
       "2    47239  123067.599782\n",
       "3    13916  110253.849219\n",
       "4    48117   65405.722250\n",
       "..     ...            ...\n",
       "85   90600   43382.088470\n",
       "86   69196   36574.783483\n",
       "87   88759   51791.112277\n",
       "88   44724   69810.449005\n",
       "89   28609   72213.027236\n",
       "\n",
       "[90 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing Actual vs Predicted\n",
    "\n",
    "df = pd.DataFrame({'Actual': Y_test, 'Predicted': Y_pred}) \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "156ba68e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.33862433396438363\n",
      "Testing Score: 0.32381268356516313\n"
     ]
    }
   ],
   "source": [
    "#Estimating training and testing score\n",
    "\n",
    "print(\"Training Score:\",regsr.score(X_train,Y_train))\n",
    "print(\"Testing Score:\",regsr.score(X_test,Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a31b36f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted cases on 2021-06-05  is : 92234.51249033213\n"
     ]
    }
   ],
   "source": [
    "date = \"2021-06-05\"\n",
    "dte=dt.datetime.strptime(date, '%Y-%M-%d').toordinal()\n",
    "prediction_cases = regsr.predict([[dte]])\n",
    "print(\"Predicted cases on {}  is : {}\".format(date,prediction_cases[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30c17f90",
   "metadata": {},
   "source": [
    "## Evaluating the model\n",
    "The final step is to evaluate the performance of our algorithm. This step is particularly important to compare how well different algorithms perform on a particular dataset. Here different errors have been calculated to compare the model performance and predict the accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ddb7b0e0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 52056.758690417475\n",
      "Mean Squared Error: 6408974847.217918\n",
      "Root Mean Squared Error: 80056.07314387783\n",
      "R-2: 0.32381268356516313\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:',metrics.mean_absolute_error(Y_test, Y_pred)) \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(Y_test, Y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)))\n",
    "print('R-2:', metrics.r2_score(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd8badc4",
   "metadata": {},
   "source": [
    "#### Root Mean Squared Error in this case is VERY HIGH\n",
    "#### R-2 gives the score of Model Fit and in this case 32.38% score of testing data indicates that above fitted Model is NOT A GOOD MODEL."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "392993f0",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "### From the above Prediction Model,  we can say that the Linear Regression is NOT a good model for predicting Covid-19 Cases.\n",
    "\n",
    "# Thank You"
   ]
  }
 ],
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