ccovid19.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>The covid 19 dataset for Pakistani patients have been downloaded from Kaggle'</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "covidpak = pd.read_csv('covid19_june_pak.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Lets explore the data first</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Travel_history</th>\n",
       "      <th>Province</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2/26/2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>China</td>\n",
       "      <td>Islamabad Capital Territory</td>\n",
       "      <td>Islamabad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2/26/2020</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Iran/Taftan</td>\n",
       "      <td>Sindh</td>\n",
       "      <td>Karachi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2/29/2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>China</td>\n",
       "      <td>Islamabad Capital Territory</td>\n",
       "      <td>Islamabad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2/29/2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Iran/Taftan</td>\n",
       "      <td>Sindh</td>\n",
       "      <td>Karachi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3/2/2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Iran/Taftan</td>\n",
       "      <td>Gilgit-Baltistan</td>\n",
       "      <td>Gilgit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Cases  Deaths  Recovered Travel_history  \\\n",
       "0  2/26/2020      1       0          0          China   \n",
       "1  2/26/2020      2       0          0    Iran/Taftan   \n",
       "2  2/29/2020      1       0          0          China   \n",
       "3  2/29/2020      1       0          0    Iran/Taftan   \n",
       "4   3/2/2020      1       0          0    Iran/Taftan   \n",
       "\n",
       "                      Province       City  \n",
       "0  Islamabad Capital Territory  Islamabad  \n",
       "1                        Sindh    Karachi  \n",
       "2  Islamabad Capital Territory  Islamabad  \n",
       "3                        Sindh    Karachi  \n",
       "4             Gilgit-Baltistan     Gilgit  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidpak.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Travel_history</th>\n",
       "      <th>Province</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2793</th>\n",
       "      <td>5/30/2020</td>\n",
       "      <td>952</td>\n",
       "      <td>36</td>\n",
       "      <td>394</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Punjab</td>\n",
       "      <td>Lahore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2794</th>\n",
       "      <td>5/31/2020</td>\n",
       "      <td>1184</td>\n",
       "      <td>22</td>\n",
       "      <td>120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Punjab</td>\n",
       "      <td>Lahore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2795</th>\n",
       "      <td>6/1/2020</td>\n",
       "      <td>1610</td>\n",
       "      <td>43</td>\n",
       "      <td>95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Punjab</td>\n",
       "      <td>Lahore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2796</th>\n",
       "      <td>6/2/2020</td>\n",
       "      <td>1639</td>\n",
       "      <td>30</td>\n",
       "      <td>353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Punjab</td>\n",
       "      <td>Lahore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2797</th>\n",
       "      <td>6/3/2020</td>\n",
       "      <td>1615</td>\n",
       "      <td>37</td>\n",
       "      <td>243</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Punjab</td>\n",
       "      <td>Lahore</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  Cases  Deaths  Recovered Travel_history Province    City\n",
       "2793  5/30/2020    952      36        394            NaN   Punjab  Lahore\n",
       "2794  5/31/2020   1184      22        120            NaN   Punjab  Lahore\n",
       "2795   6/1/2020   1610      43         95            NaN   Punjab  Lahore\n",
       "2796   6/2/2020   1639      30        353            NaN   Punjab  Lahore\n",
       "2797   6/3/2020   1615      37        243            NaN   Punjab  Lahore"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidpak.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2798, 7)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidpak.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Cases', 'Deaths', 'Recovered', 'Travel_history', 'Province',\n",
       "       'City'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidpak.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date              object\n",
       "Cases              int64\n",
       "Deaths             int64\n",
       "Recovered          int64\n",
       "Travel_history    object\n",
       "Province          object\n",
       "City              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidpak.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cases</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2798.000000</td>\n",
       "      <td>2798.000000</td>\n",
       "      <td>2798.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>30.016440</td>\n",
       "      <td>0.617584</td>\n",
       "      <td>8.847034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>128.861666</td>\n",
       "      <td>2.716284</td>\n",
       "      <td>61.362566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1639.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1431.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Cases       Deaths    Recovered\n",
       "count  2798.000000  2798.000000  2798.000000\n",
       "mean     30.016440     0.617584     8.847034\n",
       "std     128.861666     2.716284    61.362566\n",
       "min       0.000000     0.000000    -2.000000\n",
       "25%       0.000000     0.000000     0.000000\n",
       "50%       2.000000     0.000000     0.000000\n",
       "75%       9.000000     0.000000     1.000000\n",
       "max    1639.000000    43.000000  1431.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidpak.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Data Visualization</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph = covidpak.groupby('Province')['Cases'].sum().sort_index().plot(kind='bar',figsize=(18,5))\n",
    "graph.set_xlabel('Province',fontsize=12)\n",
    "graph.set_ylabel('Cases',fontsize=12)\n",
    "graph.set_title('Registered Corona Cases',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Here starts the data wrangling / preprocessing part which is the preliminary requirement for optimal data analysis</h3>\n",
    "<p>Following steps are involved\n",
    "<br>1. Finding missing values\n",
    "<br>2. Changing data types of features, if require\n",
    "<br>3. Encoding of data for categorical features</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidpak.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date               0\n",
       "Cases              0\n",
       "Deaths             0\n",
       "Recovered          0\n",
       "Travel_history    36\n",
       "Province           0\n",
       "City               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidpak.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = ['Travel_history']\n",
    "\n",
    "for c in col:\n",
    "    mode = covidpak.Travel_history.mode()\n",
    "    covidpak[c].fillna(mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets remove the null values\n",
    "covidpak.dropna(axis=\"rows\", how=\"any\", inplace = True)\n",
    "covidpak.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Islamabad Capital Territory', 'Sindh', 'Gilgit-Baltistan',\n",
       "       'Baluchistan', 'Punjab', 'Khyber Pakhtunkhwa',\n",
       "       'khyber Pakhtunkhwa', 'Azad Jummu Kashmir',\n",
       "       'Federal Administration Tribal Area',\n",
       "       'islamabad Capital Territory'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidpak['Province'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Encode values of Province</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    }
   ],
   "source": [
    "covidpak.Province [covidpak.Province == 'Islamabad Capital Territory']=1\n",
    "covidpak.Province [covidpak.Province == 'islamabad Capital Territory']=1\n",
    "covidpak.Province [covidpak.Province == 'Sindh'] = 2\n",
    "covidpak.Province [covidpak.Province == 'Gilgit-Baltistan'] = 3\n",
    "covidpak.Province [covidpak.Province == 'Baluchistan'] = 4\n",
    "covidpak.Province [covidpak.Province == 'khyber Pakhtunkhwa']= 5\n",
    "covidpak.Province [covidpak.Province == 'Khyber Pakhtunkhwa']= 5\n",
    "covidpak.Province [covidpak.Province == 'Khyber'] = 5\n",
    "covidpak.Province [covidpak.Province == 'Punjab'] = 6\n",
    "covidpak.Province [covidpak.Province == 'Azad Jummu Kashmir'] = 7\n",
    "covidpak.Province [covidpak.Province == 'Federal Administration Tribal Area'] = 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['China', 'Iran/Taftan', 'Unknown', 'Syria', 'UK',\n",
       "       'Local - Social Contact', 'USA', 'KSA', 'Local - Covid Relative',\n",
       "       'Dubai', 'International Passenger', 'Tableeghi Jamaat', 'Jail',\n",
       "       'Afghanistan', 'India'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidpak['Travel_history'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  import sys\n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if sys.path[0] == '':\n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  del sys.path[0]\n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "covidpak.Travel_history [covidpak.Travel_history == 'China'] = 1\n",
    "covidpak.Travel_history [covidpak.Travel_history == 'Iran/Taftan'] = 2\n",
    "covidpak.Travel_history [covidpak.Travel_history == 'KSA'] = 3\n",
    "covidpak.Travel_history [covidpak.Travel_history == 'Syria'] = 4\n",
    "covidpak.Travel_history [covidpak.Travel_history == 'UK'] = 5\n",
    "covidpak.Travel_history [covidpak.Travel_history == 'USA'] = 6\n",
    "covidpak.Travel_history [covidpak.Travel_history == 'Dubai'] = 7\n",
    "covidpak.Travel_history [covidpak.Travel_history == 'Tableeghi Jamaat'] = 8\n",
    "covidpak.Travel_history [covidpak.Travel_history == 'Jail'] = 9\n",
    "covidpak.Travel_history [covidpak.Travel_history == 'Afghanistan'] = 10\n",
    "covidpak.Travel_history [covidpak.Travel_history == 'India'] = 11\n",
    "covidpak.Travel_history [covidpak.Travel_history == 'International Passenger'] = 12\n",
    "covidpak.Travel_history [covidpak.Travel_history == 'Local - Social Contact'] = 13\n",
    "covidpak.Travel_history [covidpak.Travel_history == 'Local - Covid Relative'] = 14\n",
    "covidpak.Travel_history [covidpak.Travel_history == 'Unknown'] = 15\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       2\n",
       "2       1\n",
       "3       2\n",
       "4       3\n",
       "       ..\n",
       "2757    7\n",
       "2758    7\n",
       "2759    7\n",
       "2760    7\n",
       "2761    7\n",
       "Name: Province, Length: 2762, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidpak['Province']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        2\n",
       "2        1\n",
       "3        2\n",
       "4        2\n",
       "        ..\n",
       "2757    13\n",
       "2758    13\n",
       "2759    13\n",
       "2760    13\n",
       "2761    13\n",
       "Name: Travel_history, Length: 2762, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidpak['Travel_history']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covidpak.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting the types according to our domain\n",
    "col = ['Deaths','Cases','Recovered']\n",
    "\n",
    "for c in col:\n",
    "    covidpak[c] = covidpak[c].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22c0f216ec8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "corr = covidpak.corr()\n",
    "sns.set(rc={'figure.figsize':(10,5)})\n",
    "sns.heatmap(corr, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Splitting the data into train and test set</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_set = covidpak['Cases'] | covidpak['Deaths'] | covidpak['Recovered']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "covidpak = covidpak.drop(columns=['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "covidpak = covidpak.drop(columns=['City'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Train and test splitting</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn import metrics\n",
    "#Lets split the data into 60% training and 40% testing set\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(covidpak, target_set, test_size=0.4, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "\n",
    "mscaler = MinMaxScaler()\n",
    "\n",
    "# Lets input the independent data into the scaler\n",
    "xtrain = mscaler.fit_transform(xtrain)\n",
    "xtest = mscaler.fit_transform(xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Classification</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets create two arrays to store results of classification model\n",
    "names=[] \n",
    "scores=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "\n",
    "mscaler = MinMaxScaler()\n",
    "\n",
    "# Lets input the independent data into the scaler\n",
    "xtrain = mscaler.fit_transform(xtrain)\n",
    "xtest = mscaler.fit_transform(xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Multiple Linear Regression</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy : 0.9714407550050432\n",
      "Testing accuracy : 0.9601068089060244\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "linreg = LinearRegression()\n",
    "\n",
    "linreg.fit(xtrain, ytrain)\n",
    "\n",
    "ypred = linreg.predict(xtest)\n",
    "\n",
    "print(\"Training accuracy :\", linreg.score(xtrain, ytrain))\n",
    "print(\"Testing accuracy :\", linreg.score(xtest, ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "names.append(\"Linear Regression\")\n",
    "scores.append(linreg.score(xtest,ytest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Logistic Regression</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "labelencoding = preprocessing.LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in list(covidpak.columns):\n",
    "    covidpak[i] = labelencoding.fit_transform(covidpak[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adminpc\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy : 0.2395896197948099\n",
      "Testing accuracy : 0.2515837104072398\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lreg = LogisticRegression()\n",
    "\n",
    "lreg.fit(xtrain, ytrain)\n",
    "\n",
    "ypred = lreg.predict(xtest)\n",
    "\n",
    "print(\"Training accuracy :\", lreg.score(xtrain, ytrain))\n",
    "print(\"Testing accuracy :\", lreg.score(xtest, ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "names.append(\"Logistic Reg.\")\n",
    "scores.append(lreg.score(xtest,ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Method Score')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JnTEbAAAAHajdAFy9enV22223tuVBgwZl3bp1FR0KAACAjtduANbU1OTFF19MVVVVkuTJJ5+s+FAAAAB0vJr2NjjxxBNz7LHH5vnnn88ZZ5yRRYsWZcKECZ0xGwAAAB2o3QD8xCc+kfe85z1ZtGhRWlpacvLJJ+c973lPZ8wGAABAB2o3AI888sjMnj07O+20U2fMAwAAQIW0ew/gtttum+eee64zZgEAAKCC2j0DuGrVqgwePDi9e/dObW1t2+Nz586t6GAAAAB0rHYD8LzzzuuMOQAAAKiwdi8B/djHPpatt946d999dxYtWtT2GAAAAFuWdgNw1qxZGTNmTF588cWsXLkyZ555ZmbMmNEZswEAANCB2r0E9IYbbsitt96ahoaGJMno0aNz/PHH59Of/nTFhwMAAKDjtHsGsKWlpS3+kqRXr17p0qXdpwEAALCZabfkevbsmYULF7YtL1y4MD169KjoUAAAAHS8di8BPf/88/PlL385F198caqqqlJTU5Np06Z1xmwAAAB0oHYD8L3vfW9uu+22NDY2Zt26denRo0d69erVGbMBAADQgdq9BHTevHn51Kc+lfe85z3p2rVrDjvssNx+++2dMRsAAAAdqN0A/Pa3v50f/OAHSZJddtklM2fOzFVXXVXxwQAAAOhYr+tTQHv37t22/K53vSstLS0VHQoAAICO124AvvOd78wtt9yStWvXZt26dfnXf/3XbLfddp0xGwAAAB2o3QCcMGFCZsyYkb59+6Zv376ZMWNGLrrook4YDQAAgI7U7qeA7rzzzpk5c2ZefPHFVFdXp1u3bp0xFwAAAB3sNc8ALl68OL///e+TJL/5zW/yla98Jd/61rfcAwgAALAF2mgA3nrrrRk/fnxefPHFPP300znttNPyoQ99KE8//XSuvPLKzpwRAACADrDRAPzRj36U6dOn5wMf+EDmzZuXj33sYznppJMyadKkLFy4sDNnBAAAoANsNADXrVuX+vr6JMl9992XffbZJ0lSU1OTLl3a/ewYAAAANjMbLbm/3ue3Zs2a3H///fnYxz6WJFm7dm1efvnlzpkOAACADrPRTwH98Ic/nEmTJuXPf/5z6urqsueee2b58uW5+uqr079//86cEQAAgA6w0TOA48aNyyuvvJLnn38+V111VZJkypQpefjhh3P22Wd32oAAAAB0jI2eAXzb296WCy64YL3HLrzwwlRXV1d8KAAAADreG/o0F/EHAACw5droGcCOMHfu3FxzzTVZu3ZtPv/5z+eYY45pW/fII49k3LhxbcvNzc3p0aNHfvKTn+S2227L5Zdfnrq6uiTJfvvtl7Fjx1ZyVAAAgLe8igVgY2Njpk6dmpkzZ2arrbbK0Ucfnf79+2e33XZLkrz//e/P7NmzkySrVq3KUUcdlYsuuihJ8tBDD2XcuHEZNmxYpcYDAAAozkYD8Nlnn33NJ/7DP/zDa65fvHhxBgwYkJ49eyZJhgwZkvnz5+eUU0551bbXXntt/vEf/zH9+vVLkixZsiRPP/10rr322uy+++45//zz06NHj3bfDAAAABu30QA85JBDUlVVldbW1qxevTq1tbWpqanJ8uXLU1dXl1/96lev+cJLly5t+yL5JGloaMiDDz74qu1WrFiRGTNmZO7cuW2P1dfX5wtf+EI+8pGP5Jvf/GYmTJiQyy+//HW/qbq6bq97W+gM9fXdN/UIAABUwJZ2nLfRAPzNb36TJLngggvSv3//HHLIIUmSn/3sZ1m4cGG7L9zS0pKqqqq25dbW1vWW/2rOnDk58MAD2+73S5Jp06a1/fMXv/jF/NM//dPreCv/a9myl9LS0vqGngOVUl/fPU1NKzb1GAB0gi3tQBB48za347wuXape84RYu58C+tBDD7XFX5IMHjw4jz76aLs77t27d5qamtqWm5qa0tDQ8KrtFi5cmKFDh7Ytr1ixIjfccEPbcmtrq08fBQAA6ADtBmBLS0vuuuuutuU77rhjg2fy/t7AgQNz5513prm5OatWrcqCBQuy7777rrdNa2trHn744ey1115tj9XW1ua73/1uHnjggSTJjTfe+IbPAAIAAPBq7X4K6Pjx43P66aena9euaWlpSbL+JZob06tXr4wdOzajRo3KmjVrcuSRR6Zv374ZPXp0xowZkz59+qS5uTldu3bN1ltv3fa86urqXHHFFbnooouyevXq7Lzzzpk8efKbeIsAAAAkSVVra2u7N8utWbMmjz/+eKqqqvK+970vNTUV/frAN809gGxO3AMIUI76+u4ZfubsTT0G0EnmXj5iszvOa+8ewHZL7uWXX87kyZNzxx13ZO3atdlnn31y3nnnpVs3n7QJAACwJWn3HsCvf/3reeWVVzJt2rRcffXVqaqqysUXX9wZswEAANCB2j0D+MADD2TOnDlty5dccsl6nwoKAADAlqHdM4Dr1q1r+/CX5C+fCuprGQAAALY87Z4B3HvvvXP66afns5/9bJLkRz/6Ufr371/xwQAAAOhY7QbguHHjcvXVV+eb3/xm1q1bl0984hP58pe/3BmzAQAA0IHaDcCampqMGTMmY8aM6Yx5AAAAqJB2A/A//uM/Mnny5DQ3N+dvvzLwvvvuq+hgAAAAdKx2A/Cyyy7LOeeckz322CNVVVWdMRMAAAAV0G4AduvWLQceeGBnzAIAAEAFtfs1EH369MnChQs7YxYAAAAqaKNnAPfaa69UVVVl3bp1mT59erbaaqvU1NSktbU1VVVV7gEEAADYwmw0AH/yk59s9El/+2EwAAAAbBk2GoDbb799kuSLX/xivvvd76637tOf/nRmzJhR2ckAAADoUBsNwDFjxuSpp57KM888k+HDh7c9vnbt2my11VadMhwAAAAdZ6MBePbZZ+cPf/hDzj///Jx//vltj1dXV2e33XbrlOEAAADoOBsNwB122CE77LBD5s+fn1deeSX/7//9v7zvfe/L6tWrs+2223bmjAAAAHSAdr8GYsmSJTnwwANzwgknpLGxMfvtt59PAAUAANgCtRuA3/jGN3LDDTekZ8+e6d27dyZPnpyJEyd2xmwAAAB0oHYDcPXq1evd8zdo0KCsW7euokMBAADQ8doNwJqamrz44oupqqpKkjz55JMVHwoAAICOt9EPgfmrk046Kccee2yef/75nHHGGVm0aFEmTJjQGbMBAADQgdoNwP333z+77rprFi1alJaWlpx88sl5z3ve0xmzAQAA0IE2GoAvvPBC2z/36NEjQ4cOXW9dz549KzsZAAAAHWqjAThgwIC2+/6SpLW1NVVVVW3/+8gjj3TKgAAAAHSMjQbgYYcdlt/85jc54IADcsQRR6z3SaAAAABseTYagJMmTcqqVauyYMGCTJw4MS+//HIOPfTQDB8+PG9/+9s7c0YAAAA6wGt+CMy2226bESNGZMSIEXnuuecye/bsjBo1KjvvvHOuuOKKzpoRAACADtDu9wD+VXNzc5qbm/OnP/0pK1asqORMAAAAVMBrngH84x//mDlz5mT27Nmprq7OoYcemhkzZqRXr16dNR8AAAAdZKMBOHLkyDz11FMZOnRopkyZkj333LMz5wIAAKCDbTQA77nnnmy99da59dZb86//+q9tj//1ayDuu+++ThkQAACAjrHRAPzZz37WmXMAAABQYRsNwO23374z5wAAAKDCXvengAIAALBlE4AAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFEIAAAACFqKnki8+dOzfXXHNN1q5dm89//vM55phj2tY98sgjGTduXNtyc3NzevTokZ/85Cd59tlnc9ZZZ2XZsmXZZZddMmXKlLztbW+r5KgAAABveRU7A9jY2JipU6fm5ptvzqxZszJ9+vQ88cQTbevf//73Z/bs2Zk9e3ZuueWW9OjRIxdddFGS5Gtf+1o+97nPZf78+fngBz+Yq6++ulJjAgAAFKNiAbh48eIMGDAgPXv2TG1tbYYMGZL58+dvcNtrr702//iP/5h+/fplzZo1ueeeezJkyJAkyac+9amNPg8AAIDXr2KXgC5dujT19fVtyw0NDXnwwQdftd2KFSsyY8aMzJ07N0nypz/9Kd26dUtNzV9Gq6+vT2Nj4xvad11dtzcxOXS8+vrum3oEAAAqYEs7zqtYALa0tKSqqqptubW1db3lv5ozZ04OPPDA1NXVbXS7DT3vtSxb9lJaWlr/D1NDx6uv756mphWbegwAOsGWdiAIvHmb23Fely5Vr3lCrGKXgPbu3TtNTU1ty01NTWloaHjVdgsXLszQoUPblt/5zndmxYoVWbdu3Ws+DwAAgDemYgE4cODA3HnnnWlubs6qVauyYMGC7Lvvvutt09ramocffjh77bVX22Ndu3ZNv379Mm/evCTJrFmzXvU8AAAA3riKBWCvXr0yduzYjBo1KocddliGDRuWvn37ZvTo0VmyZEmSv3z1Q9euXbP11luv99wLL7wwM2bMyNChQ3Pvvffm9NNPr9SYAAAAxahqbW19y90s5x5ANifuAQQoR3199ww/c/amHgPoJHMvH7HZHedtsnsAAQAA2LwIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEIIQAAAgEJUNADnzp2boUOH5qCDDspNN930qvVPPvlkRo4cmUMPPTTHH398XnzxxSTJbbfdlo9//OMZMWJERowYkalTp1ZyTAAAgCLUVOqFGxsbM3Xq1MycOTNbbbVVjj766PTv3z+77bZbkqS1tTUnnXRSzjvvvOy7776ZMmVKrrvuupx11ll56KGHMm7cuAwbNqxS4wEAABSnYmcAFy9enAEDBqRnz56pra3NkCFDMn/+/Lb1Dz/8cGpra7PvvvsmSU488cQcc8wxSZIlS5bktttuy/Dhw/OVr3yl7cwgAAAA/3cVC8ClS5emvr6+bbmhoSGNjY1ty7/73e+y3Xbb5dxzz83hhx+eCy+8MLW1tUmS+vr6fPnLX86cOXPyrne9KxMmTKjUmAAAAMWo2CWgLS0tqaqqaltubW1db3nt2rW5++67c+ONN6ZPnz654oorMmnSpEyaNCnTpk1r2+6LX/xi/umf/ukN7buurtubfwPQgerru2/qEQAAqIAt7TivYgHYu3fv3HvvvW3LTU1NaWhoaFuur6/PTjvtlD59+iRJhg0bljFjxmTFihX58Y9/nOOOOy7JX8Kxurr6DUggTz8AABR1SURBVO172bKX0tLS+ubfBHSA+vruaWpasanHAKATbGkHgsCbt7kd53XpUvWaJ8QqdgnowIEDc+edd6a5uTmrVq3KggUL2u73S5K99torzc3NefTRR5Mkt99+ez7wgQ+ktrY23/3ud/PAAw8kSW688cY3fAYQAACAV6vYGcBevXpl7NixGTVqVNasWZMjjzwyffv2zejRozNmzJj06dMn06ZNy/jx47Nq1ar07t07kydPTnV1da644opcdNFFWb16dXbeeedMnjy5UmMCAAAUo6q1tfUtd62kS0DZnLgEFKAc9fXdM/zM2Zt6DKCTzL18xGZ3nLfJLgEFAABg8yIAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAACiEAAQAAClHRAJw7d26GDh2agw46KDfddNOr1j/55JMZOXJkDj300Bx//PF58cUXkyTPPvtsjjnmmHzyk5/MSSedlJUrV1ZyTAAAgCJULAAbGxszderU3HzzzZk1a1amT5+eJ554om19a2trTjrppIwePTpz5szJ+9///lx33XVJkq997Wv53Oc+l/nz5+eDH/xgrr766kqNCQAAUIyaSr3w4sWLM2DAgPTs2TNJMmTIkMyfPz+nnHJKkuThhx9ObW1t9t133yTJiSeemOXLl2fNmjW55557Mm3atCTJpz71qRx77LE566yzXve+u3Sp6uB3A2+OfycBytHwjm039QhAJ9rcjvPam6diAbh06dLU19e3LTc0NOTBBx9sW/7d736X7bbbLueee24eeeSR7Lrrrjn//PPzpz/9Kd26dUtNzV9Gq6+vT2Nj4xva9zve8baOeRPQQerqum3qEQDoJNePP2hTjwB0oi3tOK9il4C2tLSkqup/67O1tXW95bVr1+buu+/OZz/72dx2221597vfnUmTJr1quySvWgYAAOCNq1gA9u7dO01NTW3LTU1NaWhoaFuur6/PTjvtlD59+iRJhg0blgcffDDvfOc7s2LFiqxbt26DzwMAAOD/pmIBOHDgwNx5551pbm7OqlWrsmDBgrb7/ZJkr732SnNzcx599NEkye23354PfOAD6dq1a/r165d58+YlSWbNmrXe8wAAAPi/qWptbW2t1IvPnTs31157bdasWZMjjzwyo0ePzujRozNmzJj06dMnDzzwQC6++OKsWrUqvXv3zuTJk1NXV5c//OEPGTduXJYtW5Z3vetd+eY3v5kePXpUakwAAIAiVDQAAQAA2HxU9IvgAQAA2HwIQAAAgEIIQAAAgEIIQAAAgEIIQN7y7rrrrowcObJDXmvEiBGvuf5v99Petn/rgAMOyNChQzNixIiMGDEiBxxwQMaMGZOXX375/zwrAGypNva7e8mSJTnvvPM6bY6rrroq++yzT9vv54MPPjjDhw/Pr3/9606boT3nnXdelixZsqnHYAtSs6kHgC3J7NmzX3P93Xff/bq3/XvXXXdddthhhyTJK6+8ks997nOZNWtWPve5z73xQQHgLahPnz7p06dPp+7z6KOPzqmnntq2fMMNN2TSpEm59dZbO3WOjZk4ceKmHoEtjACkaN/+9rczZ86cVFdXZ5999slZZ52V6urq/OAHP8iNN96Y7t27Z9ddd82OO+6YU089Nbvvvnsee+yx3HnnnbnsssuSJD169Mjll1+eq6++Okly1FFH5dZbb23b9oUXXsh5552XJ598MltttVXGjRuXvffe+zXnWrFiRVasWJGePXsmSe6444788z//c9auXZsddtghF198cd7xjnfkrrvuyiWXXJLq6up8+MMfzv/8z//khz/8YWV/aACwidx111351re+lR/+8IcZOXJk+vTpk1//+tdpbm7O+PHjM2jQoDz//PO54IIL8txzz6WqqipnnnlmBg4cmMbGxpx77rlZsWJFli5dmsMPPzynnXZaZs6cmdtuuy0vvPBC9t9//5xxxhkb3X9LS0uee+65tu+n3ti+VqxYkbPPPju/+93v8u53vzvPPfdcvvWtb+Xuu+9eb1+jRo3a4PM3dJyx1VZb5Ywzzsjzzz+fJDn55JMzePDgjBw5Mqecckr69++/weOaP/7xjznllFPy3ve+N4888kjq6upy5ZVXth1jUB4BSLF++ctf5vbbb8+Pf/zjdO3aNaeeempuueWWfPSjH81NN92UmTNnpmvXrhk5cmR23HHH9Z579dVX56KLLkrfvn3zne98J7/97W8zfvz4/PCHP3zVXwSvvPLK7Ljjjpk2bVoee+yxXHDBBRsMwC996Uuprq7OsmXL0rt37xx77LE5+OCD09zcnMsvvzw/+MEP0qNHj9xyyy2ZMmVKLrroopx99tm59tprs8cee+SSSy6p6M8LADY3a9asyfTp03P77bfnyiuvzKBBgzJx4sQcccQRGTx4cJYuXdp2Rc1PfvKTDBs2LIcffnhWrFiRQYMGtV1m2tjYmHnz5qWm5tWHxrfccksWLlyY5cuXp6WlJfvtt18uvfTSJNnovqZNm5Zddtkl11xzTZYsWZLPfOYzba/3t/saO3bsBp+/oeOMpqambL/99rnuuuvyyCOPZM6cORk8eHDb627suGbQoEF59NFHc+mll2bPPffMqaeemrlz53bY7TFseQQgxfqv//qvHHLIIdl2222TJEcccURmzZqVV155Jfvvv3+6deuWJDnkkEOyfPny9Z47ePDgnHLKKTnwwAMzePDg7LPPPhvdzz333JMpU6YkSXbfffdMnz59g9v99RLQf//3f8+kSZPyyU9+MlVVVXnggQfyxz/+MaNGjUryl78+9ujRI48//njq6uqyxx57JEmOPPJIl4EAUJRPfOITSZL3vve9eeGFF5IkixcvzpNPPpl//ud/TpKsXbs2zzzzTI4//vj813/9V66//vr893//d9asWZNVq1YlSfbcc88Nxl/yv5eANjU15fOf/3w+/OEPp6Gh4TX3tWjRorbf/X369Mn73ve+ttf7231t7PkbOs54+umn881vfjONjY3Zb7/9cvLJJ68358aOawYNGpS6urrsueeebT+rF1988c382NnCCUCK1dLS8qrH1q5dmy5dumxw3d867rjjsv/+++fnP/95Lrvssjz44IM56aSTNrhtTU1Nqqqq2pb/53/+J7vssku6dNnwZzANGTIkixYtyrnnnpvvfOc7WbduXT7ykY/k29/+dpLkz3/+c1auXJmlS5e2OycAvJVtvfXWSbLe79mWlpb8y7/8S9sljkuXLk1dXV0mTZqUZ555JsOGDcuBBx6YxYsXp7W1NUmyzTbbtLuv+vr6XHLJJTn++OPTr1+/vPvd797ovqqrq9te++/97b429vz3v//9GzzO+OlPf5r//M//zM9//vN873vfy7x589Z7rb+3du3a9X5Of/1ZbWw2yuBTQCnWgAED8m//9m9ZvXp11q5dmx//+McZMGBA9t577/zyl7/MSy+9lFdeeSULFixY7xdL8pf7/FauXJnjjjsuxx13XH77298mSaqrq9v+Y/tX/fr1y7/9278l+Uv8jR49+lWv9/dOO+20/PrXv84vfvGLfOhDH8r999+fp556KslfLj+dPHlydt111yxfvjyPPfZYkmTu3Lkd8nMBgC3ZgAEDcvPNNydJnnjiiQwfPjyrVq3KokWLcvzxx+fggw/OU089lcbGxjf8h9SPfOQj2W+//druz9vYvvbee++238uPPfZY/vu//3uDv/s39vwNHWfceOONueqqq3LwwQfnwgsvTHNzc1566aX1XmtDxzXw95wBpAj33ntv9tprr7bl4cOHZ8KECXnkkUdyxBFHZO3atfn4xz+eY489NjU1NRk1alQ+85nPpLa2Nu94xzvW+8tZkpxxxhkZN25campqUltb23b/3eDBgzNixIjMnDmzbdsxY8Zk/PjxOfTQQ1NTU5PJkye3G4B1dXUZPXp0Jk+enDlz5uTSSy/N6aefnpaWlvTq1SuXXXZZttpqq0yePDnnnHNOunTpkl122aXtr4o/+tGPsnTp0px22mkd9SMEgE61od/dhxxySLvPGz9+fC644IIMHz48STJ58uR069YtJ5xwQs4+++xss8026d27dz74wQ/m97///Rue64wzzsjQoUNz7733bnRfJ598cr761a9m+PDh2XHHHbPddttt8Czjxp6/oeOM7bbbLmeccUaGDx+e6urqnHXWWXn729/e9lr777//Bo9rnnvuuTf8Hnlrq2p1DhjW89RTT+WXv/xljjvuuCTJSSedlKOOOioHHHDAph3s77S0tGTKlCk55ZRTUltbm+9///tpbGzMuHHjNvVoAFC02bNnZ4cddshHP/rRPPvsszn22GOzcOHCjd7+AZ3JGUD4O9tvv32WLFmSYcOGpaqqKh//+Mez//77b+qxXqVLly7p2bNnjjzyyHTt2jXbb7+9D4EBgM3ArrvumgsvvDAtLS3p0qVLJkyYIP7YbDgDCAAAUAh/igAAACiEAAQAACiEAAQAACiEAARgi/X73/8+u+++e4499thXrRs3blx23333NDc3v+ZrPPPMMzn11FPbXu9vP3b+/2qvvfba4MfLjxw5MiNHjlzvu8eam5uz++67v+l9AsDrIQAB2KJtvfXWeeqpp/KHP/yh7bGXX34599133+t6/rPPPpunnnqqUuO9yv33359vf/vbnbY/APhbvgYCgC1adXV1Dj744MydOzcnnnhikmTBggUZPHhwvve977Vtd/vtt+eaa67JmjVrss022+Scc85J3759M378+DQ2Nub444/P1772taxbty4XXHBBlixZkhUrVuSss87KkCFDsmbNmkyaNCl33nlnqqur07dv33z1q19Nt27dcu+99+biiy9OVVVV+vTps94Zvr/35S9/Oddff30GDhyYD3/4w+uta2lpyaWXXpoHHnggK1euTGtray655JJ89KMfzbhx47LNNtvk8ccfz7Jly3LAAQekZ8+e+fnPf56mpqZccskl2XvvvfPKK69kypQpueeee7Ju3brsueeeGT9+fLp161aZ/wMA2KI4AwjAFu+www7L7Nmz25ZnzZqVww8/vG356aefztSpU3Pddddl1qxZufjii3Pqqafmz3/+cy655JLsuOOOuf7665Mkf/7zn7PPPvvktttuyznnnJPLLrssSXLNNddk6dKlmT17dmbPnp2WlpZMnjw5r7zySk477bSMGzcus2bNSv/+/bN69eqNzrrLLrvk7LPPzle+8pW89NJL66174IEHsnTp0kyfPj3z5s3L4Ycfnu985ztt63/729/mX/7lX3LjjTfme9/7Xmpra3PLLbdk1KhRbdtdd911qa6uzsyZMzNnzpw0NDRkypQpb/6HDMBbgjOAAGzxPvjBD6a6ujoPPfRQ6urqsnLlyrzvfe9rW79o0aIsXbo0xx13XNtjVVVV+d3vfveq1+ratWuGDBmSJNljjz2ybNmyJMkdd9yRsWPHpmvXrkn+cj/fySefnMcffzw1NTXZe++9kyTDhg3LBRdc8JrzfvrTn86vfvWrXHTRRTn33HPbHt9rr73So0eP3HLLLXnmmWdy11135W1ve1vb+v333z9du3ZNfX19amtr84lPfCJJsuOOO+aFF15IkvziF7/IihUrsnjx4iTJmjVrUldX9/p+kP+/nft3pS+O4zj+/ObXYCCJQUrcspjVDUkmN85xyyCbkdEkspBJ4Q7yF9zsBimUO9xF/gWbMhsULud8h29ujm+5rOc8H9v5vM95n09n+fTq8+lIklLPAChJSoUgCDg9PaWrq4swDBO1KIrI5/McHh7Wxx4eHujp6eH29jZx70fAg38h8XOPr9e1Wg2AOI4TPZqbGy+vOzs79Tl/uL6+Znd3l+XlZaanpxkcHEzUW1tbG74niiI2NjaYnJwE4OnpiZeXl4bzkSRlg0dAJUmpEIYh5+fnnJ2dMTs7m6jl83mq1Sp3d3cAVCoVgiDg+fmZpqamepD7zsTEBCcnJ9RqNaIoolwuMzY2xvDwMHEcU6lUALi6uuLx8bFhv46ODvb29jg4OKiPVatVpqamWFpaYmRkhMvLS97f33/zGRgfH6dcLvP6+koURWxtbbG/v/+rHpKk9DIASpJSobe3l6GhIQYGBujs7EzUcrkc29vbrK2tEQQBpVKJ4+Nj2tvbyeVytLW1sbCw8N9O3mcrKyt0d3czPz/PzMwMb29vbG5u0tLSwtHREaVSiTAMubi4+PGRy9HR0cSx1MXFRW5ubpibm6NYLNLf38/9/f23P5X5anV1lb6+PorFIoVCgTiOWV9f//HzkqR0+xN/t9pJkiRJklLDHUBJkiRJyggDoCRJkiRlhAFQkiRJkjLCAChJkiRJGWEAlCRJkqSMMABKkiRJUkYYACVJkiQpIwyAkiRJkpQRfwF8naWSChdWFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.ylim([0.60,0.90])\n",
    "plt.bar(names,scores,width=0.5)\n",
    "plt.xlabel('Method Name')\n",
    "plt.ylabel('Method Score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>As we can clearly see that multiple linear regression performs much better than Logistic regression</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}