Covid19_Europe.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<h2> Models to Predict the Numbers of Deaths due to Covid19 in Europe</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> By Michael Kumakech</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> PART 1: Model Development</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will develop several models that will predict the number deaths due to Convid-19 Pandemic in Europe using the variables or features. This is just an estimate but should give us an objective idea of how much the Pandemic will cost."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Analytics, we often use Model Development to help us predict future observations from the data we have. In Artificial Intelligence, a Model will help us understand the exact relationship between different variables and how these variables are used to predict the result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Import Libaries</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import lxml.html as lh\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "WHO_url = 'https://github.com/mkumakech/Covid-19_Pandemic/blob/master/europe_covid19.csv' #assign the wiki page\n",
    "#WHO_url = 'https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases'\n",
    "\n",
    "page = requests.get(WHO_url) # create a handle to for contents of the wiki page\n",
    "\n",
    "doc = lh.fromstring(page.content) # store content of the wiki page under doc\n",
    "\n",
    "tr_elements = doc.xpath('//tr') # parse data stored between tr in the html\n",
    "\n",
    "[len(T) for T in tr_elements[:12]] # check the length of the first 12 rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Check the table headers</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:\n",
      "2:Name\n",
      "3:WHO Region\n",
      "4:Cases _cumulative total\n",
      "5:Cases _cumulative total per 1 million population\n",
      "6:Cases _newly reported in last 7 days\n",
      "7:Cases _newly reported in last 24 hours\n",
      "8:Deaths _cumulative total\n",
      "9:Deaths_ cumulative total per 1 million population\n",
      "10:Deaths _newly reported in last 7 days\n",
      "11:Deaths _newly reported in last 24 hours\n",
      "12:Transmission Classification\n"
     ]
    }
   ],
   "source": [
    "tr_elements = doc.xpath('//tr') # parse first row as header\n",
    "\n",
    "col = [] # create empty list\n",
    "i = 0\n",
    "\n",
    "for t in tr_elements[0]: # for each row, store each first element (header) and an empty list\n",
    "    i+=1\n",
    "    name=t.text_content()\n",
    "    print(\"%d:%s\" % (i,name))\n",
    "    col.append((name,[]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> \n",
    "Check the data in other Row</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "for j in range(1,len(tr_elements)): # Because header is the first row, data would be store in the subsequent rows.\n",
    "    T = tr_elements[j] #T is j'th row\n",
    "    \n",
    "    if len(T)!=12: #if row is not size 3, //tr data is not from the table.\n",
    "        break\n",
    "        \n",
    "    i = 0 #i is the index of the first column\n",
    "    \n",
    "    for t in T.iterchildren(): #iterate through each element of the row\n",
    "        data=t.text_content()\n",
    "            \n",
    "        col[i][1].append(data) #append the data to the empty list of the i'th column\n",
    "            \n",
    "        i+=1 #increment i for the next column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> \n",
    "What about the numbers of rows and columns</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[len(C) for (title,C) in col]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> \n",
    "Displays the data frame with three columns.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Dict = {title:column for (title,column) in col}\n",
    "df = pd.DataFrame(Dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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></th>\n",
       "      <th>Name</th>\n",
       "      <th>WHO Region</th>\n",
       "      <th>Cases _cumulative total</th>\n",
       "      <th>Cases _cumulative total per 1 million population</th>\n",
       "      <th>Cases _newly reported in last 7 days</th>\n",
       "      <th>Cases _newly reported in last 24 hours</th>\n",
       "      <th>Deaths _cumulative total</th>\n",
       "      <th>Deaths_ cumulative total per 1 million population</th>\n",
       "      <th>Deaths _newly reported in last 7 days</th>\n",
       "      <th>Deaths _newly reported in last 24 hours</th>\n",
       "      <th>Transmission Classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>Europe</td>\n",
       "      <td>3655839</td>\n",
       "      <td>25051.24</td>\n",
       "      <td>160023</td>\n",
       "      <td>21887</td>\n",
       "      <td>67832</td>\n",
       "      <td>464.81</td>\n",
       "      <td>3892</td>\n",
       "      <td>612</td>\n",
       "      <td>Clusters of cases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td></td>\n",
       "      <td>The United Kingdom</td>\n",
       "      <td>Europe</td>\n",
       "      <td>3505758</td>\n",
       "      <td>51641.83</td>\n",
       "      <td>294178</td>\n",
       "      <td>38905</td>\n",
       "      <td>93290</td>\n",
       "      <td>1374.22</td>\n",
       "      <td>8523</td>\n",
       "      <td>1820</td>\n",
       "      <td>Community transmission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td></td>\n",
       "      <td>France</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2916577</td>\n",
       "      <td>44682.4</td>\n",
       "      <td>132669</td>\n",
       "      <td>26565</td>\n",
       "      <td>71261</td>\n",
       "      <td>1091.73</td>\n",
       "      <td>2613</td>\n",
       "      <td>310</td>\n",
       "      <td>Community transmission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td></td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2414166</td>\n",
       "      <td>39928.76</td>\n",
       "      <td>95130</td>\n",
       "      <td>13568</td>\n",
       "      <td>83681</td>\n",
       "      <td>1384.03</td>\n",
       "      <td>3355</td>\n",
       "      <td>524</td>\n",
       "      <td>Clusters of cases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td></td>\n",
       "      <td>Spain</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2412318</td>\n",
       "      <td>51595.11</td>\n",
       "      <td>184705</td>\n",
       "      <td>18500</td>\n",
       "      <td>54637</td>\n",
       "      <td>1168.59</td>\n",
       "      <td>1146</td>\n",
       "      <td>80</td>\n",
       "      <td>Community transmission</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Name WHO Region Cases _cumulative total  \\\n",
       "0    Russian Federation     Europe                 3655839   \n",
       "1    The United Kingdom     Europe                 3505758   \n",
       "2                France     Europe                 2916577   \n",
       "3                 Italy     Europe                 2414166   \n",
       "4                 Spain     Europe                 2412318   \n",
       "\n",
       "  Cases _cumulative total per 1 million population  \\\n",
       "0                                         25051.24   \n",
       "1                                         51641.83   \n",
       "2                                          44682.4   \n",
       "3                                         39928.76   \n",
       "4                                         51595.11   \n",
       "\n",
       "  Cases _newly reported in last 7 days Cases _newly reported in last 24 hours  \\\n",
       "0                               160023                                  21887   \n",
       "1                               294178                                  38905   \n",
       "2                               132669                                  26565   \n",
       "3                                95130                                  13568   \n",
       "4                               184705                                  18500   \n",
       "\n",
       "  Deaths _cumulative total Deaths_ cumulative total per 1 million population  \\\n",
       "0                    67832                                            464.81   \n",
       "1                    93290                                           1374.22   \n",
       "2                    71261                                           1091.73   \n",
       "3                    83681                                           1384.03   \n",
       "4                    54637                                           1168.59   \n",
       "\n",
       "  Deaths _newly reported in last 7 days  \\\n",
       "0                                  3892   \n",
       "1                                  8523   \n",
       "2                                  2613   \n",
       "3                                  3355   \n",
       "4                                  1146   \n",
       "\n",
       "  Deaths _newly reported in last 24 hours Transmission Classification  \n",
       "0                                     612           Clusters of cases  \n",
       "1                                    1820      Community transmission  \n",
       "2                                     310      Community transmission  \n",
       "3                                     524           Clusters of cases  \n",
       "4                                      80      Community transmission  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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></th>\n",
       "      <th>Name</th>\n",
       "      <th>WHO Region</th>\n",
       "      <th>Cases _cumulative total</th>\n",
       "      <th>Cases _cumulative total per 1 million population</th>\n",
       "      <th>Cases _newly reported in last 7 days</th>\n",
       "      <th>Cases _newly reported in last 24 hours</th>\n",
       "      <th>Deaths _cumulative total</th>\n",
       "      <th>Deaths_ cumulative total per 1 million population</th>\n",
       "      <th>Deaths _newly reported in last 7 days</th>\n",
       "      <th>Deaths _newly reported in last 24 hours</th>\n",
       "      <th>Transmission Classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td></td>\n",
       "      <td>Isle of Man</td>\n",
       "      <td>Europe</td>\n",
       "      <td>432</td>\n",
       "      <td>5080.44</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>294.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No cases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td></td>\n",
       "      <td>Guernsey</td>\n",
       "      <td>Europe</td>\n",
       "      <td>309</td>\n",
       "      <td>4889.55</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>205.71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Community transmission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td></td>\n",
       "      <td>Greenland</td>\n",
       "      <td>Europe</td>\n",
       "      <td>30</td>\n",
       "      <td>528.43</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No cases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td></td>\n",
       "      <td>Holy See</td>\n",
       "      <td>Europe</td>\n",
       "      <td>26</td>\n",
       "      <td>32138.44</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sporadic cases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td></td>\n",
       "      <td>Turkmenistan</td>\n",
       "      <td>Europe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No cases</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name WHO Region Cases _cumulative total  \\\n",
       "57     Isle of Man     Europe                     432   \n",
       "58        Guernsey     Europe                     309   \n",
       "59       Greenland     Europe                      30   \n",
       "60        Holy See     Europe                      26   \n",
       "61    Turkmenistan     Europe                       0   \n",
       "\n",
       "   Cases _cumulative total per 1 million population  \\\n",
       "57                                          5080.44   \n",
       "58                                          4889.55   \n",
       "59                                           528.43   \n",
       "60                                         32138.44   \n",
       "61                                                0   \n",
       "\n",
       "   Cases _newly reported in last 7 days  \\\n",
       "57                                   26   \n",
       "58                                    2   \n",
       "59                                    1   \n",
       "60                                    0   \n",
       "61                                    0   \n",
       "\n",
       "   Cases _newly reported in last 24 hours Deaths _cumulative total  \\\n",
       "57                                      0                       25   \n",
       "58                                      0                       13   \n",
       "59                                      0                        0   \n",
       "60                                      0                        0   \n",
       "61                                      0                        0   \n",
       "\n",
       "   Deaths_ cumulative total per 1 million population  \\\n",
       "57                                            294.01   \n",
       "58                                            205.71   \n",
       "59                                                 0   \n",
       "60                                                 0   \n",
       "61                                                 0   \n",
       "\n",
       "   Deaths _newly reported in last 7 days  \\\n",
       "57                                     0   \n",
       "58                                     0   \n",
       "59                                     0   \n",
       "60                                     0   \n",
       "61                                     0   \n",
       "\n",
       "   Deaths _newly reported in last 24 hours Transmission Classification  \n",
       "57                                       0                    No cases  \n",
       "58                                       0      Community transmission  \n",
       "59                                       0                    No cases  \n",
       "60                                       0              Sporadic cases  \n",
       "61                                       0                    No cases  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Checking the shapes</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62, 12)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Data Analysis</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler,PolynomialFeatures\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "! pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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></th>\n",
       "      <th>Name</th>\n",
       "      <th>WHO Region</th>\n",
       "      <th>Cases _cumulative total</th>\n",
       "      <th>Cases _cumulative total per 1 million population</th>\n",
       "      <th>Cases _newly reported in last 7 days</th>\n",
       "      <th>Cases _newly reported in last 24 hours</th>\n",
       "      <th>Deaths _cumulative total</th>\n",
       "      <th>Deaths_ cumulative total per 1 million population</th>\n",
       "      <th>Deaths _newly reported in last 7 days</th>\n",
       "      <th>Deaths _newly reported in last 24 hours</th>\n",
       "      <th>Transmission Classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>61</td>\n",
       "      <td>53</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>53</td>\n",
       "      <td>38</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td></td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>395078</td>\n",
       "      <td>13342.88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Community transmission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name WHO Region Cases _cumulative total  \\\n",
       "count   62     62         62                      62   \n",
       "unique   1     62          1                      62   \n",
       "top         Italy     Europe                  395078   \n",
       "freq    62      1         62                       1   \n",
       "\n",
       "       Cases _cumulative total per 1 million population  \\\n",
       "count                                                62   \n",
       "unique                                               62   \n",
       "top                                            13342.88   \n",
       "freq                                                  1   \n",
       "\n",
       "       Cases _newly reported in last 7 days  \\\n",
       "count                                    62   \n",
       "unique                                   61   \n",
       "top                                       0   \n",
       "freq                                      2   \n",
       "\n",
       "       Cases _newly reported in last 24 hours Deaths _cumulative total  \\\n",
       "count                                      62                       62   \n",
       "unique                                     53                       60   \n",
       "top                                         0                        0   \n",
       "freq                                       10                        3   \n",
       "\n",
       "       Deaths_ cumulative total per 1 million population  \\\n",
       "count                                                 62   \n",
       "unique                                                60   \n",
       "top                                                    0   \n",
       "freq                                                   3   \n",
       "\n",
       "       Deaths _newly reported in last 7 days  \\\n",
       "count                                     62   \n",
       "unique                                    53   \n",
       "top                                        0   \n",
       "freq                                       9   \n",
       "\n",
       "       Deaths _newly reported in last 24 hours Transmission Classification  \n",
       "count                                       62                          62  \n",
       "unique                                      38                           5  \n",
       "top                                          0      Community transmission  \n",
       "freq                                        18                          38  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Let us  see we have missing values for the columns </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of NaN values for the column Name : 0\n",
      "number of NaN values for the column WHO Region: 0\n",
      "number of NaN values for the column Cases _newly reported in last 7 days: 0\n",
      "number of NaN values for the column Cases _newly reported in last 24 hours : 0\n",
      "number of NaN values for the column Deaths _cumulative total : 0\n",
      "number of NaN values for the column Transmission Classification : 0\n"
     ]
    }
   ],
   "source": [
    "print(\"number of NaN values for the column Name :\", df['Name'].isnull().sum())\n",
    "print(\"number of NaN values for the column WHO Region:\", df['WHO Region'].isnull().sum())\n",
    "print(\"number of NaN values for the column Cases _newly reported in last 7 days:\", df['Cases _newly reported in last 7 days'].isnull().sum())\n",
    "print(\"number of NaN values for the column Cases _newly reported in last 24 hours :\", df['Cases _newly reported in last 24 hours'].isnull().sum())\n",
    "print(\"number of NaN values for the column Deaths _cumulative total :\", df['Deaths _cumulative total'].isnull().sum())\n",
    "print(\"number of NaN values for the column Transmission Classification :\", df['Transmission Classification'].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No missing values obereved in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Exploratory data analysis</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Transmission Classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Community transmission</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clusters of cases</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sporadic cases</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No cases</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pending</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Transmission Classification\n",
       "Community transmission                           38\n",
       "Clusters of cases                                16\n",
       "Sporadic cases                                    4\n",
       "No cases                                          3\n",
       "Pending                                           1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Transmission Classification'].value_counts().to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 38 community transmission out of the total of 62 records."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Model Development with Linear Regression Methods</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import libraries \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<b> We can Fit a linear regression model using the Total  Cases feature and caculate the R^2 for Total Deaths </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9370958576231244"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[['Cases _cumulative total']] \n",
    "Y = df['Deaths _cumulative total'] \n",
    "lm = LinearRegression() \n",
    "lm \n",
    "lm.fit(X,Y)\n",
    "lm.score(X, Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> How could identified cases help us predict deaths?</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([86057.36321965, 82485.99408999, 68465.67949202, 56510.1678963 ,\n",
       "       56466.1923754 ])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[['Cases _cumulative total']] \n",
    "Y = df['Deaths _cumulative total'] \n",
    "lm = LinearRegression() \n",
    "lm \n",
    "lm.fit(X,Y)\n",
    "Yhat=lm.predict(X)\n",
    "Yhat[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> What is the value of the intercept ?</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-937.9962701051063"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> What is the value of the Slope ?</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02379628])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Plot the  the linear graph </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> What is the final estimated linear model we get?</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we saw above, we should get a final linear model with the structure: $$ Yhat = a + b X $$\n",
    "\n",
    "Plugging in the actual values we get:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Deaths _cumulative total = -938 + 0.0238 x Cases _cumulative total</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As of data update of 22nd Jan  2021 by WHO on her website. However, this Linear model keeps changing as the numbers of cases and deaths increases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "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>Deaths _cumulative total</th>\n",
       "      <th>Cases _cumulative total</th>\n",
       "      <th>Cases _newly reported in last 7 days</th>\n",
       "      <th>Cases _newly reported in last 24 hours</th>\n",
       "      <th>Transmission Classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67832</td>\n",
       "      <td>3655839</td>\n",
       "      <td>160023</td>\n",
       "      <td>21887</td>\n",
       "      <td>Clusters of cases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93290</td>\n",
       "      <td>3505758</td>\n",
       "      <td>294178</td>\n",
       "      <td>38905</td>\n",
       "      <td>Community transmission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>71261</td>\n",
       "      <td>2916577</td>\n",
       "      <td>132669</td>\n",
       "      <td>26565</td>\n",
       "      <td>Community transmission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83681</td>\n",
       "      <td>2414166</td>\n",
       "      <td>95130</td>\n",
       "      <td>13568</td>\n",
       "      <td>Clusters of cases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54637</td>\n",
       "      <td>2412318</td>\n",
       "      <td>184705</td>\n",
       "      <td>18500</td>\n",
       "      <td>Community transmission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>49783</td>\n",
       "      <td>2088400</td>\n",
       "      <td>109810</td>\n",
       "      <td>20398</td>\n",
       "      <td>Community transmission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24487</td>\n",
       "      <td>1591878</td>\n",
       "      <td>50377</td>\n",
       "      <td>6435</td>\n",
       "      <td>Community transmission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>34561</td>\n",
       "      <td>1457755</td>\n",
       "      <td>43393</td>\n",
       "      <td>7008</td>\n",
       "      <td>Community transmission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21499</td>\n",
       "      <td>1177621</td>\n",
       "      <td>38857</td>\n",
       "      <td>5583</td>\n",
       "      <td>Community transmission</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Deaths _cumulative total Cases _cumulative total  \\\n",
       "0                    67832                 3655839   \n",
       "1                    93290                 3505758   \n",
       "2                    71261                 2916577   \n",
       "3                    83681                 2414166   \n",
       "4                    54637                 2412318   \n",
       "5                    49783                 2088400   \n",
       "6                    24487                 1591878   \n",
       "7                    34561                 1457755   \n",
       "8                    21499                 1177621   \n",
       "\n",
       "  Cases _newly reported in last 7 days Cases _newly reported in last 24 hours  \\\n",
       "0                               160023                                  21887   \n",
       "1                               294178                                  38905   \n",
       "2                               132669                                  26565   \n",
       "3                                95130                                  13568   \n",
       "4                               184705                                  18500   \n",
       "5                               109810                                  20398   \n",
       "6                                50377                                   6435   \n",
       "7                                43393                                   7008   \n",
       "8                                38857                                   5583   \n",
       "\n",
       "  Transmission Classification  \n",
       "0           Clusters of cases  \n",
       "1      Community transmission  \n",
       "2      Community transmission  \n",
       "3           Clusters of cases  \n",
       "4      Community transmission  \n",
       "5      Community transmission  \n",
       "6      Community transmission  \n",
       "7      Community transmission  \n",
       "8      Community transmission  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf = df[['Deaths _cumulative total','Cases _cumulative total','Cases _newly reported in last 7 days', 'Cases _newly reported in last 24 hours','Transmission Classification']]\n",
    "cdf.head(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Deaths</th>\n",
       "      <th>TotalCases</th>\n",
       "      <th>Cases_in_7days</th>\n",
       "      <th>Cases_in_24hrs</th>\n",
       "      <th>Transmission_Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67832</td>\n",
       "      <td>3655839</td>\n",
       "      <td>160023</td>\n",
       "      <td>21887</td>\n",
       "      <td>Clusters of cases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93290</td>\n",
       "      <td>3505758</td>\n",
       "      <td>294178</td>\n",
       "      <td>38905</td>\n",
       "      <td>Community transmission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>71261</td>\n",
       "      <td>2916577</td>\n",
       "      <td>132669</td>\n",
       "      <td>26565</td>\n",
       "      <td>Community transmission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83681</td>\n",
       "      <td>2414166</td>\n",
       "      <td>95130</td>\n",
       "      <td>13568</td>\n",
       "      <td>Clusters of cases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54637</td>\n",
       "      <td>2412318</td>\n",
       "      <td>184705</td>\n",
       "      <td>18500</td>\n",
       "      <td>Community transmission</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Deaths TotalCases Cases_in_7days Cases_in_24hrs      Transmission_Class\n",
       "0  67832    3655839         160023          21887       Clusters of cases\n",
       "1  93290    3505758         294178          38905  Community transmission\n",
       "2  71261    2916577         132669          26565  Community transmission\n",
       "3  83681    2414166          95130          13568       Clusters of cases\n",
       "4  54637    2412318         184705          18500  Community transmission"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "cdf.columns = [\"Deaths\",\"TotalCases\", \"Cases_in_7days\",\"Cases_in_24hrs\",\"Transmission_Class\"]#(columns = {'Deaths _cumulative total': 'Deaths', 'Deaths','Cases _cumulative total': 'Cases', 'Cases _newly reported in last 7 days' : 'Cases_7days', 'Cases _newly reported in last 24 hours': 'Cases_24hrs'})\n",
    "cdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "! pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deaths                object\n",
      "TotalCases            object\n",
      "Cases_in_7days        object\n",
      "Cases_in_24hrs        object\n",
      "Transmission_Class    object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(cdf.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  app.launch_new_instance()\n",
      "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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"
     ]
    }
   ],
   "source": [
    "cdf['Deaths'] = cdf['Deaths'].astype(float)\n",
    "cdf['TotalCases'] = cdf['TotalCases'].astype(float)\n",
    "cdf['Cases_in_7days'] = cdf['Cases_in_7days'].astype(float)\n",
    "cdf['Cases_in_24hrs'] = cdf['Cases_in_24hrs'].astype(float)\n",
    "#cdf['Transmission_Class'] = cdf['Transmission_Class'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deaths                float64\n",
      "TotalCases            float64\n",
      "Cases_in_7days        float64\n",
      "Cases_in_24hrs        float64\n",
      "Transmission_Class     object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(cdf.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Deaths</th>\n",
       "      <th>TotalCases</th>\n",
       "      <th>Cases_in_7days</th>\n",
       "      <th>Cases_in_24hrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Deaths</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.968037</td>\n",
       "      <td>0.905407</td>\n",
       "      <td>0.908342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCases</th>\n",
       "      <td>0.968037</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.923210</td>\n",
       "      <td>0.926035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cases_in_7days</th>\n",
       "      <td>0.905407</td>\n",
       "      <td>0.923210</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cases_in_24hrs</th>\n",
       "      <td>0.908342</td>\n",
       "      <td>0.926035</td>\n",
       "      <td>0.974000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Deaths  TotalCases  Cases_in_7days  Cases_in_24hrs\n",
       "Deaths          1.000000    0.968037        0.905407        0.908342\n",
       "TotalCases      0.968037    1.000000        0.923210        0.926035\n",
       "Cases_in_7days  0.905407    0.923210        1.000000        0.974000\n",
       "Cases_in_24hrs  0.908342    0.926035        0.974000        1.000000"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 104898.7408001912)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.regplot(x=\"TotalCases\", y=\"Deaths\", data=cdf)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "As the TotalCases goes up, the Deaths goes up: this indicates a positive direct correlation between these two variables. TotalCases seem like a pretty good predictor of Deaths since the regression line is almost a perfect diagonal line.\n",
    "\n",
    "We can examine the correlation between 'TotalCases' and 'Deaths' and see it's approximately 0.968"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>TotalCases</th>\n",
       "      <th>Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TotalCases</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.968037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deaths</th>\n",
       "      <td>0.968037</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            TotalCases    Deaths\n",
       "TotalCases    1.000000  0.968037\n",
       "Deaths        0.968037  1.000000"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf[[\"TotalCases\", \"Deaths\"]].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Cases_in_24hrs is a potential predictor variable of Deaths</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7af13e7610>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.regplot(x=\"Cases_in_24hrs\", y=\"Deaths\", data=cdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the Cases_in_24hrs goes up, the Deaths goes up: this indicates a positive direct correlation between these two variables. Cases_in_24hrs seem like a pretty good predictor of Deaths since the regression line is almost a perfect diagonal line.\n",
    "\n",
    "We can examine the correlation between 'Cases_in_24hrs' and 'Deaths' and see it's approximately 0.908"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>Cases_in_24hrs</th>\n",
       "      <th>Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cases_in_24hrs</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.908342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deaths</th>\n",
       "      <td>0.908342</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Cases_in_24hrs    Deaths\n",
       "Cases_in_24hrs        1.000000  0.908342\n",
       "Deaths                0.908342  1.000000"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf[['Cases_in_24hrs', 'Deaths']].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Cases_in_7days is a potential predictor variable of Deaths</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7af13dd290>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.regplot(x=\"Cases_in_7days\", y=\"Deaths\", data=cdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the Cases_in_7days goes up, the Deaths goes up: this indicates a positive direct correlation between these two variables. Cases_in_7days seem like a pretty good predictor of Deaths since the regression line is almost a perfect diagonal line.\n",
    "\n",
    "We can examine the correlation between 'Cases_in_7days' and 'Deaths' and see it's approximately 0.905"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Cases_in_7days</th>\n",
       "      <th>Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cases_in_7days</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deaths</th>\n",
       "      <td>0.905407</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Cases_in_7days    Deaths\n",
       "Cases_in_7days        1.000000  0.905407\n",
       "Deaths                0.905407  1.000000"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf[['Cases_in_7days', 'Deaths']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7af220d210>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEHCAYAAACEKcAKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dfZxcZX338c93wwrBAEI2IiZg0IAtIKW6UqjVWjGRVVu0hZr2tgzeVCrSIFLtDdZa7I0tKIhNLLTWhwzUCoi0gmVtUh6tcgPL04bwIAtEWImQDQqEh7BJfvcf1zVmspndnd2zs5PZ/b5fr33Nmeuc65zfObNnfuc658x1FBGYmZmNV1uzAzAzs9bmRGJmZoU4kZiZWSFOJGZmVogTiZmZFbJTswOYbB0dHTF//vxmh2Fm1lJuv/32gYiYU2vctEsk8+fPp6enp9lhmJm1FEk/GW6cT22ZmVkhTiRmZlaIE4mZmRXiRGJmZoU4kVhTDQwMsGTJEtavX9/sUMxsnJxIrKnK5TK9vb2Uy+Vmh2Jm4+REYk0zMDBAd3c3EUF3d7dbJWYtyonEmqZcLlN5jMGWLVvcKjFrUU4k1jQrV65kcHAQgMHBQVasWNHkiMxsPJxIrGkWLlxIe3s7AO3t7SxatKjJEZnZeDiRWNOUSiUkAdDW1kapVGpyRGY2Hk4k1jQdHR10dXUhia6uLmbPnt3skMxsHKZdp422YymVSqxZs8atEbMW5kRiTdXR0cGyZcuaHYaZFeBTW2ZmVogTiZmZFeJEYmZmhTiRmJlZIU4kZmZWiBOJmZkV4kRiZmaFOJGYmVkhTiRmZlaIE4mZmRXiRGJmZoU4kTTBwMAAS5Ys8aNlzWxKcCJpgnK5TG9vrx8ta2ZTghPJJBsYGKC7u5uIoLu7260SM2t5TiSTrFwuExEAbNmyxa0SM2t5TiSTbOXKlQwODgIwODjIihUrmhyRmVkxTiSTbOHChbS3twPQ3t7OokWLmhyRmVkxTiSTrFQqIQmAtrY2P2LWzFqeE8kk6+jooKurC0l0dXUxe/bsZodkZlZIQxOJpI9LWi3pHknfkrSLpL0krZT0YH7ds2r6MyX1SXpA0ruqyt8kaVUet1T5kF7SzpIuy+W3SJrfyPWZKKVSiUMPPdStETObEhqWSCTNBU4FOiPiEGAGsBg4A7g2Ig4Ars3vkXRQHn8wcDRwoaQZeXYXAScBB+S/o3P5icDPI2IBcAFwbqPWZyJ1dHSwbNkyt0bMbEpo9KmtnYCZknYCdgUeB44BKve8loH35eFjgEsjYmNEPAL0AYdL2gfYPSJujnTf7MVD6lTmdQVwVKW1YmZmk6NhiSQifgqcBzwKrAWejogVwN4RsTZPsxZ4Za4yF3isahb9uWxuHh5avk2diNgEPA1sd5gv6SRJPZJ61q1bNzEraGZmQGNPbe1JajHsD7waeLmkD45UpUZZjFA+Up1tCyK+EhGdEdE5Z86ckQM3M7MxaeSprXcCj0TEuogYBK4EfhN4Ip+uIr8+mafvB/atqj+PdCqsPw8PLd+mTj59tgfwVEPWxszMampkInkUOELSrvm6xVHAfcBVQOV2pRLw3Tx8FbA434m1P+mi+q359Nezko7I8zl+SJ3KvI4FrotK/yNmZjYpdmrUjCPiFklXAHcAm4A7ga8As4DLJZ1ISjbH5elXS7ocuDdPf0pEbM6zOxlYDswEuvMfwNeASyT1kVoiixu1PmZmVpum2wF8Z2dn9PT0NDsMM7OWIun2iOisNc6/bDczs0KcSMzMrBAnEjMzK8SJxMzMCnEiMTOzQpxIzMysECcSMzMrxInEzMwKcSIxM7NCnEisqQYGBliyZAnr169vdihmNk5OJNZU5XKZ3t5eyuXy6BOb2Q7JicSaZmBggO7ubiKC7u5ut0rMWpQTiTVNuVym0mnoli1b3Coxa1FOJNY0K1euZHBwEIDBwUFWrFjR5IjMbDycSKxpFi5cSHt7OwDt7e0sWrSoyRGZ2Xg4kVjTlEol0kMvoa2tjVKpNEoNM9sROZE0gW95TTo6Oujq6kISXV1dzJ49u9khmdk4OJE0gW953apUKnHooYe6NWLWwpxIJplved1WR0cHy5Ytc2vErIU5kUwy3/JqZlONE8kk8y2vZjbVOJFMMt/yamZTjRPJJPMtr2Y21TiRTDLf8mpmU81OzQ5gOiqVSqxZs8atETObEpxImqByy6uZ2VTgU1tmZlaIE4mZmRXiRGJmZoU4kZiZWSFOJGZmVogTiZmZFeJEYmZmhTiRmJlZIQ1NJJJeIekKSfdLuk/SkZL2krRS0oP5dc+q6c+U1CfpAUnvqip/k6RVedxS5c6qJO0s6bJcfouk+Y1cHzMz216jWyT/AHw/In4F+DXgPuAM4NqIOAC4Nr9H0kHAYuBg4GjgQkkz8nwuAk4CDsh/R+fyE4GfR8QC4ALg3Aavj5mZDdGwRCJpd+BtwNcAIuKliPgFcAxQeZpTGXhfHj4GuDQiNkbEI0AfcLikfYDdI+LmSE+EunhIncq8rgCOqrRWzMxscjSyRfJaYB3wDUl3SvqqpJcDe0fEWoD8+so8/Vzgsar6/blsbh4eWr5NnYjYBDwNbNedrqSTJPVI6lm3bt1ErZ+ZmdHYRLIT8Ebgooj4deA58mmsYdRqScQI5SPV2bYg4isR0RkRnXPmzBk5ajMzG5NGJpJ+oD8ibsnvryAllify6Sry65NV0+9bVX8e8Hgun1ejfJs6knYC9gCemvA1MTOzYTUskUTEz4DHJL0+Fx0F3AtcBVQexFECvpuHrwIW5zux9iddVL81n/56VtIR+frH8UPqVOZ1LHBdvo5iZmaTpNHPI1kCfFPSy4CHgQ+Rktflkk4EHgWOA4iI1ZIuJyWbTcApEbE5z+dkYDkwE+jOf5Au5F8iqY/UElnc4PUxM7MhNN0O4Ds7O6Onp6fZYZiZtRRJt0dEZ61x/mW7mZkV4kRiTTUwMMCSJUtYv359s0Mxs3FyIrGmKpfL9Pb2Ui6XR5/YzHZITiTWNAMDA3R3dxMRdHd3u1Vi1qKcSKxpyuUylZs9tmzZ4laJWYtyIrGmWblyJYODgwAMDg6yYsWKJkdkZuPhRGJNs3DhQtrb2wFob29n0aJFTY7IzMbDicSaplQqUemsua2tjVKpNEoNM9sR1ZVIJH1e0u6S2iVdK2lA0gcbHZxNbR0dHXR1dSGJrq4uZs/eruNmM2sB9bZIFkXEM8B7SR0lHgh8smFR2bRRKpU49NBD3Roxa2H19rXVnl/fDXwrIp7y86NsInR0dLBs2bJmh2FmBdSbSK6WdD/wAvBRSXOAFxsXlpmZtYq6Tm1FxBnAkUBnRAySHlJ1TCMDs+nBXaSYtb6x3LX1q8AHJB1PevaH79W0wtxFilnrq/eurUuA84DfAt6c/2p2J2xWL3eRYjY11HuNpBM4yE8ftIlUq4uU008/vclRmdlY1Xtq6x7gVY0MxKYfd5FiNjWM2CKRdDUQwG7AvZJuBTZWxkfE7zU2PJvKFi5cyDXXXMPg4KC7SDFrYaOd2jpvUqKwaalUKtHd3Q24ixSzVjbiqa2IuDEibgTeXRmuLpucEG2qchcpZlNDvddIFtYo65rIQGx6chcpZq1vtGskJwMfBV4rqbdq1G7AjxoZmE0P7iLFrPWNdo3k34Bu4O+BM6rKn42IpxoWlZmZtYwRE0lEPA08DfwRgKRXArsAsyTNiohHGx+imZntyOr9ZfvvSnoQeAS4EVhDaqmYmdk0V+/F9rOBI4AfR8T+wFHADxsWlZmZtYx6E8lgRKwH2iS1RcT1wGENjMvMzFpEvX1t/ULSLOAHwDclPQlsalxYZmbWKuptkRwDPA+cBnwfeAj43UYFZWZmraOuFklEPCfpNcABEVGWtCswo7GhmZlZK6j3rq0PA1cA/5yL5gL/0aigzMysddR7ausU4C3AMwAR8SDwykYFZWZmraPeRLIxIl6qvJG0E6l7eTMzm+bqTSQ3SvoUMFPSQuDbwNX1VJQ0Q9Kdkr6X3+8laaWkB/PrnlXTnimpT9IDkt5VVf4mSavyuKWSlMt3lnRZLr9F0vw618fMzCZIvYnkDGAdsAr4M+Aa4NN11v0YcN+QeV0bEQcA1+b3SDoIWAwcDBwNXCipckH/IuAk4ID8d3QuPxH4eUQsAC4Azq0zJjMzmyB1JZKI2EK6uP7RiDg2Iv6lnue3S5oHvAf4alXxMUA5D5eB91WVXxoRGyPiEaAPOFzSPsDuEXFzXubFQ+pU5nUFcFSltWJmZpNjxESi5CxJA8D9wAOS1kn6TJ3z/xLwl8CWqrK9I2ItQH6tXLSfCzxWNV1/Lpubh4eWb1MnIjaROpjc7ulIkk6S1COpZ926dXWGbmZm9RitRXIa6W6tN0fE7IjYC/gN4C2SPj5SRUnvBZ6MiNvrjKVWSyJGKB+pzrYFEV+JiM6I6JwzZ06d4ZiZWT1GSyTHA3+UTzUBEBEPAx/M40byFuD3JK0BLgXeIelfgSfy6Sry65N5+n5g36r684DHc/m8GuXb1Ml3ku0B+DkpZmaTaLRE0h4RA0MLI2Id0D5SxYg4MyLmRcR80kX06yLig8BVQOW5qiXgu3n4KmBxvhNrf9JF9Vvz6a9nJR2Rr38cP6ROZV7H5mX4tmQzs0k0WhcpL41z3EjOAS6XdCLwKHAcQESslnQ5cC+pQ8hTImJzrnMysByYSXoOSuVZKF8DLpHUR2qJLB5nTGZmNk4a6QBe0mbguVqjgF0iYsRWyY6os7Mzenp6mh2GmVlLkXR7RHTWGjfao3bdMaOZmY2o3h8kmpmZ1eREYk01MDDAkiVLWL9+fbNDMbNxciKxpiqXy/T29lIul0ef2Mx2SE4k1jQDAwN0d3cTEXR3d7tVYtainEisacrlMpW7Brds2eJWiVmLciKxplm5ciWDg4MADA4OsmLFiiZHZGbj4URiTbNw4ULa29NPkdrb21m0aFGTIzKz8XAisaYplUpUev1va2ujVCqNUsPMdkROJNY0HR0ddHV1IYmuri5mz97uCQBm1gJG62vLrKFKpRJr1qxxa8SshTmRWFN1dHSwbNmyZodhZgX41JaZmRXiRGJmZoU4kZiZWSFOJGZmVogTiZmZFeJEYmZmhTiRmJlZIU4kZmZWiBOJmZkV4kRiZmaFOJGYmVkhTiRmZlaIE4mZmRXiRGJmZoW4G3mbEEuXLqWvr2/M9fr7+wGYN2/emOotWLCAU089dczLM7OJ50RiTfXCCy80OwQzK8iJxCbEeFsHlXpLly6dyHDMbBL5GomZmRXiRGJmZoU4kZiZWSFOJGZmVkjDEomkfSVdL+k+SaslfSyX7yVppaQH8+ueVXXOlNQn6QFJ76oqf5OkVXncUknK5TtLuiyX3yJpfqPWx8zMamtki2QT8BcR8avAEcApkg4CzgCujYgDgGvze/K4xcDBwNHAhZJm5HldBJwEHJD/js7lJwI/j4gFwAXAuQ1cHzMzq6FhiSQi1kbEHXn4WeA+YC5wDFDOk5WB9+XhY4BLI2JjRDwC9AGHS9oH2D0ibo6IAC4eUqcyryuAoyqtFTMzmxyTco0kn3L6deAWYO+IWAsp2QCvzJPNBR6rqtafy+bm4aHl29SJiE3A08DsRqyDmZnV1vBEImkW8B3gtIh4ZqRJa5TFCOUj1Rkaw0mSeiT1rFu3brSQzcxsDBqaSCS1k5LINyPiylz8RD5dRX59Mpf3A/tWVZ8HPJ7L59Uo36aOpJ2APYCnhsYREV+JiM6I6JwzZ85ErJqZmWWNvGtLwNeA+yLii1WjrgJKebgEfLeqfHG+E2t/0kX1W/Ppr2clHZHnefyQOpV5HQtcl6+jmJnZJGlkX1tvAf4EWCXprlz2KeAc4HJJJwKPAscBRMRqSZcD95Lu+DolIjbneicDy4GZQHf+g5SoLpHUR2qJLG7g+piZWQ0NSyQR8T/UvoYBcNQwdT4HfK5GeQ9wSI3yF8mJyMzMmsO/bDczs0KcSMzMrBAnEjMzK8SJxMzMCnEiMTOzQvyoXdvO0qVL6evrm5RlPfjgg8D4H9U7FgsWLJiU5ZhNN04ktp2+vj5+fM8d7Ddr8+gTF/SywdQofnHNbQ1dzqMbZow+kZmNixOJ1bTfrM18unNDs8OYMGf3zGp2CGZTlq+RmJlZIU4kZmZWiBOJmZkV4kRiZmaFOJGYmVkhTiRmZlaIE4mZmRXiRGJmZoX4B4kFjLcrkf7+fgDmzZs3ypTbchcfZrYjciJpghdeeKHZIZiZTRgnkgLG2zqo1Fu6dOlEhmNm1hS+RmJmZoU4kZiZWSFOJGZmVogTiZmZFeJEYmZmhTiRmJlZIb79N/Nzys3MxseJJOvr6+POVfeyZde9Gr4svRQA3P7Qzxq6nLbnn2ro/M3MwIlkG1t23YsXD3pvs8OYMLvc+71mh2Bm04ATiW2nv7+f556dwdk9s5odyoT5ybMzeHnu48zMJpYvtpuZWSFukdh25s2bx4ub1vLpzg3NDmXCnN0zi13G2NuymdXHiSTr7++n7fmnp9R1hbbn19Pfv6nZYZjZFOdTW2ZmVohbJNm8efN4YuNOU+6urXnzXjWuuo9umJyL7U88n45l9t51S0OX8+iGGRzY0CXYRBkYGOCzn/0sZ511FrNnz252OFaHlk8kko4G/gGYAXw1Is5pckgtb8GCBZO2rJfyjzN3mX9AQ5dzIJO7XjZ+5XKZ3t5eyuUyp59+erPDsTq0dCKRNAP4R2Ah0A/cJumqiLh3PPNre/6pSblGohefASB22b2hy0k/SBx7i2Qyfwnvh3xZtYGBAbq7u4kIuru7KZVKbpW0gJZOJMDhQF9EPAwg6VLgGGDMiWQ8R6v9/f3jemzuC5tfBGDmFo2p3syZM8f4nPdXTdpR+Hi7mBlvdzGT1fXL0qVL6e7uHlOd559/nohoUETbk8Suu+46pjpdXV1j3n6TvS02btzI+9///jHVmarbYjwma1tA6yeSucBjVe/7gd8YOpGkk4CTAPbbb7+aMxrPxhvvl2d//mHc2JLC1Ow3a+bMmc0OwcwK0mRmyIkm6TjgXRHxp/n9nwCHR8SS4ep0dnZGT0/PZIVoZmNw/vnnc8011zA4OEh7ezvvec97fJ1kByHp9ojorDWu1W//7Qf2rXo/D3i8SbGYWUGlUgkpnfJta2ujVCo1OSKrR6snktuAAyTtL+llwGLgqibHZGbj1NHRQVdXF5Lo6uryhfYW0dLXSCJik6Q/B/6LdPvv1yNidZPDMrMCSqUSa9ascWukhbT0NZLx8DUSM7Oxm8rXSMzMrMmcSMzMrBAnEjMzK8SJxMzMCpl2F9slrQN+0uw4gA5goNlB7CC8LRJvh628LbbaUbbFayJiTq0R0y6R7Cgk9Qx3B8R0422ReDts5W2xVStsC5/aMjOzQpxIzMysECeS5vlKswPYgXhbJN4OW3lbbLXDbwtfIzEzs0LcIjEzs0KcSMzMrJApl0gkvUrSpZIeknSvpGskHShpvqR7xjnPEyS9eqJjrbGc6tgHJD0r6auNXu54SXq1pCvy8GGS3j3G+m+X9JuNiW7sJP1oHHW2SOqTdI+kb0sa27NNa8/zBElfzsMfkXR80XnWscy/krRaUq+kuyRt96TRBi//BkmdefgaSa+YzOVPFEkh6fyq95+QdFYTQ5oUUyqRKD0R59+BGyLidRFxEPApYO+Csz4BGFMikTSmLvqHxg68DHgHUB7LfCZTRDweEcfmt4cBY0okwNuBmolkrNtvIkTEeJLa8xGxICIOAV4CPjLBMf1TRFw8kfMcStKRwHuBN0bEocA72fYR1uOd77g+w4h4d0T8oujym2Qj8PuSOpodyKSKiCnzR/rivWmYcfOBe/LwCcCXq8Z9j/SlNgNYDtwDrAI+DhwLbAAeAO4CZgJvAm4Ebic9C2WfPJ8bgL/L4/4COC7P6+5acQECvpCneRi4L5dfBWzOy/vAkOlWA9fn+B7K7y8HfgGsJT018qd5/OuAc/K49cCjeTm/B6wBXiD9YvYtebnP52XeBTwC/GsuXw4sBX6U6x9bvU1JSe9RYF1VzA8Cc/J0bUAf0DHk8/hZjvUu4K15OV/M63c+cHhe5p359fVVn9+VwPfzcj6fy7f7/Ko+lwuAm4D7gDfn+g8CZ1fFtCG/7pOnvSvP660jzHuwantckLfBPcCPgZ4c+xPAZ/M2fxr4QXXcue6Hcp0bgX8h/38CZwGfqFqPc4Fb87RvzeW7kv4HeoHLgFuAzjHsN78PXD3MuDVVy7wVWJDLXwNcm5d5LbBf1f9KPZ/hTODSWjHnZXbk4ePzNHcDl9SIbxbwjfyZ9AJ/kMsvytt/NfDZqunPAe7N056Xy+YA3yE9KO82tu4Pv83W/eFOYLc6tuUG4Ezgc/n9J4CzRtpmO/L61P0/1Iwv/Eb9AacCFwwzbj6jJ5I3ASuryl+RX2+o+idvzztE5UvyA6QHalWmu7Cq/ipgbvW8hsT0B8BK0pfUXwHPsDUpbRhmumWV6XLMT+fhV5G+lP+WrV84F5ES4HLSTvsK4BjSl9/xpC/4XuDhvJxNVf90HwTKVV8O387THwT01bFN/wY4LQ8vAr5TY/3PIn9JVi3ne8CM/H53YKc8/M7KPPKyHgb2AHYhdXmz7yif37l5+GOkxzHvA+xMSryzq7c56SDgr/LwDGC3EeY9SDrYeDkpMf8N6WDiB8BpeZsP5nmeQErc5SFx70NKxHNISfmHDJ9Izs/D7wb+Ow9/AvjnPHxI/hzHkkhmkb5cfgxcCPx21bg1VdvieOB7efhqoJSH/zfwH2P8DE9n635zaHXMeZkdwMGk/99KUtmrRuznAl+qer9n9bT587shL2OvPL/K3aqVz/DfgN/Kw/ux9YDuarbuD7Mq6zHKttyQ13kN6f+zOpHU3GY78vrU+zelTm1NgIeB10paJulo0hf2UK8n7awrJd0FfJr0rPiKy6qGfwgsl/Rh0j/AUL8FfCsiNgPPkr7U3jzKdL8JXFc13a0RsRZ4H+kf+I9JO+BLpNNxL+Y6a0ktjlWkFs7pwB059j0k7UZqBX1R0qmko9yoiuE/ImJLRNxLfacKv0764oG003yjjjoA387rCWlH/Ha+tnVBXq+KayPi6Yh4kXRE9hpG/vwqj2BeBayOiLURsTHX2XdIDLcBH8rntt8QEc+OMO8ZpNbi7cBTwN+TEuc8UhK4gbS9K09T+x/SwUV13L9BOqW5LiJeYtv/oaGuzK+3kxI5pP+PSwEi4h7SwUHdImIDKVGeRGpRXSbphKpJvlX1emQePpL0hQVwSY6hop7P8G3Av+bl9w4T8zuAKyJiIE/3VI1p3gn8Y9W6/DwP/qGkO0hH3geTDoCeIe0PX5X0+6T9oTKPL+f9+Spg97w//JCt+8MrImJTjeVvJyKeAS4mHdhWG2mb7bDrU4+plkhWk3aI0Wxi23XfBX75of0aaec/Bah1oVukL6LD8t8bImJR1fjnKgMR8RFSotkXuEvS0AdQa0jswz2gWsMMA2yUtD/pyOdu0nPr/5PUcppBOrXwE+DXSaeCtuR6R0bEYaR/tD/LX5YvAX9KOu1wDulL4JfLGSGG7UTEY8ATkt5B+qLsHq1O9lzV8P8Fro90/eF3yZ9TjXg2k46uRvr8KtNvGVJ3C0MeOR0RN5G+6H4KXCLp+BHmvRn4JGm7P5QTgUhJ5Lq8jftJn28ljp2q6laGq5P2SCqxV9cd9fMYTURsjogbIuJvgD8ntYJ/OXqYYYYpr/czHG2dNZ5pqvaHoyJd8/lPYJf8xXk46bTP+0j7A6TvgiOr9um5EfFsRJzD1v3h/0n6lVFiqfYl4ERSS3U4tdZtR12fEU21RHIdsHNuAQAg6c2SfnvIdGuAwyS1SdqX9GGQL5C1RcR3gL8G3pinf5Z0egNSU3JOvkCJpHZJ1UfKvyTpdRFxS0R8hnRKY+iR703AByTNIB2RvQI4oEbs1dP9gHSkdmuerJ3UEnmOlCD3ArryuBmkZPBT0imHw3L5BtKXRcX8/Lo+z+MLpCOd6kQymuptVPFV0lHn5VVHqKPVqVaJHdJpoRGN8PmNiaTXAE9GxL8AXwPeWMe87wfmS1pAum52Bul6B6TTVSO5BXi7pNmS2knX1sbif4A/zLEfBLxhLJUlvV7SAVVFh7FtD9kfqHq9OQ//iJQ8Af5XjqGW4T7Dm3I9JB1COlUz1LWkI/HZebq9akyzgqr/ZUl7snV/eFrS3uT9QdIsYI+IuIZ02vGwYeZxWH59XUSsiohzSS3Kur94c+vpclIyqahnm+2Q6zOaSb8zppEiIiS9H/iSpDNIX4ZrSBu52g9JF5NXkS6M3pHL5wLfkFRJsGfm1+XAP0l6gdQ8PRZYKmkP0jb8EluPOKt9Ie+gIu0Udw8Z/+95fneTjkI+BrxD0kOko4azcuw3VU0nUvP2v0lHd7+IiLsl3Un6AvpMXj9ybN8DFgC/Q7p5ANJprk5JvaRzqK8GziOdarmLdE7/cdKRdL2uB87Izem/j4jLSK2dbzD8aa2rgSskHQMsqTH+80BZ0umkg4TRDPf5jdXbgU9KGiQl3eNHm3dEvCjpQ6RrSe2kz+nD+aBmxFtZI2JtPo12M+mzuYPap0KHcyFpO/WS/jd6SdfO6jULWJZvud1EujHipKrxO0u6hXTg+Ue57FTg65I+STod9qFh5j3cZ3gRaXv2kv7nbh1aMSJWS/occKOkzXndThgy2dnAP+ZTZ5tJF6KvzPvDatIpycr+sBvwXUm7kD6fyv5wap5HL2mfuYl0991pkn4nz/de6m9VV5zPtgds9WyzHXl9huUuUqxh8u8CLoiItzY7lqkst1TbczJ7Hemg5cB8mq3ovNeQLoLvCM/DsB3UlGqR2I4jtwhPJp++sIbaFbg+nxYTcPJEJBGzerlFYmZmhUy1i+1mZjbJnEjMzKwQJxIzMyvEicTMzApxIrEpI/+g76789zNJP616P9qPAhsV03i6pv9bSe+c4Di6JPVIuk/S/ZLOy+VnSfrERC7Lph/f/mtTRkSsJ/+6N//Ab0NEnFcZL2mniexfqM6Yxtw1fe4JYcLkX45/GXhPRNyv1L37SaNUM6ubWyQ2pUlaLjdBD24AAAJaSURBVOmLkq4HzpV0uKQfSbozv74+T3eCpCslfV/Sg5I+n8tn5HncI2mVpI/n8hskXSDppnyU/+Zc/0FJZ1ctf0N+3SdPe1ee11tHmPdyScfm4aNyrKskfV3Szrl8jaTPSrojjxupu4u/JHVrfj9ARGyKiAtrbKsPS7pN0t2SvqP8kC5Jx+UY75Z0Uy47WNKteX16tW0XKzbNuEVi08GBwDsjYrOk3YG3RcSmfPro79jaQeFhpM4tNwIPSFoGvJLUW+8hANr2yX0vRcTbJH0M+C6pw9CngIckXZBbSBV/DPxXRHwu/xJ917y84eZN7vpiOamzvh9Lupj0I88v5UkGIuKNkj5K6tTvT4dZ/0NI3XWM5srcvxg5GZ5IemzBZ4B3RcRPq2L8CPAPEfHNfNpwLF262BTjFolNB63WNX3F64FHIuLH+X2Z1CtxRa0u5Ys4RNIPJK0i9UhQ2Ta1HodwM/ApSf8HeE1EvDABy7cW5URi00GrdU1fMVr38LW6lK+l3scrLAf+PCLeQHqiY+XxCts9DiEi/o30pM0XgP9SelyATVNOJDbdtGrX9AB/wtau6cfiC6TWw4E5ljal3niH2g1Ym/vs+mUfaarxOARJryU9WXMpqWVWqxt4myZ8jcSmm5bsmj7faXUb8E9jXXBE9Eo6DfhWvoAepIcjDfXXpGej/IR0uq7yrJhaj0M4A/hgXp+fkR7xbNOUO200M7NCfGrLzMwK8aktsykinwb72JDiH0bEKc2Ix6YPn9oyM7NCfGrLzMwKcSIxM7NCnEjMzKwQJxIzMyvk/wORN6bNrt/CswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Transmission_Class\", y=\"Deaths\", data=cdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that the distribution of Deaths between these two transmission classification categories, cluster transmission  and Community transmission, are distinct enough to take Transmission classification  as a potential good predictor ofCummulative total Deaths."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> \n",
    "Measures for In-Sample Evaluation of Linear Regression</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>R-squared</h2>\n",
    "R squared, also known as the coefficient of determination, is a measure to indicate how close the data is to the fitted regression line. The value of the R-squared is the percentage of variation of the response variable (y) that is explained by a linear model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9370958576231244"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[['Cases _cumulative total']] \n",
    "Y = df['Deaths _cumulative total'] \n",
    "lm = LinearRegression() \n",
    "lm \n",
    "lm.fit(X,Y)\n",
    "lm.score(X, Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Thus, we can say that ~ <b>93.7% </b> of the variation of the Deaths is explained by this simple linear model \"Cases_fit\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Mean Squared Error (MSE)</h2>\n",
    "The Mean Squared Error measures the average of the squares of errors, that is, the difference between actual value (y) and the estimated value (ŷ)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The output of the first four predicted value is:  [86057.36321965 82485.99408999 68465.67949202 56510.1678963 ]\n"
     ]
    }
   ],
   "source": [
    "Yhat=lm.predict(X)\n",
    "print('The output of the first four predicted value is: ', Yhat[0:4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ets import the function <b> mean_squared_error</b> from the module metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> We compare the predicted results with the actual results. </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean square error of deaths cummulative total and predicted value is:  27711998.782093927\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(df['Deaths _cumulative total'], Yhat)\n",
    "print('The mean square error of deaths cummulative total and predicted value is: ', mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Multiple Linear Regression</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> What if we want to predict car price using more than one variable?</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "If we want to use more variables in our model to predict Deaths, we can use Multiple Linear Regression. Multiple Linear Regression is very similar to Simple Linear Regression, but this method is used to explain the relationship between one continuous response (dependent) variable and two or more predictor (independent) variables. Most of the real-world regression models involve multiple predictors. We will illustrate the structure by using four predictor variables, but these results can generalize to any integer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> \n",
    "Let's develop a model using these variables as the predictor variables.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = df[['Cases _cumulative total', 'Cases _newly reported in last 7 days', 'Cases _newly reported in last 24 hours']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> \n",
    "Fit the linear model using the four above-mentioned variables.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(Z, df['Deaths _cumulative total'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> What is the value of the intercept?</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-840.6463062640614"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02176024, 0.01472714, 0.15135957])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<b>What is the final estimated linear model that we get?</b>\n",
    "\n",
    "As we saw above, we should get a final linear function with the structure\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Deaths _cumulative total = -840.65 + 0.02176*Cases _cumulative total + 0.01473*Cases _newly reported in last 7 days + 0.15136* Cases _newly reported in last 24 hours </h2> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As on  22 Jan 2021 data by WHO. This model keeps on changing as data is being updated daily"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Measures for In-Sample Evaluation of Multiple Linear Regression</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Model 2: Multiple Linear Regression</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Let's calculate the R^2</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R-square is:  0.9381547702684265\n"
     ]
    }
   ],
   "source": [
    "# fit the model \n",
    "lm.fit(Z, df['Deaths _cumulative total'])\n",
    "# Find the R^2\n",
    "print('The R-square is: ', lm.score(Z, df['Deaths _cumulative total']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We can say that ~ <b> 93.8 % </b> of the variation of Deaths _cumulative total is explained by this multiple linear regression \"multi_fit\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Let's calculate the MSE for Multiple linear Regression</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We produce a prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_predict_multifit = lm.predict(Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We compare the predicted results with the actual results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean square error of price and predicted value using multifit is:  27245501.9056061\n"
     ]
    }
   ],
   "source": [
    "print('The mean square error of price and predicted value using multifit is: ', \\\n",
    "      mean_squared_error(df['Deaths _cumulative total'], Y_predict_multifit))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Conclusion:</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing these three models, we conclude that the Multiple Linear Regression model is the best model to be able to predict Deaths from our dataset. This result makes sense, since we have 3 variables in total, and we know that more than one of those variables are potential predictors of the final numbers of Deaths."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model for predicting deaths based on cases and Confirmed cases in the last 14 days for PART 1 of our project is"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Deaths _cumulative total = -840.65 + 0.02176*Cases _cumulative total + 0.01473*Cases _newly reported in last 7 days + 0.15136* Cases _newly reported in last 24 hours </h2> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As on  22 Jan 2021 data by WHO. This model keeps on changing as data is being updated daily"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> PART 3: Use of Machine Learning Algorithms </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Feature before One Hot Encoding</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import NullFormatter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.ticker as ticker\n",
    "from sklearn import preprocessing\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Deaths _cumulative total</th>\n",
       "      <th>Cases _cumulative total</th>\n",
       "      <th>Cases _newly reported in last 7 days</th>\n",
       "      <th>Cases _newly reported in last 24 hours</th>\n",
       "      <th>Transmission Classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67832</td>\n",
       "      <td>3655839</td>\n",
       "      <td>160023</td>\n",
       "      <td>21887</td>\n",
       "      <td>Clusters of cases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93290</td>\n",
       "      <td>3505758</td>\n",
       "      <td>294178</td>\n",
       "      <td>38905</td>\n",
       "      <td>Community transmission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>71261</td>\n",
       "      <td>2916577</td>\n",
       "      <td>132669</td>\n",
       "      <td>26565</td>\n",
       "      <td>Community transmission</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83681</td>\n",
       "      <td>2414166</td>\n",
       "      <td>95130</td>\n",
       "      <td>13568</td>\n",
       "      <td>Clusters of cases</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54637</td>\n",
       "      <td>2412318</td>\n",
       "      <td>184705</td>\n",
       "      <td>18500</td>\n",
       "      <td>Community transmission</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Deaths _cumulative total Cases _cumulative total  \\\n",
       "0                    67832                 3655839   \n",
       "1                    93290                 3505758   \n",
       "2                    71261                 2916577   \n",
       "3                    83681                 2414166   \n",
       "4                    54637                 2412318   \n",
       "\n",
       "  Cases _newly reported in last 7 days Cases _newly reported in last 24 hours  \\\n",
       "0                               160023                                  21887   \n",
       "1                               294178                                  38905   \n",
       "2                               132669                                  26565   \n",
       "3                                95130                                  13568   \n",
       "4                               184705                                  18500   \n",
       "\n",
       "  Transmission Classification  \n",
       "0           Clusters of cases  \n",
       "1      Community transmission  \n",
       "2      Community transmission  \n",
       "3           Clusters of cases  \n",
       "4      Community transmission  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Deaths _cumulative total','Cases _cumulative total','Cases _newly reported in last 7 days','Cases _newly reported in last 24 hours', 'Transmission Classification']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Use one hot encoding technique to convert categorical varables to binary variables and append them to the feature Data Frame</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Cases _cumulative total</th>\n",
       "      <th>Deaths _cumulative total</th>\n",
       "      <th>Clusters of cases</th>\n",
       "      <th>Community transmission</th>\n",
       "      <th>Sporadic cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3655839</td>\n",
       "      <td>67832</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3505758</td>\n",
       "      <td>93290</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2916577</td>\n",
       "      <td>71261</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2414166</td>\n",
       "      <td>83681</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2412318</td>\n",
       "      <td>54637</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cases _cumulative total Deaths _cumulative total  Clusters of cases  \\\n",
       "0                 3655839                    67832                  1   \n",
       "1                 3505758                    93290                  0   \n",
       "2                 2916577                    71261                  0   \n",
       "3                 2414166                    83681                  1   \n",
       "4                 2412318                    54637                  0   \n",
       "\n",
       "   Community transmission  Sporadic cases  \n",
       "0                       0               0  \n",
       "1                       1               0  \n",
       "2                       1               0  \n",
       "3                       0               0  \n",
       "4                       1               0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Feature = df[['Cases _cumulative total','Deaths _cumulative total']]\n",
    "Feature = pd.concat([Feature,pd.get_dummies(df['Transmission Classification'])], axis=1)\n",
    "Feature.drop(['No cases'], axis = 1,inplace=True)\n",
    "Feature.drop(['Pending'], axis = 1,inplace=True)\n",
    "#Feature.drop(['Not applicable'], axis = 1,inplace=True)\n",
    "Feature.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> \n",
    "Feature selection</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets defind feature sets, X:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Cases _cumulative total</th>\n",
       "      <th>Deaths _cumulative total</th>\n",
       "      <th>Clusters of cases</th>\n",
       "      <th>Community transmission</th>\n",
       "      <th>Sporadic cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3655839</td>\n",
       "      <td>67832</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3505758</td>\n",
       "      <td>93290</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2916577</td>\n",
       "      <td>71261</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2414166</td>\n",
       "      <td>83681</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2412318</td>\n",
       "      <td>54637</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cases _cumulative total Deaths _cumulative total  Clusters of cases  \\\n",
       "0                 3655839                    67832                  1   \n",
       "1                 3505758                    93290                  0   \n",
       "2                 2916577                    71261                  0   \n",
       "3                 2414166                    83681                  1   \n",
       "4                 2412318                    54637                  0   \n",
       "\n",
       "   Community transmission  Sporadic cases  \n",
       "0                       0               0  \n",
       "1                       1               0  \n",
       "2                       1               0  \n",
       "3                       0               0  \n",
       "4                       1               0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Feature\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> \n",
    "What are our lables?</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Clusters of cases', 'Community transmission',\n",
       "       'Community transmission', 'Clusters of cases',\n",
       "       'Community transmission'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['Transmission Classification'].values\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 2, 1, 2], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_collection = df['Transmission Classification'].replace(to_replace=['Community transmission', 'Clusters of cases','Sporadic cases'], value=[2,1,0]).values\n",
    "y_collection[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> \n",
    "Normalize Data</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Data Standardization give data zero mean and unit variance (technically should be done after train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.68799811,  2.70179488,  1.6955825 , -1.25830574, -0.26261287],\n",
       "       [ 3.51222662,  3.9147088 , -0.58976782,  0.79471941, -0.26261287],\n",
       "       [ 2.8221911 ,  2.86516521, -0.58976782,  0.79471941, -0.26261287],\n",
       "       [ 2.23377865,  3.45690026,  1.6955825 , -1.25830574, -0.26261287],\n",
       "       [ 2.23161431,  2.07313595, -0.58976782,  0.79471941, -0.26261287],\n",
       "       [ 1.85224885,  1.84187331, -0.58976782,  0.79471941, -0.26261287],\n",
       "       [ 1.27073346,  0.63667768, -0.58976782,  0.79471941, -0.26261287],\n",
       "       [ 1.11365162,  1.11664055, -0.58976782,  0.79471941, -0.26261287],\n",
       "       [ 0.78556499,  0.49431823, -0.58976782,  0.79471941, -0.26261287],\n",
       "       [ 0.49217215,  0.10120987, -0.58976782,  0.79471941, -0.26261287]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "X_initial = X\n",
    "scaler = preprocessing.StandardScaler().fit(X_initial)\n",
    "X= scaler.transform(X)\n",
    "X[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> \n",
    "Classification</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> \n",
    "Classification with 75% Training dataset and 25% Testing dataset</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following algorithms were used K Nearest Neighbor(KNN), Decision Tree, Support Vector Machine and Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> \n",
    "K Nearest Neighbor(KNN)</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the initial train set in train and cross validation set. Use 75% of the data set for training and 25% for cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (46, 5) (46,)\n",
      "Cross validation set: (16, 5) (16,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_cv, y_train, y_cv = train_test_split( X, y, test_size=0.25, random_state=4)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Cross validation set:', X_cv.shape,  y_cv.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Import library</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import jaccard_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import log_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Find the best k by training and predicting</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model for various k between 1 and 9 and choose the one with the best accuracy on the cross validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9375, 0.9375, 0.9375, 0.875 , 0.875 , 0.875 , 0.875 , 0.875 ,\n",
       "       0.875 ])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ks = 10\n",
    "mean_acc = np.zeros((Ks-1))\n",
    "std_acc = np.zeros((Ks-1))\n",
    "ConfustionMx = [];\n",
    "for n in range(1,Ks):\n",
    "    \n",
    "    #Train Model   \n",
    "    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n",
    "#Predict on the Cross Validation set\n",
    "    yhat_cv=neigh.predict(X_cv)\n",
    "    #Calculate \n",
    "    mean_acc[n-1] = metrics.accuracy_score(y_cv, yhat_cv)\n",
    "\n",
    "    \n",
    "    std_acc[n-1]=np.std(yhat_cv==y_cv)/np.sqrt(yhat_cv.shape[0])\n",
    "\n",
    "mean_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Display the accuracy of the model for the various K</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best accuracy was 0.9375 with k= 1\n"
     ]
    }
   ],
   "source": [
    "plt.plot(range(1,Ks),mean_acc,'g')\n",
    "plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\n",
    "plt.legend(('Accuracy ', '+/- 3xstd'))\n",
    "plt.ylabel('Accuracy ')\n",
    "plt.xlabel('Number of Neighbours (K)')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print( \"The best accuracy was\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Now generate and store the model on the global train set for the optimal k = 5</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh = KNeighborsClassifier(n_neighbors = mean_acc.argmax()+1).fit(X, y)\n",
    "neigh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> \n",
    "KNN Evaluation</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> \n",
    "Jaccard index for accuracy evaluation.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7777777777777778"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_score\n",
    "jaccard_score(y_cv, yhat_cv,average='micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Jaccard</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>LogLoss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Jaccard  F1-score LogLoss\n",
       "KNN                       0         0      NA\n",
       "Decision Tree             0         0      NA\n",
       "SVM                       0         0      NA\n",
       "LogisticRegression        0         0       0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfr = {'Jaccard': [0, 0, 0, 0], 'F1-score': [0, 0, 0, 0], 'LogLoss': ['NA', 'NA', 'NA', 0]}\n",
    "df_report = pd.DataFrame (data=dfr, index=['KNN', 'Decision Tree', 'SVM', 'LogisticRegression'])\n",
    "df_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> \n",
    "Calculate accuracy and F1 for KNN algorithm</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is  0.7777777777777778  F1 is 0.875\n",
      "                        precision    recall  f1-score   support\n",
      "\n",
      "     Clusters of cases       1.00      1.00      1.00         2\n",
      "Community transmission       0.86      1.00      0.92        12\n",
      "              No cases       0.00      0.00      0.00         1\n",
      "               Pending       0.00      0.00      0.00         1\n",
      "\n",
      "              accuracy                           0.88        16\n",
      "             macro avg       0.46      0.50      0.48        16\n",
      "          weighted avg       0.77      0.88      0.82        16\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 2,  0,  0,  0],\n",
       "       [ 0, 12,  0,  0],\n",
       "       [ 0,  1,  0,  0],\n",
       "       [ 0,  1,  0,  0]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#yhat_test=neigh.predict(X_test)\n",
    "kn_jacc_test=metrics.jaccard_score(y_cv,  yhat_cv, average='micro')\n",
    "kn_f1_test=metrics.f1_score(y_cv,  yhat_cv, average='micro')\n",
    "kn_cnf_matrix = confusion_matrix(y_cv,  yhat_cv)\n",
    "print(\"Accuracy is \", kn_jacc_test, \" F1 is\" , kn_f1_test)\n",
    "print(classification_report(y_cv,  yhat_cv))\n",
    "kn_cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_report.loc['KNN','Jaccard'] = kn_jacc_test\n",
    "df_report.loc['KNN','F1-score'] = kn_f1_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> \n",
    "Decision Tree</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> \n",
    "Import necessary libraries</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "Requirement already satisfied: six in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install six"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/secretstorage/dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/secretstorage/util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
      "  from cryptography.utils import int_from_bytes\n",
      "Collecting pydotplus\n",
      "  Downloading pydotplus-2.0.2.tar.gz (278 kB)\n",
      "\u001b[K     |████████████████████████████████| 278 kB 13.6 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: pyparsing>=2.0.1 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from pydotplus) (2.4.7)\n",
      "Building wheels for collected packages: pydotplus\n",
      "  Building wheel for pydotplus (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for pydotplus: filename=pydotplus-2.0.2-py3-none-any.whl size=24566 sha256=433be7cfce0f7710dad91f2504132bfd7eb76488ea41ccfd1a4782644f699c78\n",
      "  Stored in directory: /tmp/wsuser/.cache/pip/wheels/1e/7b/04/7387cf6cc9e48b4a96e361b0be812f0708b394b821bf8c9c50\n",
      "Successfully built pydotplus\n",
      "Installing collected packages: pydotplus\n",
      "Successfully installed pydotplus-2.0.2\n"
     ]
    }
   ],
   "source": [
    "!pip install pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import six\n",
    "import sys\n",
    "sys.modules['sklearn.externals.six'] = six\n",
    "from six import StringIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.externals.six import StringIO\n",
    "import pydotplus\n",
    "import matplotlib.image as mpimg\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Build an empty DecisionTree object with depth 5</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=5)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CovidTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 5)\n",
    "CovidTree # it shows the default parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.68799811,  2.70179488,  1.6955825 , -1.25830574, -0.26261287],\n",
       "       [ 3.51222662,  3.9147088 , -0.58976782,  0.79471941, -0.26261287],\n",
       "       [ 2.8221911 ,  2.86516521, -0.58976782,  0.79471941, -0.26261287],\n",
       "       [ 2.23377865,  3.45690026,  1.6955825 , -1.25830574, -0.26261287],\n",
       "       [ 2.23161431,  2.07313595, -0.58976782,  0.79471941, -0.26261287]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import MinMaxScaler \n",
    "from sklearn.preprocessing import Binarizer\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "X= preprocessing.StandardScaler().fit(X).transform(X)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Train the decision tree using the global X data set. </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=5)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CovidTree.fit(X_initial, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Print the Decision Tree</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f9cbfd7f210>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 7200x14400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dot_data = StringIO()\n",
    "filename = \"TCtree.png\"\n",
    "featureNames = Feature.columns\n",
    "targetNames = df['Transmission Classification'].tolist()\n",
    "out=tree.export_graphviz(CovidTree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y), filled=True,  special_characters=True,rotate=False)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "graph.write_png(filename)\n",
    "img = mpimg.imread(filename)\n",
    "plt.figure(figsize=(100, 200))\n",
    "plt.imshow(img,interpolation='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn import tree\n",
    "clf = tree.DecisionTreeClassifier()\n",
    "iris = load_iris()\n",
    "clf = clf.fit(iris.data, iris.target)\n",
    "tree.export_graphviz(clf, out_file='tree.dot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Evaluating the Accuracy of Decison Tree Algorithm </h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Use Decision Tree object previously trained. </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is  1.0  F1 is 1.0\n",
      "                        precision    recall  f1-score   support\n",
      "\n",
      "     Clusters of cases       1.00      1.00      1.00        16\n",
      "Community transmission       1.00      1.00      1.00        38\n",
      "              No cases       1.00      1.00      1.00         3\n",
      "               Pending       1.00      1.00      1.00         1\n",
      "        Sporadic cases       1.00      1.00      1.00         4\n",
      "\n",
      "              accuracy                           1.00        62\n",
      "             macro avg       1.00      1.00      1.00        62\n",
      "          weighted avg       1.00      1.00      1.00        62\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[16,  0,  0,  0,  0],\n",
       "       [ 0, 38,  0,  0,  0],\n",
       "       [ 0,  0,  3,  0,  0],\n",
       "       [ 0,  0,  0,  1,  0],\n",
       "       [ 0,  0,  0,  0,  4]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat_tree=CovidTree.predict(X_initial)\n",
    "tree_jacc_test=metrics.jaccard_score(y, yhat_tree, average='micro')\n",
    "tree_f1_test=metrics.f1_score(y, yhat_tree, average ='macro')\n",
    "tree_cnf_matrix = confusion_matrix(y, yhat_tree)\n",
    "print(\"Accuracy is \", tree_jacc_test, \" F1 is\" , tree_f1_test)\n",
    "print(classification_report(y, yhat_tree))\n",
    "tree_cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_report.loc['Decision Tree','Jaccard'] = tree_jacc_test\n",
    "df_report.loc['Decision Tree','F1-score'] = tree_f1_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Support Vector Machine</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "TCSVM = svm.SVC(kernel='rbf')\n",
    "TCSVM.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Calculate accuracy and F1 for Support Vector Machine</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Use SVM object previously trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is  0.75  F1 is 0.7714285714285715\n",
      "                        precision    recall  f1-score   support\n",
      "\n",
      "     Clusters of cases       1.00      1.00      1.00        16\n",
      "Community transmission       1.00      1.00      1.00        38\n",
      "              No cases       0.75      1.00      0.86         3\n",
      "               Pending       0.00      0.00      0.00         1\n",
      "        Sporadic cases       1.00      1.00      1.00         4\n",
      "\n",
      "              accuracy                           0.98        62\n",
      "             macro avg       0.75      0.80      0.77        62\n",
      "          weighted avg       0.97      0.98      0.98        62\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[16,  0,  0,  0,  0],\n",
       "       [ 0, 38,  0,  0,  0],\n",
       "       [ 0,  0,  3,  0,  0],\n",
       "       [ 0,  0,  1,  0,  0],\n",
       "       [ 0,  0,  0,  0,  4]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat_svm=TCSVM.predict(X)\n",
    "svm_jacc_test=metrics.jaccard_score(y, yhat_svm, average ='macro')\n",
    "svm_f1_test=metrics.f1_score(y, yhat_svm, average ='macro')\n",
    "svm_cnf_matrix = confusion_matrix(y, yhat_svm)\n",
    "print(\"Accuracy is \", svm_jacc_test, \" F1 is\" , svm_f1_test)\n",
    "print(classification_report(y, yhat_svm))\n",
    "svm_cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_report.loc['SVM','Jaccard'] = svm_jacc_test\n",
    "df_report.loc['SVM','F1-score'] = svm_f1_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> \n",
    "Logistic Regression</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(solver='liblinear')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "LR = LogisticRegression(C=1.0, solver='liblinear').fit(X,y)\n",
    "LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat_LR = LR.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>\n",
    "Calculate accuracy, F1 and log_loss for Logistic Regression.</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is  0.75  F1 is 0.7714285714285715  log_loss is 0.23512131893458152\n",
      "                        precision    recall  f1-score   support\n",
      "\n",
      "     Clusters of cases       1.00      1.00      1.00        16\n",
      "Community transmission       1.00      1.00      1.00        38\n",
      "              No cases       0.75      1.00      0.86         3\n",
      "               Pending       0.00      0.00      0.00         1\n",
      "        Sporadic cases       1.00      1.00      1.00         4\n",
      "\n",
      "              accuracy                           0.98        62\n",
      "             macro avg       0.75      0.80      0.77        62\n",
      "          weighted avg       0.97      0.98      0.98        62\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[16,  0,  0,  0,  0],\n",
       "       [ 0, 38,  0,  0,  0],\n",
       "       [ 0,  0,  3,  0,  0],\n",
       "       [ 0,  0,  1,  0,  0],\n",
       "       [ 0,  0,  0,  0,  4]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat_proba_LR = LR.predict_proba(X)\n",
    "yhat_LR=LR.predict(X)\n",
    "LR_jacc_test=metrics.jaccard_score(y, yhat_LR, average ='macro')\n",
    "LR_f1_test=metrics.f1_score(y, yhat_LR, average ='macro')\n",
    "LR_cnf_matrix = confusion_matrix(y, yhat_LR)\n",
    "LR_log_loss=metrics.log_loss(y, yhat_proba_LR)\n",
    "print(\"Accuracy is \", LR_jacc_test, \" F1 is\" , LR_f1_test, \" log_loss is\", LR_log_loss)\n",
    "print(classification_report(y, yhat_LR))\n",
    "LR_cnf_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_report.loc['LogisticRegression','Jaccard'] = LR_jacc_test\n",
    "df_report.loc['LogisticRegression','F1-score'] = LR_f1_test\n",
    "df_report.loc['LogisticRegression','LogLoss'] = LR_log_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Report</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>Jaccard</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>LogLoss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.771429</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.771429</td>\n",
       "      <td>0.235121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Jaccard  F1-score   LogLoss\n",
       "KNN                 0.777778  0.875000        NA\n",
       "Decision Tree       1.000000  1.000000        NA\n",
       "SVM                 0.750000  0.771429        NA\n",
       "LogisticRegression  0.750000  0.771429  0.235121"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Author</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Michael Kumakech is a consultant in the fields of AI, Data Science, computer vision, IoT and a Lecturer at Makerere University, College of Computing and Information Sciences Uganda. You can reach him at mkumakech@mak.ac.ug, +256706290029."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7",
   "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}