Covid-19.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "import plotly.offline as pyo\n",
    "from plotly.offline import init_notebook_mode,plot,iplot\n",
    "import cufflinks as cf #to bind plotly and pandas together\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Binding Plotly and Pandas bcoz Plotly has its own dashboard online & we need it offline.\n",
    "pyo.init_notebook_mode(connected=True)\n",
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting scientific (e+8 or smething) notation to float \n",
    "pd.options.display.float_format = '{:.2f}'.format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Dataset\n",
    "### Link:  https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset?select=covid_19_data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>ObservationDate</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Last Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Anhui</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Chongqing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Fujian</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Gansu</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SNo ObservationDate Province/State  Country/Region      Last Update  \\\n",
       "0    1      01/22/2020          Anhui  Mainland China  1/22/2020 17:00   \n",
       "1    2      01/22/2020        Beijing  Mainland China  1/22/2020 17:00   \n",
       "2    3      01/22/2020      Chongqing  Mainland China  1/22/2020 17:00   \n",
       "3    4      01/22/2020         Fujian  Mainland China  1/22/2020 17:00   \n",
       "4    5      01/22/2020          Gansu  Mainland China  1/22/2020 17:00   \n",
       "\n",
       "   Confirmed  Deaths  Recovered  \n",
       "0       1.00    0.00       0.00  \n",
       "1      14.00    0.00       0.00  \n",
       "2       6.00    0.00       0.00  \n",
       "3       1.00    0.00       0.00  \n",
       "4       0.00    0.00       0.00  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"covid_19_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(172480, 8)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>Date</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Last Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Anhui</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Chongqing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Fujian</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Gansu</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SNo        Date      State         Country      Last Update  Confirmed  \\\n",
       "0    1  01/22/2020      Anhui  Mainland China  1/22/2020 17:00       1.00   \n",
       "1    2  01/22/2020    Beijing  Mainland China  1/22/2020 17:00      14.00   \n",
       "2    3  01/22/2020  Chongqing  Mainland China  1/22/2020 17:00       6.00   \n",
       "3    4  01/22/2020     Fujian  Mainland China  1/22/2020 17:00       1.00   \n",
       "4    5  01/22/2020      Gansu  Mainland China  1/22/2020 17:00       0.00   \n",
       "\n",
       "   Deaths  Recovered  \n",
       "0    0.00       0.00  \n",
       "1    0.00       0.00  \n",
       "2    0.00       0.00  \n",
       "3    0.00       0.00  \n",
       "4    0.00       0.00  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rename columns\n",
    "df.rename(columns={'Country/Region' : 'Country'}, inplace=True)\n",
    "df.rename(columns={'ObservationDate' : 'Date'}, inplace=True)\n",
    "df.rename(columns={'Province/State' : 'State'}, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>Date</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Last Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>172475</th>\n",
       "      <td>172476</td>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>Zaporizhia Oblast</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>2020-12-07 05:26:14</td>\n",
       "      <td>36539.00</td>\n",
       "      <td>337.00</td>\n",
       "      <td>6556.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172476</th>\n",
       "      <td>172477</td>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>Zeeland</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2020-12-07 05:26:14</td>\n",
       "      <td>6710.00</td>\n",
       "      <td>104.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172477</th>\n",
       "      <td>172478</td>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>2020-12-07 05:26:14</td>\n",
       "      <td>1295.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1288.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172478</th>\n",
       "      <td>172479</td>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>Zhytomyr Oblast</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>2020-12-07 05:26:14</td>\n",
       "      <td>31967.00</td>\n",
       "      <td>531.00</td>\n",
       "      <td>22263.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172479</th>\n",
       "      <td>172480</td>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>Zuid-Holland</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2020-12-07 05:26:14</td>\n",
       "      <td>154813.00</td>\n",
       "      <td>2414.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           SNo        Date              State         Country  \\\n",
       "172475  172476  12/06/2020  Zaporizhia Oblast         Ukraine   \n",
       "172476  172477  12/06/2020            Zeeland     Netherlands   \n",
       "172477  172478  12/06/2020           Zhejiang  Mainland China   \n",
       "172478  172479  12/06/2020    Zhytomyr Oblast         Ukraine   \n",
       "172479  172480  12/06/2020       Zuid-Holland     Netherlands   \n",
       "\n",
       "                Last Update  Confirmed  Deaths  Recovered  \n",
       "172475  2020-12-07 05:26:14   36539.00  337.00    6556.00  \n",
       "172476  2020-12-07 05:26:14    6710.00  104.00       0.00  \n",
       "172477  2020-12-07 05:26:14    1295.00    1.00    1288.00  \n",
       "172478  2020-12-07 05:26:14   31967.00  531.00   22263.00  \n",
       "172479  2020-12-07 05:26:14  154813.00 2414.00       0.00  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 172480 entries, 0 to 172479\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   SNo          172480 non-null  int64  \n",
      " 1   Date         172480 non-null  object \n",
      " 2   State        124597 non-null  object \n",
      " 3   Country      172480 non-null  object \n",
      " 4   Last Update  172480 non-null  object \n",
      " 5   Confirmed    172480 non-null  float64\n",
      " 6   Deaths       172480 non-null  float64\n",
      " 7   Recovered    172480 non-null  float64\n",
      "dtypes: float64(3), int64(1), object(4)\n",
      "memory usage: 10.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SNo                0\n",
       "Date               0\n",
       "State          47883\n",
       "Country            0\n",
       "Last Update        0\n",
       "Confirmed          0\n",
       "Deaths             0\n",
       "Recovered          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>172480.00</td>\n",
       "      <td>172480.00</td>\n",
       "      <td>172480.00</td>\n",
       "      <td>172480.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>86240.50</td>\n",
       "      <td>33231.56</td>\n",
       "      <td>1050.03</td>\n",
       "      <td>21028.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>49790.83</td>\n",
       "      <td>103134.89</td>\n",
       "      <td>3499.44</td>\n",
       "      <td>115659.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00</td>\n",
       "      <td>-302844.00</td>\n",
       "      <td>-178.00</td>\n",
       "      <td>-854405.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>43120.75</td>\n",
       "      <td>457.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>86240.50</td>\n",
       "      <td>4016.50</td>\n",
       "      <td>76.00</td>\n",
       "      <td>858.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>129360.25</td>\n",
       "      <td>18843.00</td>\n",
       "      <td>554.00</td>\n",
       "      <td>7229.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172480.00</td>\n",
       "      <td>2290891.00</td>\n",
       "      <td>54804.00</td>\n",
       "      <td>5624444.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            SNo  Confirmed    Deaths  Recovered\n",
       "count 172480.00  172480.00 172480.00  172480.00\n",
       "mean   86240.50   33231.56   1050.03   21028.25\n",
       "std    49790.83  103134.89   3499.44  115659.79\n",
       "min        1.00 -302844.00   -178.00 -854405.00\n",
       "25%    43120.75     457.00      6.00      10.00\n",
       "50%    86240.50    4016.50     76.00     858.00\n",
       "75%   129360.25   18843.00    554.00    7229.00\n",
       "max   172480.00 2290891.00  54804.00 5624444.00"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>555.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/23/2020</td>\n",
       "      <td>653.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/24/2020</td>\n",
       "      <td>941.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/25/2020</td>\n",
       "      <td>1438.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/26/2020</td>\n",
       "      <td>2118.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>12/02/2020</td>\n",
       "      <td>64530517.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>12/03/2020</td>\n",
       "      <td>65221040.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>12/04/2020</td>\n",
       "      <td>65899441.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>12/05/2020</td>\n",
       "      <td>66540034.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>67073728.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>320 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date   Confirmed\n",
       "0    01/22/2020      555.00\n",
       "1    01/23/2020      653.00\n",
       "2    01/24/2020      941.00\n",
       "3    01/25/2020     1438.00\n",
       "4    01/26/2020     2118.00\n",
       "..          ...         ...\n",
       "315  12/02/2020 64530517.00\n",
       "316  12/03/2020 65221040.00\n",
       "317  12/04/2020 65899441.00\n",
       "318  12/05/2020 66540034.00\n",
       "319  12/06/2020 67073728.00\n",
       "\n",
       "[320 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get confirmed cases on each date\n",
    "confirmed = df.groupby('Date').sum()['Confirmed'].reset_index()\n",
    "confirmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/23/2020</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/24/2020</td>\n",
       "      <td>26.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/25/2020</td>\n",
       "      <td>42.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/26/2020</td>\n",
       "      <td>56.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>12/02/2020</td>\n",
       "      <td>1493742.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>12/03/2020</td>\n",
       "      <td>1506260.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>12/04/2020</td>\n",
       "      <td>1518670.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>12/05/2020</td>\n",
       "      <td>1528868.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>1536056.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>320 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date     Deaths\n",
       "0    01/22/2020      17.00\n",
       "1    01/23/2020      18.00\n",
       "2    01/24/2020      26.00\n",
       "3    01/25/2020      42.00\n",
       "4    01/26/2020      56.00\n",
       "..          ...        ...\n",
       "315  12/02/2020 1493742.00\n",
       "316  12/03/2020 1506260.00\n",
       "317  12/04/2020 1518670.00\n",
       "318  12/05/2020 1528868.00\n",
       "319  12/06/2020 1536056.00\n",
       "\n",
       "[320 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get Deaths cases on each date\n",
    "deaths = df.groupby('Date').sum()['Deaths'].reset_index()\n",
    "deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>28.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/23/2020</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/24/2020</td>\n",
       "      <td>36.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/25/2020</td>\n",
       "      <td>39.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/26/2020</td>\n",
       "      <td>52.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>12/02/2020</td>\n",
       "      <td>41496318.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>12/03/2020</td>\n",
       "      <td>41932091.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>12/04/2020</td>\n",
       "      <td>42352021.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>12/05/2020</td>\n",
       "      <td>42789879.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>12/06/2020</td>\n",
       "      <td>43103827.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>320 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date   Recovered\n",
       "0    01/22/2020       28.00\n",
       "1    01/23/2020       30.00\n",
       "2    01/24/2020       36.00\n",
       "3    01/25/2020       39.00\n",
       "4    01/26/2020       52.00\n",
       "..          ...         ...\n",
       "315  12/02/2020 41496318.00\n",
       "316  12/03/2020 41932091.00\n",
       "317  12/04/2020 42352021.00\n",
       "318  12/05/2020 42789879.00\n",
       "319  12/06/2020 43103827.00\n",
       "\n",
       "[320 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get Recovered cases on each date\n",
    "recovered = df.groupby('Date').sum()['Recovered'].reset_index()\n",
    "recovered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 19,
     "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": [
    "# Data visualization using Pandas\n",
    "confirmed.plot(kind='bar',x='Date', y='Confirmed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date', ylabel='Confirmed'>"
      ]
     },
     "execution_count": 20,
     "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": [
    "# Data visualization using Seaborn\n",
    "sns.barplot(x=confirmed['Date'], y=confirmed['Confirmed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 320 artists>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data visualization using Matplotlib\n",
    "\n",
    "plt.bar(x=confirmed['Date'], height=confirmed['Confirmed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "marker": {
          "color": "rgba(255, 153, 51, 0.6)",
          "line": {
           "color": "rgba(255, 153, 51, 1.0)",
           "width": 1
          }
         },
         "name": "Confirmed",
         "orientation": "v",
         "text": "",
         "type": "bar",
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       "            Plotly.purge(gd);\n",
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data visualization using Plotly\n",
    "\n",
    "confirmed.iplot(kind='bar', x='Date', y='Confirmed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
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       "                            \n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "recovered.iplot(kind='bar', x='Date', y='Recovered')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using FBProphet\n",
    "\n",
    "from fbprophet import Prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>555.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>653.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>941.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>1438.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2118.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>2020-12-02</td>\n",
       "      <td>64530517.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>2020-12-03</td>\n",
       "      <td>65221040.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>65899441.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>2020-12-05</td>\n",
       "      <td>66540034.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>2020-12-06</td>\n",
       "      <td>67073728.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>320 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds           y\n",
       "0   2020-01-22      555.00\n",
       "1   2020-01-23      653.00\n",
       "2   2020-01-24      941.00\n",
       "3   2020-01-25     1438.00\n",
       "4   2020-01-26     2118.00\n",
       "..         ...         ...\n",
       "315 2020-12-02 64530517.00\n",
       "316 2020-12-03 65221040.00\n",
       "317 2020-12-04 65899441.00\n",
       "318 2020-12-05 66540034.00\n",
       "319 2020-12-06 67073728.00\n",
       "\n",
       "[320 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Predicting Confirmed cases against Date using fbprophet\n",
    "# https://facebook.github.io/prophet/docs/quick_start.html#python-api\n",
    "\n",
    "#Renaming columns to ds and y (as required by fbprophet)\n",
    "\n",
    "confirmed.columns=['ds','y']\n",
    "\n",
    "\n",
    "#Changing datestamp format to YYYY-MM-DD for a date or YYYY-MM-DD HH:MM:SS\n",
    "confirmed['ds']=pd.to_datetime(confirmed['ds'])\n",
    "confirmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:numexpr.utils:NumExpr defaulting to 4 threads.\n",
      "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x7ff82f2d7bd0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# instantiating a new Prophet object\n",
    "# and training the model\n",
    "\n",
    "model = Prophet(interval_width=0.95)\n",
    "model.fit(confirmed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>2021-03-02</td>\n",
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       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>2021-03-03</td>\n",
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       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>2021-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>2021-03-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>2021-03-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>2021-03-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>2021-03-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>2021-03-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>2021-03-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>2021-03-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>2021-03-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>2021-03-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>2021-03-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>2021-03-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>2021-03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>2021-03-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>2021-03-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>2021-03-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>2021-03-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>2021-03-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>2021-03-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>2021-03-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>2021-03-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>2021-03-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>2021-03-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>2021-03-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>2021-03-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>2021-03-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>2021-03-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>2021-03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>2021-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>2021-04-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>2021-04-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>2021-04-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>2021-04-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds\n",
       "405 2021-03-02\n",
       "406 2021-03-03\n",
       "407 2021-03-04\n",
       "408 2021-03-05\n",
       "409 2021-03-06\n",
       "410 2021-03-07\n",
       "411 2021-03-08\n",
       "412 2021-03-09\n",
       "413 2021-03-10\n",
       "414 2021-03-11\n",
       "415 2021-03-12\n",
       "416 2021-03-13\n",
       "417 2021-03-14\n",
       "418 2021-03-15\n",
       "419 2021-03-16\n",
       "420 2021-03-17\n",
       "421 2021-03-18\n",
       "422 2021-03-19\n",
       "423 2021-03-20\n",
       "424 2021-03-21\n",
       "425 2021-03-22\n",
       "426 2021-03-23\n",
       "427 2021-03-24\n",
       "428 2021-03-25\n",
       "429 2021-03-26\n",
       "430 2021-03-27\n",
       "431 2021-03-28\n",
       "432 2021-03-29\n",
       "433 2021-03-30\n",
       "434 2021-03-31\n",
       "435 2021-04-01\n",
       "436 2021-04-02\n",
       "437 2021-04-03\n",
       "438 2021-04-04\n",
       "439 2021-04-05"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating future dataframe periods=30 means next 30 days\n",
    "# change period for more future predictions\n",
    "future=model.make_future_dataframe(periods=120)\n",
    "future.tail(35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "forcast_confirmed=model.predict(future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "      <th>additive_terms_lower</th>\n",
       "      <th>additive_terms_upper</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>-46471.05</td>\n",
       "      <td>-968216.21</td>\n",
       "      <td>834881.15</td>\n",
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       "      <td>-18306.81</td>\n",
       "      <td>-18306.81</td>\n",
       "      <td>-18306.81</td>\n",
       "      <td>-18306.81</td>\n",
       "      <td>-18306.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-64777.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>-42557.91</td>\n",
       "      <td>-967520.78</td>\n",
       "      <td>901218.51</td>\n",
       "      <td>-42557.91</td>\n",
       "      <td>-42557.91</td>\n",
       "      <td>5076.57</td>\n",
       "      <td>5076.57</td>\n",
       "      <td>5076.57</td>\n",
       "      <td>5076.57</td>\n",
       "      <td>5076.57</td>\n",
       "      <td>5076.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-37481.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>-38644.76</td>\n",
       "      <td>-908438.85</td>\n",
       "      <td>933455.89</td>\n",
       "      <td>-38644.76</td>\n",
       "      <td>-38644.76</td>\n",
       "      <td>36906.10</td>\n",
       "      <td>36906.10</td>\n",
       "      <td>36906.10</td>\n",
       "      <td>36906.10</td>\n",
       "      <td>36906.10</td>\n",
       "      <td>36906.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1738.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>-34731.62</td>\n",
       "      <td>-929786.55</td>\n",
       "      <td>1023540.40</td>\n",
       "      <td>-34731.62</td>\n",
       "      <td>-34731.62</td>\n",
       "      <td>41658.99</td>\n",
       "      <td>41658.99</td>\n",
       "      <td>41658.99</td>\n",
       "      <td>41658.99</td>\n",
       "      <td>41658.99</td>\n",
       "      <td>41658.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6927.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>-30818.47</td>\n",
       "      <td>-995187.11</td>\n",
       "      <td>873790.90</td>\n",
       "      <td>-30818.47</td>\n",
       "      <td>-30818.47</td>\n",
       "      <td>15787.28</td>\n",
       "      <td>15787.28</td>\n",
       "      <td>15787.28</td>\n",
       "      <td>15787.28</td>\n",
       "      <td>15787.28</td>\n",
       "      <td>15787.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-15031.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>123208202.55</td>\n",
       "      <td>109401228.80</td>\n",
       "      <td>135802460.23</td>\n",
       "      <td>109633608.65</td>\n",
       "      <td>136382937.77</td>\n",
       "      <td>5076.57</td>\n",
       "      <td>5076.57</td>\n",
       "      <td>5076.57</td>\n",
       "      <td>5076.57</td>\n",
       "      <td>5076.57</td>\n",
       "      <td>5076.57</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>123213279.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>2021-04-02</td>\n",
       "      <td>123706983.44</td>\n",
       "      <td>110012199.14</td>\n",
       "      <td>136569544.87</td>\n",
       "      <td>109942580.84</td>\n",
       "      <td>136956955.26</td>\n",
       "      <td>36906.10</td>\n",
       "      <td>36906.10</td>\n",
       "      <td>36906.10</td>\n",
       "      <td>36906.10</td>\n",
       "      <td>36906.10</td>\n",
       "      <td>36906.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>123743889.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>2021-04-03</td>\n",
       "      <td>124205764.34</td>\n",
       "      <td>109779740.58</td>\n",
       "      <td>137640120.72</td>\n",
       "      <td>110251553.03</td>\n",
       "      <td>137530972.76</td>\n",
       "      <td>41658.99</td>\n",
       "      <td>41658.99</td>\n",
       "      <td>41658.99</td>\n",
       "      <td>41658.99</td>\n",
       "      <td>41658.99</td>\n",
       "      <td>41658.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>124247423.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>2021-04-04</td>\n",
       "      <td>124704545.24</td>\n",
       "      <td>110332695.12</td>\n",
       "      <td>138116183.41</td>\n",
       "      <td>110578535.76</td>\n",
       "      <td>138104990.25</td>\n",
       "      <td>15787.28</td>\n",
       "      <td>15787.28</td>\n",
       "      <td>15787.28</td>\n",
       "      <td>15787.28</td>\n",
       "      <td>15787.28</td>\n",
       "      <td>15787.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>124720332.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>2021-04-05</td>\n",
       "      <td>125203326.14</td>\n",
       "      <td>110767218.78</td>\n",
       "      <td>138669964.99</td>\n",
       "      <td>110905932.69</td>\n",
       "      <td>138679007.74</td>\n",
       "      <td>-40092.26</td>\n",
       "      <td>-40092.26</td>\n",
       "      <td>-40092.26</td>\n",
       "      <td>-40092.26</td>\n",
       "      <td>-40092.26</td>\n",
       "      <td>-40092.26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>125163233.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds        trend   yhat_lower   yhat_upper  trend_lower  \\\n",
       "0   2020-01-22    -46471.05   -968216.21    834881.15    -46471.05   \n",
       "1   2020-01-23    -42557.91   -967520.78    901218.51    -42557.91   \n",
       "2   2020-01-24    -38644.76   -908438.85    933455.89    -38644.76   \n",
       "3   2020-01-25    -34731.62   -929786.55   1023540.40    -34731.62   \n",
       "4   2020-01-26    -30818.47   -995187.11    873790.90    -30818.47   \n",
       "..         ...          ...          ...          ...          ...   \n",
       "435 2021-04-01 123208202.55 109401228.80 135802460.23 109633608.65   \n",
       "436 2021-04-02 123706983.44 110012199.14 136569544.87 109942580.84   \n",
       "437 2021-04-03 124205764.34 109779740.58 137640120.72 110251553.03   \n",
       "438 2021-04-04 124704545.24 110332695.12 138116183.41 110578535.76   \n",
       "439 2021-04-05 125203326.14 110767218.78 138669964.99 110905932.69   \n",
       "\n",
       "     trend_upper  additive_terms  additive_terms_lower  additive_terms_upper  \\\n",
       "0      -46471.05       -18306.81             -18306.81             -18306.81   \n",
       "1      -42557.91         5076.57               5076.57               5076.57   \n",
       "2      -38644.76        36906.10              36906.10              36906.10   \n",
       "3      -34731.62        41658.99              41658.99              41658.99   \n",
       "4      -30818.47        15787.28              15787.28              15787.28   \n",
       "..           ...             ...                   ...                   ...   \n",
       "435 136382937.77         5076.57               5076.57               5076.57   \n",
       "436 136956955.26        36906.10              36906.10              36906.10   \n",
       "437 137530972.76        41658.99              41658.99              41658.99   \n",
       "438 138104990.25        15787.28              15787.28              15787.28   \n",
       "439 138679007.74       -40092.26             -40092.26             -40092.26   \n",
       "\n",
       "       weekly  weekly_lower  weekly_upper  multiplicative_terms  \\\n",
       "0   -18306.81     -18306.81     -18306.81                  0.00   \n",
       "1     5076.57       5076.57       5076.57                  0.00   \n",
       "2    36906.10      36906.10      36906.10                  0.00   \n",
       "3    41658.99      41658.99      41658.99                  0.00   \n",
       "4    15787.28      15787.28      15787.28                  0.00   \n",
       "..        ...           ...           ...                   ...   \n",
       "435   5076.57       5076.57       5076.57                  0.00   \n",
       "436  36906.10      36906.10      36906.10                  0.00   \n",
       "437  41658.99      41658.99      41658.99                  0.00   \n",
       "438  15787.28      15787.28      15787.28                  0.00   \n",
       "439 -40092.26     -40092.26     -40092.26                  0.00   \n",
       "\n",
       "     multiplicative_terms_lower  multiplicative_terms_upper         yhat  \n",
       "0                          0.00                        0.00    -64777.86  \n",
       "1                          0.00                        0.00    -37481.33  \n",
       "2                          0.00                        0.00     -1738.66  \n",
       "3                          0.00                        0.00      6927.37  \n",
       "4                          0.00                        0.00    -15031.20  \n",
       "..                          ...                         ...          ...  \n",
       "435                        0.00                        0.00 123213279.12  \n",
       "436                        0.00                        0.00 123743889.54  \n",
       "437                        0.00                        0.00 124247423.33  \n",
       "438                        0.00                        0.00 124720332.51  \n",
       "439                        0.00                        0.00 125163233.87  \n",
       "\n",
       "[440 rows x 16 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forcast_confirmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>2021-03-07</td>\n",
       "      <td>110754467.38</td>\n",
       "      <td>100769364.97</td>\n",
       "      <td>120034807.17</td>\n",
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       "      <th>411</th>\n",
       "      <td>2021-03-08</td>\n",
       "      <td>111197368.74</td>\n",
       "      <td>101176959.53</td>\n",
       "      <td>120681105.80</td>\n",
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       "      <th>412</th>\n",
       "      <td>2021-03-09</td>\n",
       "      <td>111695212.03</td>\n",
       "      <td>101821906.37</td>\n",
       "      <td>121138445.72</td>\n",
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       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>2021-03-10</td>\n",
       "      <td>112216715.99</td>\n",
       "      <td>101595613.03</td>\n",
       "      <td>121562320.34</td>\n",
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       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>2021-03-11</td>\n",
       "      <td>112738880.27</td>\n",
       "      <td>102370839.90</td>\n",
       "      <td>122272443.79</td>\n",
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       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>2021-03-12</td>\n",
       "      <td>113269490.69</td>\n",
       "      <td>102691377.05</td>\n",
       "      <td>123196174.30</td>\n",
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       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>2021-03-13</td>\n",
       "      <td>113773024.48</td>\n",
       "      <td>102983414.64</td>\n",
       "      <td>123635350.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>2021-03-14</td>\n",
       "      <td>114245933.67</td>\n",
       "      <td>103323389.97</td>\n",
       "      <td>124268339.44</td>\n",
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       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>2021-03-15</td>\n",
       "      <td>114688835.02</td>\n",
       "      <td>103813920.38</td>\n",
       "      <td>125155205.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>2021-03-16</td>\n",
       "      <td>115186678.32</td>\n",
       "      <td>103817410.60</td>\n",
       "      <td>125810285.82</td>\n",
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       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>2021-03-17</td>\n",
       "      <td>115708182.27</td>\n",
       "      <td>104376974.82</td>\n",
       "      <td>126446413.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>2021-03-18</td>\n",
       "      <td>116230346.55</td>\n",
       "      <td>104672950.55</td>\n",
       "      <td>127138393.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>2021-03-19</td>\n",
       "      <td>116760956.98</td>\n",
       "      <td>104866170.43</td>\n",
       "      <td>127987965.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>2021-03-20</td>\n",
       "      <td>117264490.77</td>\n",
       "      <td>105249107.16</td>\n",
       "      <td>128473639.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>2021-03-21</td>\n",
       "      <td>117737399.95</td>\n",
       "      <td>106010146.79</td>\n",
       "      <td>129390961.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>2021-03-22</td>\n",
       "      <td>118180301.31</td>\n",
       "      <td>106077483.91</td>\n",
       "      <td>129402137.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>2021-03-23</td>\n",
       "      <td>118678144.60</td>\n",
       "      <td>105965677.51</td>\n",
       "      <td>130067531.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>2021-03-24</td>\n",
       "      <td>119199648.56</td>\n",
       "      <td>107018660.81</td>\n",
       "      <td>131088857.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>2021-03-25</td>\n",
       "      <td>119721812.84</td>\n",
       "      <td>106757975.57</td>\n",
       "      <td>131384880.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>2021-03-26</td>\n",
       "      <td>120252423.26</td>\n",
       "      <td>107576772.82</td>\n",
       "      <td>132443743.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>2021-03-27</td>\n",
       "      <td>120755957.05</td>\n",
       "      <td>107879727.79</td>\n",
       "      <td>133083868.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>2021-03-28</td>\n",
       "      <td>121228866.23</td>\n",
       "      <td>108251632.19</td>\n",
       "      <td>133637335.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>2021-03-29</td>\n",
       "      <td>121671767.59</td>\n",
       "      <td>108776832.22</td>\n",
       "      <td>134215938.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>2021-03-30</td>\n",
       "      <td>122169610.88</td>\n",
       "      <td>108701767.50</td>\n",
       "      <td>134648038.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>2021-03-31</td>\n",
       "      <td>122691114.84</td>\n",
       "      <td>109038912.51</td>\n",
       "      <td>135274384.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>123213279.12</td>\n",
       "      <td>109401228.80</td>\n",
       "      <td>135802460.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>2021-04-02</td>\n",
       "      <td>123743889.54</td>\n",
       "      <td>110012199.14</td>\n",
       "      <td>136569544.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>2021-04-03</td>\n",
       "      <td>124247423.33</td>\n",
       "      <td>109779740.58</td>\n",
       "      <td>137640120.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>2021-04-04</td>\n",
       "      <td>124720332.51</td>\n",
       "      <td>110332695.12</td>\n",
       "      <td>138116183.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>2021-04-05</td>\n",
       "      <td>125163233.87</td>\n",
       "      <td>110767218.78</td>\n",
       "      <td>138669964.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds         yhat   yhat_lower   yhat_upper\n",
       "410 2021-03-07 110754467.38 100769364.97 120034807.17\n",
       "411 2021-03-08 111197368.74 101176959.53 120681105.80\n",
       "412 2021-03-09 111695212.03 101821906.37 121138445.72\n",
       "413 2021-03-10 112216715.99 101595613.03 121562320.34\n",
       "414 2021-03-11 112738880.27 102370839.90 122272443.79\n",
       "415 2021-03-12 113269490.69 102691377.05 123196174.30\n",
       "416 2021-03-13 113773024.48 102983414.64 123635350.55\n",
       "417 2021-03-14 114245933.67 103323389.97 124268339.44\n",
       "418 2021-03-15 114688835.02 103813920.38 125155205.47\n",
       "419 2021-03-16 115186678.32 103817410.60 125810285.82\n",
       "420 2021-03-17 115708182.27 104376974.82 126446413.58\n",
       "421 2021-03-18 116230346.55 104672950.55 127138393.19\n",
       "422 2021-03-19 116760956.98 104866170.43 127987965.26\n",
       "423 2021-03-20 117264490.77 105249107.16 128473639.96\n",
       "424 2021-03-21 117737399.95 106010146.79 129390961.19\n",
       "425 2021-03-22 118180301.31 106077483.91 129402137.63\n",
       "426 2021-03-23 118678144.60 105965677.51 130067531.00\n",
       "427 2021-03-24 119199648.56 107018660.81 131088857.75\n",
       "428 2021-03-25 119721812.84 106757975.57 131384880.74\n",
       "429 2021-03-26 120252423.26 107576772.82 132443743.08\n",
       "430 2021-03-27 120755957.05 107879727.79 133083868.91\n",
       "431 2021-03-28 121228866.23 108251632.19 133637335.83\n",
       "432 2021-03-29 121671767.59 108776832.22 134215938.61\n",
       "433 2021-03-30 122169610.88 108701767.50 134648038.18\n",
       "434 2021-03-31 122691114.84 109038912.51 135274384.14\n",
       "435 2021-04-01 123213279.12 109401228.80 135802460.23\n",
       "436 2021-04-02 123743889.54 110012199.14 136569544.87\n",
       "437 2021-04-03 124247423.33 109779740.58 137640120.72\n",
       "438 2021-04-04 124720332.51 110332695.12 138116183.41\n",
       "439 2021-04-05 125163233.87 110767218.78 138669964.99"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we need only 4 columns ds, yhat(prediction), yhat_lower(lower range), yhat_upper(upper_range)\n",
    "\n",
    "forcast_confirmed[['ds','yhat','yhat_lower','yhat_upper']].tail(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confirmed_forcast_plot = model.plot(forcast_confirmed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confirmed_forcast_plot_components=model.plot_components(forcast_confirmed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deaths Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>26.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>42.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>56.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>2020-12-02</td>\n",
       "      <td>1493742.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>2020-12-03</td>\n",
       "      <td>1506260.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>1518670.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>2020-12-05</td>\n",
       "      <td>1528868.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>2020-12-06</td>\n",
       "      <td>1536056.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>320 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds          y\n",
       "0   2020-01-22      17.00\n",
       "1   2020-01-23      18.00\n",
       "2   2020-01-24      26.00\n",
       "3   2020-01-25      42.00\n",
       "4   2020-01-26      56.00\n",
       "..         ...        ...\n",
       "315 2020-12-02 1493742.00\n",
       "316 2020-12-03 1506260.00\n",
       "317 2020-12-04 1518670.00\n",
       "318 2020-12-05 1528868.00\n",
       "319 2020-12-06 1536056.00\n",
       "\n",
       "[320 rows x 2 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Deaths Prediction\n",
    "deaths.columns=['ds','y']\n",
    "\n",
    "#Changing datestamp format to YYYY-MM-DD for a date or YYYY-MM-DD HH:MM:SS\n",
    "deaths['ds']=pd.to_datetime(deaths['ds'])\n",
    "deaths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x7ff82ad59250>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "death_model = Prophet(interval_width=0.95)\n",
    "death_model.fit(deaths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>-1328.72</td>\n",
       "      <td>-20605.72</td>\n",
       "      <td>16813.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>-366.01</td>\n",
       "      <td>-18712.79</td>\n",
       "      <td>16116.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>469.81</td>\n",
       "      <td>-18616.49</td>\n",
       "      <td>19350.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>449.15</td>\n",
       "      <td>-18218.53</td>\n",
       "      <td>19288.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>-730.13</td>\n",
       "      <td>-18944.61</td>\n",
       "      <td>17725.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>2374055.60</td>\n",
       "      <td>1996693.34</td>\n",
       "      <td>2691955.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>2021-04-02</td>\n",
       "      <td>2382358.76</td>\n",
       "      <td>2001181.03</td>\n",
       "      <td>2708310.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>2021-04-03</td>\n",
       "      <td>2389805.45</td>\n",
       "      <td>2011751.99</td>\n",
       "      <td>2714827.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>2021-04-04</td>\n",
       "      <td>2396093.52</td>\n",
       "      <td>2009650.70</td>\n",
       "      <td>2731521.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>2021-04-05</td>\n",
       "      <td>2401899.55</td>\n",
       "      <td>2007895.54</td>\n",
       "      <td>2741615.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds       yhat  yhat_lower  yhat_upper\n",
       "0   2020-01-22   -1328.72   -20605.72    16813.84\n",
       "1   2020-01-23    -366.01   -18712.79    16116.04\n",
       "2   2020-01-24     469.81   -18616.49    19350.17\n",
       "3   2020-01-25     449.15   -18218.53    19288.95\n",
       "4   2020-01-26    -730.13   -18944.61    17725.29\n",
       "..         ...        ...         ...         ...\n",
       "435 2021-04-01 2374055.60  1996693.34  2691955.07\n",
       "436 2021-04-02 2382358.76  2001181.03  2708310.69\n",
       "437 2021-04-03 2389805.45  2011751.99  2714827.67\n",
       "438 2021-04-04 2396093.52  2009650.70  2731521.91\n",
       "439 2021-04-05 2401899.55  2007895.54  2741615.46\n",
       "\n",
       "[440 rows x 4 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "death_forcast_confirmed=death_model.predict(future)\n",
    "#death_forcast_confirmed\n",
    "death_forcast_confirmed[['ds','yhat','yhat_lower','yhat_upper']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6Nu2p4oKTWvP7M7VDxbFQQBYREREJkSqPn/UFFSTFOEMSjo0xzNm8l/sW7MTpcHD/Od0Y0UU7VBwrBWQRERGRENhT4ebL/HJinHYSooIfub4qKOeJZbmsK6jgpJax3HfuSbROign6+zRHCsgiIiIiQeb1W2woqiQlJopoZ3APASmt8fLPhdv4bNs+0uKiuGN4Z4a1tpGmcBw0CsgiIiIiQWRZhm0l1QQsE/RwvHJXKXd+spVyt48bB7bnir5tiY1yUFayJ6jv09wpIIuIiIgESa0vwIbCCoqrvbSMjw7a6/oDFk9n7+S/q3fTPjWWx8/vwQktE4L2+nIoBWQRERGRICiu8vBVfjl2m41WCcE7rW53eS1/nrOFjXsqOf+7HSpitUNFSCkgi4iIiByjnftr2FhUSUpsFK4gLquYu3kv9y7chg24b+yJjDyhZdBeW45MAVlERETkFzLGkFNSTU5xNS3jo3EE6RCQ3eW1zFiWy/ycEnq1SeIfY7rSRjfhNRgFZBEREZFfoNrjZ0NRBftrfGQkRAflOOeyWh8zv9jJO+uLcNhtTB5wHNedelxIj6aWwykgi4iIiPxMbl+AlbvKAENGkNYbr8or4865W9hf4+W8k1pzw2nH0TKIa5nl6Ckgi4iIiPwMXr/FuoJyLAwpMVHH/Hr+gMUzX+xi1qo8jkuN5ZHzTubEDO1QEU4KyCIiIiJHqcbrZ3VeOR5/gBZxx76NW365mz9/vJkNRZWc16MVfxh6vHaoiAAKyCIiIiJHocbrZ8XOMmwQlHB8YIcKgH+OOZGzumqHikihgCwiIiLyEyrcPlbnlWPDkHiMyyoq3X7+teRbPty0h15tEvn72SeSmawdKiKJArKIiIjIEQQsw879NWwpriIh2klc9C+PTm5/gDe+KmTWqjwqPX4mndqOGwa01w4VESi4B4QfJC8vj2HDhtGtWzd69OjBY489dtgYYww333wznTt3plevXnz55ZehKkdERETkZzHG8HVBOVtKqmgRF01c9C9fG7xmdxkX/XcNjy/bwUmtE3np8pO56fQOCscRKmQzyE6nk4cffpi+fftSWVlJv379GDVqFN27d68f8/HHH5OTk0NOTg4rVqzgpptuYsWKFaEqSUREROSo5e6vpbDCQ6vEX77Vmt8y/GfFTp5bmUdWcixPj+9J/3YpwSuykbKMocYbIMoRsrnaYxKygNymTRvatGkDQGJiIt26dSM/P/+QgPz+++8zceJEbDYbAwYMoKysjMLCwvrvExEREWloxhh27Kvhm72VtIz/5eG4oNzN/83dwteFFZzbvRW3DT3+mGahm4qAZSiu9tI60YXbH8AYgnLISjA1yBrk3Nxc1q5dy2mnnXbI4/n5+bRr167+66ysLPLz8w8LyDNnzmTmzJkAFBUVUVBQQHFxcegLbwbUx+BRL4NL/Qw+9TR41MvgirR+7q30sLWkmtSYKCo9v+w1Fu2s5JEv9mCAP53RmuEdk/BWlOANaqX/U1W2P0SvfOwCVt1fOgLGUOsLANChRRxtHAFwgIk2UL2fgprICckhD8hVVVWMHz+eRx99lKSkpEOeM8YcNt72A3+DmDx5MpMnTwagf//+ZGZmAtT/KsdGfQwe9TK41M/gU0+DR70MrkjpZ4Xbx9fl+2nfNhnnL/jn/1pfgIcWbef9jXvo2TqRv485kbYNtENFSnqrBnmfn2IZQ6XHjzHgtNvw+gIkxThx2O1kJcXQMjEalzOyZ9JDGpB9Ph/jx4/niiuu4MILLzzs+aysLPLy8uq/3r17d8T8BhEREZHmpaCslvVFlcRFO352OLaM4dOtJcxYnktBuZtrT2nHrwcc94tCdmNkGcP+Gh+WMTjsNtokxeCwQ0mVlzM6tiApCCcONqSQBWRjDJMmTaJbt27ceuutPzhm3LhxTJ8+nQkTJrBixQqSk5O1/lhEREQa3L5qL2sLKkiLi/rZN459VVDOw4u+5Zu9VRyfFsdTTfRGPL9lCFgGl9OOx2/hsAE2G6W1XjA2OrSIo1NaHNHOg/oXGZPaP1vIAvLnn3/Oiy++SM+ePenTpw8A//znP9m1axcAN954I2PHjmXOnDl07tyZuLg4nn/++VCVIyIiIvKDarx+1uaXkxLr/Fnh2G8Znl2xi2dX7iIjwcVdZ53AmBMzcDTBrduqvX6qvAHiohyUu33ERzupCVj4LYsTMxLJTIo5NBg3ciELyIMGDfrBNcYHs9lszJgxI1QliIiIiBxRpdtPldfPhsJKnHaI+RnrYgsr6naoWFdQwa+6ZXDbsOOJP4ZDRCJRrS+AN2Bhx4YFDOrYgoRoJ9XeAAkuB5YBX8AiJiqy1xP/Ek3r/6SIiIjIUdhdWsv6ogpsNkh0OX9WOF6QU8LfP83BMoa/n92Vs0/MCGGlDcMyBssyVHkD+AKGaKcNy0BGQjSltT5Oy0oh3lUXGxNj6n512MBhb3rhGBSQRUREpBkxxrC7rJavCytJj4/+WSfZuX0BHl78Le9uKKJHq0T+MbYrWcmxIay2Ybh9AcrdfmKjHbRKdJES66SowssJGfGN7ua6YFFAFhERkWbBGMPGokp2ldX+7HC8vrCCu+dvJXd/Ldf0z+LGge0b9Q4VB26481sW1d4AAzukkhoXXf98VkpcGKsLPwVkERERaRZ2ldays7SGVgmuHzx34Yfs2F/Dk5/n8tn2fbSMj2bGhSdx6nGpIa40eCxjqNts4n/XW+H24bMgIdpBQrSTLukJh4RjUUAWERGRZmBftZeNeypJjz+6cBywDM+t3MV/VuwiJsrBjQPbc/nJbRvVUdEHjnQGU/+XgiqPn4CBQR1TiWtiNxUGkzojIiIiTVpZrY/VeaWkxEQd1bKKokoPd87dzJf5FYw5MYNbh3RsNDOsljGU1viwMFgGerROpNLtI7e0FqfdTnKsk76tkxSOf4K6IyIiIk1WUYWbtfnlJLqcuI5in96F20r4+/wc/Jbh7tFdGdutkexQYcDjtyit9XF8WjzHpcYQsOp2nPAHLFolxuBy2kmKcR718pLmTAFZREREmpyAZdhaXMW3+6pJi4v+yQNA3L4A/1ryLe+sL6J7qwT+MeZE2qVE9g4VxhiqvQGqvQFq3X7wBTj1uBRaJrgOGed02MlIdB3hVeSHKCCLiIhIk2JZhvWFFRRWuo/qhrxtJdX8ac5mvt1fw8R+Wdx0evuffdx0Q6vy+Kn2BWiV4KJXZhK1ZQHatEnX7HCQKCCLiIhIk+EPWGzcU0lhpZuM+B+fNS2t8fLcyjzeWl9IksvJ9AtOYkD7yNuhwhhDpcePN2CIjbLj9lk4HXaGdEoj4bvDO2rLUDgOIgVkERERaRLcvgBr88updPt/NBwHLMOLa3bz/Ko8an0BxvVozZTT29Migm7Eq/b6qfEFMAbsNshIcNEyIZrCCg9tkmLISokltgke8RwpFJBFRESk0at0+1m9uwzLGNLijxx091R6+MvcLXyZX86ZndL4zaAOdGgRGYdi+AIWpTU+bHZIjY2mS3o8ybFRxDgd2L/bfaO5H+DRUBSQRUREpNEyxpBf5mZ9UQXx0Q6SXEc+GnnRthLu+TQHb8DirrNO4JxuGWFfluD1W+yv9RHjtOPxW/TOTCIj0RXxa6CbOgVkERERaZQOrDfeXeYmPS7qiEc/u/0BHluygze/LqRbRt0OFcelhn+HiiqPn1p/gJPbJpFf7iY2ykHbCN85o7lQQBYREZFG58BOFXurvLT+kS3MtpVU8+ePN7N9Xw1X9WvLlNM7NPjsbMAylFR7sdtsYAMMGCApxsmgdikkuJy0SYpp0Jrkxykgi4iISKNijGHz3koKKz20SvjhcOzxW7y5roCnlu8kweXgifNPYmCHht2h4sAMsT9g6NYqgbbJsXgDFgHL4LDbiI924vhubXG4l3rIoRSQRUREpNHw+i127K8hd38tGQmH34wXsAwfb97L09k7Kar0MKhjC/4yssuP3rgXCmW1XqIcDk5um4LdBunx0dhsNmK080SjoIAsIiIijUJheS3rCysxQMuE6MNmXfdW1e1QsWZ3Od1bJXDnqC6celzoZ43dvrrT7JJjo3DYYF+Nj5RYJ33aJuNyKhA3RgrIIiIiEvFy99ewsaiStLioH1xDvHj7Pu6evxVvwOL/RnbhvB6tQr5sweO3KKv1kRzjpH2LOL7dV40B2iXH0K1V4hFvGpTIp4AsIiIiEa0uHFfQMt5Vv2b3ALc/wGNLd/DmukK6toznn2NPpH1q6PYKrvUFqPT4AYiPdtI7M4nM5BhsNhttklwYA8mxR95qThoHBWQRERGJSMYYckqq2VZc/YPh+Nt91fzp481sK6nhir5tmXp6B6KdoZm1NcYQsOqOfD6lXQrJsYfPZCfFKBg3FQrIIiIiEnHqdqqoYsf+GlomRNdtkXbQc++sL+JfS74lPtrB4+f34PQOLYJegy9gUeML4PFb2G02LGPo1SaJ9CPsnCFNhwKyiIiIRBRjDNtLqvl2Xw2tvncz3uq8Mp5YlsvGPZUMOC6Fu0Z3JT2IO1RYxuANWFR7AjgddtokxpCR6CI1NgoDh81iS9OkgCwiIiIRo9YXYFNRJXuqPLSM/184Lq7y8I8F21i2Yz+tEl3cOaoLv+re6pCZ5WC8d4XbT2pcFO1bxNEpLU5HPjdTCsgiIiISdpZlyC+vZdOeKpx2DjkAZOm3+7hr3lY8fotbBnfkkt6ZuIKw1tgXsKj0+AkYA0BslIPTO7YgRTfZNXsKyCIiIhJWfsuwOq+MfTVeWsRG1W+P5vFbPL5sB69/VcAJLeP555gT6dDi2HaocPsCVHkDWAZioux0aBFHenw0CS6nZoulngKyiIiIhE3AMuQUVxGIjSLjoFnjHftr+NOczeSUVHPZyZn85oyOx7xDRaXbjwG6ZSSQHBtFUoxTRzzLD1JAFhERkbDwBSzW5ZdTVuujQ3rdjXbGGN7bUMRDi78lLsrBo+f1YFDHY9uhotrrp8Zn4XLaOe24FOKiFX/kx+knRERERBpchdvHuoIKar2B+jW/O0treGzpDpZ8u59Tj0vh7l+4Q0Xtd0c/G2Mw1B3c0TcjkdQjnMIn8n0KyCIiItJgjDHkl7lZX1RBfLSDtPhovt3vZ8anOXywsQiX08EtgztyRd+2P3uHCl/AotztJ8Zpp3urBOKinTjtNi2lkJ9NAVlEREQahMcf4Js9lRSUe0iLq7sZb9mO/fx17k6qfRYX9c5k0qntaBH382aNy2q9eC2Dy2Hn+PQ42qdqezY5NgrIIiIiEnL7a7ys3V2OwdAq0YXXb/Hwou28+lUBx6e6+M+5J9HxZ+5Q4fYFKHf7aZPkonN6Agkuh2aKJSgUkEVERCSkiqs8rM4rI8nlJCbKQe7+Gv708Wa2FlczoU8mE7vFkXEU4bjS7cftt7DZwBhIcDnonZlEZnKMgrEElQKyiIiIhIQvYLG30sPXhRWkxEQR5bDV7VCxaDsxUXYeGdedwZ3SKCvZ86Ov4/XXHegR5bQzqFML4qMdAArFEjIKyCIiIhJ0NV4/K3eV4fYHSImJosYX4P5525i/tYRT26Xwt9En0PKgfY9/SLXXT7UvQJzTQfsWcRyXGktslKOBrkCaMwVkERERCapqj59VeWVgICHayYtf7uaF1btx+y2mndGBif2zfnCHCssY3D4Lb8DC47dIcDkZ2L4FydqFQhqYArKIiIgEzf4aL6t3leFy2llXWME9n+ZQUu3lzE5pTDmjPcenxR/2PV6/xf4aL34L0uOjaJXoIjHGSXp8tHajkLBQQBYREZGgKK7ysCqvjNgoOzO/2MUra/PpnB7H/ed0o3dm0mHja7wBKj1+HHYb7VPjaJscQ7xL0UTCTz+FIiIicswKymr5qqCCCrePW9/fypbiai7p3YZbBnfC5Tx8FrjK48dnGQZ2SKW2LEBmRkIYqhb5YQrIIiIi8otVuH3kFFdTWOFmxc5SHlr8LS6nnYfP7c6Zx6cdNr7M7cPrN8RF2xnQPpUEl5PasoavW+THKCCLiIjIL7Kv2suqXaX4A4YnPt/BvK0l9M9K5u6zu5Lx3Q4VljHU+gL4AgZvwCItLpqT2icSF60IIpFLP50iIiLys7h9AbaXVLOjtIZVu8qY+cUuiqs8TDm9PVf3b4fDbsMYQ1mtD59lyEhwkehy0iIuitS4aBx27UghkU0BWURERI5aWa2PNXllrNldxn9W7CKnpIauLeO5d+yJ9GxTdyOe2x+grNZPVkoMXdLjNVssjY5+YkVEROSo5JfVsmZ3Ga+uLeC1rwrISo7hH2O6MuqElthtdbPG5W4/ljEMaJ9KWnx0uEsW+UUUkEVERORH+QMWW4qrWL6jlH8t2c7mvdVc3LsNtwzuSIyz7mS7crcPj9/QNtnF8Wnx2q5NGjX99IqIiMgR1foCrMkr470NRTy5PJdoh52Hzu3O0OPTsL5bZ+wJWGQkuDgxI4EEBWNpAvRTLCIiIocxxrCn0sMXO0uZvmwHC7bto19WMneP7kp6fDT7qr1YBrJSYmiXEktybFS4SxYJGgVkEREROURpjZdv9lax9Nt9PL50B3sqPdx0enuu6d8Ob8CipMZL5/R42qfG4vpuiYVIU6KALCIi0oxkZ2ezaNEihg4dysCBAw95zhewyCmu4rNt+3j5y91k7yyjTaKLmRf3plebRPbX+Ihy2jm9QwtSNGMsTZgCsoiISDMxc+ZMpk2bRiAQwOVysWDBgvqQvL/Gy+pddVu3vfV1IQkuJ9PO6MCEPpnYbTb2VHlonxpH14wEohyHHx0t0pQoIIuIiDRx2dnZvPDCC/z73/8mEAgA4PF4WLRoEQMGDGBbSTWLtu2r36Hiwp6tmXZGB5Jioiir9WEZQ/+sFFolxYT5SkQaRsgC8nXXXcfs2bPJyMhgw4YNhz2/aNEizjvvPDp27AjAhRdeyJ133hmqckRERJql7OxsRowYgdvtxhhT/7jD4WDQ4CF8s6eS51bk8VT2Tpx2Gw/+qhvDOqdT5fGzp9JD60QX3VonEhultcbSfIQsIF9zzTVMmzaNiRMnHnHM4MGDmT17dqhKEBERafZeeOGFw8Kx0+nknvsfYX9KJ/767gY+27aPvm2TuPvsE0mJdbKn0kOLuCh6ZSaRGqfDPqT5CVlAHjJkCLm5uaF6eREREfkJM2fO5N///nd9OI6KiuKqq6/l1NEXUNuyC1Pf3kBhpZsbB7bn2lPaUe724fUb+rdLpmWCC5vNFuYrEAmPsK6yz87Opnfv3owZM4aNGzeGsxQREZEmJTs7m6lTp9avObbZbJw/4UqG/fr/+LiiBX+Y/Q0BY5h5cS8mndqOkhov6fHRnNGxBRmJMQrH0qyF7Sa9vn37snPnThISEpgzZw7nn38+OTk5Pzh25syZzJw5E4CioiIKCgooLi5uyHKbLPUxeNTL4FI/g089DZ5I7+Xq1av517/+VR+OAWzOaEq7jeGGN9bhswzndEnm2t7pxDhq+TavgqzkODJsfkr21jR4vZHez8ZAPQyusAXkpKSk+v8eO3YsU6ZMoaSkhPT09MPGTp48mcmTJwPQv39/MjMzAep/lWOjPgaPehlc6mfwqafBE6m9zM7OZsKECXg8Howx2Gw2bC0yybjuET4tiWHUCWlMOb0D7VJiKa31AjbObJ0Y9h0qIrWfjYl6GDxhC8hFRUW0atUKm83GypUrsSyLtLS0cJUjIiLSJBx8U57dbue4cyZTdOJYqp1OHhjThWHHp1Hu9rG32kvrhGi6t07UaXgi3xOygHzZZZexaNEiSkpKyMrK4m9/+xs+nw+AG2+8kbfeeounnnoKp9NJbGwsr732mtY7iYiI/AIHTscrKyv73015UbGYUVPI7XImJ7dO4p7RXUmNi2ZPlYd2KbF0bBFPYoyOQxD5ISH7nfHqq6/+6PPTpk1j2rRpoXp7ERGRZuHAPscejwfLsuoebNUZxv4Bk9KGyQOO49pT2lHp8VPjC+jAD5GjoL86ioiINGKH7HPsjIZ+58OACVBTyh29ozn/lLodKtqlxNI1I0HLKUSOggKyiIhII3Tg+Ohnn30Wgw16ngUDJ0BCGracz/ntgEzOGXwK+2p8nJyZTGZKbLhLFmk0FJBFREQamZkzZzJt2jT8fj8mKQPG/gHadCWpuogRjm8459phtO12MpaBAR1SaaHT8ER+FgVkERGRRuTAASB+vx+6DoGRU8BYOOc9xiN330bnXhdQ5QnQKS2Ozmnx2O26AV7k51JAFhERaUSefX4WfpsTzpoCJ42Egm84O3onF9x1G61P7IPTYee09kmkxWvWWOSXUkAWERFpBBYtXcZzz/+Xl+cugSsfgeTWsOJ17jinPyPH/xWwcVLrRDISXdo2VeQY2cNdgIiIiByZZRn++cgTjBg+nBe/Lsa65N663Sre+j8uaB/N4PMvIzkmijM6tqBVUozCsUgQaAZZREQkQlV7/Nzz8BPc/5+X4NIHoNXxkJMN86fjws/p59xP15bxdGyhtcYiwaSALCIiEoE++nQRDz3/Oou8beGCu6CsCOY8hD3nc8ZefCUjz7uYy381kpYJrnCXKtLkKCCLiIhEEH/A4o25C5l4xz8JnDm57sGFz8DXn2DHYuqd93PlNZPomZlEbJQO/RAJhZ9cgzx9+nRKS0sbohYREZFmrdLtZ9G2Em7/cBOBs34H+3bBi7fAVx/hsMPUO+/nN1NupH+7FIVjkRD6yRnkoqIiTjnlFPr27ct1113H6NGjdQOAiIhIEAUsQ15ZLbM3FnHnB2spjWsPX7wO2a/icNg5+9KJTLr2GsaOGKKjokUawE/OIP/9738nJyeHSZMmMWvWLLp06cKf/vQntm/f3hD1iYiINFnGGIqrPCz7dh+/fWEht7z7NaXlFfDm/8HylwHD+ZdexVsvPscFo4cpHIs0kKNag2yz2WjdujWtW7fG6XRSWlrKRRddxKhRo3jggQdCXaOIiEiTU+Xxs66ggnUF5cxYsJFN5Qa2r4J5T4C7EgCnw8GtU64nRsspRBrUTwbkxx9/nP/+97+kp6dz/fXX8+CDDxIVFYVlWXTp0kUBWURE5GcwxlBU4WZBTgkvfZnPvC3FOCwffPYsrJtTP87pdDJjxgxOP/30MFYr0jz9ZEAuKSnhnXfeoX379oc8brfbmT17dsgKExERaWr8AYuNeyp57ct8HluyHY8/QNa+DeS/8y+oqrsh3umM4rrrruOaa65m4MCBYa5YpHn6yYB89913H/G5bt26BbUYERGRpqrK4+eL3P08vPhb5m4phsItMOch8sr31I+x2Wxcf/0knnrqqTBWKiLaB1lERCSEApZhd1ktszcVcf9n29ld5sa28k3M8lfACtSPs9lsxMTEMHHixDBWKyKggCwiIhIypTVevi6o4JW1BTy/chfxTsOJW97km89fAmPqx0VFRTFp0iQmTpyoZRUiEUABWUREJMg8/gA5xdWsK6hgxue5ZO8spUt0Nd9On0JZdRkYg81mw263c+655/LHP/5RwVgkgiggi4iIBFFxlYfluft5a10hb68vJGAZzkooZv7fb8BYFlC3nGLUqFHcddddCsYiEUgBWUREJAgsy7BtXzXPr8zj6eW5lLn99E+302LzPD59+cn6cAx1W7gpHItELgVkERGRY1TrC7BqZyn3LtzG3C3FdG+VwPkxm3npzpvxBwKHrDe22+1Mnz5d4Vgkgikgi4iI/EL+gMW2khoWbivm/oXbySurZWyWA9eal3jhtf9iBQKHjD9w+MfkyZPDVLGIHA0FZBERkV+g1hfg6/xyZq3O47mVeSS6nExILuDNP047bNbY4XBwww03aJcKkUZCAVlERORn8PgDfFtSze49hTy+tG6HikEdW9CvYg2P/+V3h6w1Bs0aizRGCsgiIiJHwRhDUYWbdQUVfLihhNe/KafC4+eyTg7c2c/yxGsvHBKONWss0ngpIIuIiPwEty/AN3sqeX/DHv67Oo/c0lp6tErkfNcmZv32FgI/cCPek08+qVljkUZKAVlERORHVLh9LNm+j2eydzL7m720T43lr0Pa4N20jPv++nstqRBpghSQRUREfoAxhj2VHt7dUMgDC7eTW1rLxH5ZDIrZy7v/fZBPPnhbSypEmigFZBERke/x+OuWVDyTvYvnVu4i0eXk1pOiWD3rL7y0cC5WwAK0pEKkqVJAFhER+Y4/YJFf7mbN7jIeWrSd7J1l9Ey1c9yW93jsvucI+H2HfY+WVIg0PQrIIiIiwN5KNxuLqlieu4/HluZSWutjZHwJn/3t16wP+A+5CQ+0pEKkKVNAFhGRZs0fsNhaXMX8rcW89GU+q/PKaRVrY0jxAha+9MRhp+FBXTjWkgqRpksBWUREmq1Kt58Vu/bz2JIdzP5mL8kxzrpZ4wemsNDrOew0vHPPPZfWrVszZswYxo0bF8bKRSSUFJBFRKRZKqpw8/6GQu77boeK3rYiuu3fzBszH8X6ia3bCgoKwlGyiDQQBWQREWlWApZh694qpn++g/+syMNpebG/90/W7VjDOq0zFhEUkEVEpJkwxlBW6yM7dz+3v72KDWWQGdhH4bO3YqpKDxuvrdtEmi8FZBERafKqPH42FlWwePs+7vl4PVU+YNkLFKz5gIP3Mwaw2Ww4HA5t3SbSjCkgi4hIkxWwDHlltSzZXsLTC74me68FZcXw0YOwd/shY+12O3/4wx9ISUlh6NChWlIh0owpIIuISJNUVutjTV4p/3h/BYsK/BjLwLo5sPxl8Lnrx2nGWES+TwFZRESaFGMM785bxCMvvM2Xyf2piU2DzUtg+StQsbd+nNPp5NZbb9WMsYgcRgFZRESajBqvn3sens79r8/DnHkdeGvhnbsg98v6MQ6Hg0mTrueaa65WKBaRH6SALCIijV52djYzn32ejXnFrIrpBiNugty1MPcRqCmrH6cT8ETkaCggi4hIo5Sdnc0LL7zA7oJC5s6Zg7/VCTD2VohLhcXPwZr3AYPD6WTEWWPo2C6Tq6/WrLGI/DQFZBERaXRmzpzJlClTCAQCEBUDp10Kp4yvW2P82h9hzzbsDgfDzhrDnf/vDoYMPiPcJYtII6KALCIijcKBGeOioiI+/PBDAsYGfc6pC8fxKbBxASycicP4GDPhaqZcfy1jRpwZ7rJFpBFSQBYRkYh3yIwxQIt2cM4foGVHyFuPffZ9DO7RgfgLL2b8pZdzxbkjcTkd4S1aRBotBWQREYlYB2aN//3vf/8vHPc8C4beAD43tg/vZUinFlzx+L9o260P7VNj6ZaRiN1uC2/hItKoKSCLiEjEyc7O5oEHHuDDDz/EsiyMMeCKh1HT4IQzYNc6xsblc84//kLHk/oSE+WgQ2osHVrEYbMpHIvIsVFAFhGRiHDwGuOPPvoIn8/3vyczu8HYP0B8Kp2KvuBPlw+hXbfJxEc76NY6kdTYKAVjEQkaBWQREQmrA8H42WefPTQUA9jscNrFMGAC8VYtv+sXy5hBv6es1kerJBc9WiXidNjDU7iINFkhC8jXXXcds2fPJiMjgw0bNhz2vDGGW265hTlz5hAXF8esWbPo27dvqMoREZEINHPmTKZNm4bf769bRnGwTqfCoKsgvT09XJXMuO4sAKo8fvplJZOR6NKssYiERMgC8jXXXMO0adOYOHHiDz7/8ccfk5OTQ05ODitWrOCmm25ixYoVoSpHREQiRHZ2NosWLaKsrIyHHnoIy7L+96TDCZ1Og37jILMbCf4qJnZzcu1ZY9hf48PltHN6xxYkuPQPoCISOiH7E2bIkCHk5uYe8fn333+fiRMnYrPZGDBgAGVlZRQWFtKmTZtQlSQiImF2xBnjtHbQczRRvUbhc8aS5oLJZ3TmvB6tMMDeKi/HpcbSNSOBKC2pEJEQC9tfwfPz82nXrl3911lZWeTn5/9gQJ45cyYzZ84EoKioiIKCAoqLixus1qZMfQwe9TK41M/gC1dPV69ezfLsbEr2l/Hcf2ZiDswYR8XACYOg5yjI7IYdi4HHJTGmczJ9W8dhDOTlF+JwQKfUOFqYAMV7qsJyDd+nn8/gUj+PnXoYXGELyIetNYMjriWbPHkykydPBqB///5kZmYC1P8qx0Z9DB71MrjUz+Br6J4+9fQz3PybafgDAXBEQ5dBkNUDWh0P6R3AGQ378xgRX8IdV5xDalw0lW4/tf4A0Q47p7dKICPBFZE34unnM7jUz2OnHgZP2AJyVlYWeXl59V/v3r1b/2NFRJqIZZ8v56n/PMdrL87CyugCvc6GLgMgOg7cVVD8Ld1NId0c1Ywd14cefU+l0u1jb5WHjAQXPVLrtm6LxGAsIk1f2ALyuHHjmD59OhMmTGDFihUkJydr/bGISCO1fPlyPvl0Ia7EFObNncuShfMwHU+Bi++FzBPrQvGWpfDNYuxFW7jjnge48IorcfsDVLj9VHr8tEuNo02Si6SYqHBfjog0cyELyJdddhmLFi2ipKSErKws/va3v9Xvb3njjTcyduxY5syZQ+fOnYmLi+P5558PVSkiIhIixhgemf4Uf7z1FgKWgYxOcFwfmDgdUtpAWSEseBr7lsVcec31xPU5n259B9Cld3+Kq724nHb6tk2mZYJLx0OLSMQIWUB+9dVXf/R5m83GjBkzQvX2IiISQsuXL+ejTxawo6iEV19/HQZdA92HQUxi3YCCb2DpLOw7VnPepVcw9I7X6dKnP7FOB22SYkiPjyY2yo7L6cChYCwiEUYbSYqIyFHz+ANMf+oZ7rj1FvwtjoP+F8C1T4MxkPM5bF8Ju76G2nLsDgdT/3IfF1xxDZlJMbROdJEU49ThHiIS8RSQRUTkRwUsw8cLF/Of5//Lnj17WPHtHsx5d0L7PuCpgS8/gC8/xF6znysmT6O0rBOWMYw6/xLOP2sY7VJiNUssIo2KArKIiPwgty9AQbmbx598msdnPovp0A86jYHeHaBqHyyZBes/weatwe5wMOXO+/nVhKvJSnaRFu+iRVyUDvUQkUZJAVlERA5hWYb35i/iP8/P4ltfAlsSu8Ol94MVgKIc+OQx2LwYhw3GX/NrYhOSGTr0TM4Zcaa2ZhORJkEBWUREgLodKfZVe7nvsSf518xZmDOugg5doWQXfPI45CwHbw0OZxSjL7qcUedfwq9Gnknb5FhioxzhLl9EJGgUkEVEmrlaX4BPFi5h1gsv8G2Zj/Wu4+Giv0NlcV0w3rQQjIXd4eC0EWO45PppnDtyCO1S4oh2arZYRJoeBWQRkWaqxutnd7mbGU8+zROPPYI59RLoPrTuxrslz8NXH4Hfi8PpZPT4Kznvksv51YghtExwaW2xiDRpCsgiIs1MhdvHO598xsuvvsF2k8oOe8u6rdr8Hlj1Dqx6GzzV2B0OTh85lttuu43RwwfjcmoZhYg0DwrIIiLNhGUMb8/9jJnPv8D8HZWYUy6sO9ijJBeWvQibFkDV/roZ40sncvXEiVwwephmi0Wk2VFAFhFp4gJW3c13/5z+H56evQwz4DIY3BJ2rIbPX4a92wGwOxycOmIMU27+HReOHka8Sx8RItI86U8/EZEmqsbrZ+5nS5k9bwFbq518XpMGZ9383VZtj0LeeoDv1hhfzvmXXM7FY4aTEhsV3sJFRMJMAVlEpIkprfHyziefMXP6E6zaWYw5eRwc1wt8hTD7Adj6OXaHndNGjKFlRiuuv/Zqxgwfov2LRUS+o4AsItJE1PoCvP3xQp579S0W7QHT9VLokwI1ZbDwGfj6E7D82Ox2/nDPg9x+y1RSY6Ow2XQMtIjIwRSQRUQaucVLl/HBJ5/ybVEp728tw/Q5F1rZYdsK+GYR5H4Jlh9sNhxOJ/c9/Ci//80UBWMRkSNQQBYRaaQ8/gAPPf4kd/6/27C6j4QBl8IpKbB5MSx7CSr2AHU33100aSoZSbFcOv4CBp1xengLFxGJcArIIiKNiGUZZi9YzCefLmTvvv28teRLuPwRaJFVd9Pdu3fDnm31O1JkZLTiyquu5Fcjh1JavIfMzMxwX4KISMRTQBYRaQSMMZTW+rj/0Rk89I+7sI4fACeNgnNHw75ddcF4x2psNht2p5Pb73mQ3067ifT46PqlFKVhvgYRkcZCAVlEJMJ9umgpb300j7yyWubk+eD658AZDft3w/wZsGE+NhuMnzSVtNRULjrnLEYOHRzuskVEGi0FZBGRCOX1W9z36Az+duefsPpeAP3GQVcbbJgPGxfAnm11M8YOB7f89X5unXYTbZJisNt1852IyLFQQBYRiTABy1BY7ubBJ57k8XcWwMQnIT6lbkeKZS9CZTF2u52LJk0lPimZ80aP4lejzsShYCwiEhQKyCIiEeDzz5cz99OFuBKSWbF6DdvcsWxO7gkjboLdG+G9u/83Y+x0cvOd9zH1pl+TlRKLy+kId/kiIk2KArKISJg9Mv1JbvvdLQRcCXDCEOg+DDodD6X58P4/YPsKbHY7F06aSssWqYwfqzXGIiKhpIAsIhIm1R4/9z82g3vu+RuceQP0PAvsDijKgU+frFtrbAVwOJ389q4HuP2WKYfsSiEiIqGhgCwi0sAWL13Gk/95jtyCYlZVxcHVT9XtSvH1XPhqDuzPA8DhdDL20quZfN01jB1+pm6+ExFpIArIIiINxO0L8Mj0p/jzbb/DnDAIzrgKklrCti9g6X+xlxdyyaQplJdX4LDbuP6aa3TznYhIGCggi4iE2NyFS3j62efZs2cPK7buxlx6P7TuAnu2wdxHYPcGHE4nU+68n+tvuIG2ybGkxkZpxlhEJEwUkEVEQmTR0mU8+e/nePv9D7G6DILuY6B3B6gsho//Bd8sxuF0MPrSiVxx1VVccNYwYqO0I4WISLgpIIuIBJnXb/HAYzP461++O+DjmqfA6YLCLfDpU7BpAXYT4NQRZ3PDtN9y8dnDSYzRH8ciIpFCfyKLiASJL2BRXOXhgcee5LG3Pz30gI8vXofSfBzOKM4efxmjzr+UCWOH0yopJtxli4jI9yggi4gcI7cvwAfzF/HCCy+w3RPD5qReMHJK/QEfjpIdDBh2FslpIxh9waWMHTGE41LiiHbaw126iIj8AAVkEZFfaOHipXw471N279nH23PmYQZeAZ36QlkhfHAvbMvGZrcz5c77ufq66+nQIk4334mINAIKyCIiP5PbF+D+x2Zw911/xTpxKHQfDpeNBncVLPpP3V7Glh+H08nv736AP/9uGkkxUeEuW0REjpICsojIUfL6LT6Yv4hn//sCn2wvx1w9A2IS626+W/AUbF4Cnur6Az6uu/pqzh01VPsYi4g0MgrIIiJHoaCslvsfm8H0V9/DOv0qGJIJuWth6Swo3oHNZsNmtzNg5Bj++Ic/cu4onXwnItJYKSCLiByBL2Axd+ESPv50IZvL/HxWlgTn3A4lu+CduyD3SxxOJxdOmkp8UjKjRwznwtHDdPOdiEgjp4AsIvI9NV4/H8xfzBOPPEz2hq2YvudBt6FgK4P5M2DDfOx2G2dfOpFzxl/GuSOHkJHowuXUIR8iIk2BArKIyHf8AYsPPl3MzOdfYN72Mky3s6H3TeD3wIo3YdVb4K3FZrfz27se4M+3TiM1NgqbTUspRESaEgVkEWn2LMuQV1bLv6Y/xRNvzKnbrm1Iy//dfLdlGbgrwWbD4XTyt/v/xW03T9VSChGRJkoBWUSatdIaLxuLKnnk+dd451sbjP4tFOXAx/+C/I0A2B0Oxk+aynGt0znv7FEMHnRGeIsWEZGQUkAWkWbH67fYW+lhZ1kNHy7K5r+rd1MUcxzEF9cF428WY3fYOW3EGNIzMrjyyqv41cgziYvWH5kiIs2B/rQXkWaj1hfg25Jq5i1aymervmKzL5UcR2uwt4DPX4I174Hfi81u5/a/P8St026iRVy0tmsTEWlmFJBFpMkLWIbCcjevf7yAp977jO0x7aFlL3D4YONCWP4y1JQB4HA6uf9fj/H730wJb9EiIhI2Csgi0mQZYyip9vLGxwuZ9eFnrLa3h3ZDYc82WPA0bFlad/MddeuML7riaqZcfy1nDh4U3sJFRCSsFJBFpEnyBSxe+2gBDz3zX76OPQHaD4ayQvjwXsjJ/t9Amw2nw8Gjjz/B1JtuDF/BIiISMRSQRaRJMMZQVuujqNLD4qWf89bb77BwvwvT/SLw1sKiZ2HdRxDwA3UzxldNnka7VumMPWsEAwcODPMViIhIpFBAFpFGr7TGy9tzP2Px0iXk1UaxZPtezIkjoVUUfPURfPE6uCuxOxycPmoMnY/L4vprr+GMM04Pd+kiIhKBFJBFpNGqdPt5c+5CnnzySdbUJkOvsyE+Abp5YPsKWP4KlBUAdTPGdz/wCLffMhWnQwd8iIjIkSkgi0ijU+3xM3vBYv794msszPdiel8GUTF1a4s3LYRd68Dvxe5wcNqIMbRp05op11/LiDMHh7t0ERFpBBSQRaRRqPUF2Lq3ikVLl/Hic/9hTW0S9DkH2rjqjoJe8TrsywPAZrNhdzq5/Z4Hue2WqaTERoW5ehERaUwUkEUkYhlj2FPpYfani3nnjVcorLZYVxGF6TWhbsb4e8HY7nBwyaQptE5vwTmjRjByqGaMRUTk51NAFpGIY4xhyfIvWLbqK3bsr2bO1v3QZTC0bQXGOiQYH1hG0TazNZOvu5ZRCsUiInKMFJBFJKLsq/Zy/6MzePihB7H6nQ89RkJfO+R+CSvegG9X1Z96Z3c4+L97/8Xvf3MTSTFaRiEiIsER0oA8d+5cbrnlFgKBANdffz133HHHIc8vWrSI8847j44dOwJw4YUXcuedd4ayJBGJMJZlKHP7WLT0c157+UW2lrhZZ1rBNU/WzRZvXAir3obyIqDuKOhTR4yhZatWTL3hOs7SjLGIiARZyAJyIBBg6tSpzJ8/n6ysLE455RTGjRtH9+7dDxk3ePBgZs+eHaoyRCRCuX0B3vh4IYsWLaK8ooJ35y3C9BoLJ54Ofl/d/sWr34WqfdhsNmwOB6cNPYsbpv2Wc0YMIT3ehd1uC/dliIhIExSygLxy5Uo6d+5Mp06dAJgwYQLvv//+YQFZRJoXf8Bi7mdLuPf+B8jevhfTYwQcdzpcfBZ4qmHl2/DlB1BbjsPp5IJJU8lIa8E5Z41g1NDBRGkPYxERCbGQBeT8/HzatWtX/3VWVhYrVqw4bFx2dja9e/cmMzOThx56iB49ehw2ZubMmcycOROAoqIiCgoKKC4uDlXpzYr6GDzq5Y8LWIYFy7J55fW3WPBNAVb/C6HX8VBdCjnLIXcN7PwKvLU4HE6GjB3HJROuYNSQgcRGOQAo3lMU3oto5PQzGjzqZXCpn8dOPQyukAVkY8xhj9lsh/5zaN++fdm5cycJCQnMmTOH888/n5ycnMO+b/LkyUyePBmA/v37k5mZCVD/qxwb9TF41MtDefwBPl20jAWffcauvft5Z+UWTN8L4Jy2sH83fPIYfLMYLH/dMgq7ndNGjOGGab9lyMndOL59u59+E/lZ9DMaPOplcKmfx049DJ6QBeSsrCzy8vLqv969e/dh/+OSkpLq/3vs2LFMmTKFkpIS0tPTQ1WWiISYZRmKqzzMXrCY/0x/jBWrV2E6nw79zoORo2DPdvjwPtj2BRgLu8PB+ElTSUhOYfjQoZw78kySY6MoKCgI96WIiEgzFbKAfMopp5CTk8OOHTto27Ytr732Gq+88sohY4qKimjVqhU2m42VK1diWRZpaWmhKklEQsgYw75qL2/N/YzX33mPxfkeTIcx0OemugF562HedNi5tm7v4uGjSWuZwbXXXM1ZQwcTF+XQTXciIhIRQhaQnU4n06dPZ/To0QQCAa677jp69OjB008/DcCNN97IW2+9xVNPPYXT6SQ2NpbXXnvtsGUYIhLZ/AGL/HI3sxcsZuasl/naZMAJoyED2L0BNj1ft4dxyc76I6Bvuet+/jDtJlomuHDqpjsREYkwId0HeezYsYwdO/aQx2688cb6/542bRrTpk0LZQkiEgKVbj9VXj/7a7wsWLyMV2YvYEV1Apx4KfjcsG4OrHkPKksA6pdRJCancO5ZIzh31FAcmi0WEZEIpZP0ROSo1Xj9vD9/MS++8AJ+exRVqR3ILnNBm6FQUw7LX4av5oC7sv4I6NT0lpx3yeWcO2KIZoxFRKRRUEAWkR/lD1jMWbiEOfMXULS/gg++2onpcga07gJ2B3j3wMJnYMN88HuBuhnjv93/CDdPvRGX047L6QjzVYiIiBw9BWQR+UFuX4CiSg+PzniKJx59BKvXGOgxEs4cCUU5sOKNun2LC7fU70YxYMQYsjLbcNMN1zJ08KBwX4KIiMgvooAsIkDdTHGVN0BJtYdPPlvKyuXL2FVjZ1FRACZOB2Ng67K6U+72bPvfN9psOJxO7n3oUX437SYtoRARkUZPAVmkmTPGsLfSw4aiStauXsELr77Jhj3VmBPOgFadoW0FrHq77sa7qv11B3o4HJw29CzOHDEKm7uSsWeNZNAZp4f7UkRERIJCAVmkmXL7AhRXefjw08W88e4HbIvKIj/xeDjhIjgBKM6F+TPgm8/A78VmtzN+0lTSW6Tyq7NGMmroYKKdmi0WEZGmRwFZpBkxxlDtDTD708W88OIL7Kp1spHW0Gl03RKKnM/rTrjbvRFqygDq9y7+y70P8/vfTCHBpT82RESkadMnnUgT5wtYlFR5+GjBEpYvW0J5RQXvZm/E9PkVdO4CtRWw6h1Y9xFU7a//PrvDwfhrbiQzI41xo0cy/MzBYbwKERGRhqOALNJEuX0Bvt1fzScLl/LSC/9lbWEVpm0POO5UOPss2Jd3yBIKAIfTyYBho2ifmcnlV17FyKGDtEWbiIg0OwrIIk2Mxx/g/XmLmPXCC+TVOthAGzjxcujugKp9dVuzfbMIctcCBofTyakjxtCubRuuv/YaRp45SEe+i4hIs6aALNLIHdierdrjZ/Hnn/Piiy/xyYY8TO9zDl1CsXEBlBUAfLcThZ0BQ8/iN7/7PeedNZTYKM0Ui4iIgAKySKPl8Qd455PPmL/gMxKTU1mWvYK15XZMrzEwOg3274ZPn4RNCw854W78NTeSkpLKr0aPYMzwIURp32IREZFDKCCLNDJVHj+ffLaUfz38INlb8jAnnglZ7aDHxLoBuWth3hOHLaHIbNOaqydOZPiQQcRFO7SMQkRE5AgUkEUagYBl+HTxUp59fha7CveysrC2bglFry7gqYHd62HdXMhdA/t3Y3c4OG342aRlZHDtNVczdvgQYrSEQkRE5KgoIItEKI8/wPxFS5m/4DOKSvbz5qsvYbqPhJMvgl4HL6H4DPye+u+zOxzcds+D3H7LVFJiozRTLCIi8jMpIItEmAWLl/L+3E8pKt7H26++iNW6K3Q6Fa77N0S5DltCYXc4uHDSVGqrKomJcnD9tdcwethgBWMREZFfSAFZJEJUuH088OgM7r37L1hdz4RuQ2Hyf8HuqJsh/mYRfPkh7NuFwxnFqSPOJj0jg0lXX8OoYYOIjdK6YhERkWBQQBYJI2MMCxYvY/a8T9leXMHsTcVw7UyISYC938KKNyBvPRRtxRbwYbPbOW3EGK668WYuOnsYaXHR2O0KxSIiIsGkgCzSwLx+i72VHuYvXsp/pj9K9qrVmJPHQa/RMCAGcrJh1VtQlIPNZqtbQnH1r4lLTKb/6YMYf9ZQWiXGKBiLiIiEiAKySAOpdPv58NNFvPzSixSXlrM6rwzToR9ccz3Y7bB5Max6G/blYbPbGT9pKolJKQwZeiYjBp9BUkwUiS6ngrGIiEiIKSCLhJjXb/H6nAW8+vJLfLJsBVa/C6H7BXCSo+6Uuw3zYfW7ULGnbsbY6eSP9zzIrdNuIjUuGocCsYiISINSQBYJEV/AYu7CJdz34ANkb8nH9B8PEy4CdxWseQ+2fQFFOWAs7A4HF0yaSpv0Fpx39ihGDh0c7vJFRESaLQVkkSCrcPuYvWAxL734Ip+s/gar/3i4qDtUl8GSWfD1x+CtrTvhbvhoUtNbcvkVVzFu1FASY/RbUkREJNz0aSwSJFUeP8+9N4/Hn3mer0s8mK5D4LyLoKIYFj4DG+bjMAEGDD2L5PSWXHjp5Zw3aijJsVFEOezhLl9ERES+o4Ascow+W7KU6TOfZ2txJRviu0P3y+ueKMmFTx6v27/Y8mN3OJj6l/u48cZfk5kUQ1KMU/sWi4iIRCAFZJFfaNnny/n3c8/z0quvYZ1yMfS9AjzVsPxl2LAAqkqwOxycNuwsUtNbctVVE7lg9FBcTke4SxcREZEfoYAs8gs88sST/OHWW7COPx2uegLiU2H9PFj2IrgrAbA7HPzmzvv5/W9uomWCi5goBWMREZHGQAFZ5Gcoq/Xx4GMz+OfDj8D5d8Fxvep2ovjgn1CUU3fj3YgxtGndmqsnTmT08MGaMRYREWlkFJBFfoIxhrkLl/DMc7PI21/Nl+5kuOJR8NXCp09i37SA04eNJHZAHy67+nouHjOcBJd+a4mIiDRW+hQXOYLs7GzmLVjI9vy9vPTRZ5i+50G3/mAMbFoIS/+L3VPF1DvvZ8LV19HCqqRrx+N0452IiEgjp4As8gOeevoZbv7NNPyuRBhyLVz8j7p9jFe8Ces/gcoSHE4nU++8n9/fPIWs5FiKitwKxyIiIk2AArLI9zwy/Ulu/e0tcNJZcMaV4HTBF6/ByrfA78XhdHLWJRO55PIruHTsCGJ1852IiEiTooAsAliW4eOFS3h21n9577PlcOn90LoL7PwKFj6DrawAm93OaSPGMPk3v2XcqKG0iIsOd9kiIiISAgrI0qwtX76cj+cvZHtBMa+/8gLWaRPqwnF1GXz0ILacz7nkupuIS0zm3NEjGTtiiHalEBERaeIUkKXZenzGU/z+tzfjT82CPufAtf8GhxPWzoblr+CwvNx05/1cfe0kTmqTpH2MRUREmgkFZGl2Apbh3kem85e77oJRN0O3oeDz1B0JvfYDHGUFjL7oCs4ZP4ELRw8jI8GF3a6b70RERJoLBWRpVuYtWsr0mc8x+5u9MHH6dzfgvQ5fvo/NU43d4eCmv9zHb6feSIcW8TgUjEVERJodBWRpFjz+APc9OoO7/zUda+ivYfgJsGsdLHgae3khl0yaiis+kX4DzmDCOSNomeAKd8kiIiISJgrI0qRVefzsLnPz2FPP8PTizXDpA1BbCXMehs2LcTidTLnzfq657no6pMWREhOl5RQiIiLNnAKyNElltT7e+nghr735Fjm0ZFfSCXDyr+DrT2DZC9j9bkZfMpEJV1zJhaOH6WhoERERqadUIE2KZRk+nL+YJ2a9zMKSaEz30XXrjHd9DR/eB4VbsNnt/Oav9/On300jIzEm3CWLiIhIhFFAliajpMrDvY/O4JE35mIGXQ2t4mDjAlj7IezbBYDD6eRP/3iIO347lbho/fiLiIjI4ZQQpNHzByy2FFfxjydn8eoWD4z6DeRvgk+fPCQYj7v0SqbecB0jzhwc5opFREQkkikgS6NV4/Wzt8rLe/MWM33hBrbHdYa0Gvjkcdi4ALvDzqnDx5DVtjU3XHcto84chM2mG/BERETkxykgS6NT6wuQV1bLB/MX8e+357I1tQ8knwgbPoUlz4O7Epvdzh/+9iB3/G4qqXHR4S5ZREREGhEFZGk0LMvw3vxFPP38S2yxtWJXQmfoMBpKdsHr/w/yNwJ1yynueeARbr9lqrZsExERkZ9NAVkiXo3XT365myefeobH3pqHOf1KiE+p25nis5nw7SowFnaHg0uvvJpfT7qWMwcPCnfZIiIi0kgpIEvE8Qcs/Jah3O1nzoLFvP7qK+zwJ7A1thOMmgb538C7f4O92+u/x+5w8MAjj/P730wJY+UiIiLSFCggS0Tw+APsKq0lv9zN2lUr+PS9NyiqqGVtVQym+whIaAHle+Djf8E3i4C6pRQDho3iuMw23DjpOoYMPiO8FyEiIiJNggKyhI0xhkpP3fKJTxYu4YtVq9juS2DNjj2YlgOgbSYYAztWw/y5kLu2finFwGGjueP22zhnxJnamUJERESCSgFZQs7jD1DlCeANWNR6A7icdr5ctYKP5i3AGZ/MwnXb+cakQcf+EO2ANi1g7zbYuBA2L4LKEqBuxnj0+Cu54qqruGj0cKKd9vBemIiIiDRJIQ3Ic+fO5ZZbbiEQCHD99ddzxx13HPK8MYZbbrmFOXPmEBcXx6xZs+jbt28oS2p0LMsAYLfbsCyDzcYPzphalqkf47cMBoPL6QDq+nzge/wBC4/f4sBLeP0WxhgsA4ED72UDt79unGUMMU4HUQ4bdpsNyxgClsFQN7lrGUOtL0CtL4DNZiMh2oHHb/HpkmW89erLAHQ68SS2bVpP6b5iWqS3JC4hkbffex+r2zDoPhw6dYXqMljzPmyYD6X59dflcDo5dcQY0lpmMOmaqzln5JlEORSMRUREJHRCFpADgQBTp05l/vz5ZGVlccoppzBu3Di6d+9eP+bjjz8mJyeHnJwcVqxYwU033cSKFStCVdIxKa/11YVGX4Diai8By+By2kl0OYmJcuANWLh9gbpQSd3OCx6fRZTTTnKMky3r1vDBm69QvHcvaekZHN+9J1+v+4qAZeh6Ui+2bVpPyd49tGnTmr4n9+XrdWvZXVBEUot0OnfrybZv1gPQuVtPcrdsoLK0hBbpGXQ8sSeb1n9FaUkxqekt68eWlhTTqlUrTjipFxu++gqbDTp370nOxrqgemDsN2tXUVVV+b/v3bQebBzyOt9/3R/72gC2xJZ8vOAzTFJrSM0ETyWknA4t4sDngYAPrh4NVgByv4QFT9cto7ACdc222bDb7Zw29Cyun/Zbxo8eRlKMU0spREREpEGELCCvXLmSzp0706lTJwAmTJjA+++/f0hAfv/995k4cSI2m40BAwZQVlZGYWEhbdq0CVVZv8jny5cz64NPqYlKZsfOnVRWVpKcnEy3TseRv+lLykr2kprekuO69yEnZxs+exStO55I7q7dVJfvI9pbybqFH2AFLIhJAFc8LFwFMfHgSmD2lv1QXQoVe2HVl7w1d9F34xKgwAvbKuq+BwMb59XdrGYscJXA2l11z8Wkwl4XlOWDOwoqHLArB77aVfc6MfGwZwu4WkB8O/A6YdkOKLfA74BqDxRtBldK3Xtv9IKrL6R7oGIPfLoe7E6I6QI18bDBAzF9oUN83fuXx0HrTmAAZxRccGZd87y1UFYIe78FdyVEucDpgk2fwaYFULW/vs92h4Orfz2NzJZpnDl0KEMGnV4/Cy4iIiLSUEIWkPPz82nXrl3911lZWYfNDv/QmPz8/IgKyNnZ2Qz9f8/g738R+ICWnaBl3XOflwNtjqsLka44KAcyOtc9WQmktoXU717ohEuPrZADs6v2IwRGY4Gnpi6QxibVBdHv81SDu6ruVysArY6HuJSDnq+pe85TVTeuohiiY6HNiXBCOgS83z1fDe5qqCyGktz/PWZ3gs1WF4hL86G0EKr3H14HdctE7A4HF0yaSk1VJSmx0dxw3dUM1f7FIiIiEmYhC8jGmMMe+/4/kR/NGICZM2cyc+ZMAIqKiigoKKC4uDhIlf64Dz74AGv7aqgsA3cF1JSD3wOOaEhqCUkZdSHSXfVdsKz+X8j01IAzum5MUksIBL57vPqgsVV1yw7iU+vGxSaBt6bu9dwHjfW6wW6HhLS6cTbb/77fU133XhzUz9jkutf01f7veWMdfoFOV12Nnuoffv6Xstmw2+z0HzKckwcM5tst31BeWkJKizQ6ntCNyvJyBp1xOoMHnkZCtKP+hruCgoLg1dDAGupnsrlQP4NPPQ0e9TK41M9jpx4GV8gCclZWFnl5efVf7969m8zMzJ89BmDy5MlMnjwZgP79+9eP+aGxwTZu3Dgee+wxPIWbsaxfGCCLtuJwRjFo+CiMiSE94zg6d+/FpvXrsIyhc7eefPvNeir2l9CiZQZduvdiy8Z17CveS2p6Rv1NbtigS/ee5G7eSGnJXpLSWtY99/21wN/dEJfWMoMu3XuyZePXAIc8l5rekuO79WTz2lVUV1WSctA6YoDju/Vk+1GuQU7PyOCknr3ZuH4dfsvQtUcvvFXlnDn0TIYPGUyUw4YNG1EOG37LYANcTjvOJnizXUP8TDYn6mfwqafBo14Gl/p57NTD4AlZQD7llFPIyclhx44dtG3bltdee41XXnnlkDHjxo1j+vTpTJgwgRUrVpCcnBxRyysABg4cyIIFC3hj9ifY45L4dtMGSkv2kJ6RwQnde7Fpw9eUFO8huUVdaPx28wbioh307NWHjV9/xb7ivbRomcGwcRfT8aS+xEY5cPsCuJwOJl3vIi7ajsNup8LtZ0+lB4cdoux2XE470Q47DruNRJeDuGgn3oBFhdtHea0ft9+iRXwUidFOohx27DZwOuzYAL9lcPsCVHvrlmXERzvqd35w2G3EOO0YwOO32L07n+SWrYiNcuC02+pn9WOjHNjttrrdKb57TV/AYIwhJspBtMOO02GrD7sHZv59AQun3aYb6kRERKTRCllAdjqdTJ8+ndGjRxMIBLjuuuvo0aMHTz/9NAA33ngjY8eOZc6cOXTu3Jm4uDief/75UJVzTAYOHMjAgQN/dIwxBmPqtmP7sTE2m+2QbdcOaJMEXTMSfrKWlgk/sLb4F0pwgSfRRWaLuCOOiY36eTfJaQs2ERERaexCug/y2LFjGTt27CGP3XjjjfX/bbPZmDFjRihLaDA2m42fmjQ9EIo1uyoiIiISuTTdJyIiIiJyEAVkEREREZGDKCCLiIiIiBxEAVlERERE5CAKyCIiIiIiB1FAFhERERE5iAKyiIiIiMhBFJBFRERERA6igCwiIiIichAFZBERERGRgyggi4iIiIgcRAFZREREROQgCsgiIiIiIgdRQBYREREROYjNGGPCXcTPkZ6eTocOHSguLqZly5bhLqfRUx+DR70MLvUz+NTT4FEvg0v9PHbq4S+Tm5tLSUnJYY83uoB8QP/+/Vm9enW4y2j01MfgUS+DS/0MPvU0eNTL4FI/j516GFxaYiEiIiIichAFZBERERGRgzTagDx58uRwl9AkqI/Bo14Gl/oZfOpp8KiXwaV+Hjv1MLga7RpkEREREZFQaLQzyCIiIiIioaCALCIiIiJykAYJyHl5eQwbNoxu3brRo0cPHnvsMQD279/PqFGj6NKlC6NGjaK0tBSA+fPn069fP3r27Em/fv1YuHBh/WutWbOGnj170rlzZ26++WaOtELkSOOWLFlC3759cTqdvPXWWyG+8uCKpD4+/fTT9OzZkz59+jBo0CA2bdoU4qsPvkjq56xZs2jZsiV9+vShT58+/Oc//wnx1QdfJPXzd7/7XX0vTzjhBFJSUkJ78SESST3duXMnI0aMoFevXgwdOpTdu3eH+OqDKxy9/POf/0y7du1ISEg45PHG/Dl0QDD7eaQ+fV9T+lyPpP41hc/zkDANoKCgwKxZs8YYY0xFRYXp0qWL2bhxo7ntttvMvffea4wx5t577zV//OMfjTHGfPnllyY/P98YY8z69etNZmZm/WudcsopZvny5cayLHP22WebOXPm/OB7Hmncjh07zLp168xVV11l3nzzzZBdcyhEUh/Ly8vrx7z//vtm9OjRwb/gEIukfj7//PNm6tSpIbvWhhBJ/TzY448/bq699tqgXmtDiaSeXnTRRWbWrFnGGGMWLFhgrrzyytBcdIiEo5fZ2dmmoKDAxMfHH/J4Y/4cOiCY/TxSn76vKX2uR1L/msLneSg0SED+vnHjxpl58+aZE044wRQUFBhj6n5YTjjhhMPGWpZlWrRoYdxutykoKDBdu3atf+6VV14xkydPPux7jmbc1Vdf3Wh+Ix1JJPTxwONnn312MC4prMLZz6YQkL8vUn4+Bw4caObNmxeMSwq7cPa0e/fuJi8vr/61ExMTg3ptDS3UvTzYkYJLU/gcOuCX9vNgPxbwmvrneiT078DjTeHzPBgafA1ybm4ua9eu5bTTTmPPnj20adMGgDZt2rB3797Dxr/99tucfPLJuFwu8vPzycrKqn8uKyuL/Pz8w77naMc1ZpHQxxkzZnD88cfzxz/+kccffzyYl9fgIqGfb7/9Nr169eKiiy4iLy8vmJfX4CKhn1C3LGDHjh0MHz48WJcWNuHuae/evXn77bcBePfdd6msrGTfvn1BvcaG0hC9bE6OpZ9Hqyn3PRL615Q+z4OlQQNyVVUV48eP59FHHyUpKeknx2/cuJHbb7+dZ555BuAH13nZbLbDHjvacY1VpPRx6tSpbN++nfvvv5+///3vP+cSIkok9PPcc88lNzeXr7/+mpEjR3L11Vf/3MuIGJHQzwNee+01LrroIhwOx9GWH5EioacPPfQQixcv5uSTT2bx4sW0bdsWp9P5cy8l7Bqql83FsfbzaDXVvkdK/5rK53kwNVhA9vl8jB8/niuuuIILL7wQgFatWlFYWAhAYWEhGRkZ9eN3797NBRdcwAsvvMDxxx8P1P2N5+AbQ3bv3k1mZiaBQKD+hpw777zziOOagkjs44QJE3jvvfdCcbkhFyn9TEtLq58NuOGGG1izZk1oLzxEIqWfB7z22mtcdtllIbvehhApPc3MzOSdd95h7dq1/OMf/wAgOTk5tBcfZA3Zy+YgGP08kubwuR6J/WvMn+dB1xDrOCzLMldddZW55ZZbDnn8D3/4wyGL0W+77TZjjDGlpaWmV69e5q233jrstfr372+ys7PrF5l/9NFHP/iePzWuMa5ViqQ+bt26tX7MBx98YPr16xeMS2xQkdTPA2vOjDHmnXfeMaeddlowLrFBRVI/jTFm8+bNpn379sayrCBdYcOLpJ4WFxebQCBgjDHmT3/6k/nLX/4SrMtsEOHo5QFNcQ1yMPt5wE/dZNaUPtcjqX9N4fM8FBokIC9dutQApmfPnqZ3796md+/e5qOPPjIlJSVm+PDhpnPnzmb48OFm3759xhhj7rnnHhMXF1c/tnfv3mbPnj3GGGNWrVplevToYTp16mSmTp16xA+/I41buXKladu2rYmLizMtWrQw3bt3b4gWBEUk9fHmm2823bt3N7179zZDhw41GzZsaJgmBFEk9fOOO+4w3bt3N7169TJDhw4133zzTcM0IYgiqZ/GGPPXv/7V3H777aG/8BCKpJ6++eabpnPnzqZLly5m0qRJh90gFOnC0cvbbrvNtG3b1thsNtO2bVvz17/+1RjTuD+HDghmP4/Up+9rSp/rkdS/pvB5Hgo6alpERERE5CA6SU9ERERE5CAKyCIiIiIiB1FAFhERERE5iAKyiIiIiMhBFJBFRERERA6igCwi0sTcddddPPTQQ+EuQ0Sk0VJAFhERERE5iAKyiEgT8I9//IOuXbsycuRItmzZAsDjjz9O9+7d6dWrFxMmTAhzhSIijYcz3AWIiMixWbNmDa+99hpr167F7/fTt29f+vXrx3333ceOHTtwuVyUlZWFu0wRkUZDM8giIo3c0qVLueCCC4iLiyMpKYlx48YB0KtXL6644gpeeuklnE7Nh4iIHC0FZBGRJsBmsx322EcffcTUqVNZs2YN/fr1w+/3h6EyEZHGRwFZRKSRGzJkCO+++y61tbVUVlby4YcfYlkWeXl5DBs2jAceeICysjKqqqrCXaqISKOgf3MTEWnk+vbty6WXXkqfPn1o3749gwcPxmazceWVV1JeXo4xht/97nekpKSEu1QRkUbBZowx4S5CRERERCRSaImFiIiIiMhBFJBFRERERA6igCwiIiIichAFZBERERGRgyggi4iIiIgcRAFZREREROQgCsgiIiIiIgf5/xy7EM74k9QpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "death_confirmed_forcast_plot = death_model.plot(death_forcast_confirmed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "death_confirmed_forcast_plot_components=death_model.plot_components(death_forcast_confirmed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recovered prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>28.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>36.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>39.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>52.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>2020-12-02</td>\n",
       "      <td>41496318.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>2020-12-03</td>\n",
       "      <td>41932091.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>2020-12-04</td>\n",
       "      <td>42352021.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>2020-12-05</td>\n",
       "      <td>42789879.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>2020-12-06</td>\n",
       "      <td>43103827.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>320 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds           y\n",
       "0   2020-01-22       28.00\n",
       "1   2020-01-23       30.00\n",
       "2   2020-01-24       36.00\n",
       "3   2020-01-25       39.00\n",
       "4   2020-01-26       52.00\n",
       "..         ...         ...\n",
       "315 2020-12-02 41496318.00\n",
       "316 2020-12-03 41932091.00\n",
       "317 2020-12-04 42352021.00\n",
       "318 2020-12-05 42789879.00\n",
       "319 2020-12-06 43103827.00\n",
       "\n",
       "[320 rows x 2 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recovered Prediction\n",
    "recovered.columns=['ds','y']\n",
    "\n",
    "#Changing datestamp format to YYYY-MM-DD for a date or YYYY-MM-DD HH:MM:SS\n",
    "recovered['ds']=pd.to_datetime(recovered['ds'])\n",
    "recovered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x7ff82b4437d0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recovered_model = Prophet(interval_width=0.95)\n",
    "recovered_model.fit(recovered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>-31371.04</td>\n",
       "      <td>-634984.57</td>\n",
       "      <td>594963.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>-26810.28</td>\n",
       "      <td>-637773.32</td>\n",
       "      <td>573754.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>-36071.28</td>\n",
       "      <td>-646311.58</td>\n",
       "      <td>600948.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>-10283.58</td>\n",
       "      <td>-601411.13</td>\n",
       "      <td>519578.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>-21761.15</td>\n",
       "      <td>-650812.56</td>\n",
       "      <td>591837.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>73983037.97</td>\n",
       "      <td>67287650.00</td>\n",
       "      <td>81000968.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>2021-04-02</td>\n",
       "      <td>74251203.63</td>\n",
       "      <td>67682969.60</td>\n",
       "      <td>81390606.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>2021-04-03</td>\n",
       "      <td>74554417.99</td>\n",
       "      <td>67650164.98</td>\n",
       "      <td>81875089.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>2021-04-04</td>\n",
       "      <td>74820367.07</td>\n",
       "      <td>68007359.79</td>\n",
       "      <td>82110765.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>2021-04-05</td>\n",
       "      <td>75065118.37</td>\n",
       "      <td>68215475.40</td>\n",
       "      <td>82353895.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds        yhat  yhat_lower  yhat_upper\n",
       "0   2020-01-22   -31371.04  -634984.57   594963.35\n",
       "1   2020-01-23   -26810.28  -637773.32   573754.86\n",
       "2   2020-01-24   -36071.28  -646311.58   600948.03\n",
       "3   2020-01-25   -10283.58  -601411.13   519578.74\n",
       "4   2020-01-26   -21761.15  -650812.56   591837.25\n",
       "..         ...         ...         ...         ...\n",
       "435 2021-04-01 73983037.97 67287650.00 81000968.30\n",
       "436 2021-04-02 74251203.63 67682969.60 81390606.83\n",
       "437 2021-04-03 74554417.99 67650164.98 81875089.19\n",
       "438 2021-04-04 74820367.07 68007359.79 82110765.02\n",
       "439 2021-04-05 75065118.37 68215475.40 82353895.88\n",
       "\n",
       "[440 rows x 4 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recovered_forcast_confirmed=recovered_model.predict(future)\n",
    "#recovered_forcast_confirmed\n",
    "recovered_forcast_confirmed[['ds','yhat','yhat_lower','yhat_upper']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "recovered_confirmed_forcast_plot = recovered_model.plot(recovered_forcast_confirmed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "recovered_confirmed_forcast_plot_components=recovered_model.plot_components(recovered_forcast_confirmed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}