forcasting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "from datetime import timedelta\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVR\n",
    "from statsmodels.tsa.api import Holt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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        1.0     0.0        0.0  \n",
       "1       14.0     0.0        0.0  \n",
       "2        6.0     0.0        0.0  \n",
       "3        1.0     0.0        0.0  \n",
       "4        0.0     0.0        0.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid=pd.read_csv(\"covid_19_data_1.csv\")\n",
    "covid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size/shape of the dataset (48095, 8)\n",
      "checking fot the null values SNo                    0\n",
      "ObservationDate        0\n",
      "Province/State     19705\n",
      "Country/Region         0\n",
      "Last Update            0\n",
      "Confirmed              0\n",
      "Deaths                 0\n",
      "Recovered              0\n",
      "dtype: int64\n",
      "checking the date-type SNo                  int64\n",
      "ObservationDate     object\n",
      "Province/State      object\n",
      "Country/Region      object\n",
      "Last Update         object\n",
      "Confirmed          float64\n",
      "Deaths             float64\n",
      "Recovered          float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(\"size/shape of the dataset\",covid.shape)\n",
    "print(\"checking fot the null values\",covid.isnull().sum())\n",
    "print(\"checking the date-type\",covid.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#droping the columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid.drop([\"SNo\"],1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ObservationDate        0\n",
       "Province/State     19705\n",
       "Country/Region         0\n",
       "Last Update            0\n",
       "Confirmed              0\n",
       "Deaths                 0\n",
       "Recovered              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid[\"ObservationDate\"]=pd.to_datetime(covid[\"ObservationDate\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2020-01-22\n",
       "1       2020-01-22\n",
       "2       2020-01-22\n",
       "3       2020-01-22\n",
       "4       2020-01-22\n",
       "           ...    \n",
       "48090   2020-06-22\n",
       "48091   2020-06-22\n",
       "48092   2020-06-22\n",
       "48093   2020-06-22\n",
       "48094   2020-06-22\n",
       "Name: ObservationDate, Length: 48095, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid[\"ObservationDate\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouping different types of cases as per the date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "datewise=covid.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basic information\n",
      "Total number of confirmed cases aroud the world 9098641.0\n",
      "Total number of Recoverd cases aroud the world 4526333.0\n",
      "Total number of Deaths cases aroud the world 472171.0\n",
      "Total number of Active cases around the world 4100137.0\n",
      "Total number of Close cases around the world 4998504.0\n"
     ]
    }
   ],
   "source": [
    "print(\"basic information\")\n",
    "print(\"Total number of confirmed cases aroud the world\",datewise[\"Confirmed\"].iloc[-1])\n",
    "print(\"Total number of Recoverd cases aroud the world\",datewise[\"Recovered\"].iloc[-1])\n",
    "print(\"Total number of Deaths cases aroud the world\",datewise[\"Deaths\"].iloc[-1])\n",
    "print(\"Total number of Active cases around the world\",datewise[\"Confirmed\"].iloc[-1]-datewise[\"Recovered\"].iloc[-1]-datewise[\"Deaths\"].iloc[-1])\n",
    "print(\"Total number of Close cases around the world\",datewise[\"Recovered\"].iloc[-1]+datewise[\"Deaths\"].iloc[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "         13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "         26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "         39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "         52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "         65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "         78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "         91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "        104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "        117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "        130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
       "        143, 144, 145, 146, 147, 148, 149, 150, 151, 152]),\n",
       " <a list of 153 Text xticklabel objects>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.barplot(x=datewise.index.date,y=datewise[\"Confirmed\"]-datewise[\"Recovered\"]-datewise[\"Deaths\"])\n",
    "plt.title(\"Distributions plot for active cases\")\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,\n",
       "         13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,\n",
       "         26,  27,  28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,\n",
       "         39,  40,  41,  42,  43,  44,  45,  46,  47,  48,  49,  50,  51,\n",
       "         52,  53,  54,  55,  56,  57,  58,  59,  60,  61,  62,  63,  64,\n",
       "         65,  66,  67,  68,  69,  70,  71,  72,  73,  74,  75,  76,  77,\n",
       "         78,  79,  80,  81,  82,  83,  84,  85,  86,  87,  88,  89,  90,\n",
       "         91,  92,  93,  94,  95,  96,  97,  98,  99, 100, 101, 102, 103,\n",
       "        104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
       "        117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
       "        130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142,\n",
       "        143, 144, 145, 146, 147, 148, 149, 150, 151, 152]),\n",
       " <a list of 153 Text xticklabel objects>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.barplot(x=datewise.index.date,y=datewise[\"Recovered\"]+datewise[\"Deaths\"])\n",
    "plt.title(\"Distributions plot for close cases\")\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'weakly progress of different cases')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "datewise[\"WeekofYear\"]=datewise.index.weekofyear\n",
    "week_num=[]\n",
    "weekwise_confirmed=[]\n",
    "weekwise_recovered=[]\n",
    "weekwise_deaths=[]\n",
    "w=1\n",
    "for i in list(datewise[\"WeekofYear\"].unique()):\n",
    "    weekwise_confirmed.append(datewise[datewise[\"WeekofYear\"]==i][\"Confirmed\"].iloc[-1])\n",
    "    weekwise_recovered.append(datewise[datewise[\"WeekofYear\"]==i][\"Recovered\"].iloc[-1])\n",
    "    weekwise_deaths.append(datewise[datewise[\"WeekofYear\"]==i][\"Deaths\"].iloc[-1])\n",
    "    week_num.append(w)\n",
    "    w=w+1\n",
    "    \n",
    "plt.figure(figsize=(8.,5))\n",
    "plt.plot(week_num,weekwise_confirmed,linewidth=3)\n",
    "plt.plot(week_num,weekwise_recovered,linewidth=3)\n",
    "plt.plot(week_num,weekwise_deaths,linewidth=3)\n",
    "plt.xlabel(\"WeekNumber\")\n",
    "plt.ylabel(\"Number of cases\")\n",
    "plt.title(\"weakly progress of different cases\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2)=plt.subplots(1,2,figsize=(12,4))\n",
    "sns.barplot(x=week_num,y=pd.Series(weekwise_confirmed).diff().fillna(0),ax=ax1)\n",
    "sns.barplot(x=week_num,y=pd.Series(weekwise_deaths).diff().fillna(0),ax=ax2)\n",
    "ax1.set_xlabel(\"Week Number\")\n",
    "ax2.set_xlabel(\"Week Number\")\n",
    "ax1.set_ylabel(\"Number of Confirmend cases\")\n",
    "ax2.set_ylabel(\"Number of deat cases\")\n",
    "ax1.set_title(\"Weekly increse in number of confirmed cases\")\n",
    "ax2.set_title(\"Weekly increse in number of death cases\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average increse in number of Confirmed cases everyday 59465.0\n",
      "Average increse in number of Recovered cases everyday 29584.0\n",
      "Average increse in number of Death cases everyday 3086.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Average increse in number of Confirmed cases everyday\",np.round(datewise[\"Confirmed\"].diff().fillna(0).mean()))\n",
    "print(\"Average increse in number of Recovered cases everyday\",np.round(datewise[\"Recovered\"].diff().fillna(0).mean()))\n",
    "print(\"Average increse in number of Death cases everyday\",np.round(datewise[\"Deaths\"].diff().fillna(0).mean()))\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(datewise[\"Confirmed\"].diff().fillna(0),label=\"Daily incress in Confirmed cases\",linewidth=3)\n",
    "plt.plot(datewise[\"Recovered\"].diff().fillna(0),label=\"Daily incress in Recovered cases\",linewidth=3)\n",
    "plt.plot(datewise[\"Deaths\"].diff().fillna(0),label=\"Daily incress in Deaths cases\",linewidth=3)\n",
    "plt.xlabel(\"TimeStamp\")\n",
    "plt.ylabel(\"Daily increse\")\n",
    "plt.title(\"Daily Increse in cases\")\n",
    "plt.legend()\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#country wise analsis\n",
    "#Country wise mortality rate number of death cases/ number of recoverd cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "countrywise=covid[covid[\"ObservationDate\"]==covid[\"ObservationDate\"].max()].groupby([\"Country/Region\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"}).sort_values([\"Confirmed\"],ascending=False)\n",
    "countrywise[\"Mortality\"]=(countrywise[\"Deaths\"]/countrywise[\"Recovered\"])*100\n",
    "countrywise[\"Recovered\"]=(countrywise[\"Recovered\"]/countrywise[\"Confirmed\"])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Top 15 countries as per number of death cases')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2)=plt.subplots(1,2,figsize=(25,10))\n",
    "top_15confirmed=countrywise.sort_values([\"Confirmed\"],ascending=False).head(15)\n",
    "top_15deaths=countrywise.sort_values([\"Deaths\"],ascending=False).head(15)\n",
    "sns.barplot(x=top_15confirmed[\"Confirmed\"],y=top_15confirmed.index,ax=ax1)\n",
    "ax1.set_title(\"Top 15 countries as per number of confirmed cases\")\n",
    "sns.barplot(x=top_15deaths[\"Deaths\"],y=top_15deaths.index,ax=ax2)\n",
    "ax2.set_title(\"Top 15 countries as per number of death cases\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data analysis of pakistan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confirmed    185034.0\n",
      "Recovered     73471.0\n",
      "Deaths         3695.0\n",
      "Name: 2020-06-22 00:00:00, dtype: float64\n",
      "Total active cases 107868.0\n",
      "Total Number of close cases 77166.0\n"
     ]
    }
   ],
   "source": [
    "pak_data=covid[covid[\"Country/Region\"]==\"Pakistan\"]\n",
    "datewise_pak=pak_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
    "print(datewise_pak.iloc[-1])\n",
    "print(\"Total active cases\",datewise_pak[\"Confirmed\"].iloc[-1]-datewise_pak[\"Recovered\"].iloc[-1]-datewise_pak[\"Deaths\"].iloc[-1])\n",
    "print(\"Total Number of close cases\",datewise_pak[\"Recovered\"].iloc[-1]+datewise_pak[\"Deaths\"].iloc[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'weakly progress of different cases')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "datewise_pak[\"WeekofYear\"]=datewise_pak.index.weekofyear\n",
    "week_num_pak=[]\n",
    "pak_weekwise_confirmed=[]\n",
    "pak_weekwise_recovered=[]\n",
    "pak_weekwise_deaths=[]\n",
    "w=1\n",
    "for i in list(datewise_pak[\"WeekofYear\"].unique()):\n",
    "    pak_weekwise_confirmed.append(datewise_pak[datewise_pak[\"WeekofYear\"]==i][\"Confirmed\"].iloc[-1])\n",
    "    pak_weekwise_recovered.append(datewise_pak[datewise_pak[\"WeekofYear\"]==i][\"Recovered\"].iloc[-1])\n",
    "    pak_weekwise_deaths.append(datewise_pak[datewise_pak[\"WeekofYear\"]==i][\"Deaths\"].iloc[-1])\n",
    "    week_num_pak.append(w)\n",
    "    w=w+1\n",
    "    \n",
    "plt.figure(figsize=(8.,5))\n",
    "plt.plot(week_num_pak,pak_weekwise_confirmed,linewidth=3)\n",
    "plt.plot(week_num_pak,pak_weekwise_recovered,linewidth=3)\n",
    "plt.plot(week_num_pak,pak_weekwise_deaths,linewidth=3)\n",
    "plt.xlabel(\"WeekNumber\")\n",
    "plt.ylabel(\"Number of cases\")\n",
    "plt.title(\"weakly progress of different cases\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#how many days pakistan taken to reach present situtation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It took  118 days in Pakistan to reach 185034.0 Confirmed cases\n",
      "It took  82 days in Italy to reach Confirmed cases\n",
      "It took  82 days in US to reach Confirmed cases\n",
      "It took  153 days in China to reach Confirmed cases\n",
      "It took  76 days in Spain to reach Confirmed cases\n"
     ]
    }
   ],
   "source": [
    "max_pak=datewise_pak[\"Confirmed\"].max()\n",
    "china_data=covid[covid[\"Country/Region\"]==\"Mainland China\"]\n",
    "Italy_data=covid[covid[\"Country/Region\"]==\"Italy\"]\n",
    "Us_data=covid[covid[\"Country/Region\"]==\"US\"]\n",
    "spain_data=covid[covid[\"Country/Region\"]==\"Spain\"]\n",
    "datewise_china=china_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
    "datewise_Italy=Italy_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
    "datewise_Us=Us_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
    "datewise_spain=spain_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
    "print(\"It took \",datewise_pak[(datewise_pak[\"Confirmed\"]>0)].shape[0],\"days in Pakistan to reach\",max_pak,\"Confirmed cases\")\n",
    "print(\"It took \",datewise_Italy[(datewise_Italy[\"Confirmed\"]>0)&(datewise_Italy[\"Confirmed\"]<=max_pak)].shape[0],\"days in Italy to reach Confirmed cases\")\n",
    "print(\"It took \",datewise_Us[(datewise_Us[\"Confirmed\"]>0)&(datewise_Italy[\"Confirmed\"]<=max_pak)].shape[0],\"days in US to reach Confirmed cases\")\n",
    "print(\"It took \",datewise_china[(datewise_china[\"Confirmed\"]>0)&(datewise_china[\"Confirmed\"]<=max_pak)].shape[0],\"days in China to reach Confirmed cases\")\n",
    "print(\"It took \",datewise_spain[(datewise_spain[\"Confirmed\"]>0)&(datewise_spain[\"Confirmed\"]<=max_pak)].shape[0],\"days in Spain to reach Confirmed cases\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_pak=datewise_pak[\"Confirmed\"].max()\n",
    "Us_data=covid[covid[\"Country/Region\"]==\"US\"]\n",
    "brazil_data=covid[covid[\"Country/Region\"]==\"Brazil\"]\n",
    "uk_data=covid[covid[\"Country/Region\"]==\"UK\"]\n",
    "Italy_data=covid[covid[\"Country/Region\"]==\"Italy\"]\n",
    "france_data=covid[covid[\"Country/Region\"]==\"France\"]\n",
    "spain_data=covid[covid[\"Country/Region\"]==\"Spain\"]\n",
    "maxico_data=covid[covid[\"Country/Region\"]==\"Mexico\"]\n",
    "india_data=covid[covid[\"Country/Region\"]==\"India\"]\n",
    "iran_data=covid[covid[\"Country/Region\"]==\"Iran\"]\n",
    "belgium_data=covid[covid[\"Country/Region\"]==\"Belgium\"]\n",
    "canada_data=covid[covid[\"Country/Region\"]==\"Canada\"]\n",
    "peru_data=covid[covid[\"Country/Region\"]==\"Peru\"]\n",
    "russia_data=covid[covid[\"Country/Region\"]==\"Russia\"]\n",
    "netherlands_data=covid[covid[\"Country/Region\"]==\"Netherlands\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "datewise_Us=Us_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
    "datewise_brazil=brazil_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
    "datewise_uk=uk_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
    "datewise_Italy=Italy_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
    "datewise_france=france_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
    "datewise_spain=spain_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
    "datewise_maxico=maxico_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
    "datewise_india=india_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
    "datewise_iran=iran_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
    "datewise_belgium=belgium_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
    "datewise_canada=canada_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
    "datewise_peru=peru_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
    "datewise_russia=russia_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})\n",
    "datewise_netherlands=netherlands_data.groupby([\"ObservationDate\"]).agg({\"Confirmed\":\"sum\",\"Recovered\":\"sum\",\"Deaths\":\"sum\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It took  118 days in Pakistan to reach 185034.0 Confirmed cases\n",
      "It took  69 days in US to reach Confirmed cases\n",
      "It took  77 days in Brazil to reach Confirmed cases\n",
      "It took  93 days in UK to reach Confirmed cases\n",
      "It took  82 days in Italy to reach Confirmed cases\n",
      "It took  125 days in France to reach Confirmed cases\n",
      "It took  76 days in Spain to reach Confirmed cases\n",
      "It took  115 days in Maxico to reach Confirmed cases\n",
      "It took  122 days in India to reach Confirmed cases\n",
      "It took  116 days in Iran to reach Confirmed cases\n",
      "It took  140 days in Belgium to reach Confirmed cases\n",
      "It took  149 days in Canada to reach Confirmed cases\n",
      "It took  91 days in Peru to reach Confirmed cases\n",
      "It took  98 days in Russia to reach Confirmed cases\n",
      "It took  117 days in Netherlands to reach Confirmed cases\n"
     ]
    }
   ],
   "source": [
    "print(\"It took \",datewise_pak[(datewise_pak[\"Confirmed\"]>0)].shape[0],\"days in Pakistan to reach\",max_pak,\"Confirmed cases\")\n",
    "print(\"It took \",datewise_Us[(datewise_Us[\"Confirmed\"]>0)&(datewise_Us[\"Confirmed\"]<=max_pak)].shape[0],\"days in US to reach Confirmed cases\")\n",
    "print(\"It took \",datewise_brazil[(datewise_brazil[\"Confirmed\"]>0)&(datewise_brazil[\"Confirmed\"]<=max_pak)].shape[0],\"days in Brazil to reach Confirmed cases\")\n",
    "print(\"It took \",datewise_uk[(datewise_uk[\"Confirmed\"]>0)&(datewise_uk[\"Confirmed\"]<=max_pak)].shape[0],\"days in UK to reach Confirmed cases\")\n",
    "print(\"It took \",datewise_Italy[(datewise_Italy[\"Confirmed\"]>0)&(datewise_Italy[\"Confirmed\"]<=max_pak)].shape[0],\"days in Italy to reach Confirmed cases\")\n",
    "print(\"It took \",datewise_france[(datewise_france[\"Confirmed\"]>0)&(datewise_france[\"Confirmed\"]<=max_pak)].shape[0],\"days in France to reach Confirmed cases\")\n",
    "print(\"It took \",datewise_spain[(datewise_spain[\"Confirmed\"]>0)&(datewise_spain[\"Confirmed\"]<=max_pak)].shape[0],\"days in Spain to reach Confirmed cases\")\n",
    "print(\"It took \",datewise_maxico[(datewise_maxico[\"Confirmed\"]>0)&(datewise_maxico[\"Confirmed\"]<=max_pak)].shape[0],\"days in Maxico to reach Confirmed cases\")\n",
    "print(\"It took \",datewise_india[(datewise_india[\"Confirmed\"]>0)&(datewise_india[\"Confirmed\"]<=max_pak)].shape[0],\"days in India to reach Confirmed cases\")\n",
    "print(\"It took \",datewise_iran[(datewise_iran[\"Confirmed\"]>0)&(datewise_iran[\"Confirmed\"]<=max_pak)].shape[0],\"days in Iran to reach Confirmed cases\")\n",
    "print(\"It took \",datewise_belgium[(datewise_belgium[\"Confirmed\"]>0)&(datewise_belgium[\"Confirmed\"]<=max_pak)].shape[0],\"days in Belgium to reach Confirmed cases\")\n",
    "print(\"It took \",datewise_canada[(datewise_canada[\"Confirmed\"]>0)&(datewise_canada[\"Confirmed\"]<=max_pak)].shape[0],\"days in Canada to reach Confirmed cases\")\n",
    "print(\"It took \",datewise_peru[(datewise_peru[\"Confirmed\"]>0)&(datewise_peru[\"Confirmed\"]<=max_pak)].shape[0],\"days in Peru to reach Confirmed cases\")\n",
    "print(\"It took \",datewise_russia[(datewise_russia[\"Confirmed\"]>0)&(datewise_russia[\"Confirmed\"]<=max_pak)].shape[0],\"days in Russia to reach Confirmed cases\")\n",
    "print(\"It took \",datewise_netherlands[(datewise_netherlands[\"Confirmed\"]>0)&(datewise_netherlands[\"Confirmed\"]<=max_pak)].shape[0],\"days in Netherlands to reach Confirmed cases\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "datewise[\"Days Since\"]=datewise.index-datewise.index[0]\n",
    "datewise[\"Days Since\"]=datewise[\"Days Since\"].dt.days\n",
    "trian_ml=datewise.iloc[:int(datewise.shape[0]*0.95)]\n",
    "valid_ml=datewise.iloc[:int(datewise.shape[0]*0.95):]\n",
    "model_scores=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Zubair\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVR(C=1, cache_size=200, coef0=0.0, degree=5, epsilon=0.001, gamma='scale',\n",
       "    kernel='poly', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg=LinearRegression(normalize=True)\n",
    "svm=SVR(C=1,degree=5,kernel='poly',epsilon=0.001)\n",
    "lin_reg.fit(np.array(trian_ml[\"Days Since\"]).reshape(-1,1),np.array(trian_ml[\"Confirmed\"]).reshape(-1,1))\n",
    "svm.fit(np.array(trian_ml[\"Days Since\"]).reshape(-1,1),np.array(trian_ml[\"Confirmed\"]).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_valid_lin_reg=lin_reg.predict(np.array(valid_ml[\"Days Since\"]).reshape(-1,1))\n",
    "prediction_valid_svm=svm.predict(np.array(valid_ml[\"Days Since\"]).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dates</th>\n",
       "      <th>LR</th>\n",
       "      <th>SVR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-06-23</td>\n",
       "      <td>6579294</td>\n",
       "      <td>8393327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-06-24</td>\n",
       "      <td>6633463</td>\n",
       "      <td>8640650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-06-25</td>\n",
       "      <td>6687632</td>\n",
       "      <td>8894481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>6741802</td>\n",
       "      <td>9154948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>6795971</td>\n",
       "      <td>9422180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-06-28</td>\n",
       "      <td>6850141</td>\n",
       "      <td>9696307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-06-29</td>\n",
       "      <td>6904310</td>\n",
       "      <td>9977463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>6958480</td>\n",
       "      <td>10265782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>7012649</td>\n",
       "      <td>10561400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-07-02</td>\n",
       "      <td>7066818</td>\n",
       "      <td>10864454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Dates      LR      SVR\n",
       "0 2020-06-23 6579294  8393327\n",
       "1 2020-06-24 6633463  8640650\n",
       "2 2020-06-25 6687632  8894481\n",
       "3 2020-06-26 6741802  9154948\n",
       "4 2020-06-27 6795971  9422180\n",
       "5 2020-06-28 6850141  9696307\n",
       "6 2020-06-29 6904310  9977463\n",
       "7 2020-06-30 6958480 10265782\n",
       "8 2020-07-01 7012649 10561400\n",
       "9 2020-07-02 7066818 10864454"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_date=[]\n",
    "new_prediction_lr=[]\n",
    "new_prediction_svm=[]\n",
    "for i in range(1,18):\n",
    "    new_date.append(datewise.index[-1]+timedelta(days=i))\n",
    "    new_prediction_lr.append(lin_reg.predict(np.array(datewise[\"Days Since\"].max()+i).reshape(-1,1))[0][0])\n",
    "    new_prediction_svm.append(svm.predict(np.array(datewise[\"Days Since\"].max()+i).reshape(-1,1))[0])\n",
    "\n",
    "pd.set_option(\"display.float_format\",lambda x: '%.f'%x)\n",
    "model_predictions=pd.DataFrame(zip(new_date,new_prediction_lr,new_prediction_svm),columns=[\"Dates\",\"LR\",\"SVR\"])\n",
    "model_predictions.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#time series analysis\n",
    "model_train=datewise.iloc[:int(datewise.shape[0]*0.85)]\n",
    "valid=datewise.iloc[int(datewise.shape[0]*0.85):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Zubair\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\holtwinters.py:731: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  loc = initial_p >= ub\n"
     ]
    }
   ],
   "source": [
    "#arema model\n",
    "holt=Holt(np.asarray(model_train[\"Confirmed\"])).fit(smoothing_level=1.4,smoothing_slope=0.2)\n",
    "y_pred=valid.copy()\n",
    "y_pred[\"Holt\"]=holt.forecast(len(valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dates</th>\n",
       "      <th>LR</th>\n",
       "      <th>SVR</th>\n",
       "      <th>Holts Linear Model Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-06-23</td>\n",
       "      <td>6579294</td>\n",
       "      <td>8393327</td>\n",
       "      <td>8751515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-06-24</td>\n",
       "      <td>6633463</td>\n",
       "      <td>8640650</td>\n",
       "      <td>8863225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-06-25</td>\n",
       "      <td>6687632</td>\n",
       "      <td>8894481</td>\n",
       "      <td>8974934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>6741802</td>\n",
       "      <td>9154948</td>\n",
       "      <td>9086644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-06-27</td>\n",
       "      <td>6795971</td>\n",
       "      <td>9422180</td>\n",
       "      <td>9198354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Dates      LR     SVR  Holts Linear Model Prediction\n",
       "0 2020-06-23 6579294 8393327                        8751515\n",
       "1 2020-06-24 6633463 8640650                        8863225\n",
       "2 2020-06-25 6687632 8894481                        8974934\n",
       "3 2020-06-26 6741802 9154948                        9086644\n",
       "4 2020-06-27 6795971 9422180                        9198354"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holt_new_date=[]\n",
    "holt_new_prediction=[]\n",
    "for i in range(1,18):\n",
    "    holt_new_date.append((datewise.index[-1]+timedelta(days=i)))\n",
    "    holt_new_prediction.append(holt.forecast((len(valid)+i))[-1])\n",
    "model_predictions[\"Holts Linear Model Prediction\"]=holt_new_prediction \n",
    "model_predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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