Untitled (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from pandas.io.json import json_normalize\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\abhi7\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\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>countrycode</th>\n",
       "      <th>date</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39618</th>\n",
       "      <td>FJ</td>\n",
       "      <td>9/23/20</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39619</th>\n",
       "      <td>KN</td>\n",
       "      <td>9/23/20</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39620</th>\n",
       "      <td>VA</td>\n",
       "      <td>9/23/20</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39621</th>\n",
       "      <td>LS</td>\n",
       "      <td>9/23/20</td>\n",
       "      <td>1507</td>\n",
       "      <td>35</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39622</th>\n",
       "      <td>SC</td>\n",
       "      <td>9/23/20</td>\n",
       "      <td>143</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39623</th>\n",
       "      <td>PG</td>\n",
       "      <td>9/23/20</td>\n",
       "      <td>527</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39624</th>\n",
       "      <td>EH</td>\n",
       "      <td>9/23/20</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39625</th>\n",
       "      <td>NANA</td>\n",
       "      <td>9/23/20</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39626</th>\n",
       "      <td>GL</td>\n",
       "      <td>9/23/20</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39627</th>\n",
       "      <td>LU</td>\n",
       "      <td>9/23/20</td>\n",
       "      <td>8090</td>\n",
       "      <td>124</td>\n",
       "      <td>6367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      countrycode     date cases deaths recovered\n",
       "39618          FJ  9/23/20    32      2        18\n",
       "39619          KN  9/23/20    19      0        15\n",
       "39620          VA  9/23/20    12      0        12\n",
       "39621          LS  9/23/20  1507     35        11\n",
       "39622          SC  9/23/20   143      0        11\n",
       "39623          PG  9/23/20   527      7         8\n",
       "39624          EH  9/23/20    10      1         8\n",
       "39625        NANA  9/23/20     9      2         0\n",
       "39626          GL  9/23/20    13      0         0\n",
       "39627          LU  9/23/20  8090    124      6367"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = requests.get('https://thevirustracker.com/timeline/map-data.json')\n",
    "api_data = response.json()\n",
    "api_data = api_data['data']\n",
    "data = json_normalize(api_data)\n",
    "data.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39628 entries, 0 to 39627\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   countrycode  39628 non-null  object\n",
      " 1   date         39628 non-null  object\n",
      " 2   cases        39628 non-null  object\n",
      " 3   deaths       39628 non-null  object\n",
      " 4   recovered    39628 non-null  object\n",
      "dtypes: object(5)\n",
      "memory usage: 774.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.recovered.replace('','0',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 39628 entries, 0 to 39627\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype         \n",
      "---  ------       --------------  -----         \n",
      " 0   countrycode  39628 non-null  object        \n",
      " 1   date         39628 non-null  datetime64[ns]\n",
      " 2   cases        39628 non-null  int64         \n",
      " 3   deaths       39628 non-null  int64         \n",
      " 4   recovered    39628 non-null  int64         \n",
      "dtypes: datetime64[ns](1), int64(3), object(1)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data['date'] = pd.to_datetime(data['date'])\n",
    "data['cases'] = data['cases'].apply(int)\n",
    "data['recovered'] = data['recovered'].apply(int)\n",
    "data['deaths'] = data['deaths'].apply(int)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>date</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>recovered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>countrycode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CN</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>548</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KR</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TH</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TW</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date  cases  deaths  recovered\n",
       "countrycode                                     \n",
       "CN          2020-01-22    548      24         28\n",
       "JP          2020-01-22      2       0          0\n",
       "KR          2020-01-22      1       0          0\n",
       "TH          2020-01-22      2       0          0\n",
       "TW          2020-01-22      1       0          0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by=['countrycode','date'])\n",
    "data.set_index('countrycode',inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter country Name:india\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\abhi7\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\ipykernel_launcher.py:6: MatplotlibDeprecationWarning: Non-1D inputs to pie() are currently squeeze()d, but this behavior is deprecated since 3.1 and will be removed in 3.3; pass a 1D array instead.\n",
      "  \n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def analysis(ct):\n",
    "    country = {'india':'IN','usa':'US','canada':'CA'}\n",
    "    countryData = data.xs(country[ct.lower()])\n",
    "    fig,ax = plt.subplots(nrows=2,ncols=1,figsize=(15,15))\n",
    "    score=[countryData.iloc[-1:]['cases']-countryData.iloc[-1:]['recovered']-countryData.iloc[-1:]['deaths'],countryData.iloc[-1:]['deaths'],countryData.iloc[-1:]['recovered']]\n",
    "    ax[0].pie(score, labels=['current cases','Death','recovered'], autopct='%1.2f%%')\n",
    "    ax[1].plot(countryData['date'],countryData.drop('date',axis=1))\n",
    "country = input('Enter country Name:')\n",
    "\n",
    "analysis(country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['DayNo']=data['date'].apply(lambda x: int(str(x-np.datetime64('2020-01-21')).split()[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>recovered</th>\n",
       "      <th>DayNo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>countrycode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CN</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>548</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KR</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TH</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TW</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date  cases  deaths  recovered  DayNo\n",
       "countrycode                                            \n",
       "CN          2020-01-22    548      24         28      1\n",
       "JP          2020-01-22      2       0          0      1\n",
       "KR          2020-01-22      1       0          0      1\n",
       "TH          2020-01-22      2       0          0      1\n",
       "TW          2020-01-22      1       0          0      1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.reset_index().sort_values(by=['countrycode','date']).set_index(['countrycode','date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error,r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Country Name:india\n",
      "Enter the Date(yy-mm-dd):2020-09-25\n",
      "Root Mean Square Evaluation: 30863.11124098576\n",
      "R2 score of designed model: 0.9996708808577287\n",
      "\n",
      "Prediction: 5837214\n",
      "\n",
      "\t\tActual Report\n",
      "              cases  deaths  recovered  DayNo\n",
      "date                                         \n",
      "2020-09-25  5818570   92290     439934    248\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def predictor(country,day):\n",
    "    X_poly=PolynomialFeatures(5).fit_transform(np.array(data.xs(country)['DayNo']).reshape(-1,1))\n",
    "    n_poly=PolynomialFeatures(5).fit_transform(np.array([day]).reshape(-1,1))\n",
    "    lm=LinearRegression(normalize=True)\n",
    "    lm.fit(X_poly,data.xs(country)['cases'])\n",
    "    pred=lm.predict(X_poly)\n",
    "    \n",
    "    \n",
    "    sns.set_style('darkgrid')\n",
    "    fig=plt.figure()\n",
    "    \n",
    "    ax1=fig.add_axes([0.1,0.1,0.9,0.9])\n",
    "    ax1.set_title('Prediction Function')\n",
    "    ax1.set_xlabel('Predicted Confirmed')\n",
    "    ax1.set_ylabel('Actaul Confirmed')\n",
    "    ax1.scatter(data.xs(country)['cases'],pred,color='red')\n",
    "    \n",
    "    ax2=fig.add_axes([0.7,0.7,0.3,0.3])\n",
    "    ax2.set_title('Error Distribution')\n",
    "    ax2.set_xlabel('Actual-Predicted')\n",
    "    ax2.set_ylabel('Error rate')\n",
    "    ax2.hist(data.xs(country)['cases']-pred,color='green',bins=30)\n",
    "    \n",
    "    \n",
    "    rmse = np.sqrt(mean_squared_error(data.xs(country)['cases'],pred))\n",
    "    r2 = r2_score(data.xs(country)['cases'],pred)\n",
    "    print('Root Mean Square Evaluation:',rmse)\n",
    "    print('R2 score of designed model:',r2)\n",
    "    \n",
    "    print('\\nPrediction:',int(lm.predict(n_poly)[0]))\n",
    "    if(day<=data.describe()['DayNo']['max']):\n",
    "        print('\\n\\t\\tActual Report')\n",
    "        print(data.xs(country)[data.xs(country)['DayNo']==day])\n",
    "\n",
    "country=input('Country Name:')\n",
    "ct = {'india':'IN','usa':'US','canada':'CA'}\n",
    "date=input('Enter the Date(yy-mm-dd):')\n",
    "n=int(str(np.datetime64(date)-np.datetime64('2020-01-21')).split()[0])\n",
    "predictor(ct[country.lower()],n)"
   ]
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}