Covid-19 prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import seaborn as sns\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "from statsmodels.tsa.stattools import adfuller, kpss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\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>Id</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>ConfirmedCases</th>\n",
       "      <th>Fatalities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2020-01-22</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>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2020-01-23</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>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2020-01-24</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>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2020-01-25</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>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id Province/State Country/Region   Lat  Long        Date  ConfirmedCases  \\\n",
       "0   1            NaN    Afghanistan  33.0  65.0  2020-01-22             0.0   \n",
       "1   2            NaN    Afghanistan  33.0  65.0  2020-01-23             0.0   \n",
       "2   3            NaN    Afghanistan  33.0  65.0  2020-01-24             0.0   \n",
       "3   4            NaN    Afghanistan  33.0  65.0  2020-01-25             0.0   \n",
       "4   5            NaN    Afghanistan  33.0  65.0  2020-01-26             0.0   \n",
       "\n",
       "   Fatalities  \n",
       "0         0.0  \n",
       "1         0.0  \n",
       "2         0.0  \n",
       "3         0.0  \n",
       "4         0.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid=pd.read_csv(r'E:\\Documents\\Kaggle Projects\\Covid 19 time series prediction\\train.csv', sep=',')\n",
    "covid.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Province/state has all nan values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                   0\n",
       "Province/State    9240\n",
       "Country/Region       0\n",
       "Lat                  0\n",
       "Long                 0\n",
       "Date                 0\n",
       "ConfirmedCases       0\n",
       "Fatalities           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping Provice/state column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid=covid.drop(columns=['Province/State','Country/Region','Lat','Long','Id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date               object\n",
       "ConfirmedCases    float64\n",
       "Fatalities        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting object to date and time format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid['Date'] = pd.to_datetime(covid['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date              datetime64[ns]\n",
       "ConfirmedCases           float64\n",
       "Fatalities               float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>ConfirmedCases</th>\n",
       "      <th>Fatalities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  ConfirmedCases  Fatalities\n",
       "0 2020-01-22             0.0         0.0\n",
       "1 2020-01-23             0.0         0.0\n",
       "2 2020-01-24             0.0         0.0\n",
       "3 2020-01-25             0.0         0.0\n",
       "4 2020-01-26             0.0         0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "covid = covid.sort_values('Date', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_converter.py:129: FutureWarning:\n",
      "\n",
      "Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.lineplot(x=\"Date\", y=\"Fatalities\",markers=True, data=covid)\n",
    "ax.set_xticklabels(covid.Date,rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(16, 6))\n",
    "ax = sns.swarmplot(x=\"Date\", y=\"Fatalities\",color='blue', data=covid)\n",
    "ax.set_xticklabels(covid.Date,rotation=90)\n",
    "ax = sns.relplot(x=\"Date\", y=\"ConfirmedCases\",kind='line',color='red', data=covid)\n",
    "ax.set_xticklabels(covid.Date,rotation=90)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Date is indexed for rolling mean and standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "covidindexed=covid.set_index(['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing Stationary of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rolling statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "rolmean=covidindexed.rolling(30).mean()\n",
    "rolstd=covidindexed.rolling(30).mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00'),\n",
       " Text(0, 0, '2020-01-22 00:00:00')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 6))\n",
    "ax = sns.lineplot(x=\"Date\", y=\"ConfirmedCases\",markers=True, dashes=False,color='red', data=covid)\n",
    "ax = sns.lineplot(x=rolmean.reset_index().Date, y=rolmean['ConfirmedCases'].values,markers=True,color='green', data=rolmean)\n",
    "ax = sns.lineplot(x=rolstd.reset_index().Date, y=rolstd['ConfirmedCases'].values,markers=True,color='orange', data=rolstd)\n",
    "ax.set_xticklabels(covid.Date,rotation=90)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test statistics         -130.968645\n",
      "p-value                    0.000000\n",
      "#Lags used                 0.000000\n",
      "# of obs               17039.000000\n",
      "Critical Value(1%)        -3.430734\n",
      "Critical Value(5%)        -2.861710\n",
      "Critical Value(10%)       -2.566860\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "dftest=adfuller(covidindexed['ConfirmedCases'],autolag='AIC')\n",
    "dfoutput=pd.Series(dftest[0:4],index=['Test statistics','p-value','#Lags used','# of obs'])\n",
    "for key,value in dftest[4].items():\n",
    "    dfoutput['Critical Value(%s)'%key]=value\n",
    "print(dfoutput)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([          nan,           nan,           nan, ..., 1325.26666667,\n",
       "       1320.16666667, 1101.06666667])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rolmean['ConfirmedCases'].values\n",
    "\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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}