Team Vector- Air quality Detection (1).ipynb

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  {
   "cell_type": "code",
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   "id": "173407b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "from statsmodels.graphics.tsaplots import plot_pacf\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4616d0b0",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Datetime</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>NH3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>Xylene</th>\n",
       "      <th>AQI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>1/1/2015 1:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>40.01</td>\n",
       "      <td>36.37</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>122.07</td>\n",
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       "      <td>0.02</td>\n",
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       "      <td>19.73</td>\n",
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       "      <td>0.02</td>\n",
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       "      <td>1/1/2015 3:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.08</td>\n",
       "      <td>19.32</td>\n",
       "      <td>11.08</td>\n",
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       "      <td>0.08</td>\n",
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       "      <td>0.30</td>\n",
       "      <td>16.45</td>\n",
       "      <td>9.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.30</td>\n",
       "      <td>39.53</td>\n",
       "      <td>153.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1/1/2015 5:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.12</td>\n",
       "      <td>14.90</td>\n",
       "      <td>7.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.12</td>\n",
       "      <td>32.63</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>103.00</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>331.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>341.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1535 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           City        Datetime  PM2.5  PM10    NO    NO2    NOx  NH3    CO  \\\n",
       "0     Ahmedabad   1/1/2015 1:00    NaN   NaN  1.00  40.01  36.37  NaN  1.00   \n",
       "1     Ahmedabad   1/1/2015 2:00    NaN   NaN  0.02  27.75  19.73  NaN  0.02   \n",
       "2     Ahmedabad   1/1/2015 3:00    NaN   NaN  0.08  19.32  11.08  NaN  0.08   \n",
       "3     Ahmedabad   1/1/2015 4:00    NaN   NaN  0.30  16.45   9.20  NaN  0.30   \n",
       "4     Ahmedabad   1/1/2015 5:00    NaN   NaN  0.12  14.90   7.85  NaN  0.12   \n",
       "...         ...             ...    ...   ...   ...    ...    ...  ...   ...   \n",
       "1530  Ahmedabad  3/5/2015 19:00    NaN   NaN   NaN  21.00    NaN  NaN   NaN   \n",
       "1531  Ahmedabad  3/5/2015 20:00    NaN   NaN   NaN  32.00    NaN  NaN   NaN   \n",
       "1532  Ahmedabad  3/5/2015 21:00    NaN   NaN   NaN  27.00    NaN  NaN   NaN   \n",
       "1533  Ahmedabad  3/5/2015 22:00    NaN   NaN   NaN    NaN    NaN  NaN   NaN   \n",
       "1534  Ahmedabad  3/5/2015 23:00    NaN   NaN   NaN    NaN    NaN  NaN   NaN   \n",
       "\n",
       "         SO2      O3  Benzene  Toluene  Xylene    AQI  \n",
       "0     122.07     NaN      0.0      0.0     0.0    NaN  \n",
       "1      85.90     NaN      0.0      0.0     0.0    NaN  \n",
       "2      52.83     NaN      0.0      0.0     0.0    NaN  \n",
       "3      39.53  153.58      0.0      0.0     0.0    NaN  \n",
       "4      32.63     NaN      0.0      0.0     0.0    NaN  \n",
       "...      ...     ...      ...      ...     ...    ...  \n",
       "1530  103.00  160.00      NaN      NaN     NaN  331.0  \n",
       "1531   99.00  189.00      NaN      NaN     NaN  334.0  \n",
       "1532  124.00  185.00      NaN      NaN     NaN  337.0  \n",
       "1533     NaN     NaN      NaN      NaN     NaN  340.0  \n",
       "1534     NaN     NaN      NaN      NaN     NaN  341.0  \n",
       "\n",
       "[1535 rows x 15 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"newhour.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "32fbfe09",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Datetime']=pd.to_datetime(data['Datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8d526525",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 1800x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import missingno as msno\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "missing=pd.DataFrame(data.isna().sum()/len(data))\n",
    "missing.columns=['Proportion']\n",
    "\n",
    "msno.bar(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "22b2461b",
   "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>City</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>PM</th>\n",
       "      <th>Nitric</th>\n",
       "      <th>CO</th>\n",
       "      <th>NH3</th>\n",
       "      <th>O3</th>\n",
       "      <th>SO2</th>\n",
       "      <th>BTX</th>\n",
       "      <th>AQI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>01:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77.38</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>122.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>02:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.50</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>85.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>03:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.48</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>52.83</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.95</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>153.58</td>\n",
       "      <td>39.53</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>05:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.87</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>32.63</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City        date      time   PM  Nitric    CO  NH3      O3     SO2  \\\n",
       "0  Ahmedabad  2015-01-01  01:00:00  0.0   77.38  1.00  0.0    0.00  122.07   \n",
       "1  Ahmedabad  2015-01-01  02:00:00  0.0   47.50  0.02  0.0    0.00   85.90   \n",
       "2  Ahmedabad  2015-01-01  03:00:00  0.0   30.48  0.08  0.0    0.00   52.83   \n",
       "3  Ahmedabad  2015-01-01  04:00:00  0.0   25.95  0.30  0.0  153.58   39.53   \n",
       "4  Ahmedabad  2015-01-01  05:00:00  0.0   22.87  0.12  0.0    0.00   32.63   \n",
       "\n",
       "   BTX  AQI  \n",
       "0  0.0  0.0  \n",
       "1  0.0  0.0  \n",
       "2  0.0  0.0  \n",
       "3  0.0  0.0  \n",
       "4  0.0  0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.fillna(0,inplace=True)\n",
    "\n",
    "data['date'] = pd.DatetimeIndex(data['Datetime']).date\n",
    "data['time'] = pd.DatetimeIndex(data['Datetime']).time\n",
    "\n",
    "\n",
    "data['PM']=data['PM2.5'] + data['PM10']\n",
    "\n",
    "\n",
    "data['Nitric']=data['NO'] + data['NO2']+ data['NOx']\n",
    "\n",
    "\n",
    "data['BTX']=data['Benzene'] + data['Toluene']+ data['Xylene']\n",
    "\n",
    "\n",
    "datagrp=data.groupby(['City','date','time'])[['PM','Nitric','CO','NH3','O3','SO2','BTX','AQI']].mean()\n",
    "\n",
    "datagrp=datagrp.reset_index(['City','date','time'])\n",
    "datagrp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1d8261e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "very poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "Satisfactory\n",
      "Satisfactory\n",
      "Satisfactory\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
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      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
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      "moderately polluted\n",
      "severe\n",
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      "severe\n",
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      "poor\n",
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      "poor\n",
      "very poor\n",
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      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "poor\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "poor\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "poor\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
      "poor\n",
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      "severe\n",
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      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "poor\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "very poor\n",
      "severe\n",
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      "severe\n",
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      "severe\n",
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      "very poor\n",
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      "severe\n",
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      "severe\n",
      "severe\n",
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      "very poor\n",
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      "very poor\n",
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      "very poor\n",
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      "very poor\n",
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      "very poor\n",
      "very poor\n",
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      "severe\n",
      "severe\n",
      "poor\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "Satisfactory\n",
      "severe\n",
      "severe\n",
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      "moderately polluted\n",
      "moderately polluted\n",
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      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "very poor\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
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      "severe\n",
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      "poor\n",
      "moderately polluted\n",
      "moderately polluted\n",
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      "severe\n",
      "severe\n",
      "severe\n",
      "very poor\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "moderately polluted\n",
      "poor\n",
      "severe\n",
      "severe\n",
      "severe\n",
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      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
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      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "severe\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n",
      "very poor\n"
     ]
    }
   ],
   "source": [
    "for i in datagrp:\n",
    "    if i==\"AQI\":\n",
    "        for j in datagrp[\"AQI\"]:\n",
    "            if j>0 and j<50:\n",
    "                print(\"Good\")\n",
    "            elif j>51 and j<100:\n",
    "                print(\"Satisfactory\")\n",
    "            elif j>101 and j<200:\n",
    "                print(\"moderately polluted\")\n",
    "            elif j>201 and j<300:\n",
    "                print(\"poor\")\n",
    "            elif j>301 and j<400:\n",
    "                print(\"very poor\")\n",
    "            else:\n",
    "                print(\"severe\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "89c666ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "statiscal result          -3.089505\n",
       "p value                    0.027340\n",
       "no of lags                24.000000\n",
       "observation count       1510.000000\n",
       "critical value (1%)       -3.434688\n",
       "critical value (5%)       -2.863456\n",
       "critical value (10%)      -2.567790\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftest = adfuller(data['AQI'], autolag='AIC')\n",
    "dfoutput = pd.Series(dftest[0:4], index=['statiscal result','p value','no of lags','observation count'])\n",
    "for key,value in dftest[4].items():\n",
    "    dfoutput['critical value (%s)'%key] = value\n",
    "dfoutput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d7f49e74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pmdarima in c:\\users\\thispc\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (1.8.4)\n",
      "Requirement already satisfied: numpy>=1.19.3 in c:\\users\\thispc\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from pmdarima) (1.21.4)\n",
      "Requirement already satisfied: joblib>=0.11 in c:\\users\\thispc\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from pmdarima) (1.1.0)\n",
      "Requirement already satisfied: scikit-learn>=0.22 in c:\\users\\thispc\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from pmdarima) (1.0.1)\n",
      "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in c:\\users\\thispc\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from pmdarima) (39.0.1)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\thispc\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from pmdarima) (1.7.2)\n",
      "Requirement already satisfied: Cython!=0.29.18,>=0.29 in c:\\users\\thispc\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from pmdarima) (0.29.24)\n",
      "Requirement already satisfied: urllib3 in c:\\users\\thispc\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from pmdarima) (1.26.7)\n",
      "Requirement already satisfied: pandas>=0.19 in c:\\users\\thispc\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from pmdarima) (1.1.5)\n",
      "Requirement already satisfied: statsmodels!=0.12.0,>=0.11 in c:\\users\\thispc\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from pmdarima) (0.13.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\thispc\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from pandas>=0.19->pmdarima) (2021.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\thispc\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from pandas>=0.19->pmdarima) (2.8.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\thispc\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from scikit-learn>=0.22->pmdarima) (3.0.0)\n",
      "Requirement already satisfied: patsy>=0.5.2 in c:\\users\\thispc\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from statsmodels!=0.12.0,>=0.11->pmdarima) (0.5.2)\n",
      "Requirement already satisfied: six in c:\\users\\thispc\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from patsy>=0.5.2->statsmodels!=0.12.0,>=0.11->pmdarima) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pmdarima;\n",
    "from pmdarima import auto_arima;  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6ed632d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                    <td>y</td>                  <th>  No. Observations:  </th>   <td>1535</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 0)x(2, 0, [1, 2], 12)</td> <th>  Log Likelihood     </th> <td>-8890.556</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                     <td>Thu, 25 Nov 2021</td>           <th>  AIC                </th> <td>17793.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                         <td>11:57:25</td>               <th>  BIC                </th> <td>17825.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                           <td>0</td>                  <th>  HQIC               </th> <td>17805.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                               <td> - 1535</td>               <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                 <td>opg</td>                 <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>    <td>    0.2511</td> <td>    0.014</td> <td>   18.378</td> <td> 0.000</td> <td>    0.224</td> <td>    0.278</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L12</th> <td>   -0.1408</td> <td>    0.055</td> <td>   -2.577</td> <td> 0.010</td> <td>   -0.248</td> <td>   -0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L24</th> <td>    0.6493</td> <td>    0.055</td> <td>   11.771</td> <td> 0.000</td> <td>    0.541</td> <td>    0.757</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th> <td>    0.0898</td> <td>    0.064</td> <td>    1.410</td> <td> 0.158</td> <td>   -0.035</td> <td>    0.215</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L24</th> <td>   -0.4664</td> <td>    0.057</td> <td>   -8.219</td> <td> 0.000</td> <td>   -0.578</td> <td>   -0.355</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td> 6321.9309</td> <td>   58.471</td> <td>  108.121</td> <td> 0.000</td> <td> 6207.330</td> <td> 6436.531</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.01</td> <th>  Jarque-Bera (JB):  </th> <td>99746.41</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.92</td> <th>  Prob(JB):          </th>   <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th>  <td>inf</td> <th>  Skew:              </th>   <td>-1.90</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>   <td>42.32</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                        SARIMAX Results                                        \n",
       "===============================================================================================\n",
       "Dep. Variable:                                       y   No. Observations:                 1535\n",
       "Model:             SARIMAX(1, 1, 0)x(2, 0, [1, 2], 12)   Log Likelihood               -8890.556\n",
       "Date:                                 Thu, 25 Nov 2021   AIC                          17793.112\n",
       "Time:                                         11:57:25   BIC                          17825.126\n",
       "Sample:                                              0   HQIC                         17805.025\n",
       "                                                - 1535                                         \n",
       "Covariance Type:                                   opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.2511      0.014     18.378      0.000       0.224       0.278\n",
       "ar.S.L12      -0.1408      0.055     -2.577      0.010      -0.248      -0.034\n",
       "ar.S.L24       0.6493      0.055     11.771      0.000       0.541       0.757\n",
       "ma.S.L12       0.0898      0.064      1.410      0.158      -0.035       0.215\n",
       "ma.S.L24      -0.4664      0.057     -8.219      0.000      -0.578      -0.355\n",
       "sigma2      6321.9309     58.471    108.121      0.000    6207.330    6436.531\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):             99746.41\n",
       "Prob(Q):                              0.92   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):                inf   Skew:                            -1.90\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        42.32\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto_arima(y=data['AQI'],start_p=1,start_P=1,start_q=1,start_Q=1,seasonal=True,m=12, stepwise=\\\n",
    "          True).summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "540cf59e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>                   <td>AQI</td>                 <th>  No. Observations:  </th>   <td>1535</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 0)x(2, 0, [1, 2], 12)</td> <th>  Log Likelihood     </th> <td>-8890.556</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                     <td>Thu, 25 Nov 2021</td>           <th>  AIC                </th> <td>17793.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                         <td>12:06:34</td>               <th>  BIC                </th> <td>17825.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                           <td>0</td>                  <th>  HQIC               </th> <td>17805.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                               <td> - 1535</td>               <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>                 <td>opg</td>                 <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>    <td>    0.2511</td> <td>    0.014</td> <td>   18.378</td> <td> 0.000</td> <td>    0.224</td> <td>    0.278</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L12</th> <td>   -0.1408</td> <td>    0.055</td> <td>   -2.577</td> <td> 0.010</td> <td>   -0.248</td> <td>   -0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L24</th> <td>    0.6493</td> <td>    0.055</td> <td>   11.771</td> <td> 0.000</td> <td>    0.541</td> <td>    0.757</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L12</th> <td>    0.0898</td> <td>    0.064</td> <td>    1.410</td> <td> 0.158</td> <td>   -0.035</td> <td>    0.215</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.S.L24</th> <td>   -0.4664</td> <td>    0.057</td> <td>   -8.219</td> <td> 0.000</td> <td>   -0.578</td> <td>   -0.355</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td> 6321.9309</td> <td>   58.471</td> <td>  108.121</td> <td> 0.000</td> <td> 6207.330</td> <td> 6436.531</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.01</td> <th>  Jarque-Bera (JB):  </th> <td>99746.41</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.92</td> <th>  Prob(JB):          </th>   <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th>  <td>inf</td> <th>  Skew:              </th>   <td>-1.90</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>   <td>42.32</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                        SARIMAX Results                                        \n",
       "===============================================================================================\n",
       "Dep. Variable:                                     AQI   No. Observations:                 1535\n",
       "Model:             SARIMAX(1, 1, 0)x(2, 0, [1, 2], 12)   Log Likelihood               -8890.556\n",
       "Date:                                 Thu, 25 Nov 2021   AIC                          17793.112\n",
       "Time:                                         12:06:34   BIC                          17825.126\n",
       "Sample:                                              0   HQIC                         17805.025\n",
       "                                                - 1535                                         \n",
       "Covariance Type:                                   opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.2511      0.014     18.378      0.000       0.224       0.278\n",
       "ar.S.L12      -0.1408      0.055     -2.577      0.010      -0.248      -0.034\n",
       "ar.S.L24       0.6493      0.055     11.771      0.000       0.541       0.757\n",
       "ma.S.L12       0.0898      0.064      1.410      0.158      -0.035       0.215\n",
       "ma.S.L24      -0.4664      0.057     -8.219      0.000      -0.578      -0.355\n",
       "sigma2      6321.9309     58.471    108.121      0.000    6207.330    6436.531\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.01   Jarque-Bera (JB):             99746.41\n",
       "Prob(Q):                              0.92   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):                inf   Skew:                            -1.90\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        42.32\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset=data['AQI'][:2018]\n",
    "testset=data['AQI'][:2019]\n",
    "model=SARIMAX(trainset,order=(1,1,0),seasonal_order=(2,0,[1,2],12), trend='n')\n",
    "final=model.fit()\n",
    "final.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6444cd37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax= plt.subplots(figsize=(10,6))\n",
    "forecastdata= final.get_forecast(steps=12, dynamic=True)\n",
    "conf_int=forecastdata.conf_int()\n",
    "low = conf_int.loc[:,'lower AQI']\n",
    "upper = conf_int.loc[:,'upper AQI']\n",
    "forecastdata.predicted_mean.plot(legend=True, ax=ax, label ='Predicted AQI')\n",
    "plt.fill_between(conf_int.index, low, upper, color='green')\n",
    "\n",
    "testset.plot(legend=True, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97889e11",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}