Handling_Missing_Values.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "98e5bd53-9d3d-463b-bb54-b5528d9241ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eb7402ca-679d-4042-b43b-6263350fddd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./city_day.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9cae8e1b-f16a-4ff5-aff7-04324bf7af82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Date</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",
       "      <th>AQI_Bucket</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>18.22</td>\n",
       "      <td>17.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>27.64</td>\n",
       "      <td>133.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97</td>\n",
       "      <td>15.69</td>\n",
       "      <td>16.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97</td>\n",
       "      <td>24.55</td>\n",
       "      <td>34.06</td>\n",
       "      <td>3.68</td>\n",
       "      <td>5.50</td>\n",
       "      <td>3.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City        Date  PM2.5  PM10    NO    NO2    NOx  NH3    CO    SO2  \\\n",
       "0  Ahmedabad  2015-01-01    NaN   NaN  0.92  18.22  17.15  NaN  0.92  27.64   \n",
       "1  Ahmedabad  2015-01-02    NaN   NaN  0.97  15.69  16.46  NaN  0.97  24.55   \n",
       "\n",
       "       O3  Benzene  Toluene  Xylene  AQI AQI_Bucket  \n",
       "0  133.36     0.00     0.02    0.00  NaN        NaN  \n",
       "1   34.06     3.68     5.50    3.77  NaN        NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "58a2081c-ad2a-43d2-95be-75320b6f9618",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.drop(['AQI_Bucket','Xylene','O3','Benzene'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fd2209aa-cfc0-4432-b4d9-4b04c643aa91",
   "metadata": {},
   "outputs": [],
   "source": [
    "non_numeric_cols = df[['City','Date']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a8ea52ad-6ac4-4d3c-b0ea-de13543894b4",
   "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>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>Toluene</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>18.22</td>\n",
       "      <td>17.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>27.64</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97</td>\n",
       "      <td>15.69</td>\n",
       "      <td>16.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97</td>\n",
       "      <td>24.55</td>\n",
       "      <td>5.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City        Date  PM2.5  PM10    NO    NO2    NOx  NH3    CO    SO2  \\\n",
       "0  Ahmedabad  2015-01-01    NaN   NaN  0.92  18.22  17.15  NaN  0.92  27.64   \n",
       "1  Ahmedabad  2015-01-02    NaN   NaN  0.97  15.69  16.46  NaN  0.97  24.55   \n",
       "\n",
       "   Toluene  AQI  \n",
       "0     0.02  NaN  \n",
       "1     5.50  NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "08b3afc2-824a-4fda-ad5f-b99a42d59771",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29531, 12)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "86cc4ffc-7396-454a-90f7-eb7b5db5ecda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City           0\n",
       "Date           0\n",
       "PM2.5       4598\n",
       "PM10       11140\n",
       "NO          3582\n",
       "NO2         3585\n",
       "NOx         4185\n",
       "NH3        10328\n",
       "CO          2059\n",
       "SO2         3854\n",
       "Toluene     8041\n",
       "AQI         4681\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3cec809d-80b5-40db-88a4-a14f6eed5962",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['City', 'Date', 'PM2.5', 'PM10', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2',\n",
       "       'Toluene', 'AQI'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6b82698b-5038-4922-8d1b-097a9b1dc782",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['PM2.5', 'PM10', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2',\n",
    "       'Toluene', 'AQI']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b50780cd-5eab-492d-8a14-e21d81b34b7b",
   "metadata": {},
   "source": [
    "# Handling Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c54efdf-d5d8-4f98-962f-289fd08b4d4a",
   "metadata": {},
   "source": [
    "<h3>Visualization of Outliers using Boxplot</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "200b2460-ff68-4e28-8f4b-deeca0ce02d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.boxplot(df[['PM2.5', 'PM10', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2',\n",
    "        'Toluene',  'AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e48cb444-337b-4454-bc8c-ce0f1cf72974",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "57feb31b-3f8d-4bb5-af5b-f2c426fe04d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in cols:\n",
    "  percentile25=df[i].quantile(0.25)\n",
    "  percentile75=df[i].quantile(0.75)\n",
    "\n",
    "  iqr = percentile75 - percentile25\n",
    "\n",
    "  upper_limit = percentile75 + 1.5*iqr\n",
    "  lower_limit = percentile25 - 1.5*iqr\n",
    "\n",
    "\n",
    "\n",
    "  new_df[i] = np.where(new_df[i]>upper_limit,\n",
    "                          upper_limit,\n",
    "                          np.where(\n",
    "                              new_df[i]<lower_limit,\n",
    "                              lower_limit,\n",
    "                              new_df[i]\n",
    "                          ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "554a2451-d63b-48fe-805e-533a51c59076",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.boxplot(new_df[['PM2.5', 'PM10', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2',\n",
    "        'Toluene',  'AQI']])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ffd8bab-2a5f-425e-adf2-bab59c3cbf8b",
   "metadata": {},
   "source": [
    "<p>Now as we can see all the outliers have been removed from our dataset</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6cad0d14-e3fb-4d22-8b55-249f688e2138",
   "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>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>Toluene</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>18.22</td>\n",
       "      <td>17.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>27.64</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97</td>\n",
       "      <td>15.69</td>\n",
       "      <td>16.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97</td>\n",
       "      <td>24.55</td>\n",
       "      <td>5.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City        Date  PM2.5  PM10    NO    NO2    NOx  NH3    CO    SO2  \\\n",
       "0  Ahmedabad  2015-01-01    NaN   NaN  0.92  18.22  17.15  NaN  0.92  27.64   \n",
       "1  Ahmedabad  2015-01-02    NaN   NaN  0.97  15.69  16.46  NaN  0.97  24.55   \n",
       "\n",
       "   Toluene  AQI  \n",
       "0     0.02  NaN  \n",
       "1     5.50  NaN  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4f6e65a0-3270-4d9c-885b-0b20630baf64",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['Date'] = pd.to_datetime(new_df['Date'])\n",
    "new_df['year'] = new_df['Date'].dt.year\n",
    "new_df['month'] = new_df['Date'].dt.month\n",
    "new_df['day'] = new_df['Date'].dt.day\n",
    "new_df['weekday'] = new_df['Date'].dt.weekday\n",
    "new_df['dayofyear'] = new_df['Date'].dt.dayofyear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2336ac78-1c2e-4ccd-bdc3-09d206164b3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df.drop('Date', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9ad2e031-f5f4-4ee2-b576-b3b22442aa33",
   "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>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>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>dayofyear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>18.22</td>\n",
       "      <td>17.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.92</td>\n",
       "      <td>27.64</td>\n",
       "      <td>0.02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97</td>\n",
       "      <td>15.69</td>\n",
       "      <td>16.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.97</td>\n",
       "      <td>24.55</td>\n",
       "      <td>5.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City  PM2.5  PM10    NO    NO2    NOx  NH3    CO    SO2  Toluene  AQI  \\\n",
       "0  Ahmedabad    NaN   NaN  0.92  18.22  17.15  NaN  0.92  27.64     0.02  NaN   \n",
       "1  Ahmedabad    NaN   NaN  0.97  15.69  16.46  NaN  0.97  24.55     5.50  NaN   \n",
       "\n",
       "   year  month  day  weekday  dayofyear  \n",
       "0  2015      1    1        3          1  \n",
       "1  2015      1    2        4          2  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "25e2852d-b371-486e-92da-87181b046c3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting categorical data into a numerical format using oneHotEncoder, so that it can be used by our MICE algorithm for imputation.\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "cities = new_df[['City']]\n",
    "\n",
    "encoder = OneHotEncoder(sparse_output=False, handle_unknown='ignore')\n",
    "\n",
    "city_encoded = encoder.fit_transform(cities)\n",
    "city_encoded_df = pd.DataFrame(city_encoded, columns=encoder.get_feature_names_out(['City']))\n",
    "\n",
    "df_encoded = pd.concat([new_df.drop('City', axis=1), city_encoded_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "458ceafe-5bf9-4872-a885-685f4beb26d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PM2.5', 'PM10', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2', 'Toluene',\n",
       "       'AQI', 'year', 'month', 'day', 'weekday', 'dayofyear', 'City_Ahmedabad',\n",
       "       'City_Aizawl', 'City_Amaravati', 'City_Amritsar', 'City_Bengaluru',\n",
       "       'City_Bhopal', 'City_Brajrajnagar', 'City_Chandigarh', 'City_Chennai',\n",
       "       'City_Coimbatore', 'City_Delhi', 'City_Ernakulam', 'City_Gurugram',\n",
       "       'City_Guwahati', 'City_Hyderabad', 'City_Jaipur', 'City_Jorapokhar',\n",
       "       'City_Kochi', 'City_Kolkata', 'City_Lucknow', 'City_Mumbai',\n",
       "       'City_Patna', 'City_Shillong', 'City_Talcher',\n",
       "       'City_Thiruvananthapuram', 'City_Visakhapatnam'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# These are our columns after encoding our cities\n",
    "df_encoded.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "71f6eea8-74b7-403d-8d37-f5c5f6f8ea3f",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PM2.5</th>\n",
       "      <td>24933.0</td>\n",
       "      <td>61.148955</td>\n",
       "      <td>42.803676</td>\n",
       "      <td>0.04</td>\n",
       "      <td>28.820</td>\n",
       "      <td>48.57</td>\n",
       "      <td>80.5900</td>\n",
       "      <td>158.24500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM10</th>\n",
       "      <td>18391.0</td>\n",
       "      <td>112.877919</td>\n",
       "      <td>73.706540</td>\n",
       "      <td>0.01</td>\n",
       "      <td>56.255</td>\n",
       "      <td>95.68</td>\n",
       "      <td>149.7450</td>\n",
       "      <td>289.98000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>25949.0</td>\n",
       "      <td>14.507996</td>\n",
       "      <td>12.246567</td>\n",
       "      <td>0.02</td>\n",
       "      <td>5.630</td>\n",
       "      <td>9.89</td>\n",
       "      <td>19.9500</td>\n",
       "      <td>41.43000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO2</th>\n",
       "      <td>25946.0</td>\n",
       "      <td>27.259387</td>\n",
       "      <td>19.691661</td>\n",
       "      <td>0.01</td>\n",
       "      <td>11.750</td>\n",
       "      <td>21.69</td>\n",
       "      <td>37.6200</td>\n",
       "      <td>76.42500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOx</th>\n",
       "      <td>25346.0</td>\n",
       "      <td>29.539227</td>\n",
       "      <td>22.721584</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.820</td>\n",
       "      <td>23.52</td>\n",
       "      <td>40.1275</td>\n",
       "      <td>81.08875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH3</th>\n",
       "      <td>19203.0</td>\n",
       "      <td>21.361087</td>\n",
       "      <td>16.688309</td>\n",
       "      <td>0.01</td>\n",
       "      <td>8.580</td>\n",
       "      <td>15.85</td>\n",
       "      <td>30.0200</td>\n",
       "      <td>62.18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>27472.0</td>\n",
       "      <td>1.079514</td>\n",
       "      <td>0.815915</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.4500</td>\n",
       "      <td>2.86000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO2</th>\n",
       "      <td>25677.0</td>\n",
       "      <td>11.743115</td>\n",
       "      <td>8.220634</td>\n",
       "      <td>0.01</td>\n",
       "      <td>5.670</td>\n",
       "      <td>9.16</td>\n",
       "      <td>15.2200</td>\n",
       "      <td>29.54500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toluene</th>\n",
       "      <td>21490.0</td>\n",
       "      <td>6.231113</td>\n",
       "      <td>7.332004</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.600</td>\n",
       "      <td>2.97</td>\n",
       "      <td>9.1500</td>\n",
       "      <td>21.97500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQI</th>\n",
       "      <td>24850.0</td>\n",
       "      <td>157.342455</td>\n",
       "      <td>103.870019</td>\n",
       "      <td>13.00</td>\n",
       "      <td>81.000</td>\n",
       "      <td>118.00</td>\n",
       "      <td>208.0000</td>\n",
       "      <td>398.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>2017.887914</td>\n",
       "      <td>1.533783</td>\n",
       "      <td>2015.00</td>\n",
       "      <td>2017.000</td>\n",
       "      <td>2018.00</td>\n",
       "      <td>2019.0000</td>\n",
       "      <td>2020.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>6.267143</td>\n",
       "      <td>3.431974</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.000</td>\n",
       "      <td>6.00</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>12.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>15.762758</td>\n",
       "      <td>8.804537</td>\n",
       "      <td>1.00</td>\n",
       "      <td>8.000</td>\n",
       "      <td>16.00</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>31.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekday</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>2.998747</td>\n",
       "      <td>2.000110</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>6.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dayofyear</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>175.360164</td>\n",
       "      <td>104.833505</td>\n",
       "      <td>1.00</td>\n",
       "      <td>86.000</td>\n",
       "      <td>167.00</td>\n",
       "      <td>266.0000</td>\n",
       "      <td>366.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Ahmedabad</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.068030</td>\n",
       "      <td>0.251802</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Aizawl</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.003826</td>\n",
       "      <td>0.061741</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Amaravati</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.032203</td>\n",
       "      <td>0.176543</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Amritsar</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.041346</td>\n",
       "      <td>0.199093</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Bengaluru</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.068030</td>\n",
       "      <td>0.251802</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Bhopal</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.009786</td>\n",
       "      <td>0.098442</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Brajrajnagar</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.031763</td>\n",
       "      <td>0.175372</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Chandigarh</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.010294</td>\n",
       "      <td>0.100939</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Chennai</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.068030</td>\n",
       "      <td>0.251802</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Coimbatore</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.013071</td>\n",
       "      <td>0.113581</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Delhi</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.068030</td>\n",
       "      <td>0.251802</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Ernakulam</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.005486</td>\n",
       "      <td>0.073864</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Gurugram</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.056856</td>\n",
       "      <td>0.231570</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Guwahati</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.016999</td>\n",
       "      <td>0.129270</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Hyderabad</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.067929</td>\n",
       "      <td>0.251628</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Jaipur</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.037723</td>\n",
       "      <td>0.190529</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Jorapokhar</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.039586</td>\n",
       "      <td>0.194987</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Kochi</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.005486</td>\n",
       "      <td>0.073864</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Kolkata</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.027564</td>\n",
       "      <td>0.163723</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Lucknow</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.068030</td>\n",
       "      <td>0.251802</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Mumbai</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.068030</td>\n",
       "      <td>0.251802</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Patna</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.062917</td>\n",
       "      <td>0.242818</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Shillong</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.010497</td>\n",
       "      <td>0.101920</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Talcher</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.031323</td>\n",
       "      <td>0.174192</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Thiruvananthapuram</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.037655</td>\n",
       "      <td>0.190365</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City_Visakhapatnam</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>0.049507</td>\n",
       "      <td>0.216928</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count         mean         std      min       25%  \\\n",
       "PM2.5                    24933.0    61.148955   42.803676     0.04    28.820   \n",
       "PM10                     18391.0   112.877919   73.706540     0.01    56.255   \n",
       "NO                       25949.0    14.507996   12.246567     0.02     5.630   \n",
       "NO2                      25946.0    27.259387   19.691661     0.01    11.750   \n",
       "NOx                      25346.0    29.539227   22.721584     0.00    12.820   \n",
       "NH3                      19203.0    21.361087   16.688309     0.01     8.580   \n",
       "CO                       27472.0     1.079514    0.815915     0.00     0.510   \n",
       "SO2                      25677.0    11.743115    8.220634     0.01     5.670   \n",
       "Toluene                  21490.0     6.231113    7.332004     0.00     0.600   \n",
       "AQI                      24850.0   157.342455  103.870019    13.00    81.000   \n",
       "year                     29531.0  2017.887914    1.533783  2015.00  2017.000   \n",
       "month                    29531.0     6.267143    3.431974     1.00     3.000   \n",
       "day                      29531.0    15.762758    8.804537     1.00     8.000   \n",
       "weekday                  29531.0     2.998747    2.000110     0.00     1.000   \n",
       "dayofyear                29531.0   175.360164  104.833505     1.00    86.000   \n",
       "City_Ahmedabad           29531.0     0.068030    0.251802     0.00     0.000   \n",
       "City_Aizawl              29531.0     0.003826    0.061741     0.00     0.000   \n",
       "City_Amaravati           29531.0     0.032203    0.176543     0.00     0.000   \n",
       "City_Amritsar            29531.0     0.041346    0.199093     0.00     0.000   \n",
       "City_Bengaluru           29531.0     0.068030    0.251802     0.00     0.000   \n",
       "City_Bhopal              29531.0     0.009786    0.098442     0.00     0.000   \n",
       "City_Brajrajnagar        29531.0     0.031763    0.175372     0.00     0.000   \n",
       "City_Chandigarh          29531.0     0.010294    0.100939     0.00     0.000   \n",
       "City_Chennai             29531.0     0.068030    0.251802     0.00     0.000   \n",
       "City_Coimbatore          29531.0     0.013071    0.113581     0.00     0.000   \n",
       "City_Delhi               29531.0     0.068030    0.251802     0.00     0.000   \n",
       "City_Ernakulam           29531.0     0.005486    0.073864     0.00     0.000   \n",
       "City_Gurugram            29531.0     0.056856    0.231570     0.00     0.000   \n",
       "City_Guwahati            29531.0     0.016999    0.129270     0.00     0.000   \n",
       "City_Hyderabad           29531.0     0.067929    0.251628     0.00     0.000   \n",
       "City_Jaipur              29531.0     0.037723    0.190529     0.00     0.000   \n",
       "City_Jorapokhar          29531.0     0.039586    0.194987     0.00     0.000   \n",
       "City_Kochi               29531.0     0.005486    0.073864     0.00     0.000   \n",
       "City_Kolkata             29531.0     0.027564    0.163723     0.00     0.000   \n",
       "City_Lucknow             29531.0     0.068030    0.251802     0.00     0.000   \n",
       "City_Mumbai              29531.0     0.068030    0.251802     0.00     0.000   \n",
       "City_Patna               29531.0     0.062917    0.242818     0.00     0.000   \n",
       "City_Shillong            29531.0     0.010497    0.101920     0.00     0.000   \n",
       "City_Talcher             29531.0     0.031323    0.174192     0.00     0.000   \n",
       "City_Thiruvananthapuram  29531.0     0.037655    0.190365     0.00     0.000   \n",
       "City_Visakhapatnam       29531.0     0.049507    0.216928     0.00     0.000   \n",
       "\n",
       "                             50%        75%         max  \n",
       "PM2.5                      48.57    80.5900   158.24500  \n",
       "PM10                       95.68   149.7450   289.98000  \n",
       "NO                          9.89    19.9500    41.43000  \n",
       "NO2                        21.69    37.6200    76.42500  \n",
       "NOx                        23.52    40.1275    81.08875  \n",
       "NH3                        15.85    30.0200    62.18000  \n",
       "CO                          0.89     1.4500     2.86000  \n",
       "SO2                         9.16    15.2200    29.54500  \n",
       "Toluene                     2.97     9.1500    21.97500  \n",
       "AQI                       118.00   208.0000   398.50000  \n",
       "year                     2018.00  2019.0000  2020.00000  \n",
       "month                       6.00     9.0000    12.00000  \n",
       "day                        16.00    23.0000    31.00000  \n",
       "weekday                     3.00     5.0000     6.00000  \n",
       "dayofyear                 167.00   266.0000   366.00000  \n",
       "City_Ahmedabad              0.00     0.0000     1.00000  \n",
       "City_Aizawl                 0.00     0.0000     1.00000  \n",
       "City_Amaravati              0.00     0.0000     1.00000  \n",
       "City_Amritsar               0.00     0.0000     1.00000  \n",
       "City_Bengaluru              0.00     0.0000     1.00000  \n",
       "City_Bhopal                 0.00     0.0000     1.00000  \n",
       "City_Brajrajnagar           0.00     0.0000     1.00000  \n",
       "City_Chandigarh             0.00     0.0000     1.00000  \n",
       "City_Chennai                0.00     0.0000     1.00000  \n",
       "City_Coimbatore             0.00     0.0000     1.00000  \n",
       "City_Delhi                  0.00     0.0000     1.00000  \n",
       "City_Ernakulam              0.00     0.0000     1.00000  \n",
       "City_Gurugram               0.00     0.0000     1.00000  \n",
       "City_Guwahati               0.00     0.0000     1.00000  \n",
       "City_Hyderabad              0.00     0.0000     1.00000  \n",
       "City_Jaipur                 0.00     0.0000     1.00000  \n",
       "City_Jorapokhar             0.00     0.0000     1.00000  \n",
       "City_Kochi                  0.00     0.0000     1.00000  \n",
       "City_Kolkata                0.00     0.0000     1.00000  \n",
       "City_Lucknow                0.00     0.0000     1.00000  \n",
       "City_Mumbai                 0.00     0.0000     1.00000  \n",
       "City_Patna                  0.00     0.0000     1.00000  \n",
       "City_Shillong               0.00     0.0000     1.00000  \n",
       "City_Talcher                0.00     0.0000     1.00000  \n",
       "City_Thiruvananthapuram     0.00     0.0000     1.00000  \n",
       "City_Visakhapatnam          0.00     0.0000     1.00000  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistical data about encoded dataframe.\n",
    "df_encoded.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afcc1945-f995-4573-9fbb-efecac5c4331",
   "metadata": {},
   "source": [
    "<h3>Now, we will divide our dataset into training and testing sets using train_test_split</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "51d675db-8e90-4585-9cda-62466ec2b14e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test = train_test_split(df_encoded, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "961e5a04-3f5f-4b47-acfb-c71e5d0cafc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((23624, 41), (5907, 41))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c2378ad-b36e-4e59-8a00-5fd66e50d977",
   "metadata": {},
   "source": [
    "<h3>For filling missing data we'll be using <strong>MICE</strong> technique (Multivariate Imputation by Chained Equations) which we can implement using iterative imputer method from scikit-learn.</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a62b7c51-0400-4227-a887-e5bba222a1bd",
   "metadata": {},
   "source": [
    "<h2>Implementing Iterative Imputer using ElasticNet estimator.</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0e917627-270f-43d5-91e6-d1403994d606",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[IterativeImputer] Completing matrix with shape (23624, 41)\n",
      "[IterativeImputer] Ending imputation round 1/200, elapsed time 0.76\n",
      "[IterativeImputer] Change: 470.7214350419918, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 2/200, elapsed time 1.70\n",
      "[IterativeImputer] Change: 161.18175828013182, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 3/200, elapsed time 2.68\n",
      "[IterativeImputer] Change: 50.87049369129261, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 4/200, elapsed time 3.66\n",
      "[IterativeImputer] Change: 42.44897024163242, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 5/200, elapsed time 4.58\n",
      "[IterativeImputer] Change: 35.655140891362095, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 6/200, elapsed time 5.60\n",
      "[IterativeImputer] Change: 29.975944328508234, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 7/200, elapsed time 6.80\n",
      "[IterativeImputer] Change: 25.210031257188888, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 8/200, elapsed time 7.85\n",
      "[IterativeImputer] Change: 21.203255106177238, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 9/200, elapsed time 9.01\n",
      "[IterativeImputer] Change: 17.833279407898388, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 10/200, elapsed time 9.99\n",
      "[IterativeImputer] Change: 14.99864774654958, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 11/200, elapsed time 10.98\n",
      "[IterativeImputer] Change: 12.614348571739342, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 12/200, elapsed time 11.94\n",
      "[IterativeImputer] Change: 10.608914474801253, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 13/200, elapsed time 12.96\n",
      "[IterativeImputer] Change: 8.922191993358012, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 14/200, elapsed time 13.95\n",
      "[IterativeImputer] Change: 7.503578288315495, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 15/200, elapsed time 15.20\n",
      "[IterativeImputer] Change: 6.310500234425419, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 16/200, elapsed time 16.46\n",
      "[IterativeImputer] Change: 5.468667383011123, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 17/200, elapsed time 17.61\n",
      "[IterativeImputer] Change: 4.787751713824783, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 18/200, elapsed time 18.67\n",
      "[IterativeImputer] Change: 4.191746560452806, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 19/200, elapsed time 19.73\n",
      "[IterativeImputer] Change: 3.670049665091484, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 20/200, elapsed time 21.00\n",
      "[IterativeImputer] Change: 3.21337681883213, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 21/200, elapsed time 22.00\n",
      "[IterativeImputer] Change: 2.906516060962171, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 22/200, elapsed time 22.95\n",
      "[IterativeImputer] Change: 2.6303820555058435, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 23/200, elapsed time 23.92\n",
      "[IterativeImputer] Change: 2.3805835996873057, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 24/200, elapsed time 24.86\n",
      "[IterativeImputer] Change: 2.1545923032992733, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 25/200, elapsed time 25.83\n",
      "[IterativeImputer] Change: 1.9501240239062263, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 26/200, elapsed time 27.00\n",
      "[IterativeImputer] Change: 1.765116202729394, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 27/200, elapsed time 28.09\n",
      "[IterativeImputer] Change: 1.5977062562937638, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 28/200, elapsed time 29.36\n",
      "[IterativeImputer] Change: 1.4462117965139214, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 29/200, elapsed time 30.48\n",
      "[IterativeImputer] Change: 1.3091128391780447, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 30/200, elapsed time 31.55\n",
      "[IterativeImputer] Change: 1.185035835595883, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 31/200, elapsed time 32.65\n",
      "[IterativeImputer] Change: 1.0727393322427492, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 32/200, elapsed time 33.72\n",
      "[IterativeImputer] Change: 0.9711010675744791, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 33/200, elapsed time 34.85\n",
      "[IterativeImputer] Change: 0.8791063691394505, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 34/200, elapsed time 35.93\n",
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      "[IterativeImputer] Change: 6.707433630737114e-07, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Completing matrix with shape (5907, 41)\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kavyasmac/anaconda3/envs/new_env/lib/python3.11/site-packages/sklearn/impute/_iterative.py:785: ConvergenceWarning: [IterativeImputer] Early stopping criterion not reached.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Imputed X_train:\n",
      "           0           1          2          3          4          5   \\\n",
      "0  142.270000  252.490000  17.110000  33.820000  50.910000   9.800000   \n",
      "1   75.610000  121.940000   2.950000  27.030000  16.780000  15.920000   \n",
      "2   15.010000   27.650000   3.250000   7.990000   9.710000   6.010000   \n",
      "3   51.383514  110.719213  13.414774  24.707616  27.955343  18.924246   \n",
      "4   16.660000   45.859543   7.440000  28.000000  23.790000  28.830000   \n",
      "\n",
      "         6          7          8           9   ...   31   32   33   34   35  \\\n",
      "0  2.860000  17.180000  10.051466  325.000000  ...  0.0  0.0  0.0  0.0  0.0   \n",
      "1  1.120000   8.240000   7.530000  128.000000  ...  0.0  0.0  0.0  0.0  0.0   \n",
      "2  0.480000   5.800000   2.722763   46.000000  ...  0.0  0.0  0.0  0.0  0.0   \n",
      "3  0.994469  12.273934   6.319510  133.649517  ...  0.0  0.0  0.0  0.0  0.0   \n",
      "4  0.820000   5.030000   0.210000   69.000000  ...  0.0  0.0  0.0  1.0  0.0   \n",
      "\n",
      "    36   37   38   39   40  \n",
      "0  0.0  0.0  0.0  0.0  0.0  \n",
      "1  0.0  0.0  0.0  0.0  1.0  \n",
      "2  0.0  0.0  0.0  1.0  0.0  \n",
      "3  0.0  0.0  1.0  0.0  0.0  \n",
      "4  0.0  0.0  0.0  0.0  0.0  \n",
      "\n",
      "[5 rows x 41 columns]\n",
      "\n",
      "Imputed X_test:\n",
      "           0           1          2          3          4          5     6   \\\n",
      "0   59.849203  124.814425  12.327533  24.389322  24.867095  23.099132  0.00   \n",
      "1   60.748578  121.232452  13.205756  25.683567  25.622796  23.694019  0.00   \n",
      "2   58.500000  111.338723  11.230000  17.050000  28.290000  23.712785  1.14   \n",
      "3   78.570000  161.749819   6.850000   5.450000   5.570000  11.054171  1.33   \n",
      "4  158.245000  284.324788  40.040000  50.590000  60.420000  35.470000  1.22   \n",
      "\n",
      "          7          8           9   ...   31   32   33   34   35   36   37  \\\n",
      "0  10.457695   3.806393  137.810883  ...  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
      "1   9.577667   4.288386  139.097360  ...  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
      "2   5.130000   0.640000  137.000000  ...  0.0  0.0  0.0  0.0  0.0  1.0  0.0   \n",
      "3  29.545000  16.320000  190.000000  ...  0.0  0.0  0.0  0.0  0.0  1.0  0.0   \n",
      "4   9.710000   1.190000  339.000000  ...  0.0  0.0  0.0  1.0  0.0  0.0  0.0   \n",
      "\n",
      "    38   39   40  \n",
      "0  0.0  0.0  0.0  \n",
      "1  0.0  0.0  0.0  \n",
      "2  0.0  0.0  0.0  \n",
      "3  0.0  0.0  0.0  \n",
      "4  0.0  0.0  0.0  \n",
      "\n",
      "[5 rows x 41 columns]\n"
     ]
    }
   ],
   "source": [
    "#importing required methods\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "#model creation\n",
    "elastic_net_imputer = IterativeImputer(\n",
    "    estimator=ElasticNet(alpha=1.0, l1_ratio=0.5, random_state=42),\n",
    "    max_iter=200,\n",
    "    random_state=42,\n",
    "    verbose=2,\n",
    "    tol = 1e-10\n",
    ")\n",
    "\n",
    "#fitting and transforming X_train and X_test\n",
    "X_train_imputed = elastic_net_imputer.fit_transform(X_train)\n",
    "X_test_imputed = elastic_net_imputer.transform(X_test)\n",
    "\n",
    "print(\"Imputed X_train:\")\n",
    "print(pd.DataFrame(X_train_imputed).head())\n",
    "print(\"\\nImputed X_test:\")\n",
    "print(pd.DataFrame(X_test_imputed).head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "dde11550-fa41-4796-956c-aaac04bc2482",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting imputed result into dataframe\n",
    "X_train_imputed = pd.DataFrame(X_train_imputed, columns=X_train.columns)\n",
    "X_test_imputed = pd.DataFrame(X_test_imputed, columns=X_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e218fac0-480d-4060-83b2-40376c317f03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combining imputed X_train and X_test for analysing\n",
    "X_combined = pd.concat([X_train_imputed, X_test_imputed], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "da6e0379-d5f1-48bb-a0ed-1d6582140967",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PM2.5                      0\n",
       "PM10                       0\n",
       "NO                         0\n",
       "NO2                        0\n",
       "NOx                        0\n",
       "NH3                        0\n",
       "CO                         0\n",
       "SO2                        0\n",
       "Toluene                    0\n",
       "AQI                        0\n",
       "year                       0\n",
       "month                      0\n",
       "day                        0\n",
       "weekday                    0\n",
       "dayofyear                  0\n",
       "City_Ahmedabad             0\n",
       "City_Aizawl                0\n",
       "City_Amaravati             0\n",
       "City_Amritsar              0\n",
       "City_Bengaluru             0\n",
       "City_Bhopal                0\n",
       "City_Brajrajnagar          0\n",
       "City_Chandigarh            0\n",
       "City_Chennai               0\n",
       "City_Coimbatore            0\n",
       "City_Delhi                 0\n",
       "City_Ernakulam             0\n",
       "City_Gurugram              0\n",
       "City_Guwahati              0\n",
       "City_Hyderabad             0\n",
       "City_Jaipur                0\n",
       "City_Jorapokhar            0\n",
       "City_Kochi                 0\n",
       "City_Kolkata               0\n",
       "City_Lucknow               0\n",
       "City_Mumbai                0\n",
       "City_Patna                 0\n",
       "City_Shillong              0\n",
       "City_Talcher               0\n",
       "City_Thiruvananthapuram    0\n",
       "City_Visakhapatnam         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for null/missing values in combined dataframe\n",
    "X_combined.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dbd93c5-b104-41bd-b258-283f916ed19a",
   "metadata": {},
   "source": [
    "<h5>Changes in Statistical Data of numeric columns before and after Imputing</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "57da58a5-dc6a-4206-a8a0-93a985da37f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PM2.5</th>\n",
       "      <td>24933.0</td>\n",
       "      <td>61.148955</td>\n",
       "      <td>42.803676</td>\n",
       "      <td>0.04</td>\n",
       "      <td>28.820</td>\n",
       "      <td>48.57</td>\n",
       "      <td>80.5900</td>\n",
       "      <td>158.24500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM10</th>\n",
       "      <td>18391.0</td>\n",
       "      <td>112.877919</td>\n",
       "      <td>73.706540</td>\n",
       "      <td>0.01</td>\n",
       "      <td>56.255</td>\n",
       "      <td>95.68</td>\n",
       "      <td>149.7450</td>\n",
       "      <td>289.98000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>25949.0</td>\n",
       "      <td>14.507996</td>\n",
       "      <td>12.246567</td>\n",
       "      <td>0.02</td>\n",
       "      <td>5.630</td>\n",
       "      <td>9.89</td>\n",
       "      <td>19.9500</td>\n",
       "      <td>41.43000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO2</th>\n",
       "      <td>25946.0</td>\n",
       "      <td>27.259387</td>\n",
       "      <td>19.691661</td>\n",
       "      <td>0.01</td>\n",
       "      <td>11.750</td>\n",
       "      <td>21.69</td>\n",
       "      <td>37.6200</td>\n",
       "      <td>76.42500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOx</th>\n",
       "      <td>25346.0</td>\n",
       "      <td>29.539227</td>\n",
       "      <td>22.721584</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.820</td>\n",
       "      <td>23.52</td>\n",
       "      <td>40.1275</td>\n",
       "      <td>81.08875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH3</th>\n",
       "      <td>19203.0</td>\n",
       "      <td>21.361087</td>\n",
       "      <td>16.688309</td>\n",
       "      <td>0.01</td>\n",
       "      <td>8.580</td>\n",
       "      <td>15.85</td>\n",
       "      <td>30.0200</td>\n",
       "      <td>62.18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>27472.0</td>\n",
       "      <td>1.079514</td>\n",
       "      <td>0.815915</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.4500</td>\n",
       "      <td>2.86000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO2</th>\n",
       "      <td>25677.0</td>\n",
       "      <td>11.743115</td>\n",
       "      <td>8.220634</td>\n",
       "      <td>0.01</td>\n",
       "      <td>5.670</td>\n",
       "      <td>9.16</td>\n",
       "      <td>15.2200</td>\n",
       "      <td>29.54500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toluene</th>\n",
       "      <td>21490.0</td>\n",
       "      <td>6.231113</td>\n",
       "      <td>7.332004</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.600</td>\n",
       "      <td>2.97</td>\n",
       "      <td>9.1500</td>\n",
       "      <td>21.97500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQI</th>\n",
       "      <td>24850.0</td>\n",
       "      <td>157.342455</td>\n",
       "      <td>103.870019</td>\n",
       "      <td>13.00</td>\n",
       "      <td>81.000</td>\n",
       "      <td>118.00</td>\n",
       "      <td>208.0000</td>\n",
       "      <td>398.50000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count        mean         std    min     25%     50%       75%  \\\n",
       "PM2.5    24933.0   61.148955   42.803676   0.04  28.820   48.57   80.5900   \n",
       "PM10     18391.0  112.877919   73.706540   0.01  56.255   95.68  149.7450   \n",
       "NO       25949.0   14.507996   12.246567   0.02   5.630    9.89   19.9500   \n",
       "NO2      25946.0   27.259387   19.691661   0.01  11.750   21.69   37.6200   \n",
       "NOx      25346.0   29.539227   22.721584   0.00  12.820   23.52   40.1275   \n",
       "NH3      19203.0   21.361087   16.688309   0.01   8.580   15.85   30.0200   \n",
       "CO       27472.0    1.079514    0.815915   0.00   0.510    0.89    1.4500   \n",
       "SO2      25677.0   11.743115    8.220634   0.01   5.670    9.16   15.2200   \n",
       "Toluene  21490.0    6.231113    7.332004   0.00   0.600    2.97    9.1500   \n",
       "AQI      24850.0  157.342455  103.870019  13.00  81.000  118.00  208.0000   \n",
       "\n",
       "               max  \n",
       "PM2.5    158.24500  \n",
       "PM10     289.98000  \n",
       "NO        41.43000  \n",
       "NO2       76.42500  \n",
       "NOx       81.08875  \n",
       "NH3       62.18000  \n",
       "CO         2.86000  \n",
       "SO2       29.54500  \n",
       "Toluene   21.97500  \n",
       "AQI      398.50000  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Before ElasticNet Imputation\n",
    "new_df.describe().transpose()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "6f188f0c-74ba-49c3-b512-bcf022a652c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PM2.5</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>61.259746</td>\n",
       "      <td>40.365000</td>\n",
       "      <td>-7.230246</td>\n",
       "      <td>31.14</td>\n",
       "      <td>51.930000</td>\n",
       "      <td>78.384357</td>\n",
       "      <td>158.245000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM10</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>125.957189</td>\n",
       "      <td>74.008758</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>70.57</td>\n",
       "      <td>110.223276</td>\n",
       "      <td>162.545000</td>\n",
       "      <td>328.696143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>14.447791</td>\n",
       "      <td>11.794683</td>\n",
       "      <td>-2.236934</td>\n",
       "      <td>5.93</td>\n",
       "      <td>10.591630</td>\n",
       "      <td>18.480000</td>\n",
       "      <td>41.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO2</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>27.014502</td>\n",
       "      <td>18.806881</td>\n",
       "      <td>-5.098843</td>\n",
       "      <td>12.38</td>\n",
       "      <td>22.656950</td>\n",
       "      <td>35.725000</td>\n",
       "      <td>76.425000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOx</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>28.805694</td>\n",
       "      <td>21.464161</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.65</td>\n",
       "      <td>23.960000</td>\n",
       "      <td>36.805000</td>\n",
       "      <td>81.370011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH3</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>22.605571</td>\n",
       "      <td>14.222345</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>11.78</td>\n",
       "      <td>21.229203</td>\n",
       "      <td>29.120000</td>\n",
       "      <td>62.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>1.081697</td>\n",
       "      <td>0.789660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>2.860000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO2</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>11.547939</td>\n",
       "      <td>7.724630</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>6.09</td>\n",
       "      <td>9.460000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>29.545000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toluene</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>5.992702</td>\n",
       "      <td>6.440144</td>\n",
       "      <td>-3.246351</td>\n",
       "      <td>1.14</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.808458</td>\n",
       "      <td>21.975000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQI</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>156.426263</td>\n",
       "      <td>98.172839</td>\n",
       "      <td>-5.033760</td>\n",
       "      <td>85.00</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>419.746783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count        mean        std       min    25%         50%  \\\n",
       "PM2.5    29531.0   61.259746  40.365000 -7.230246  31.14   51.930000   \n",
       "PM10     29531.0  125.957189  74.008758  0.010000  70.57  110.223276   \n",
       "NO       29531.0   14.447791  11.794683 -2.236934   5.93   10.591630   \n",
       "NO2      29531.0   27.014502  18.806881 -5.098843  12.38   22.656950   \n",
       "NOx      29531.0   28.805694  21.464161  0.000000  13.65   23.960000   \n",
       "NH3      29531.0   22.605571  14.222345  0.010000  11.78   21.229203   \n",
       "CO       29531.0    1.081697   0.789660  0.000000   0.54    0.940000   \n",
       "SO2      29531.0   11.547939   7.724630  0.010000   6.09    9.460000   \n",
       "Toluene  29531.0    5.992702   6.440144 -3.246351   1.14    4.000000   \n",
       "AQI      29531.0  156.426263  98.172839 -5.033760  85.00  125.000000   \n",
       "\n",
       "                75%         max  \n",
       "PM2.5     78.384357  158.245000  \n",
       "PM10     162.545000  328.696143  \n",
       "NO        18.480000   41.430000  \n",
       "NO2       35.725000   76.425000  \n",
       "NOx       36.805000   81.370011  \n",
       "NH3       29.120000   62.180000  \n",
       "CO         1.400000    2.860000  \n",
       "SO2       14.000000   29.545000  \n",
       "Toluene    7.808458   21.975000  \n",
       "AQI      200.000000  419.746783  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After ElasticNet Imputation\n",
    "X_combined.describe().transpose()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "061060e5-5e58-4afc-9501-063e739d6d7c",
   "metadata": {},
   "source": [
    "<h5>Changes in Correlation of various important columns with 'AQI' after Imputing</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "b0aa1127-5438-46aa-ab30-2a1949dc4995",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PM2.5      0.846661\n",
       "PM10       0.850268\n",
       "NO         0.513341\n",
       "NO2        0.510401\n",
       "NOx        0.529350\n",
       "NH3        0.379542\n",
       "CO         0.579902\n",
       "SO2        0.399403\n",
       "Toluene    0.400889\n",
       "AQI        1.000000\n",
       "Name: AQI, dtype: float64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Before ElasticNet Imputation\n",
    "new_df.corr(numeric_only=True)['AQI'][:-5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "35f61e51-1a31-424e-92d7-f6781b830966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PM2.5      0.852206\n",
       "PM10       0.883562\n",
       "NO         0.517950\n",
       "NO2        0.510100\n",
       "NOx        0.504211\n",
       "NH3        0.421930\n",
       "CO         0.566103\n",
       "SO2        0.401233\n",
       "Toluene    0.416770\n",
       "AQI        1.000000\n",
       "year      -0.234248\n",
       "Name: AQI, dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After ElasticNet Imputation\n",
    "X_combined.corr(numeric_only=True)['AQI'][:11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e4b00be8-3acf-4722-8e8a-07f7c1cd8858",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.boxplot(X_combined[['PM2.5', 'PM10', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2',\n",
    "        'Toluene',  'AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0b854ee4-51df-4c39-8d55-d3446fbfcfb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GxsbOlg0AAADQrXQ61DnkkEPimWeeiSeffDL+8R//Mc4999x4/vnnC8dzuVyH9lmWbbHvnd6tzRVXXBGrVq0qbEuXLu1s2QAAAADdSqdDnd69e8dBBx0URx99dEyZMiWOPPLI+MY3vhH19fUREVvMuFmxYkVh9k59fX1s2LAhVq5cuc02W1NdXV2449bmDQAAAKAn2+lr6myWZVm0tbXF4MGDo76+PubMmVM4tmHDhnj88cfjuOOOi4iI4cOHR1VVVYc2y5cvj+eee67QBgAAAIB3V9mZxldeeWWMGTMmGhsbY82aNTFz5syYO3duPPTQQ5HL5WLixIkxefLkGDJkSAwZMiQmT54ce+yxR5xzzjkREVFbWxvnn39+XHbZZbHPPvtE//794/LLL49hw4bFySefXJITBAAAAOiOOhXq/OlPf4pPf/rTsXz58qitrY0jjjgiHnrooRg1alRERHzxi1+M9evXx0UXXRQrV66MY445Jh5++OHo27dvoY9p06ZFZWVljB8/PtavXx8nnXRS3H777VFRUVHcMwMAAADoxnJZlmXlLqKzOnPPdgAAAKB7Wb9+fTQ1NUVERHNzc9TU1JS5ouLpTOaxy9fUAQAAAGD3E+oAAAAAJEioAwAAAJAgoQ4AAABAgoQ6AAAAAAnq1C3NIUVZlkVra2tJ+m1ra4uIiOrq6sjlckV/jnw+X5J+AQAASJ9Qh26vtbW1cKu71HS3W/MBAABQPJZfAQAAACTITB26vXw+H83NzUXvt7W1NcaOHRsREbNnz458Pl/05yhFnwAAAHQPQh26vVwuV/IlTPl83jIpAAAAdivLrwAAAAASJNQBAAAASJBQBwAAACBBQh0AAACABAl1AAAAABIk1AEAAABIkFAHAAAAIEFCHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgAQJdQAAAAASJNQBAAAASJBQBwAAACBBQh0AAACABAl1AAAAABIk1AEAAABIkFAHAAAAIEFCHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgAQJdQAAAAASJNQBAAAASJBQBwAAACBBQh0AAACABAl1AAAAABIk1AEAAABIkFAHAAAAIEFCHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgAQJdQAAAAASJNQBAAAASJBQBwAAACBBQh0AAACABAl1AAAAABIk1AEAAABIkFAHAAAAIEFCHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgAQJdQAAAAAS1KlQZ8qUKfG3f/u30bdv39hvv/3iYx/7WLz44osd2px33nmRy+U6bB/84Ac7tGlra4sJEybEgAEDok+fPnH66afHsmXLdv1sAAAAAHqIToU6jz/+eFx88cXx5JNPxpw5c+LNN9+M0aNHx7p16zq0O+WUU2L58uWF7cEHH+xwfOLEiTFr1qyYOXNmzJs3L9auXRunnnpqtLe37/oZAQAAAPQAlZ1p/NBDD3X4fsaMGbHffvvFwoUL4/jjjy/sr66ujvr6+q32sWrVqrjtttvirrvuipNPPjkiIu6+++5obGyMRx55JJqamjp7DgAAAAA9zi5dU2fVqlUREdG/f/8O++fOnRv77bdfHHzwwXHBBRfEihUrCscWLlwYGzdujNGjRxf2NTQ0xNChQ2P+/PlbfZ62trZYvXp1hw0AAACgJ9vpUCfLsrj00kvjwx/+cAwdOrSwf8yYMXHPPffEo48+GlOnTo0FCxbEiSeeGG1tbRER0dLSEr1794699967Q391dXXR0tKy1eeaMmVK1NbWFrbGxsadLRsAAACgW+jU8qu3u+SSS+LXv/51zJs3r8P+M888s/D10KFD4+ijj45BgwbFz372sxg3btw2+8uyLHK53FaPXXHFFXHppZcWvl+9erVgBwAAAOjRdmqmzoQJE+KBBx6Ixx57LA444IDtth04cGAMGjQoXn755YiIqK+vjw0bNsTKlSs7tFuxYkXU1dVttY/q6uro169fhw0AAACgJ+tUqJNlWVxyySVx//33x6OPPhqDBw9+18e8/vrrsXTp0hg4cGBERAwfPjyqqqpizpw5hTbLly+P5557Lo477rhOlg8AAADQM3Vq+dXFF18c9957b8yePTv69u1buAZObW1t1NTUxNq1a2PSpEnx8Y9/PAYOHBivvvpqXHnllTFgwIA444wzCm3PP//8uOyyy2KfffaJ/v37x+WXXx7Dhg0r3A0LAAAAgO3rVKhz6623RkTEiBEjOuyfMWNGnHfeeVFRURGLFi2KO++8M954440YOHBgjBw5Mu67777o27dvof20adOisrIyxo8fH+vXr4+TTjopbr/99qioqNj1MwIAAADoAToV6mRZtt3jNTU10dzc/K795PP5mD59ekyfPr0zTw8AAADAf9jpW5oDAAAAUD5CHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgAQJdQAAAAASJNQBAAAASJBQBwAAACBBQh0AAACABAl1AAAAABIk1AEAAABIkFAHAAAAIEFCHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgAQJdQAAAAASJNQBAAAASJBQBwAAACBBQh0AAACABAl1AAAAABIk1AEAAABIkFAHAAAAIEFCHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgAQJdQAAAAASJNQBAAAASJBQBwAAACBBQh0AAACABAl1AAAAABIk1AEAAABIkFAHAAAAIEFCHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgAQJdQAAAAASJNQBAAAASJBQBwAAACBBQh0AAACABAl1AAAAABIk1AEAAABIkFAHAAAAIEFCHQAAAIAEVZa7AAAAAKD8siyL1tbWcpexQ95eZyo1R0Tk8/nI5XJF60+oAwAAAERra2s0NTWVu4xOGzt2bLlL2GHNzc1RU1NTtP4svwIAAABIkJk6AAAAQAer/2FiZFVV5S5j27Is4s2Nb31dWRVRxCVNxZbbuDH6ffemkvQt1AEAAAA6yKqqIqp6l7uM7etdXe4KdkhWwr4tvwIAAABIkFAHAAAAIEFCHQAAAIAECXUAAAAAEtSpUGfKlCnxt3/7t9G3b9/Yb7/94mMf+1i8+OKLHdpkWRaTJk2KhoaGqKmpiREjRsTixYs7tGlra4sJEybEgAEDok+fPnH66afHsmXLdv1sAAAAAHqIToU6jz/+eFx88cXx5JNPxpw5c+LNN9+M0aNHx7p16wptrr/++rjxxhvj5ptvjgULFkR9fX2MGjUq1qxZU2gzceLEmDVrVsycOTPmzZsXa9eujVNPPTXa29uLd2YAAAAA3Vinbmn+0EMPdfh+xowZsd9++8XChQvj+OOPjyzL4qabboqrrroqxo0bFxERd9xxR9TV1cW9994bF154YaxatSpuu+22uOuuu+Lkk0+OiIi77747Ghsb45FHHommpqYinRoAAABA97VL19RZtWpVRET0798/IiKWLFkSLS0tMXr06EKb6urqOOGEE2L+/PkREbFw4cLYuHFjhzYNDQ0xdOjQQpt3amtri9WrV3fYAAAAAHqynQ51siyLSy+9ND784Q/H0KFDIyKipaUlIiLq6uo6tK2rqysca2lpid69e8fee++9zTbvNGXKlKitrS1sjY2NO1s2AAAAQLew06HOJZdcEr/+9a/j+9///hbHcrlch++zLNti3zttr80VV1wRq1atKmxLly7d2bIBAAAAuoWdCnUmTJgQDzzwQDz22GNxwAEHFPbX19dHRGwx42bFihWF2Tv19fWxYcOGWLly5TbbvFN1dXX069evwwYAAADQk3Uq1MmyLC655JK4//7749FHH43Bgwd3OD548OCor6+POXPmFPZt2LAhHn/88TjuuOMiImL48OFRVVXVoc3y5cvjueeeK7QBAAAAYPs6dferiy++OO69996YPXt29O3btzAjp7a2NmpqaiKXy8XEiRNj8uTJMWTIkBgyZEhMnjw59thjjzjnnHMKbc8///y47LLLYp999on+/fvH5ZdfHsOGDSvcDQsAAACA7etUqHPrrbdGRMSIESM67J8xY0acd955ERHxxS9+MdavXx8XXXRRrFy5Mo455ph4+OGHo2/fvoX206ZNi8rKyhg/fnysX78+TjrppLj99tujoqJi184GAAAAoIfoVKiTZdm7tsnlcjFp0qSYNGnSNtvk8/mYPn16TJ8+vTNPDwAAAMB/2Om7XwEAAABQPkIdAAAAgAQJdQAAAAASJNQBAAAASJBQBwAAACBBQh0AAACABAl1AAAAABIk1AEAAABIkFAHAAAAIEFCHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgAQJdQAAAAASJNQBAAAASJBQBwAAACBBQh0AAACABAl1AAAAABIk1AEAAABIkFAHAAAAIEFCHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgAQJdQAAAAASJNQBAAAASJBQBwAAACBBQh0AAACABAl1AAAAABIk1AEAAABIkFAHAAAAIEFCHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgAQJdQAAAAASJNQBAAAASJBQBwAAACBBQh0AAACABAl1AAAAABIk1AEAAABIkFAHAAAAIEFCHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgARVlruAFGVZFq2trSXpt62tLSIiqqurI5fLFbX/fD5f9D4BAACA8hDq7ITW1tZoamoqdxmd1tzcHDU1NeUuAwAAACgCy68AAAAAEmSmzk7I5/PR3Nxc9H5bW1tj7NixERExe/bsyOfzRe2/2P0BAAAA5SPU2Qm5XK7ky5jy+bylUgAAAMA2WX4FAAAAkCChDgAAAECChDoAAAAACRLqAAAAACRIqAMAAACQoE6HOk888UScdtpp0dDQELlcLn784x93OH7eeedFLpfrsH3wgx/s0KatrS0mTJgQAwYMiD59+sTpp58ey5Yt26UTAQAAAOhJOh3qrFu3Lo488si4+eabt9nmlFNOieXLlxe2Bx98sMPxiRMnxqxZs2LmzJkxb968WLt2bZx66qnR3t7e+TMAAAAA6IEqO/uAMWPGxJgxY7bbprq6Ourr67d6bNWqVXHbbbfFXXfdFSeffHJERNx9993R2NgYjzzySDQ1NW3xmLa2tmhrayt8v3r16s6WTSKyLIvW1tZyl7FD3l5nKjVHROTz+cjlcuUuAwAAgF3U6VBnR8ydOzf222+/2GuvveKEE06Ia6+9Nvbbb7+IiFi4cGFs3LgxRo8eXWjf0NAQQ4cOjfnz52811JkyZUpcc801pSiVLqa1tXWr/wa6urFjx5a7hB3W3NwcNTU15S4DAACAXVT0CyWPGTMm7rnnnnj00Udj6tSpsWDBgjjxxBMLM21aWlqid+/esffee3d4XF1dXbS0tGy1zyuuuCJWrVpV2JYuXVrssgEAAACSUvSZOmeeeWbh66FDh8bRRx8dgwYNip/97Gcxbty4bT4uy7JtLgmprq6O6urqYpdKF/et49+I6oqs3GVsU5ZFbNj01te9e0V05RVNbe25uPiJvcpdBgAAAEVUkuVXbzdw4MAYNGhQvPzyyxERUV9fHxs2bIiVK1d2mK2zYsWKOO6440pdDgmprsgiX1HuKrYvnUVMXTccAwAAYOcUffnVO73++uuxdOnSGDhwYEREDB8+PKqqqmLOnDmFNsuXL4/nnntOqAMAAACwgzo9U2ft2rXxyiuvFL5fsmRJPPPMM9G/f//o379/TJo0KT7+8Y/HwIED49VXX40rr7wyBgwYEGeccUZERNTW1sb5558fl112Weyzzz7Rv3//uPzyy2PYsGGFu2EBAAAAsH2dDnWeeuqpGDlyZOH7Sy+9NCIizj333Lj11ltj0aJFceedd8Ybb7wRAwcOjJEjR8Z9990Xffv2LTxm2rRpUVlZGePHj4/169fHSSedFLfffntUVHTxtTYAAAAAXUSnQ50RI0ZElm37+hzNzc3v2kc+n4/p06fH9OnTO/v0AAAAAMRuuKYOAAAAAMUn1AEAAABIkFAHAAAAIEFCHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgAQJdQAAAAASJNQBAAAASJBQBwAAACBBQh0AAACABAl1AAAAABIk1AEAAABIkFAHAAAAIEFCHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgAQJdQAAAAASJNQBAAAASJBQBwAAACBBQh0AAACABAl1AAAAABIk1AEAAABIkFAHAAAAIEFCHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgAQJdQAAAAASJNQBAAAASJBQBwAAACBBleUuAAAAAOhiNm4odwXdRwlfS6EOAAAA0EHtd79R7hLYAZZfAQAAACTITB0AAACgg1X/8IWIqt7lLqN72LihZDOfhDoAAABAR1W9hToJsPwKAAAAIEFCHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgAQJdQAAAAASJNQBAAAASJBQBwAAACBBQh0AAACABAl1AAAAABIk1AEAAABIkFAHAAAAIEFCHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgAQJdQAAAAAS1OlQ54knnojTTjstGhoaIpfLxY9//OMOx7Msi0mTJkVDQ0PU1NTEiBEjYvHixR3atLW1xYQJE2LAgAHRp0+fOP3002PZsmW7dCIAAAAAPUmnQ51169bFkUceGTfffPNWj19//fVx4403xs033xwLFiyI+vr6GDVqVKxZs6bQZuLEiTFr1qyYOXNmzJs3L9auXRunnnpqtLe37/yZAAAAAPQglZ19wJgxY2LMmDFbPZZlWdx0001x1VVXxbhx4yIi4o477oi6urq4995748ILL4xVq1bFbbfdFnfddVecfPLJERFx9913R2NjYzzyyCPR1NS0C6cDAAAA0DMU9Zo6S5YsiZaWlhg9enRhX3V1dZxwwgkxf/78iIhYuHBhbNy4sUObhoaGGDp0aKHNO7W1tcXq1as7bAAAAAA9WVFDnZaWloiIqKur67C/rq6ucKylpSV69+4de++99zbbvNOUKVOitra2sDU2NhazbAAAAIDklOTuV7lcrsP3WZZtse+dttfmiiuuiFWrVhW2pUuXFq1WAAAAgBQVNdSpr6+PiNhixs2KFSsKs3fq6+tjw4YNsXLlym22eafq6uro169fhw0AAACgJytqqDN48OCor6+POXPmFPZt2LAhHn/88TjuuOMiImL48OFRVVXVoc3y5cvjueeeK7QBAAAAYPs6ffertWvXxiuvvFL4fsmSJfHMM89E//794z3veU9MnDgxJk+eHEOGDIkhQ4bE5MmTY4899ohzzjknIiJqa2vj/PPPj8suuyz22Wef6N+/f1x++eUxbNiwwt2wICKizR3ui8ZrCQAA0P10OtR56qmnYuTIkYXvL7300oiIOPfcc+P222+PL37xi7F+/fq46KKLYuXKlXHMMcfEww8/HH379i08Ztq0aVFZWRnjx4+P9evXx0knnRS33357VFRUFOGU6C4ufmLvd28EAAAAPVSnQ50RI0ZElmXbPJ7L5WLSpEkxadKkbbbJ5/Mxffr0mD59emefHgAAAIDYiVAHdpdvHb8yqk3eKoq2djOfAAAAuhuhDl1WdUVEXqgDAAAAW1XUu18BAAAAsHsIdQAAAAASJNQBAAAASJBQBwAAACBBQh0AAACABAl1AAAAABIk1AEAAABIkFAHAAAAIEFCHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgAQJdQAAAAASJNQBAAAASFBluQsAAHavLMuitbW1JP22tbVFRER1dXXkcrmi9p/P54veJwCwdbmNGyMrdxHbk2URb2586+vKqogu/B4ht3Fjyfru1qFOqd60lsrba02pbm+yAdLS2toaTU1N5S6j05qbm6OmpqbcZQBAj9DvuzeVuwR2QLcOdVJ90xoRMXbs2HKXsMO8yQYAAIDdr1uHOgDAlvL5fDQ3Nxe939bW1sKHErNnz458Pl/U/ovdHwDQUaneI5RCqd93lEqx6+wxoc66oz4Z0auLn26WRWx6862ve1V26TWBsenN6PPv95S7CgB2Qi6XK/kMy3w+bxYnACRmd7xHKIWe/L6ji6ccRdSrMqKiqtxV7IDe5S4AAAAASIBbmgMAAAAkqOfM1AGgJFK9PXaEu/cBAJA2oQ4AuyTlOw26ex8AACmz/AoAAAAgQWbqALBLUr09doRbZAMAkDahDgC7xO2xAQCgPCy/AgAAAEiQUAcAAAAgQUIdAAAAgAQJdQAAAAASJNQBAAAASJBQBwAAACBBQh0AAACABAl1AAAAABIk1AEAAABIkFAHAAAAIEFCHQAAAIAECXUAAAAAEiTUAQAAAEiQUAcAAAAgQUIdAAAAgARVlrsAAAC2LcuyaG1tLUm/bW1tERFRXV0duVyu6M+Rz+dL0i8A8BahDgBAF9ba2hpNTU3lLmOnNDc3R01NTbnLAIBuy/IrAAAAgASZqQMA0IXl8/lobm4uer+tra0xduzYiIiYPXt25PP5oj9HKfoEAP6TUAcAoAvL5XIlX8KUz+ctkwKABFl+BQAAAJAgoQ4AAABAgoQ6AAAAAAlyTR26rLb2XERk5S5jm7IsYsOmt77u3SsilytvPdvz1msJAABAdyLUocu6+Im9yl0CAAAAdFmWXwEAAAAkyEwdupR8Ph/Nzc3lLmOHtLa2xtixYyMiYvbs2ZHP58tc0Y5JpU4AAAC2T6hDl5LL5aKmpqbcZXRaPp9Psm4AAADSZfkVAAAAQIKEOgAAAAAJKnqoM2nSpMjlch22+vr6wvEsy2LSpEnR0NAQNTU1MWLEiFi8eHGxywAAAADo1koyU+d973tfLF++vLAtWrSocOz666+PG2+8MW6++eZYsGBB1NfXx6hRo2LNmjWlKAUAAACgWypJqFNZWRn19fWFbd99942It2bp3HTTTXHVVVfFuHHjYujQoXHHHXfEX//617j33ntLUQoAAABAt1SSUOfll1+OhoaGGDx4cJx11lnxu9/9LiIilixZEi0tLTF69OhC2+rq6jjhhBNi/vz52+yvra0tVq9e3WEDAAAA6MmKfkvzY445Ju688844+OCD409/+lN89atfjeOOOy4WL14cLS0tERFRV1fX4TF1dXXx+9//fpt9TpkyJa655ppdK6x94649no68ngAAAFBWRQ91xowZU/h62LBhceyxx8aBBx4Yd9xxR3zwgx+MiIhcLtfhMVmWbbHv7a644oq49NJLC9+vXr06GhsbO1VXn6ct7wIAAAC6j5Lf0rxPnz4xbNiwePnllwt3wdo8Y2ezFStWbDF75+2qq6ujX79+HTYAAACAnqzoM3Xeqa2tLX7zm9/ERz7ykRg8eHDU19fHnDlz4gMf+EBERGzYsCEef/zxuO6660pax7oPnBNRUVXS5+hR2jea/QQAAABlVPRQ5/LLL4/TTjst3vOe98SKFSviq1/9aqxevTrOPffcyOVyMXHixJg8eXIMGTIkhgwZEpMnT4499tgjzjnnnGKX0lFFlVAHAAAA6DaKHuosW7Yszj777Pjzn/8c++67b3zwgx+MJ598MgYNGhQREV/84hdj/fr1cdFFF8XKlSvjmGOOiYcffjj69u1b7FIAAAAAuq2ihzozZ87c7vFcLheTJk2KSZMmFfupAQAAAHqMkl8oGQAAAIDiE+oAAAAAJEioAwAAAJCgkt/SHKCzsiyL1tbWkvTb1tYWERHV1dWRy+WK2n8+ny96nwAAANsi1AG6nNbW1mhqaip3GZ3W3NwcNTU15S4DAADoIYQ6ANBFlWrWWqm8vdaU6jbLDgBIlVAH6HLy+Xw0NzcXvd/W1tYYO3ZsRETMnj078vl8Ufsvdn+Q6qy1iCj8rKXALDsAIFVCHaDLyeVyJf8DK5/P+yMOAABImlAHoAdJaTmPpTwdnf7+i6KyV1XR+y2mLMuifdObERFR0auySy9penPTxnjgmVvKXQYAwC4R6gD0IKku57GUJ6KyV1VUVvQuer/FVhXV5S4BAKDH6FXuAgAAAADoPDN1AHqoL0VEV573kUXExv/4uioiuu5CnogNEXFduYsAAKDHEeoA9FC9I6J3l45KIqGFPFm5CwAAoAey/AoAAAAgQUIdAAAAgAQJdQAAAAASJNQBAAAASJBQBwAAACBBQh0AAACABLmlOQBAkWRZFq2treUuY4e8vc5Uao6IyOfzkcvlyl0GAHQJQh0AgCJpbW2NpqamcpfRaWPHji13CTusubk5ampqyl0G3Uipwtgsy6KtrS0iIqqrq0sSRgo5gZ4T6mx6s9wVvLss+886e1VGdOVf0Cm8ngAA8C5SDWMjhJxADwp1+vz7PeUuAQDoQUbsPyAquvAHNFmWxabsra975aJLf9rfnmUx949/LncZANDl9JhQBwBgd6rI5aKyV9cNSiK6cm3vsKncBdCd5fP5aG5uLnq/ra2thaWNs2fPjnw+X/TnKEWfQFq6dahTql/QpbI7fvGXQip1AgDAO+VyuZIvYcrn85ZJASXRrUOd3fELulT84gcAAAC2p1e5CwAAAACg84Q6AAAAAAnq1suvAKC7eLN9Y7lL6Fa8ngBAdyDUAYAEPPDsLeUuAQCALkaoAwAAQDKyLIvW1taS9NvW1hYREdXV1ZHL5Yrafz6fL3qfINQB6KE2REREVuYquocNu+E5Tj/yoqisqNoNz9QzvNm+0ewngES1trZGU1NTucvotObmZnc4puiEOgA91HXlLoBOqayoisqK3uUuAwCALkSoA+y0Uk19LZW315pS3abqAgD8p3w+H83NzUXvt7W1NcaOHRsREbNnz458Pl/U/ovdH0QIdYBdkOrU14go/IedglJN1f1SRJj3URwbwswnANhdcrlcyZcx5fN5S6VIglAHoIfqHRG9wwyg4nBtIgAAdj+hDlAU7ae1d/3fKFlEtP/H1xURXTrPeDOi4icV5a4CAADowrr6n2BAKiojjd8obh4EAAB0E73KXQAAAAAAnZfC5+oAAMlp3+RaS8XitQSArRPqAACUwNzX/lzuEqDbybIsWltby13GDnl7nanUHPHWXZ9yua584UHg7YQ6AABAElpbW6OpqancZXTa2LFjy13CDmtubnYrb0iIUAcAoARGNAyIil4+7S6G9k2ZmU8AsBVCHQBIwJubNpa7hHeVZVm0b3ozIiIqelV26en7u+P1rOiVi0qhDpTMhz/84aioqCh3GduUZVls2rQpIiJ69erVpX8ntre3x7x588pdBt1UqZZN7o4ljiksRxTqAEACHnjmlnKXANClVFRUdOlQB3jL7lg2WaoljiksRxTqAPRQGyIiouveUSaLiM1zKaoioit/RrKh3AUAANAjCXUAeqjryl0A7yqfz0dzc3O5y9hhra2thU/KZs+eHfl8vswV7ZhU6gSAFJXq/UyWZdHW1hYREdXV1SVZJpXCewShDlAcb5a7gG7G60lE5HK5Lj/ld1vy+XyytQMAxVPK9zN77LFHSfpNiVAHKIqKn1jTnoKUZn6Y9QHA9rS3t5e7hG6jVK9lqS6QWyq748K7pZDCxXwpHaEOQA+S6swPsz5IUXuWRWwqdxXblmVZbPqPy2r1ykWX/oOgPeu61/+ifNytqevbHRfILZVSXXi3FFK4mC+lI9QBiqL9tHa/UYrpTbOfIHVz//jncpcAAHRz/gQDiqMy/EYBAHabD3/4w25pXiTt7e0ln/n02fNbo6qqpE+xy7Is4s3/uK5hZWVEF57AGBs3RnzvNku+8ScYAEDRuG5V6aVSJ6VXUVEh1ElIVVV0+VAnIqJ373JXAJ0j1AGKI4W7NWURsfk6gBUR0YU/fUni9QS24LpVsPt09QslZ1kWmza9dWGtXr16de3rVnXx1xLYNqEOUBSu/wIA7E4ulAwQ0avcBQAAAADQeWbqADstpWtHRLh+BACkLqX3Ht53ALuDUAfYaaleOyLC9SMAKJ0sy6K1tbUk/ba1tUVERHV1ddGv0ZLP57v0dV8i0n3v4X3HW3droni8nmwm1AEAgCJqbW2NpqamcpfRac3NzT0+eKB03H4bSkOoAwBAj1SqGTWl6HN3KFXdKcwAAkhVWUOdW265Jb7+9a/H8uXL433ve1/cdNNN8ZGPfKScJQEA0EOkOqOmVDZf/6XYzAAiIuLT/7U1qqrKXcX2ZVnEm2++9XVlZURXziI3boy4606znyhjqHPffffFxIkT45ZbbokPfehD8e1vfzvGjBkTzz//fLznPe8pV1k7ZHd8qlOK/nvqpySpjleEMSs2P2Olkep4RRizYvMzVhqpjldEzx0z0uJnbPcQQEBp5LIsy8rxxMccc0wcddRRceuttxb2HXbYYfGxj30spkyZ0qFtW1tb4aJwERGrV6+OxsbGWLVqVfTr12+31bzZ+vXrk/xUp6d+SpLqeEUYs9QYr/QYs7QYr/R09TFL+bVNiX8HpeO1JaLr/zug81avXh21tbU7lHmUZabOhg0bYuHChfFP//RPHfaPHj065s+fv0X7KVOmxDXXXLO7ygMAAGAXleoW9G+/XXxKSnVre7eh79nKMlPntddei/333z9+8YtfxHHHHVfYP3ny5LjjjjvixRdf7NC+q83UcZvKtKQ6XhHGrBT9+hkrvlTHK8KYlaJfP2PFl+p4RXT9Mdsdr21K/Dsofr89/WesVFIds546XnRel5+ps9k7/0FnWbbVf+TV1dVRXV29u8p6V7lcrmTT2/bYY4+S9NuTGa/0GLO0GK/0GLO0GK/S8doS4d9BiowZ/Kde5XjSAQMGREVFRbS0tHTYv2LFiqirqytHSQAAAABJKUuo07t37xg+fHjMmTOnw/45c+Z0WI4FAAAAwNaVbfnVpZdeGp/+9Kfj6KOPjmOPPTa+853vxB/+8If43Oc+V66SAAAAAJJRtlDnzDPPjNdffz2+8pWvxPLly2Po0KHx4IMPxqBBg8pVEgAAAEAyynL3q13VmStBAwAAAKSiM5lHWa6pAwAAAMCuEeoAAAAAJEioAwAAAJAgoQ4AAABAgoQ6AAAAAAkS6gAAAAAkSKgDAAAAkCChDgAAAECChDoAAAAACRLqAAAAACRIqAMAAACQIKEOAAAAQIKEOgAAAAAJEuoAAAAAJEioAwAAAJAgoQ4AAABAgoQ6AAAAAAkS6gAAAAAkSKgDAAAAkKDKchewM7Isi4iI1atXl7kSAAAAgOLZnHVszj62J8lQZ82aNRER0djYWOZKAAAAAIpvzZo1UVtbu902uWxHop8uZtOmTfHaa69F3759I5fLlbucolq9enU0NjbG0qVLo1+/fuUuh3dhvNJjzNJivNJjzNJivNJjzNJivNJjzNLSXccry7JYs2ZNNDQ0RK9e279qTpIzdXr16hUHHHBAucsoqX79+nWrf5TdnfFKjzFLi/FKjzFLi/FKjzFLi/FKjzFLS3ccr3ebobOZCyUDAAAAJEioAwAAAJAgoU4XU11dHVdffXVUV1eXuxR2gPFKjzFLi/FKjzFLi/FKjzFLi/FKjzFLi/FK9ELJAAAAAD2dmToAAAAACRLqAAAAACRIqAMAAACQIKEOAAAAQIKEOgAAAHRruVwufvzjH5e7DCg6oU4RnHfeeZHL5SKXy0VVVVW8973vjcsvvzzWrVsXr776auRyuaisrIw//vGPHR63fPnyqKysjFwuF6+++mpERDz77LNx9tlnR2NjY9TU1MRhhx0W3/jGN961hhEjRhRq2LydddZZpTjdbqOY4xYR8YUvfCGGDx8e1dXV8f73v3+rz7lo0aI44YQToqamJvbff//4yle+Em5AVzybx/RrX/tah/0//vGPI5fLFb5vb2+PadOmxRFHHBH5fD722muvGDNmTPziF7/Y3SX3KMUen/vvvz9GjRoV++67b/Tr1y+OPfbYaG5u3i3n0lP4mUrPjozZ3LlzI5fLxRtvvLHF4//mb/4mbrrppsL3F154YRx44IFRU1MT++67b4wdOzZeeOGFUp4CEdHS0hITJkyI9773vVFdXR2NjY1x2mmnxf/5P/+n0Gb+/Pnx0Y9+NPbee+/I5/MxbNiwmDp1arS3t5ex8p5hxYoVceGFF8Z73vOeqK6ujvr6+mhqaopf/vKXhTY7Mj6vvvpqnH/++TF48OCoqamJAw88MK6++urYsGFDOU6ry3vn3zrv3M4777xyl0gJzJ8/PyoqKuKUU07Z4tj69evj6quvjkMOOSSqq6tjwIAB8fd///exePHiDu0mTZq0zb/PugOhTpGccsopsXz58vjd734XX/3qV+OWW26Jyy+/vHC8oaEh7rzzzg6PueOOO2L//ffvsG/hwoWx7777xt133x2LFy+Oq666Kq644oq4+eab37WGCy64IJYvX17Yvv3tbxfn5LqxYo1bRESWZfHZz342zjzzzK0+1+rVq2PUqFHR0NAQCxYsiOnTp8cNN9wQN954Y3FPqofL5/Nx3XXXxcqVK7d6PMuyOOuss+IrX/lKfP7zn4/f/OY38fjjj0djY2OMGDHCJzglVszxeeKJJ2LUqFHx4IMPxsKFC2PkyJFx2mmnxdNPP72bzqZn8DOVnncbs84YPnx4zJgxI37zm99Ec3NzZFkWo0ePFhyU0KuvvhrDhw+PRx99NK6//vpYtGhRPPTQQzFy5Mi4+OKLIyJi1qxZccIJJ8QBBxwQjz32WLzwwgvxhS98Ia699to466yzfGBUYh//+Mfj2WefjTvuuCNeeumleOCBB2LEiBHxl7/8JSJ2fHxeeOGF2LRpU3z729+OxYsXx7Rp0+J//a//FVdeeWU5T6/LevvfOTfddFP069evw74d+SCc9Hzve9+LCRMmxLx58+IPf/hDYX9bW1ucfPLJ8b3vfS/+5//8n/HSSy/Fgw8+GO3t7XHMMcfEk08+Wcaqd7OMXXbuuedmY8eO7bDvH/7hH7L6+vpsyZIlWURkX/7yl7MhQ4Z0aHPIIYdk//zP/5xFRLZkyZJt9n/RRRdlI0eO3G4NJ5xwQvaFL3xhJ8+gZyrVuF199dXZkUceucX+W265Jautrc1aW1sL+6ZMmZI1NDRkmzZtKsYp9Xjnnntuduqpp2aHHnpo9j/+x/8o7J81a1a2+dfdzJkzs4jIHnjggS0eP27cuGyfffbJ1q5du9tq7kl2x/gcfvjh2TXXXFP84nuoYo7Zpk2bspNOOilramoq/M5buXJl1tjYmF155ZW754R6gB0Zs8ceeyyLiGzlypVbPH7QoEHZtGnTttn/s88+m0VE9sorrxS7dP7DmDFjsv3333+rv+tWrlyZrV27Nttnn32ycePGbXH8gQceyCIimzlz5u4otUdauXJlFhHZ3Llzt3p8V8fn+uuvzwYPHly0erurGTNmZLW1tR323XLLLdl73/verKqqKjv44IOzO++8s8PxiMhmzZqVZdnWfw8+/fTTW7y//8UvfpF95CMfyfL5fHbAAQdkEyZM6PCzOWjQoOzaa6/NPvOZz2R77rln1tjYmH3729/u8LzLli3Lxo8fn+21115Z//79s9NPP327f/vxn9auXZv17ds3e+GFF7Izzzyzw3u8r33ta1kul8ueeeaZDo9pb2/Pjj766Ozwww8vvN/Y1t9n3YWZOiVSU1MTGzduLHx/+umnx8qVK2PevHkRETFv3rz4y1/+Eqeddtq79rVq1aro37//u7a75557YsCAAfG+970vLr/88lizZs3On0APVcxxe6df/vKXccIJJ0R1dXVhX1NTU7z22msdlnGxayoqKmLy5Mkxffr0WLZs2RbH77333jj44IO3OoaXXXZZvP766zFnzpzdUWqPVMrx2bRpU6xZs2aHfl+y44o1ZrlcLu6444741a9+Fd/85jcjIuJzn/tc1NXVxaRJk0p9Gj3Ku43Zzlq3bl3MmDEjBg8eHI2NjUXrl//0l7/8JR566KG4+OKLo0+fPlsc32uvveLhhx+O119/vcPM4s1OO+20OPjgg+P73//+7ii3R9pzzz1jzz33jB//+MfR1ta2xfFdHZ8dfd9PR7NmzYovfOELcdlll8Vzzz0XF154YXzmM5+Jxx57bKf7XLRoUTQ1NcW4cePi17/+ddx3330xb968uOSSSzq0mzp1ahx99NHx9NNPx0UXXRT/+I//WFim+te//jVGjhwZe+65ZzzxxBMxb9682HPPPeOUU06xzG4H3HfffXHIIYfEIYccEp/61KdixowZhZlu9957b4waNSqOPPLIDo/p1atX/Pf//t/j+eefj2effbYcZe92Qp0S+NWvfhX33ntvnHTSSYV9VVVV8alPfSq+973vRcRb08g+9alPRVVV1Xb7+uUvfxk/+MEP4sILL9xuu09+8pPx/e9/P+bOnRv//M//HP/6r/8a48aN2/WT6UGKOW5b09LSEnV1dR32bf6+paVlFyrnnc4444x4//vfH1dfffUWx1566aU47LDDtvq4zftfeumlktbX05VqfKZOnRrr1q2L8ePHF69YIqJ4Y7b//vvHt7/97fjSl74UV155ZfzkJz+Je+65Z6d+p7J92xuzzQ444IDCH6ibt7dPbd/slltuKRx/6KGHYs6cOdG7d+9Slt9jvfLKK5FlWRx66KHbbLP552lbP3eHHnqo/8dKqLKyMm6//fa44447Yq+99ooPfehDceWVV8avf/3riNi18fntb38b06dPj8997nOlKb4bu+GGG+K8886Liy66KA4++OC49NJLY9y4cXHDDTfsdJ9f//rX45xzzomJEyfGkCFD4rjjjotvfvObceedd0Zra2uh3Uc/+tG46KKL4qCDDoovfelLMWDAgJg7d25ERMycOTN69eoV3/3ud2PYsGFx2GGHxYwZM+IPf/hDoQ3bdtttt8WnPvWpiHjrshlr164tXFvMe/r/JNQpkp/+9Kex5557Rj6fj2OPPTaOP/74mD59eoc2559/fvzwhz+MlpaW+OEPfxif/exnt9vn4sWLY+zYsfEv//IvMWrUqO22veCCC+Lkk0+OoUOHxllnnRU/+tGP4pFHHol///d/3+Vz685KMW7b8/YLi0ZEIWl+53523XXXXRd33HFHPP/8851+rPEovWKPz/e///2YNGlS3HfffbHffvsVo0TeoVhj9olPfCLGjRsXU6ZMialTp8bBBx9czDJ5m3cbs3/7t3+LZ555psPW0NCwRbtPfvKT8fTTT8fjjz8eQ4YMifHjx3f4g4bi6cz7gmwb183Jssz/YyX28Y9/PF577bV44IEHoqmpKebOnRtHHXVU3H777YU2nR2f1157LU455ZT4xCc+Ef/wD/9QqtK7rd/85jfxoQ99qMO+D33oQ/Gb3/xmp/tcuHBh3H777R2C76ampti0aVMsWbKk0O6II44ofJ3L5aK+vj5WrFhR6OOVV16Jvn37Fvro379/tLa2xm9/+9udrq0nePHFF+NXv/pV4eY/lZWVceaZZxY+bN+ezT9/PeUDCKFOkYwcOTKeeeaZePHFF6O1tTXuv//+Lf6wGDp0aBx66KFx9tlnx2GHHRZDhw7dZn/PP/98nHjiiXHBBRfEl7/85U7Xc9RRR0VVVVW8/PLLnX5sT1Lscdue+vr6LWbkbP6F/84ZPOy6448/Ppqamra42ODBBx+8zT9wNv/HP2TIkJLX19MVc3zuu+++OP/88+MHP/hBnHzyyaUpmKKN2V//+tdYuHBhVFRU+D+qxLY1ZpsNHjw4DjrooA5bZWXlFu1qa2tjyJAhcfzxx8ePfvSjeOGFF2LWrFmlLr9HGjJkSORyue3+Ibo5CN1WmxdeeMH/Y7tBPp+PUaNGxb/8y7/E/Pnz47zzzourr756p8bntddei5EjR8axxx4b3/nOd0pee3e1tQ9PtxVw9urVq9Bms7dfgiHirWXdF154YYfg+9lnn42XX345DjzwwEK7d842zeVysWnTpkIfw4cP3yJAf+mll+Kcc87Z+ZPtAW677bZ48803Y//994/KysqorKyMW2+9Ne6///5YuXJlDBkyZJvvPzYvf+spHxwJdYqkT58+cdBBB8WgQYO2O438s5/9bMydO3e7sz0WL14cI0eOjHPPPTeuvfbanapn8eLFsXHjxhg4cOBOPb6nKOa4vZtjjz02nnjiiQ7rZx9++OFoaGiIv/mbv9npftm2r33ta/GTn/wk5s+fX9h31llnxcsvvxw/+clPtmg/derU2Geffd51ZhzFUYzx+f73vx/nnXde3HvvvfF3f/d3u6XunqwYY3bZZZdFr1694uc//3l885vfjEcffXS31N5TbW3MdlWWZVu9lgi7rn///tHU1BTf+ta3Yt26dVscf+ONN2L06NHRv3//mDp16hbHH3jggXj55Zfj7LPP3h3l8jaHH354rFu3rtPj88c//jFGjBgRRx11VMyYMaMQNtA5hx12WOEamJvNnz9/m8tz9t1334h4645amz3zzDMd2hx11FGxePHiLcLvgw46aIdngBx11FHx8ssvx3777bdFH7W1tZ04w57lzTffjDvvvDOmTp26Rag2aNCguOeee+Lss8+ORx55ZIvr5mzatCmmTZsWRx99dBx++OFlOoPdrDzXZ+5etnYXpc0230Xp6aefzrIsyzZu3Jj9v//3/7KNGzdmWbblVdafe+65bN99980++clPZsuXLy9sK1asKPS5bNmy7JBDDsn+7//9v1mWZdkrr7ySXXPNNdmCBQuyJUuWZD/72c+yQw89NPvABz6QvfnmmyU779QVc9yyLMtefvnl7Omnn84uvPDC7OCDD86efvrp7Omnn87a2tqyLMuyN954I6urq8vOPvvsbNGiRdn999+f9evXL7vhhhtKeZo9ytbG9NOf/nSWz+cLd33ZtGlTdsYZZ2R777139t3vfjdbsmRJ9uyzz2b/7b/9t6yysrJwVwSKr9jjc++992aVlZXZt771rQ6/L994443deFbdW7HH7Kc//WnWu3fvbOHChVmWZdmXv/zl7IADDsj+8pe/7K5T6vZ2ZMx29O5Xv/3tb7PJkydnTz31VPb73/8+mz9/fjZ27Nisf//+2Z/+9KcSn0nP9bvf/S6rr6/PDj/88OxHP/pR9tJLL2XPP/989o1vfCM79NBDsyzLsh/+8IdZRUVFdsEFF2TPPvtstmTJkuy73/1utvfee2d///d/766aJfTnP/85GzlyZHbXXXdlzz77bPa73/0u+8EPfpDV1dVln/3sZ7Ms2/Hx+eMf/5gddNBB2YknnpgtW7asw/9lbN877341a9asrKqqKrv11luzl156KZs6dWpWUVGRPfbYY4U28ba7X23YsCFrbGzMPvGJT2Qvvvhi9tOf/jQ75JBDOry/f/bZZ7Oamprsoosuyp5++unspZdeymbPnp1dcsklhT63dsfAI488Mrv66quzLMuydevWZUOGDMlGjBiRPfHEE9nvfve7bO7cudnnP//5bOnSpSV4ZbqHWbNmZb17997qe7orr7wye//735+tX78+O+aYY7LGxsbsBz/4Qfb73/8++9WvfpV97GMfy2pra7Pnnnuu8JjufvcroU4RdCYceKd3hgNXX311FhFbbIMGDdqiz82/pP7whz9kxx9/fNa/f/+sd+/e2YEHHph9/vOfz15//fXinWQ3VMxxy7K3biu/tbF7e5tf//rX2Uc+8pGsuro6q6+vzyZNmuSNVxFtbUxfffXVrLq6Ont7hr1x48bshhtuyN73vvdl1dXVWb9+/bKmpqbs3/7t33ZzxT1LscdnWz9z55577m44m56hmGO2YsWKrK6uLps8eXKHx/2X//JfsvHjx5f8XHqKHRmzHQ11/vjHP2ZjxozJ9ttvv6yqqio74IADsnPOOSd74YUXSnwWvPbaa9nFF1+cDRo0KOvdu3e2//77Z6effnqHP1CfeOKJ7JRTTslqa2uz3r17Z4cffnh2ww03+ECvxFpbW7N/+qd/yo466qistrY222OPPbJDDjkk+/KXv5z99a9/LbTbkfGZMWPGVv8f87n7u9vVW5pnWZbNmzcvGzZsWJbP57OPfOQj2Q9/+MMt3rv/6le/ykaNGpXtueeeWZ8+fbIjjjgiu/baawvH3y3UybIsW758efZf/+t/zQYMGJBVV1dn733ve7MLLrggW7Vq1a6+DN3Wqaeemn30ox/d6rGFCxdmEZEtXLgwW7t2bXbVVVdlBx54YFZZWZlFRHbQQQdtEZh191Anl2XbuIoXAAAAQAJ+/vOfxxlnnBE33HDDFree784s2gQAAACSNmbMmPj5z38ef/nLX+LPf/5zucvZbczUAQAAAEiQmToAAAAACRLqAAAAACRIqAMAAACQIKEOAAAAQIKEOgAAAAAJEuoAAAAAJEioAwAAAJAgoQ4AAABAgoQ6AAAAAAn6/3cDdK4PumGuAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.boxplot(new_df[['PM2.5', 'PM10', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2',\n",
    "        'Toluene',  'AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3b83a833-8bcb-41d6-8e38-14f2e3cb5a32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PM2.5', 'PM10', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2', 'Toluene',\n",
       "       'AQI', 'year', 'month', 'day', 'weekday', 'dayofyear', 'City_Ahmedabad',\n",
       "       'City_Aizawl', 'City_Amaravati', 'City_Amritsar', 'City_Bengaluru',\n",
       "       'City_Bhopal', 'City_Brajrajnagar', 'City_Chandigarh', 'City_Chennai',\n",
       "       'City_Coimbatore', 'City_Delhi', 'City_Ernakulam', 'City_Gurugram',\n",
       "       'City_Guwahati', 'City_Hyderabad', 'City_Jaipur', 'City_Jorapokhar',\n",
       "       'City_Kochi', 'City_Kolkata', 'City_Lucknow', 'City_Mumbai',\n",
       "       'City_Patna', 'City_Shillong', 'City_Talcher',\n",
       "       'City_Thiruvananthapuram', 'City_Visakhapatnam'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_combined.columns"
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  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c1df91e6-89cf-47a7-85de-d115722d821f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>dayofyear</th>\n",
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       "      <td>72.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>2019-12-17</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
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       "      <td>75.0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>245.0</td>\n",
       "      <td>2018-09-02</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>2018-10-14</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>2018-09-27</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>2018-10-08</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>2018-08-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2016-03-28</td>\n",
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       "     year  month   day  weekday  dayofyear       Date\n",
       "0  2018.0    3.0  13.0      1.0       72.0 2018-03-13\n",
       "1  2019.0   12.0  17.0      1.0      351.0 2019-12-17\n",
       "2  2020.0    4.0  20.0      0.0      111.0 2020-04-20\n",
       "3  2020.0    3.0  15.0      6.0       75.0 2020-03-15\n",
       "4  2018.0    9.0   2.0      6.0      245.0 2018-09-02\n",
       "5  2018.0   10.0  14.0      6.0      287.0 2018-10-14\n",
       "6  2018.0    9.0  27.0      3.0      270.0 2018-09-27\n",
       "7  2018.0   10.0   8.0      0.0      281.0 2018-10-08\n",
       "8  2018.0    8.0   4.0      5.0      216.0 2018-08-04\n",
       "9  2016.0    3.0  28.0      0.0       88.0 2016-03-28"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_imputed['Date'] = pd.to_datetime(X_train_imputed[['year', 'month', 'day']])\n",
    "X_train_imputed[['year', 'month', 'day', 'weekday', 'dayofyear','Date']].head(10)"
   ]
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   "cell_type": "code",
   "execution_count": 102,
   "id": "60a402fb-ce22-46f9-865d-ebed75a246ef",
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       "      <td>0.0</td>\n",
       "      <td>310.0</td>\n",
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       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>2019-09-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2019-06-18</td>\n",
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       "    <tr>\n",
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       "      <td>261.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2019-02-27</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>2017-11-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "     year  month   day  weekday  dayofyear       Date\n",
       "0  2017.0    1.0   8.0      6.0        8.0 2017-01-08\n",
       "1  2016.0    8.0  27.0      5.0      240.0 2016-08-27\n",
       "2  2016.0    5.0  15.0      6.0      136.0 2016-05-15\n",
       "3  2019.0    5.0   8.0      2.0      128.0 2019-05-08\n",
       "4  2017.0   11.0   6.0      0.0      310.0 2017-11-06\n",
       "5  2019.0    9.0  24.0      1.0      267.0 2019-09-24\n",
       "6  2019.0    6.0  18.0      1.0      169.0 2019-06-18\n",
       "7  2017.0    9.0  18.0      0.0      261.0 2017-09-18\n",
       "8  2019.0    2.0  27.0      2.0       58.0 2019-02-27\n",
       "9  2017.0   11.0   4.0      5.0      308.0 2017-11-04"
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     },
     "execution_count": 102,
     "metadata": {},
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    "X_test_imputed['Date'] = pd.to_datetime(X_test_imputed[['year', 'month', 'day']])\n",
    "X_test_imputed[['year', 'month', 'day', 'weekday', 'dayofyear','Date']].head(10)"
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  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "fa1b6abc-b54e-41e5-bec4-07a7ba5a3f33",
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   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
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       "      <th>City</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>142.270000</td>\n",
       "      <td>252.490000</td>\n",
       "      <td>17.110000</td>\n",
       "      <td>33.820000</td>\n",
       "      <td>50.910000</td>\n",
       "      <td>9.800000</td>\n",
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       "      <td>17.180000</td>\n",
       "      <td>10.051466</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>2018.0</td>\n",
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       "      <td>72.0</td>\n",
       "      <td>2018-03-13</td>\n",
       "      <td>Brajrajnagar</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75.610000</td>\n",
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       "      <td>2.950000</td>\n",
       "      <td>27.030000</td>\n",
       "      <td>16.780000</td>\n",
       "      <td>15.920000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>8.240000</td>\n",
       "      <td>7.530000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>2019-12-17</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.010000</td>\n",
       "      <td>27.650000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>7.990000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>6.010000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>2.722763</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2020-04-20</td>\n",
       "      <td>Thiruvananthapuram</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.383514</td>\n",
       "      <td>110.719213</td>\n",
       "      <td>13.414774</td>\n",
       "      <td>24.707616</td>\n",
       "      <td>27.955343</td>\n",
       "      <td>18.924246</td>\n",
       "      <td>0.994469</td>\n",
       "      <td>12.273934</td>\n",
       "      <td>6.319510</td>\n",
       "      <td>133.649517</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>Talcher</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.660000</td>\n",
       "      <td>45.859543</td>\n",
       "      <td>7.440000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>23.790000</td>\n",
       "      <td>28.830000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>5.030000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>2018-09-02</td>\n",
       "      <td>Lucknow</td>\n",
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      ],
      "text/plain": [
       "        PM2.5        PM10         NO        NO2        NOx        NH3  \\\n",
       "0  142.270000  252.490000  17.110000  33.820000  50.910000   9.800000   \n",
       "1   75.610000  121.940000   2.950000  27.030000  16.780000  15.920000   \n",
       "2   15.010000   27.650000   3.250000   7.990000   9.710000   6.010000   \n",
       "3   51.383514  110.719213  13.414774  24.707616  27.955343  18.924246   \n",
       "4   16.660000   45.859543   7.440000  28.000000  23.790000  28.830000   \n",
       "\n",
       "         CO        SO2    Toluene         AQI    year  month   day  weekday  \\\n",
       "0  2.860000  17.180000  10.051466  325.000000  2018.0    3.0  13.0      1.0   \n",
       "1  1.120000   8.240000   7.530000  128.000000  2019.0   12.0  17.0      1.0   \n",
       "2  0.480000   5.800000   2.722763   46.000000  2020.0    4.0  20.0      0.0   \n",
       "3  0.994469  12.273934   6.319510  133.649517  2020.0    3.0  15.0      6.0   \n",
       "4  0.820000   5.030000   0.210000   69.000000  2018.0    9.0   2.0      6.0   \n",
       "\n",
       "   dayofyear       Date                City  \n",
       "0       72.0 2018-03-13        Brajrajnagar  \n",
       "1      351.0 2019-12-17       Visakhapatnam  \n",
       "2      111.0 2020-04-20  Thiruvananthapuram  \n",
       "3       75.0 2020-03-15             Talcher  \n",
       "4      245.0 2018-09-02             Lucknow  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Handling multiple city columns and converting to a single 'City' column\n",
    "city_columns = [col for col in X_train_imputed.columns if col.startswith('City_')]\n",
    "X_train_imputed['City'] = X_train_imputed[city_columns].idxmax(axis=1).str.replace('City_', '')\n",
    "\n",
    "# Dropping the encoded city columns\n",
    "X_train_imputed.drop(columns=city_columns, inplace=True)\n",
    "X_train_imputed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "5f8372d8-5bd6-470e-ad80-508fadc14bbc",
   "metadata": {},
   "outputs": [
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       "      <th>Date</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59.849203</td>\n",
       "      <td>124.814425</td>\n",
       "      <td>12.327533</td>\n",
       "      <td>24.389322</td>\n",
       "      <td>24.867095</td>\n",
       "      <td>23.099132</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.457695</td>\n",
       "      <td>3.806393</td>\n",
       "      <td>137.810883</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2017-01-08</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60.748578</td>\n",
       "      <td>121.232452</td>\n",
       "      <td>13.205756</td>\n",
       "      <td>25.683567</td>\n",
       "      <td>25.622796</td>\n",
       "      <td>23.694019</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9.577667</td>\n",
       "      <td>4.288386</td>\n",
       "      <td>139.097360</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58.500000</td>\n",
       "      <td>111.338723</td>\n",
       "      <td>11.230000</td>\n",
       "      <td>17.050000</td>\n",
       "      <td>28.290000</td>\n",
       "      <td>23.712785</td>\n",
       "      <td>1.14</td>\n",
       "      <td>5.130000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>2016-05-15</td>\n",
       "      <td>Patna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78.570000</td>\n",
       "      <td>161.749819</td>\n",
       "      <td>6.850000</td>\n",
       "      <td>5.450000</td>\n",
       "      <td>5.570000</td>\n",
       "      <td>11.054171</td>\n",
       "      <td>1.33</td>\n",
       "      <td>29.545000</td>\n",
       "      <td>16.320000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>2019-05-08</td>\n",
       "      <td>Patna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>158.245000</td>\n",
       "      <td>284.324788</td>\n",
       "      <td>40.040000</td>\n",
       "      <td>50.590000</td>\n",
       "      <td>60.420000</td>\n",
       "      <td>35.470000</td>\n",
       "      <td>1.22</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>1.190000</td>\n",
       "      <td>339.000000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>2017-11-06</td>\n",
       "      <td>Lucknow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PM2.5        PM10         NO        NO2        NOx        NH3    CO  \\\n",
       "0   59.849203  124.814425  12.327533  24.389322  24.867095  23.099132  0.00   \n",
       "1   60.748578  121.232452  13.205756  25.683567  25.622796  23.694019  0.00   \n",
       "2   58.500000  111.338723  11.230000  17.050000  28.290000  23.712785  1.14   \n",
       "3   78.570000  161.749819   6.850000   5.450000   5.570000  11.054171  1.33   \n",
       "4  158.245000  284.324788  40.040000  50.590000  60.420000  35.470000  1.22   \n",
       "\n",
       "         SO2    Toluene         AQI    year  month   day  weekday  dayofyear  \\\n",
       "0  10.457695   3.806393  137.810883  2017.0    1.0   8.0      6.0        8.0   \n",
       "1   9.577667   4.288386  139.097360  2016.0    8.0  27.0      5.0      240.0   \n",
       "2   5.130000   0.640000  137.000000  2016.0    5.0  15.0      6.0      136.0   \n",
       "3  29.545000  16.320000  190.000000  2019.0    5.0   8.0      2.0      128.0   \n",
       "4   9.710000   1.190000  339.000000  2017.0   11.0   6.0      0.0      310.0   \n",
       "\n",
       "        Date     City  \n",
       "0 2017-01-08   Mumbai  \n",
       "1 2016-08-27   Mumbai  \n",
       "2 2016-05-15    Patna  \n",
       "3 2019-05-08    Patna  \n",
       "4 2017-11-06  Lucknow  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Handling multiple city columns and converting to a single 'City' column\n",
    "city_columns = [col for col in X_test_imputed.columns if col.startswith('City_')]\n",
    "X_test_imputed['City'] = X_test_imputed[city_columns].idxmax(axis=1).str.replace('City_', '')\n",
    "\n",
    "# Dropping the encoded city columns\n",
    "X_test_imputed.drop(columns=city_columns, inplace=True)\n",
    "X_test_imputed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7c18103-dfbf-41b0-8268-e2ee977c1cb1",
   "metadata": {},
   "source": [
    "<h6>Exporting our dataset as csv file for further computation</h6>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "2a094867-8766-4396-b733-231a2341dc1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame saved to ./train_elastic.csv\n",
      "DataFrame saved to ./test_elastic.csv\n"
     ]
    }
   ],
   "source": [
    "trainPath = './train_elastic.csv'\n",
    "testPath = './test_elastic.csv'\n",
    "X_train_imputed.to_csv(trainPath, index=False)\n",
    "X_test_imputed.to_csv(testpath, index=False)\n",
    "print(f\"DataFrame saved to {trainPath}\")\n",
    "print(f\"DataFrame saved to {testPath}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7722870-6fc7-4a79-ba02-0789b803096d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "672c0ab9-70a1-4324-988f-5cad7571b770",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "13e5f71a-a323-42f4-a4ac-8d7222aabb99",
   "metadata": {},
   "source": [
    "<h2>Implementing Iterative Imputer using Ridge estimator</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "c2e4c184-0205-408a-8c99-221c8989a5c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[IterativeImputer] Completing matrix with shape (23624, 41)\n",
      "[IterativeImputer] Ending imputation round 1/200, elapsed time 0.37\n",
      "[IterativeImputer] Change: 480.6850266835447, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 2/200, elapsed time 0.75\n",
      "[IterativeImputer] Change: 207.8677078680362, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 51.398201359716126, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 42.09033540896152, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 34.69609919118574, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 28.595001422406938, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 23.55672789071491, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 19.399064187313712, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 16.13205873221859, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 14.51483697931869, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 13.06278587180042, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 9.532270384296364, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 8.584333372948944, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 7.731478143045358, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 2.4545921696269737, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 2.211841559000046, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 1.9931235494497948, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 1.7960530325704127, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 1.6184829676786663, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 1.0673185666210827, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 0.020486865016810185, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 0.018463596452185982, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 0.010978391984945546, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 0.009894342434009218, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 2.0177947135380236e-07, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Early stopping criterion reached.\n",
      "[IterativeImputer] Completing matrix with shape (5907, 41)\n",
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      "[IterativeImputer] Ending imputation round 183/184, elapsed time 0.90\n",
      "[IterativeImputer] Ending imputation round 184/184, elapsed time 0.90\n",
      "Imputed X_train:\n",
      "           0           1          2          3         4          5   \\\n",
      "0  142.270000  252.490000  17.110000  33.820000  50.91000   9.800000   \n",
      "1   75.610000  121.940000   2.950000  27.030000  16.78000  15.920000   \n",
      "2   15.010000   27.650000   3.250000   7.990000   9.71000   6.010000   \n",
      "3   51.444474  142.311269  22.749114  12.857405  29.96521  10.478798   \n",
      "4   16.660000   38.682937   7.440000  28.000000  23.79000  28.830000   \n",
      "\n",
      "         6          7          8           9   ...   31   32   33   34   35  \\\n",
      "0  2.860000  17.180000  12.134088  325.000000  ...  0.0  0.0  0.0  0.0  0.0   \n",
      "1  1.120000   8.240000   7.530000  128.000000  ...  0.0  0.0  0.0  0.0  0.0   \n",
      "2  0.480000   5.800000   3.031510   46.000000  ...  0.0  0.0  0.0  0.0  0.0   \n",
      "3  1.669762  23.740929   1.505868  159.222498  ...  0.0  0.0  0.0  0.0  0.0   \n",
      "4  0.820000   5.030000   0.210000   69.000000  ...  0.0  0.0  0.0  1.0  0.0   \n",
      "\n",
      "    36   37   38   39   40  \n",
      "0  0.0  0.0  0.0  0.0  0.0  \n",
      "1  0.0  0.0  0.0  0.0  1.0  \n",
      "2  0.0  0.0  0.0  1.0  0.0  \n",
      "3  0.0  0.0  1.0  0.0  0.0  \n",
      "4  0.0  0.0  0.0  0.0  0.0  \n",
      "\n",
      "[5 rows x 41 columns]\n",
      "\n",
      "Imputed X_test:\n",
      "           0           1          2          3          4          5     6   \\\n",
      "0   33.013438   85.533611  21.946129  18.862555  40.358101  14.719075  0.00   \n",
      "1   34.245307   79.672067  22.891953  19.275128  41.073732  15.446875  0.00   \n",
      "2   58.500000   95.625289  11.230000  17.050000  28.290000  13.118423  1.14   \n",
      "3   78.570000  163.410049   6.850000   5.450000   5.570000   4.680443  1.33   \n",
      "4  158.245000  264.320757  40.040000  50.590000  60.420000  35.470000  1.22   \n",
      "\n",
      "          7          8           9   ...   31   32   33   34   35   36   37  \\\n",
      "0  11.689613  -0.155693   75.070079  ...  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
      "1  10.824266  -0.207162   75.977044  ...  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
      "2   5.130000   0.640000  137.000000  ...  0.0  0.0  0.0  0.0  0.0  1.0  0.0   \n",
      "3  29.545000  16.320000  190.000000  ...  0.0  0.0  0.0  0.0  0.0  1.0  0.0   \n",
      "4   9.710000   1.190000  339.000000  ...  0.0  0.0  0.0  1.0  0.0  0.0  0.0   \n",
      "\n",
      "    38   39   40  \n",
      "0  0.0  0.0  0.0  \n",
      "1  0.0  0.0  0.0  \n",
      "2  0.0  0.0  0.0  \n",
      "3  0.0  0.0  0.0  \n",
      "4  0.0  0.0  0.0  \n",
      "\n",
      "[5 rows x 41 columns]\n"
     ]
    }
   ],
   "source": [
    "#Importing required methods\n",
    "from sklearn.experimental import enable_iterative_imputer \n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.impute import IterativeImputer\n",
    "\n",
    "# Initializing Iterative Imputer with Ridge Regression\n",
    "ridge_imputer = IterativeImputer(\n",
    "    estimator=Ridge(alpha=1.0, solver='auto', max_iter=1000, tol=1e-4, random_state=0),\n",
    "    max_iter=200,\n",
    "    random_state=42,\n",
    "    verbose=2,\n",
    "    tol=1e-10\n",
    ")\n",
    "\n",
    "# Fit and transform the training and testind data\n",
    "X_train_imputed_ridge = ridge_imputer.fit_transform(X_train)\n",
    "X_test_imputed_ridge = ridge_imputer.transform(X_test)\n",
    "\n",
    "# Displaying the imputed data\n",
    "print(\"Imputed X_train:\")\n",
    "print(pd.DataFrame(X_train_imputed_ridge).head())\n",
    "print(\"\\nImputed X_test:\")\n",
    "print(pd.DataFrame(X_test_imputed_ridge).head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "3885f5e5-fb8f-439f-8c86-4a33eb1b6999",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting imputed result into dataframe\n",
    "X_train_imputed_ridge = pd.DataFrame(X_train_imputed_ridge, columns=X_train.columns)\n",
    "X_test_imputed_ridge = pd.DataFrame(X_test_imputed_ridge, columns=X_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "cbe3fa53-c989-42f1-af1c-2725255170b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_imputed_ridge['Date'] = pd.to_datetime(X_train_imputed_ridge[['year', 'month', 'day']])\n",
    "X_test_imputed_ridge['Date'] = pd.to_datetime(X_test_imputed_ridge[['year', 'month', 'day']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "a5ff6c3d-5700-470d-9322-b79bdbf96e31",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\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>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>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>...</th>\n",
       "      <th>City_Kochi</th>\n",
       "      <th>City_Kolkata</th>\n",
       "      <th>City_Lucknow</th>\n",
       "      <th>City_Mumbai</th>\n",
       "      <th>City_Patna</th>\n",
       "      <th>City_Shillong</th>\n",
       "      <th>City_Talcher</th>\n",
       "      <th>City_Thiruvananthapuram</th>\n",
       "      <th>City_Visakhapatnam</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>142.27</td>\n",
       "      <td>252.49</td>\n",
       "      <td>17.11</td>\n",
       "      <td>33.82</td>\n",
       "      <td>50.91</td>\n",
       "      <td>9.80</td>\n",
       "      <td>2.86</td>\n",
       "      <td>17.18</td>\n",
       "      <td>12.134088</td>\n",
       "      <td>325.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-03-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75.61</td>\n",
       "      <td>121.94</td>\n",
       "      <td>2.95</td>\n",
       "      <td>27.03</td>\n",
       "      <td>16.78</td>\n",
       "      <td>15.92</td>\n",
       "      <td>1.12</td>\n",
       "      <td>8.24</td>\n",
       "      <td>7.530000</td>\n",
       "      <td>128.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-12-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PM2.5    PM10     NO    NO2    NOx    NH3    CO    SO2    Toluene    AQI  \\\n",
       "0  142.27  252.49  17.11  33.82  50.91   9.80  2.86  17.18  12.134088  325.0   \n",
       "1   75.61  121.94   2.95  27.03  16.78  15.92  1.12   8.24   7.530000  128.0   \n",
       "\n",
       "   ...  City_Kochi  City_Kolkata  City_Lucknow  City_Mumbai  City_Patna  \\\n",
       "0  ...         0.0           0.0           0.0          0.0         0.0   \n",
       "1  ...         0.0           0.0           0.0          0.0         0.0   \n",
       "\n",
       "   City_Shillong  City_Talcher  City_Thiruvananthapuram  City_Visakhapatnam  \\\n",
       "0            0.0           0.0                      0.0                 0.0   \n",
       "1            0.0           0.0                      0.0                 1.0   \n",
       "\n",
       "        Date  \n",
       "0 2018-03-13  \n",
       "1 2019-12-17  \n",
       "\n",
       "[2 rows x 42 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_imputed_ridge.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "949c3d43-2ff4-4265-bd08-1bd49e6c4fa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></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",
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       "      <th>SO2</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>...</th>\n",
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       "      <th>City_Kolkata</th>\n",
       "      <th>City_Lucknow</th>\n",
       "      <th>City_Mumbai</th>\n",
       "      <th>City_Patna</th>\n",
       "      <th>City_Shillong</th>\n",
       "      <th>City_Talcher</th>\n",
       "      <th>City_Thiruvananthapuram</th>\n",
       "      <th>City_Visakhapatnam</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33.013438</td>\n",
       "      <td>85.533611</td>\n",
       "      <td>21.946129</td>\n",
       "      <td>18.862555</td>\n",
       "      <td>40.358101</td>\n",
       "      <td>14.719075</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.689613</td>\n",
       "      <td>-0.155693</td>\n",
       "      <td>75.070079</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.245307</td>\n",
       "      <td>79.672067</td>\n",
       "      <td>22.891953</td>\n",
       "      <td>19.275128</td>\n",
       "      <td>41.073732</td>\n",
       "      <td>15.446875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.824266</td>\n",
       "      <td>-0.207162</td>\n",
       "      <td>75.977044</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-08-27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       PM2.5       PM10         NO        NO2        NOx        NH3   CO  \\\n",
       "0  33.013438  85.533611  21.946129  18.862555  40.358101  14.719075  0.0   \n",
       "1  34.245307  79.672067  22.891953  19.275128  41.073732  15.446875  0.0   \n",
       "\n",
       "         SO2   Toluene        AQI  ...  City_Kochi  City_Kolkata  \\\n",
       "0  11.689613 -0.155693  75.070079  ...         0.0           0.0   \n",
       "1  10.824266 -0.207162  75.977044  ...         0.0           0.0   \n",
       "\n",
       "   City_Lucknow  City_Mumbai  City_Patna  City_Shillong  City_Talcher  \\\n",
       "0           0.0          1.0         0.0            0.0           0.0   \n",
       "1           0.0          1.0         0.0            0.0           0.0   \n",
       "\n",
       "   City_Thiruvananthapuram  City_Visakhapatnam       Date  \n",
       "0                      0.0                 0.0 2017-01-08  \n",
       "1                      0.0                 0.0 2016-08-27  \n",
       "\n",
       "[2 rows x 42 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_imputed_ridge.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "744d2f94-9ffc-4c7e-8906-ebcc05bbae49",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_combined_ridge = pd.concat([X_train_imputed_ridge, X_test_imputed_ridge], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "20f1ea75-e401-4071-9710-bbfae8aee8be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PM2.5                      0\n",
       "PM10                       0\n",
       "NO                         0\n",
       "NO2                        0\n",
       "NOx                        0\n",
       "NH3                        0\n",
       "CO                         0\n",
       "SO2                        0\n",
       "Toluene                    0\n",
       "AQI                        0\n",
       "year                       0\n",
       "month                      0\n",
       "day                        0\n",
       "weekday                    0\n",
       "dayofyear                  0\n",
       "City_Ahmedabad             0\n",
       "City_Aizawl                0\n",
       "City_Amaravati             0\n",
       "City_Amritsar              0\n",
       "City_Bengaluru             0\n",
       "City_Bhopal                0\n",
       "City_Brajrajnagar          0\n",
       "City_Chandigarh            0\n",
       "City_Chennai               0\n",
       "City_Coimbatore            0\n",
       "City_Delhi                 0\n",
       "City_Ernakulam             0\n",
       "City_Gurugram              0\n",
       "City_Guwahati              0\n",
       "City_Hyderabad             0\n",
       "City_Jaipur                0\n",
       "City_Jorapokhar            0\n",
       "City_Kochi                 0\n",
       "City_Kolkata               0\n",
       "City_Lucknow               0\n",
       "City_Mumbai                0\n",
       "City_Patna                 0\n",
       "City_Shillong              0\n",
       "City_Talcher               0\n",
       "City_Thiruvananthapuram    0\n",
       "City_Visakhapatnam         0\n",
       "Date                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_combined_ridge.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f47a5340-5348-47af-a4a3-f0c3fdd75662",
   "metadata": {},
   "source": [
    "<h5>Changes in Statistical Data of numeric columns before and after Imputing</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "8c58b16a-0a63-4fce-8a9f-7ae02bfde4a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PM2.5</th>\n",
       "      <td>24933.0</td>\n",
       "      <td>61.148955</td>\n",
       "      <td>42.803676</td>\n",
       "      <td>0.04</td>\n",
       "      <td>28.820</td>\n",
       "      <td>48.57</td>\n",
       "      <td>80.5900</td>\n",
       "      <td>158.24500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM10</th>\n",
       "      <td>18391.0</td>\n",
       "      <td>112.877919</td>\n",
       "      <td>73.706540</td>\n",
       "      <td>0.01</td>\n",
       "      <td>56.255</td>\n",
       "      <td>95.68</td>\n",
       "      <td>149.7450</td>\n",
       "      <td>289.98000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>25949.0</td>\n",
       "      <td>14.507996</td>\n",
       "      <td>12.246567</td>\n",
       "      <td>0.02</td>\n",
       "      <td>5.630</td>\n",
       "      <td>9.89</td>\n",
       "      <td>19.9500</td>\n",
       "      <td>41.43000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO2</th>\n",
       "      <td>25946.0</td>\n",
       "      <td>27.259387</td>\n",
       "      <td>19.691661</td>\n",
       "      <td>0.01</td>\n",
       "      <td>11.750</td>\n",
       "      <td>21.69</td>\n",
       "      <td>37.6200</td>\n",
       "      <td>76.42500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOx</th>\n",
       "      <td>25346.0</td>\n",
       "      <td>29.539227</td>\n",
       "      <td>22.721584</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.820</td>\n",
       "      <td>23.52</td>\n",
       "      <td>40.1275</td>\n",
       "      <td>81.08875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH3</th>\n",
       "      <td>19203.0</td>\n",
       "      <td>21.361087</td>\n",
       "      <td>16.688309</td>\n",
       "      <td>0.01</td>\n",
       "      <td>8.580</td>\n",
       "      <td>15.85</td>\n",
       "      <td>30.0200</td>\n",
       "      <td>62.18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>27472.0</td>\n",
       "      <td>1.079514</td>\n",
       "      <td>0.815915</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.4500</td>\n",
       "      <td>2.86000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO2</th>\n",
       "      <td>25677.0</td>\n",
       "      <td>11.743115</td>\n",
       "      <td>8.220634</td>\n",
       "      <td>0.01</td>\n",
       "      <td>5.670</td>\n",
       "      <td>9.16</td>\n",
       "      <td>15.2200</td>\n",
       "      <td>29.54500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toluene</th>\n",
       "      <td>21490.0</td>\n",
       "      <td>6.231113</td>\n",
       "      <td>7.332004</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.600</td>\n",
       "      <td>2.97</td>\n",
       "      <td>9.1500</td>\n",
       "      <td>21.97500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQI</th>\n",
       "      <td>24850.0</td>\n",
       "      <td>157.342455</td>\n",
       "      <td>103.870019</td>\n",
       "      <td>13.00</td>\n",
       "      <td>81.000</td>\n",
       "      <td>118.00</td>\n",
       "      <td>208.0000</td>\n",
       "      <td>398.50000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count        mean         std    min     25%     50%       75%  \\\n",
       "PM2.5    24933.0   61.148955   42.803676   0.04  28.820   48.57   80.5900   \n",
       "PM10     18391.0  112.877919   73.706540   0.01  56.255   95.68  149.7450   \n",
       "NO       25949.0   14.507996   12.246567   0.02   5.630    9.89   19.9500   \n",
       "NO2      25946.0   27.259387   19.691661   0.01  11.750   21.69   37.6200   \n",
       "NOx      25346.0   29.539227   22.721584   0.00  12.820   23.52   40.1275   \n",
       "NH3      19203.0   21.361087   16.688309   0.01   8.580   15.85   30.0200   \n",
       "CO       27472.0    1.079514    0.815915   0.00   0.510    0.89    1.4500   \n",
       "SO2      25677.0   11.743115    8.220634   0.01   5.670    9.16   15.2200   \n",
       "Toluene  21490.0    6.231113    7.332004   0.00   0.600    2.97    9.1500   \n",
       "AQI      24850.0  157.342455  103.870019  13.00  81.000  118.00  208.0000   \n",
       "\n",
       "               max  \n",
       "PM2.5    158.24500  \n",
       "PM10     289.98000  \n",
       "NO        41.43000  \n",
       "NO2       76.42500  \n",
       "NOx       81.08875  \n",
       "NH3       62.18000  \n",
       "CO         2.86000  \n",
       "SO2       29.54500  \n",
       "Toluene   21.97500  \n",
       "AQI      398.50000  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Before \n",
    "new_df.describe().transpose()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "11b5c24c-375c-4940-b95a-11caa0dcd1be",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PM2.5</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>60.052013</td>\n",
       "      <td>-12.842243</td>\n",
       "      <td>28.77</td>\n",
       "      <td>49.62</td>\n",
       "      <td>78.12</td>\n",
       "      <td>170.604312</td>\n",
       "      <td>41.216835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM10</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>120.393433</td>\n",
       "      <td>-11.776707</td>\n",
       "      <td>63.95</td>\n",
       "      <td>103.4</td>\n",
       "      <td>159.105841</td>\n",
       "      <td>346.093805</td>\n",
       "      <td>73.601024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>14.741266</td>\n",
       "      <td>-5.743611</td>\n",
       "      <td>5.9</td>\n",
       "      <td>10.68</td>\n",
       "      <td>20.439849</td>\n",
       "      <td>45.062252</td>\n",
       "      <td>11.94978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO2</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>26.538188</td>\n",
       "      <td>-18.503685</td>\n",
       "      <td>11.71</td>\n",
       "      <td>21.11</td>\n",
       "      <td>36.87</td>\n",
       "      <td>76.425</td>\n",
       "      <td>19.137502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOx</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>28.996357</td>\n",
       "      <td>-5.849839</td>\n",
       "      <td>13.028707</td>\n",
       "      <td>23.74</td>\n",
       "      <td>39.301146</td>\n",
       "      <td>87.92691</td>\n",
       "      <td>21.728381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH3</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>21.326691</td>\n",
       "      <td>0.01</td>\n",
       "      <td>10.41</td>\n",
       "      <td>18.22</td>\n",
       "      <td>28.54</td>\n",
       "      <td>62.18</td>\n",
       "      <td>14.597113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>1.107686</td>\n",
       "      <td>-0.012333</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.51993</td>\n",
       "      <td>3.211698</td>\n",
       "      <td>0.823734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO2</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>12.053735</td>\n",
       "      <td>0.01</td>\n",
       "      <td>6.02</td>\n",
       "      <td>9.85</td>\n",
       "      <td>15.91</td>\n",
       "      <td>30.186612</td>\n",
       "      <td>8.000897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toluene</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>5.89464</td>\n",
       "      <td>-10.320435</td>\n",
       "      <td>0.85</td>\n",
       "      <td>3.550596</td>\n",
       "      <td>8.35717</td>\n",
       "      <td>22.337775</td>\n",
       "      <td>6.677608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQI</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>156.373922</td>\n",
       "      <td>-19.636346</td>\n",
       "      <td>80.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>210.174192</td>\n",
       "      <td>488.935019</td>\n",
       "      <td>102.585627</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count        mean        min        25%       50%         75%  \\\n",
       "PM2.5    29531.0   60.052013 -12.842243      28.77     49.62       78.12   \n",
       "PM10     29531.0  120.393433 -11.776707      63.95     103.4  159.105841   \n",
       "NO       29531.0   14.741266  -5.743611        5.9     10.68   20.439849   \n",
       "NO2      29531.0   26.538188 -18.503685      11.71     21.11       36.87   \n",
       "NOx      29531.0   28.996357  -5.849839  13.028707     23.74   39.301146   \n",
       "NH3      29531.0   21.326691       0.01      10.41     18.22       28.54   \n",
       "CO       29531.0    1.107686  -0.012333       0.53      0.91     1.51993   \n",
       "SO2      29531.0   12.053735       0.01       6.02      9.85       15.91   \n",
       "Toluene  29531.0     5.89464 -10.320435       0.85  3.550596     8.35717   \n",
       "AQI      29531.0  156.373922 -19.636346       80.0     120.0  210.174192   \n",
       "\n",
       "                max         std  \n",
       "PM2.5    170.604312   41.216835  \n",
       "PM10     346.093805   73.601024  \n",
       "NO        45.062252    11.94978  \n",
       "NO2          76.425   19.137502  \n",
       "NOx        87.92691   21.728381  \n",
       "NH3           62.18   14.597113  \n",
       "CO         3.211698    0.823734  \n",
       "SO2       30.186612    8.000897  \n",
       "Toluene   22.337775    6.677608  \n",
       "AQI      488.935019  102.585627  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#After\n",
    "X_combined_ridge.describe().transpose() [:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af0f119b-35cb-454f-a314-a3e4e287a7ec",
   "metadata": {},
   "source": [
    "<h5>Changes in Correlation of various important columns with 'AQI' after Imputing</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "dfb779ee-64c4-4a28-97fd-8c5b6ad23b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PM2.5      0.846661\n",
       "PM10       0.850268\n",
       "NO         0.513341\n",
       "NO2        0.510401\n",
       "NOx        0.529350\n",
       "NH3        0.379542\n",
       "CO         0.579902\n",
       "SO2        0.399403\n",
       "Toluene    0.400889\n",
       "AQI        1.000000\n",
       "Name: AQI, dtype: float64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Before Ridge Imputation\n",
    "new_df.corr(numeric_only=True)['AQI'][:-5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "3c8bd0e6-fec1-4021-b88c-e60a846252f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PM2.5      0.852206\n",
       "PM10       0.883562\n",
       "NO         0.517950\n",
       "NO2        0.510100\n",
       "NOx        0.504211\n",
       "NH3        0.421930\n",
       "CO         0.566103\n",
       "SO2        0.401233\n",
       "Toluene    0.416770\n",
       "AQI        1.000000\n",
       "year      -0.234248\n",
       "Name: AQI, dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After Ridge Imputation\n",
    "X_combined.corr(numeric_only=True)['AQI'][:11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "6a86fc31-24c0-404e-bb15-3ca066cbac6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.boxplot(X_combined_ridge[['PM2.5', 'PM10', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2',\n",
    "        'Toluene',  'AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "430773b6-8c85-466e-a752-20e9b720a926",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.boxplot(new_df[['PM2.5', 'PM10', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2',\n",
    "        'Toluene',  'AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "2e117b4a-25bd-49e2-b78b-bc018aeb37c5",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2018-03-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>2019-12-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2020-04-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2020-03-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>2018-09-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>287.0</td>\n",
       "      <td>2018-10-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>2018-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>2018-10-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>2018-08-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2016-03-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  month   day  weekday  dayofyear       Date\n",
       "0  2018.0    3.0  13.0      1.0       72.0 2018-03-13\n",
       "1  2019.0   12.0  17.0      1.0      351.0 2019-12-17\n",
       "2  2020.0    4.0  20.0      0.0      111.0 2020-04-20\n",
       "3  2020.0    3.0  15.0      6.0       75.0 2020-03-15\n",
       "4  2018.0    9.0   2.0      6.0      245.0 2018-09-02\n",
       "5  2018.0   10.0  14.0      6.0      287.0 2018-10-14\n",
       "6  2018.0    9.0  27.0      3.0      270.0 2018-09-27\n",
       "7  2018.0   10.0   8.0      0.0      281.0 2018-10-08\n",
       "8  2018.0    8.0   4.0      5.0      216.0 2018-08-04\n",
       "9  2016.0    3.0  28.0      0.0       88.0 2016-03-28"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_imputed_ridge['Date'] = pd.to_datetime(X_train_imputed_ridge[['year', 'month', 'day']])\n",
    "X_train_imputed_ridge[['year', 'month', 'day', 'weekday', 'dayofyear','Date']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "ab49524f-2eaf-4a5d-a08b-cf32f109749c",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2017-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>2016-08-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>2016-05-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>2019-05-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>2017-11-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>2019-09-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2019-06-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>2017-09-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2019-02-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>2017-11-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  month   day  weekday  dayofyear       Date\n",
       "0  2017.0    1.0   8.0      6.0        8.0 2017-01-08\n",
       "1  2016.0    8.0  27.0      5.0      240.0 2016-08-27\n",
       "2  2016.0    5.0  15.0      6.0      136.0 2016-05-15\n",
       "3  2019.0    5.0   8.0      2.0      128.0 2019-05-08\n",
       "4  2017.0   11.0   6.0      0.0      310.0 2017-11-06\n",
       "5  2019.0    9.0  24.0      1.0      267.0 2019-09-24\n",
       "6  2019.0    6.0  18.0      1.0      169.0 2019-06-18\n",
       "7  2017.0    9.0  18.0      0.0      261.0 2017-09-18\n",
       "8  2019.0    2.0  27.0      2.0       58.0 2019-02-27\n",
       "9  2017.0   11.0   4.0      5.0      308.0 2017-11-04"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_imputed_ridge['Date'] = pd.to_datetime(X_test_imputed_ridge[['year', 'month', 'day']])\n",
    "X_test_imputed_ridge[['year', 'month', 'day', 'weekday', 'dayofyear','Date']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "aebe0ea5-7b7f-4a89-97f8-b9c5ad487303",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling multiple city columns and converting to a single 'City' column\n",
    "city_columns = [col for col in X_train_imputed_ridge.columns if col.startswith('City_')]\n",
    "X_train_imputed_ridge['City'] = X_train_imputed_ridge[city_columns].idxmax(axis=1).str.replace('City_', '')\n",
    "\n",
    "city_columns = [col for col in X_test_imputed_ridge.columns if col.startswith('City_')]\n",
    "X_test_imputed_ridge['City'] = X_test_imputed_ridge[city_columns].idxmax(axis=1).str.replace('City_', '')\n",
    "\n",
    "\n",
    "# Dropping the encoded city columns\n",
    "X_train_imputed_ridge.drop(columns=city_columns, inplace=True)\n",
    "X_test_imputed_ridge.drop(columns=city_columns, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "bd11e0c4-7d7b-4770-a407-22988a51af54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame saved to ./train_ridge.csv\n",
      "DataFrame saved to ./test_ridge.csv\n"
     ]
    }
   ],
   "source": [
    "trainPath = './train_ridge.csv'\n",
    "testPath = './test_ridge.csv'\n",
    "X_train_imputed_ridge.to_csv(trainPath, index=False)\n",
    "X_test_imputed_ridge.to_csv(testpath, index=False)\n",
    "print(f\"DataFrame saved to {trainPath}\")\n",
    "print(f\"DataFrame saved to {testPath}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f31d996-76df-4cd2-a1db-7f9cda84db12",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "557f386d-6f48-41cf-b2c8-7063ef39c618",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4ecb54c6-8b3f-47e6-9f49-71b21ee421bf",
   "metadata": {},
   "source": [
    "<h2>Implementing Iterative Imputer using Bayesian estimator</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "3e238b60-e947-41cb-b1a8-eb8ddf2a919b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[IterativeImputer] Completing matrix with shape (23624, 41)\n",
      "[IterativeImputer] Ending imputation round 1/123, elapsed time 1.26\n",
      "[IterativeImputer] Change: 480.59121211379875, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 2/123, elapsed time 2.69\n",
      "[IterativeImputer] Change: 207.69565271036586, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 3/123, elapsed time 4.14\n",
      "[IterativeImputer] Change: 51.51598651161808, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 4/123, elapsed time 5.61\n",
      "[IterativeImputer] Change: 42.15589592138684, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 5/123, elapsed time 7.00\n",
      "[IterativeImputer] Change: 34.72757774678172, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 6/123, elapsed time 8.27\n",
      "[IterativeImputer] Change: 28.604004569327543, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 7/123, elapsed time 9.60\n",
      "[IterativeImputer] Change: 23.55072324852543, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 8/123, elapsed time 11.02\n",
      "[IterativeImputer] Change: 19.383230766591737, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 9/123, elapsed time 12.39\n",
      "[IterativeImputer] Change: 16.247263262546277, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 10/123, elapsed time 13.77\n",
      "[IterativeImputer] Change: 14.615791677548284, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 11/123, elapsed time 15.07\n",
      "[IterativeImputer] Change: 13.151029037120196, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 12/123, elapsed time 16.40\n",
      "[IterativeImputer] Change: 11.83538215191951, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 13/123, elapsed time 17.76\n",
      "[IterativeImputer] Change: 10.653139782205159, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 14/123, elapsed time 19.04\n",
      "[IterativeImputer] Change: 9.590345014599745, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 15/123, elapsed time 20.42\n",
      "[IterativeImputer] Change: 8.63459960701993, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 16/123, elapsed time 21.86\n",
      "[IterativeImputer] Change: 7.774872445324199, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 17/123, elapsed time 23.43\n",
      "[IterativeImputer] Change: 7.001330498721181, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 18/123, elapsed time 24.85\n",
      "[IterativeImputer] Change: 6.305193663815473, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 19/123, elapsed time 26.10\n",
      "[IterativeImputer] Change: 5.678610561233555, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 20/123, elapsed time 27.42\n",
      "[IterativeImputer] Change: 5.114551775709259, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 21/123, elapsed time 28.73\n",
      "[IterativeImputer] Change: 4.606717496541563, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 22/123, elapsed time 30.14\n",
      "[IterativeImputer] Change: 4.14945711590709, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 23/123, elapsed time 31.49\n",
      "[IterativeImputer] Change: 3.7376988421594888, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 24/123, elapsed time 32.89\n",
      "[IterativeImputer] Change: 3.3668878055358107, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 25/123, elapsed time 34.26\n",
      "[IterativeImputer] Change: 3.0329314072864904, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 26/123, elapsed time 35.41\n",
      "[IterativeImputer] Change: 2.732150918619702, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 27/123, elapsed time 36.49\n",
      "[IterativeImputer] Change: 2.4612385056115187, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 28/123, elapsed time 37.85\n",
      "[IterativeImputer] Change: 2.2172189671509273, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 29/123, elapsed time 39.14\n",
      "[IterativeImputer] Change: 1.9974156385081159, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 30/123, elapsed time 40.57\n",
      "[IterativeImputer] Change: 1.7994199266199615, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 31/123, elapsed time 41.89\n",
      "[IterativeImputer] Change: 1.6210640764371007, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 32/123, elapsed time 43.24\n",
      "[IterativeImputer] Change: 1.460396795060504, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 33/123, elapsed time 44.43\n",
      "[IterativeImputer] Change: 1.315661410660148, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 34/123, elapsed time 45.69\n",
      "[IterativeImputer] Change: 1.185276279472042, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 35/123, elapsed time 47.19\n",
      "[IterativeImputer] Change: 1.0678172158801615, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 36/123, elapsed time 48.43\n",
      "[IterativeImputer] Change: 0.9620017129449678, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 37/123, elapsed time 49.57\n",
      "[IterativeImputer] Change: 0.8666747698829411, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 38/123, elapsed time 50.86\n",
      "[IterativeImputer] Change: 0.7807961464327846, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 39/123, elapsed time 52.22\n",
      "[IterativeImputer] Change: 0.7034289244648022, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 40/123, elapsed time 53.55\n",
      "[IterativeImputer] Change: 0.6337292022717875, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 41/123, elapsed time 54.84\n",
      "[IterativeImputer] Change: 0.5709368324954909, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 42/123, elapsed time 56.09\n",
      "[IterativeImputer] Change: 0.514367092059409, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 43/123, elapsed time 57.28\n",
      "[IterativeImputer] Change: 0.4634031836045338, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 0.4174894791767869, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 45/123, elapsed time 59.84\n",
      "[IterativeImputer] Change: 0.3761254470445792, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 46/123, elapsed time 61.17\n",
      "[IterativeImputer] Change: 0.33886018919666583, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 0.30528750263059123, scaled tolerance: 2.02e-07 \n",
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      "[IterativeImputer] Change: 0.2750414514425259, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 49/123, elapsed time 64.58\n",
      "[IterativeImputer] Change: 0.24779237375295793, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 50/123, elapsed time 65.72\n",
      "[IterativeImputer] Change: 0.2232432797601902, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 51/123, elapsed time 66.87\n",
      "[IterativeImputer] Change: 0.2011266165534522, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 52/123, elapsed time 68.22\n",
      "[IterativeImputer] Change: 0.1812013435455313, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 53/123, elapsed time 69.67\n",
      "[IterativeImputer] Change: 0.1632503132706944, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 54/123, elapsed time 71.00\n",
      "[IterativeImputer] Change: 0.14707789233928992, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 55/123, elapsed time 72.32\n",
      "[IterativeImputer] Change: 0.13250783646414988, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 56/123, elapsed time 73.59\n",
      "[IterativeImputer] Change: 0.11938136884781159, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 57/123, elapsed time 74.93\n",
      "[IterativeImputer] Change: 0.10755544198185163, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 58/123, elapsed time 76.15\n",
      "[IterativeImputer] Change: 0.09690118966803851, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 59/123, elapsed time 77.45\n",
      "[IterativeImputer] Change: 0.08730250989922439, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 60/123, elapsed time 78.65\n",
      "[IterativeImputer] Change: 0.07865480881662279, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 61/123, elapsed time 79.97\n",
      "[IterativeImputer] Change: 0.07086385830272945, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 62/123, elapsed time 81.21\n",
      "[IterativeImputer] Change: 0.0638447647997964, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 63/123, elapsed time 82.48\n",
      "[IterativeImputer] Change: 0.05752104798223456, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 64/123, elapsed time 83.77\n",
      "[IterativeImputer] Change: 0.05182380942439835, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 65/123, elapsed time 84.92\n",
      "[IterativeImputer] Change: 0.04669097191077043, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 66/123, elapsed time 86.04\n",
      "[IterativeImputer] Change: 0.04206661550304602, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 67/123, elapsed time 87.32\n",
      "[IterativeImputer] Change: 0.03790035898710897, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 68/123, elapsed time 88.60\n",
      "[IterativeImputer] Change: 0.03414681346504267, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 69/123, elapsed time 89.88\n",
      "[IterativeImputer] Change: 0.03076508928773336, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 70/123, elapsed time 91.23\n",
      "[IterativeImputer] Change: 0.027718344390912364, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 71/123, elapsed time 92.61\n",
      "[IterativeImputer] Change: 0.024973394658921677, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 72/123, elapsed time 93.88\n",
      "[IterativeImputer] Change: 0.022500336124977593, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 73/123, elapsed time 95.27\n",
      "[IterativeImputer] Change: 0.020272234783696774, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 74/123, elapsed time 96.44\n",
      "[IterativeImputer] Change: 0.018264821741617254, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 75/123, elapsed time 97.69\n",
      "[IterativeImputer] Change: 0.01645623346957592, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 76/123, elapsed time 99.12\n",
      "[IterativeImputer] Change: 0.014826772816050493, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 77/123, elapsed time 100.68\n",
      "[IterativeImputer] Change: 0.01335869461394168, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 78/123, elapsed time 102.23\n",
      "[IterativeImputer] Change: 0.012036012824019338, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 79/123, elapsed time 103.77\n",
      "[IterativeImputer] Change: 0.010844324382276227, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 80/123, elapsed time 105.22\n",
      "[IterativeImputer] Change: 0.009770651475513148, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 81/123, elapsed time 106.58\n",
      "[IterativeImputer] Change: 0.00880330552588049, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 82/123, elapsed time 108.06\n",
      "[IterativeImputer] Change: 0.007931753988472678, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 83/123, elapsed time 109.42\n",
      "[IterativeImputer] Change: 0.007146508812404306, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 84/123, elapsed time 110.65\n",
      "[IterativeImputer] Change: 0.006439021120918653, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 85/123, elapsed time 112.06\n",
      "[IterativeImputer] Change: 0.005801589829601994, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 86/123, elapsed time 113.46\n",
      "[IterativeImputer] Change: 0.005227274071295085, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 87/123, elapsed time 114.83\n",
      "[IterativeImputer] Change: 0.004709826159292163, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 88/123, elapsed time 116.17\n",
      "[IterativeImputer] Change: 0.0042436109144006195, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 89/123, elapsed time 117.57\n",
      "[IterativeImputer] Change: 0.0038235572665854534, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 90/123, elapsed time 118.90\n",
      "[IterativeImputer] Change: 0.003445090048110444, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 91/123, elapsed time 120.28\n",
      "[IterativeImputer] Change: 0.0031040948352512032, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 92/123, elapsed time 121.48\n",
      "[IterativeImputer] Change: 0.0027968581611190757, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 93/123, elapsed time 122.83\n",
      "[IterativeImputer] Change: 0.002520038589402418, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 94/123, elapsed time 124.12\n",
      "[IterativeImputer] Change: 0.0022706220478951877, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 95/123, elapsed time 125.40\n",
      "[IterativeImputer] Change: 0.0020458980150479533, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 96/123, elapsed time 126.61\n",
      "[IterativeImputer] Change: 0.0018434188831406573, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 97/123, elapsed time 127.72\n",
      "[IterativeImputer] Change: 0.0016609833647507344, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 98/123, elapsed time 128.99\n",
      "[IterativeImputer] Change: 0.0014966067307753406, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 99/123, elapsed time 130.25\n",
      "[IterativeImputer] Change: 0.0013485006979081504, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 100/123, elapsed time 131.38\n",
      "[IterativeImputer] Change: 0.001215054148623551, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 101/123, elapsed time 132.53\n",
      "[IterativeImputer] Change: 0.0010948174735503358, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 102/123, elapsed time 133.68\n",
      "[IterativeImputer] Change: 0.0009864803579304748, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 103/123, elapsed time 135.06\n",
      "[IterativeImputer] Change: 0.0008888653773837518, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 104/123, elapsed time 136.44\n",
      "[IterativeImputer] Change: 0.0008009128792281217, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 105/123, elapsed time 137.76\n",
      "[IterativeImputer] Change: 0.0007216638556286625, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 106/123, elapsed time 139.08\n",
      "[IterativeImputer] Change: 0.0006502587874592791, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 107/123, elapsed time 140.51\n",
      "[IterativeImputer] Change: 0.0005859201433580097, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 108/123, elapsed time 141.92\n",
      "[IterativeImputer] Change: 0.0005279479104203233, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 109/123, elapsed time 143.24\n",
      "[IterativeImputer] Change: 0.0004757137114168586, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 110/123, elapsed time 144.37\n",
      "[IterativeImputer] Change: 0.00042864771554462777, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 111/123, elapsed time 145.61\n",
      "[IterativeImputer] Change: 0.0003862396708385063, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 112/123, elapsed time 146.97\n",
      "[IterativeImputer] Change: 0.0003480276465452903, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 113/123, elapsed time 148.36\n",
      "[IterativeImputer] Change: 0.0003135963373352979, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 114/123, elapsed time 149.60\n",
      "[IterativeImputer] Change: 0.0002825729166247015, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 115/123, elapsed time 150.89\n",
      "[IterativeImputer] Change: 0.00025461840412788206, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 116/123, elapsed time 152.16\n",
      "[IterativeImputer] Change: 0.00022943107139639096, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 117/123, elapsed time 153.26\n",
      "[IterativeImputer] Change: 0.00020673491787448484, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 118/123, elapsed time 154.37\n",
      "[IterativeImputer] Change: 0.0001862843511943879, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 119/123, elapsed time 155.47\n",
      "[IterativeImputer] Change: 0.0001678567638663253, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 120/123, elapsed time 156.74\n",
      "[IterativeImputer] Change: 0.0001512531882852386, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 121/123, elapsed time 157.87\n",
      "[IterativeImputer] Change: 0.00013629195392184101, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 122/123, elapsed time 159.16\n",
      "[IterativeImputer] Change: 0.00012281074037900908, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Ending imputation round 123/123, elapsed time 160.64\n",
      "[IterativeImputer] Change: 0.0001106630544072118, scaled tolerance: 2.02e-07 \n",
      "[IterativeImputer] Completing matrix with shape (5907, 41)\n",
      "[IterativeImputer] Ending imputation round 1/123, elapsed time 0.00\n",
      "[IterativeImputer] Ending imputation round 2/123, elapsed time 0.01\n",
      "[IterativeImputer] Ending imputation round 3/123, elapsed time 0.02\n",
      "[IterativeImputer] Ending imputation round 4/123, elapsed time 0.03\n",
      "[IterativeImputer] Ending imputation round 5/123, elapsed time 0.03\n",
      "[IterativeImputer] Ending imputation round 6/123, elapsed time 0.04\n",
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      "[IterativeImputer] Ending imputation round 47/123, elapsed time 0.21\n",
      "[IterativeImputer] Ending imputation round 48/123, elapsed time 0.22\n",
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      "[IterativeImputer] Ending imputation round 50/123, elapsed time 0.23\n",
      "[IterativeImputer] Ending imputation round 51/123, elapsed time 0.23\n",
      "[IterativeImputer] Ending imputation round 52/123, elapsed time 0.23\n",
      "[IterativeImputer] Ending imputation round 53/123, elapsed time 0.24\n",
      "[IterativeImputer] Ending imputation round 54/123, elapsed time 0.25\n",
      "[IterativeImputer] Ending imputation round 55/123, elapsed time 0.25\n",
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      "[IterativeImputer] Ending imputation round 57/123, elapsed time 0.26\n",
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      "[IterativeImputer] Ending imputation round 60/123, elapsed time 0.26\n",
      "[IterativeImputer] Ending imputation round 61/123, elapsed time 0.27\n",
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      "[IterativeImputer] Ending imputation round 63/123, elapsed time 0.27\n",
      "[IterativeImputer] Ending imputation round 64/123, elapsed time 0.28\n",
      "[IterativeImputer] Ending imputation round 65/123, elapsed time 0.29\n",
      "[IterativeImputer] Ending imputation round 66/123, elapsed time 0.29\n",
      "[IterativeImputer] Ending imputation round 67/123, elapsed time 0.29\n",
      "[IterativeImputer] Ending imputation round 68/123, elapsed time 0.29\n",
      "[IterativeImputer] Ending imputation round 69/123, elapsed time 0.30\n",
      "[IterativeImputer] Ending imputation round 70/123, elapsed time 0.31\n",
      "[IterativeImputer] Ending imputation round 71/123, elapsed time 0.31\n",
      "[IterativeImputer] Ending imputation round 72/123, elapsed time 0.31\n",
      "[IterativeImputer] Ending imputation round 73/123, elapsed time 0.31\n",
      "[IterativeImputer] Ending imputation round 74/123, elapsed time 0.32\n",
      "[IterativeImputer] Ending imputation round 75/123, elapsed time 0.32\n",
      "[IterativeImputer] Ending imputation round 76/123, elapsed time 0.32\n",
      "[IterativeImputer] Ending imputation round 77/123, elapsed time 0.34\n",
      "[IterativeImputer] Ending imputation round 78/123, elapsed time 0.34\n",
      "[IterativeImputer] Ending imputation round 79/123, elapsed time 0.34\n",
      "[IterativeImputer] Ending imputation round 80/123, elapsed time 0.34\n",
      "[IterativeImputer] Ending imputation round 81/123, elapsed time 0.34\n",
      "[IterativeImputer] Ending imputation round 82/123, elapsed time 0.35\n",
      "[IterativeImputer] Ending imputation round 83/123, elapsed time 0.35\n",
      "[IterativeImputer] Ending imputation round 84/123, elapsed time 0.35\n",
      "[IterativeImputer] Ending imputation round 85/123, elapsed time 0.36\n",
      "[IterativeImputer] Ending imputation round 86/123, elapsed time 0.36\n",
      "[IterativeImputer] Ending imputation round 87/123, elapsed time 0.36\n",
      "[IterativeImputer] Ending imputation round 88/123, elapsed time 0.36\n",
      "[IterativeImputer] Ending imputation round 89/123, elapsed time 0.37\n",
      "[IterativeImputer] Ending imputation round 90/123, elapsed time 0.37\n",
      "[IterativeImputer] Ending imputation round 91/123, elapsed time 0.38\n",
      "[IterativeImputer] Ending imputation round 92/123, elapsed time 0.38\n",
      "[IterativeImputer] Ending imputation round 93/123, elapsed time 0.38\n",
      "[IterativeImputer] Ending imputation round 94/123, elapsed time 0.39\n",
      "[IterativeImputer] Ending imputation round 95/123, elapsed time 0.39\n",
      "[IterativeImputer] Ending imputation round 96/123, elapsed time 0.39\n",
      "[IterativeImputer] Ending imputation round 97/123, elapsed time 0.40\n",
      "[IterativeImputer] Ending imputation round 98/123, elapsed time 0.40\n",
      "[IterativeImputer] Ending imputation round 99/123, elapsed time 0.41\n",
      "[IterativeImputer] Ending imputation round 100/123, elapsed time 0.41\n",
      "[IterativeImputer] Ending imputation round 101/123, elapsed time 0.42\n",
      "[IterativeImputer] Ending imputation round 102/123, elapsed time 0.44\n",
      "[IterativeImputer] Ending imputation round 103/123, elapsed time 0.44\n",
      "[IterativeImputer] Ending imputation round 104/123, elapsed time 0.45\n",
      "[IterativeImputer] Ending imputation round 105/123, elapsed time 0.45\n",
      "[IterativeImputer] Ending imputation round 106/123, elapsed time 0.45\n",
      "[IterativeImputer] Ending imputation round 107/123, elapsed time 0.46\n",
      "[IterativeImputer] Ending imputation round 108/123, elapsed time 0.46\n",
      "[IterativeImputer] Ending imputation round 109/123, elapsed time 0.47\n",
      "[IterativeImputer] Ending imputation round 110/123, elapsed time 0.47\n",
      "[IterativeImputer] Ending imputation round 111/123, elapsed time 0.48\n",
      "[IterativeImputer] Ending imputation round 112/123, elapsed time 0.48\n",
      "[IterativeImputer] Ending imputation round 113/123, elapsed time 0.48\n",
      "[IterativeImputer] Ending imputation round 114/123, elapsed time 0.49\n",
      "[IterativeImputer] Ending imputation round 115/123, elapsed time 0.49\n",
      "[IterativeImputer] Ending imputation round 116/123, elapsed time 0.50\n",
      "[IterativeImputer] Ending imputation round 117/123, elapsed time 0.50\n",
      "[IterativeImputer] Ending imputation round 118/123, elapsed time 0.50\n",
      "[IterativeImputer] Ending imputation round 119/123, elapsed time 0.50\n",
      "[IterativeImputer] Ending imputation round 120/123, elapsed time 0.51\n",
      "[IterativeImputer] Ending imputation round 121/123, elapsed time 0.52\n",
      "[IterativeImputer] Ending imputation round 122/123, elapsed time 0.52\n",
      "[IterativeImputer] Ending imputation round 123/123, elapsed time 0.53\n",
      "Imputed X_train:\n",
      "           0           1          2          3          4          5   \\\n",
      "0  142.270000  252.490000  17.110000  33.820000  50.910000   9.800000   \n",
      "1   75.610000  121.940000   2.950000  27.030000  16.780000  15.920000   \n",
      "2   15.010000   27.650000   3.250000   7.990000   9.710000   6.010000   \n",
      "3   51.226705  141.931893  22.695044  12.817709  29.873171  10.470932   \n",
      "4   16.660000   38.762942   7.440000  28.000000  23.790000  28.830000   \n",
      "\n",
      "         6          7          8           9   ...   31   32   33   34   35  \\\n",
      "0  2.860000  17.180000  12.180105  325.000000  ...  0.0  0.0  0.0  0.0  0.0   \n",
      "1  1.120000   8.240000   7.530000  128.000000  ...  0.0  0.0  0.0  0.0  0.0   \n",
      "2  0.480000   5.800000   3.022075   46.000000  ...  0.0  0.0  0.0  0.0  0.0   \n",
      "3  1.666053  23.719064   1.540898  158.786662  ...  0.0  0.0  0.0  0.0  0.0   \n",
      "4  0.820000   5.030000   0.210000   69.000000  ...  0.0  0.0  0.0  1.0  0.0   \n",
      "\n",
      "    36   37   38   39   40  \n",
      "0  0.0  0.0  0.0  0.0  0.0  \n",
      "1  0.0  0.0  0.0  0.0  1.0  \n",
      "2  0.0  0.0  0.0  1.0  0.0  \n",
      "3  0.0  0.0  1.0  0.0  0.0  \n",
      "4  0.0  0.0  0.0  0.0  0.0  \n",
      "\n",
      "[5 rows x 41 columns]\n",
      "\n",
      "Imputed X_test:\n",
      "           0           1          2          3          4          5     6   \\\n",
      "0   33.383709   86.162348  21.860745  18.868379  40.326258  14.692778  0.00   \n",
      "1   34.683662   80.431764  22.793898  19.287181  41.043836  15.413135  0.00   \n",
      "2   58.500000   95.607623  11.230000  17.050000  28.290000  13.137121  1.14   \n",
      "3   78.570000  163.063553   6.850000   5.450000   5.570000   4.766307  1.33   \n",
      "4  158.245000  264.336473  40.040000  50.590000  60.420000  35.470000  1.22   \n",
      "\n",
      "          7          8           9   ...   31   32   33   34   35   36   37  \\\n",
      "0  11.700778  -0.159122   75.558379  ...  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
      "1  10.839105  -0.209469   76.559657  ...  0.0  0.0  0.0  0.0  1.0  0.0  0.0   \n",
      "2   5.130000   0.640000  137.000000  ...  0.0  0.0  0.0  0.0  0.0  1.0  0.0   \n",
      "3  29.545000  16.320000  190.000000  ...  0.0  0.0  0.0  0.0  0.0  1.0  0.0   \n",
      "4   9.710000   1.190000  339.000000  ...  0.0  0.0  0.0  1.0  0.0  0.0  0.0   \n",
      "\n",
      "    38   39   40  \n",
      "0  0.0  0.0  0.0  \n",
      "1  0.0  0.0  0.0  \n",
      "2  0.0  0.0  0.0  \n",
      "3  0.0  0.0  0.0  \n",
      "4  0.0  0.0  0.0  \n",
      "\n",
      "[5 rows x 41 columns]\n"
     ]
    }
   ],
   "source": [
    "# Importing required methods\n",
    "from sklearn.experimental import enable_iterative_imputer  \n",
    "from sklearn.linear_model import BayesianRidge\n",
    "\n",
    "# Initializing the Iterative Imputer with BayesianRidge estimator\n",
    "bayesian_imputer = IterativeImputer(\n",
    "    estimator=BayesianRidge(\n",
    "    n_iter=500,            \n",
    "    alpha_1=1e-6,          \n",
    "    alpha_2=1e-6,           \n",
    "    lambda_1=1e-6,          \n",
    "    lambda_2=1e-6,         \n",
    "    tol=1e-4,               \n",
    "    fit_intercept=True     \n",
    ")\n",
    ",\n",
    "    max_iter=123,\n",
    "    random_state=42,\n",
    "    verbose=2,\n",
    "    tol=1e-10\n",
    ")\n",
    "\n",
    "# Fitting and transforming the training and testing data\n",
    "X_train_imputed_bayes = bayesian_imputer.fit_transform(X_train)\n",
    "X_test_imputed_bayes = bayesian_imputer.transform(X_test)\n",
    "\n",
    "# Displaying the imputed data\n",
    "print(\"Imputed X_train:\")\n",
    "print(pd.DataFrame(X_train_imputed_bayes).head())\n",
    "print(\"\\nImputed X_test:\")\n",
    "print(pd.DataFrame(X_test_imputed_bayes).head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "36ccd95f-2962-4e95-b664-8078d7864f2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_imputed_bayes = pd.DataFrame(X_train_imputed_bayes, columns=X_train.columns)\n",
    "X_test_imputed_bayes = pd.DataFrame(X_test_imputed_bayes, columns=X_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "8cdcfee6-e82f-4755-b8b8-f6d8f4343174",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_imputed_bayes['Date'] = pd.to_datetime(X_train_imputed_bayes[['year', 'month', 'day']])\n",
    "X_test_imputed_bayes['Date'] = pd.to_datetime(X_test_imputed_bayes[['year', 'month', 'day']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "e3bac984-1c60-4797-9a43-3213e9dfc564",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>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>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>...</th>\n",
       "      <th>City_Kochi</th>\n",
       "      <th>City_Kolkata</th>\n",
       "      <th>City_Lucknow</th>\n",
       "      <th>City_Mumbai</th>\n",
       "      <th>City_Patna</th>\n",
       "      <th>City_Shillong</th>\n",
       "      <th>City_Talcher</th>\n",
       "      <th>City_Thiruvananthapuram</th>\n",
       "      <th>City_Visakhapatnam</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>142.27</td>\n",
       "      <td>252.49</td>\n",
       "      <td>17.11</td>\n",
       "      <td>33.82</td>\n",
       "      <td>50.91</td>\n",
       "      <td>9.80</td>\n",
       "      <td>2.86</td>\n",
       "      <td>17.18</td>\n",
       "      <td>12.180105</td>\n",
       "      <td>325.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018-03-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75.61</td>\n",
       "      <td>121.94</td>\n",
       "      <td>2.95</td>\n",
       "      <td>27.03</td>\n",
       "      <td>16.78</td>\n",
       "      <td>15.92</td>\n",
       "      <td>1.12</td>\n",
       "      <td>8.24</td>\n",
       "      <td>7.530000</td>\n",
       "      <td>128.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019-12-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PM2.5    PM10     NO    NO2    NOx    NH3    CO    SO2    Toluene    AQI  \\\n",
       "0  142.27  252.49  17.11  33.82  50.91   9.80  2.86  17.18  12.180105  325.0   \n",
       "1   75.61  121.94   2.95  27.03  16.78  15.92  1.12   8.24   7.530000  128.0   \n",
       "\n",
       "   ...  City_Kochi  City_Kolkata  City_Lucknow  City_Mumbai  City_Patna  \\\n",
       "0  ...         0.0           0.0           0.0          0.0         0.0   \n",
       "1  ...         0.0           0.0           0.0          0.0         0.0   \n",
       "\n",
       "   City_Shillong  City_Talcher  City_Thiruvananthapuram  City_Visakhapatnam  \\\n",
       "0            0.0           0.0                      0.0                 0.0   \n",
       "1            0.0           0.0                      0.0                 1.0   \n",
       "\n",
       "        Date  \n",
       "0 2018-03-13  \n",
       "1 2019-12-17  \n",
       "\n",
       "[2 rows x 42 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_imputed_bayes.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "b64c6aee-3364-4d31-9731-0a55c0f9b520",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>...</th>\n",
       "      <th>City_Kochi</th>\n",
       "      <th>City_Kolkata</th>\n",
       "      <th>City_Lucknow</th>\n",
       "      <th>City_Mumbai</th>\n",
       "      <th>City_Patna</th>\n",
       "      <th>City_Shillong</th>\n",
       "      <th>City_Talcher</th>\n",
       "      <th>City_Thiruvananthapuram</th>\n",
       "      <th>City_Visakhapatnam</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33.383709</td>\n",
       "      <td>86.162348</td>\n",
       "      <td>21.860745</td>\n",
       "      <td>18.868379</td>\n",
       "      <td>40.326258</td>\n",
       "      <td>14.692778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.700778</td>\n",
       "      <td>-0.159122</td>\n",
       "      <td>75.558379</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.683662</td>\n",
       "      <td>80.431764</td>\n",
       "      <td>22.793898</td>\n",
       "      <td>19.287181</td>\n",
       "      <td>41.043836</td>\n",
       "      <td>15.413135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.839105</td>\n",
       "      <td>-0.209469</td>\n",
       "      <td>76.559657</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016-08-27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       PM2.5       PM10         NO        NO2        NOx        NH3   CO  \\\n",
       "0  33.383709  86.162348  21.860745  18.868379  40.326258  14.692778  0.0   \n",
       "1  34.683662  80.431764  22.793898  19.287181  41.043836  15.413135  0.0   \n",
       "\n",
       "         SO2   Toluene        AQI  ...  City_Kochi  City_Kolkata  \\\n",
       "0  11.700778 -0.159122  75.558379  ...         0.0           0.0   \n",
       "1  10.839105 -0.209469  76.559657  ...         0.0           0.0   \n",
       "\n",
       "   City_Lucknow  City_Mumbai  City_Patna  City_Shillong  City_Talcher  \\\n",
       "0           0.0          1.0         0.0            0.0           0.0   \n",
       "1           0.0          1.0         0.0            0.0           0.0   \n",
       "\n",
       "   City_Thiruvananthapuram  City_Visakhapatnam       Date  \n",
       "0                      0.0                 0.0 2017-01-08  \n",
       "1                      0.0                 0.0 2016-08-27  \n",
       "\n",
       "[2 rows x 42 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_imputed_bayes.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "426dc6a2-4b72-47e0-9d77-3aff6fb4542e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_combined_bayes = pd.concat([X_train_imputed_bayes, X_test_imputed_bayes], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "5dba0352-9618-4c4d-9739-1597c63d7de5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PM2.5                      0\n",
       "PM10                       0\n",
       "NO                         0\n",
       "NO2                        0\n",
       "NOx                        0\n",
       "NH3                        0\n",
       "CO                         0\n",
       "SO2                        0\n",
       "Toluene                    0\n",
       "AQI                        0\n",
       "year                       0\n",
       "month                      0\n",
       "day                        0\n",
       "weekday                    0\n",
       "dayofyear                  0\n",
       "City_Ahmedabad             0\n",
       "City_Aizawl                0\n",
       "City_Amaravati             0\n",
       "City_Amritsar              0\n",
       "City_Bengaluru             0\n",
       "City_Bhopal                0\n",
       "City_Brajrajnagar          0\n",
       "City_Chandigarh            0\n",
       "City_Chennai               0\n",
       "City_Coimbatore            0\n",
       "City_Delhi                 0\n",
       "City_Ernakulam             0\n",
       "City_Gurugram              0\n",
       "City_Guwahati              0\n",
       "City_Hyderabad             0\n",
       "City_Jaipur                0\n",
       "City_Jorapokhar            0\n",
       "City_Kochi                 0\n",
       "City_Kolkata               0\n",
       "City_Lucknow               0\n",
       "City_Mumbai                0\n",
       "City_Patna                 0\n",
       "City_Shillong              0\n",
       "City_Talcher               0\n",
       "City_Thiruvananthapuram    0\n",
       "City_Visakhapatnam         0\n",
       "Date                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_combined_bayes.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c2a7250-4876-453f-861c-afe364e6a015",
   "metadata": {},
   "source": [
    "<h5>Changes in Statistical Data of numeric columns before and after Imputing</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "05a216a5-5f4f-47d9-b9a9-5c621610b4b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PM2.5</th>\n",
       "      <td>24933.0</td>\n",
       "      <td>61.148955</td>\n",
       "      <td>42.803676</td>\n",
       "      <td>0.04</td>\n",
       "      <td>28.820</td>\n",
       "      <td>48.57</td>\n",
       "      <td>80.5900</td>\n",
       "      <td>158.24500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM10</th>\n",
       "      <td>18391.0</td>\n",
       "      <td>112.877919</td>\n",
       "      <td>73.706540</td>\n",
       "      <td>0.01</td>\n",
       "      <td>56.255</td>\n",
       "      <td>95.68</td>\n",
       "      <td>149.7450</td>\n",
       "      <td>289.98000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>25949.0</td>\n",
       "      <td>14.507996</td>\n",
       "      <td>12.246567</td>\n",
       "      <td>0.02</td>\n",
       "      <td>5.630</td>\n",
       "      <td>9.89</td>\n",
       "      <td>19.9500</td>\n",
       "      <td>41.43000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO2</th>\n",
       "      <td>25946.0</td>\n",
       "      <td>27.259387</td>\n",
       "      <td>19.691661</td>\n",
       "      <td>0.01</td>\n",
       "      <td>11.750</td>\n",
       "      <td>21.69</td>\n",
       "      <td>37.6200</td>\n",
       "      <td>76.42500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOx</th>\n",
       "      <td>25346.0</td>\n",
       "      <td>29.539227</td>\n",
       "      <td>22.721584</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12.820</td>\n",
       "      <td>23.52</td>\n",
       "      <td>40.1275</td>\n",
       "      <td>81.08875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH3</th>\n",
       "      <td>19203.0</td>\n",
       "      <td>21.361087</td>\n",
       "      <td>16.688309</td>\n",
       "      <td>0.01</td>\n",
       "      <td>8.580</td>\n",
       "      <td>15.85</td>\n",
       "      <td>30.0200</td>\n",
       "      <td>62.18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>27472.0</td>\n",
       "      <td>1.079514</td>\n",
       "      <td>0.815915</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.510</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.4500</td>\n",
       "      <td>2.86000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO2</th>\n",
       "      <td>25677.0</td>\n",
       "      <td>11.743115</td>\n",
       "      <td>8.220634</td>\n",
       "      <td>0.01</td>\n",
       "      <td>5.670</td>\n",
       "      <td>9.16</td>\n",
       "      <td>15.2200</td>\n",
       "      <td>29.54500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toluene</th>\n",
       "      <td>21490.0</td>\n",
       "      <td>6.231113</td>\n",
       "      <td>7.332004</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.600</td>\n",
       "      <td>2.97</td>\n",
       "      <td>9.1500</td>\n",
       "      <td>21.97500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQI</th>\n",
       "      <td>24850.0</td>\n",
       "      <td>157.342455</td>\n",
       "      <td>103.870019</td>\n",
       "      <td>13.00</td>\n",
       "      <td>81.000</td>\n",
       "      <td>118.00</td>\n",
       "      <td>208.0000</td>\n",
       "      <td>398.50000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count        mean         std    min     25%     50%       75%  \\\n",
       "PM2.5    24933.0   61.148955   42.803676   0.04  28.820   48.57   80.5900   \n",
       "PM10     18391.0  112.877919   73.706540   0.01  56.255   95.68  149.7450   \n",
       "NO       25949.0   14.507996   12.246567   0.02   5.630    9.89   19.9500   \n",
       "NO2      25946.0   27.259387   19.691661   0.01  11.750   21.69   37.6200   \n",
       "NOx      25346.0   29.539227   22.721584   0.00  12.820   23.52   40.1275   \n",
       "NH3      19203.0   21.361087   16.688309   0.01   8.580   15.85   30.0200   \n",
       "CO       27472.0    1.079514    0.815915   0.00   0.510    0.89    1.4500   \n",
       "SO2      25677.0   11.743115    8.220634   0.01   5.670    9.16   15.2200   \n",
       "Toluene  21490.0    6.231113    7.332004   0.00   0.600    2.97    9.1500   \n",
       "AQI      24850.0  157.342455  103.870019  13.00  81.000  118.00  208.0000   \n",
       "\n",
       "               max  \n",
       "PM2.5    158.24500  \n",
       "PM10     289.98000  \n",
       "NO        41.43000  \n",
       "NO2       76.42500  \n",
       "NOx       81.08875  \n",
       "NH3       62.18000  \n",
       "CO         2.86000  \n",
       "SO2       29.54500  \n",
       "Toluene   21.97500  \n",
       "AQI      398.50000  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Before\n",
    "new_df.describe().transpose()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "6545d2e1-4c82-4363-8e25-84d405336917",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PM2.5</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>60.05554</td>\n",
       "      <td>-12.991184</td>\n",
       "      <td>28.771665</td>\n",
       "      <td>49.62</td>\n",
       "      <td>78.211041</td>\n",
       "      <td>170.763578</td>\n",
       "      <td>41.223249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM10</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>120.392273</td>\n",
       "      <td>-11.721768</td>\n",
       "      <td>63.951464</td>\n",
       "      <td>103.39</td>\n",
       "      <td>159.099535</td>\n",
       "      <td>346.259325</td>\n",
       "      <td>73.594991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>14.738302</td>\n",
       "      <td>-5.693557</td>\n",
       "      <td>5.9</td>\n",
       "      <td>10.68</td>\n",
       "      <td>20.42</td>\n",
       "      <td>44.971859</td>\n",
       "      <td>11.9479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO2</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>26.53815</td>\n",
       "      <td>-18.484664</td>\n",
       "      <td>11.71</td>\n",
       "      <td>21.12</td>\n",
       "      <td>36.87</td>\n",
       "      <td>76.425</td>\n",
       "      <td>19.13603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOx</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>28.994658</td>\n",
       "      <td>-5.868862</td>\n",
       "      <td>13.025</td>\n",
       "      <td>23.75</td>\n",
       "      <td>39.275</td>\n",
       "      <td>87.724401</td>\n",
       "      <td>21.726497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH3</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>21.32792</td>\n",
       "      <td>0.01</td>\n",
       "      <td>10.41</td>\n",
       "      <td>18.23</td>\n",
       "      <td>28.542951</td>\n",
       "      <td>62.18</td>\n",
       "      <td>14.594955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>1.107686</td>\n",
       "      <td>-0.011634</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.52</td>\n",
       "      <td>3.209917</td>\n",
       "      <td>0.823628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO2</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>12.053189</td>\n",
       "      <td>0.01</td>\n",
       "      <td>6.02</td>\n",
       "      <td>9.85</td>\n",
       "      <td>15.91</td>\n",
       "      <td>30.175205</td>\n",
       "      <td>8.00009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toluene</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>5.895873</td>\n",
       "      <td>-10.191174</td>\n",
       "      <td>0.85</td>\n",
       "      <td>3.556692</td>\n",
       "      <td>8.353744</td>\n",
       "      <td>22.33395</td>\n",
       "      <td>6.675284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQI</th>\n",
       "      <td>29531.0</td>\n",
       "      <td>156.373048</td>\n",
       "      <td>-19.881622</td>\n",
       "      <td>80.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>488.757922</td>\n",
       "      <td>102.57849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count        mean        min        25%       50%         75%  \\\n",
       "PM2.5    29531.0    60.05554 -12.991184  28.771665     49.62   78.211041   \n",
       "PM10     29531.0  120.392273 -11.721768  63.951464    103.39  159.099535   \n",
       "NO       29531.0   14.738302  -5.693557        5.9     10.68       20.42   \n",
       "NO2      29531.0    26.53815 -18.484664      11.71     21.12       36.87   \n",
       "NOx      29531.0   28.994658  -5.868862     13.025     23.75      39.275   \n",
       "NH3      29531.0    21.32792       0.01      10.41     18.23   28.542951   \n",
       "CO       29531.0    1.107686  -0.011634       0.53      0.91        1.52   \n",
       "SO2      29531.0   12.053189       0.01       6.02      9.85       15.91   \n",
       "Toluene  29531.0    5.895873 -10.191174       0.85  3.556692    8.353744   \n",
       "AQI      29531.0  156.373048 -19.881622       80.0     120.0       210.0   \n",
       "\n",
       "                max        std  \n",
       "PM2.5    170.763578  41.223249  \n",
       "PM10     346.259325  73.594991  \n",
       "NO        44.971859    11.9479  \n",
       "NO2          76.425   19.13603  \n",
       "NOx       87.724401  21.726497  \n",
       "NH3           62.18  14.594955  \n",
       "CO         3.209917   0.823628  \n",
       "SO2       30.175205    8.00009  \n",
       "Toluene    22.33395   6.675284  \n",
       "AQI      488.757922  102.57849  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After\n",
    "X_combined_bayes.describe().transpose() [:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32518a95-4492-44ab-9752-2cd0cbacca37",
   "metadata": {},
   "source": [
    "<h5>Changes in Correlation of various important columns with 'AQI' after Imputing</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "9a4e55aa-f057-4979-97a7-5787b477978c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PM2.5      0.846661\n",
       "PM10       0.850268\n",
       "NO         0.513341\n",
       "NO2        0.510401\n",
       "NOx        0.529350\n",
       "NH3        0.379542\n",
       "CO         0.579902\n",
       "SO2        0.399403\n",
       "Toluene    0.400889\n",
       "AQI        1.000000\n",
       "Name: AQI, dtype: float64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Before ElasticNet Imputation\n",
    "new_df.corr(numeric_only=True)['AQI'][:-5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "ee4c9071-668c-4742-a160-df5c763e4977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PM2.5      0.852206\n",
       "PM10       0.883562\n",
       "NO         0.517950\n",
       "NO2        0.510100\n",
       "NOx        0.504211\n",
       "NH3        0.421930\n",
       "CO         0.566103\n",
       "SO2        0.401233\n",
       "Toluene    0.416770\n",
       "AQI        1.000000\n",
       "year      -0.234248\n",
       "Name: AQI, dtype: float64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After ElasticNet Imputation\n",
    "X_combined.corr(numeric_only=True)['AQI'][:11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "1c1d37b0-e04a-48c1-b8ca-e89cdf28731a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.boxplot(new_df[['PM2.5', 'PM10', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2',\n",
    "        'Toluene',  'AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "a3c50851-1a66-4b51-ae21-ced4e02b5c3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "sns.boxplot(X_combined_bayes[['PM2.5', 'PM10', 'NO', 'NO2', 'NOx', 'NH3', 'CO', 'SO2',\n",
    "        'Toluene',  'AQI']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "56119eef-a887-4f72-8de9-2f7ccb89539c",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2018-03-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>2019-12-17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  month   day  weekday  dayofyear       Date\n",
       "0  2018.0    3.0  13.0      1.0       72.0 2018-03-13\n",
       "1  2019.0   12.0  17.0      1.0      351.0 2019-12-17"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_imputed['Date'] = pd.to_datetime(X_train_imputed[['year', 'month', 'day']])\n",
    "X_train_imputed[['year', 'month', 'day', 'weekday', 'dayofyear','Date']].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "e122f459-2a13-44d6-9e6e-70d98ed2f7cd",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2017-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>2016-08-27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  month   day  weekday  dayofyear       Date\n",
       "0  2017.0    1.0   8.0      6.0        8.0 2017-01-08\n",
       "1  2016.0    8.0  27.0      5.0      240.0 2016-08-27"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_imputed['Date'] = pd.to_datetime(X_test_imputed[['year', 'month', 'day']])\n",
    "X_test_imputed[['year', 'month', 'day', 'weekday', 'dayofyear','Date']].head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "867c84ba-b6d1-4af8-a42d-4b82a710682c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>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>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>Date</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>142.270000</td>\n",
       "      <td>252.490000</td>\n",
       "      <td>17.110000</td>\n",
       "      <td>33.820000</td>\n",
       "      <td>50.910000</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>2.860000</td>\n",
       "      <td>17.180000</td>\n",
       "      <td>12.180105</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>2018-03-13</td>\n",
       "      <td>Brajrajnagar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75.610000</td>\n",
       "      <td>121.940000</td>\n",
       "      <td>2.950000</td>\n",
       "      <td>27.030000</td>\n",
       "      <td>16.780000</td>\n",
       "      <td>15.920000</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>8.240000</td>\n",
       "      <td>7.530000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>2019-12-17</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.010000</td>\n",
       "      <td>27.650000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>7.990000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>6.010000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.022075</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2020-04-20</td>\n",
       "      <td>Thiruvananthapuram</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.226705</td>\n",
       "      <td>141.931893</td>\n",
       "      <td>22.695044</td>\n",
       "      <td>12.817709</td>\n",
       "      <td>29.873171</td>\n",
       "      <td>10.470932</td>\n",
       "      <td>1.666053</td>\n",
       "      <td>23.719064</td>\n",
       "      <td>1.540898</td>\n",
       "      <td>158.786662</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2020-03-15</td>\n",
       "      <td>Talcher</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.660000</td>\n",
       "      <td>38.762942</td>\n",
       "      <td>7.440000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>23.790000</td>\n",
       "      <td>28.830000</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>5.030000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>2018-09-02</td>\n",
       "      <td>Lucknow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PM2.5        PM10         NO        NO2        NOx        NH3  \\\n",
       "0  142.270000  252.490000  17.110000  33.820000  50.910000   9.800000   \n",
       "1   75.610000  121.940000   2.950000  27.030000  16.780000  15.920000   \n",
       "2   15.010000   27.650000   3.250000   7.990000   9.710000   6.010000   \n",
       "3   51.226705  141.931893  22.695044  12.817709  29.873171  10.470932   \n",
       "4   16.660000   38.762942   7.440000  28.000000  23.790000  28.830000   \n",
       "\n",
       "         CO        SO2    Toluene         AQI    year  month   day  weekday  \\\n",
       "0  2.860000  17.180000  12.180105  325.000000  2018.0    3.0  13.0      1.0   \n",
       "1  1.120000   8.240000   7.530000  128.000000  2019.0   12.0  17.0      1.0   \n",
       "2  0.480000   5.800000   3.022075   46.000000  2020.0    4.0  20.0      0.0   \n",
       "3  1.666053  23.719064   1.540898  158.786662  2020.0    3.0  15.0      6.0   \n",
       "4  0.820000   5.030000   0.210000   69.000000  2018.0    9.0   2.0      6.0   \n",
       "\n",
       "   dayofyear       Date                City  \n",
       "0       72.0 2018-03-13        Brajrajnagar  \n",
       "1      351.0 2019-12-17       Visakhapatnam  \n",
       "2      111.0 2020-04-20  Thiruvananthapuram  \n",
       "3       75.0 2020-03-15             Talcher  \n",
       "4      245.0 2018-09-02             Lucknow  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Handling multiple city columns and converting to a single 'City' column\n",
    "city_columns = [col for col in X_train_imputed_bayes.columns if col.startswith('City_')]\n",
    "X_train_imputed_bayes['City'] = X_train_imputed_bayes[city_columns].idxmax(axis=1).str.replace('City_', '')\n",
    "\n",
    "# Dropping the encoded city columns\n",
    "X_train_imputed_bayes.drop(columns=city_columns, inplace=True)\n",
    "X_train_imputed_bayes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "614e5e8e-7e90-4f42-a8f4-2d248395f5be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>Toluene</th>\n",
       "      <th>AQI</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>weekday</th>\n",
       "      <th>dayofyear</th>\n",
       "      <th>Date</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33.383709</td>\n",
       "      <td>86.162348</td>\n",
       "      <td>21.860745</td>\n",
       "      <td>18.868379</td>\n",
       "      <td>40.326258</td>\n",
       "      <td>14.692778</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.700778</td>\n",
       "      <td>-0.159122</td>\n",
       "      <td>75.558379</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2017-01-08</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34.683662</td>\n",
       "      <td>80.431764</td>\n",
       "      <td>22.793898</td>\n",
       "      <td>19.287181</td>\n",
       "      <td>41.043836</td>\n",
       "      <td>15.413135</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10.839105</td>\n",
       "      <td>-0.209469</td>\n",
       "      <td>76.559657</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>Mumbai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58.500000</td>\n",
       "      <td>95.607623</td>\n",
       "      <td>11.230000</td>\n",
       "      <td>17.050000</td>\n",
       "      <td>28.290000</td>\n",
       "      <td>13.137121</td>\n",
       "      <td>1.14</td>\n",
       "      <td>5.130000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>2016-05-15</td>\n",
       "      <td>Patna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78.570000</td>\n",
       "      <td>163.063553</td>\n",
       "      <td>6.850000</td>\n",
       "      <td>5.450000</td>\n",
       "      <td>5.570000</td>\n",
       "      <td>4.766307</td>\n",
       "      <td>1.33</td>\n",
       "      <td>29.545000</td>\n",
       "      <td>16.320000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>2019-05-08</td>\n",
       "      <td>Patna</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>158.245000</td>\n",
       "      <td>264.336473</td>\n",
       "      <td>40.040000</td>\n",
       "      <td>50.590000</td>\n",
       "      <td>60.420000</td>\n",
       "      <td>35.470000</td>\n",
       "      <td>1.22</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>1.190000</td>\n",
       "      <td>339.000000</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>2017-11-06</td>\n",
       "      <td>Lucknow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PM2.5        PM10         NO        NO2        NOx        NH3    CO  \\\n",
       "0   33.383709   86.162348  21.860745  18.868379  40.326258  14.692778  0.00   \n",
       "1   34.683662   80.431764  22.793898  19.287181  41.043836  15.413135  0.00   \n",
       "2   58.500000   95.607623  11.230000  17.050000  28.290000  13.137121  1.14   \n",
       "3   78.570000  163.063553   6.850000   5.450000   5.570000   4.766307  1.33   \n",
       "4  158.245000  264.336473  40.040000  50.590000  60.420000  35.470000  1.22   \n",
       "\n",
       "         SO2    Toluene         AQI    year  month   day  weekday  dayofyear  \\\n",
       "0  11.700778  -0.159122   75.558379  2017.0    1.0   8.0      6.0        8.0   \n",
       "1  10.839105  -0.209469   76.559657  2016.0    8.0  27.0      5.0      240.0   \n",
       "2   5.130000   0.640000  137.000000  2016.0    5.0  15.0      6.0      136.0   \n",
       "3  29.545000  16.320000  190.000000  2019.0    5.0   8.0      2.0      128.0   \n",
       "4   9.710000   1.190000  339.000000  2017.0   11.0   6.0      0.0      310.0   \n",
       "\n",
       "        Date     City  \n",
       "0 2017-01-08   Mumbai  \n",
       "1 2016-08-27   Mumbai  \n",
       "2 2016-05-15    Patna  \n",
       "3 2019-05-08    Patna  \n",
       "4 2017-11-06  Lucknow  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Handling multiple city columns and converting to a single 'City' column\n",
    "city_columns = [col for col in X_test_imputed_bayes.columns if col.startswith('City_')]\n",
    "X_test_imputed_bayes['City'] = X_test_imputed_bayes[city_columns].idxmax(axis=1).str.replace('City_', '')\n",
    "\n",
    "# Dropping the encoded city columns\n",
    "X_test_imputed_bayes.drop(columns=city_columns, inplace=True)\n",
    "X_test_imputed_bayes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "11ae7480-8bdc-4b62-98b0-335a899c5a47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame saved to ./train_bayes.csv\n",
      "DataFrame saved to ./test_bayes.csv\n"
     ]
    }
   ],
   "source": [
    "trainPath = './train_bayes.csv'\n",
    "testPath = './test_bayes.csv'\n",
    "X_train_imputed.to_csv(trainPath, index=False)\n",
    "X_test_imputed.to_csv(testpath, index=False)\n",
    "print(f\"DataFrame saved to {trainPath}\")\n",
    "print(f\"DataFrame saved to {testPath}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed8d8ef4-2e64-4303-8e61-73b85a0e99e2",
   "metadata": {},
   "source": [
    "# Summary\n",
    "In this analysis, we addressed missing values in the dataset using the Multiple Imputation by Chained Equations (MICE) technique. To enhance the accuracy and robustness of the imputation process, we used three different estimators: Bayesian Regression, Elastic Net, and Ridge Regression. These estimators were used in an iterative process to predict and fill in missing values based on the relationships between features.\n",
    "\n",
    "Before applying the MICE technique, we preprocessed the dataset by removing outliers. This step was crucial to ensure that the imputation models were not skewed by extreme values, thereby improving the quality and reliability of the imputations.\n",
    "\n",
    "Overall, this approach allowed us to handle missing data effectively and prepare a cleaner dataset for subsequent analysis and modeling tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a317d99c-8745-4bdf-a8be-c35dbd6cf008",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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