Air Quality Classification for Indian Cities with ANOVA and SVM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f796824b",
   "metadata": {},
   "source": [
    "### Air Quality Classification for Indian Cities (2015- 2020) : Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6c9f4da",
   "metadata": {},
   "source": [
    "Kaggle Dataset link: https://www.kaggle.com/datasets/rohanrao/air-quality-data-in-india\n",
    "\n",
    "Used Dataset: 'city_day.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d569c208",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1ab37f3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading data\n",
    "df=pd.read_csv(r\"city_day.csv\",parse_dates=True,index_col='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7f95fe2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 29531 entries, 2015-01-01 to 2020-07-01\n",
      "Data columns (total 15 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   City        29531 non-null  object \n",
      " 1   PM2.5       24933 non-null  float64\n",
      " 2   PM10        18391 non-null  float64\n",
      " 3   NO          25949 non-null  float64\n",
      " 4   NO2         25946 non-null  float64\n",
      " 5   NOx         25346 non-null  float64\n",
      " 6   NH3         19203 non-null  float64\n",
      " 7   CO          27472 non-null  float64\n",
      " 8   SO2         25677 non-null  float64\n",
      " 9   O3          25509 non-null  float64\n",
      " 10  Benzene     23908 non-null  float64\n",
      " 11  Toluene     21490 non-null  float64\n",
      " 12  Xylene      11422 non-null  float64\n",
      " 13  AQI         24850 non-null  float64\n",
      " 14  AQI_Bucket  24850 non-null  object \n",
      "dtypes: float64(13), object(2)\n",
      "memory usage: 3.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ca22340e",
   "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>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",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</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>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>2015-01-02</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>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",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.40</td>\n",
       "      <td>19.30</td>\n",
       "      <td>29.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.40</td>\n",
       "      <td>29.07</td>\n",
       "      <td>30.70</td>\n",
       "      <td>6.80</td>\n",
       "      <td>16.40</td>\n",
       "      <td>2.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.48</td>\n",
       "      <td>17.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.59</td>\n",
       "      <td>36.08</td>\n",
       "      <td>4.43</td>\n",
       "      <td>10.14</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.10</td>\n",
       "      <td>21.42</td>\n",
       "      <td>37.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.10</td>\n",
       "      <td>39.33</td>\n",
       "      <td>39.31</td>\n",
       "      <td>7.01</td>\n",
       "      <td>18.89</td>\n",
       "      <td>2.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-27</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>15.02</td>\n",
       "      <td>50.94</td>\n",
       "      <td>7.68</td>\n",
       "      <td>25.06</td>\n",
       "      <td>19.54</td>\n",
       "      <td>12.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>8.55</td>\n",
       "      <td>23.30</td>\n",
       "      <td>2.24</td>\n",
       "      <td>12.07</td>\n",
       "      <td>0.73</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-28</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>24.38</td>\n",
       "      <td>74.09</td>\n",
       "      <td>3.42</td>\n",
       "      <td>26.06</td>\n",
       "      <td>16.53</td>\n",
       "      <td>11.99</td>\n",
       "      <td>0.52</td>\n",
       "      <td>12.72</td>\n",
       "      <td>30.14</td>\n",
       "      <td>0.74</td>\n",
       "      <td>2.21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-29</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>22.91</td>\n",
       "      <td>65.73</td>\n",
       "      <td>3.45</td>\n",
       "      <td>29.53</td>\n",
       "      <td>18.33</td>\n",
       "      <td>10.71</td>\n",
       "      <td>0.48</td>\n",
       "      <td>8.42</td>\n",
       "      <td>30.96</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>16.64</td>\n",
       "      <td>49.97</td>\n",
       "      <td>4.05</td>\n",
       "      <td>29.26</td>\n",
       "      <td>18.80</td>\n",
       "      <td>10.03</td>\n",
       "      <td>0.52</td>\n",
       "      <td>9.84</td>\n",
       "      <td>28.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>54.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>15.00</td>\n",
       "      <td>66.00</td>\n",
       "      <td>0.40</td>\n",
       "      <td>26.85</td>\n",
       "      <td>14.05</td>\n",
       "      <td>5.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2.10</td>\n",
       "      <td>17.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29531 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     City  PM2.5   PM10     NO    NO2    NOx    NH3     CO  \\\n",
       "Date                                                                         \n",
       "2015-01-01      Ahmedabad    NaN    NaN   0.92  18.22  17.15    NaN   0.92   \n",
       "2015-01-02      Ahmedabad    NaN    NaN   0.97  15.69  16.46    NaN   0.97   \n",
       "2015-01-03      Ahmedabad    NaN    NaN  17.40  19.30  29.70    NaN  17.40   \n",
       "2015-01-04      Ahmedabad    NaN    NaN   1.70  18.48  17.97    NaN   1.70   \n",
       "2015-01-05      Ahmedabad    NaN    NaN  22.10  21.42  37.76    NaN  22.10   \n",
       "...                   ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "2020-06-27  Visakhapatnam  15.02  50.94   7.68  25.06  19.54  12.47   0.47   \n",
       "2020-06-28  Visakhapatnam  24.38  74.09   3.42  26.06  16.53  11.99   0.52   \n",
       "2020-06-29  Visakhapatnam  22.91  65.73   3.45  29.53  18.33  10.71   0.48   \n",
       "2020-06-30  Visakhapatnam  16.64  49.97   4.05  29.26  18.80  10.03   0.52   \n",
       "2020-07-01  Visakhapatnam  15.00  66.00   0.40  26.85  14.05   5.20   0.59   \n",
       "\n",
       "              SO2      O3  Benzene  Toluene  Xylene   AQI    AQI_Bucket  \n",
       "Date                                                                     \n",
       "2015-01-01  27.64  133.36     0.00     0.02    0.00   NaN           NaN  \n",
       "2015-01-02  24.55   34.06     3.68     5.50    3.77   NaN           NaN  \n",
       "2015-01-03  29.07   30.70     6.80    16.40    2.25   NaN           NaN  \n",
       "2015-01-04  18.59   36.08     4.43    10.14    1.00   NaN           NaN  \n",
       "2015-01-05  39.33   39.31     7.01    18.89    2.78   NaN           NaN  \n",
       "...           ...     ...      ...      ...     ...   ...           ...  \n",
       "2020-06-27   8.55   23.30     2.24    12.07    0.73  41.0          Good  \n",
       "2020-06-28  12.72   30.14     0.74     2.21    0.38  70.0  Satisfactory  \n",
       "2020-06-29   8.42   30.96     0.01     0.01    0.00  68.0  Satisfactory  \n",
       "2020-06-30   9.84   28.30     0.00     0.00    0.00  54.0  Satisfactory  \n",
       "2020-07-01   2.10   17.05      NaN      NaN     NaN  50.0          Good  \n",
       "\n",
       "[29531 rows x 15 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "330fe9de",
   "metadata": {},
   "source": [
    "# Analysis of Variance (ANOVA) to check if 'AQI' and 'AQI_Bucket' columns are the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f9423311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=21992.753952732455, pvalue=0.0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using f_oneway from scipy.stats to perform ANOVA\n",
    "# This tests if there's a significant difference in 'AQI' values among different 'AQI_Bucket' categories\n",
    "from scipy.stats import f_oneway\n",
    "# ANOVA\n",
    "m1=df['AQI_Bucket']=='Moderate' \n",
    "m2=df['AQI_Bucket']=='Satisfactory'\n",
    "m3=df['AQI_Bucket']=='Poor'\n",
    "m4=df['AQI_Bucket']=='Very Poor' \n",
    "m5=df['AQI_Bucket']=='Good'\n",
    "m6=df['AQI_Bucket']=='Severe'\n",
    "\n",
    "f_oneway(df[m1]['AQI'],df[m2]['AQI'],df[m3]['AQI'],df[m4]['AQI'],df[m5]['AQI'],df[m6]['AQI'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3c145a0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# since p value is 0 we can asume that AQI and AQI_Bucket are nothing but same colums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "290a4139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City              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",
       "O3             4022\n",
       "Benzene        5623\n",
       "Toluene        8041\n",
       "Xylene        18109\n",
       "AQI            4681\n",
       "AQI_Bucket     4681\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aede659e",
   "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>PM2.5</th>\n",
       "      <th>PM10</th>\n",
       "      <th>NO</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>NH3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>Benzene</th>\n",
       "      <th>Toluene</th>\n",
       "      <th>Xylene</th>\n",
       "      <th>AQI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PM2.5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.089912</td>\n",
       "      <td>0.132325</td>\n",
       "      <td>0.161238</td>\n",
       "      <td>0.023911</td>\n",
       "      <td>0.117080</td>\n",
       "      <td>0.114579</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.112588</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.022265</td>\n",
       "      <td>0.169335</td>\n",
       "      <td>0.081700</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.185621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.170322</td>\n",
       "      <td>0.014580</td>\n",
       "      <td>0.035771</td>\n",
       "      <td>0.150857</td>\n",
       "      <td>0.094237</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.025260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.171701</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOx</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.166224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.093170</td>\n",
       "      <td>0.039121</td>\n",
       "      <td>0.189386</td>\n",
       "      <td>0.087398</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.185621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.166224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.104891</td>\n",
       "      <td>-0.038998</td>\n",
       "      <td>0.094972</td>\n",
       "      <td>-0.015650</td>\n",
       "      <td>0.013227</td>\n",
       "      <td>-0.019813</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>0.089912</td>\n",
       "      <td>0.112588</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.104891</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.041736</td>\n",
       "      <td>0.061861</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.154889</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO2</th>\n",
       "      <td>0.132325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.170322</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.038998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.162142</td>\n",
       "      <td>0.036110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O3</th>\n",
       "      <td>0.161238</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.014580</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.093170</td>\n",
       "      <td>0.094972</td>\n",
       "      <td>0.041736</td>\n",
       "      <td>0.162142</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.020255</td>\n",
       "      <td>0.130209</td>\n",
       "      <td>0.111410</td>\n",
       "      <td>0.198991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benzene</th>\n",
       "      <td>0.023911</td>\n",
       "      <td>0.022265</td>\n",
       "      <td>0.035771</td>\n",
       "      <td>0.025260</td>\n",
       "      <td>0.039121</td>\n",
       "      <td>-0.015650</td>\n",
       "      <td>0.061861</td>\n",
       "      <td>0.036110</td>\n",
       "      <td>0.020255</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.044407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toluene</th>\n",
       "      <td>0.117080</td>\n",
       "      <td>0.169335</td>\n",
       "      <td>0.150857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.189386</td>\n",
       "      <td>0.013227</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.130209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xylene</th>\n",
       "      <td>0.114579</td>\n",
       "      <td>0.081700</td>\n",
       "      <td>0.094237</td>\n",
       "      <td>0.171701</td>\n",
       "      <td>0.087398</td>\n",
       "      <td>-0.019813</td>\n",
       "      <td>0.154889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.111410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.165532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQI</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.198991</td>\n",
       "      <td>0.044407</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.165532</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            PM2.5      PM10        NO       NO2       NOx       NH3        CO  \\\n",
       "PM2.5         NaN       NaN       NaN       NaN       NaN       NaN  0.089912   \n",
       "PM10          NaN       NaN       NaN       NaN       NaN       NaN  0.112588   \n",
       "NO            NaN       NaN       NaN       NaN       NaN  0.185621       NaN   \n",
       "NO2           NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "NOx           NaN       NaN       NaN       NaN       NaN  0.166224       NaN   \n",
       "NH3           NaN       NaN  0.185621       NaN  0.166224       NaN  0.104891   \n",
       "CO       0.089912  0.112588       NaN       NaN       NaN  0.104891       NaN   \n",
       "SO2      0.132325       NaN  0.170322       NaN       NaN -0.038998       NaN   \n",
       "O3       0.161238       NaN  0.014580       NaN  0.093170  0.094972  0.041736   \n",
       "Benzene  0.023911  0.022265  0.035771  0.025260  0.039121 -0.015650  0.061861   \n",
       "Toluene  0.117080  0.169335  0.150857       NaN  0.189386  0.013227       NaN   \n",
       "Xylene   0.114579  0.081700  0.094237  0.171701  0.087398 -0.019813  0.154889   \n",
       "AQI           NaN       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "\n",
       "              SO2        O3   Benzene   Toluene    Xylene       AQI  \n",
       "PM2.5    0.132325  0.161238  0.023911  0.117080  0.114579       NaN  \n",
       "PM10          NaN       NaN  0.022265  0.169335  0.081700       NaN  \n",
       "NO       0.170322  0.014580  0.035771  0.150857  0.094237       NaN  \n",
       "NO2           NaN       NaN  0.025260       NaN  0.171701       NaN  \n",
       "NOx           NaN  0.093170  0.039121  0.189386  0.087398       NaN  \n",
       "NH3     -0.038998  0.094972 -0.015650  0.013227 -0.019813       NaN  \n",
       "CO            NaN  0.041736  0.061861       NaN  0.154889       NaN  \n",
       "SO2           NaN  0.162142  0.036110       NaN       NaN       NaN  \n",
       "O3       0.162142       NaN  0.020255  0.130209  0.111410  0.198991  \n",
       "Benzene  0.036110  0.020255       NaN       NaN       NaN  0.044407  \n",
       "Toluene       NaN  0.130209       NaN       NaN       NaN       NaN  \n",
       "Xylene        NaN  0.111410       NaN       NaN       NaN  0.165532  \n",
       "AQI           NaN  0.198991  0.044407       NaN  0.165532       NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a mask to identify columns with correlation coefficients <= 0.2\n",
    "mask=df.corr()<=0.2\n",
    "df.corr()[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6dd0d750",
   "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>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",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</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>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>2015-01-02</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>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",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.40</td>\n",
       "      <td>19.30</td>\n",
       "      <td>29.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.40</td>\n",
       "      <td>29.07</td>\n",
       "      <td>30.70</td>\n",
       "      <td>6.80</td>\n",
       "      <td>16.40</td>\n",
       "      <td>2.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.48</td>\n",
       "      <td>17.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.59</td>\n",
       "      <td>36.08</td>\n",
       "      <td>4.43</td>\n",
       "      <td>10.14</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.10</td>\n",
       "      <td>21.42</td>\n",
       "      <td>37.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.10</td>\n",
       "      <td>39.33</td>\n",
       "      <td>39.31</td>\n",
       "      <td>7.01</td>\n",
       "      <td>18.89</td>\n",
       "      <td>2.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-27</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>15.02</td>\n",
       "      <td>50.94</td>\n",
       "      <td>7.68</td>\n",
       "      <td>25.06</td>\n",
       "      <td>19.54</td>\n",
       "      <td>12.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>8.55</td>\n",
       "      <td>23.30</td>\n",
       "      <td>2.24</td>\n",
       "      <td>12.07</td>\n",
       "      <td>0.73</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-28</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>24.38</td>\n",
       "      <td>74.09</td>\n",
       "      <td>3.42</td>\n",
       "      <td>26.06</td>\n",
       "      <td>16.53</td>\n",
       "      <td>11.99</td>\n",
       "      <td>0.52</td>\n",
       "      <td>12.72</td>\n",
       "      <td>30.14</td>\n",
       "      <td>0.74</td>\n",
       "      <td>2.21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-29</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>22.91</td>\n",
       "      <td>65.73</td>\n",
       "      <td>3.45</td>\n",
       "      <td>29.53</td>\n",
       "      <td>18.33</td>\n",
       "      <td>10.71</td>\n",
       "      <td>0.48</td>\n",
       "      <td>8.42</td>\n",
       "      <td>30.96</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>16.64</td>\n",
       "      <td>49.97</td>\n",
       "      <td>4.05</td>\n",
       "      <td>29.26</td>\n",
       "      <td>18.80</td>\n",
       "      <td>10.03</td>\n",
       "      <td>0.52</td>\n",
       "      <td>9.84</td>\n",
       "      <td>28.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>54.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>15.00</td>\n",
       "      <td>66.00</td>\n",
       "      <td>0.40</td>\n",
       "      <td>26.85</td>\n",
       "      <td>14.05</td>\n",
       "      <td>5.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2.10</td>\n",
       "      <td>17.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29531 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     City  PM2.5   PM10     NO    NO2    NOx    NH3     CO  \\\n",
       "Date                                                                         \n",
       "2015-01-01      Ahmedabad    NaN    NaN   0.92  18.22  17.15    NaN   0.92   \n",
       "2015-01-02      Ahmedabad    NaN    NaN   0.97  15.69  16.46    NaN   0.97   \n",
       "2015-01-03      Ahmedabad    NaN    NaN  17.40  19.30  29.70    NaN  17.40   \n",
       "2015-01-04      Ahmedabad    NaN    NaN   1.70  18.48  17.97    NaN   1.70   \n",
       "2015-01-05      Ahmedabad    NaN    NaN  22.10  21.42  37.76    NaN  22.10   \n",
       "...                   ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "2020-06-27  Visakhapatnam  15.02  50.94   7.68  25.06  19.54  12.47   0.47   \n",
       "2020-06-28  Visakhapatnam  24.38  74.09   3.42  26.06  16.53  11.99   0.52   \n",
       "2020-06-29  Visakhapatnam  22.91  65.73   3.45  29.53  18.33  10.71   0.48   \n",
       "2020-06-30  Visakhapatnam  16.64  49.97   4.05  29.26  18.80  10.03   0.52   \n",
       "2020-07-01  Visakhapatnam  15.00  66.00   0.40  26.85  14.05   5.20   0.59   \n",
       "\n",
       "              SO2      O3  Benzene  Toluene  Xylene   AQI    AQI_Bucket  \n",
       "Date                                                                     \n",
       "2015-01-01  27.64  133.36     0.00     0.02    0.00   NaN           NaN  \n",
       "2015-01-02  24.55   34.06     3.68     5.50    3.77   NaN           NaN  \n",
       "2015-01-03  29.07   30.70     6.80    16.40    2.25   NaN           NaN  \n",
       "2015-01-04  18.59   36.08     4.43    10.14    1.00   NaN           NaN  \n",
       "2015-01-05  39.33   39.31     7.01    18.89    2.78   NaN           NaN  \n",
       "...           ...     ...      ...      ...     ...   ...           ...  \n",
       "2020-06-27   8.55   23.30     2.24    12.07    0.73  41.0          Good  \n",
       "2020-06-28  12.72   30.14     0.74     2.21    0.38  70.0  Satisfactory  \n",
       "2020-06-29   8.42   30.96     0.01     0.01    0.00  68.0  Satisfactory  \n",
       "2020-06-30   9.84   28.30     0.00     0.00    0.00  54.0  Satisfactory  \n",
       "2020-07-01   2.10   17.05      NaN      NaN     NaN  50.0          Good  \n",
       "\n",
       "[29531 rows x 15 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "711eb2ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# since 'O3' ,'benzen' and 'Xylen' have less correlation with y. we will remove them\n",
    "df=df.iloc[:,[0,1,2,3,4,5,6,7,8,11,13,14]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eedfb584",
   "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>AQI_Bucket</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-01</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-02</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-03</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.40</td>\n",
       "      <td>19.30</td>\n",
       "      <td>29.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.40</td>\n",
       "      <td>29.07</td>\n",
       "      <td>16.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-04</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.48</td>\n",
       "      <td>17.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.70</td>\n",
       "      <td>18.59</td>\n",
       "      <td>10.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.10</td>\n",
       "      <td>21.42</td>\n",
       "      <td>37.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.10</td>\n",
       "      <td>39.33</td>\n",
       "      <td>18.89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-27</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>15.02</td>\n",
       "      <td>50.94</td>\n",
       "      <td>7.68</td>\n",
       "      <td>25.06</td>\n",
       "      <td>19.54</td>\n",
       "      <td>12.47</td>\n",
       "      <td>0.47</td>\n",
       "      <td>8.55</td>\n",
       "      <td>12.07</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-28</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>24.38</td>\n",
       "      <td>74.09</td>\n",
       "      <td>3.42</td>\n",
       "      <td>26.06</td>\n",
       "      <td>16.53</td>\n",
       "      <td>11.99</td>\n",
       "      <td>0.52</td>\n",
       "      <td>12.72</td>\n",
       "      <td>2.21</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-29</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>22.91</td>\n",
       "      <td>65.73</td>\n",
       "      <td>3.45</td>\n",
       "      <td>29.53</td>\n",
       "      <td>18.33</td>\n",
       "      <td>10.71</td>\n",
       "      <td>0.48</td>\n",
       "      <td>8.42</td>\n",
       "      <td>0.01</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>16.64</td>\n",
       "      <td>49.97</td>\n",
       "      <td>4.05</td>\n",
       "      <td>29.26</td>\n",
       "      <td>18.80</td>\n",
       "      <td>10.03</td>\n",
       "      <td>0.52</td>\n",
       "      <td>9.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>54.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>15.00</td>\n",
       "      <td>66.00</td>\n",
       "      <td>0.40</td>\n",
       "      <td>26.85</td>\n",
       "      <td>14.05</td>\n",
       "      <td>5.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29531 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     City  PM2.5   PM10     NO    NO2    NOx    NH3     CO  \\\n",
       "Date                                                                         \n",
       "2015-01-01      Ahmedabad    NaN    NaN   0.92  18.22  17.15    NaN   0.92   \n",
       "2015-01-02      Ahmedabad    NaN    NaN   0.97  15.69  16.46    NaN   0.97   \n",
       "2015-01-03      Ahmedabad    NaN    NaN  17.40  19.30  29.70    NaN  17.40   \n",
       "2015-01-04      Ahmedabad    NaN    NaN   1.70  18.48  17.97    NaN   1.70   \n",
       "2015-01-05      Ahmedabad    NaN    NaN  22.10  21.42  37.76    NaN  22.10   \n",
       "...                   ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "2020-06-27  Visakhapatnam  15.02  50.94   7.68  25.06  19.54  12.47   0.47   \n",
       "2020-06-28  Visakhapatnam  24.38  74.09   3.42  26.06  16.53  11.99   0.52   \n",
       "2020-06-29  Visakhapatnam  22.91  65.73   3.45  29.53  18.33  10.71   0.48   \n",
       "2020-06-30  Visakhapatnam  16.64  49.97   4.05  29.26  18.80  10.03   0.52   \n",
       "2020-07-01  Visakhapatnam  15.00  66.00   0.40  26.85  14.05   5.20   0.59   \n",
       "\n",
       "              SO2  Toluene   AQI    AQI_Bucket  \n",
       "Date                                            \n",
       "2015-01-01  27.64     0.02   NaN           NaN  \n",
       "2015-01-02  24.55     5.50   NaN           NaN  \n",
       "2015-01-03  29.07    16.40   NaN           NaN  \n",
       "2015-01-04  18.59    10.14   NaN           NaN  \n",
       "2015-01-05  39.33    18.89   NaN           NaN  \n",
       "...           ...      ...   ...           ...  \n",
       "2020-06-27   8.55    12.07  41.0          Good  \n",
       "2020-06-28  12.72     2.21  70.0  Satisfactory  \n",
       "2020-06-29   8.42     0.01  68.0  Satisfactory  \n",
       "2020-06-30   9.84     0.00  54.0  Satisfactory  \n",
       "2020-07-01   2.10      NaN  50.0          Good  \n",
       "\n",
       "[29531 rows x 12 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "23e4fbc9",
   "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>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>PM2.5</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.846498</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM10</th>\n",
       "      <td>0.846498</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.803313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.79489</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NOx</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.79489</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toluene</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQI</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.803313</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            PM2.5      PM10       NO  NO2      NOx  NH3   CO  SO2  Toluene  \\\n",
       "PM2.5    1.000000  0.846498      NaN  NaN      NaN  NaN  NaN  NaN      NaN   \n",
       "PM10     0.846498  1.000000      NaN  NaN      NaN  NaN  NaN  NaN      NaN   \n",
       "NO            NaN       NaN  1.00000  NaN  0.79489  NaN  NaN  NaN      NaN   \n",
       "NO2           NaN       NaN      NaN  1.0      NaN  NaN  NaN  NaN      NaN   \n",
       "NOx           NaN       NaN  0.79489  NaN  1.00000  NaN  NaN  NaN      NaN   \n",
       "NH3           NaN       NaN      NaN  NaN      NaN  1.0  NaN  NaN      NaN   \n",
       "CO            NaN       NaN      NaN  NaN      NaN  NaN  1.0  NaN      NaN   \n",
       "SO2           NaN       NaN      NaN  NaN      NaN  NaN  NaN  1.0      NaN   \n",
       "Toluene       NaN       NaN      NaN  NaN      NaN  NaN  NaN  NaN      1.0   \n",
       "AQI           NaN  0.803313      NaN  NaN      NaN  NaN  NaN  NaN      NaN   \n",
       "\n",
       "              AQI  \n",
       "PM2.5         NaN  \n",
       "PM10     0.803313  \n",
       "NO            NaN  \n",
       "NO2           NaN  \n",
       "NOx           NaN  \n",
       "NH3           NaN  \n",
       "CO            NaN  \n",
       "SO2           NaN  \n",
       "Toluene       NaN  \n",
       "AQI      1.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Creating a mask to identify columns with correlation coefficients >= 0.70\n",
    "mask=df.corr()>=0.70\n",
    "df.corr()[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "32167d39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 29531 entries, 2015-01-01 to 2020-07-01\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   City        29531 non-null  object \n",
      " 1   PM2.5       24933 non-null  float64\n",
      " 2   PM10        18391 non-null  float64\n",
      " 3   NO          25949 non-null  float64\n",
      " 4   NO2         25946 non-null  float64\n",
      " 5   NOx         25346 non-null  float64\n",
      " 6   NH3         19203 non-null  float64\n",
      " 7   CO          27472 non-null  float64\n",
      " 8   SO2         25677 non-null  float64\n",
      " 9   Toluene     21490 non-null  float64\n",
      " 10  AQI         24850 non-null  float64\n",
      " 11  AQI_Bucket  24850 non-null  object \n",
      "dtypes: float64(10), object(2)\n",
      "memory usage: 2.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "482e22cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ceac6116",
   "metadata": {},
   "outputs": [],
   "source": [
    "# In 'PM2.5' and 'PM10' there is multicolinearity so will remove 'PM2.5' since PM10 has more corr with 'AQI'\n",
    "# In 'NO' and 'NOx' there is multicolinearity so will remove NO since 'NOx' has more corr with 'AQI'\n",
    "df=df.iloc[:,[0,2,4,5,6,7,8,9,10,11]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "53e967ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 29531 entries, 2015-01-01 to 2020-07-01\n",
      "Data columns (total 10 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   City        29531 non-null  object \n",
      " 1   PM10        18391 non-null  float64\n",
      " 2   NO2         25946 non-null  float64\n",
      " 3   NOx         25346 non-null  float64\n",
      " 4   NH3         19203 non-null  float64\n",
      " 5   CO          27472 non-null  float64\n",
      " 6   SO2         25677 non-null  float64\n",
      " 7   Toluene     21490 non-null  float64\n",
      " 8   AQI         24850 non-null  float64\n",
      " 9   AQI_Bucket  24850 non-null  object \n",
      "dtypes: float64(8), object(2)\n",
      "memory usage: 2.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e4b76ed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City              0\n",
       "PM10          11140\n",
       "NO2            3585\n",
       "NOx            4185\n",
       "NH3           10328\n",
       "CO             2059\n",
       "SO2            3854\n",
       "Toluene        8041\n",
       "AQI            4681\n",
       "AQI_Bucket     4681\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values again\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9e0c4800",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Moderate        8829\n",
       "Satisfactory    8224\n",
       "Poor            2781\n",
       "Very Poor       2337\n",
       "Good            1341\n",
       "Severe          1338\n",
       "Name: AQI_Bucket, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counting the occurrences of each category in the 'AQI_Bucket' column\n",
    "df['AQI_Bucket'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "174030d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['NH3','Toluene'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2f41ccc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 29531 entries, 2015-01-01 to 2020-07-01\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   City        29531 non-null  object \n",
      " 1   PM10        18391 non-null  float64\n",
      " 2   NO2         25946 non-null  float64\n",
      " 3   NOx         25346 non-null  float64\n",
      " 4   CO          27472 non-null  float64\n",
      " 5   SO2         25677 non-null  float64\n",
      " 6   AQI         24850 non-null  float64\n",
      " 7   AQI_Bucket  24850 non-null  object \n",
      "dtypes: float64(6), object(2)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4f8c3a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City              0\n",
       "PM10          11140\n",
       "NO2            3585\n",
       "NOx            4185\n",
       "CO             2059\n",
       "SO2            3854\n",
       "AQI            4681\n",
       "AQI_Bucket     4681\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e980b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling missing values by filling them with the mean values of their respective columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "02f6de31",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PM10'].fillna(value=df['PM10'].mean(),inplace=True)\n",
    "df['NO2'].fillna(value=df['NO2'].mean(),inplace=True)\n",
    "df['NOx'].fillna(value=df['NOx'].mean(),inplace=True)\n",
    "df['CO'].fillna(value=df['CO'].mean(),inplace=True)\n",
    "df['SO2'].fillna(value=df['SO2'].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f0acaae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City             0\n",
       "PM10             0\n",
       "NO2              0\n",
       "NOx              0\n",
       "CO               0\n",
       "SO2              0\n",
       "AQI           4681\n",
       "AQI_Bucket    4681\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for missing values in the DataFrame after filling missing values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "afb36485",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing rows with any remaining missing values\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bf833331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City          0\n",
       "PM10          0\n",
       "NO2           0\n",
       "NOx           0\n",
       "CO            0\n",
       "SO2           0\n",
       "AQI           0\n",
       "AQI_Bucket    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c3939504",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting feature columns (X) and the target column (y)\n",
    "X=df.iloc[:,1:6].values\n",
    "y=df.iloc[:,-1].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7364a687",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling the feature columns\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c40017f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5295cb38",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_sc=sc.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6aa9a3e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.09273670e-03, -1.07161505e-02,  4.83876096e-02,\n",
       "         6.54140048e-01,  2.04200840e+00],\n",
       "       [-3.09273670e-03, -1.19440435e-02,  2.97521283e-01,\n",
       "         1.64109841e+00,  1.98217648e+00],\n",
       "       [-3.09273670e-03,  1.50956431e-01,  6.87808139e-01,\n",
       "         3.14435580e+00,  3.08006320e+00],\n",
       "       ...,\n",
       "       [-6.95635824e-01,  2.28462587e-02, -4.72559026e-01,\n",
       "        -2.65785015e-01, -3.45459558e-01],\n",
       "       [-9.03938926e-01,  1.17952216e-02, -4.56649675e-01,\n",
       "        -2.60080053e-01, -2.62972830e-01],\n",
       "       [-6.92067180e-01, -8.68455177e-02, -6.17435673e-01,\n",
       "        -2.50096371e-01, -7.12583584e-01]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ad6e9127",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1316177a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((19880, 5), (4970, 5), (19880,), (4970,))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting the dataset into training and testing sets (80% train, 20% test)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=0)\n",
    "X_train.shape,X_test.shape,y_train.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a0d6b15e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing Support Vector Model\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d9bab224",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating an SVM model with the radial basis function (RBF) kernel\n",
    "model=SVC(kernel='rbf')\n",
    "# Training the SVM model on the training data\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ac8f6036",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Moderate' 'Moderate' 'Satisfactory' ... 'Moderate' 'Moderate' 'Moderate']\n"
     ]
    }
   ],
   "source": [
    "# Making predictions on the test data\n",
    "y_pred=model.predict(X_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7375b247",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6142857142857143"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the accuracy score\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9bd79583",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Romoving the outliers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4a102ed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 24850 entries, 2015-01-29 to 2020-07-01\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   City        24850 non-null  object \n",
      " 1   PM10        24850 non-null  float64\n",
      " 2   NO2         24850 non-null  float64\n",
      " 3   NOx         24850 non-null  float64\n",
      " 4   CO          24850 non-null  float64\n",
      " 5   SO2         24850 non-null  float64\n",
      " 6   AQI         24850 non-null  float64\n",
      " 7   AQI_Bucket  24850 non-null  object \n",
      "dtypes: float64(6), object(2)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "69e4b842",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for outliers in column 1\n",
    "plt.boxplot(df.iloc[:,1],vert=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d184afe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "q3=np.percentile(df.iloc[:,1],75)\n",
    "q1=np.percentile(df.iloc[:,1],25)\n",
    "iqr=q3-q1\n",
    "u_b=q3+1.5*iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bab1d317",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "199.72375"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c03f3a00",
   "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>PM10</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>AQI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>24850.000000</td>\n",
       "      <td>24850.000000</td>\n",
       "      <td>24850.000000</td>\n",
       "      <td>24850.000000</td>\n",
       "      <td>24850.000000</td>\n",
       "      <td>24850.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>118.361096</td>\n",
       "      <td>28.971818</td>\n",
       "      <td>32.290515</td>\n",
       "      <td>2.343536</td>\n",
       "      <td>14.367049</td>\n",
       "      <td>166.463581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>75.660501</td>\n",
       "      <td>24.432587</td>\n",
       "      <td>29.542968</td>\n",
       "      <td>7.011582</td>\n",
       "      <td>17.215237</td>\n",
       "      <td>140.696585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>71.780000</td>\n",
       "      <td>12.090000</td>\n",
       "      <td>14.030000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>5.790000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>118.127103</td>\n",
       "      <td>22.535000</td>\n",
       "      <td>25.720000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>9.430000</td>\n",
       "      <td>118.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>122.957500</td>\n",
       "      <td>37.910000</td>\n",
       "      <td>38.170000</td>\n",
       "      <td>1.530000</td>\n",
       "      <td>14.890000</td>\n",
       "      <td>208.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>917.080000</td>\n",
       "      <td>362.210000</td>\n",
       "      <td>378.240000</td>\n",
       "      <td>175.810000</td>\n",
       "      <td>186.080000</td>\n",
       "      <td>2049.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               PM10           NO2           NOx            CO           SO2  \\\n",
       "count  24850.000000  24850.000000  24850.000000  24850.000000  24850.000000   \n",
       "mean     118.361096     28.971818     32.290515      2.343536     14.367049   \n",
       "std       75.660501     24.432587     29.542968      7.011582     17.215237   \n",
       "min        0.030000      0.010000      0.000000      0.000000      0.010000   \n",
       "25%       71.780000     12.090000     14.030000      0.590000      5.790000   \n",
       "50%      118.127103     22.535000     25.720000      0.950000      9.430000   \n",
       "75%      122.957500     37.910000     38.170000      1.530000     14.890000   \n",
       "max      917.080000    362.210000    378.240000    175.810000    186.080000   \n",
       "\n",
       "                AQI  \n",
       "count  24850.000000  \n",
       "mean     166.463581  \n",
       "std      140.696585  \n",
       "min       13.000000  \n",
       "25%       81.000000  \n",
       "50%      118.000000  \n",
       "75%      208.000000  \n",
       "max     2049.000000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "36b0bcb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.289372157324728\n"
     ]
    }
   ],
   "source": [
    "# Checking skewness and 90th percentile for column 1\n",
    "print(df.iloc[:,1].skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c1a4d78a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "198.5050000000001\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[:,1].quantile(0.90))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e9a4f280",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[:,1]=np.where(df.iloc[:,1]>u_b,u_b,df.iloc[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1dc76f8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.17725911518006135\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[:,1].skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fcef3dda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for outliers in column 2\n",
    "plt.boxplot(df.iloc[:,2],vert=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "50f36f7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "q3=np.percentile(df.iloc[:,2],75)\n",
    "q1=np.percentile(df.iloc[:,2],25)\n",
    "iqr=q3-q1\n",
    "u_b=q3+1.5*iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2e5d135e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[:,2]=np.where(df.iloc[:,2]>u_b,u_b,df.iloc[:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e2cb19e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df.iloc[:,2],vert=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "63f8883f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD4CAYAAADFAawfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAMsklEQVR4nO3dX2yddR3H8c/3tIfWuI2ta7MQ9qedgTi6GBzVYCDEuLgBF0wTLjBL5GIJyXREL0xkWSLzwgtNqomJscGA4J8OFDVyYyJaErMLwU4HDBGYAlGCbOiGugvmn68X5zmH0/b0f3ueT9f3K3nS0+c855xvf/S81/OcrURmCgDgq1L2AACAmRFqADBHqAHAHKEGAHOEGgDMdS7Hnfb29mZ/f/9y3DUAXJJOnDjxZmb2tbpuWULd39+v8fHx5bhrALgkRcSr013HqQ8AMEeoAcAcoQYAc4QaAMwRagAwR6gBwByhBgBzhBoAzBFqADBHqAHAHKEGAHOEGgDMEWoAMEeoAcAcoQYAc4QaAMwRagAwR6gBwByhBgBzhBoAzBFqADBHqAHAHKEGAHOEGgDMEWoAMEeoAcAcoQYAc4QaAMwRagAwR6gBwByhBgBzhBoAzBFqADBHqAHAHKEGAHOEGgDMEWoAMEeoAcAcoQYAc4QaAMwRagAwR6gBwByhBgBzhBoAzBFqADBHqAHAHKEGAHOEGgDMEWoAMEeoAcAcoQYAc4QaAMwRagAwR6gBwByhBgBzhBoAzBFqADBHqAHAHKEGAHOEGgDMEWoAMEeoAcAcoQYAc4QaAMwRagAwR6gBwByhBgBzhBoAzBFqADBHqAHAHKEGAHOEGgDMEWoAMEeoAcAcoQYAc4QaAMwRagAwR6gBwByhBgBzhBoAzBFqADBHqAHAHKEGAHOEGgDMEWoAMEeoAcAcoQYAc4QaAMwRagAwR6gBwByhBgBzKzrUPT09iogZNx29fNZj6ltPT0/ZXxIATLGiQ33u3Dll5oybpFmPqW/nzp0r+SsCgKlWdKgBYDUg1ABgjlADgDlCDQDmCDUAmCPUAGDOLtQRUfYIbbXavl4A82cXagDARIQaAMwRagAwR6gBwNysoY6IByLiTEScasdAq0Xjl0Y1XWa7dLdqtaotW7aoUqmou7tblUpFO3fu1N69e1WtVqccX6lUtHHjxinHHzt2THfffbe6u7sbx0VE4/P6sZNvsxjHjh3Tzp071dHR0bi/VvtWs2Vfjzn8UqObJO2SdGquv9zouuuuy4WqjbSEx967blkeezEksa3QrVKpTNm3bt26aY+PiAm37e3tzeHh4RwYGMihoaGUlJ2dnROO6+joaFxeu3Zt4/gjR47k2rVrs1Kp5P79+3Pbtm25e/fulJRXX311dnZ25g033JAdHR25f//+xm0GBgZydHR0Qd+ro6OjOTAwkGNjY3nx4sUcGxvLvr6+7Ovrm7BvMY+x0rVao4Wsh6TxnK7D012RE2PdL0K9ZMqODdvEbc2aNS3333bbbVP2VavVKRFujmzzcfWoN4e3v78/MzPHxsYyInLDhg2NY4aHh3N4eDglNfZXq9XG8YODg1mtVnPTpk05ODjY2Hfw4MGMiBweHs6urq4cHh6ecH3940LUb9+sv7+/8XXULeYxVrpWa7SQ9VA7Qi3pLknjksa3bt26sK845x+xWc0z1Gxs9e3s2bNLfp8RkZmZFy9ebHxev+7ChQt54cKFKbepH9/803ylUmnsO3/+fOP29Y/N19c/LkT99pP31b+OusU8xkrXao0Wsh6aIdRL9mZiZt6XmUOZOdTX17fY+5rrHyBLbq6PvZgNXtasWdNy/4EDB6bsq1arjcsRMeHj5OMqldrTq6Ojo7F/27ZtkqTjx48rIrR+/frGMSMjIxoZGZEkbdiwYcLjHT9+XDt27FC1WtWmTZu0Y8eOxr7Dhw8rIjQyMqKuri6NjIxMuL7+cSHqt2+2devWxtdRt5jHWOlardGSr8ccw9IvTn0sGRn8tMi2sI1z1Jyjnoxz1EtxrGGo64/Ftnq2zs7O3Lx5c0ZEdnV1ZUTk4OBg7tmzJzs7O6ccHxHZ09Mz5fjR0dE8dOhQdnV1NY6T1Pi8fuzk2yzG6OhoDg4OZqVSadxfq32r2VKsh2YIdeQsL8Uj4pikD0vqlfSGpHsz8/6ZbjM0NJTj4+Mz3u8Mjzfn0wNzOvbo5dLRt5b8sZdKGY8JwE9EnMjMoVbXdc5248z8xNKPBACYK/5lIgCYI9QAYI5QA4A5u1CvtjfWVtvXC2D+7EINAJiIUAOAOUINAOYINQCYI9QAYI5QA4C5FR/q2f4XSHM5pr7Vf7UkADiZ9Xd9OJvr30HOo8s7BwAspxX/EzUAXOoINQCYI9QAYI5QA4A5Qg0A5gg1AJgj1ABgjlADgDlCDQDmCDUAmCPUAGCOUAOAOUINAOYINQCYI9QAYI5QA4A5Qg0A5gg1AJgj1ABgjlADgDlCDQDmCDUAmCPUAGCOUAOAOUINAOYINQCYI9QAYI5QA4A5Qg0A5gg1AJgj1ABgjlADgDlCDQDmCDUAmCPUAGCOUAOAOUINAOYINQCYI9QAYI5QA4A5Qg0A5gg1AJgj1ABgjlADgDlCDQDmCDUAmCPUAGCOUAOAOUINAOYINQCYI9QAYI5QA4A5Qg0A5gg1AJgj1ABgjlADgDlCDQDmCDUAmCPUAGCOUAOAOUINAOYINQCYI9QAYI5QA4A5Qg0A5gg1AJgj1ABgjlADgDlCDQDmCDUAmCPUAGCOUAOAOUINAOYINQCYI9QAYI5QA4A5Qg0A5gg1AJgj1ABgjlADgDlCDQDmCDUAmCPUAGCOUAOAOUINAOYINQCYI9QAYI5QA4A5Qg0A5gg1AJgj1ABgLjJz6e804qykVxd4815Jby7hOEuN+RbHfT7Jf0bmWxzX+bZlZl+rK5Yl1IsREeOZOVT2HNNhvsVxn0/yn5H5Fsd9vlY49QEA5gg1AJhzDPV9ZQ8wC+ZbHPf5JP8ZmW9x3Oebwu4cNQBgIsefqAEATQg1AJizCXVE3BwRL0TE6Yi4p+x56iLilYh4NiJORsR4sa8nIh6PiJeKjxvaOM8DEXEmIk417Ws5T9R8vVjTZyJiV0nzHY2I14o1PBkRtzZdd7iY74WI2NuG+bZExBMR8fuIeC4iPlPst1jDGeZzWsPuiHgqIp4uZvxisX8gIp4sZnkkIi4r9ncVn58uru8vab4HI+LlpjW8ttjf9ufJvGVm6ZukDkl/lLRd0mWSnpZ0TdlzFbO9Iql30r6vSLqnuHyPpC+3cZ6bJO2SdGq2eSTdKulnkkLS9ZKeLGm+o5I+1+LYa4r/1l2SBorvgY5lnu8KSbuKy2slvVjMYbGGM8zntIYhaU1xuSrpyWJtfiDpjmL/iKSDxeVPSRopLt8h6ZGS5ntQ0u0tjm/782S+m8tP1B+UdDoz/5SZFyU9LGlfyTPNZJ+kh4rLD0n6WLseODN/Jenvc5xnn6TvZM2vJa2PiCtKmG86+yQ9nJlvZ+bLkk6r9r2wbDLz9cz8bXH5n5Kel3SlTNZwhvmmU8YaZmb+q/i0Wmwp6SOSHi32T17D+to+Kml3REQJ802n7c+T+XIJ9ZWS/tz0+V808zdnO6Wkn0fEiYi4q9i3KTNfLy7/VdKmckZrmG4ep3U9VLysfKDpVFGp8xUvwd+v2k9cdms4aT7JaA0joiMiTko6I+lx1X6SP5+Z/2kxR2PG4vq3JG1s53yZWV/DLxVr+LWI6Jo8X4vZLbiE2tmNmblL0i2SPh0RNzVfmbXXTjZ/x9FtnsI3Jb1H0rWSXpc0XOo0kiJijaQfSfpsZv6j+TqHNWwxn9UaZuZ/M/NaSZtV+wn+vWXOM9nk+SJip6TDqs35AUk9kj5f3oTz4xLq1yRtafp8c7GvdJn5WvHxjKSfqPZN+Ub9pVHx8Ux5E0ozzGOxrpn5RvHE+Z+kb+mdl+alzBcRVdUi+P3M/HGx22YNW83ntoZ1mXle0hOSPqTaKYPOFnM0Ziyuv1zS39o8383FaaXMzLclfVsmazgXLqH+jaSrineNL1PtDYfHSp5JEfHuiFhbvyxpj6RTqs12Z3HYnZJ+Ws6EDdPN85ikTxbval8v6a2ml/dtM+l838dVW8P6fHcUfytgQNJVkp5a5llC0v2Sns/MrzZdZbGG081ntoZ9EbG+uPwuSR9V7Vz6E5JuLw6bvIb1tb1d0ljxqqWd8/2h6Q/iUO38efMalv48mVHZ72bWN9XeeX1RtXNdR8qep5hpu2rvqD8t6bn6XKqdX/ulpJck/UJSTxtnOqbaS99/q3Yu7cB086j2LvY3ijV9VtJQSfN9t3j8Z1R7UlzRdPyRYr4XJN3ShvluVO20xjOSThbbrS5rOMN8Tmv4Pkm/K2Y5JekLxf7tqv0hcVrSDyV1Ffu7i89PF9dvL2m+sWINT0n6nt75myFtf57Md+OfkAOAOZdTHwCAaRBqADBHqAHAHKEGAHOEGgDMEWoAMEeoAcDc/wFI3UQqPTZDuQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for outliers in column 3\n",
    "plt.boxplot(df.iloc[:,3],vert=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "db19ca57",
   "metadata": {},
   "outputs": [],
   "source": [
    "q3=np.percentile(df.iloc[:,3],75)\n",
    "q1=np.percentile(df.iloc[:,3],25)\n",
    "iqr=q3-q1\n",
    "u_b=q3+1.5*iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a1ac0f61",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[:,3]=np.where(df.iloc[:,3]>u_b,u_b,df.iloc[:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5bf68d0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df.iloc[:,3],vert=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "eaad65f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for outliers in column 4\n",
    "plt.boxplot(df.iloc[:,4],vert=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4e3f26fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "q3=np.percentile(df.iloc[:,4],75)\n",
    "q1=np.percentile(df.iloc[:,4],25)\n",
    "iqr=q3-q1\n",
    "u_b=q3+1.5*iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "522038e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[:,4]=np.where(df.iloc[:,4]>u_b,u_b,df.iloc[:,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3c190c51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df.iloc[:,4],vert=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1ecd75f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for outliers in column 5\n",
    "plt.boxplot(df.iloc[:,5],vert=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "40616b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "q3=np.percentile(df.iloc[:,5],75)\n",
    "q1=np.percentile(df.iloc[:,5],25)\n",
    "iqr=q3-q1\n",
    "u_b=q3+1.5*iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "35158c3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[:,5]=np.where(df.iloc[:,5]>u_b,u_b,df.iloc[:,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1e68c0cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD4CAYAAADFAawfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAH4ElEQVR4nO3cP4jkZx3H8c/XRJto5I4LEqLxVMTGQuWwEkmjRBu1CaZSEGKhoJ1ik1gIIip2QsRgBI0I/i21EGIhkjsRjYp/kIgJMafcgaYSzGOxE7jobnKX2818Zu/1anZ2hh2+D8/e+2afnf3NWisA9HrRtgcA4NkJNUA5oQYoJ9QA5YQaoNz1R/Gkp06dWqdPnz6KpwY4ls6dO/ePtdZN+z12JKE+ffp0zp49exRPDXAszcxfDnrM0QdAOaEGKCfUAOWEGqCcUAOUE2qAckINUE6oAcoJNUA5oQYoJ9QA5YQaoJxQA5QTaoByQg1QTqgBygk1QDmhBign1ADlhBqgnFADlBNqgHJCDVBOqAHKCTVAOaEGKCfUAOWEGqCcUAOUE2qAckINUE6oAcoJNUA5oQYoJ9QA5YQaoJxQA5QTaoByQg1QTqgBygk1QDmhBign1ADlhBqgnFADlBNqgHJCDVBOqAHKCTVAOaEGKCfUAOWEGqCcUAOUE2qAckINUE6oAcoJNUA5oQYoJ9QA5YQaoJxQA5QTaoByQg1QTqgBygk1QDmhBign1ADlhBqgnFADlBNqgHJCDVBOqAHKCTVAOaEGKCfUAOWEGqCcUAOUE2qAckINUE6oAcoJNUA5oQYoJ9QA5YQaoJxQA5QTaoByQg1QTqgBygk1QDmhBign1ADlrt/2ACQnT57MxYsXtz3GvtbdN2Y+/c9tj/GCOHHiRC5cuLDtMeD/CHWBixcvZq217TH2d8/Le2c7ZDOz7RFgX44+AMoJNUA5oQYoJ9QA5YQaoJxQA5SrC7W3SAG76qj6VRdqAJ5JqAHKCTVAOaEGKPecoZ6Z+2bm/Mw8/EIMBMAzXc4r6q8luf2I5wDgAM8Z6rXWg0lc+xFgSw7tMqczc1eSu5Lk1ltvvdrnOoyR4Ir53qPRoYV6rXVvknuT5MyZM1d1AeNr5frHTxOHHtfa9x6Hyx+8AFyjhBqg3OW8Pe+BJD9L8oaZeXRmPnT0YwHwtOc8o15r3flCDALA/hx9AJQTaoByQg1Qri7U3scK7Kqj6lddqAF4JqEGKCfUAOWEGqCcUAOUE2qAcod2mVOuTuulTtfdN9bOdthOnDix7RFgX0JdoP294+uebU8A1zZHHwDlhBqgnFADlBNqgHJCDVBOqAHKCTVAOaEGKCfUAOWEGqCcUAOUE2qAckINUE6oAcoJNUA5oQYoJ9QA5YQaoJxQA5QTaoByQg1QTqgBygk1QDmhBign1ADlhBqgnFADlBNqgHJCDVBOqAHKCTVAOaEGKCfUAOWEGqCcUAOUE2qAckINUE6oAcoJNUA5oQYoJ9QA5YQaoJxQA5QTaoByQg1QTqgBygk1QDmhBign1ADlhBqgnFADlBNqgHJCDVBOqAHKCTVAOaEGKCfUAOWEGqCcUAOUE2qAckINUE6oAcoJNUA5oQYoJ9QA5YQaoJxQA5QTaoByQg1QTqgBygk1QDmhBign1ADlhBqgnFADlBNqgHJCDVBOqAHKCTVAOaEGKCfUAOWEGqCcUAOUE2qAckINUE6oAcoJNUA5oQYoJ9QA5YQaoJxQA5SbtdbhP+nM35P85Xl++akk/zjEcRpY0+44juuypt3w6rXWTfs9cCShvhozc3atdWbbcxwma9odx3Fd1rT7HH0AlBNqgHKNob532wMcAWvaHcdxXda04+rOqAF4psZX1ABcQqgBytWEemZun5nfz8yfZuaT257nsMzMIzPz65n55cyc3fY8z8fM3Dcz52fm4UvuOzkzP56ZP24+ntjmjFfqgDXdMzOPbfbqlzPz7m3OeKVm5lUz85OZ+e3M/GZmPra5f9f36qB17fR+XYmKM+qZuS7JH5K8I8mjSR5Kcuda67dbHewQzMwjSc6stXb2zfkz8/YkTyb5+lrrjZv7Ppfkwlrrs5v/WE+stT6xzTmvxAFruifJk2utz29ztudrZm5OcvNa6xcz87Ik55K8N8kHs9t7ddC67sgO79eVaHlF/dYkf1pr/Xmt9e8k30ryni3PxMZa68EkF/7n7vckuX9z+/7s/cPZGQesaaettR5fa/1ic/tfSX6X5Jbs/l4dtK5rRkuob0ny10s+fzTHZyNWkh/NzLmZuWvbwxyiV6y1Ht/c/luSV2xzmEP00Zn51eZoZKeOCC41M6eTvDnJz3OM9up/1pUck/16Li2hPs7ettZ6S5J3JfnI5kfuY2XtnZ9t/wzt6n05yeuSvCnJ40m+sNVpnqeZeWmS7yT5+Frrn5c+tst7tc+6jsV+XY6WUD+W5FWXfP7KzX07b6312Obj+STfy94xz3HwxObs8OkzxPNbnueqrbWeWGv9Z631VJKvZAf3amZenL2YfWOt9d3N3Tu/V/ut6zjs1+VqCfVDSV4/M6+ZmZckeX+SH255pqs2MzdsfvmRmbkhyTuTPPzsX7UzfpjkA5vbH0jygy3OciiejtnG+7JjezUzk+SrSX631vriJQ/t9F4dtK5d368rUfGujyTZvLXmS0muS3LfWusz253o6s3Ma7P3KjpJrk/yzV1c18w8kOS27F1a8okkdyf5fpJvJ7k1e5e0vWOttTO/nDtgTbdl78foleSRJB++5Gy33sy8LclPk/w6yVObuz+VvfPcXd6rg9Z1Z3Z4v65ETagB2F/L0QcABxBqgHJCDVBOqAHKCTVAOaEGKCfUAOX+C3v3tEBYlyglAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df.iloc[:,5],vert=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5965cc03",
   "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>PM10</th>\n",
       "      <th>NO2</th>\n",
       "      <th>NOx</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>AQI</th>\n",
       "      <th>AQI_Bucket</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-29</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>118.127103</td>\n",
       "      <td>28.71</td>\n",
       "      <td>33.72</td>\n",
       "      <td>2.94</td>\n",
       "      <td>28.54</td>\n",
       "      <td>209.0</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-30</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>118.127103</td>\n",
       "      <td>28.68</td>\n",
       "      <td>41.08</td>\n",
       "      <td>2.94</td>\n",
       "      <td>28.54</td>\n",
       "      <td>328.0</td>\n",
       "      <td>Very Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-31</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>118.127103</td>\n",
       "      <td>32.66</td>\n",
       "      <td>52.61</td>\n",
       "      <td>2.94</td>\n",
       "      <td>28.54</td>\n",
       "      <td>514.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>118.127103</td>\n",
       "      <td>42.08</td>\n",
       "      <td>74.38</td>\n",
       "      <td>2.94</td>\n",
       "      <td>28.54</td>\n",
       "      <td>782.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-02</th>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>118.127103</td>\n",
       "      <td>35.31</td>\n",
       "      <td>72.80</td>\n",
       "      <td>2.94</td>\n",
       "      <td>28.54</td>\n",
       "      <td>914.0</td>\n",
       "      <td>Severe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-27</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>50.940000</td>\n",
       "      <td>25.06</td>\n",
       "      <td>19.54</td>\n",
       "      <td>0.47</td>\n",
       "      <td>8.55</td>\n",
       "      <td>41.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-28</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>74.090000</td>\n",
       "      <td>26.06</td>\n",
       "      <td>16.53</td>\n",
       "      <td>0.52</td>\n",
       "      <td>12.72</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-29</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>65.730000</td>\n",
       "      <td>29.53</td>\n",
       "      <td>18.33</td>\n",
       "      <td>0.48</td>\n",
       "      <td>8.42</td>\n",
       "      <td>68.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-30</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>49.970000</td>\n",
       "      <td>29.26</td>\n",
       "      <td>18.80</td>\n",
       "      <td>0.52</td>\n",
       "      <td>9.84</td>\n",
       "      <td>54.0</td>\n",
       "      <td>Satisfactory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-01</th>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>26.85</td>\n",
       "      <td>14.05</td>\n",
       "      <td>0.59</td>\n",
       "      <td>2.10</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24850 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     City        PM10    NO2    NOx    CO    SO2    AQI  \\\n",
       "Date                                                                      \n",
       "2015-01-29      Ahmedabad  118.127103  28.71  33.72  2.94  28.54  209.0   \n",
       "2015-01-30      Ahmedabad  118.127103  28.68  41.08  2.94  28.54  328.0   \n",
       "2015-01-31      Ahmedabad  118.127103  32.66  52.61  2.94  28.54  514.0   \n",
       "2015-02-01      Ahmedabad  118.127103  42.08  74.38  2.94  28.54  782.0   \n",
       "2015-02-02      Ahmedabad  118.127103  35.31  72.80  2.94  28.54  914.0   \n",
       "...                   ...         ...    ...    ...   ...    ...    ...   \n",
       "2020-06-27  Visakhapatnam   50.940000  25.06  19.54  0.47   8.55   41.0   \n",
       "2020-06-28  Visakhapatnam   74.090000  26.06  16.53  0.52  12.72   70.0   \n",
       "2020-06-29  Visakhapatnam   65.730000  29.53  18.33  0.48   8.42   68.0   \n",
       "2020-06-30  Visakhapatnam   49.970000  29.26  18.80  0.52   9.84   54.0   \n",
       "2020-07-01  Visakhapatnam   66.000000  26.85  14.05  0.59   2.10   50.0   \n",
       "\n",
       "              AQI_Bucket  \n",
       "Date                      \n",
       "2015-01-29          Poor  \n",
       "2015-01-30     Very Poor  \n",
       "2015-01-31        Severe  \n",
       "2015-02-01        Severe  \n",
       "2015-02-02        Severe  \n",
       "...                  ...  \n",
       "2020-06-27          Good  \n",
       "2020-06-28  Satisfactory  \n",
       "2020-06-29  Satisfactory  \n",
       "2020-06-30  Satisfactory  \n",
       "2020-07-01          Good  \n",
       "\n",
       "[24850 rows x 8 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "521495ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting feature columns (X) and the target column (y) after outlier removal\n",
    "X=df.iloc[:,1:6].values\n",
    "y=df.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cfb9d813",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.1905674 ,  0.05302815,  0.21010528,  2.20178063,  2.1479357 ],\n",
       "       [ 0.1905674 ,  0.05150117,  0.56890615,  2.20178063,  2.1479357 ],\n",
       "       [ 0.1905674 ,  0.25407992,  1.13099502,  2.20178063,  2.1479357 ],\n",
       "       ...,\n",
       "       [-0.88410766,  0.09476547, -0.54015905, -0.84341368, -0.41861593],\n",
       "       [-1.20734842,  0.0810227 , -0.51724649, -0.79389832, -0.23747759],\n",
       "       [-0.87856991, -0.04164433, -0.74880956, -0.70724645, -1.22480908]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scaling the feature columns again after outlier removal\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc=StandardScaler()\n",
    "\n",
    "X_sc=sc.fit_transform(X)\n",
    "\n",
    "X_sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "56542322",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying the model to data once again after outlier removal\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "9ec87beb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X_sc,y,test_size=0.2,random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0b27d961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((19880, 5), (4970, 5), (19880,), (4970,))"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape,X_test.shape,y_train.shape,y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9c9f2b3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4a12e88f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=SVC(kernel='rbf')\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "497aa847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Moderate' 'Moderate' 'Moderate' ... 'Moderate' 'Very Poor' 'Moderate']\n"
     ]
    }
   ],
   "source": [
    "y_pred=model.predict(X_test)\n",
    "\n",
    "# Displaying the predicted labels after outlier removal\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7ffa1427",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6394366197183099"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking model score\n",
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "2e0097e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting the confusion matrix to visualize the performance of the SVM model after outlier removal\n",
    "from sklearn.metrics import plot_confusion_matrix,classification_report "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b6b07456",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SAURABH\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEKCAYAAAACS67iAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABM+ElEQVR4nO2dd3wVVfbAvyc9tIQkECJdRRARERBBUUNR1NXFXXtZy7pgQRexd111XX9rXRFUVMCKBRsqCipgQUE60on0EiCF0NPe+f0xE3gJKS/htYTz/Xzm82bOvXPvmXnvnblz7r3niqpiGIZh1H4iQq2AYRiG4R/MoBuGYdQRzKAbhmHUEcygG4Zh1BHMoBuGYdQRzKAbhmHUEcygG4Zh+BERGS0iW0VkUTlpd4iIikiKeywi8qKIZIjIQhHp6pX3GhFZ6W7X+FK3GXTDMAz/MhY4u6xQRFoCZwHrvMTnAO3cbTDwsps3CXgEOBnoATwiIo2rqtgMumEYhh9R1R+BnHKSngfuBrxncw4E3lKHGUCiiKQBA4BvVTVHVXOBbynnIVGWqEPW3qiUmMh4jY9sFGo19qNFRaFW4WDCbLayxMaEWoVSaH5BqFUIa/axmwLNl0MpY0Cf+pqdU+xT3jkL8yepapXG1RsRGQhsVNUFIqVUbQ6s9zre4MoqkleKGfQAEx/ZiFOaXR5qNfZTvGVbqFU4CC0ML4MV2aptqFUoRXHG6lCrENbM1O8PuYysnGJmTmrhU97otD86iMhsL9EoVR1VUX4RqQfcj+NuCShm0A3DMFCK1eNr5ixV7V6Nwo8C2gIlrfMWwFwR6QFsBFp65W3hyjYC6WXk06qqyHzohmEc9ijgQX3aql226u+q2lRV26hqGxz3SVdVzQQmAFe7o116AnmquhmYBJwlIo3dztCzXFmlWAvdMAwD8OBzC71SRGQcTus6RUQ2AI+o6hsVZJ8InAtkAHuA6wBUNUdEHgdmufkeU9XyOlpLYQbdMIzDHkUp9N3lUnlZqpV2mrmt9JJ9BYZUkG80MLo6dZtBNwzjsEeB4hq4U8INM+iGYRhQI/94uGEG3TCMwx4FisNsPkRNMINuGIYBfuoSDS1m0A3DOOxR1HzohmEYdQFVKKz99twMumEYBgjFHFI4mLDADLphGIc9CnishW74g6EPLqRH761sz41hyOWnA9C732auGLSSlm12Mey6U8hYmljqnCape3n5gx9577V2fPLukQHTLSUtn7ueX01iSiEoTHyvCZ+PacbVd2yg15nb8Xhge3Y0z97RlpytoYlS2D19Bzc+vonICOXrcUl8+FJqwOu87Z659Dglk+25sdx8bb9SaX+5dCWDhizmsvPPYUdeLADHd9nG4Ft/JypK2ZEXwz3/PC3gOpYQivtTG3WqCy30Oh/LRURSReQ9EVklInNE5FcR+Ysfyp0mItUJ0FMh333VgoeHnlRKtvaPhvz77q4smpdU7jn/uG0pc35t4o/qK8VTLLz2REtu6H88t13QkfOv3kqrdnsZ/2oaN53diSHnduK37xO5cuimgOtSHhERypAnN/LglW0ZlN6ePgO306rdvoDX+903rXjorlMOkqc03UPXk7axNTN+v6x+gwKG3L6Qx+7ryU3X9OPJh3sEXL8SQnV/aptOzsQi8WkLZ+q0QRcntNlnwI+qeqSqdgMuw4lcFjYsnpfEzh3RpWTr1zRg47oG5ebveUYmWzbFs3ZV+en+JGdrDBmL6gOwd3ck6zPiSU4tYM+uyP154uoVhyykefsT97BpTQyZ62IpKoxg2ueJ9BqQF/B6Fy1IOeg7Axh8yyJGv3xcqfuR3n8Dv/yYxrat9QDI2x4bcP1KCNX9qW06KVCoET5t4Ux4a3fo9AUKVPWVEoGqrlXV4SISJyJjROR3EZknIn0AKpHHi8j7IrJURD4F4suvMrDExRdx0dWreO/1dkGvO7VFPkcdt4fl850HyTV3beDtX+fT54Ic3n6uytj7ASG5WSHbNh1w9WRtjiYlrTAkuvTsvZnsrDhW/5FQSt685S4aNCzkqf/9xP9em0rfAesqKMH/hNP9KSEcdVKEYiJ82sKZ8Nbu0DkOmFtB2hCc2DjHA5cDb4pIXCXym4A9qnoszlp/3SqqVEQGi8hsEZld4Nnrx8uBKwet5LNxbdm3N7jdH3H1innwlQxefazl/tb5m0+34G+9ujD1syTOv2ZrUPUJN2Jji7j0qhW8/caxB6VFRipHH7OdR+7pxUN3nsLl1yyneYtdIdDSqAyPik9bOHNYdYqKyAigN1CAE5N4OICqLhORtcAxbnp58tOBF135QhFZWFE97uolowASYlL96ow4ptN2Tu2byd9vWUb9hoWoRygoiODLj9r4s5pSREZ5eOiVDKZ+lsz0bw726U/5LJnHx67kneeD30rPzoymyREHVjxKSSska/PBrpBAk9Z8N6lpuxkxeoqjR5N9vPj6NIbdcAZZ2+LZkRdD/r4o8vdFsWhBMm2PzmPjhsC7zMLl/ngTjjqV+NBrO3XdoC8GLiw5UNUhIpICzMYx6LWOewb32r9/xaAV7NsTFVBjDsqw/65hXUY8n7zebL/0iDb72LQmDoBeZ21n/R9xAdShYpbPr0fztgWktswnOzOa9IHbeWpI66DrsWZVAlcMPHf/8ZgPJjF0cDo78mKZ8XMaN922gIhID9FRHtofm8tnHx4dFL3C5f6Eu04gFIe5f9wX6rpBnwI8KSI3qerLrqye+/kTcCUwRUSOAVoByyuR/whc4co7AZ39peTdj8/j+G45NEos4M0vpvDua+3YuSOaG+9YQkLjAh59bjarVjbi4X8Gb3RECcd130X/C7NZvTSeERMXATD26RYMuDSLFkfuQz2wZWMMw+9vE3TdwBmFM+KB5jz53ioiImHy+0msXRH4h8vdD8+i84lZNEoo4K3x3/DOmA5M/qpNuXnXr23InJmpjBwzFY8HJn3VmrWrg7NweKjuT23TyVmxqPYbdNE6EGGsMkQkDXgeOBnYBuwGXgE+B14GugNFwO2qOtX1l5cnjwfGACcAS3FW4B6iqrOphISYVLVFoisn7BaJPtoWia5NzNTv2aE5h+QvOeb4eB0xoY1Pec86ctmcaq4pGjTqegsdd32+yypIvq6c/PsqkO+tpBzDMGo5HvOhG4Zh1H6cTtHa73Ixg24YhmGdooZhGHWDutIpagbdMAwDKA7zSUO+UPsfSYZhGIeIIhRqlE9bVYjIaBHZKiKLvGRPi8gyEVkoIp+KSKJX2n0ikiEiy0VkgJf8bFeWISL3+nIdZtANwzjsKekU9VMsl7HA2WVk3wKdVLUzsAK4D0BEOuKMnjvOPWekiESKSCQwAjgH6Ahc7uatFDPohmEc9ihCsfq2VVmW6o9AThnZZFUtcg9ncCDi60DgfVXNV9XVQAbQw90yVHWVqhYA77t5K8UMumEYBk6nqC+bH/g78LW73xxY75W2wZVVJK8U6xQ1DOOwR5XqDFtMERHvGeKj3IB8VSIiD+DMQH+3mir6hBl0wzAOe5xO0ciqMzpk1WTqv4hcC5wH9NMDMVc2Ai29srVwZVQirxBzuRiGYeDXTtGDEJGzgbuBP6vqHq+kCcBlIhIrIm2BdsBvwCygnYi0FZEYnI7TCVXVYy10wzAOexT/LV4hIuOAdBzXzAacBXHuA2KBb52VMZmhqjeq6mIR+RBYguOKGaKqxW45twCTgEhgtKourqpuM+gBRgsLKdpQ5ZtS0Ji0aX6oVTiIczucHmoVSlHYLKHqTEEkIiPUGhyMRIWR6SiqOosv+CuWi6qWF171jUry/xv4dznyicDE6tQdRt+KYRhGaFDAY7FcDMMw6gJiS9AZhmHUBRSqM8olbDGDbhjGYY+qmMvFMAyjrmDx0A3DMOoATjx086EbhmHUAWzFIsMwjDqBM2zRWuiGYRi1nmrGcglbzKAbhmFga4oahmHUCZzwueZyMQzDqBOYD90wDKMO4ERbNJeLEWC6p+/gxsc3ERmhfD0uiQ9fSg1YXc8Oa8nM7xqRmFLEqKnLS6WNf6UJrz3WnA9//52E5GJU4eWHmvPblEbExXu44/l1tOu8F4BzWpxAmw77AGjavIB/vbna77rWb1jE0CdW0LrdHlThhQeOYcPqeO57bhlNm+9j68Y4/jOsA7t2RPu9boAmybu5e8jPNE7ciypM/O4YPv26I3+7eD7n9ltB3o44AEaP68pv81rQt/cqLvnz/kXgadsql5vvOZ8/1iYFRL8SomM9PPtJBtExSmSU8tNXibz9TLOA1lmWlLQC7np+NYlNikBh4nspfD46lQYJRdw/chWpLQrYsiGGJ28+kl15oTFJztR/M+h+Q0QUeFdVr3KPo4DNwExVPa8a5awBuqtqlh90uhaYrKqbDrWsmhARoQx5ciP3XXYkWZujGT5xJTMmJbBuZVxA6jvr0hz+fF0WTw9tVUq+dWM0c39oSNPmBftls6Y0ZOPqWMZMX8qyufUYfl8LXvxqJQAxcR5e/q70A8Hf3PDAH8z5KYknh3YkKtpDbJyHS29Yz/wZiXz0WksuHrSeiwdtYMyzbQNSf3Gx8Orb3clYnUx8XCEjn/qSOQuPAODjrzoy/otOpfJP+flIpvx8JABtWubyr7umBNyYAxTmC3dffBT79kQSGaU891kGs6Y0ZNnc+gGvuwRPsfDaEy3JWFSP+PrFDP9qKfN+asSZF2czf3ojPhzZjEtuzuSSmzMZ/Z8WVRcYEOpGCz2crmA30ElE4t3jM/FhyaVDRUQqG6t0LXBEoHWoiPYn7mHTmhgy18VSVBjBtM8T6TUgL2D1Hd9zNw0bFx8kf/XR5lz/4CbEy8X466QE+l+Ugwgc220Pu/Miyd4SnPZBvQZFdOqex6TxzttKUWEEu3dG0bNfNt995si++yyVXv2zA6ZDzvZ6ZKxOBmDvvmjWbUwgJWlPFWc59O29mmm/BOZBczDCvj3OTzwqWomMVvYvfhYkcrZGk7GoHgB7d0eyPiOO5GaF9DpzO9+Nd+7hd+OTOeWs7cFVrAwexKctnAkngw5OMPc/ufuXA+NKEkQkSUQ+E5GFIjJDRDq78mQRmSwii0XkdThwx0XkKhH5TUTmi8irJcZbRHaJyLMisgDoJSIPi8gsEVkkIqPE4SKgO/Cue368iHQTkR9EZI6ITBKRtEDejORmhWzbFLP/OGtzNClphYGs8iB++aYRKc0KOeq4faXkWZnRNDnigC4pRxSSnem4NwryI7jl7GMYel47fvna/4tFNGuxj7ycaIb9ZwXDP5nL0MdXEBtfTGJyAbnbnPuVuy2axOSCKkryD6lNdnF02xyWZaQAMHDAMl59egJ33DSdBvXzD8p/Rq/VTJ0eLIPuvOmN/HY5HyxczLwfG7B8XvBa52VJbZHPUcftYfm8+iSmFJGz1fnN5GyNIjHFTytV1ICSUS6+bOFMuBn093HW14sDOgMzvdL+BcxT1c7A/cBbrvwR4GdVPQ74FGgFICLHApcCp6pqF6AYuNI9pz6OK+cEVf0ZeElVT1LVTkA8cJ6qjgdmA1e65xcBw4GLVLUbMJpyVhmpS+zbI7w/PJWr79pcrfPe/m0JL32zgntHrOWVR5qzaU1M1SdVg8go5eiOu5g4Lo1b/9qVfXsjuWTQ+jK5BA3Cny8utpCH75jKy2NPYs/eGL6Y3J5rbv0rN959Pjm58dxw9exS+TscvY38gijWrG8ccN1K8HiEm89sz5XdOtK+yx5at98btLq9iatXzIOvruLVf7Vkz66yL8ZCkF8cDsKjET5t4UxYaaeqC4E2OK3zsksv9QbedvNNAZJFpBFwOvCOK/8KyHXz9wO6AbNEZL57fKSbVgx87FV2HxGZKSK/A32B48pRrz3QCWdNwPnAgzgrcR+EiAwWkdkiMruQg1tovpKdGU2TIw60MlPSCsnaHJhOvvLYvDaWzHUx3NS/A1f36Mi2zdEMGdCenK1RpDQrZNumA7pkbYomuVnhfj0B0loX0PmUXfyxKL7c8mtKVmYsWVtiWb6wEQA/T0rhqI672J4dQ+Mmzv1q3KSAvJzA3qvISA+P3DGNKT8dyc+/tQZge148Ho1AVZj4/TG0P6p0V076qcFtnXuze0ckC35pwEl9dga97sgo5aFXVzH10ySmf+M8zLZnRZHU1PmtJDUtJC8rdF16JWuK+rKFM2Fl0F0mAM/g5W6pIQK8qapd3K29qj7qpu3zWog1DhiJ0/I+HngNKK/XUYDFXuUdr6pnlVexqo5S1e6q2j2a2BpfwPL59WjetoDUlvlERXtIH7idGZODt95l22P38eHvi3nrtyW89dsSmqQVMmLScpKaFtHzrB18Nz4JVVg6px71GhWTnFrEzu2RFOQ7P/q87EgWz6pPq2P2VVFT9cjNimHb5liat3V81l16bWfdH/WYMSWJ/hdsAaD/BVuY8X2yX+stjXLHjdNZtzGBj7868PxPSjzgRz+1x1rWrE/cfyyinNFrTVANekJSEfUbOf0iMXEeup6+i/UZgelUrxhl2NNrWJcRxyevHxilNePbRPpf5PRz9L8om1+/TQyyXgdQoEgjfNrCmbAZ5eLFaGC7qv4uIule8p9wXCaPu/IsVd0hIj8CVwBPiMg5QMm77PfA5yLyvKpuFZEkoKGqri1TX8mvO0tEGgAXAeNd2U6gobu/HGgiIr1U9VcRiQaO8WUl7priKRZGPNCcJ99bRUQkTH4/ibUrAvdn/M9NrVn4awPycqK4sltH/nZHJmdfkVNu3h79djDr+4Zcd8qxxLrDFgHWrYzlxXtaIhGgHrh0yBZaH1Pzt5SKeOWJo7j76eVERXvIXB/P8/e3QyLgvueXctaFmWzd5AxbDBTHtd/KmWesYtXaxrzy3wmAM0Sxz6mrOapNDqrClm31eWFUr/3nHH/sFrZl1Sdza8OKivU7SamF3Pm/dUREQEQE/PhFAjO/axS0+gGOO2k3/S/MYfXSeEZ8vQSAsf9tzgcjm3H/y6sYcGkWWzfG8O+bjqyipMAS7u4UXxANdpd3BYjILlVtUEaWDtypque5Bnk0jttkDzBYVReKSDJOa7458AtwFtBNVbNE5FLgPpw3kUJgiKrOKFuXiDyB4+bJBFYAa1X1URG5EHgS2Av0wnG7vAgk4DwMX1DV1yq7rkaSpCdLv0O5NX5l0qb5oVbhIM7tcHqoVShFYefQGpayRPw8P9QqHIREhU9bcEbRJHZ4cg7JF5LUoan2G32hT3nHn/rKHFXtfij1BYqw+VbKGnNXNg2Y5u7nABeUkycbx4iXV+YHwAdV1aWqD+L4xMvm+5jSvvb5OD57wzDqEP5c4EJERgPnAVvdgRa4DdIPcPoI1wCXqGquiAjwP+BcnIbqtao61z3nGg7YpSdU9c2q6q797xiGYRh+wI+domOBs8vI7gW+V9V2OO7ge135OUA7dxsMvAz7HwCPACcDPYBHRKTKoVFm0A3DOOwpWeDCHwZdVX8EynY+DQRKWthvcsDbMBB4Sx1mAInu/JYBwLeqmqOqucC3HPyQOIiwcbkYhmGECkUo8gS0fZuqqiUTOjKBkuE+zQHvSRQbXFlF8koxg24YhkG1fOgpIuI9Y2yUqo7y9WRVVTd2ld8xg24YhqHVioeeVYNRLltEJE1VN7sula2ufCPQ0itfC1e2EUgvI59WVSXmQzcM47DHnz70CpgAXOPuXwN87iW/2o0f1RPIc10zk4CzRKSx2xl6liurFGuhG4Zh4L8Vi0RkHE7rOkVENuCMVnkK+FBErgfWApe42SfiDFnMwBm2eB04w7RF5HFglpvvMXfodqWYQTcM47BHEYr91CmqqpdXkHTQDEN1ZnYOqaCc0TiTKX3GDLphGAb+m1gUSsygG4Zx2KPV6xQNW8ygG4ZhQFDi5wcaM+iGYRiEf6xzXzCDbhiGgbXQjVpI+qBBoVbhIOqlBm4x55oQMX1BqFUojYSfoYmoVy/UKuxHdh366BRVKPaE332uLmbQDcMwsFEuhmEYdQLFXC6GYRh1BOsUNQzDqDOEyWqch4QZdMMwDMzlYhiGUSdwRrnU/uCzZtANwzAwl4thGEadwVwuhmEYdQBFzKAbhmHUFeqAx8UMumEYBgpqU/8NwzDqBnXa5SIiw6nkLURV/xkQjYxSdE/fwY2PbyIyQvl6XBIfvpQa8Dpjoor4391fER1VTGSkhx/mtGXshG488I+ptG+dRXFxBEtXN+HZd3pTXHxgqFf7NtsYee8EHhvVlx/mtvWrTrfdM4cevTLZnhvLzdf1B+DKa5cy4Lw15G2PBeDN1zoye2YzjumQw613zgdARHl37LH8+tMRftWnMv4yaCvnXJ6DKqxeFsezt7eiMD90Q+IuuH4b51yRjQh8/V4Sn77eNOg61G9YxNAnVtC63R5U4YUHjmHD6njue24ZTZvvY+vGOP4zrAO7dkQHXbcS6vool9lB0yKIiEgx8DvOtS8FrlHVPaHVqnwiIpQhT27kvsuOJGtzNMMnrmTGpATWrYwLaL0FRZHc/uy57M2PJjLSw/C7v+C3RS35bsbR/Pv1dAAeGjSVP/VexoQfOjq6iocbLvyNWUuaB0Sn775uzRefHMUd95f+WX720dF88kG7UrK1qxsx9IZ0PMURNE7ax4jR3zPzl2Z4igNvVJObFXDB37MY1KcDBfsieOCVNaQPzOXbD5MDXnd5tG6/l3OuyOaffzqGwkLhyXf/YOZ3CWxaExtUPW544A/m/JTEk0M7EhXtITbOw6U3rGf+jEQ+eq0lFw9az8WDNjDmWf82BHylrsRyqfAXrqpvem/AR2WOayt7VbWLqnYCCoAbD6UwEQmY26r9iXvYtCaGzHWxFBVGMO3zRHoNyAtUdV4Ie/OdllJUpIeoSA+qMHNRS0AAYenqJjRpvHv/GX/tu4Qf57Rl+874gGi0aGEKO3f61nrLz4/ab7xjYoqD/keNjFJi4zxERCqx8R6yM0PX6mzVLp9l8+qRvy8CT7GwcEYDTj1ne1B1qNegiE7d85g03nm7LCqMYPfOKHr2y+a7zxzZd5+l0qt/CMMoK6Di2xbGVNlkEZFeIrIEWOYenyAiIwOuWXD4CThaRJJE5DMRWSgiM0SkM0Al8kdF5G0RmQ68HSjlkpsVsm1TzP7jrM3RpKQVBqq6UkSIh9cf/oTPnn2H2Uubs3T1gdf0yEgPZ/XM4LfFLQFISdxN7xPX8PkPxwZFN2/O/8sqRoz+ntvumUODBgX75e2PzeHlsd8xcsz3vPRcl6C0zgGyM2MY/0pT3v5tCePmLWL3jkjm/tgoKHWXx5plcXQ6eTcNGxcRG+fhpL47aHJEcH5DJTRrsY+8nGiG/WcFwz+Zy9DHVxAbX0xicgG525zfd+62aBKTC6ooKbCo+raFM778yl8ABgDZAKq6ADg9gDoFBbdlfQ6O++VfwDxV7QzcD7zlZqtIDtAR6K+qlwdP6+Dh0Qj+8dhfufjuyzm2zTbaHpGzP23YFdNZuLIZv69sBsAtl85g1Cc9gt4S/urztlx/xVnccn1fcrLj+MeQ3/enLV+axE3X9ue2G9O55MoVRMcUB0WnBglF9BqQxzU9O3JF107E1Sum719zqj4xQKzPiOPDEU35z3t/8O93/2DV4ng8nuDqEBmlHN1xFxPHpXHrX7uyb28klwxaXyZXqMeBC+rxbauyJJFhIrJYRBaJyDgRiRORtiIyU0QyROQDEYlx88a6xxlueptDuQqfmi2qWvbuB+ffERjiRWQ+Th/BOuANoDduS1tVpwDJItKoEjnABFXdW14FIjJYRGaLyOxC8musaHZmNE2OONBqSUkrJGtzcF/fd+2NZd7yNHp02gDANefPJbHhPkZ82HN/nvZttvHwoCm8/5/3OaPram67cjq9u6wJuG7bc+PweBxD8M2XbTimQ+5BedavbcS+vZG0absj4PoAnHjaLjLXxZCXE0VxkTD960Q6dt9d9YkBZNL7ydxyTnvuvLAdu/Ii2bAqsH0wZcnKjCVrSyzLFzp/nZ8npXBUx11sz46hcRPn9924SQF5OaFzTQGu28WHrRJEpDnwT6C769aNBC4D/g94XlWPBnKB691TrgdyXfnzbr4a44tBXy8ipwAqItEicidOZ2JtpcSH3kVVb1XVmr7nVfgvVdVRqtpdVbtHU/POp+Xz69G8bQGpLfOJivaQPnA7MyYn1Lg8X0losJcG8c6DKCa6iO4dN7IuM5E/9V7GSR038NhrfUq1pi6/7zIuc7cf5rblhXdP5ef5bQKuZ+Okffv3TzltM2tXOwYjtdluIiKdZmjT1D20aLWLLZnBWTJt68Zoju26h9g4D6B06b0z4J3YVZGQ7LhYmhxRwKnn5DH108Sg1p+bFcO2zbE0b+uMPejSazvr/qjHjClJ9L9gCwD9L9jCjO9D03EMOOPQVXzafCAKp+EYBdQDNgN9gfFu+pvABe7+QPcYN72fSM3XHPSlQ+9G4H9Ac2ATMAkYUtMKw5SfgCuBx0UkHchS1R0iUpE8KEp5ioURDzTnyfdWEREJk99PYu2KwBuH5IQ93Pf3H4mI8BAhMHV2W35d2IrvX3mDzOwGjLxvAgA/zm3DW192Dbg+AHc/PIvOXbbRKKGAtz76mnfGHEvnE7M48ug8VGFLZj2GP3MiAMd1zubiK1ZQVBSBKox8/gR25AVnVMfyefX56asERkxaTnGRkLE4nq/fDaGhAh5+bQ0NGxdRXCS89EALdu8I/vSTV544irufXk5UtIfM9fE8f387JALue34pZ12YydZNzrDFkOIH/7iqbhSRZ3De/vcCk4E5wHZVLXKzbcCxp7if691zi0QkD0gGsmpSv2i4e/n9jIjsUtUGZWRJwGjgSGAPMFhVF1YifxTYparPVFVfI0nSk6Wfvy+jxuT/6aRQq3AQ9VaE1yLRxRmrQ61C2BPZsGGoVdjPr7s+J68o65BaWbFtW2jav271Ke/aa+5dS2mDO0pVRwGISGPgY+BSYDvwEU7L+1HXrYKItAS+VtVOIrIIOFtVN7hpfwAnq2qNDHqVj2oRORKnhd4T5xn2KzBMVVfVpMJQU9aYu7IcDrwC+SJ/NACqGYYRSnzvLM5S1e4VpPUHVqvqNgAR+QQ4FUgUkSi3ld4C2Ojm3wi0BDa4LpoE3AEoNcEXH/p7wIdAGnAEzhNnXE0rNAzDCDv8Nw59HdBTROq5vvB+wBJgKnCRm+ca4HN3f4J7jJs+RQ/BbeKLQa+nqm+rapG7vQOEtpfHMAzDz/hjHLqqzsRxsczFGRIdAYwC7gFuF5EMHB/5G+4pb+CMnssAbgfuPZRrqCyWS5K7+7WI3Au8j/McuxSYeCiVGoZhhB1+6k5U1UeAR8qIVwE9ysm7D7jYPzVX7kOfg3OJJe8YN3jrAdznLyUMwzBCTphP6/eFCg26qoYmSo5hGEYIkDow4M+nAaki0glnqvt+37mqvlXxGYZhGLUIFTgcFrgQkUeAdByDPhEn/snPlI5rYhiGUbupAy10X0a5XIQz9CZTVa8DTsAZK2kYhlF38EMsl1Dji8tlr6p6RKTIDUy1FWcgvGEYRt0hzI21L/hi0GeLSCLwGs7Il104s0UNwzDqBiUTi2o5VRp0Vb3Z3X1FRL4BGqnqwsCqZRiGEVzq9CgXEakwjJ6IdFXVuYFRyTAMIwTUZYMOPFtJmuLE9zUMw6gT1OkWuqr2CaYidRYRJDqm6nxBYl9iZKhVOIjYleEVuPP6FeEVPveNY8Jvjl/xjuCsAOULqn5aU+9w8KEbhmHUeWrBkERfMINuGIYBZtANwzDqCuInz00oqXKmqDhcJSIPu8etROSgMJCGYRi1mjowU9SXqf8jgV7A5e7xTmBEwDQyDMMIMqK+b+GMLy6Xk1W1q4jMA1DVXBEJn2EbhmEY/uAwGeVSKCKRuC8bItKE6iynahiGURsI89a3L/jicnkR+BRoKiL/xgmd+2RAtTIMwwgyh4XLRVXfFZE5OCF0BbhAVZcGXDPDMIxgoXVjlIsvC1y0AvYAX3jLVHVdIBUzDMMIKmHe+vYFX3zoX3Fgseg4oC2wHDgugHoZhmEElzpg0Kv0oavq8ara2f1sB/TA4qEbhlHH8KcPXUQSRWS8iCwTkaUi0ktEkkTkWxFZ6X42dvOKiLwoIhkisrCySLdVUe2Zoqo6V0ROrmmFRuWkpOVz1/OrSUwpBIWJ7zXh8zHNuOq2jZx9+Tbysp2vbOzTLZg1NTGgukSIhzFDP2FbXn3uHHMOF52yiEtP+52WKTsY8MjV5O2JB6B1k1wevHQa7Ztn8co3PXjvhxMCqldZuqfv4MbHNxEZoXw9LokPX0oNSD0/3pfC+qn1iEsu5sKvNgLw2/81Zt2UekTEQKOWhZz2VBaxjTxsnB7HrGeS8BQKEdFKj7tzOKLXPgC+uT6VvVsj8RQLzbrvo9cj2UQEMGba7c+t4+T+O9meFcUNfdsHrqJaqk+A+B/wjape5A7zrgfcD3yvqk+JyL3AvcA9OOs0t3O3k4GX3c9q44sP/XavwwigK7DJl8JF5AHgCqAYZ6jjDao6s4K81wKTVXWTe/w68JyqLqkgfwfgfZwXpYtU9Q9fdHLPTQcKVPUXX88JFp5i4bUnWpKxqD7x9YsZ/uVi5v3sLOH66RupfDwqLWi6XHraItZsbUz92AIAFq5pxvSlrRl544RS+XbsieO5z07ljE5rgqZbCRERypAnN3LfZUeStTma4RNXMmNSAutWxvm9rnZ/3UXHq3bww91N9suOOHUf3e/IJSIKfnu6MQteTaDHXbnENvZw5itbqJ9aTM6KaCb9vRmX/7wegL7/20pMA0UVptzalNVf1+eo83b7Xd8SJn+QxIQxKdz1v/UBq6M6hJs++/GTy0VEEoDTgWsBVLUAKBCRgUC6m+1NYBqOQR8IvKWqCsxwW/dpqrq5unX7MmyxodcWi+NTH1jVSSLSCzgP6KqqnYH+QGXf4LXAESUHqvqPioy5ywXAeFU9sTrG3CUdOKU6J4hIUOLe5GyNIWNRfQD27o5kfUY8yakFwai6FE0SdnFKh7VMmNlhv2zFphQ25zY8KG/u7niWbmhKUbEvPyf/0v7EPWxaE0PmuliKCiOY9nkivQbkBaSutJP2EZtQeihEi957iXB/GU1PyGdPpnOQ0rGA+qnFADRuV0hRvlDsfo0xDRzLoUVQXChIgOezLJrZgJ254RO2Kdz0AfaPcvFl84G2wDZgjIjME5HXRaQ+kOplpDOBklfJ5pS2jRtcWbWp9B/oTihqqKr/crd/q+q7qrrPh7LTgCxVzQdQ1SxV3SQiD4vILBFZJCKjXP/RRUB34F0RmS8i8SIyTUS6i0ikiIx18/8uIsNE5FzgNuAmEZnq6vqZiMwRkcUiMtjrGs4WkbkiskBEvheRNsCNwDC3rtNEpI2ITHH9V9+7I3tw631FRGYC/3V9X03ctAjX53WgueZnUlvkc9Rxe1g+vwEAf756Ky9/s4hhT6+mQaOiQFULwLA//8JLX/VEw3z2XHKzQrZtOjBxOWtzNClphSHRZcXHDWlx+p6D5Gsm1SOlYwGRXvOrv/l7Ku/2akV0fQ9tzg5c69yoBr7HckkRkdle2+AyJUXheDJeVtUTgd047pUDVTmtcb93w1Zo0EUkSlWLgVNrWPZkoKWIrBCRkSJyhit/SVVPUtVOQDxwnqqOB2YDV6pqF1Xd61VOF6C5qnZS1eOBMao6EXgFeN5rIY6/q2o3nAfDP0Uk2TW2rwEXquoJwMWqusbr3C6q+hMwHHjTfZN4F2cyVQktgFNU9XbgHeBKV94fWKCq22p4fyolrl4xD76SwauPtWTPrki+fKcp153emZvPOY6crdEMeihwr6unHruW3F3xLN8YsGdVnWP+ywlERCpH/bm0cc5dGc2sp5M49fGsUvKzR2/h8unr8RQIm2f43z1kVA+hWp2iWara3WsbVaa4DcAGL/fyeBwDv0VE0gDcz61u+kagpdf5LVxZtamshf6b+zlfRCaIyN9E5K8lW1UFq+ouoBswGOf14wPXT95HRGaKyO84y9hVNfxxFXCkiAwXkbOBipZK+aeILABm4NycdkBP4EdVXe3qlFPBub2A99z9t4HeXmkfuQ82gNHA1e7+34Ex5RUmIoNLnt6FPr3MlCYyysNDr2Qw9bNkpn+TBMD2rGg8HkFV+GZcE9qfELhWXec2mZzWcS2f3vcuj1/1Hd2P3sSjl38fsPoOhezMaJocccAllZJWSNbm6KDqsOKTBqybWo/0Z7eVcp/szozkuyGpnPHfbTRqdfAbVVSs0qrfHtZ+Vz+I2hoV4qdoi6qaCawXkZIe337AEmACcI0ruwb43N2fAFzteit6Ank18Z+Db6Nc4oBsHONbMh5dgU+qOtE1hNOAaa4BvwHoDHRX1fUi8qhbfmVl5IrICcAAHFfJJTjGdD9uJ2d/oJeq7hGRaVWVWw32W05X5y0i0hdn+OaV5Z3gPrFHATSKSK7ma5Uy7L9rWJcRzyevN9svTWpaQM5W5539lAG5rFkeX83L8J2Xvz6Zl792Otm7HrmJK85YwKPj+gWsvkNh+fx6NG9bQGrLfLIzo0kfuJ2nhrQOWv0bfozn99cSOPfdzUTFH/iq83dEMHlQKifdkUNqt/z98sLdQuHuCOo1LcZTBOunxdOse/Uf+oaf8f+0/ltxXMgxOI3S63Aa0B+KyPXAWhxbBjAROBfIwJnEeV1NK63MoDd1R7gs4oAhL6HKS3efTh5VXemKuuBMSOoMZIlIA+AinNcRcMLyHtTjJiIpOCNSPhaR5Thuj7IkALmuMe+A0zIHp7U+UkTaqupqEUlyW+k7gUZe5/8CXIbTOr8S+KmSS3vd1eFtr5a73ziu+y76X5jN6qXxjJi4CHCGKKb/OYcjO+4BhS0bYnnx/uAZrRIuOfV3rkpfQFLDPbxz+3h+XdaKJ8efQVLDPYz95yfUjyvAo8JlvX/nsmcuYU9+4INyeoqFEQ8058n3VhERCZPfT2LtisC4MKYOa8Lm3+LYlxvJuNNa0vWfuSx4NRFPgfDNtc7Dt2mXfE59LJsl7zRix7po5o1IZN6IRADOHpMJCt/emEpxoaAeOOLkvXS4fGdA9C3h3pFr6dxrFwlJRbwzewlvP5vKpHHJAa2zNumzHz9O/VfV+Tju37Ic1DJy/elD/FGvOGWVkyCyGWc8ZHm9Yqqqj1VasEg3HN90IlCE8/QZjNOZeTlOL+8KYK2qPioiF+IE/dqL4wL5GrgTKMRxbZS4h+5T1a/d1v0uVX1GRGKBz4A2OA+NROBRVZ0mIue45UYAW1X1TBE5BudB4sF5kq5z60jBcQ9dp6rrRGQs8KXr4y+5rmicN5YeqrqssnsATgu9Z/TZVWULGtsvqfGchYCR8O6MUKtQClskunYxU79nh+YcUu99fFpLPfLa26vOCCx56vY5qlqesQ45lbXQN1dltCtDVedQ/tDAB92tbP6PgY+9ROle+wdZIVV91Gs/H2dwfnl6fI3zcPCWrcB5U/CmbznnXltOkSfgdIZWacwNw6hF1IGp/5UZ9PAerxYC3NldN1GB79wwjFpKLVhezhcqG+USnr1gIURVn1LV1qr6c6h1MQzDv9TpeOiVDPEzDMOoe4S5sfaFMJt/axiGERoOiwUuDMMw6jx1xIduBt0wjMMeoW6MAjGDbhiGAdZCNwzDqCuE+wgWXzCDbhiGAdZCNwzDqBOojXIxDMOoO1gL3TAMo25gPnTDMIy6ghl0o0pU0cLgL/JcEY0/+z3UKhyERgc+bnp1CLdwtZFHh5c+ALolq+pMQUJ2+WdxcmuhG4Zh1AUUvy5wESrMoBuGcdhTskh0bccMumEYBpgP3TAMo64gFSzHWZswg24YhmHRFg3DMOoOdcGH7p/xPoZhGLUc8fi2+VSWSKSIzBORL93jtiIyU0QyROQDEYlx5bHucYab3uZQrsEMumEYBhxwu1S1+cZQYKnX8f8Bz6vq0UAucL0rvx7IdeXPu/lqjBl0wzAMHxeI9sUtIyItgD8Br7vHAvQFxrtZ3gQucPcHuse46f3c/DXCDLphGAb4s4X+AnA3B6YqJQPbVbXIPd4ANHf3mwPrAdz0PDd/jTCDbhjGYU/JxCIfW+gpIjLbaxu8vxyR84CtqjonFNdho1wMwzAA8fjsIM9S1e4VpJ0K/FlEzgXigEbA/4BEEYlyW+EtgI1u/o1AS2CDiEQBCUB2DS/BWuiGYRg+u1uqsPmqep+qtlDVNsBlwBRVvRKYClzkZrsG+Nzdn+Ae46ZPUa35DCdroYc53dN3cOPjm4iMUL4el8SHL6WGRI+ICOXFTxeStSWGRwcfCyjXDFtH73Oy8XiEr95rxoS30oKiS0paPnc9v5rElEJQmPheEz4f02x/+l8HZTL4wfVc0qULO3Kjg6JTCbc/t46T++9ke1YUN/RtH7R6b7tnLj1OyWR7biw3X9uvVNpfLl3JoCGLuez8c9iRF8uFl60k/cz1AERGKi1b7+TyP5/Lrp2Bi3o59vtZ7NkdiccjFBcLQy/swvV3r+bkPjkUFQqb18Xx3H3HsHtn6ExSgFcsugd4X0SeAOYBb7jyN4C3RSQDyMF5CNSYWm/QReQB4AqgGKcT4gZVnRlarfxDRIQy5MmN3HfZkWRtjmb4xJXMmJTAupVxQddl4DWbWfdHPPUaFANw5oVbSUkrYPCAE1EVEpKCFyLYUyy89kRLMhbVJ75+McO/XMy8nxNYtzKelLR8up2Wx5YNoQnJO/mDJCaMSeGu/60Par3ffdOKLz49kjvuL+26TWm6h64nbWNrZvx+2cfvt+Pj99sB0OOUzfzlkj8CasxLuPea40s9YOdNT2TMs23wFAt/v3M1l96wntHPhDBUsJ8nFqnqNGCau78K6FFOnn3Axf6qs1a7XESkF3Ae0FVVOwP9cXuMA1BX0B9+7U/cw6Y1MWSui6WoMIJpnyfSa0BesNUgpVk+PdJzmfThgbeDP12+hfdeaoGqM8IqLyd4BjRnawwZi+oDsHd3JOsz4klOdR4oNzy8ntf/0zJk07gXzWzAztzgt5MWLUhh546D30YG37KI0S8fR0Uv8en9NjLtuxYB1q585k5vjKfY+f0sm9+QlGahXTfAX8MWQ0mtNuhAGk4HRT6Aqmap6iYR6SYiP4jIHBGZJCJpItJBRH4rOVFE2ojI7+7+Qfld+TQReUFEZgNDK8oXKJKbFbJt0wFDmbU5mpS0wkBWWS43PLCaN/7bGo/XK2laq32c8ads/vfJAh57fQlHtN4bdL0AUlvkc9Rxe1g+vwE9z8wlOzOa1UvrhUSXcKNn781kZ8Wx+o+EctNjY4vodvIWpv9wRMB1UeDfbyzixY/ncc4lmQeln3XhFmb92DjgelSIAqq+bWFMbTfok4GWIrJCREaKyBkiEg0MBy5S1W7AaODfqroMiBGRkne6S4EPKsrvVUeM26P9YhX59iMig0uGNBWS7/+rDiI9+uSwPTuajMUNSsmjYzwU5AtD/3oC33yYyrD/ZARdt7h6xTz4SgavPtaS4iK4bMhm3nquedUnHgbExhZx6VUrePuNYyvMc/KpmSz5PSko7pY7L+/MrX89kYcGHcd5V26iU/cDb5qX3bie4mJh6oQmAdejMvw59T9U1GofuqruEpFuwGlAH+AD4AmgE/CtO+EqEtjsnvIhjiF/yv28FGhfSX7cMvEhn7deo4BRAI0kqcaP9OzMaJocceA1NCWtkKzNwe3k69h1Jz375XLSGXOIjvVQr0Exdz2zgqzMWKZPduY//DI5idufCq5Bj4zy8NArGUz9LJnp3yTRpv0emrXM5+WvFwOQklbAS18tYejAjuRuC+49CwfSmu8mNW03I0ZPASClyT5efH0aw244g9wcpw/m9L4b+eH74LhbsrfGAo5r7pdvk2nfeSeLZifQ/y9b6JGew33XdsIZDR4abIGLMEFVi3E6Hqa5LpQhwGJV7VVO9g+Aj0TkE+dUXSkix1eSH2C3+ylV5PM7y+fXo3nbAlJb5pOdGU36wO08NaR1sKoHYOyzrRn7rFPn8T3yuPAfm3j6zmO47s61nNAzj8nj4zi+xw42rg5mR60y7L9rWJcRzyevO6Nb1iyvx2XdTtyf482fF3Dr+R2DPsolXFizKoErBp67/3jMB5MYOjidHXmOYa1Xv5Dju2Tx9BPdAq5LbHwxERHK3t1RxMYX0/XU7bw3shXdTsvl4n9s4O6rOpO/LzLgelRKLXCn+EKtNugi0h7wqOpKV9QFJyDOWSLSS1V/dV0qx6jqYlX9Q0SKgYc40PJeDjQpL3+Z6nzN5zc8xcKIB5rz5HuriIiEye8nsXZF8Ee4lMeHrzbn7udWcMG1m9m3J4IXHjg6aHUf130X/S/MZvXSeEZMXATA2KdbMGtqYtB0qIh7R66lc69dJCQV8c7sJbz9bCqTxtV4JrfP3P3wLDqfmEWjhALeGv8N74zpwOSv2lSY/5TTNjF3VlPy9wXeBDROLuShEUsAiIyEaV82Yc5PjXlj8myiYzz8e4zzHS5b0JCXHgne76gsdaGFLocwhj3kuO6W4UAiUARkAINxZmK9iDPrKgp4QVVfc8+5E3gaaKuqa1xZl/Lyi8g04E5VnV1Zvsp0bCRJerL0qyxLUImoXz/UKhyEFgS/o7cytDC0oy3KEnl0CIfyVYBuyQq1CvuZsWsCecVZh+SvaZjYQk88fahPeX/64u45lcwUDSm1uoXuxks4pZykLOD0Cs55BnimjGx+eflVNd2XfIZh1H7qQgu9Vht0wzAMv6BAce236GbQDcMwsBa6YRhG3aEW9yeWYAbdMAwDa6EbhmHUDaq3XmjYYgbdMIzDHgHEOkUNwzDqBmI+dMMwjDqAuVwMwzDqChbLxTAMo85go1wMwzDqCtZCNwzDqAOojXIxDMOoO9R+e24G/bCjbctQa3AQEdtyQq1CKYq3bA21CqXQzeGlD0BE05RQq3AAP8V0rwvDFmv7mqKGYRj+wU+LRItISxGZKiJLRGSxiAx15Uki8q2IrHQ/G7tyEZEXRSRDRBaKSNeaXoIZdMMwDAU8Pm5VUwTcoaodgZ7AEBHpCNwLfK+q7YDv3WOAc4B27jYYeLmml2EG3TCMwx5BEfVtqwpV3ayqc939nTjLYjYHBgJvutneBC5w9wcCb6nDDCBRRNJqch3mQzcMwwDw+Nb8rg4i0gY4EZgJpKrqZjcpE0h195sD671O2+DKNlNNzKAbhmGUuFx8I0VEZnsdj1LVUWUziUgD4GPgNlXdIXJg2VNVVRH/T2Uyg24YhkG1RrlkVbVItIhE4xjzd1X1E1e8RUTSVHWz61IpGb60EfAeftbClVUb86EbhmGAP0e5CPAGsFRVn/NKmgBc4+5fA3zuJb/aHe3SE8jzcs1UC2uhG4Zh+Dc416nA34DfRWS+K7sfeAr4UESuB9YCl7hpE4FzgQxgD3BdTSs2g24YhqGAn6b+q+rPOGtmlEe/cvIrMMQfdZtBNwzDoG7MFDWDbhiGARZt0TAMo06ggMcMuhFguqfv4MbHNxEZoXw9LokPX0qt+iQ/MOz23+jRcxPbt8dy0+BzADjyyFxuHTqb6BgPxcXCiOHdWLE8mT5913DxJctAYO+eKF4a3o3VqxoHVL8LrlzLgL9sRBXWZDTg+UeOo+MJeVw/bAVR0R4yljbihX91xFMc3IFctz+3jpP772R7VhQ39G0f1LrLEhGhvPjpQrK2xPDo4GM5oWce/7h3jXN/FjXg+fuPxlNckav30Bl63zx6nLqF7bmxDPlbHwCuGrSMnr03oypsz43l+X+fSE5WHADHn5jF4KGLiIxSdmyP4d5bTg2YbgdTN1YsCpthi24wmwFlZLeJSI3jGlRQz1gRWS0i80Vkroj08mf5/iQiQhny5EYevLItg9Lb02fgdlq12xeUur/9tg0P3n96Kdn1gxbw7juduOWmAbzzZieu/8cCADIzG3D3nX25+YazGffecfzzttnlFek3kpvs48+Xr2PolSdz88WnEBkB6edkcvtji/i/e4/n5otPYevmOPqfX6ORX4fE5A+SeODKtkGvtzwGXrOZdX/EAyCi3PHflTx12zHc9KcT2boplv5/CWwUx+8mtuLh23uWkn387lHcck0fbr02nd+mp3L5dcsBqN+gkJvvWMhj9/Tg5qv68J8HKx3mHRj8NGwxlISNQQfGAZeVkV3myqtERCKrUdddqtoFJzjOq9U4r7x6RUQCch/bn7iHTWtiyFwXS1FhBNM+T6TXgLxAVHUQi35vys6dsaVkqkK9eoUA1KtfSHa2YyyWLklh164YAJYtTSYlZW/A9YuMVGJiPUREeoiNKyZ/byRFhRFsXFcfgHkzkjm135aA61GWRTMbsDM39C++Kc3y6ZGey6QPnTe6Ro2LKCoUNq5xvrO50xPpPSA7oDosXpDMzh0xpWR790Tv34+LL95vH9PP3MAvP6SxbUs9APK2l/7tBRwFij2+bWFMOBn08cCfRCQG9sdAOAL4SUTOEpFf3Rb1R+6UWkRkjYj8n4jMBe51P3HT2nkfV8CPwNFu/ttFZJG73eZVzkFyEWkjIstF5C1gEaVnefmN5GaFbNt04A+RtTmalLTCQFTlE6++fCLXD1rAW+9O4B+DFzB2dOeD8gw4exWzZzULqB7Z2+L45K02vPn1T7z77Y/s3hXFj5NTiYxS2nV0Hni9+2+hSWp+QPUIZ254YDVv/Lf1/vAkeTlRREQq7TrtAqD32dmkpBWERLerBy9l7CeTST9rA++83gGAI1rtpkHDQv4zfDr/e+MH+p69vopS/I2CenzbwpiwMeiqmgP8hhNKEpzW+YdAMvAg0F9VuwKzgdu9Ts1W1a6q+m8gT0S6uPLrgDFVVHs+zuD/bm7+k3HCXQ4SkRMrkrvntgNGqupxqrq2ptddm/jT+RmMeqULV1/5Z0a90oXbbp9VKr3zCVs46+xVjH79hIDq0aBhIT3Tt3Ldeb256qzTiYsvps+5mTx17/EMumMFz789k727o8K9MRUwevTJYXt2NBmLG3hJhadua8/g+1fzwviF7N0dGYhYVD7x1qhjufavZzFtcgvOv3A1AJGRHo7ukMejd53MQ7f35LJrV3BEy13BVcxcLn7H2+1S4m7pCXQEpruzrq4BWnud84HX/uvAda775VLgvQrqedotazBwPdAb+FRVd6vqLuAT4LRK5ABr3VCXByEig0VktojMLqTmrcTszGiaHHGgFZWSVkjW5uhKzggs/c9cw/SfWwDw048tad/+wCt7m7bbuW3YLB575LSDXDX+psvJOWRuimdHbgzFRRFMn9KUY0/YzrKFidx9/UkM+9vJ/D63MZvW1g+oHuFKx6476dkvl7FT53DvCys4oWcedz2zgmXzG3LXFcdz20WdWTSrERtXx4dUz2mTm3NKutPPkb01nrkzm5C/L4odebEsnp/MkUfvCJ4yJaNcfNnCmHAz6J8D/dwVO+qp6hycGVffqmoXd+uoqtd7nbPba/9jnBb+ecAcVa3ISXiXW9aZqrqohrrurihBVUepandV7R5NzY3b8vn1aN62gNSW+URFe0gfuJ0ZkxNqXN6hkp0dx/GdtwHQpctWNm5qCECTJrt56OHpPP3fnmzc2DDgemzLjKPD8XnExhUDSpceOaxfXZ+Exs7DLyraw8XXrmHi+BYB1yUcGftsa/52Wneu7dONp247hgUzEnj6zmNISHLuT3SMh4sHbWTiuMC6xsrjiBYHWt09T8tkw1rnLWLGT83o2DnH6ROJLeKY43JZv6ZBRcUEhjrQQg99740XqrpLRKYCoznQGToDGCEiR6tqhojUB5qr6opyzt8nIpNwVvy4vmx6JfwEjBWRp3AeIH/BicUgFciDgqdYGPFAc558bxURkTD5/STWrogLSt333PcrnTtvpVFCPm+/O4G33+7Ei8+fxA03zyMywkNBYSQvvuCMRLjiqsU0bJTPkFvnAFBcLAy95ayA6bZ8UQI/f5fKi+/NoLhYWLWsEV9/3IKrh2TQ47QsIiKUrz5qwYJZSQHToSLuHbmWzr12kZBUxDuzl/D2s6lMGpccdD3K46JBm+jRJ5cIUb4a14wFMwLbOLj70Tkcf2IWjRILePPTybz7Rnu699pK81a7UA9szazHiKedfpj1axsyZ2ZTRrw5DY8Kk79oxdrVjQKq30GEubH2BdEwuwgRuQD4FDhWVZe5sr7A/8H+5u6DqjpBRNYA3VU1y+v8njgdrK1Vtbic8scCX6rq+DLy24G/u4evq+oLFcndDtsvVbVTVdfTSJL0ZDkofEPIiOjUIdQqHITYItGVElE//FxH4bRI9C8b3iEvP/OQBtQnRDfVU1Iu9invN5kj51QVPjdUhFULHUBVP6NMYBtVnQKcVE7eNuUU0RsYU54xd8+5tgL5c8BzvshVdQ1QpTE3DKMWEWaN25oQdgb9UBCRT4GjgL6h1sUwjFqGGfTwQlX/EmodDMOojYT/CBZfqFMG3TAMo0YoaJhPGvIFM+iGYRgQ9tP6fcEMumEYhiohmzrrR8ygG4ZhgHWKGoZh1BXUWuiGYRh1gfCf1u8LZtANwzDqyBJ04RacyzAMI+gooMXFPm1VISJnu+slZIjIvYHX/gBm0A3DMNQ/C1y4obtH4ER97QhcLiIdg3AFgBl0wzAMANSjPm1V0APIUNVVqloAvA8MDLjyLmbQDcMwwF9L0DUHvNfP2+DKgoJ1igaYneRmfafj/bFEXQqQVWWuqvj90BVx8Y8+/iPc9AF/6eS/ldj8d4/8o5O/9GlddZbK2UnupO90vK8xgeNEZLbX8ShVHXWoOvgDM+gBRlWb+KMcEZkdTjGYTZ+qCTedTJ+KUdWz/VTURkovGt/ClQUFc7kYhmH4j1lAOxFpKyIxOGsjTwhW5dZCNwzD8BOqWiQitwCTgEhgtKouDlb9ZtBrD2Hho/PC9KmacNPJ9AkCqjoRmBiKusNuTVHDMAyjZpgP3TAMo45gBj1EiEiqiLwnIqtEZI6I/Coih7yEnohME5Hu7r6KyDteaVEisk1EvqxmmWtExC/LvIvItSJyRDXyF4vIfBFZJCIfiUi9Gtb7gIgsFpGFbnkn+6qjiLxe2Ww/EengljlPRI6qpl7pInKKv6/BH4jIVBEZUEZ2m4i87Od6xorIavea5opIL3+WfzhhBj0EiIgAnwE/quqRqtoNpze8hZ+r2g10EpF49/hMgjCEyp3+XBHXAj4bdGCvqnZR1U5AAXBjDfTpBZwHdHW3/pSe/FGpjqr6D1VdUkn+C4Dxqnqiqv5RTfXSgSoNeg2uocaISEnf2jic36U3l7lyX8qp7HdQlrtUtQtwL/BqNc4rr14RkcPTtqmqbUHegH7ADxWkxQFjcKYAzQP6VCGPx5levBT4FJgJdHfTdgFPAhe5x28B9wBfusdJOA+WhcAMoLMrTwYmA4uB14G1QIqbdhXwGzAf548X6VXXs8ACoDfwMM4QrkU4nV8CXOTmW+6eHw90A34A5uCMDEgrcz92ee3fCIysRO+K5B/gzNibDozzKs9XHacB3XFGLYx18/8ODAPOBTJxHpRT3XI/c69nMTDYq76zgbnuPfoeaON17nzgNFc2xb2G74FW7rlTgDXu9/scsBJo4nX/9rl50oAOwG9e9bYBfnf3y73f7jW+AMwG7nDzTQcKcX4LaW4569z7dBbwq3s9HwEN3HLWAP/nyh8A5nrp0c772Es+lgO/0Thgj7t/u3uvFwG3eeU/SO7qthznN74YaB3q/3lIbEuoFTgcN+CfwPMVpN2BM9QJ94+5zv2RVyS/3UveGSiitEHvDIx3887HaRGWGPThwCPufl9gvrv/IvCwu/8nnGB0KcCxwBdAtJs2Erja3VfgEq/rSPLafxs4392f5qVfNPAL0MQ9vrTkWrzO3eV+RgGfAzdVondF8ieBPThGcCRwhq86eh/jGLlvveSJ7uejwJ1lrx3nYbAI5wHZBKdF3bZMnrLnfgFc4+7/HfjM3X8HyANWuNcw2v3ufwEuBj72vn/ud11S1z3Ag5Xdb/caR5b9XoAvgafd+u4FnsH5LfwI1Pcqv+T3sga42+t6pgJdvL6HW8v5zY/lgEG/GOeh1Q3noVkfaIBjpE+sRN4G8AA9Q/3/DuVmwxbDABEZgdOqLcBpSQ4HUNVlIrIWOMZNL09+Oo4BRlUXishC77JdWRvgcg4eStUbuNDNN0VEkkWkkVvmX135VyKS6+bvh/OHmuV4jYgHtrppxThGpYQ+InI3UA+n5bwYx1h50x7oBHzrlhcJbC6TJ15E5rv7PwFv4Pzhy9O7ouspAP6LY7T6AB+4YU13+qCjN6uAI0VkOPAVTsu1PP7p1R/SEqdl2gTHxbba1S+ngnN74d57nIfMf939ImAojsHsg/NgOxNIwHm7yMX5PZTcvw9xDPZT7uelVH2/P3A/9+cDGuPc199wXD3XAz1xIglOd8uJwWmtly0HnDe860TkdleHHhVc99Mi8iCwza2jH/Cpqu4GEJFPcN5gpAL5BGCtqs6ooPzDAjPooWExruEBUNUhbqfjbByD7m8m4LSs0nFaizVFgDdV9b5y0vapajGAiMThtCK7q+p6EXkU5w2hvPIWq2plnWB71fGtHjjJMSLVZZeqTgOmicjvwA04by9V6bgfVc0VkROAATjun0twWtHeuqXj+Ld7qeoeEZlWVbmHcA2v4rT644F2Jfff5QPgI9fgqaquFJHjqfx+7y65jJJ8ItIA50F2L/C+qs4RkfNx3lQur6IccB7yj+C4g+aoanYF59ylquNLDkSkXwX5KmN31VnqNodnx0HomYIT4OcmL1nJ6I2fgCsBROQYoBWOb7Ai+Y/AFa68E46RKsto4F+qWjY0l3eZ6UCWqu4oU+Y5OK00cHy6F4lIUzctSUTKC4xUYsCyXINwkVfaTqChu78caFIyqkFEokXkuHLKK0tFelckT8ZxE5TQxa3bFx334z50I1T1YxwXRtdydEsAcl1j3gGnNQuOT/90EWnrlpVUQV2/cKAj8kr3mgAaAc3KXMMsnNb0dFUt9r5/6nTOFgMPcaDF7Ov93p9PVXfhvNm8x4HO0BnAqSJytFtOffc3eRCqug/HV/8yTh+Qr/wEXCAi9USkPvAXV1aR3ADzoYdqw+lkeh9YjfM6OxXnlfRQOkU/oUynaDn1plO9TtHXKN0peimOf3YhTsdaz/LqAp4A/sDpWBsDPOrKL6R0h2MXnAfIAre+QWXKKe8aqtsp+iqOq2KJm/YJjoH3VcdpOD70E3A6++a72zlu/kdx/eBALPC1+3185p6b7qad4353C3B98ThukoUc6BRtTfmdol+4OnlfQzOcfpJZ5d0/4E6cvo02XrJy7zcH9xt451vnltPBK72vW+9Cd/uzK1+D+1vxytsT580zsoL/wlhcH3oZeXU7RReF+n8d6s1mihpGLUWc+QbPq+ppodalMkTkTiBBVR8KtS51HfOhG0YtxO3UvQnXxRSuiMinwFE4LXojwFgL3TAMo45gnaKGYRh1BDPohmEYdQQz6IZhGHUEM+hGSPFXNEW3rLEicpG7X1WERJ+jHJY5r9zIkxXJy+Sp1tLKIvKoO0LEMHzCDLoRaiqNpugV+a9aaNUREtPxIcqhYdQmzKAb4cRPwNFu6/knEZkALBGRSBF5WkRmiRML/AbYHyb1JRFZLiLfAU1LCpLSceHPFifO9gIR+d6NbXMjMMx9OzhNRJqIyMduHbNE5FT33GQRmSxOHPLXcabFV4qIfCZOjPvFIjK4TNrzrvx7EWniyo4SkW/cc35yZ5gaRrWxcehGWOC2xM8BvnFFXYFOqrraNYp5qnqSiMTiBIWajBNlrz1OoKhUnFmUo8uU2wRntuvpbllJqpojIq/gzEJ9xs33Hs4knZ9FpBXOdPVjceKQ/Kyqj4nIn3ACR1XF39064nECmX2sTgyT+sBsVR0mIg+7Zd+CE1zrRnXirZyMEwfHxm0b1cYMuhFqyoumeApOPO/VrvwsoHOJfxwnXko7nKiQ49QJSrVJRKaUU35PfIty2B/oKAcCfzVyY7xUFHmyMsqLtpiNE961JK7KO8Anbh2n4ATSKjk/1oc6DOMgzKAboaaiaIrekfMEJ472pDL5zvWjHhE4cWn2laOLz0j1oi2qW+/2svfAMGqC+dCN2sAk4CYRiQYn2qQbae9H4FLXx56GEye8LL5GOZwM3FpyICJd3N2KIk9WREXRFsH5v5W8ZVyB48rZAawWkYvdOkScEL2GUW3MoBu1gddx/ONzRWQRTvTEKJwl91a6aW9RepEFAFR1GzAYx72xgAMujy+Av5R0iuKsItXd7XRdwoHRNv/CeSAsxnG9rKtC12+AKBFZirO4hPeCC7uBHu419AUec+VXAte7+i0GBvpwTwzjICyWi2EYRh3BWuiGYRh1BDPohmEYdQQz6IZhGHUEM+iGYRh1BDPohmEYdQQz6IZhGHUEM+iGYRh1BDPohmEYdYT/B2GYCUpAyxQDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(model,X_test,y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1081a13",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}