co2Emission.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression, Lasso, Ridge\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import mean_squared_error ,r2_score, mean_absolute_error\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = pd.read_csv('CO2 Emissions_India.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2 Emissions(g/km)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>33</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>AV7</td>\n",
       "      <td>Z</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>48</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX 4WD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>27</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Make       Model Vehicle Class  Engine Size(L)  Cylinders Transmission  \\\n",
       "0  ACURA         ILX       COMPACT             2.0          4          AS5   \n",
       "1  ACURA         ILX       COMPACT             2.4          4           M6   \n",
       "2  ACURA  ILX HYBRID       COMPACT             1.5          4          AV7   \n",
       "3  ACURA     MDX 4WD   SUV - SMALL             3.5          6          AS6   \n",
       "4  ACURA     RDX AWD   SUV - SMALL             3.5          6          AS6   \n",
       "\n",
       "  Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "0         Z                               9.9   \n",
       "1         Z                              11.2   \n",
       "2         Z                               6.0   \n",
       "3         Z                              12.7   \n",
       "4         Z                              12.1   \n",
       "\n",
       "   Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "0                              6.7                               8.5   \n",
       "1                              7.7                               9.6   \n",
       "2                              5.8                               5.9   \n",
       "3                              9.1                              11.1   \n",
       "4                              8.7                              10.6   \n",
       "\n",
       "   Fuel Consumption Comb (mpg)  CO2 Emissions(g/km)  \n",
       "0                           33                  196  \n",
       "1                           29                  221  \n",
       "2                           48                  136  \n",
       "3                           25                  255  \n",
       "4                           27                  244  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.rename(columns={'CO2 Emissions(g/km)':'CO2_emission'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2_emission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>AS5</td>\n",
       "      <td>Z</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>33</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>M6</td>\n",
       "      <td>Z</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>AV7</td>\n",
       "      <td>Z</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>48</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX 4WD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>AS6</td>\n",
       "      <td>Z</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>27</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Make       Model Vehicle Class  Engine Size(L)  Cylinders Transmission  \\\n",
       "0  ACURA         ILX       COMPACT             2.0          4          AS5   \n",
       "1  ACURA         ILX       COMPACT             2.4          4           M6   \n",
       "2  ACURA  ILX HYBRID       COMPACT             1.5          4          AV7   \n",
       "3  ACURA     MDX 4WD   SUV - SMALL             3.5          6          AS6   \n",
       "4  ACURA     RDX AWD   SUV - SMALL             3.5          6          AS6   \n",
       "\n",
       "  Fuel Type  Fuel Consumption City (L/100 km)  \\\n",
       "0         Z                               9.9   \n",
       "1         Z                              11.2   \n",
       "2         Z                               6.0   \n",
       "3         Z                              12.7   \n",
       "4         Z                              12.1   \n",
       "\n",
       "   Fuel Consumption Hwy (L/100 km)  Fuel Consumption Comb (L/100 km)  \\\n",
       "0                              6.7                               8.5   \n",
       "1                              7.7                               9.6   \n",
       "2                              5.8                               5.9   \n",
       "3                              9.1                              11.1   \n",
       "4                              8.7                              10.6   \n",
       "\n",
       "   Fuel Consumption Comb (mpg)  CO2_emission  \n",
       "0                           33           196  \n",
       "1                           29           221  \n",
       "2                           48           136  \n",
       "3                           25           255  \n",
       "4                           27           244  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7385 entries, 0 to 7384\n",
      "Data columns (total 12 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   Make                              7385 non-null   object \n",
      " 1   Model                             7385 non-null   object \n",
      " 2   Vehicle Class                     7385 non-null   object \n",
      " 3   Engine Size(L)                    7385 non-null   float64\n",
      " 4   Cylinders                         7385 non-null   int64  \n",
      " 5   Transmission                      7385 non-null   object \n",
      " 6   Fuel Type                         7385 non-null   object \n",
      " 7   Fuel Consumption City (L/100 km)  7385 non-null   float64\n",
      " 8   Fuel Consumption Hwy (L/100 km)   7385 non-null   float64\n",
      " 9   Fuel Consumption Comb (L/100 km)  7385 non-null   float64\n",
      " 10  Fuel Consumption Comb (mpg)       7385 non-null   int64  \n",
      " 11  CO2_emission                      7385 non-null   int64  \n",
      "dtypes: float64(4), int64(3), object(5)\n",
      "memory usage: 692.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# Checking for the data types and null values\n",
    "\n",
    "ds.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                                0\n",
       "Model                               0\n",
       "Vehicle Class                       0\n",
       "Engine Size(L)                      0\n",
       "Cylinders                           0\n",
       "Transmission                        0\n",
       "Fuel Type                           0\n",
       "Fuel Consumption City (L/100 km)    0\n",
       "Fuel Consumption Hwy (L/100 km)     0\n",
       "Fuel Consumption Comb (L/100 km)    0\n",
       "Fuel Consumption Comb (mpg)         0\n",
       "CO2_emission                        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for total null values if any\n",
    "\n",
    "ds.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <td>7385.0</td>\n",
       "      <td>3.160068</td>\n",
       "      <td>1.354170</td>\n",
       "      <td>0.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cylinders</th>\n",
       "      <td>7385.0</td>\n",
       "      <td>5.615030</td>\n",
       "      <td>1.828307</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <td>7385.0</td>\n",
       "      <td>12.556534</td>\n",
       "      <td>3.500274</td>\n",
       "      <td>4.2</td>\n",
       "      <td>10.1</td>\n",
       "      <td>12.1</td>\n",
       "      <td>14.6</td>\n",
       "      <td>30.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <td>7385.0</td>\n",
       "      <td>9.041706</td>\n",
       "      <td>2.224456</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.2</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <td>7385.0</td>\n",
       "      <td>10.975071</td>\n",
       "      <td>2.892506</td>\n",
       "      <td>4.1</td>\n",
       "      <td>8.9</td>\n",
       "      <td>10.6</td>\n",
       "      <td>12.6</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <td>7385.0</td>\n",
       "      <td>27.481652</td>\n",
       "      <td>7.231879</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO2_emission</th>\n",
       "      <td>7385.0</td>\n",
       "      <td>250.584699</td>\n",
       "      <td>58.512679</td>\n",
       "      <td>96.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>522.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   count        mean        std   min    25%  \\\n",
       "Engine Size(L)                    7385.0    3.160068   1.354170   0.9    2.0   \n",
       "Cylinders                         7385.0    5.615030   1.828307   3.0    4.0   \n",
       "Fuel Consumption City (L/100 km)  7385.0   12.556534   3.500274   4.2   10.1   \n",
       "Fuel Consumption Hwy (L/100 km)   7385.0    9.041706   2.224456   4.0    7.5   \n",
       "Fuel Consumption Comb (L/100 km)  7385.0   10.975071   2.892506   4.1    8.9   \n",
       "Fuel Consumption Comb (mpg)       7385.0   27.481652   7.231879  11.0   22.0   \n",
       "CO2_emission                      7385.0  250.584699  58.512679  96.0  208.0   \n",
       "\n",
       "                                    50%    75%    max  \n",
       "Engine Size(L)                      3.0    3.7    8.4  \n",
       "Cylinders                           6.0    6.0   16.0  \n",
       "Fuel Consumption City (L/100 km)   12.1   14.6   30.6  \n",
       "Fuel Consumption Hwy (L/100 km)     8.7   10.2   20.6  \n",
       "Fuel Consumption Comb (L/100 km)   10.6   12.6   26.1  \n",
       "Fuel Consumption Comb (mpg)        27.0   32.0   69.0  \n",
       "CO2_emission                      246.0  288.0  522.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insight of different statistical distribution of features and label\n",
    "\n",
    "ds.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ACURA' 'ALFA ROMEO' 'ASTON MARTIN' 'AUDI' 'BENTLEY' 'BMW' 'BUICK'\n",
      " 'CADILLAC' 'CHEVROLET' 'CHRYSLER' 'DODGE' 'FIAT' 'FORD' 'GMC' 'HONDA'\n",
      " 'HYUNDAI' 'INFINITI' 'JAGUAR' 'JEEP' 'KIA' 'LAMBORGHINI' 'LAND ROVER'\n",
      " 'LEXUS' 'LINCOLN' 'MASERATI' 'MAZDA' 'MERCEDES-BENZ' 'MINI' 'MITSUBISHI'\n",
      " 'NISSAN' 'PORSCHE' 'RAM' 'ROLLS-ROYCE' 'SCION' 'SMART' 'SRT' 'SUBARU'\n",
      " 'TOYOTA' 'VOLKSWAGEN' 'VOLVO' 'GENESIS' 'BUGATTI']\n"
     ]
    }
   ],
   "source": [
    "# checking for unique variables\n",
    "print(ds['Make'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Automatic of Selective type' 'Manual' 'CVT' 'Automated Manual'\n",
      " 'Automatic']\n"
     ]
    }
   ],
   "source": [
    "# Putting different transmission sub-catagories into their respective catagories\n",
    "ds['Transmission'] = np.where(ds['Transmission'].isin(['A4','A5','A6','A7','A8','A9','A10']),'Automatic',ds['Transmission'])\n",
    "ds['Transmission'] = np.where(ds['Transmission'].isin(['AS4','AS5','AS6','AS7','AS8','AS9','AS10']),'Automatic of Selective type',ds['Transmission'])\n",
    "ds['Transmission'] = np.where(ds['Transmission'].isin(['AM5','AM6','AM7','AM8','AM9']),'Automated Manual',ds['Transmission'])\n",
    "ds['Transmission'] = np.where(ds['Transmission'].isin(['AV','AV6','AV7','AV8','AV10']),'CVT',ds['Transmission'])\n",
    "ds['Transmission'] = np.where(ds['Transmission'].isin(['M5','M6','M7']),'Manual',ds['Transmission'])\n",
    "\n",
    "print(ds['Transmission'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X    3637\n",
      "Z    3202\n",
      "E     370\n",
      "D     175\n",
      "N       1\n",
      "Name: Fuel Type, dtype: int64\n",
      "['Premium gasoline' 'Diesel' 'Regular gasoline' 'Ethanol' 'Natural gas']\n"
     ]
    }
   ],
   "source": [
    "# Renaming fuel types for better understanding\n",
    "\n",
    "print(ds['Fuel Type'].value_counts())\n",
    "\n",
    "ds['Fuel Type']= np.where(ds['Fuel Type']=='X','Regular gasoline',ds['Fuel Type'])\n",
    "ds['Fuel Type']= np.where(ds['Fuel Type']=='Z','Premium gasoline',ds['Fuel Type'])\n",
    "ds['Fuel Type']= np.where(ds['Fuel Type']=='E','Ethanol',ds['Fuel Type'])\n",
    "ds['Fuel Type']= np.where(ds['Fuel Type']=='D','Diesel',ds['Fuel Type'])\n",
    "ds['Fuel Type']= np.where(ds['Fuel Type']=='N','Natural gas',ds['Fuel Type'])\n",
    "\n",
    "print(ds['Fuel Type'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['COMPACT' 'SUV - SMALL' 'MID-SIZE' 'TWO-SEATER' 'MINICOMPACT'\n",
      " 'SUBCOMPACT' 'FULL-SIZE' 'STATION WAGON - SMALL' 'SUV - STANDARD'\n",
      " 'VAN - CARGO' 'VAN - PASSENGER' 'PICKUP TRUCK - STANDARD' 'MINIVAN'\n",
      " 'SPECIAL PURPOSE VEHICLE' 'STATION WAGON - MID-SIZE'\n",
      " 'PICKUP TRUCK - SMALL']\n"
     ]
    }
   ],
   "source": [
    "print(ds['Vehicle Class'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7385, 12)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2_emission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Automatic of Selective type</td>\n",
       "      <td>Premium gasoline</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>33</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Premium gasoline</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>CVT</td>\n",
       "      <td>Premium gasoline</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>48</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX 4WD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>Automatic of Selective type</td>\n",
       "      <td>Premium gasoline</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>Automatic of Selective type</td>\n",
       "      <td>Premium gasoline</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>27</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Make       Model Vehicle Class  Engine Size(L)  Cylinders  \\\n",
       "0  ACURA         ILX       COMPACT             2.0          4   \n",
       "1  ACURA         ILX       COMPACT             2.4          4   \n",
       "2  ACURA  ILX HYBRID       COMPACT             1.5          4   \n",
       "3  ACURA     MDX 4WD   SUV - SMALL             3.5          6   \n",
       "4  ACURA     RDX AWD   SUV - SMALL             3.5          6   \n",
       "\n",
       "                  Transmission         Fuel Type  \\\n",
       "0  Automatic of Selective type  Premium gasoline   \n",
       "1                       Manual  Premium gasoline   \n",
       "2                          CVT  Premium gasoline   \n",
       "3  Automatic of Selective type  Premium gasoline   \n",
       "4  Automatic of Selective type  Premium gasoline   \n",
       "\n",
       "   Fuel Consumption City (L/100 km)  Fuel Consumption Hwy (L/100 km)  \\\n",
       "0                               9.9                              6.7   \n",
       "1                              11.2                              7.7   \n",
       "2                               6.0                              5.8   \n",
       "3                              12.7                              9.1   \n",
       "4                              12.1                              8.7   \n",
       "\n",
       "   Fuel Consumption Comb (L/100 km)  Fuel Consumption Comb (mpg)  CO2_emission  \n",
       "0                               8.5                           33           196  \n",
       "1                               9.6                           29           221  \n",
       "2                               5.9                           48           136  \n",
       "3                              11.1                           25           255  \n",
       "4                              10.6                           27           244  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fuel Consumption Comb (mpg)        -0.907426\n",
       "Cylinders                           0.832644\n",
       "Engine Size(L)                      0.851145\n",
       "Fuel Consumption Hwy (L/100 km)     0.883536\n",
       "Fuel Consumption Comb (L/100 km)    0.918052\n",
       "Fuel Consumption City (L/100 km)    0.919592\n",
       "CO2_emission                        1.000000\n",
       "Name: CO2_emission, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.corr()['CO2_emission'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2_emission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.927653</td>\n",
       "      <td>0.831379</td>\n",
       "      <td>0.761526</td>\n",
       "      <td>0.817060</td>\n",
       "      <td>-0.757854</td>\n",
       "      <td>0.851145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cylinders</th>\n",
       "      <td>0.927653</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.800702</td>\n",
       "      <td>0.715252</td>\n",
       "      <td>0.780534</td>\n",
       "      <td>-0.719321</td>\n",
       "      <td>0.832644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <td>0.831379</td>\n",
       "      <td>0.800702</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.948180</td>\n",
       "      <td>0.993810</td>\n",
       "      <td>-0.927059</td>\n",
       "      <td>0.919592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <td>0.761526</td>\n",
       "      <td>0.715252</td>\n",
       "      <td>0.948180</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.977299</td>\n",
       "      <td>-0.890638</td>\n",
       "      <td>0.883536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <td>0.817060</td>\n",
       "      <td>0.780534</td>\n",
       "      <td>0.993810</td>\n",
       "      <td>0.977299</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.925576</td>\n",
       "      <td>0.918052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <td>-0.757854</td>\n",
       "      <td>-0.719321</td>\n",
       "      <td>-0.927059</td>\n",
       "      <td>-0.890638</td>\n",
       "      <td>-0.925576</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.907426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO2_emission</th>\n",
       "      <td>0.851145</td>\n",
       "      <td>0.832644</td>\n",
       "      <td>0.919592</td>\n",
       "      <td>0.883536</td>\n",
       "      <td>0.918052</td>\n",
       "      <td>-0.907426</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Engine Size(L)  Cylinders  \\\n",
       "Engine Size(L)                          1.000000   0.927653   \n",
       "Cylinders                               0.927653   1.000000   \n",
       "Fuel Consumption City (L/100 km)        0.831379   0.800702   \n",
       "Fuel Consumption Hwy (L/100 km)         0.761526   0.715252   \n",
       "Fuel Consumption Comb (L/100 km)        0.817060   0.780534   \n",
       "Fuel Consumption Comb (mpg)            -0.757854  -0.719321   \n",
       "CO2_emission                            0.851145   0.832644   \n",
       "\n",
       "                                  Fuel Consumption City (L/100 km)  \\\n",
       "Engine Size(L)                                            0.831379   \n",
       "Cylinders                                                 0.800702   \n",
       "Fuel Consumption City (L/100 km)                          1.000000   \n",
       "Fuel Consumption Hwy (L/100 km)                           0.948180   \n",
       "Fuel Consumption Comb (L/100 km)                          0.993810   \n",
       "Fuel Consumption Comb (mpg)                              -0.927059   \n",
       "CO2_emission                                              0.919592   \n",
       "\n",
       "                                  Fuel Consumption Hwy (L/100 km)  \\\n",
       "Engine Size(L)                                           0.761526   \n",
       "Cylinders                                                0.715252   \n",
       "Fuel Consumption City (L/100 km)                         0.948180   \n",
       "Fuel Consumption Hwy (L/100 km)                          1.000000   \n",
       "Fuel Consumption Comb (L/100 km)                         0.977299   \n",
       "Fuel Consumption Comb (mpg)                             -0.890638   \n",
       "CO2_emission                                             0.883536   \n",
       "\n",
       "                                  Fuel Consumption Comb (L/100 km)  \\\n",
       "Engine Size(L)                                            0.817060   \n",
       "Cylinders                                                 0.780534   \n",
       "Fuel Consumption City (L/100 km)                          0.993810   \n",
       "Fuel Consumption Hwy (L/100 km)                           0.977299   \n",
       "Fuel Consumption Comb (L/100 km)                          1.000000   \n",
       "Fuel Consumption Comb (mpg)                              -0.925576   \n",
       "CO2_emission                                              0.918052   \n",
       "\n",
       "                                  Fuel Consumption Comb (mpg)  CO2_emission  \n",
       "Engine Size(L)                                      -0.757854      0.851145  \n",
       "Cylinders                                           -0.719321      0.832644  \n",
       "Fuel Consumption City (L/100 km)                    -0.927059      0.919592  \n",
       "Fuel Consumption Hwy (L/100 km)                     -0.890638      0.883536  \n",
       "Fuel Consumption Comb (L/100 km)                    -0.925576      0.918052  \n",
       "Fuel Consumption Comb (mpg)                          1.000000     -0.907426  \n",
       "CO2_emission                                        -0.907426      1.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation between features and label\n",
    "\n",
    "ds.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "VISUALISATIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# VISUALISATIONS\n",
    "\n",
    "corr = ds.corr()\n",
    "\n",
    "plt.rcParams['figure.figsize']=(10,8)\n",
    "sns.heatmap(corr, cmap='coolwarm', linewidth=0.5, fmt='0.2f', annot=True)\n",
    "\n",
    "plt.title('Correlation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'Engine Size(L)'}>,\n",
       "        <AxesSubplot:title={'center':'Cylinders'}>,\n",
       "        <AxesSubplot:title={'center':'Fuel Consumption City (L/100 km)'}>],\n",
       "       [<AxesSubplot:title={'center':'Fuel Consumption Hwy (L/100 km)'}>,\n",
       "        <AxesSubplot:title={'center':'Fuel Consumption Comb (L/100 km)'}>,\n",
       "        <AxesSubplot:title={'center':'Fuel Consumption Comb (mpg)'}>],\n",
       "       [<AxesSubplot:title={'center':'CO2_emission'}>, <AxesSubplot:>,\n",
       "        <AxesSubplot:>]], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# distribution of numerical features\n",
    "# we can seee that the numerical features are little ight skewed.\n",
    "\n",
    "ds.hist(figsize=(10,8),bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FREQUENCY DISTRIBUTION OF DIFFFRENT FEATURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Make\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "ds.groupby('Make')['Make'].count().sort_values(ascending=False).plot(kind='bar',color='red')\n",
    "\n",
    "plt.title('Frequency distribution of cars of different companies', fontsize=25)\n",
    "plt.xlabel('Company', fontsize=20)\n",
    "plt.ylabel('Frequency', fontsize=20)\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# MODEL\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "ds.groupby('Model')['Model'].count().sort_values(ascending=False)[:25].plot(kind='bar')\n",
    "\n",
    "plt.title('Distribution of models', fontsize=25)\n",
    "plt.xlabel('Models', fontsize=20)\n",
    "plt.ylabel('Frequency', fontsize=20)\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABZgAAAFgCAYAAAA2IxyjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAACekklEQVR4nOzdeZy9c/nH8dfbvmbJlqWoqNSvVaQkkkiFZM2+plJpR8lS1iKUJSE7yRLJkiVJSFQiS0jZtyxRdu/fH9fn+B5f893mOzNnlvfz8ZjHzNznPmc+c+bMfe77+lyf65JtIiIiIiIiIiIiIiKm1DS9HkBEREREREREREREjEwJMEdEREREREREREREvyTAHBERERERERERERH9kgBzRERERERERERERPRLAswRERERERERERER0S8JMEdEREREREREREREvyTAHBERETECSFpUktvHokN9/wk85mbt8f45EI832AbjORiJJB3dnoOj+7jtknbbrkM/skmTtGsb3yV93DbB32s46XoNrtDrsUREREQMhASYIyIiIiaDpJ+0oNC/Jc04Bfe7td3vrMEcX8Rw1CYhdh0LwVRJ27ff9e29HktERETEUJqu1wOIiIiIGCGOBLYC5gbWAE6Z1B0kfQB4Xdf9e+lZ4OauryP6cgf1OnlogB5vM+AD7etLBuDxHqLGd8cAPNZA2x54DfBP4C8T2a/zf/i/wR1ORERExNBIgDkiIiJiMti+UtINwJLA5kxGgLntB3A/8KvBGtvksH038MZejiGGP9ub9HoME2P7R8CPej2OqWE7/4cRERExqqRERkRERMTk62Qhf1jSwhPbUdLswNrt22NtPzeoI4uIiIiIiOiBBJgjIiIiJt9xVHmJaYBNJ7HvesCs7eujum+QNIekb0r6g6RHJD0t6U5JJ0l6z+QMRNL8kg6UdLukpyTdL+lkSX1mR05OgztJ00haV9IvJN3dxvWgpGsk7S3pLZMztvEecyZJX5D0W0kPSXpG0n3tZ6w6pY/Xx+MvI+mnrdb1fyX9R9INko6S9OEpfKx3Svq2pEsl/as9r49KulLSNyTNNpH7zizpq5KuaH/TZ9tzd4OkYyR9so/7TCdpm9ZY76F2n39LulnSzyRt0Z/npD32hpJ+L+lxSY+119o2kjSJ+02wyd+UjLfVXjbjymPs0vX6e9nrsLvxnaT5JO0v6e+S/tcep7PfBJv8jTdWSdpW0lXt9/+PpMskbTiR+/yzPfZmE9nnZY0EO2OiymMA/HT833W8x5hok7/2P7O9pMvba+mp9no8VhOp79w9fkkzSPqapGvb/8Vjki4eiP+5iIiIiPGlREZERETEZLL9oKpZ3yep2rJ7TGT3TnmM39u+qbNR0jLAmcD8bdPzVC3WhYH1gfUkfdP2XhN57DdTQev5GFfHdT4qqP0RScvbvnZKfjdJ8wCnAct3bX4MmAN4Z/t4I7DmFDzm4lRpkMXbJgP/oX73NYA1JB1q+7NTMtb22NMC+wNf6Nr8X2Ba4E3tYy1gzil42Gu6vn6hjXVOYJn2sYmkFW0/MN5YZgd+B7ytbTL13M0JzNPG8gHq+e0e/znAyl0P9Rg1KTE3sASwLuNNTkxKCyAfybjXn4FHgaWApYEVgaen5DH7Od4nqdIwcwPTU3+bJ8Z72Of7+FGvB06mXiNP0f964SdR/w8vMO5v8T7gfZJWAra07QnffYo8Qf2u81KTT/+hfv8pJmkh4DygM5nzLPU//mpgY2BDSdvb/uFEHmY24FLqNfss9fd+BfW3X0HSVran6HUVERERMTHJYI6IiIiYMp0yGa+XtHxfO0h6A/De9u1RXdsXpYJH8wOnAu8CZrL9irbtO1TQbU9Ja05kDMcBtwDvtj0rFVBaGbiXCiRNLPjU13inA35BBZefBr4BzGd7TmBmYDHg08ANU/CYcwK/poLLF7fHnrk95pzAl6nA3GckfXFKxtvsybjg8lHAG2zP1p6P+alA+HlT+JgXAltQmagz2p4LmIUKVN9M1d8+rI/7fZEKLj9MTT7M3O47I7AQsAn1XHTbgPqbPUU1j5y96/mev/3M05hyn2dccPlH1N9xbirQuysVdF2jH487ReO1/TPbCwCXt03ft73AeB939vFzfkAFxFcCZm3/G2+YwrGuSQW7dwbmar///Iyr3bw59TwNCNvfb79r5/f54vi/6+Q8Tgvin0YFlx8DNgJma8/z64Czqeu3gyR9ZCIPtTs1YbUm9RzOTk0OXQkIOFDSHFP4a0ZERERMUALMEREREVPmfOCu9vWEShh0tj/BS5sBfo8Krh5nex3bf+rUZrb9gO1vA19v++46kTHcD6xs++p23+dsX0gFgQHer0nUiB7PplR2p4G1bO9r+8H22M/b/qftw23vNAWP+U1gUSq4vIrt39l+uj3mY7Z/QAVeAb7VgtyTRdISwFfbt/va3tL23zu3t+fyTNvrT8F4sb2y7Z/avqPr7/Kk7TOogOfTwJqSXj3eXTuTCd+3fXrX7/mC7XtsH2d7mwnc51jbR9p+ot3Hbfxn2F6bKSBpJmCX9u1xtj9v+6H2uI/Z3g3YhynL6h608U7AC8CHbF9s+4X2M/4+ifuMbw7gu7a/a/s/7TEetP154Pi2zy7t+RpO1qayjgHWs32C7WcAbP8D+ATwh3b7vhN5nFmo5/BM28+2+98MrE5NEMwGfGwQxh8RERFjVALMEREREVOgBb2Oad+urfHq8rYsxI3btz/rBOIkzU1leQLsPZEfcWz7/DZJ809gn/1s97UE/1zgmfb1/03kZ4yvExA/x/Y5U3C/PrUyDZ3H3G8iDQ5/QZUTmIfK5p5cm1Lnsf9mXEB1UNm+G7iWygB973g3P9o+v2oKHrJzn8nKbp1MH6YylaGyWPuyNxVknFKPts8DOd6+HGf7rknvNlFPAt+fwG2d52VuXlruYzhYr32+wvb549/Y/o92a9++RdKE/sdP7S7L03X/B4Er2rdvndrBRkRERHQkwBwREREx5Y6isn1nZVxQqOMjjAs0dtc5XZZx514XqxrdvewD+FvXfV5D3/7Q18YWgHqwfTt3X/uMr2UOv7t9+8vJuc9kWLLr5x89kd/1XiqbEib8u/alE+C9wHZ/gqV9UjU5/JSksyTdIenJ8Rq1Ld12HT87/Oz2eTtVo8Y1W03riTmHeg2tLulcSRtIWnAqf4Wl2uc7bd/a1w62H+OltaYn12CMty+/H4DHuLqTuTw+27cwbgXCUn3t00Od8Vw4kX1+w7ja1RMaf5/Hh+ae9nmyjg8RERERkyMB5oiIiIgp1JarX9K+Hb9MRuf7m2xf3rW9Oxg3/yQ+OmaZwBAen8jwOtnC009kn26v7Nr3X5N5n0np/l3nZeK/a+d8dEK/a186WbQDNV4kzUIF9k4APg4s0sb2MFWS5H7GNZybtfu+tk8EDqQCsOsDZwAPSrpF0sGSXpadbfsyqtb1M8CqwInA3ZLulPRTSSv249eYr32+exL7TXGG8CCNty8PTHqXSZrU79+5fb6J7jX0Jvn3axMqD423//gG8vgQERERMUkJMEdERET0T6fZ33tbUz9a1urHxru9Y9r2+UnbmsyPSwb/13gJD9DjTNv19QKT+bse3cPxQtWMXpEqr/AlKqN6Jtuv7GrU1skM1csGYm9PNaPbiSpV8ijweuCzwNWSDujjPt+jGih+iSoX8gCVHb0ZleX+c0n9CQQO5PMy7kEHb7zdnp/0LpM0KL//EJrc8Y/03zMiIiJGiQSYIyIiIvrnNMbVpd28fd6Yygx8DjhuvP3va59nlvT6QR/d5Ps34zJzFx2gx7yv6+spqQU9ue5tnxcdwMfsNATc3fYBrdHf+AG8idYftn2r7b1sr0Zlhi9LBWIBvihp9T7uc0/7eZ+wPT9VG/eIdvPawGem4HfoZP9OqsHjQlPwmC8xwOMdLJP7+4+fLd3J7p1Y8785+jWiydMZzyIT2qE1Jnxl+/bBCe0XERERMZQSYI6IiIjoh7ZU/cT27SatuV8n0Hy27fvHu8vljMs4XJ9hotVtvqp9+/EBetjrqeZ9MDi/a6f0yMot4DYQOkG9P/d1o6RFqYzkyWL7BdtXUkHXO9rmSTaVs32d7a0ZV4t4ShrRXd0+LyLpdX3tIOkVTFlDxYmajPG+0PnRA/UzJ8NSkmbv64Y2udMJQF893s2PtM99BnglTcPE6zZP7e/aGc9KE9lnBWC69vUf+/lzIiIiIgZUAswRERER/dcpg/EqYGfGZeuOXx4D2w8AZ7ZvvyZpiYk9sKShbMLVGe9qklab2gdrQetOg8NNJS03sf378bseTZVSeCWw2xQPsG+Ptc9vm8Dte0/ojpJmnNBttp+n6hZDV/mHid2neXL8+0yGCxgXJN15Avt8HZh5Ch4TmKrxdiYa5pzSnzkVZga+MoHbvtU+P0w9X92ubZ8/IamvIPGmTDw7emp/15Pb52UlfXj8G1tDzm+3b6+3fX0/f05ERETEgEqAOSIiIqKfbP8J+Ev7thPQu5eqwduXr1AlKV4BXCZpC0kvLrmXNI+ktSSdDpw0OKPu03HAZVTm5WmSvtbqSSNpWkmLSvqSpH2m4DG/A9xGZVueJ+nLkubt3ChpDkmrSjoG+N2UDNb2rcD32rdfl3SEpMW7HnteSetJOmMKHva89vlb7W8wXXusxSSdCKzLuODt+P4g6SBJK0h6sQGgpAUl/ZBxmc/ndN3nF5KOkvQRSXN23WduSd9iXBZr930myvaT1PMOFdg/QNIr2+O+QtLOVI3oRyf3MQdgvJ0g6GqS+l2aYwo9BuwsacdOJnP73zqQChIDfKetQujW+Z97E3D4eM/dl4DDqMD0hHR+17UlzdWPcZ/GuDrfp0j6VKemtaTF2u3Lttu/3o/Hj4iIiBgUCTBHRERETJ1O9m/nvOqYlrX6Mrb/QZUQ+Ccwb7vvI5IelvQ4VVP1NOATDOF5Wss4/gQV6J0J2Bd4QNIjVGbq7cD+VBO7yX3Mh6nf9VpgVmC/zmNKeowKcp4LbALM0I9hfws4uH29JfB3SY9L+i9Vy/ZkqmnflDze/cDs1N/gSUmPAv8ANqCaAP51AvedE/g88Bvg8fY7PgHcDWzX9vmB7V933WdmqqTKOdRr4LH2vPybChILOJVx9Y0n14GMq//9Reo5f5gKjO4O/IxxmfRTor/jPQZ4igqy3yHpPkn/bB+TqpXcX78Afg7s2cb6MPWa+EK7/VjgoPHvZPuidhvAVsBD7X/gEer1fzjwy4n83MOpMjjvBR6UdE/nd52cQbfjxieBv1G1nk8Anmhj+AewOlWG44u2JzSJFRERETHkEmCOiIiImDonUAG0jqMmtCOA7T8DS1KBxwuBh6ig5jTALVRd5/WBtQZjsBMZ10NUfdeNqMDvg1Rg+BHgGqpExE5T+Ji3UzVrNwHOprK7Z6UCyrcDZwBbMC4rc0oe+3nb2wHLUX+DO6gGi89QAbojqWDd5D7ev9pYjwTuaZufauNexfZeE7n7+sAuwEXU7zVDG8u/qIDuSra/PN59Pg98gwrY3kIFaGduP/ss4JO217H9AlOg1X7ehHrOr6QmCKYD/gRsC3xqSh5vasdr+xYq0H8W9Zp6JfCa9jEdg2cDquHgn9vP+S9wBbCJ7U0n8rxuQQXm/0I9d9NQ9aXXs/35if1A25cCH6X+rx8D5mfc7zpZbN9NvQ6/zLi/3yzAndTEwbtsvyw4HhEREdFLenlz7IiIiIiIiIiIiIiISUsGc0RERERERERERET0SwLMEREREREREREREdEvCTBHRERERERERERERL8kwBwRERERERERERER/TKYnZuHvXnmmceLLrpor4cRERERERERERERMaxdc801D9med/ztYzrAvOiii3L11Vf3ehgRERERERERERERw5qkf/W1PSUyIiIiIiIiIiIiIqJfEmCOiIiIiIiIiIiIiH5JgDkiIiIiIiIiIiIi+iUB5oiIiIiIiIiIiIjolwSYIyIiIiIiIiIiIqJfEmCOiIiIiIiIiIiIiH7paYBZ0lGSHpB0fde270m6SdJfJZ0hac6u23aUdKukmyWt0rX9XZKua7cdJElD/KtEREREREREREREjDnT9fjnHw38CDi2a9sFwI62n5O0D7Aj8A1JSwLrA28GFgQulLSE7eeBQ4FtgCuBc4BVgXOH7Lfoot1GTmzbu7jXQ4iIiIiIiIiIiIgRrKcZzLYvBR4eb9uvbT/Xvr0SWLh9vQZwsu2nbd8O3AosLelVwCtsX2HbVLB6zSH5BSIiIiIiIiIiIiLGsOFeg3kLxmUiLwTc2XXbXW3bQu3r8bf3SdI2kq6WdPWDDz44wMONiIiIiIiIiIiIGDuGbYBZ0jeB54ATOpv62M0T2d4n24fbXsr2UvPOO+/UDzQiIiIiIiIiIiJijOp1DeY+SdoU+BiwUit7AZWZvEjXbgsD97TtC/exPSIiIiIiIiIiIiIG0bDLYJa0KvANYHXb/+u66SxgfUkzSloMWBy4yva9wOOS3iNJwCbAmUM+8IiIiIiIiIiIiIgxpqcZzJJOAlYA5pF0F7ALsCMwI3BBxYu50va2tv8m6RTgBqp0xudsP98e6jPA0cDMVM3mc4mIiIiIiIiIiIiIQdXTALPtDfrYfORE9t8D2KOP7VcDbxnAoUVERERERERERETEJAy7EhkRERERERERERERMTIkwBwRERERERERERER/ZIAc0RERERERERERET0SwLMEREREREREREREdEvPW3yFzE5tJt6PYTJ5l3c6yFEREREREREREQMmWQwR0RERERERERERES/JMAcEREREREREREREf2SAHNERERERERERERE9EsCzBERERERERERERHRLwkwR0RERERERERERES/JMAcEREREREREREREf2SAHNERERERERERERE9EsCzBERERERERERERHRLwkwR0RERERERERERES/JMAcEREREREREREREf2SAHNERERERERERERE9EsCzBERERERERERERHRLwkwR0RERERERERERES/JMAcEREREREREREREf0yXS9/uKSjgI8BD9h+S9s2N/AzYFHgn8C6th9pt+0IbAk8D3zB9vlt+7uAo4GZgXOAL9r2UP4uESOJdlOvhzDZvEv+lSMiIiIiIiIihqteZzAfDaw63rYdgItsLw5c1L5H0pLA+sCb230OkTRtu8+hwDbA4u1j/MeMiIiIiIiIiIiIiAHW0wCz7UuBh8fbvAZwTPv6GGDNru0n237a9u3ArcDSkl4FvML2FS1r+diu+0RERERERERERETEIOl1BnNf5rd9L0D7PF/bvhBwZ9d+d7VtC7Wvx9/eJ0nbSLpa0tUPPvjggA48IiIiIiIiIiIiYiwZjgHmCemraKwnsr1Ptg+3vZTtpeadd94BG1xERERERERERETEWDPFAWZJ0w/GQLrc38pe0D4/0LbfBSzStd/CwD1t+8J9bI+IiIiIiIiIiIiIQdSfDOa7Je0j6fUDPppyFrBp+3pT4Myu7etLmlHSYlQzv6taGY3HJb1HkoBNuu4TEREREREREREREYOkPwHmaYCvATdLukDSJyVN158fLukk4ArgDZLukrQlsDewsqRbgJXb99j+G3AKcANwHvA528+3h/oMcATV+O824Nz+jCciIiIiIiIiIiIiJl9/AsMLAmsD2wArAR8EHpB0FHCE7dsn94FsbzCBm1aawP57AHv0sf1q4C2T+3MjIiIiIiIiIiIiYupNcQaz7Wdsn2h7BeCNwAFUoHpH4BZJ50haQ9JIaiAYEREREREREREREVNoqoLAtv9u+yvAQsBGwO+AVYHTgTsk7SppwakfZkREREREREREREQMNwOSZWz7GeBXwBnAPYCoUhrfBm6XdICkGQfiZ0VERERERERERETE8DDVAWZJ75H0Uyqw/ANgVuAg4O3AFsDNwOepUhoRERERERERERERMUr0p8kfkmYHNgY+TTXXE/An4FDgRNtPtl3/Kuk44DyqMeBnpnrEERERERERERERETEsTHGAWdIRwHrALMDTwHHAIbav6mt/289LugT44FSMMyIiIiIiIiIiIiKGmf5kMG8B3AYcBvzU9sOTcZ9LgN378bMiIiIiIiIiIiIiYpjqT4D5I7bPn5I72P498Pt+/KyIiIiIiIiIiIiIGKamuMnflAaXIyIiIiIiIiIiImJ0muIAs6SVJB0lacEJ3L5gu32FqR1cRERERERERERERAxf/SmR8Xngjbbv6etG2/dIWhaYg6q9HBERERERERERERGj0BRnMAPvBC6fxD6XAUv147EjIiIiIiIiIiIiYoToT4B5PqDP7OUu97f9IiIiIiIiIiIiImKU6k+A+TFgkUnsswjw3348dkRERERERERERESMEP0JMF8FrClpgb5ubM3/1mz7RURERERERERERMQo1Z8A8w+B2YHfSVpd0owAkmaUtAZwKTAbcNDADTMiIiIiIiIiIiIihpvppvQOtn8t6TvAzsAZgCU9AswFqH3sbvu8AR1pRERERERERERERAwr/clgxvYuwKrAOcDDwBzt86+AVWzvOlADjIiIiIiIiIiIiIjhaYozmDts/xr49QCOJSIiIiIiIiIiIiJGkH5lMA8FSV+S9DdJ10s6SdJMkuaWdIGkW9rnubr231HSrZJulrRKL8ceERERERERERERMRZMVYBZ0qySFpL06r4+puJxFwK+ACxl+y3AtMD6wA7ARbYXBy5q3yNpyXb7m6nSHYdImnZqfreIiIiIiIiIiIiImLh+BZglbSzpeuA/wB3A7X18/GMqxzYdMLOk6YBZgHuANYBj2u3HAGu2r9cATrb9tO3bgVuBpafy50dERERERERERETERExxDWZJmwFHAc8DvwPuBJ4byEHZvlvS96ng9ZPAr23/WtL8tu9t+9wrab52l4WAK7se4q62ra/xbwNsA/DqV/c7yToiIiIiIiIiIiJizOtPk7+vAo8Ay9m+cYDHA0CrrbwGsBjwKPBzSRtN7C59bHNfO9o+HDgcYKmllupzn4iIiIiIiIiIiIiYtP6UyHg9cOpgBZebDwG3237Q9rPA6cB7gfslvQqgfX6g7X8XsEjX/RemSmpERERERERERERExCDpT4D5YeCpgR7IeO4A3iNpFkkCVgJuBM4CNm37bAqc2b4+C1hf0oySFgMWB64a5DFGREREREREREREjGn9KZFxNrCCJNkelBITtv8g6VTgT1R95z9TZS1mA06RtCUVhF6n7f83SacAN7T9P2f7+cEYW0RERERERERERESU/gSYdwR+Dxwm6Su2nxjgMQFgexdgl/E2P01lM/e1/x7AHoMxloiIiIiIiIiIiIh4uf4EmH8O/A/YCviUpFuoRnzjs+0+g8ERERERERERERERMfL1J8C8QtfXswJvn8B+g1I+IyIiIiIiIiIiIiKGhykOMNvuT2PAiIiIiIiIiIiIiBhl+pPBHBERfdBu6vUQJot3yQKTiIiIiIiIiBgYyUaOiIiIiIiIiIiIiH7pV4BZ0jSSPi/pSkmPSXqu67Z3SDpE0hIDN8yIiIiIiIiIiIiIGG6mOMAsaQbgAuAA4HXA40D3uvDbgS2ADQdgfBERERERERERERExTPUng/lrwIrAbsD8wBHdN9p+FLgUWGVqBxcRERERERERERERw1d/AswbAr+3vbvtF4C+ukXdDrx6qkYWEREREREREREREcNafwLMiwFXTmKfh4G5+/HYERERERERERERETFC9CfA/CQw5yT2eTXwaD8eOyIiIiIiIiIiIiJGiP4EmP8CfLg1+3sZSXNQ9ZevmopxRURERERERERERMQw158A80+ARYATJL2i+wZJcwJHA3MBh03t4CIiIiIiIiIiIiJi+JpuSu9g+yRJHwI2B1YHHgGQdDXwZmBG4GDb5wzkQCMiIiIiIiIiIiJieOlPBjO2twS2AG4A5gUEvBO4FdjS9ucHbIQRERERERERERERMSxNcQZzh+2jgaMlzUyVxHjM9n8HamARERERERERERERMbz1O8DcYftJ4MkBGEtERMRLaDf1egiTxbu410OIiIiIiIiI6Il+lciIiIiIiIiIiIiIiJjiDGZJ/5jMXW37dVP6+BERERERERERERExMvQng3kaqqnf+B9zAou2jxn6+dgvkjSnpFMl3STpRknLSppb0gWSbmmf5+raf0dJt0q6WdIqU/OzIyIiIiIiIiIiImLSpjgIbHtR24v18TE3sARwHnAb8KapHNuBwHm23wi8DbgR2AG4yPbiwEXteyQtCawPvBlYFThE0rRT+fMjIiIiIiIiIiIiYiKmuslfN9u3SloLuB7YBdixP48j6RXA8sBm7XGfAZ6RtAawQtvtGOAS4BvAGsDJtp8Gbpd0K7A0cEV/f5eIiIjRKI0TIyIiIiIiYiANeJM/208BFwAbTMXDvBZ4EPippD9LOkLSrMD8tu9tP+deYL62/0LAnV33v6ttexlJ20i6WtLVDz744FQMMSIiIiIiIiIiImJsG/AAc/McsMBU3H864J3AobbfAfyXVg5jAvpKx+oz9cn24baXsr3UvPPOOxVDjIiIiIiIiIiIiBjbBjzALGke4BO8NKN4St0F3GX7D+37U6mA8/2SXtV+zquAB7r2X6Tr/gsD90zFz4+IiIiIiIiIiIiISZjiGsySvj2Rx1qEqoc8B/2svwxg+z5Jd0p6g+2bgZWAG9rHpsDe7fOZ7S5nASdK2h9YEFgcuKq/Pz8iIiIiIiIiIiIiJq0/Tf52ncTt/wG+a3vffjx2t88DJ0iaAfgHsDmVcX2KpC2BO4B1AGz/TdIpVAD6OeBztp+fyp8fERERERERERERERPRnwDzihPY/gLwCHCT7ef6P6Ri+y/AUn3ctNIE9t8D2GNqf25ERERERERERERETJ4pDjDb/u1gDCQiIiIiIiIiIiIiRpYBb/IXEREREREREREREWNDf5r8vbq/P8z2Hf29b0RERMRwpN3U6yFMNu/iXg8hIiIiIiJGmf7UYP4n0J+rE/fz50VERERERERERETEMNSfgO+xwKLA8sBjwF+A+4AFgLcDcwC/pQLRERERERERERERETFK9SfAvBdwBfADYDfb/+ncIOkVwG7AJsCnbf99QEYZEREREREREREREcNOf5r87Q1cZ/sr3cFlANv/sf0l4G9tv4iIiIiIiIiIiIgYpfoTYF4euGwS+1wGfKAfjx0RERERERERERERI0R/AswzUvWWJ+ZVbb+IiIiIiIiIiIiIGKX6E2D+M7C+pHf0daOkdwHrAX+amoFFRERERERERERExPDWnyZ/uwHnAVdKOgG4FLgfmJ8qi/EpKnC920ANMiIiIiIiIiIiIiKGnykOMNu+UNL6wI+BzYBNu24W8Aiwje2LBmSEERERERERERERETEs9SeDGdunSjoXWAN4JzAH8BhVFuNM2/8duCFGRERERERERERExHDUrwAzQAsin9g+IiIiIiIGhHZTr4cw2byLez2EiIiIiIie6k+Tv5eQNJekRQZiMBERERERERERERExcvQrwCxpNkn7SboPeAi4veu2ZSSdI+mdAzXIiIiIiIiIiIiIiBh+pjjALGkO4ArgS8A9wI1Uc7+O64D3AxsMxAAjIiIiIiIiIiIiYnjqTwbzN4E3A5vZfifw8+4bbf8P+C2w0tQPLyIiIiIiIiIiIiKGq/4EmNcCzrd97ET2+RewUP+GFBEREREREREREREjQX8CzAsDf53EPk8Ac/TjsV9C0rSS/izp7Pb93JIukHRL+zxX1747SrpV0s2SVpnanx0RERERERERERERE9efAPPjwHyT2Gcxqvnf1PoiVeO5YwfgItuLAxe175G0JLA+VbpjVeAQSdMOwM+PiIiIiIiIiIiIiAnoT4D5j8DHJM3e142SXgWsBlw2NQOTtDDwUeCIrs1rAMe0r48B1uzafrLtp23fDtwKLD01Pz8iIiIiIiIiIiIiJq4/AeYDgVcC50h6U/cN7fufAzMBB03l2A4Avg680LVtftv3ArTPnUzqhYA7u/a7iwnUgJa0jaSrJV394IMPTuUQIyIiIiIiIiIiIsauKQ4w2z4f2BV4H3A9sCOApIfa9+8FdrR9eX8HJeljwAO2r5ncu/Q11L52tH247aVsLzXvvPP2d4gRERERERERERERY15/MpixvTuwEnAW8AjwPBXQPQf4kO3vTeW43gesLumfwMnAByUdD9zfSnB0SnE80Pa/C1ik6/4LA/dM5RgiIiIiIiIiIiIiYiKmOMAsaXlJb7f9G9ufsP0q2zPYntf2x21fPLWDsr2j7YVtL0o177vY9kZUQHvTttumwJnt67OA9SXNKGkxYHHgqqkdR0RERERERERERERMWH8ymH8DbDPQA5lMewMrS7oFWLl9j+2/AacANwDnAZ+z/XyPxhgRERERERERERExJkzXj/s8BDw50AOZENuXAJe0r/9Nleboa789gD2GalwRERERERERERERY11/MpgvoRr5RURERERERERERMQY1p8A87eAN0j6jqTpB3pAERERERERERERETEy9KdExo7A9cBOwJaSrgXuAzzefra95VSOLyIiIiIiIiIiIiKGqf4EmDfr+nqB9tEXAwkwR0RERERERERERIxS/QkwLzbgo4iIiIiIiEGj3dTrIUw27zL+wsiIiIiIGM4mK8AsaRPgL7b/avtfgzymiIiIiIiIiIiIiBgBJrfJ39HAmt0bJG0q6eKBHlBEREREREREREREjAyTG2Duy6LABwZoHBERERERERERERExwkxNgDkiIiIiIiIiIiIixrAEmCMiIiIiIiIiIiKiXxJgjoiIiIiIiIiIiIh+mZIAswdtFBEREREREREREREx4kw3BfvuKmnX8TdKen4C+9v2lDx+RERERERERERERIwgUxIA1hQ+9pTuHxEREREREREREREjyGQFmG2nVnNEREREREQX7TYycmq8S6odRkRExOBJ4DgiIiIiIiIiIiIi+iUB5oiIiIiIiIiIiIjolwSYIyIiIiIiIiIiIqJfhmWAWdIikn4j6UZJf5P0xbZ9bkkXSLqlfZ6r6z47SrpV0s2SVund6CMiIiIiIiIiIiLGhmEZYAaeA75i+03Ae4DPSVoS2AG4yPbiwEXte9pt6wNvBlYFDpE0bU9GHhERERERERERETFGDMsAs+17bf+pff04cCOwELAGcEzb7Rhgzfb1GsDJtp+2fTtwK7D0kA46IiIiIiIiIiIiYowZlgHmbpIWBd4B/AGY3/a9UEFoYL6220LAnV13u6tti4iIiIiIiIiIiIhBMqwDzJJmA04Dtrf9n4nt2sc2T+Axt5F0taSrH3zwwYEYZkRERERERERERMSYNGwDzJKmp4LLJ9g+vW2+X9Kr2u2vAh5o2+8CFum6+8LAPX09ru3DbS9le6l55513cAYfERERERERERERMQYMywCzJAFHAjfa3r/rprOATdvXmwJndm1fX9KMkhYDFgeuGqrxRkRERERERERERIxF0/V6ABPwPmBj4DpJf2nbdgL2Bk6RtCVwB7AOgO2/SToFuAF4Dvic7eeHfNQRERERERERERERY8iwDDDbvoy+6yoDrDSB++wB7DFog4qIiIiIiIiIiIiIlxiWAeaIiIiIiIgYe7TbhPKMhhfv0mdP+YiIiDFpWNZgjoiIiIiIiIiIiIjhLwHmiIiIiIiIiIiIiOiXBJgjIiIiIiIiIiIiol8SYI6IiIiIiIiIiIiIfkmAOSIiIiIiIiIiIiL6JQHmiIiIiIiIiIiIiOiXBJgjIiIiIiIiIiIiol8SYI6IiIiIiIiIiIiIfkmAOSIiIiIiIiIiIiL6JQHmiIiIiIiIiIiIiOiX6Xo9gIiIiIiIiIgYHNpNvR7CZPMu7vUQIiKiHxJgjoiIiIiIiIiYTAnaR0S8VALMERERERERERHRMwnaR4xsqcEcEREREREREREREf2SDOaIiIiIiIiIiIhRZqRkhicrfORLBnNERERERERERERE9EsCzBERERERERERERHRLwkwR0RERERERERERES/jKoazJJWBQ4EpgWOsL13j4cUERERERERERERo0DqWvdt1GQwS5oWOBj4CLAksIGkJXs7qoiIiIiIiIiIiIjRa9QEmIGlgVtt/8P2M8DJwBo9HlNERERERERERETEqCV7aFOmB4uktYFVbW/Vvt8YWMb2duPttw2wTfv2DcDNQzrQ/pkHeKjXgxhl8pwOjjyvAy/P6cDLczrw8pwOvDyngyPP68DLczrw8pwOvDynAy/P6cDLczo48rwOvDynA28kPaevsT3v+BtHUw3mvoqgvCx6bvtw4PDBH87AkXS17aV6PY7RJM/p4MjzOvDynA68PKcDL8/pwMtzOjjyvA68PKcDL8/pwMtzOvDynA68PKeDI8/rwMtzOvBGw3M6mkpk3AUs0vX9wsA9PRpLRERERERERERExKg3mgLMfwQWl7SYpBmA9YGzejymiIiIiIiIiIiIiFFr1JTIsP2cpO2A84FpgaNs/63HwxooI6qkxwiR53Rw5HkdeHlOB16e04GX53Tg5TkdHHleB16e04GX53Tg5TkdeHlOB16e08GR53Xg5TkdeCP+OR01Tf4iIiIiIiIiIiIiYmiNphIZERERERERERERETGEEmCOiIiIiIiIiIiIiH5JgDkiekZSjkER8TKSFuz1GCKiNyTN2+sxRERExOghaW5Jo6YH3cRIUq9+doI7EZNB0ly9HsNoI2lFYNUEmXtL0nSSZun1OIY7SUtImr3X4xgLJK0CHCBpvl6PJaZcL09qh4qkaTvvXZKm7fV4RhNJHwNOl/TKXo8lIgaGpDl7PYYYeJJWkLRyr8cxXEh6o6RXS3pVr8cSL9XOLfYHZuv1WIbIS85NhzLeksBOxCRI+gjwLUlz9Hoso4WkDwOHAI/YfqHX4xmrJK0K/AS4VNIPJX2612Majtox4CwgWXWDrB0b9gIOsv1Ar8cTkybprZJWlvRZSa/0KO8e3Y4HhwHHSnqt7eczUTow2uTSXsD2tv89FiYrRhJJi/d6DKOBpBl6PYahJOm1wM6SPtDrscTAacfrHwD/7fVYhoN2bnAacCiwp6RlejykaNq1xe7ASbYfHe3nbO333U/SvpJ2lTS97ReG6pxqVD+5AZKWlLRsr8cxUrVZ2e8Bv7L9WDKVpl47Ifk5sJftK8bKUpXhpv0d9gfOAL4B3ASsLGnPng5smOlMMAHb2f6HpLEy8z3k2mvyLOAm25ep6fW4YsIkfRQ4AfgksD5wnqR1JM3d25ENjjYptwdwCfAQ8DtJC2SidOq1//+jgbuAO3s7mujWDsUzAKdJ2r/X4xnJJL0d2GeMlYGaGfgfsJqk9/V6MDH12vF6b2oy8HJJ80t6R6/H1SuS1qACmGsCOwK3A29tt+U8tofaiumTgM1tn98mvPaRNGuPhzYoJK0G/Bj4A/AP4A3ANZLmse2hCK4nwDyKSZoZ+BiwbWbRplzXP+j6ti+W9BpguwSY+q8rqHk68GFJb7b9XN58h5ak5YDjgU1sn2X7IuAIKqt8CUmb9HSAw4Skt1DBs0NtXyhpMeD3kt7a46GNOpKWp44NmwCvl7S7mx4PLSagHc+/C2xhe1vbywPHAmsDK7R9Rs2xXdISwIHAwbZPsL09dRx9dU8HNgq0ibx9qcm8XwG7Svq//P8PD+1Q/AywKvD+7ono7sQLSTP1YnwjzD+pC/7tJS3Q2TgaM+o6ZW5s/42aPPovsFY7B40Rql0PHwwcZvu3khamEocW7enAemsTYB7bt9j+K/An4H2SFgEW6u3Qxq72/vQq6rirFsM5DnjI9qjLvG+r7b8ObGv7RNuH2d6ACjZfLGm6oUiIGHVvZjGO7SeBM4Frgc0lvQdezERQ+zoZuX1oJ3pLArPYvr6dNJ8OYPuJng5uhGpLK78GbA1sCfwN2EPSm9qM2qgJRAxX7V9/OuC1wJV0lXyw/TTwe+Aq4G29GeHw0bK1nqBmvV/Vsm5+AhzdTh5jgLRjw/8BW9k+BVgX+ISk3Xo7spiQrgvMg23/sbMSxfYPgSuAHSXNPloChC1QcjfwO2D+rkmmdwHfkPQrSR+QtFTPBjlCtSDbG4GtbR9JPcf3Ap9uk3wxDLQltvcAq1OZqHsC2H6+3b4R9b+Q64rxdF1zTW/7UeBT1HnY1yQtIEmdi35Jq0ga8ZNWLWvwr5J+Kun9gIGDgIep/ivL93SA0W+2/wWcCrynreo5BTjF9hm9HdnQk/QOSQvZ/iRwi6QL200rAh+kAu9nSvqepKV7NtAxqCUKfsr2iVS2/b7AzcCPbe/Ttd+rJc3Yo2EONAPPAlcDdH4v21sDNwBfHIpBJMA8SnVmwm3fDPwSuA3YTNJ7O1lhkj4P7JvA3kupaoR9xPb3qUZT1wHXAPvbPrBrv8xITiZJHwRWBg6xfXk7kT6GCkTs2RVkzjFpELV//eeoY8LPqEySjbtufxq4Efi/sXyRKGl1KtB+H3AkMDdVV+1y2z/o2u/dGlvLXAecqsTCD4HnWsmc6W3/E1iNen0myDwMtQvMXwArqJbFvtB12wHAc8BHezK4AdYytX8MPAXsBiwMrC3pGGB66hjxF+DLwO6S5sx51eRpF4CnU0GnOwBsX0u9tu6jVuC9uWcDHONUZfaWA7D9bDs+3wt8BPiIpL3afltQF/CndgLO8RLzwkuew0eBrYDXUNlmCwBI2pIKwo6G86/bgRmo0klvBs6nsjznBR6nJpHf2bvhRX+05Ats70CVMjoAuNT2j7r22UDVUG1Ua8H1kxn3//1hYBpJ/wZeZ/vVwDrAZ4DZSemnIdMmuE4Evt6uZy8AjgLuoSawO/ttDuwJjOgAc1cg+T/USpH12vdPd60s+jMwy1CMJ8GcUUbSeyQt0gp5d4LMt1An67cCm0paVNK61MXQCaMlw2ggtEDoxcBOALb3ppZSzAWc17XfRlTjv5TLmIQWQDqUqqt4W2d7y4Q5lsqk3V21HDZ1LAeJpPdJ+rqkFahAyRlUptj79dKSGIsAt1CzoGNOCyZ9h3oTntf2n6hg/InUieOr236bUicrY/J5GgiqZfH7U2UWfgYvXoBPa/tOKsj8cUnf6+Ewo4ukV7aMU2x/lcrq3Zk2KdUVWL2ZWpI4orXjwb5Upvbz7XW5MzAH8Cbg87bPsf1NYHNgXduP5rxq0trF+f7At2wfY/u+zm22r6NW4N1DZXm+qUfDHLNUZfZWA7aR9F54WZB5NWAVSZdR58yrtVII0aUdL4+WtD68LMi8NVViZxtJXwe+SR1Dbu/ZgAeApGnaRPE7gAeBmYD3UnXrX0etUvoiNYE0phoejlSSXgdg+5muIPO3qTJRi0l6W9tvfWq16m0TeqzRoE2O7gx81vZfJM0HYPuD1LXVHO37O21f1fa7d4IPGAOmnbftR70v3dTO3R6lkqv2Bb4o6YPtb/gZYJ8WmB2RWuxqpxZvgTp3emMLsmP7qbb9eWAmNYM6ppwDjy6q5hurAh+2fVd7k+8su1oc+Dj1xr4EsEKWeo/TDkh7UNlI77a9Rddt36BmIVcGlqYyNTbKyfTESXojlZ20he0ru7Z3vy5fBWxLnXRuATybi/OBpXG1ry+jmk4cYftISfNSmUjLUQHnWYBdgA3G4mu7BTy+C3yFuvA73/Zx7balqaXBz1OlM1analyNuedpIKhKKpxAlRw5t3NM6Po8re3nJS3a9lvD9kM9HfQY1yYEdgP+A9xm+9Nt+55UiYPd24XWOtSJ/cds392zAU8lVRfu/YEtbf+hTS59DtiVykbahcqEudj25T0b6AjTLmxmpLLCT7d9piR1VjF1TzS3DMcPAsd3B6Bj8HT+Fu3rJahA8puA49yarwLTt0DTq6mM253bpECMR9LswBpUbfrjbJ/Wts9k+ylVo6mfUddla4/k6zJJSwKPtgSSzrbFqdWKe9ner217C3Xe+VvbN/ZksDHZVHVdfwz8s2UuI2kGV112JO1B1V++mTo33sT2DT0a7qBT9WP5C7CL7QPaeeovgS/avrjt82vglbbf1bOBjkGSVqJWRW7ZVkX+FvhM5/XYEgNXoRoyzgGsPJKPQS1IvieVGPVv25dImp+awJubShg7kerJ9nXqWmrQ/zenG+wfEEPL9pclPQucLmmtFmSe3vaztm+R9BfgGeCikfwPNdBaYGk/6oB0paSvtpOiW1tZgX0kmVrCeTfwiTx/k2Uu4Ib2nE7bWTrZfQFp+15Jh1KB5Wd6NdDRStK7qeDxii1IsjbwFUmn2X5Q0i+p5e1fp4L8q47mE8MJkbQs9Sb9FVfTkpXpeo+0fVU7BmwBbEaytabWdFSpgce6N3YdG2aX9B/b/5T0AVdpl+iRFmzdB9ge+BdwvqQv2D7I9k6S9qYyTW+mJrI3HuHB5VdQ9ZVf0Y6br6TK5Bzt6m/xZAus7wFMK+kaV4mhmIQWvHxK0lPUUk6okgDPdU08/5/t62z/SdL1OTcYUq+kMk2x/fcWUJ4W2LhNAFwKPCPpy8ArgHVsP9u74Q5vth9v51nPUv1wsH1aV1bZm6lgx50jNcOxvUbmBf4KXC7pemqV7LTt2vN9wO8kzWp7d9vXA9f3cMgxmVqw6nGq58KWknaxvVubYJrJ9lO2vynp+4yB4HLzILAX8A5Ja1ITz0fYvrhzrWv7w5JOlfQaV0mxGBrvpPo5XNEy7WegemrdANVHS9Kl1GqRG2z/vXdDnTrt+v5AYMOWJd+xYDsv/ySwKbA8tYrkE0P1v5kSGaOApPnaxRAAtr9BZSqe3g5sz7b9tqBeaCckODqOqjbNe6hsxCvbAekeqsFfdybtAdSyn7Xz/E1c19KLpwBBNYKRNE3nNknLt6Aetu+z/e/ejHbUM3ARsBKA7VOBfwPbtczm2ansmcOoIPRoPzF8mZZN+wSwje3ftM33UfVlp2m3A/ydmhl/T4LLU6ddXP8WeLukGTtZy/BiffuNGBfgT03PHpI0D7ADcLbti23fRi0rfKoFXjv1GG+msvQ2bQGEEUlVg/1U23sBp7aJ+fOpxjAHd+36BLAdcFiCy5NP0nSqEm6zUpMR2H5O0vRdu31Q40qxJLg8RFTNK2+S9BlVOa1OL5dfUJlQG0l6narM3ueBXya4/HKqRn2fbxne2H6MOoYcD2zRLvw7NZcvBO4fqcHlZnrbDwA/An5FBXVOppIZlmqvoaWp5q/f6OE4YwqoVi0dTzWsu5W6TniDpF1g3NJ7SWtQy/JXGc3XEJLeqmpY+RxwCNWf6Qjgb249mtq17iqS3mh77QSXh4akD0lax/b3bP++K8P+PLoa2kv6FNUf5OyRHFxuFgHOaglQneun/YBrJH2/TWSuTq3AX2Moz8sTYB7hJL2dqnN0pKQVNa7u8peBy6mLo2nbP9TuwH62H+nZgIeZFuBcyPautn/XsjOeAf4GvLtrv08AnwAOH8kXzkOlKzD/L2qGd4e2/YWu294MvEtjuJncEPkLtaz9jZJ2kbQP1b18YWrW/ULgq8DPW+BoTFEtAb6EWlp0taQZ23H0DmCe9pp9TtJWwE+Af4zwC8GekTTXeJtuBd4HLNOCzJ1A8geoZdmzwkuOJ9EDrtIkRwPztcASVL3hnYGLJB3dAiV7AcuN5AtMjSuV9W148VzqFCqr84Su/TakAijYvn/oRzrySFpE0nS2O5nKOwLvlfRVqNq0bb/NgLWABO2H3lzUcXcZ4JOSzmwTfndQx4Cbqf+Dw6gL1mt6NdDhqiWprEtdc20n6QhVA6b/2D6Zev7Wl3QClbSy3EgOQqnqo2/dvr0OWJM6t/wsda75a0nbUkGehamyeTHMqZbeH0jVrP19O+/9I1US5w2Sdm37bUj103lwNCcKqVZxHUPFBt7tqtl7AnXes0jLJkXVOP37wJO9GusYZeBnqh5ZUJMAUCXdPgIv/m12Aq4ayasiu5L45gXmhxcnNt5JnTfNRzVJ/3q77ZmuFTNDIiUyRr7rgEupTJofAb+QdH9btrq9qi7Sw8Cj1JLuEVvba5B8lDrRW9X2rdQywBeoZZtvhxffPL9JnUynCd1EtGVwa1IBTdt+WNJawNmS1DLCkLQBlQG3jtNxfMC1iadrqeWJz0m6jsq83QV4u+2F236zAgvCS8uWjCVtCfBVwMmS1nerHSjpSuoiCVUTxK2oDOchfZMeLVpm3B/aBMf1tk+1fZSkBaljwTKS/gS8HvgCsF4mQ3tLbfkrgO1j2zntR9t74mzAssDM1MqodwMX2L6jV+OdWl0XkNdQgTQAbO/Zsrh/27KXVgO+RC0FfqIngx1hJL2DyoL9s6RvUcG2OyR9Ddhf0uupyc4FqOPBuvn/H3qu8lD7UReou1BlDo4AbqcCS2dSpR4uyEq+l2rnuHaVDjgbWIGanPoccDjwD0kn2D6xTWLvCKw/Cq7LlqNWrhzs6u2xDFVz+gFqdei+1Hnmh4HNXY3nYxhr1wbbUTWFL+gEtFoQ6yrqWLCVqsHnAsD7bd/UuxEPrjbxfCCwle3Lum56wvZ+qvJ5X5X0D+r/Yf2RPGk0kqga0D5s+yJJH2BcvOG4tsvV1ITIx6lVN+uO8CSIaalguoELgC+0a9eTXSXFbrL9P0n7AnNovL4WQzbOJAaNbKolhd+hMsHOoIrsH0/VmjmYKpWxFvDXkfwPNdBaAO6OFgDdGfgkFey8pd2+AnVyeC0127Vpnr+Ja6/FnwKfoiY7HqeWbvxB0pupE+1/UrOKiwGfSjb4wFPVS7uFKj9wNfCzzolfu8j/OhXg26N3o+y9Fkxa2vZ328nzd6hlgOvYvqdlbZ0MXEnVr9o8x4D+k/QG6vn8GfWedCVwsu3L28n7e6kg5YPA93Js6K0WSP0cdVF1dVd26XpUHfLjPa4B5jTU+eSInSxUdd/ei7pwnhVYHPhR90WzpO9Rmdt3UjXvcjyYTKpSKr+kMsHPpwKYJ7ka/M1HlWCZhlpaf7BTgmjItMSA590aMbcL9o1tf0bV3PZXVKbeelSfgqNs/3eCDzhGSZrHXY1oJf0I+LvtgyTtTjWzfoIKNl8DXDFaJqgkXQBcaXvnllSyBVX3dHvbZ7WApW3/r6cDjYnqTJKoGlOeTp333qWuHjptv0WoeMM3gW/YvrY3Ix587frgJ9Sk2s+6tn+fmkD5pO37WyxhayqZL+evQ0BVwuUg4FvAma6mqR8AzgI+3xIj5qFKHt7GENYgHgyqfmEbU1nZp9m+sGVsfxT4le3j234bUhNEm7pHZUASYB6BWvBzVuA3bZbi9dQJ+8epztynU5kg0wCvAT48VrMT+9L+QX9ELd+6qM3I7k7r8uxqSLEM1fX4Bkb4bNdQaoHkg6kLyWeoi/ETgTNs3ybptdRr90GnI/ygUHV7Po5annU+lYW0M3BzC/a/gzopvM72br0bae+0k5LvtY9Tbf+3Bcm+S00srWf7zpaF9HrS1LPfOhcs7etjqBOjnaiJqDXb998C7un8HfJ+1Xuq5a/fpiZZz6AyRH7UbluHOqH9HXVsf7hX4xwoqjJid9m+tC0zXJ3qwH2wq35oZ7/tgfNzPJhyktanlnP+sn0+HziVOl89qQU2pvMIXro6krTAySLUee6/gQ3ahN/swM+pHhpvAHayfYak9wD/ckpEvUwL0v8GWMv22e25XZtqFPpLamXExtR58RrAIaPpHFjVFHlNqgnsdMA5wO22t+jhsGIKSXpzZ3JP0unAJbYPat9P11ZELkwFVs+kVkmO6lV9LWP0JOp/9pK2bRtgFSq5by0qOeJZYCaP4jIhw0k75h5CNfS7arzblgPOpjLwj5H0U2CvXgVbB0JLxNmLKk81B7Ah9R7zGNVj6dtUedfHaKtKejnRkQDzCNNO/P5ElbzYDbjM9qOqmnUfprLAtmsnODNRHdAf6NV4h5uWlXUo8Dnbvx3vtm8C61PZzLdSQbrvjOZlPwNBVcP2aeDZlvm5O9WZ9WRJO1H1fW+ksr6+kWVDA687iNe+X55qSrkK45a6vpUK9p8NTE9dKI6aC5zJJWlJKhvrM66mnuM/dztTx9JPUnUo/94dYIrJJ2lFqrHPE7YPljQ3cBR1YvQeaun1H6hJp39S2fVPOScmPdOVwTQTNTF1L3A/1XRxeipb5CIqwPwR4GuuWoQjUsvWvBN4wPbTnQmONhG3OpVx+6ORfGHSK5JWAj5G9WK4gFq9dAo1iTctdTG0P/U6eowKUKXm+hBrmfnvp47DX7d9bnufPA/Y1/aPMvE3cara9CdTK/e2sH1ay9r9PXXutZrt89q+neZTI1KbgNuNei//ne2HWkbraVQj1CMlLUWtStgpx87hr02IiCoNdbXtDdprekVapmTXvltS740b2X68JwMeApLeYfvP7esDqbJgn25B9iWBm9q5wtHAobb/0MPhjjmqvjhvsv2VFht7DxXs/wOVaPmG9vVqVPb5SF5h90bq/firtk9t23anEjw6r9FFqOutJ6mmkz2NtaQG88jzNJVZsCr1QppG0vnUifquwM4tuNyZVRzVM4v98AHgFFeduTmpA9BHgKuopX//pS6E3k/NimU510S0bPAfUKUYFlfVVr4K+Jqkx6mgxKbAudSywGQmDYI+Lsgvo7LCFqRqir+HCt5tALwC2HM0ZB3205PU0tQrW8Bz47YqZA4q2PE9YB7gSGD1BDv6px0bvk/9328n6dVU5vIt1DFjeeALtn+pqs98t+00Remxrte7qCDgnC3AdC+VoXcfNXm1LnWyO2IvMNtFyUnAI8BNkn5o+/cAtv/cVjWsBuwgaS+nduhka9k2e1JLVRcA9qFeM0cBF1PH2K1tnwXsI2mRHGuHjrrqq1O1sf9NBZf2aZNM50g6jSpZFJNg+xRVebLXAz+VNFvLnNuOWqp8aTueeIQHl99l+xpV7d2Vgd1UTTovouqbfkfSb6hj6qPAQxN8sBhOFnHVxH8H1SvjQNtfVDVwXEfSu6jVJh8CvkiVkhux7/2T6RBJT9tegVr1vAPwGUmHdFY1t1IEr6OaoMbQug9YugWa16L6jb2SWsn/etvfakku94/k4HJzG1VWcFlJnYabnQbpD1OrDK+2fVovB9ktGcwjRMsmeqbNlr2BSpG/C/gfVef2V5K+QWXcpfHMBEj6BHUgOgdYhwo2LQH8mVoeu3vLYDze9u29G+nwJ+lDVKBoWyqr/mvA/9lepy2FX59acpmO0YOoZYmtQV3E/xvYx/Y/JW1NBfRE1aL6paSZgRlsP9a7EfeWpMWpZVWXA5tQy/yvA2aiLpjWcdVTmy+rP/pH0luoGuDb2T5J0mLU870adfJ3MfBN2z/o4TCji6TXUUE/qGD/XaryWydSwYM1GbdU/rNURsiIDriq+gbsSq3yOAvYg+phcbftE9o+b6aOr0favr9HQx1RVD0uLgVWdpVlWgTYm6pjeRu1guTAluU5cyaWhlYrZ7AjtULhBtp1BPXaf4RahrsFMBe14m9J6vojF4xd2kqxZ4A/ukrtbUyVJvwrNRn3OdsnSLqcOi87s4fDnSpdK1vOB+aw/Z62fXsqcedZauXnXFQSz28kzerU6h7WWubybFQprMNs79sy7/9C1bT9qqQ1qHPlTpbzzr1cej+U2kTKg7Y/IWlNKnN7RqqXyBLUcfKTTsmsIaeqrbwNVZ7kNuCElhiwFLA71WhxxK6uG5+kGanEp0eo1YRvolYULkzFW26iVhQOi0m9BJhHgBbI25c6+fu5qzbo+tSLbDGq1soJVLbyasB3x3IAaWLaMoM1qWYlv6UCyVdLWp2qs7p5L8c3UqhqUh1L1Zv6ZNv2duALwJZUVvinbG/UbsvyykHQlUG+D1XLdjMqC/9IV/H/E4HHXM16XtKkYyxrF4ZvoILKJ3SyuSX9Eti7k8UY/aOqtf5VKnvpSNv/apNOR7pq3H6RauZ1kO2neznWeLHB3e5UqYiZqRPWz9m+RNLnqBI7m9o+t4fDHDBt5cLjtp9tGVrnUavC7qCybreiMm0vpeqnPufUBZ5skmagnrsrbW/ftp1OHWtPa0uKp+ucH8TQkvQDKgvxNOqi9F/UBOB2VKLAmtQ5xUeoZti5nhhPmzS5llpVehD1HJ5CTZ5+iXp/O5sKzD0C/NsjuNSWpLd0goqSzgAWsr10+/51wKuoY+f7qBViy+W8f+SQtCy12uwI2wd2BZnPtf2Fts/0VNxoxGbgT0q7NvgfFVT+V9t2CdWDYi1Jr6GOk3NSMZfDnIa0Q07jSpmJqgP+XNck2LpUHGI924/2dqT9pyrf9gYqTtVpsj0jlU2/LPDRrtfofMCTw2lVQQLMI4CqQ+TOVI26p6gTv1Wo4Mi2VCO1lagg8x+Gy+zFcCbpFbb/03WQ2oTKaP4UVS80/xgToKo99U/q9fdj4B/U0qG9gIVtb9QuMP8CnGj7uz0a6qjWMpcPomoJX9q1/bvUm9IXgLdTy5K3ToBk4lRNvr4BfMT2Pb0ez0gk6cNUfdVvU8sGN2s3zUM19Vrb9jOqngEfBzb0KG8QM9ypGl7uAuzQAsozUwHWfam/5T1UkHVl2w+O9KCBpI9R9dW/RE2+uWXh3UetCjuGmhx5G3URubfHYK36/mivpZVa1ttMwK+p8m0PU8/n2q4u74sAB1LvS2mINERagsXN7TX/feq6YRMqm9lU2aJVbN/QLtKvtv2P3o14eJI0o6te+5eoMnBHUe9nl1IryV6wvX37fzgcWGIkZ+m30h4v1uZt286kgsxLde03K5XNfFNeN8OfpLltP9x1HfxRaiXD9rZ/2P6eV1E9ddbp7WgHX5t4vpxxjU/PBi62/TtJv6Um2zbu5RijjBdYnqmdV8xKHY8/TSVEXNfbUfZP+90EXEOdN11MnS/dYfvaFl/5CVWC6GAP0xr3CTAPY5LeRpVw+CfVKfL1wGupbJsPUhfva9s+qy1ZPS1LOCdM0nTA8+3kunNAmhbYmjogbZSZyIlr5QVuprJm96AuSo6klhg/BazaCWRK+j9qRu3WHg131GpvQIdRNZY/QdVZphP4kXQeVfblu1RN8TXHarmH8QNinUxuSfO7SmEsTpXG+Cw1451jwBRqr0eoGmFvp2r0fotaYfNFqiHch7qPBUoJkp5rmea3ApvZPlatS3y77YvUBMDSkg6mlj5vRNUQHZEnjm0CZG9gR9vndx0LPkpl370S2Mr2ee3cYIaRHBgaSu2i53Bqov6rtg9tQebTqSZnr25BjBmo84YZnVJuQ6YFOw8AtnFrcC3pBCrTdhvgNdR1xeW2r+zVOIc7SatR5wt7uJrb7UJNpu4HvAN4FxVk3sb2I5Jm8Sjo5SJpNirYeKXtLdq2M4FXdTKZY+Ro7/3bUaWuzpW0IDW5ej21muewlsk8GxXgWgO4b6S+90+KpCVs/13S2lST7wep1VyvoLLzL6T+x8+0/YnejXTskbQMdYx9F3A0VcKss+p0Ber1ug/wXipJcC+PghIuXb/bE1Syw7tpjVSpkkTHUmUxvzIcE8im6fUAom/tJOYYqsbKs1QmyO1Uh+IZqTeGrYC/A9g+JMHlcSS9Q9KqkraX9JoWUH6uBZeXBT7X3lBXo944N05gabI8RXXFfi11IJ+Wmui4ncpYnqZdmGP7ugSXB147MZwZ2J6awTwQWLBdvHcat/4MmMdV/+4DYzGQp2oqR3teXnyvawGl5YELVU15nqdOItfOMaB/3FAZ4CdSwcgfUZOj3wNOBjZrwfzOfcbca3K4aVlmewJbSnpjywbpHENOAJ5qK1b2oU5iXxipF5iSPki9NjdvweXXAnu3c4NfUedYf7J9HtRxIsHlyedaNv0r6sL8U5K+3FYnrEnVR/xBW8L6jO1nE1weOqpGR98DPtsJLgPY3pBKYjkJuM32/gkuT1hb/bAXVV7vUQDbu1Glhb4JXGv781R5oUfa3UbsMUTSspJeK2mB9v+6NNVU61gA22sAd0kalhl00TdJr6SO0/+j/p7rU+dov7D9JWBDYFtJO7S/+zK27x2p7/2TompIe5KkhakAXucYeIPtrYAfUqubrgU+0PaLIdBiYUcDi1MJK/tRr81FJS1A/W2uasfbC4FPj+Tgcvvf7LiPWo18nu2vUisJ96eu+b9PVS/YazgGlyEB5mGpLX3fm2rMdWa7dn+Iqu/1B2rZ6kdtH2v7ph4OdVhq2UgnULWpNwa+A3xd0oxdbyA3upbBX0oVgh+xB6ShZPtO4FwqwPwOapJjRqp79KupA9/cPRvgKKYyK5U9vgOVtbw1MAewo6RXd73RzM+47t1jru6yqh7VTqru5p0gs9pt8wOfAb5t+/4WZPt+jqX90ybqOu6iZtovpEor/LB9PpJ6nW7WFcCMHpH0f5Iubtn936L+Xse3LJ5OLbuHqNqhL9i+w9W1eiRbiKqT2lnKfRxVZ7FTouUo4D+qJsoxmbr//23/nMqSvRhYVdK3W+C5UzrngB4Mccxq5wyiyjccZvsiSXNKerOk7SS90/Ym1ATAxS1bMfog6VVUfeotbf+CSqaYXVVm4JvUtdkukt5NO/dqx9ERGZRrwZtTqazl01SrZBejlmy/U9JBALbXAq5WNfKNYU7Vt+Vkqnb4kdR1xA7A32wfDGD7T1Ti0DqS5urRUIdEez72pCbQ76LKLhwF3Ai8Q9KmwKXtvW0V4HVtvxhkLfC/D7WSbrd2rDmQ6hGyHhVz2Mj26e1c9umRPHHdYleHqEoI0q5JLwS+qSpZtXn7OICqaLCAh3H5tpTIGIYkfQZ42vZRqppA76AKet9GveGvTWWFnGH75J4NdBhqywD3pE4C/9S2rU0tabuTqqk0ve0/9m6UI0t7A34rcJnty9sFy5eB+6kafrdTgSRRwc+vJTtx8KhK5+xMzaZ/r20+gqrRvj31xrsj1dl4TAZNJc0CfAx4P3CL7YPa9mmo7rtzuspjpPHhVGiZcWdRwbr9qGPsStTE0/epTBgDX6Hq2j2eY0PvdAIe7Rh+GjALVXPcknamVvN8qi0V3YwqHbW67Qd7N+qp046X/wbupcoJbQO8mSqTcWzXc7IksCs1sZ/VYJNB0ruAP1KlMf5s+8eSNqBW2JwL/JS6ON9T1ZxmAbemNDF0JG3FuOW1m1HJRW+jgoe/t324pJ8A37F9R88GOoxJmofK9P4cNVH1Dapm9YzUudgXqJJkr6auP0Zs81qNKx+2KVU25RHq/H4hqon0jdR11mG2P9u7kcaUaAG7PalrtIvbttmo64YZgN/Y/k3X/jOO5NfxpEh6D7Wq6bOuslivpq5hdwRuoerTv52KvRw9koOXI03L5P0F9Zr89ni3rU4dbz9t+7YeDG/AtdUxe1PvL7favrttn5XqtbQ6VVf6nN6Ncsokg3l4mh34tKqG7XHUC+6dVDBpR9snUSeKv53wQ4w9LcPg28D5neBycybwG2rG5+5OcLmT0RgT1g5uW1LL/w6WtBOwFFWv70FgJ+qE+mvUTPjmCSANPEnLSHqvpLlsXwt8nTrx+Rp1HN+KKvNwERXMW2cMB5flqnn4S+r//o2SvgCVydxOmFeStEyCy1PtdmqZ5UbUJN6Pgdmocjn3UUHnOYA9bd+WY0PPzQIvljRZi2q+dmH7n/kO9V75E0k7UoHYrUd4cLmzvHLp9r9+PhX0vJfKrKcFl7emVjV8OsHlSes6d5qB6gnyCmALSbtS70e7UMGozwBrSPpKyy5KcLk3rqcmWQ6ngoUHUYkrl1GTLdjeOsHliXqYyib7IRV8ei3wcyrQPDewgu2dgC+N5KBcu47aR9IGto8BrgD+Qy1DXxs4hzr3vwPYWNICuZYa/loywAG04LKqdOTB1MTBcVQpzlVaQlHHM0M/0iE1PVXq5jFJS1CZ3ZfY/ntbeXAscB11vTttz0Y5xkh6lav57yHAgpLWlTRn53bbZwHPUQ3sR7y2SuDLwLauElb3tu1ylbm8gVp1f07bPiJit8lgHqYkHUc18LoROMr2n1Q1A39MLdN4saFXvGTGfVvqxO8vwCke17BoOuqNYh/bR/dsoCOQpLcCa1GN/F5BNY37MhWsWJa6kNwG2KUtq44B1N58rqWWBV1OlX+5lapvuy8VVD6COlH8DnDEWAwuS1qklXDpztScGfgI8CGqs/lBkj4FHAws59Rc7jeNa5C2CFVq6Ezq/emH1OTHXrb3U9Vdfnw4L+UaCyS9j8rA+xFwp+2TVA3X9qVWqKzU/mf2ArZlhP9/qEqNfZ/KTrpivNvWp7LrfwDMRGUub+kR2nV8qHWv/FA14FmTCjrNDtxEXRh+1/YP2oX7Uwle9lYLAi5g+96u98dtgRWprOannAvCiWplIxanzsV+4VajXdJRwMW2j+/l+KaWpFdQfX42p87tzwXOoBIY3knVqT+rve+/BvhvzvmHv3b9uzn1d1yOii38DDjOrSxG+3tuSwXu9vIoaEw5IZKWA6axfWlb9bwDdX17uO0fdO33dtt/kTRbspeHRjvG7gb8oa3iX59qEn4WcKFbbXtJ+1PH3LN7N9qB0VbH/IIqBdLnJLyk31NZ9D8ZyrFNjdRCHGY6J+62N27Zio903bwCNcs4g8fVDRzz2oz7XpLOtX1YWw64LPCCpNNcDWWek3QudfETk9AuCuegyi5cTzX3W5vqjn01VaLhfcCstq9VNfQZsVkbw9xj1AXgJlSZgQepDLHfUpkzWwNLANu7GgGMKe3CeTbgt5IOsn1ApwyA7Sclnd92fX87BrwBWH4kB896SdUsbUUqs+BcajXNctRx4QbbH5L0DlrGh+1bejbY6PYa6uJxWWBDVaNLqIDzgcBxkja2vaOk/UZB4OBdwJG2r5A0B/AWaqLp91Qm4nPURNNswIdt39izkY4gbZn1h1RliK6jsr5+TjX9vY/KcFyeOv+S7TQAGwZa8PjezteStqFK4GzoNLOcLG2S9CUTpZLWoSbo9ujJoAZIV/mEL7bAzrNU7W5s/6St8lgBmEvSsVmNMPy1c+M3AcdTDRoXoiYMFqbeGw/u2v1xYHfqmm40B5c/QiVBbN8Cx+dKepIqb3ObWlmQlojyTUkf8sjvPzEiSJrX9n2SLgfeLekZ28e3BRKrt91+LumTVF+HH/ZqrAPsCaoMy+xQcUCq94lbgt97qEn7i3s3xCmXAPMw02aG1ZawdmZqXkX9M30W2CzB5XHajPt9VAbdhyQ9a/uIdjK0HJXVeVI7IK1EZSzFRKjqG+1J1Zkz1TBuNerEZFMq2PndtkylY7QvpeoZV4O6K6hmfVsB89heTlVb9H/AJ6lsvD0Z7+JnjJivrV5YDzi2nZQc0t6cp2tLjE5v2cyvAdZKpmL/SFqDygrdj5r4+DR14XIIsAzwR0lz2t63d6OMvtg+UVUH953UBecjVF3WnamGP5+iJrA3HwXBZagJjv9TNd3akXovm4OaKP2+7WPaBfi1CYJOHlUTmgOo53Nx4I1Uc7P3U6+prdvHz9xqCMbwoqptuSLVAHtj2zf0eEgjUsu0W586J1vfI78W6BJUuZRdJH3f9nEtsPNxSZ0g8+epCfpZqLIZMYy1SaUbJN0ErGp7V1WZw49S13MASNqEKme08ih57+9TS3zYD9jE9uWd7bYvkfQtKnP2BVWT8E9TpQYTXB4Ckj4EHC5ph3Zu9jTwgXbs6QSZV2kr05ahjrm393LMA8X2U5IeBQ6VtGJn5X3z9vaxg+0RdcxNiYwRoM2Qfw7Yzvb1vR7PcDHejPtlkjamGlL80tVVdGvqImhe4P+o4Hyev4loy6iPpLJarmnbDqEuID8AzEldQM5NNYRJN91BIOnDVCDkh8Dttp9oGWPvBr4E/LazlEtVQsO2H+3VeHuhBYfmo7LnDm8nIe+klv79wPYhXfuuSV0YHTrS3qSHizbReQrwZY+rY78kFaiQ7R0kvZ5aJfIN2/v1brQBoD7Kk0j6MvW/8CvbZ7Xs3gWpFRI/HS3B1jb5/FNgLuo1ebyrSe2Hga8CH7OdidHJ1J7PXwK7276oa/teVHPIZWnnWVRJp0PHu1CKIdZJVulj+/zAc646l9EPLVC/HLVqZ8Sv0mnLtL9JNep9P5Xhena7rlqeWo5+UptAfrSHQ43JpCqN8TzVs2UJ21u27d8DFqMapX0Q2I4qETUqV/V1joOS1gY+anvz9n62EtUM/F7gGOr69igqMW3dTL4NHVWN8JOpLN0LuspjfIBqRHu8qoHwxsBXR8vfpvs9WtKZ1Pnq16meCctQfZY2HImxq2QwDyMTOhm0/XNJL9aeiRdNzoz7Z6hmJpuO1jfPAbYAVcP3ms5SIduflXQoVQPp/VSt1Q9R2W4xOJahGqc9BLxD0peAf9r+bXuNf1bSd21/a6weF9qx8v42AbJVy1w+pWUy/6wdAw5RlczZjaoxm+By//2Xej3+q2XCPmv7BknHUw1AV28By8Wp5inRQ6oyR5cCD0r6CvCA7b/Y3r9loq0j6QXgMts3Stqpr/OPkUhVauw/ktYF5rD9cJuQgnqP+w91ERmTb1qqqd/f4CXl3HZUNQP+mO0TJM0EXJfg8tCYSBC5E1R5KzCt7T93bnMaWb7MRJ7HadoqsnmB/7UVUbTg/JlDPc6B1F4b2P4r1cTwGWBJ4FDg85JeaNdVMwLvk3R2gsvDn6qe8l0e14PoMOBKSdvZ/pHtr7WJwV9RTVk3GC0BuwlYELgb+CuwraQ9qGvZ26keDE8C+9leXVWX/p7Rkh07gtxI1Xi/DlhC0pa2j2ynbctJmsm1Ov2szjF4NGjv0Z1zqTUk7QtsybiSdhuNxOAyJMA8bHSdDC4FvMn2cW37NLZfGKtBpEk4iWrodyf1pjF9V5D5I+2AdKikExJYmrj2uoMKxr+bWkL8dHtOn6Vmus8AFrR9paS/OKVaBlxbwtW5cNmcqrP8IBUgvb+9uV4oaXpgbUmvHItZSOqqT2/7ZFW9wM+34+jP2kz38ZJWpmqvruYx2PhwIEhakDrReZI6UX+17aslTasqQfI3SRdS2Vxn5cS899px+++SLqCySj8OzCfpRtu72v6hpA2pE9nnJZ3X0wEPoHbO9Hx7bT5HBU4A5lCVf/oCVQYkPQMmg6TFgH/bfkTSDcAHJZ003nP8X+p1hu0LezneMUiAu/4WwIsXrstQzYPW7tXgRpC5GHeseFELLq8AfJHK9BwVwY2Wgf0X4K62quVfVAbzgdRr6gTGXVcdIekVth/v2YBjsrTzte8Ar2/JFY/avkdVAmKZTgZ6mxi8Fzh3NGTgT4iq5vKeLRP/n8D+VBbsJVSTw1tU5W7eImlu27/v2WDHGEnvAua0fZGr9vJlwAbUyrOl2wTXT9sE11vbMWjUxXK6z6Vsf11Vg3kW4HmP4Hro0/R6AGONpKXaP1WnkDfw4snge4GfAPd0bX9h6Ec5fEl6a2fWnZfPuH9a0motOP8H4L2SZh+NB6SBpKqreBTwOuBs4E5Ja7QL9Wfbge9Z6ngxJ1TNoJ4NeJRqJ0KHAbO3jJIfAiu6SmFcDqwHHNEyducGvjRGg8uvAfZVNZsDwPZp1PP1WUmr2L6aqhc+P/BJ29f2ZrQjWzs2HEvVO3ucWsJ2uKQlbT/fteu0VE3f6LH2N9tV0jTAcVTJmL2omoJbSzqvXWyeS5WXudZNzwY9FSS9WdKK7dzqlS0gNJ2rse+Kkr7Tdv0EtSpkc6cG+2SRtBpVW3lNVYmcG6jMr7e2ybxOQPN+4Ek1PRrumNKe6iWBm1XNqp5TLYnv3D4DlTCwWYImE9euyX4kabru67J227xUU+XjPYrqirdzxw9RDd/eCqxKvdf/D5jX9slUUsmnJM2a66gR4yFqMuT3VH+Fvdtx/HqqWdjrOjvaPmiUB5dXBvYGdrJ9ve0nbJ9jewPb36Yaq0GVgFmE1pg6Bl87n7gK+KWkPVTlDc+ggst3AxcB75G0re1jgN1G2zGonaMD0N6/p2lfP2/78ZEcXIZkMPfCRlSG6PvGu0CHapjyLXfVt4txMuM+8CR9gHr+NrT9B9Xy1n9QTWCmAc5oB751qOzFB3s32tFLVU/8AGqp2t/aRfqfgU0kfYxq8Lk2cDVVj/1Sj8HO76rmG9NSJ4ZrS3re9m+hgsyqOtWHS1q2vZ4/0CZHYgq1QOU+VNbWZW3zD6mZ9ZNV5YfuaVly61MBvOghVX3h71L9Gl6QdCvwPeB3wC1UJvo5VEDhTOCDI/n/Q9LHqaaT11HnAO+U9Anbf1E1Qf0e9RqGCpSeaftlWYrxcpJWpc4NtgD+6GpEczBwELAt8CdJ51NBqm2pibwROUkx0rTgfqeB1x+o5e/L2P6vxi23fUbS0UkGmCyLAdN1X+R3eYJapnx31/M+Kti+uAXhjqKav65NNXtdUNIpwKnAaR5FS9JHO1dfgWeAr0l6C/A2qgnz/sArqWzedUZbsK5bu36aCdgG2Nv2uZJmB2al4i/X2L4HmElVEmMT6ho417dDxPa9qhJm3wDeC9xKnas9B/zO9p6qxuzvljSH7cd6ONyp1hL2Xuj6Xu0c/b3A0rYPGG0JpWnyN0Q6Jyaqpe3HASfZHtH1u3qhZS1eSF1EPwu8iZrtutZVBH5zaiZ+i5wUTVoL1D9v+0BJM7SLknmoi8olqRPvy6lGCBu2zNoYQC274BBqieYy7irlIOnnwCeB1W2f3bZN28fk1KjXAu07UwGN+YDVqf//EzpB5rbfT4BdR1O20VBqJ+ezUhedh9r+jcbVoex8/gIVUP43MAfVdCNZ4j3UJql+Bvzc9tZd2zsTBfMAn+6cd0iaz/YDPRnsAJC0NNWYZxOPazr5NWB7YBXqQmUeVwPgl5QPiIlrQbajgdNt/6Irs+YF1XLVranO5q8BTDX+HJF1AkcijVcaS9LhwArAO10NgTulzWIiJC1s+652Drap7fV6PaZeaL//PsCy7fWzmFPqasSQtCzwyq5rhPGDWW+kspc/RR2zlx7pAbvJIWlXKj5wFbWCa35qBc5pVMPa+6jknR84PZp6ol3X7QF8C7gL2JXKwt8eeAGYZiQnCnYfS/v4v3wXVbVgZ9u/6tUYB0tKZAyRrllvAX+nOm7HFLJ9MbAytfz9EKpG7dJUzeUZqBn3rRNcnriuZayLAfO2r59tB8CHqCylTmO/3wBrJrg88NoS1+8AH6FqLp8tafmuXb5GLVu8trP8eIwGl1elZrp3a0uHbqMCIDcDG7TbkfQpKhMnF9f91KolPEFlincyutxu65wcHUYF8TYE1k5wubdacHlfYHdguhZo7fgTdc6xk+0z2yoVGPmrUeYETrH9xxb0xPb3qJUgp1CNei5r2xNcnjIzUiuWXsz2bsFluWpXn2V7K2qSb/UEl4dOuyi9W9IRknZoiQHbUJNL10iaxa20WY+HOqyplmjv1s4Z/gs837ZP37XPmCj3Yvsc6vzqj6o6tJ2AyJj4/Ueydu77Y+DezrY+MiVvsn207Q9Tq5ZGdXC5axXC3VTD9IuoxtM/oXoFTAssYftPwOcSXO6dNinyXaqM26y21wC+0a7z/jvCg8uvBHZqEx2dc6juuOurqeScX43GY20CzINM0jsl/VLSa1qa/zNU1s0nJX2y1+MbiVwlRD5DFek/wfYHqNIiz7SD0qhd+jNQuiY8zqDqHL2rs61lez1FLSW6yPavW0AvBpCk/6OWr61t+0bbp1P1wo6U9P622yPUhf7aLfA35pacSJqbWta/n+1zJL1e0jHA09Tr9yrgQElHADsCG4/kzMzhoJ0EPQUs3C5W1DkxUpUh2Z6qFf70aL9YGe4kzUWVKNnG9v7UCqllJH0FaikiFWTermU2PtW2j8hjiaRF2+/8CuCjkmZ2NaSdFl4MMt9C1RaNKSDpPQCu8kt/BlZtActOXWu34/HHVHVZn3RKMAyJrgvQmamSN/NSDZH2l9Spp/4YcE77m2VSZQIkzQH8hyr99A5qcn9mSa8D3ihpljZp9bqJPMyo0oLMOwAXSpqmBSZH5HvEWNFWJ30T+IrtayTNoyoN9aKu67rO++OoXdknaaWWzT0HgO2fUM/PB9uqrl/bvp8qx7BIS6hKs98es/1zKjnih6oSZw/1ekwD5EnqGnV+STvCi0Hmzv/iGS1pcsSej09MAsyDSNKbqFT/+6nlRweoaoPeRi0DeJukmfXyml8xCZlxHzB/oE6y12tB5hdcdejWo7LEU69yELQliUcDjwN3dLKNbB9BBZl/Kun9LXj3dWDULZ+ZXK6aqR8Hvq1q8PljqiTO/2z/w/ZRVFmcg4FVbN/Qw+GOWJI+KOkgSRtSJTKOAw5RNU18oSsr5pNUU5RkifdYu8D8HLCH7T+0zb8HfkRNHH4NwPZ3qYDhPD0Z6ACRND9VJmcpaonrtdRk/UyuTtydJj0GZuvRMEecrvOmb0m6sX19KrWk+OMtiN8JWK4GrEXVuIyh0ylTchnwbapp19HAkcCJVB3Re6lj8wWQ8+G+tIzPn1KB5fOoY8jiwBpUhuMJwK+BK4HTWhbamOAqn7R8e78fdQGP0aJNAMxPvQeeafsCSa+lGvcu1Nd9RvvKR0mzArtR/7/fUpV/BHjA9l/bhMkLkragemEd4VFW83Yks30KsB/wVUmzjuT3LklvlPSado16DrWibjFJ34T6X9R4zWRHoyyhGiTtYPcFANtbSXo9dWF+bMu0W5CqhTSf7X/1bqQjV8tmnJ6acV+Ktrq71+MaSVxNYX4CbEllwvyRylxcm8qavaOnAxyFJK1EBZG3s33p+LfbPlLSC8CZklbvLPMey9oSouepJp872d6/vUG/0G5PvcCpoCqxsA/wC2Bd4Gnbp6oa+Z3csmHvp2pfb0/VY89KkR5S1a7bE9iFaswKQMvm7QSbt5X0bdu7A58d6dk6tu+X9BDwpXZR/XuqpuK0kk5z1Q9dD1iCqucXk+fNwPW2PybpdEm/s/1+SUtQpXCWbVmyS1Crx9Z1Vw3gGFzt+PzBtnrkOuBkaoJvU2q5949tH9eyy1enJplGZVbU1GgT+z8AvgTc5Wo09QtqQuqv1CTcUS3JYkHgSduP9GzAPeAqjxXD27TtvXAb4DuS/knVxT+hBbTGnHYtex41+XkycJikBYBb24rH6VWrxj8PfMr2jRN5uBhEE1od4eqjdaZHcIlTVdnLq4F7JR0KPEpNeswCLCdpJ9t7tiDzS2oyjzZp8jdI2uzLklQBeaglLE+plr7/H/BRqu7qUVTN4Pwh+knSbDkpmjqqbq3vohqo3Qv8xvbfezuq0akF7Z62fVS7IHw7VZP93paN29lvY+AK27f2ZqTDj6rj+Q+pZoiPKc27ppqk91GZMB+3/fuW9fp2qpTTLVTn8a2oE6Tngf1tX9ej4QYv1g89mTqvuFrVf2BaqlHov1uQeRZgOapO9vYjOVDSVi4sYPvX7fuLgT/b/kq7yH43dQy9AFgJ2MjpGTBZ2gq6m4GrbW/Qtp0NzGZ7BUlvpyadFqdKNh3o1KwcMm2VwgHATlSAf36qD8n7gUWoY/OtVCbjPybwMGNeO9c6k2oAfFF3kEPSnFQj6/cCf7V9WO9GGjFhbZJkXeALtv8jaTMqjnCw7c9rXCPmj1M1bU/u5XgHm6pc1gwt4D4ttdrzANvnSTqEakZ9G3UMfR64vJXJiEE0oSByZ3s7p5vW9p97MLxBJenH1ETHF4EtqPfnBYArgA8C59o+oGcDHCIJMA8wdXVvbkHmNwLbATNQbwhPtttmpTLBTspJYcTYIenr1GqGrahM5k5W4buB42zv1KuxjQSSPkKdLC7bymfEVJC0NrW8+mBqufBFwDVUMOlNwDdt/7HtO4Orj0D0kKR5gJOo8hj/ospFLU8FmW+jAs+PtcDzdLb/17PBToV2DjU31Wx2QarJ59Gq8mNfAU62faGqxNBHgAeA+23/s1djHokkzUbVsr/S9hZt29nAnLaXa99PO9qXWQ83kl4BnE297i/q2r4vVapkWarUwybU6p7DgOeTsDJOV0BjAaqUyEdtPzl+9libtFuNSgDaxektEMNMO/fdu32c3lmR1JJRDgI+1pIENqGCW+uN5gQVjatBPSNwge0dJG3bbr6CWpG3MfBWYFHg+05/liHRNdHxsiQgSctQf5u1bf++JwMcZJJOAARsQyV+dBrQr0YFnFe0/WjPBjgEEmAeQO2i5yhgjc5BrCvIvC3VqXhnIPWtIsYwScdRAZMbqSWZf5K0GFVf+BMjeYnQUJC0BlUaIKVx+knSO6lO2ie3IPPqVPbnd2wfJmk+agXOI7YP7OVY46Va1unXqGyINwEXUxdU11HnGqe4unOPCpI+QTXwfIKqDfwk8Fpq+eGBbUl7mlJNAVUzpPuB/9m+rwWZrwT+ZHuTts/pwFttv76HQx2zVPV/fwmsafuB7iC/pIOBy2yfJOnDVF+CZOaNR9JrO0k8kn4FHGL7V+376dqx4+3URNYVwPRO+acYZiQtAvwM+Krty1XlIUVNAj7QSkP9kKrLvgxVEmvUrjRpZYP2pxp0PkrVUz+AClz+gerBsI7t89r+M3cS/GLwtJjXm6jVIu9wlS17Mcjckh62AG63fX4PhzpgJC1PlQ+8xF0NClv5pWmpQPrTkmairvvtMVDWMTWYB4ikGW3fKOkG4GeS1rX9YJs5/zv1xrAtsKDtO3s72ojohc4Fou2NJc013rL1Fam6iskSmwTbZ0q6aDTXrxpMLUD5GmAbSc+66i2bmmn/D0C7aJm5bYthpGWGHANcDiwM/KJrddRWjIIGd5LeQb1Gz7R9RsvmnIe6mJyXqg08D3A3lcmc4PJkatmcp1KZXze3bJvfAW8DrpV0kO0v2F5L0omSFhsLF0TDRWeliO1/S/oHVX/5Z666jZ2L9SeAtwB0SsfES7WVoidK+o3tHakJlBUl/d32LV2ZdcsA76OWzye4HMPRQ8BNwMNt4mk76nW7qKTjbO/VXu8HAO8d5cHluanM0MuoCdHnJH0W+KTtg1TN1FZuZTI611wJLg+yrkn+G1R9QK6UtIyrPnbn7/CMpKNtP9Xr8U6trt/3B1TCw62SdgbusH2D7TUlnQKcIWnttpJwzFQsmKbXAxgNVDWR9pM0j+0tqTeB0yXNC9Ux0vblwByM8C7uEdF/7QJR7etHoJZmStqUyhb9xmh44x0KTt31fmuB+V9TFyObtwnR04BjgRUkrStpXWrS49DejTQmxPZ9tn9n+6Su4PI61HLQP/Z2dFNH0qqMW956dJsQuYnKBrmwZdQfQ2Udfl3SLJ3jakycpPlt30fV9P0V9Vp5A7Ui5CjgeGA7Vf1KbH8qweWhI+mD1Gt69bbpr1S95U4wuRMUvQ94Ss3Qj3T4ayvBPk01V/oC8D2qFua2ktaRNIekLahyAnvn3CuGMQP/oxpU3kSVfDgF+BawvKT3unq4LGz7+p6Ncgi4SuMdTyVDfEbSjNTqu87/79XA/JIWd8o6DaW5O1/Y3ohKgPizqk/W8y3rnlF0nO3E875NrR44DFgf2KkFmrG9LvU6Pa4nI+yhZDBPJVUn9z2ok/NHAGx/pi1fO13S5tSMxSpUc477ejXWiOi9PjLtlqOWeW022k8Mo7e6lwu3rIKLqInmrSQ9b/vnLVaxFdWkdhXbN/VuxDE5Wkbq+tTfbX3bt/V4SP3WtfR1I+AGqmzQb6kSGW+jgkHftH1wWx12k0dojemhpqozu5ek820f01YoLAjcbPuEtsx6NuAOYGNJu1M1rZMdPgRassp3gD2BB9vmQ6jA6OdbVth51PXEp4G18rd5OXX1CrB9raqx8hHU6rAtgS8Am1HnXTMC69q+oUfDjehTW3q/HFUS6noqmPwWqk/GLzqvcVXJuNdRAb3HezPaodHJGm2rml4AVgDOAbC9Uvv8V0nXkNWgQ0bSu4DfSzqeqjG8v+1tJH0HuEbSO2z/T6OkMbuk1wC7StqBaoa+G/Al20dK+jxwYDvfegLYgIr/jSkJME+FdlH3FWAr23+UNIOqjt10tj8n6RvUhdLjwGLA1rbv7eGQI6JHupbTvEQL6l04XrmMiAHVllWeK+lk27vAi0HmC4AXgHVVJZ7OoUq1/NVpQDtSPAvcTtVvv6XXg+kvVS3ZY6mlr3fb/jewtqTdgLWpCfoNJd1q+6e2L+jhcEeUVmLkPuBSquTC01Twfisqw/N/wKkt0+hC4L/d9QRjcKkaHx0IbGj7qq6b3tCSVraksvTWo2qvrp2g6Mup6ilvJGm/zvWW7etVpYN+DDxl+3vA99r12guZoIrhpiWv/Qj4CTArtWLnCCpwd1nXfhtSzcN2hT4TWEYV2+4KMp/Z3scWAP6oKlX6dNvva70d6djQdV07M1Vma16qYf3Ckl5HNaBeBThH0mqj6Fg7G7AQsIjtqyXtD2wqaSFqNfIGwF1UA95X2/5X74baG2nyNxUkzUXVVv4KNYOxAzXbODPVbOcLVH3EmYDHbN/do6FGRA913oQlLQW8yfZxbftLOplHDIZ2sfJmajn8AcDPbX+n6/Z5gX2AX7bMkDRM66EJPf8a15l7Xqo526hpBippJaocy27UBeN8wLm2L2m3L081TD4UOAvYYBQttRxULSt8T+CLti+TtDHVIPKXtk+XtDWVGXctcOxoyDAaaSStBbzF9u5q9Sol7QN8Bjje9mfbfrMCz+e137d2bDwBuAY4wF2NDyW9l7ou+8ZYvOCPkUHSq4HTgC/b/l3XtiOAq23v1IJ3q1LBrHU9imsu96X7HKllcC9H9Wc43PaDE7tvDBy9tPHsssDHqHrhl1DnFG8DFgc+Dlxh+32j5fpC0o7AWsCHqDjfAcDywKa2L+zh0IaF1GCeOo8C5wPfp5YELAqcDHyZmnFc2fY/XMW+E1yOGOUkLdWWCiFp2s72Flx+L5WNcE/X9gSXY1B1lXG6zfbF1AnguqpGKAC0E/J/A4v0ZpQxnj4bK7bg8grA4cCcQzieofAfqkzQCcDZwNPAKi2wjO1LbR9OLYndIQG2KbIENcG0i6RV2gTnxcDHJa1l+yfUOewbgFl6OM6x7PVU/fROr4Z3A6+gmge9sXO8tv3fvPZfTtLskl7Z3ss2pCajviJpfunFGtU3Ude9mUCJ4ewp4Bbbv5M0bSv5cgdV3mWTVkrnfqqMzhpjLbgML15TTdO+PhO4isoqzf/2EOlMXEv6oaRtgL8BZ1DvWStRZVy+SpUi2oIqSzRis+wlzd1WvXQcRE1kvr1NZP6FKil2Ydt/TPdGSAbzVGovtv+jLszP7CzPkHQk8Bvbx/dyfBExdCQdALzb9vv6uG0L6s3nV0M+sBiTWhmnk4CvtzJOs7ayGB+gsmEOBP5MlXD6BrCmR3D93tGgTVB9hVpaZ3c1qWnZeacAP3I1ZRx1urK0F6ea/E0PnG379z0e2oglaR7gm8CdVMO4I22f3TKZlwcutn2SpDltP9rDoY5ZkhamSjj81PapbduMtp9W1RCeicrIzUXbeNok6vbU8uwf2D66HSsPp4LKR9u+uQXmvgGsY/uBng04og9d731zU+UGtu+UgZI0k+2n2qqGm20fNRZXQI7/O3d/r2oml+bfQ0DSR6mM3Z2oCez5gZWp84tFqNJbt1JxsRFfak/SnFTw/Brgd21SA0l7AovZ3qB9fy5V5u3ksf5enRrMU6kdzK5oH8CLndzfRi1JjIhRrmvJz9eA4ySt0XkD6nB1eI4YSk9T9XmfkjQT8LWWAXs/VR/sg1Rw+Y3AegkuDwuLUX0cnutk6HR5AtjI9t2jZZnh+DoXi7ZvkXQc8ClgPUnP2f5Db0c3ckjqZMP+FXgYeIZq3Hko1TDuBdvHSZoReJ+ksxNcHjptOfHswKUtI/kR4FxgRUnT2z6pBZc3oDJytxiN/+9TS9JHqBU6n6YaVh4k6V7b57fSL3sD35I0A5VZt0WCyzHctBJRG0m6Hvg91dhzLUkP2v4LdS4HMAN13AAY1ceDPoLJagH49wJL2z6gfd+pyZzg8hBQ9XP4BrCt7Yu6tu9LlcZYliofuwnwrKTDqLJOI/b1avtRVR3/9wGHqnom/Ab4NnCeqhb6icCvqOTSEfu7DpRkMA8gVcfI9YCtqYv163s8pIgYQu0i5lvADLZ36PV4YmxrS7S+DHyYWiJ/IdVA7UaqadoVtk+RNLvtUd19fLiTtLDtu1qW3aa21+v1mIYDSW8EPgEckdqKk0fV0PNBahLpy8C/qJUKB1L1q+eiAvdHuholvcL2f3o13rGkHZOnA/5OldK7nurfcivwP2o58RpU49W/U3VW1871xMu1urSHAH+3/eW2bWNgRqph5aNtYvXVVAnDG23f2avxRvSllRr4LrU6aV5gDuByqnbtq4BTqZJG6wG7AKt6BDfznRRJi9m+vX09fpD5XVSpwZ2zGrQ32vnFL6kVjw+MV4f5YOCytiLqw8C17qqDPxpIWoK6flqaeq/5F9UU+StjcVXBhCTAPIAkzUxlhN1s+9ZejyciBpekd1JNqbYDHrX9mKr5xnlUndBRuYw9Ro6JlHH6KTXTfuxozYYdKdrk9HeBi4C7gU/b/lTLZHy27TNm/0bdz0NMHkkfpCaUvkutYngT9dq61vbxkjangpdbeBQ1ixzuWj3VZ1pm8hJUZuJbgGmBX1ATALNQk4L/AW7oBFtiHEnvp563aajg8XXtvexnVC3xOYHDgIdsH9GrcUZMTJtAvQH4kO2LW2moPYEfAncAq1GNKf9IZeBva/u6Xo13sLXg5d7A3bZ3bdu6y2B8AnisPVdj9pyoFzrvXe3r46leGT+zbUmdVXf7AM/Z/uZEH2yE07hGvN+l4n5LUP+fj+c1WRJgjojoB0lvAv5LLZGZDXiS6mB8RVsu8wZgL+DpzGjGcNLKOO1AdR9PWYwekjQH1ZhmXaqEQSeD6atUoOk24HlgkUxcx5Roy66PAt5JZdx8ispq3pzKvFEyl4eOpP8D3mz75LbE9iCqseWNknamJqu/Dzxhe/dejnU4axmf+wMb2v6LpC2pFTpvBgRsCiwFvJ2qCbqF7bt6NNyIiZJ0CvAa28t0ff8r28e07+emGv/NaPuR3o108EmahWrk+3HgDtt7te0vZsnG0GsT1u8F/mr7LElfB14DHNY94SHpS9T18Hdh5Db0m5TuyQ1J8wOMtkztqZUAc0TEFJI0K3UhiO3PSHo98EmqscERVC3A1wBftP2vng00okvKOA0vklYFtqGCJbdRnbc/Q9WwuwSYh8pinJXK1Pug7X/3ZLAxIrWSK/sAy9p+onv5cQwtSdsCnwPe38o3fJmquX4RlbW4L3Af1dxyp5w7vFxbdt1Znn1u2zYdsEH7+Lntn3btnyXLMey0lWVPd61QOpFaZXIx8DpgA9tPjpXXb8vkfrJzzJO0IvX//C/be7RtCTL3QDuH+A71HnVPS6KajaoTPi3wB2rV7irA14G1bN/Qq/EOlWTQT1wCzBERU6jVUVwS+Gzb9BVXh+f3U+UIPgp8hMoe2zpvQjEcpIzT8NFO2n8AfIlaBv/PdtK+BrAcVTP3qLbscEHq4mtUZy/F4Givtf2A99l+uG3LxdEQ6a5xLelIqlzRR6nVCgcB7wG2sn1e2yd/mz60CbnvUvXFF6YF6ttt01CZ+W+kamIe4WqeGDGstAz8HajSGLfb/n7b/mNq8n822/+TNNNYeA1LWhK4GriXakL7KHACda66HFV2YM+275gIuA8XbaXN8dRqkau6tr/L9jVt9chKVDKEgC8lcSUgAeaIiMk2fk1U6mJmO6qz8xdsP9lumxXYHjjJ9j96NNyIGIbaktczgV1tXzTecrs5gY8xbjniYb0baYwWktagGkQtRa1czcn/EJC0HFXu5he2j25LwA8ArrR9lKQ9qEDp8m3/BJf70CbZ9gMOahl0hwIfAt7VFbyfFtiWCj7vbfuxng04og8tuPwdqsbyC8BawKG2L2y3nwC8Hlihcz0xFrTg+lrAF4EtqKanCwBXUIHmc20f0LMBjlGS1gLeYnv3rrrD+1Ar7Y63/dm236zA82NhQiQmzzS9HkBExEjQai5fKmk+eLG21E3Aj6ju7ztLmrZdIP7X9h4JLkdER5uUgpqQepbqFA+V+QFAy8i7CLgGeGOr0RwxVWyfCSxv+4UEMIfUNMA7gB0k7Qq8Arie+t+eBdgVQNLqMHprVk4t2/dQJceuaN9/Bvg1cI2kV7Rtz1ON/RJcjmFH0sLAz4GDbR8HnA78DZi+s4/tDYE7qXOAMcP2p6n/548BawJ7UMHld1KT7Zu2yfcYWq8H3gp1fJX0buo97LXUe9g3223/TXA5uiWDOSJiEiTNaPvptrz1tVRztAfbbdMCy1CZM9+0fWcPhxoRw5Sk13YmnST9CjjE9q/a950u3G8H5qYurqZPE7aIkaeVu3nG9jOSPk7VV10CuJEKKG0E/NT2fpK+Apxg+77ejXjk6Bwr29cHAytSNcYTVI5hSdIsrezFt6kGdhvYvlXSmdQ1xXXAQ8APbd8iaYHRfDyQtDwwH3CJ7Ye6tv+Cquu7drvmmonqaeP0Dhh6bVLkx9R71altW+d6+DPATMABmRiN8SWDOSJiIjr1KyXNY3tLKmv5dEnzQs3q2r4cmIOqQxUR8RJtCeGJkvZqm64EVpS0OEAnYEJNVm1WmxJcjhhpJL0ZOBDYrGXX/gmYjmq2eClVa3R64HuthMb+ozmY1F+S3iDpja2kUGcynzYR1/n6c1T91ou6VohEDButgd2xkt5ge3fgLOA0SUdQAbrtgJ8CiwNfb9cao/J40PU/+gPgJ8C5klZtdZixvSbwJHBGC8o/ZfsfCS4PDUnLSvpwC+wDPAKcS52rbgDQgssbABsCv0pwOfqSDOaIiAmQ9DFqqdYuwC87HYxb1sxbqaYy/6C65+4CfML2vT0abkQMY5LeRpXU+TlwOHAEcD8VbP418EmqXuvaY6ELd8RoJGl64PPAa6is5Y2B9YClbG/e9lmXqmO5me1/9Wqsw1WrVXsQVSpoOaq8yz/Hq1f/YsOv0Z7xGSNbqxk+J7CL7b9L+iLwLeCDtq/r2m9224/3aJiDTtK8th+U9FFqMv1fwPupCbibbf9/e/cdZ2dVrXH896TTi4WOeKUKAWkqUjQgkSYiLUpHmoCAEBREBKRIQMJVwAsiSJfmBQKCgIIoJVLupaMUKZdOAIFQEkqe+8feBw7DpDAhc87MPN/PJ5838777nFmZTCbnrHfttQ6r686j7ODapHXR9h018T8AeACYhdLGaX9KL+zXKe91v0HpG/4AsA7ldWoG+kWnkmCOiOiEpHmBc4Ef2r5V0iBgJmCA7Rck7QesCowHPg3s0vxCMSJC0iDbbzZ9vAwlsXxWPe4JfIXSh3kwpc9oXrRH9CCNxGdjEHB9wz438CNgrXrcG7ixVjEiaRbbr7Uu6vYkaRjlBtz2tm+oQ6XWoCTj3uiwtp/tSRmOGO1GTUPB68f/SRlAeaDt+2u7jA2B79i+q1VxdhdJn6L0nN+fsuPzd8Detq+XtAdl18dJwKvAfsA8uWnUPRqvU2tl8uLARGAZSruSSyhV9zMDw4FXgPtSVR5TMqDVAUREtKmJlEFcE+p2of0plTQzSbqbkhi6kLLF7WXbT7Ys0ohoO7Wf8laSRjd2Nti+R9KOlL52E2z/nLJVflZgku3XWxdxRHTRAsATNbncv+52egHYV9K2lDfmE4CtJV1m+3ZKZVg0kdSPMuTrNqCRwBgNfIJSPfc+jQrmJJejnUhaAvhO/b//OQDbe0s6Fjhc0s62D5U0G/BfkoY1J6N7qVkpPycXsn1b/VpsK2kBYDfg28ATwDbAwtnZ0T0kDQWWBs6j7Mj9PmVnzShJP6EUWh0DvNq4ORoxNenBHBHRuZeAqyj/sT4ELEL5D3gfyhaitWtvsPuSXI6ITjxJaaWzp6R5GidrhfIPgLVrVQ+2X01yOaLnqT2Cz5C0J5S5DJL61WQpts8A/gu4ABhESTwnKdpB7Ue/GPBTYBywq6SFKG2DZgbensLDI9rJnJQdj3s15rUA2N6HUqF7dv34B8BGfSC5jO17gb8AJ0qaA7iG8l5qNLCH7fNt32h7lySXu9WqwI8lzWn7ZuB8YDdJG1GS/t8BrgWWaLxejZiatMiIiJiMWlU4FFgIGGN7Yj1/KvAX22e3Mr6IaD+1KmlQbaXzCcp27wcpb6Seq1vp56ZsB907N6gieq66w+krlMqvS2yfVM8L3p9IljS48Toi3lOHoB5I2RF2AmW41GHAkpRE3eo1cd+oDo9oO5JWABYG7gQWpbR2GQiMtj2urhkMnAHsaPvV3tzepb7OedP2q/XjRkL5XNt/ra0GR9heoV7vtV+LdiNp9sYg6fqediFgfcpukeOAL1K+R6+sa/J3E9MsFcwREZNRqwrH2r6gKbm8GbAcMLa10UVEu6mDQS8GrpW0XX1TuTOlMu/7lP52UF68z0NpwxMRPYykxWsP4An1TfixwMaSdofJVii/2cm5PqspCf8apSfrq8COlP7VBwB3A38DFqxf6ySXoy1JWgc4h1LxeRIwO2V47wBg7zrXBcrAz3kBQ+/dySBpTuC/gUMkfQPe/Xf+IvDd+vFRwLOSvp0EZveRtBpwpqTt6qk9gEeBrW0/BdwPPJzkcnRVKpgjIqaBpPkoLwx3otxxzyCuiHiXpHWBUcAuwPyUKpAdbF8l6eP12mDKNvn/oAz3yWDQiB5G0rLAX4F7gesoW4j/B/gsZaDfZbZ/U9fmzflkSPpU83Z4SUsDW1KGnv6WUsl8CPAOcHLdZh/RViStThmEtqrt+yTtA2wMrEv5mfB1ykC/3wObAd/qC9/Lkj5DacEwCjid0iLjL8CVwGmUm0q7A7/PQL/uI2kNyqDpNyitH08CNqcMoTyEUvhwDXCM7UtbFGb0YEkwR0RMA0kzAWsC99t+qNXxRET7kLQwpc/qA7XPIpK2piSUf2/7pbqVfmFKP/d/2H68VfFGRNc1tb5ZgFKl903gdqA/cAewA3Cs7XNaFWO7q62ErgEusH1M0/mlKb0/X7N9pKRFKBV2oxptBiLaSa1e/jHlJshZ9dwVwK62H5M0kDK88m3g7r72HkLS4sCmwOcpr4keo/z7Hll3JnxggGd89Grbxzdtvynp68BSlF11/6C0ctkKOM32aEkjgXOS+I+uSII5IiIiootq9dIylLZji1DeQJ4p6XxgCcrAn5OA522f0qo4I2L61MF9g22/UfuLngJcT0mUvkYZ3tmfkmC+DVjL9vhWxduuJH2Z8jPzTkpP1rNtH990fSVKBfMOtm+VNMB2hvxFW6o3j1cDdgP+QOlnuyKwqe20xQEa/dMlHU4p1lmcspNrfHZ4zHj1xt0+wM2UquXZgG2BCymvUT9LaUu0GKV3+I35e4muGtDqACIiIiJ6Iklfo/Re3dL2HZJ2AD4nqbHNe11gJeBzwOaSrrT9RMsCjogukbQepbL2U5IuB26kvEE/E/gk8FPb361rzwMeSXL5/ZqqFdcBnrJ9g6S9gF/VTiInANi+TdLfKH1sobTIiGhLtidIuoFyk/kQYB7bnwGQNNB2Zi3AJADbB0qap/7+ldaG1Kc8QGnptDRwPrA18DJwgO3tgVslvQHsCjye5HJMj1QwR0RERHxIkoYDlwEb2f5jPTeAkoT6NnCh7dOa1mcraEQPVLcTHw2MpGwlXpIyqPN8SsXiWcB9wHG2n21VnO1O0qC6PXsU8ILtn9fzq1BaDJ1n+yhJW1F6Wa9r+/9aGHLENJM0CBhGGWJ3UaNdRhTpR9+9Gl/vxk2OOlR1bsrP1rXqcW9KtfKh9TGz1GGMEV2WBHNERETEh1B7Lh4OjKMMRlnd9kv1Wj9ge0oS6jHgFNsTWhRqREwHSTNTBlKdaPu6em524MvAdsCRwOOUrcZXUXoFp+K2A0lLAD+h/Gz8PjC37R81JUGWpAxBu5lSZfcd2/e1LOCIyZhSorS2y1idkrw7x/ap3RpcRCVpwcaOuUaLkqZr2wLLAZ+htMfY3PbtuQkQH4W0yIiIiIiYRpLmp2yN38P2WEknUrYXrmj7FduTJJ1OqWJakDLUJgnmiJ7pLWAOYBZ4N7n0Sm3hsBKwqu1fStocGJjk8gfVm25vUbZkHw+8CDz03mX1t/3P2pt5IjDI9outiTaic5K+QGl989zkEnG1Xcb1lIF+D3Z7kBFAnRFwhqQxto+r/a/7AdieZPsMSYsCXwB+BrxQryW5HNMtFcwRERERH4KkT9p+runjXwHDgRUbfQUl9Qdmtf1yi8KMiC6StBQws+3/kXQMcLvtc+q/a9cbSSOALSjDvNJntROShgErAJdT+tJvRRk2NRg4GxgKvAE8A4wHtkuSI9pRvZm8CjB8SknmiFarlfRfoewWucT2SfW84P2JZEmDbU9sQZjRS6WCOSIiIuJDaCSXJQ2w/bbt3WuS+e+SVrH9cq1kTHI5ooeRtDBlEN1QSYcCVwDnSnrc9t+als5FSYy+3YIw256kDSi9q0cD420/KekUYBCwGjAKMDA/8BqlL3MSdtGWbO8q6TjgYknfrEnmd1sPSJrP9tMtDjP6MEmLAw/VtmxXSpoE7Fu/T39V2xGpw8Pe7P5IozdLBXNERETEFNT+oQKes/1ihzeVzb8/k9LPbuUkSiJ6HklrAxtSeiqvDiwA/Bj4KnAcpY/wK8DMwL7At2zf05po25ekocB5wI62xzadF7AosAswG3CA7RdaE2XElEn6ODDB9qtN504ElgU2sf1MPfcd6oC/DEmLVpC0LPBX4F7gOuBa4H8or0l/BFxm+zd1barvY4bp1+oAIiIiItqVpK8BlwIHAXdIWqT2s2tsNWzubbcNsEFeuEf0PJKGA8cA59ZK5TGUCuUjgD8DI4CFgM0oQ/62SHJ5soYAV9Q+9QOh9GJ28SBwKvAOcLikfp1U1UW0lKQVKH2Ufy3pS03/5+8K3A1cVNdtCfwU+HmSy9FCT1MSy4Mo7YZ+Rtklsj1wJfDd+r2aXssxQ6WCOSIiIqITtX/oycD2tm+QdBSwBrCm7Tc6rO1X+7KmMiSih6nJ5cuBI20f1HR+GWBjYF7gGNsP10TTQNvZWjwZkr5OSXB8uTGwr/Gzsfa3focy0G+C7WdbGGpEpyQNBv5Aucm0IvB74KmmfrbHAttRdjRsaPuuFoUafVgtcBhs+4063O8U4HrgGkrroR8A/YEdgNuAtWyPb1W80fulgjkiIiKig/qifSPKC/JH6unRwD+ASR3X255Uj0kuR/QgtS3G0ZRdCpvV4X0A1Arli4AngIMlDa1VuEkuN1HxOUl31kTyZcBNwCaSZm8sq8dNgbVtP5bkcrSbDtX0dwBXU9pf/BHYR9LvJK1GaZEzElg/yeVoBUnrAWcAV0naj9IOY1tKIcS3gSdtf9f2TsDalJZOSS7HDJUEc0REREQTSYsBi1G2vY4DdpW0EOUN5cxkqFdEr1CrFFcG9rB9JCVhNErS5o01Ncl8OXAX5edBdFCT7ncADwC31tN/BFYBtpA0T93hsQWlxcifWhNpxOTVHQvrSprZ9kRK8u5QYFZgAqX9wARgZ0p18+m2721VvNF31V0io4Fz6xHqDQ9ga2BJ4CeS5gGwfY3th1sRa/QtaZERERERUUmaBTiQ0kP0BODfwGGUF+szAavXvsvvDveLiJ5H0uLAv22Pqx832tysA5wI7Gf7gqb1g1K5/EGSVgGWAe60fYukU4BlbH9R0obAesBalErQVYGtbd/duogjOidpT8pNkTOAG2y/KmknSs/11YDv2f6DpJmB2VKBH61Qv/9OA060fV09Nzvl+3Q74Ejgccqw2quAUXm9Gt0lCeaIiIjo85p7J0saCmwODKAMoxpHqWaeQEk8Pd5oiRERPU/dpXAj5QbSSOCJWoHbuL4e8AvgMNtntSLGnqAOQT2GMvQM4C7boySdDyxk+0t13ZrAU8B420+2JtqIzkn6PDCX7ask7QEsS2mN8yfgC5Qq0R/YPj83l6PV6uDUy4DjbV/e1N9+DmAf4EXbv5Q0L2VewOMtDTj6lLTIiIiIiICFG7+p1XXnAaYMRvkkcDhlm+w+wFKtCDAipp+kwbYfpLxBfxUYDuwv6XBJM9VK5SuA/YCRkmZrZbztqiaNLwSG296C8jNzFUlz2R4BPCHplpr8uNb2P5NcjnYjaV3gLGBpSUvYPh64H9iEMtD3RsqN5m9KGpLkcrSKpKUkrWj7LeAeYM56qV/dgfMycB+wpqSBtp9Jcjm6WxLMERER0afVBNKFkvZtnKt9Fc8B3gE2tf08pVLvbeC5lgQaEdNF0vrAYZL6A7+jVCmOArYHtgHGAIdLWtT2xcCqGYo0Wc8Ds1AGoGH7UkobocXqx5tTdn9c16L4IqZI0peA44AdbR9r+34A28cA/wS+JemrwPWUQZ+DWhZs9GmSFgbWAXaXtAhwBXCspDVsv9O0q24u4BkyKyRaJC0yIiIios+S9GVq/1DKoJSzawVT4/pKwG+BHWzfKmmA7bxwj+hhJA0HjgJ2t31THdx5KfAD4F5gLOUm0mLAGpRerBOdN0uTVX8+/omys+NjlK/bJrXCrrFmgVQuRztpaimwPTC/7SOaerAPbHz/Stqd0jf8HOCWRr/2iO4kaW1gQ8qOkdWBBYAfA1+l3CD5CfAKZQj1vsC36nDaiG43oNUBRERERHS3xptJSkXIU7ZvkLQX8Kv63vMEANu3SfobMHt9aLbHRvQwtVfwecAltm8CsP24pP0ovZY/Aexse0xd/3HbE1oVb09Rfz4Opwzwe8n2p+HdHqGTamVdksvRbvrzXoXnoh2uvS1JwBdt/0rSJOC2JJejFerP158Du9Yboy8CGwNHUJLMIyhDVD9Lafm0RZLL0UppkRERERF9UeMmu4AhALb/DnwP2KEmnpC0FWUL+IN1TaoZI3qQmlw+mjKoc5KkHzVdvgv4B3Cg7TGShtTzL3RzmD2W7VuBLwNzStqynnsrvWqjHdWfBz+X1A94GlhQ0uy1ermfK+Drkr5s+0Tbz7Y26uiLanL5cmBM043ReyitnZ6hJJmfsH0wZTD1LrbvalW8EZAEc0RERPQxkpYAflur7F4A5q7nZXss8G1ga0mnUhLOm9n+v5YFHBFdImkuYFNKdfIvgDOB5SXtD2D7GeBWYE9JMzWqlnMj6cOpSY21gbNq24GItlOTy0cCl9meZPtK4FHgL5LmpOZG6o3lDeu1iG5X22IcDRwEbCZpRONaU5L5CeBgSUPrfZE3WxNtxHvSIiMiIiL6jFq19BbwMnA88CLw0HuX1d/2P2tv5onAINsvtibaiOgqSRsAKwFH2W78Gx8LTAK+J+kA2z+zfbSkRSnDkd5oUbg9Xm2XsSLweqtjieioJuyOAfawfV0dlDbM9k6Szqa00Hld0sPABpRe4o+1LuLoqyQNBlamfK9eL+lO3mvfdgGUJHMdVjuRMkw1oi1kyF9ERET0CZKGAStQthwK2IoynGowcDYwlJJgegYYD2yXSsaInqcml48ADqH0XXbTtSGUN++7Av+y/RNJg21PbEmwETHD1H7KQygVn8/a3q4O+Pw98Fvbv67rVgPmAwz8r+2HWxVz9F2SFgf+3ej53TR8ch3gRGC/RpK5Xh+UyuVoJ0kwR0RERK9XE05HA6OBK20/KenTwG7AasAOlDeW8wOvAS/YfrBV8UZE10iaFzgX+KHtWyUNouzanJvy7/oNSTMBqwBbAyOzSyGid5I0s+3Xa1L5JOBuYE3gTNsn1F1L70gaaPut1kYbfZmkxYAbgX8DIyn9le9our4eZSjtYbbPakWMEVOTBHNERET0apKGUra/7lh7LDfOizJBfhdgNuAA2xnuFdGD1b7L51PeoD8I7E+5iTQQeATYx/aLtQf7ANtpixHRC9Wqz72Bp4ArgT8DpwNv2d64ad12wKqU1wJ2EiTRzRq7aOrsj89REs2fpLRwOwJ4x/abkr4JHAysbnt8ywKOmIwM+YuIiIjebghwhe2xNanU2HboWqV8KvAOcLikfjXxHBE900vAVZR+qw8Bi1BuMP2wXl8NwPZbSS5H9E51oN/PgNMo7TEOoPa1BWaT9IO6bgtKu5xf1sF/SS5Ht5K0PnBY7an8O8r36yhge2AbYAzl9emiti8GVk1yOdpVhvxFREREbzcvsI6kI5u2whtA0lKU5PJRwATbk1oUY0R8BGxb0q+Bm4CFgDGN/sqSdgZmb2V8ETFjSVoQOAEYbfu8em5JYEnbV0raCxgt6YvAp4GtbN/Xuoijr5I0HDgc2L22anmAcnP0ZuBeylDaS4HFgAslrUIGqUYbSwVzRERE9CoqPifpTpWx25dRkk2bSGoklxpVypsCa9t+zPazLQk4Ij5Stl+1Pdb2BU3J5c2A5YCxU350RPRUkobYfgI4E1hL0vL10hLAHHX30n3AvsDbwJZJLkcr1Cr784E7bN8EYPtxYD9Kr+U7gL1sn2B7L8pr1Qmpso92lh7MERER0StJuhD4tO2VJG0EbAjcAlxs+9m6NXZ/YFPbD7Qw1IiYQSTNB4wAdgJG2L6nxSFFxAxQh6CtSxmC9pykg4AVgFeATwDr1R0OjcF+A2y/3cqYo2+qyeWjKS1chgIP2T6yXpsXOB642vZv6k2TCbVgIsm7aGupYI6IiIheQ9IqknaS9HnbmwF3SPq77UuASyjDU26Q9CtKT9Ytk1yO6NVeogz7+0aSyxG9k6QNgCOBa4AXAGwfClwHrAgc2kjO2X6nHpNcjm5XB9FuCuxs+xeUavvlJe0PYPsZ4FZgT0kz2Z5Qzye5HG0vFcwRERHRK9SKkGOAu+upu2yPknQ+sJDtL9V1a1Kmyo+3/WRroo2IiIjpVSs+zwV+aPtWSYMow32H1Erm3YGvAqOBG5Ooi1apN0JWAs62/VA9Nwj4AvA94E7bP6vnTwYOsf1Uq+KN+LBSwRwRERE9Xk0aXwgMt70FcB6wiqS5bI8AnpB0S91ieK3tfya5HBER0eNNBN4CJkgaAhwAXARcLOkk4NeUfra7AYNbFWT0bTW5fARwJ/Cvxnnbb1Iqlk8AlpF0WL20R5LL0dMkwRwRERG9wfPALMAwANuXAjNRJm9je3NgHGW7bERERPQOLwFXUXYwPQQsQrnJvA/ldcGatn8K7NloNxDRnWqV/UhgR9sXAwMlzSxpwaY2GLcBpwALSpq7MaA2oicZ0OoAIiIiIqaX7bskfQH4k6TBwMeACcDtTWvWl7RAq2KMiIiIj1Yd3Pdr4CZgIWBMIzknaWdg/rru+dZFGX1cxyr7/YHVgIHAI5L2sf2ipOuBsbbfaGGsEV2WHswRERHRa0haGbgaeMn2p+u5gcCkxmCfiIiI6N0kbQbsB4yw/a+prY+YUSSJUlE/HFga+DNwA2VmyK7ARXXnXUSPlgRzRERE9CqSlgX+CnzP9jmtjiciIiK6h6T5gBHATpTk8j0tDikCSbMCQ/lglf2pwF9sn93K+CI+CkkwR0RERK8jaSXgFmAH26e1Op6IiIiY8STNBKwJ3G/7oVbHEzE5qbKP3iYJ5oiIiOiVJC0PvG77/lbHEhERERGRKvvorZJgjoiIiIiIiIiImMFSZR+9VRLMEREREREREREREdEl/VodQERERERERERERET0TEkwR0RERERERERERESXJMEcEREREREREREREV2SBHNEREREREREREREdEkSzBERERERERERERHRJUkwR0RERESfI+l0SZa0yId4zKOSHp2Oz7ld/ZzbdfU5ZgRJi9S4Tm91LBERERHR8yTBHBERERFtQ9LvarJz12lY+6e6dqNuCK1HkbSkpOMl3SPpZUlvSnpK0uWSdpA0pNUxRkRERETvkARzRERERLSTk+txpyktqpXHawFPA3+YwTE1rFV/tTVJBwH3At8DxgNnAMcAfwSWBE4BbmhZgBERERHRqwxodQAREREREQ22r5P0ALC8pBVs/+9klu4ICDjN9tvdFNu/uuPzTA9JBwA/BR4HNrN9cydrNgBGdndsEREREdE7pYI5IiIiItrNb+qx0ypmSf2B7QBTqnEb55esvZUflzRR0rO15cYSU/pkknaRdLekCfUxJ0uao5N1k+3BLGmEpGskvVif51FJ50paaVr+wJIWlHSCpIdr7C9IulTSytPy+PociwCHAG8B63WWXAaw/QdgnWl4vsUljZJ0m6RxNa7H6tdnwU7WS9K2km6q6yfUv4urJI3osHbZ+vV5tD7vOEn/K+kXkgZO6585IiIiIlovFcwRERER0W7OAI4AtpA00vbrHa6vBywA/Mn2IwCS1gEuAgYClwEPAQsCGwPrSxo2mWroo4Gv1cdcDQyjJLYXBdacWqCSBJwGbAs8X2MYVz/3MOB+4LapPMcK9XPPDVxVn+PjwEbADZK+afuKqcUCbE/5859n+54pLbQ9cRqeb2Pgu8BfgJuAN4GlKdXjX5e0ku0nm9YfAfwIeAS4AHgZmA9YGdgMOL/+eZcFbqbcILi0rp+d8jXfDTiQkiSPiIiIiB4gCeaIiIiIaCu2x0m6BNi8/jq9w5Id6/FkAElzAecCrwNr2L6vsVDS0pRk5inACp18ui8CQ23/X10/ALgWGCbp87ZvmUq4O1GSy7cCa9t+uelz9wc+OaUH1893ATArMMz2X5uuzV+f91RJi0xDUni1erxmKuum1VnAf3b8vJKGU/o5Hwg0D2PcBXgSWKbjTQFJH2/6cFtgCLCR7TEd1s1F+XuMiIiIiB4iLTIiIiIioh01hv3t2HxS0nyUCuZngUZychtgTuDg5uQygO17KS03lpf02U4+z6GN5HJd/zalIhng89MQ5x71uEtzcrk+1zu2n57K49cHPgMc35xcro9/ilJhPS/TNlxwvnp8YhrWTpXtJztLatu+mjJE8GudPOwt4J1OHvN8J2vf6GTdv21P6kK4EREREdEiqWCOiIiIiHZ0LfAvYFVJS9n+Rz2/PeU17Om2G20UVqnH5SQd0slzLV6PSwH3dbjWWfuKx+txrikFKGkWYBngWdu3T2ntFDRi/9RkYl+sHpcCptYmQ/XoLsby/icr7T+2pPS7Xo7y9ejftOTNDg85h5Jwv1fShcBfgbEdE++UVhl7AZdI+j3wZ+DGnjBEMSIiIiI+KAnmiIiIiGg7ti3pFOBIShXzyJrw3IEOw/2Aj9Vjp0MBm8zaybmXOjn3dj327+Rasznr8ckpLZqKRuybTWVdZ7F39BSwJKX/80fhWOD7wNOU3tBP8l7V8XbApzqs35tyU+A7wP7119uSrgBG2n4IwPYtklYHfgxsCmwNIOl+4Ke2z/2I4o+IiIiIbpAEc0RERES0q9OAQ4FtJP0IWB34D+DaRrKyalTILmf7rm6M76V6XGA6nqMR+zdsXzp94XADZTDhWsCp0/NEkj4J7AncA3zJ9vgO17/d8TG23wF+CfyyPn414FuU5PnSkpZutNywPRbYQNJgYEVgHUr18+8kjbP95+mJPyIiIiK6T3owR0RERERbsv0scCnwcWAj3qtQPrnD0r/X4+rdE1lh+zVKAnYeSct38Wk+ythPo/RA3mQy/abfVRO7U/IflPcKV3eSXF6wXp8s28/Zvsj25pR2J5+htBPpuG6i7ZtsH0RJaAN8YyqxRUREREQbSYI5IiIiItrZb+pxJCXJ/DxwcYc1p1GqiQ+W9IHBfJL6SfrKDIrvuHr8taQ5Ovm883XymGZjKG0ldpe0XmcLJK0iaeapBWL7UeAQYBBwuaSVJvN86wB/nMrTPVqPq0l6t1WIpFkpfyfv2wkpabCktWobk+bzA4G564ev13Ord/xaVfM0r4uIiIiIniEtMiIiIiKinV0NPAI0Escn2H7fcDnbL0jalJJ4/ruka4B7gUnAwpRBeh8DhsyA+E6htILYBnhQ0hhgHDA/pV3FbylJ307ZfkvSxpQex5dLugm4g5JkXQhYmVItPB/TkHi1/TNJA4CDgVvr890GvEpJ4K5BGRzY2XDD5ud5RtJ5lBYXd0i6GpgDWBuYUGP8XNNDZqIM63tU0s3AY5Sv99qUAYWXNg1qHAkMl3Qd8HCNbWlgXeDffLBCPSIiIiLaWBLMEREREdG26rC/U4HD66nfTGbdNZKWBfYFvkZpOfEmZfDdtcB/z6j4gG1rAnZnYHNgMGUw3vWUFh9Te467JC0H7ANsAGxPSY4/DdxOSRY//yFiOlTShcBuwLD6fEOAFyiJ4aOAs6fhqXagJIBHALtTEueXAgfxwa/na8B+9fN9iVJtPp5Snb0rJdHe8F+URPIXgFUp70meqOdH235sWv+sEREREdF6Kq+JIyIiIiIiIiIiIiI+nPRgjoiIiIiIiIiIiIguSYI5IiIiIiIiIiIiIrokCeaIiIiIiIiIiIiI6JIkmCMiIiIiIiIiIiKiS5JgjoiIiIiIiIiIiIguSYI5IiIiIiIiIiIiIrokCeaIiIiIiIiIiIiI6JIkmCMiIiIiIiIiIiKiS5JgjoiIiIiIiIiIiIgu+X/hkjeeFqB34QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vehicle Class\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "ds.groupby('Vehicle Class')['Vehicle Class'].count().sort_values(ascending=False).plot(kind='bar', color='green')\n",
    "\n",
    "plt.title('Vehicle class distribution', fontsize=25)\n",
    "plt.xlabel('Vehicle Class', fontsize=20)\n",
    "plt.ylabel('Frequency', fontsize=20)\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Transmission\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "ds.groupby('Transmission')['Transmission'].count().sort_values(ascending=False).plot(kind='bar', color='magenta')\n",
    "\n",
    "plt.title('Distribution of transmission', fontsize=25)\n",
    "plt.xlabel('Transmission type', fontsize=20)\n",
    "plt.ylabel('Frequency', fontsize=20)\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fuel Type\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "ds.groupby('Fuel Type')['Fuel Type'].count().sort_values(ascending=False).plot(kind='bar')\n",
    "\n",
    "plt.title(' Most frequently used Fuel type', fontsize=25)\n",
    "plt.xlabel('Fuel Type', fontsize=20)\n",
    "plt.ylabel('Frequency', fontsize=20)\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABZgAAAFgCAYAAAA2IxyjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAtNUlEQVR4nO3dfbhtVV0v8O9PEMQXEORF5EUsMRVL1BNRmqGYEJmoaaFdwZfEa+a18t4SuwVoFN0yTU0N38BSCVMTTS0kMTURj0YCIkqpgCCiIqAiwuF3/1jzxHa79z5nz7PPWXvv8/k8z3rmXGOOMedv8axncfgyzhjV3QEAAAAAgMW63bQLAAAAAABgZRIwAwAAAAAwioAZAAAAAIBRBMwAAAAAAIwiYAYAAAAAYBQBMwAAAAAAowiYAQBgM6iqE6qqq+qcxVzbEqrq1OH5p07j+QAArB7bTrsAAADY3KpqmyS/nOQxSQ5OsnuSOyb5VpLPJ/lIkrd094XTqhEAAFYiATMAAKtaVR2c5LQk95nRfHOSG5LcLclDh9cLq+qdSZ7c3d/fzGV9PcklSS7bzM8BAIDNyhIZAACsWlX1S0nOySRc/kaS45Lcp7u36+67JdkuyU8mOTnJ9UmekMnM5s2qu1/V3fft7qM397MAAGBzMoMZAIBVqar2T/K3SbZP8tkkh3X3FTP7dPe6JGuTrK2qP0vyxi1eKAAArGBmMAMAsFr9UZIdk3wvyeNnh8uzdfc3u/txSa5Lkqo6fdgI730Ljauqe1fVrUPfQzamsA1sAPgDG/BV1ROr6pyq+mZVfbeqzq+q51fVgn+Wr6pfq6qPVdUNVXVdVX2iqo6tqtrIGn+0ql5ZVRdX1beHZ19cVS+vqn3nGfO0ofYvDe8fUVX/UFVXVdU6mwoCAKw+ZjADALDqVNUeSZ44vH1Ld39+Y8d2dw+nr03yq0kOq6p9u3u+9ZJ/PUkl+Xx3nzOy5DlV1auSPDfJrZks4bFDkgcmeXmSByc5Zo4xleQNSZ4+NHUmmxmuSXJQkkckuWkDz31Wkr9Kcvuh6aahhvsOr6dX1RO7+6wF7vG/hjork9B+3cKfFgCAlcgMZgAAVqNH5LY/675rzA2GsPji4T7PnKtPVd0+ydOGt6eMec4CHpvkWUl+J8nO3b1zkl2TvH64fnRVPXKOcc/LbeHyq5Ls3t27JNklyQmZhOZHzvfQqnpcbvssJyfZL5Ng+06ZhMtvz2Rm+N/PN5M5yR5J/iKTzRX37e67Dvd4yQKfFwCAFUjADADAanTAjPN/34T7/PVwfEZVbTPH9cdmEqZ+P5MwdSntnOTZ3f2y7r4+Sbr7G939rCSfGvo8eeaAqrpDkuOHt3/T3c/r7q8PY6/r7hOT/GmSu871wKraLpNQOkn+Z3cf191f7ttc0t2/kuTMTELm35mn9jskeXd3P727Lx+ev667/3PR/xQAAFjWBMwAAKxGd5tx/s1NuM9pSb6bZO8kR8xx/VnD8R3rg9wldHmSN89z7czh+BOz2h+dyUzlJHnxPGNPzmRd6rn8QpK9klyd5E0L1La+rsMW6PMnC1wDAGCVsAYzAACr0UZtZLch3f2tqvq7TJaceFaS9/z3A6rumeTnh7dLvTxGknyyu2+d59qVw3GXWe1rhuPl3X3pXAO7+7qq+lSSh85x+WHDceckVy2wH+B2w/Ge81y/Mcmn5xsMAMDqIWAGAGA1mjmbeJfcFsiO8dpMAuYjqmqv7v7K0P7rmfyNwEuWenO/wQ0LXLtlON5+Vvvuw/ErWdgV87TfYzhul8nSHxuywzzt31ggHAcAYBWxRAYAAKvRRTPOH7QpN+ru8zKZjbtNhs3+hvWY12+k97pNuf9m0iPHrV9n+gPdXRvzmuc+60Y+HwCAFUbADADAavShJOtn0D5+Ce732uH4zKq6XSbrMe+V5KYs/eZ+m+Jrw3HvDfTba572rw7HH1+acgAAWO0EzAAArDrdfXWSdwxvn1JV99nYsTX3wsNvTXJ9kn0z2dhu/eZ+79wMm/ttirXDcZ+q+tG5OlTVjkkeMs/4jw3HvarqYfP0AQCA/yZgBgBgtfq/Sb6dyTrB76yq+WbtJkmqaueqekeSnWZf6+7vJPmbGfc9YjjfHJv7bYqzklw7nP/BPH1+N/OvnfyeJFcN539ZVXdc6GFVNXuTQQAAtjICZgAAVqXu/nySpyb5fpIDkpxfVb9XVfde36eqtqmqB1XVi5P8V5InLHDL9ctk/EwmaxVvrs39RuvuG5O8ZHh7TFW9vKrulkxmLlfVHyR5UZJvzTP+e0l+I5M1nB+c5GNVdVhVbbe+T1Xdq6qeXVXnDX0BANiKCZgBAFi1uvsfkjwyyaVJdk1ycpIvVNVNVfWNTMLnT2cy23enJG9L8p157nVhko/OaFqOm/slyV/mttnWz0/ytar6ZpJvJnlxkr9L8u75Bg//zJ6a5LtJDkzygSTfqaqvV9X3MgniX5vkJzN+M0EAAFYJATMAAKtad38syX2TPDnJWzIJm7+X5C6ZhK4fTXJSkvt191O6++YFbvf24bjcNvf7b919a3cfneToJOcmuTHJtpkE6f8zyVM24h5vSXLvJH+UybrO305y10z+uZ2f5FVJHpXkT5f8AwAAsKJUt0kHAACwMarqPUkek+Rt3b3BoBYAAFY7ATMAAGyEqvqRJF/I5G8BPry7PzLlkgAAYOoskQEAABtQVTsmeU0mf37+hHAZAAAmtp12AQAAsFxV1Z8neVKSuyfZLsktSX5rmjUBAMByYgYzAADMb9ck+yb5fpKPJzm8u8+dbkkAALB8bNVrMO+666693377TbsMAAAAAIBl7VOf+tTXu3u32e1b9RIZ++23X9auXTvtMgAAAAAAlrWq+vJc7ZbIAAAAAABgFAEzAAAAAACjCJgBAAAAABhFwAwAAAAAwCgCZgAAAAAARhEwAwAAAAAwioAZAAAAAIBRBMwAAAAAAIwiYAYAAAAAYBQBMwAAAAAAowiYAQAAAAAYZdtpFwAAAAAALH8n1onTLmHVOb6Pn3YJm8wMZgAAAAAARhEwAwAAAAAwioAZAAAAAIBRBMwAAAAAAIwiYAYAAAAAYBQBMwAAAAAAowiYAQAAAAAYRcAMAAAAAMAoAmYAAAAAAEYRMAMAAAAAMIqAGQAAAACAUQTMAAAAAACMImAGAAAAAGAUATMAAAAAAKNMNWCuqjtU1XlV9R9VdVFVnTi071JVZ1XVF4bjzjPGHFdVl1bVJVV12Iz2h1TVBcO1V1RVTeMzAQAAAABsLaY9g/mmJI/s7gcmOTDJ4VV1cJIXJjm7u/dPcvbwPlV1/yRHJTkgyeFJXl1V2wz3ek2SY5PsP7wO34KfAwAAAABgqzPVgLknvj28vf3w6iRHJjltaD8tyeOG8yOTnN7dN3X3F5NcmuSgqtozyY7d/fHu7iRvnjEGAAAAAIDNYNozmFNV21TV+Um+luSs7v5Ekj26+6okGY67D933SnL5jOFXDG17Deez2+d63rFVtbaq1l5zzTVL+lkAAAAAALYmUw+Yu3tddx+YZO9MZiM/YIHuc62r3Au0z/W8U7p7TXev2W233RZdLwAAAAAAE1MPmNfr7m8lOSeTtZOvHpa9yHD82tDtiiT7zBi2d5Irh/a952gHAAAAAGAzmWrAXFW7VdVdh/MdkjwqyeeSnJnkmKHbMUnePZyfmeSoqtq+qu6VyWZ+5w3LaNxQVQdXVSU5esYYAAAAAAA2g22n/Pw9k5xWVdtkEnaf0d3vraqPJzmjqp6Z5LIkT0qS7r6oqs5I8tkktyR5bnevG+71nCSnJtkhyfuHFwAAAAAAm8lUA+bu/kySB83R/o0kh84z5qQkJ83RvjbJQus3AwAAAACwhJbNGswAAAAAAKwsAmYAAAAAAEYRMAMAAAAAMIqAGQAAAACAUQTMAAAAAACMImAGAAAAAGAUATMAAAAAAKMImAEAAAAAGEXADAAAAADAKAJmAAAAAABGETADAAAAADCKgBkAAAAAgFEEzAAAAAAAjCJgBgAAAABgFAEzAAAAAACjCJgBAAAAABhFwAwAAAAAwCgCZgAAAAAARhEwAwAAAAAwioAZAAAAAIBRBMwAAAAAAIwiYAYAAAAAYBQBMwAAAAAAowiYAQAAAAAYRcAMAAAAAMAoAmYAAAAAAEYRMAMAAAAAMIqAGQAAAACAUQTMAAAAAACMImAGAAAAAGAUATMAAAAAAKNMNWCuqn2q6kNVdXFVXVRVzx/aT6iqr1TV+cPriBljjquqS6vqkqo6bEb7Q6rqguHaK6qqpvGZAAAAAAC2FttO+fm3JHlBd3+6qu6S5FNVddZw7WXd/eczO1fV/ZMcleSAJPdI8sGquk93r0vymiTHJjk3yfuSHJ7k/VvocwAAAAAAbHWmOoO5u6/q7k8P5zckuTjJXgsMOTLJ6d19U3d/McmlSQ6qqj2T7NjdH+/uTvLmJI/bvNUDAAAAAGzdls0azFW1X5IHJfnE0PSbVfWZqnpjVe08tO2V5PIZw64Y2vYazme3z/WcY6tqbVWtveaaa5byIwAAAAAAbFWWRcBcVXdO8o4kv9Xd12ey3MWPJjkwyVVJXrq+6xzDe4H2H27sPqW713T3mt12221TSwcAAAAA2GpNPWCuqttnEi6/pbvfmSTdfXV3r+vuW5O8LslBQ/crkuwzY/jeSa4c2veeox0AAAAAgM1kqgFzVVWSNyS5uLv/Ykb7njO6PT7JhcP5mUmOqqrtq+peSfZPcl53X5Xkhqo6eLjn0UnevUU+BAAAAADAVmrbKT//oUmemuSCqjp/aHtRkidX1YGZLHPxpSTPTpLuvqiqzkjy2SS3JHlud68bxj0nyalJdkjy/uEFAAAAsKydWCdOu4RV6fg+ftolwFZhqgFzd380c6+f/L4FxpyU5KQ52tcmecDSVQcAAAAAwEKmvgYzAAAAAAArk4AZAAAAAIBRBMwAAAAAAIwiYAYAAAAAYBQBMwAAAAAAowiYAQAAAAAYRcAMAAAAAMAoAmYAAAAAAEYRMAMAAAAAMIqAGQAAAACAURYdMFfV7TdHIQAAAAAArCxjZjB/par+tKruveTVAAAAAACwYowJmG+X5P8kuaSqzqqqX66qbZe4LgAAAAAAlrkxAfM9kvyPJB9JcmiSM5JcXlUnVdW9lrI4AAAAAACWr0UHzN39/e5+a3cfkuS+SV6eZNskxyX5QlW9r6qOrCobCAIAAAAArGKbFAJ39+e7+wVJ9spts5oPT/LOJJdV1QlVdY9NLxMAAAAAgOVmSWYZd/f3k/xjkncluTJJZbKUxh8m+WJVvbyqtl+KZwEAAAAAsDxscsBcVQdX1ZsyCZZfluROSV6R5MAkz0hySZLnZbKUBgAAAAAAq8S2YwZV1V2SPDXJs5M8IJMZy59O8pokb+3uG4eun6mqv0nygSRPTPKcTa4YAAAAAIBlYdEBc1W9PsmvJrljkpuS/E2SV3f3eXP17+51VXVOkkduQp0AAAAAACwzY2YwPyPJfyZ5bZI3dfc3N2LMOUlePOJZAAAAAAAsU2MC5l/o7n9azIDu/liSj414FgAAAAAAy9SiN/lbbLgMAAAAAMDqtOiAuaoOrao3VtU95rl+j+H6IZtaHAAAAAAAy9eYJTKel+S+3X3lXBe7+8qq+ukkO2Wy9jIAAAAAAKvQomcwJ3lwkn/bQJ+PJlkz4t4AAAAAAKwQYwLm3ZPMOXt5hquHfgAAAAAArFJjAubrkuyzgT77JPnOiHsDAAAAALBCjAmYz0vyuKq6+1wXh83/Hjf0AwAAAABglRoTML8yyV2SfKSqHltV2ydJVW1fVUcm+dckd07yiqUrEwAAAACA5WbbxQ7o7n+uqpck+YMk70rSVXVtkp2T1PB6cXd/YEkrBQAAAABgWRkzgzndfXySw5O8L8k3k+w0HP8xyWHdfcLG3Keq9qmqD1XVxVV1UVU9f2jfparOqqovDMedZ4w5rqourapLquqwGe0PqaoLhmuvqKoa89kAAAAAANg4owLmZDKTubt/qbt37+7thuNju/usRdzmliQv6O77JTk4yXOr6v5JXpjk7O7eP8nZw/sM145KckAmAferq2qb4V6vSXJskv2H1+FjPxsAAAAAABs2OmBeCt19VXd/eji/IcnFSfZKcmSS04Zup2WyaWCG9tO7+6bu/mKSS5McVFV7Jtmxuz/e3Z3kzTPGAAAAAACwGSx6DeaZqupOSe6aZJu5rnf3ZYu4135JHpTkE0n26O6rhntcVVW7D932SnLujGFXDG03D+ez2+d6zrGZzHTOvvvuu7HlAQAAAAAwy6iAuaqemuT3ktxvgW69sfevqjsneUeS3+ru6xdYPnmuC71A+w83dp+S5JQkWbNmzZx9AAAAAADYsEUHzFX1tCRvTLIuyUeSXJ7JWsqjVNXtMwmX39Ld7xyar66qPYfZy3sm+drQfkWSfWYM3zvJlUP73nO0AwAAAACwmYyZwfy/k1yb5GHdffGmPLwmU5XfkOTi7v6LGZfOTHJMkpOH47tntL+1qv4iyT0y2czvvO5eV1U3VNXBmSyxcXSSV25KbQAAAAAALGxMwHzvJKdtarg8eGiSpya5oKrOH9pelEmwfEZVPTPJZUmelCTdfVFVnZHks5nMmn5ud68bxj0nyalJdkjy/uEFAAAAAMBmMiZg/maS7y3Fw7v7o5l7/eQkOXSeMSclOWmO9rVJHrAUdQEAAAAAsGG3GzHmvUkOqQV24gMAAAAAYPUbEzAfl2T7JK+tqjsvcT0AAAAAAKwQY5bIeHuS7yb59SRPqaovJPnWHP26u+dc5gIAAAAAgJVvTMB8yIzzOyU5cJ5+PeLeAAAAAACsEIsOmLt7zLIaAAAAAACsMsJiAAAAAABGETADAAAAADDKqIC5qm5XVc+rqnOr6rqqumXGtQdV1aur6j5LVyYAAAAAAMvNogPmqtouyVlJXp7kR5PckKRmdPlikmck+bUlqA8AAAAAgGVqzAzm/5PkEUlOTLJHktfPvNjd30ryr0kO29TiAAAAAABYvsYEzL+W5GPd/eLuvjVJz9Hni0n23aTKAAAAAABY1sYEzPdKcu4G+nwzyS4j7g0AAAAAwAoxJmC+McldN9Bn3yTfGnFvAAAAAABWiDEB8/lJHj1s9vdDqmqnTNZfPm8T6gIAAAAAYJkbEzC/Lsk+Sd5SVTvOvFBVd01yapKdk7x2U4sDAAAAAGD52naxA7r7bVX1qCRPT/LYJNcmSVWtTXJAku2T/FV3v28pCwUAAAAAYHkZM4M53f3MJM9I8tkkuyWpJA9OcmmSZ3b385asQgAAAAAAlqVFz2Ber7tPTXJqVe2QyZIY13X3d5aqMAAAAAAAlrfRAfN63X1jkhuXoBYAAAAAAFaQUUtkAAAAAADAomcwV9V/bWTX7u4fXez9AQAAAABYGcYskXG7JD1H+05J7jqcX5nk5pE1AQAAAACwAiw6YO7u/ea7VlX3TvKKJHdKctj4sgAAAAAAWO6WdA3m7r40yROS7JXk+KW8NwAAAAAAy8uSb/LX3d9LclaSJy/1vQEAAAAAWD6WPGAe3JLk7pvp3gAAAAAALANLHjBX1a5JHp/k8qW+NwAAAAAAy8eiN/mrqj9c4F77JDkyyU5JjtuEugAAAAAAWOYWHTAnOWED169P8kfd/f9G3BsAAAAAgBViTMD8iHnab01ybZLPdfct40sCAAAAAGAlWHTA3N0f3hyFAAAAAACwsiz5Jn+LUVVvrKqvVdWFM9pOqKqvVNX5w+uIGdeOq6pLq+qSqjpsRvtDquqC4dorqqq29GcBAAAAANjajNnkb9+xD+vuy2Y1nZrkVUnePKv9Zd3957Oee/8kRyU5IMk9knywqu7T3euSvCbJsUnOTfK+JIcnef/YOgEAAAAA2LAxazB/KUmPGNezn9fd/1pV+23k+COTnN7dNyX5YlVdmuSgqvpSkh27++NJUlVvTvK4CJgBAAAAADarMQHzm5Psl+ThSa5Lcn6Srya5e5IDk+yU5MOZBNFj/WZVHZ1kbZIXdPe1SfbKZIbyelcMbTcP57Pb51RVx2Yy2zn77jt6MjYAAAAAwFZvTMD8J0k+nuRlSU7s7uvXX6iqHZOcmOToJM/u7s+PuP9rkrwkkxnPL0ny0iTPSDLXusq9QPucuvuUJKckyZo1a8bMxAYAAAAAIOM2+Ts5yQXd/YKZ4XKSdPf13f3bSS4a+i1ad1/d3eu6+9Ykr0ty0HDpiiT7zOi6d5Irh/a952gHAAAAAGAzGhMwPzzJRzfQ56NJfm7EvVNVe854+/gkFw7nZyY5qqq2r6p7Jdk/yXndfVWSG6rq4KqqTGZPv3vMswEAAAAA2HhjlsjYPpP1lhey59BvQVX1tiSHJNm1qq5IcnySQ6rqwEyWufhSkmcnSXdfVFVnJPlskluSPLe71w23ek6SU5PskMnmfjb4AwAAAADYzMYEzP+eyUziV3X3v8++WFUPSfKrmWzQt6DufvIczW9YoP9JSU6ao31tkgds6HkAAAAAACydMQHziUk+kOTcqnpLkn9NcnWSPTJZFuMpmSy9ceJSFQkAAAAAwPKz6IC5uz9YVUcl+eskT0tyzIzLleTaJMd299lLUiEAAAAAAMvSmBnM6e6/r6r3JzkyyYOT7JTkuiSfTvLu7v7O0pUIAAAA45xY/nLtUju+j592CQAsI6MC5iQZQuS3Di8AAAAAALYyt9vUG1TVzlW1z1IUAwAAAADAyjEqYK6qO1fVS6vqq0m+nuSLM679VFW9r6oevFRFAgAAAACw/Cw6YK6qnZJ8PMlvJ7kyycWZbO633gVJfjbJk5eiQAAAAAAAlqcxM5h/P8kBSZ7W3Q9O8vaZF7v7u0k+nOTQTS8PAAAAAIDlakzA/IQk/9Tdb16gz5eT7DWuJAAAAAAAVoIxAfPeST6zgT7fTrLTiHsDAAAAALBCjAmYb0iy+wb63CuTzf8AAAAAAFilxgTMn0zymKq6y1wXq2rPJEck+eimFAYAAAAAwPI2JmD+yyR3S/K+qrrfzAvD+7cnuUOSV2x6eQAAAAAALFfbLnZAd/9TVZ2Q5IQkFya5OUmq6utJdk5SSX6vu/9t6coEAAAAAGC5GTODOd394iSHJjkzybVJ1iXpJO9L8qju/rMlqxAAAAAAgGVp0TOYq+rhSa7v7g8l+dDSlwQAAAAAwEowZgbzh5Icu9SFAAAAAACwsowJmL+e5MalLgQAAAAAgJVlTMB8TpKfWeI6AAAAAABYYcYEzP83yY9V1Uuq6vZLXRAAAAAAACvDojf5S3JckguTvCjJM6vqP5J8NUnP6tfd/cxNrA8AAAAAgGVqTMD8tBnndx9ec+kkAmYAAAAAgFVqTMB8ryWvAgAAAACAFWejAuaqOjrJ+d39me7+8mauCQAAAACAFWBjN/k7NcnjZjZU1TFV9S9LXRAAAAAAACvDxgbMc9kvyc8tUR0AAAAAAKwwmxIwAwAAAACwFRMwAwAAAAAwioAZAAAAAIBRFhMw92arAgAAAACAFWfbRfQ9oapOmN1YVevm6d/dvZj7AwAAAACwgixmBnMt8rXBe1fVG6vqa1V14Yy2XarqrKr6wnDceca146rq0qq6pKoOm9H+kKq6YLj2iqqqRXwuAAAAAABG2KiAubtvN+a1Ebc+Ncnhs9pemOTs7t4/ydnD+1TV/ZMcleSAYcyrq2qbYcxrkhybZP/hNfueAAAAAAAssalu8tfd/5rkm7Oaj0xy2nB+WpLHzWg/vbtv6u4vJrk0yUFVtWeSHbv7493dSd48YwwAAAAAAJvJVAPmeezR3VclyXDcfWjfK8nlM/pdMbTtNZzPbp9TVR1bVWurau0111yzpIUDAAAAAGxNlmPAPJ+51lXuBdrn1N2ndPea7l6z2267LVlxAAAAAABbm+UYMF89LHuR4fi1of2KJPvM6Ld3kiuH9r3naAcAAAAAYDNajgHzmUmOGc6PSfLuGe1HVdX2VXWvTDbzO29YRuOGqjq4qirJ0TPGAAAAAACwmWw7zYdX1duSHJJk16q6IsnxSU5OckZVPTPJZUmelCTdfVFVnZHks0luSfLc7l433Oo5SU5NskOS9w8vAAAAAAA2o6kGzN395HkuHTpP/5OSnDRH+9okD1jC0gAAAAAA2IDluEQGAAAAAAArgIAZAAAAAIBRBMwAAAAAAIwiYAYAAAAAYBQBMwAAAAAAowiYAQAAAAAYRcAMAAAAAMAoAmYAAAAAAEYRMAMAAAAAMIqAGQAAAACAUQTMAAAAAACMImAGAAAAAGAUATMAAAAAAKMImAEAAAAAGEXADAAAAADAKAJmAAAAAABGETADAAAAADCKgBkAAAAAgFEEzAAAAAAAjCJgBgAAAABgFAEzAAAAAACjCJgBAAAAABhFwAwAAAAAwCgCZgAAAAAARhEwAwAAAAAwioAZAAAAAIBRBMwAAAAAAIwiYAYAAAAAYBQBMwAAAAAAowiYAQAAAAAYRcAMAAAAAMAoyzZgrqovVdUFVXV+Va0d2napqrOq6gvDcecZ/Y+rqkur6pKqOmx6lQMAAAAAbB2WbcA8eER3H9jda4b3L0xydnfvn+Ts4X2q6v5JjkpyQJLDk7y6qraZRsEAAAAAAFuL5R4wz3ZkktOG89OSPG5G++ndfVN3fzHJpUkO2vLlAQAAAABsPZZzwNxJ/rmqPlVVxw5te3T3VUkyHHcf2vdKcvmMsVcMbT+kqo6tqrVVtfaaa67ZTKUDAAAAAKx+2067gAU8tLuvrKrdk5xVVZ9boG/N0dZzdezuU5KckiRr1qyZsw8AAAAAABu2bGcwd/eVw/FrSd6VyZIXV1fVnkkyHL82dL8iyT4zhu+d5MotVy0AAAAAwNZnWQbMVXWnqrrL+vMkj05yYZIzkxwzdDsmybuH8zOTHFVV21fVvZLsn+S8LVs1AAAAAMDWZbkukbFHkndVVTKp8a3d/YGq+mSSM6rqmUkuS/KkJOnui6rqjCSfTXJLkud297rplA4AAAAAsHVYlgFzd/9XkgfO0f6NJIfOM+akJCdt5tIAAAAAABgsyyUyAAAAAABY/gTMAAAAAACMImAGAAAAAGAUATMAAAAAAKMImAEAAAAAGEXADAAAAADAKAJmAAAAAABGETADAAAAADCKgBkAAAAAgFEEzAAAAAAAjCJgBgAAAABgFAEzAAAAAACjCJgBAAAAABhFwAwAAAAAwCgCZgAAAAAARhEwAwAAAAAwioAZAAAAAIBRBMwAAAAAAIwiYAYAAAAAYBQBMwAAAAAAowiYAQAAAAAYRcAMAAAAAMAo2067AAAAYGU6sU6cdgmrzvF9/LRLAABYFDOYAQAAAAAYRcAMAAAAAMAoAmYAAAAAAEaxBjMAwDJjXdvNw9q2AACw9MxgBgAAAABgFAEzAAAAAACjCJgBAAAAABhFwAwAAAAAwCirapO/qjo8yV8m2SbJ67v75CmXBMAyY/O0pWfjNAAAgK3XqgmYq2qbJH+V5OeTXJHkk1V1Znd/drqVwdZDcLf0BHcAAADAcraalsg4KMml3f1f3f39JKcnOXLKNQEAAAAArFrV3dOuYUlU1ROTHN7dvz68f2qSn+ru35zV79gkxw5vfyzJJVu00NVv1yRfn3YRsAG+p6wUvqusBL6nrAS+p6wUvqusBL6nrAS+p5vHPbt7t9mNq2aJjCQ1R9sPpefdfUqSUzZ/OVunqlrb3WumXQcsxPeUlcJ3lZXA95SVwPeUlcJ3lZXA95SVwPd0y1pNS2RckWSfGe/3TnLllGoBAAAAAFj1VlPA/Mkk+1fVvapquyRHJTlzyjUBAAAAAKxaq2aJjO6+pap+M8k/JdkmyRu7+6Ipl7U1svwIK4HvKSuF7yorge8pK4HvKSuF7yorge8pK4Hv6Ra0ajb5AwAAAABgy1pNS2QAAAAAALAFCZgBAAAAABhFwAwAAAAAwCgCZmCrUlUPq6rfqapHT7sWgJWoqg6qqp8czu8//KYeMe26AFaSqtquqo6uqkcN759SVa+qqudW1e2nXR+sV1U/VVU7Duc7VNWJVfWeqvrTqtpp2vXBfKrqzdOuYWtikz9gVauq87r7oOH8WUmem+RdSR6d5D3dffI06wNYSarq+CS/kGTbJGcl+akk5yR5VJJ/6u6TplcdwMpRVW/J5Lf0jkm+leTOSd6Z5NBM/jv9mOlVB7epqouSPLC7b6mqU5J8N8nfZ/JdfWB3P2GqBUKSqjpzdlOSRyT5lyTp7sdu8aK2MgJmllRV3a27vzHtOmC9qvr37n7QcP7JJEd09zVVdack53b3j0+3QoCVo6ouSHJgku2TfDXJ3t19fVXtkOQT3f0T06wPYKWoqs90909U1bZJvpLkHt29rqoqyX/4PWW5qKqLu/t+w/mnu/vBM66d390HTq04GFTVp5N8Nsnrk3QmAfPbkhyVJN394elVt3WwRAajVdXJVbXrcL6mqv4rySeq6stV9XNTLg/Wu11V7VxVd8vkf6pdkyTd/Z0kt0y3NLhNVe00/K5+rqq+MbwuHtruOu36YHBLd6/r7u8m+c/uvj5JuvvGJLdOtzSYqKodq+pPqupvquops669elp1wSy3q6rtktwlk1nM65ca2D6JJTJYTi6sqqcP5/9RVWuSpKruk+Tm6ZUFP2BNkk8l+f0k13X3OUlu7O4PC5e3DAEzm+IXu/vrw/mfJfnV7r53kp9P8tLplQU/YKdM/kWzNskuVXX3JKmqO2fyfzVhuTgjybVJDunuu3X33TL5a13XJnn7VCuD23y/qu44nD9kfeOwBqOAmeXiTZn8O/4dSY6qqndU1fbDtYOnVxb8gDck+VyS8zMJRN5eVa9L8skkp0+xLpjt15P8XFX9Z5L7J/n4MLnsdcM1mLruvrW7X5bk6Ul+v6pelckyRGwhlshgtKr6XJIHDGsxndvdB8+4doGlB1jOhoBkj+7+4rRrgSSpqku6+8cWew22pKravrtvmqN91yR7dvcFUygLfsDsv7JdVb+f5Igkj01y1sy/3g3TVFX3SJLuvnL420qPSnJZd5831cJgDlV1lyQ/kklod0V3Xz3lkmBeVfWLSR7a3S+adi1bCwEzo1XV85L8UpKTkzw8yV1z28YUP9LdT51edQArS1X9c5IPJjlt/R/Yq2qPJE9L8vPd/agplgewYlTVxUkO6O5bZ7Qdk+R3k9y5u+85teIAAFYhATObpKoOSfKcJPfJ5P9kXp7kH5K8sbutbwuwkapq5yQvTHJkkt2H5quTnJnk5O6+dlq1AawkVfX/kvxzd39wVvvhSV7Z3ftPpzIAgNVJwMxmUVVP7+43TbsOgNXAbyrA0vB7CgCw9ATMbBZVdVl37zvtOgBWA7+pAEvD7ykAwNKzoyKjVdVn5ruUZI8tWQvASuc3FWBp+D0FANiyBMxsij2SHJZk9rqgleTftnw5ACua31SApeH3FABgCxIwsynem8lO3OfPvlBV52zxagBWNr+pAEvD7ykAwBZkDWYAAAAAAEa53bQLAAAAAABgZRIwAwAAAAAwioAZAAA2QVUdUlVdVSfMaj+nqjbrenTzPRsAALYUATMAAFuFqrpvVb2yqi6squuq6vtVdWVV/WNVPbOq7jDtGgEAYKXZdtoFAADA5lZVf5jk+EwmWJyb5LQk306yR5JDkrw+yXOSrFnCxx6d5I5LeD8AAFh2BMwAAKxqVfWiJCcmuTzJk7r7E3P0eUySFyzlc7v7sqW8HwAALEeWyAAAYNWqqv2SnJDk5iRHzBUuJ0l3vzfJ4cMyGl1V/7LAPS+oqpur6u4bePYPrcE8c83kqjpwWJ7jW1X13ar6cFX9zDz32qOq3lBVV1fVjVV1flUds4Hn71JVf1JVFw9jrquqs6vq0XP0fdpQ19Oq6vCh9utm1l9VP1tV76mqK6rqpqr6alWdW1XHL1QHAACrm4AZAIDV7OlJbp/kHd194UIdu/um7v5ckg8leURV3Wd2nyEAfkCSd3f3VzehrjVJ/i3JHTJZnuO9SR6W5Oyq+rFZz7zb0PcZST6f5OVJzk/y2iS/PdfNq+qeST6V5IVJrhn6/l2S+yX5QFU9a566njjUcsMw5ozhfocnOWd9jUlemuQfktyU5DcW88EBAFhdLJEBAMBq9rDhePYixrw6ySOSHJvkf8+6duxw/OtNrOsXkzy9u09d31BVz84k1H1+fjC0/ZMkP5Lk5d392zP6vyrJx+e5/2lJ7pnkyd19+owxd80kKH5FVZ3Z3VfPGndEJjO9PzCr/VmZTE45pLv/Y+aFqtp1wU8KAMCqZgYzAACr2Z7D8YpFjPmHJFcmeVpVbb++cQhnfyXJfyb54CbW9bGZ4fLgjUluSXLQjGfePsmvZTKj+ISZnbt7bZK3zL5xVT0wyc9lMmv79FljvpXJZod3SPLLc9T17jnC5ZlunN3Q3V9foD8AAKucGcwAAKxmNRx7wV4zdPctVfX6JH+YSQj71uHSU5PskOSU7t7o+81j7RzPvbmqrk6y84zm+ya5Y5KPdPd1c9znnCSz12L+6eG4U1WdMMeY3Ybj/ea4dt489b4lyROSfKKq/i6TZUQ+1t2LCe4BAFiFBMwAAKxmV2YS0u69yHGnJHlRkmfntoD52CTfT/KmJajrW/O035JkmxnvdxqOs5eyWG+udaDvNhx/fnjN584beb909zur6jFJXpDJWtDPTpKq+lSS47r7rAWeAwDAKmaJDAAAVrOPDsdDFzOou7+S5D1JHl5V95uxud+7uvuaJa5xIetnLe8xz/W7LzDm+d1dC7yePsfYeWdmd/c/dvcjM5lhfWiSlyU5IMl7q+r+G/dxAABYbQTMAACsZm9KcnOSX95QCDpzveXBq4fjsVm6zf0W63NJvpvkwKraaY7rh8zRdu5w/NnNUVB3f6e7/6W7fyfJHyfZLskvbI5nAQCw/AmYAQBYtbr7S5lsjrddkn+sqjVz9auqw5O8f1bz2Uk+n8kax7+S5PPd/aHNVuwcuvvmTNY/vktmbfI3fJZfm2PM2iQfSfKEqnrGXPetqh+vqt03to6qOrSqdpjj0vqZ1d/d2HsBALC6WIMZAIBVrbv/uKq2TXJ8kk9W1b9lssnetzMJSB+eZP/M2nivu7uqXpvkL4amLT17eb0XZbIkxW8NofJHk+yZ5FeTvC/JY+cY85Qk/5LkDVX1v5J8IpN1n/dO8hOZLPfx00m+tpE1vDTJflV1TpIvZbIW9UOSPDLJl5OcvviPBQDAaiBgBgBg1evuF1fV25P8RpJHJHl6kjsk+UaS85P8aZK/nWPoqUn+PJNlNk7bErXO1t1fr6qHZrIcxS8lWZPkkiTPySTs/aGAubuvqKqHJHlekl/OZKbzNpls4vfZJK9McsEiyvjjJI8fnv2oJLcmuWxof3l3XzvmswEAsPJV97z7eAAAwFatqg5J8qEkf9vdT51uNQAAsPxYgxkAAOb3u8PxVVOtAgAAlilLZAAAwAxV9eNJHpPJGsO/kOS93f2J6VYFAADLk4AZAAB+0EMyWVv4+iTr120GAADmYA1mAAAAAABGsQYzAAAAAACjCJgBAAAAABhFwAwAAAAAwCgCZgAAAAAARhEwAwAAAAAwyv8HXLz+6A6beb8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cylinders\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "ds.groupby('Cylinders')['Cylinders'].count().sort_values(ascending=True).plot(kind='bar', color='purple')\n",
    "\n",
    "plt.title(' Cylinder', fontsize=25)\n",
    "plt.xlabel('Cylinders', fontsize=20)\n",
    "plt.ylabel('Frequency', fontsize=20)\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " FEATURE DISTRIBUTION WITH RESPECT TO CO2 EMISSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualisation wrt CO2 emision\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "ds.groupby('Make')['CO2_emission'].mean().sort_values(ascending=False)[:25].plot(kind='bar', color='red')\n",
    "\n",
    "plt.title('Car brands wrt CO2 emission', fontsize=25)\n",
    "plt.xlabel('Car brands', fontsize=20)\n",
    "plt.ylabel('CO2 emission', fontsize=20)\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "ds.groupby('Model')['CO2_emission'].mean().sort_values(ascending=False)[:25].plot(kind='bar', color='blue')\n",
    "\n",
    "plt.title(' Car models generating most of CO2', fontsize=25)\n",
    "plt.xlabel(' Car Models', fontsize=20)\n",
    "plt.ylabel('CO2 emission', fontsize=20)\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "ds.groupby('Vehicle Class')['CO2_emission'].mean().sort_values(ascending=False).plot(kind='bar',color='m')\n",
    "\n",
    "plt.title('Vehicle class wrt CO2 emission', fontsize=25)\n",
    "plt.xlabel('Vehicle class' , fontsize=20)\n",
    "plt.ylabel('Co2 emission', fontsize=20)\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "ds.groupby('Fuel Type')['CO2_emission'].mean().sort_values(ascending=False).plot(kind='bar',color='g')\n",
    "\n",
    "plt.title('Fuel type wrt CO2 emission', fontsize=25)\n",
    "plt.xlabel('Fuel Type', fontsize=20)\n",
    "plt.ylabel('Co2 emission', fontsize=20)\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAAIcCAYAAAC3otkuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAA/6UlEQVR4nO3dd5xddZ3/8dcnRVqUFpoEjBqyCnayWEEsAYIUsWLBwcWuiYgVBUSIrruWxeC6giKOu6uu+1NXRAKJdHdVFgRRUJNZjRCpA1ISiimf3x/fM3gT0ibk3HNm7uv5ePCYOWfunfuBy537vp/zLZGZSJIkqVljmi5AkiRJhjJJkqRWMJRJkiS1gKFMkiSpBQxlkiRJLWAokyRJaoFxTRfwSE2cODEnT57cdBmSJEnrddVVVw1m5g5r+tmID2WTJ0/myiuvbLoMSZKk9YqIP67tZ16+lCRJagFDmSRJUgsYyiRJklrAUCZJktQChjJJkqQWMJRJkiS1gKFMkiSpBQxlkiRJLWAokyRJagFDmSRJUgsYyiRJklrAUCZJktQChjJJkqQWMJRJNbniiivYf//9ueqqq5ouRZI0AhjKpJqcfPLJrFy5khNPPLHpUiRJI4ChTKrBFVdcwZIlSwBYsmSJ3TJJ0noZyqQanHzyyasc2y2TJK2PoUyqwVCXbG3HkiStzlAm1WDChAnrPJYkaXWGMqkGq1++PPXUU5spRJI0YhjKpBrss88+D3XHJkyYwN57791wRZKktjOUSTU5+eSTGTNmjF0ySdIGGdd0AdJotc8++3DJJZc0XYYkaYSwUyZJktQChjJJkqQWMJRJkiS1gKFMkiSpBQxlkiRJLWAokyRJagFDmSRJUgsYyiRJklrAUCZJktQChjJJkqQWMJRJkiS1QO2hLCIWRcSvIuKaiLiyOrddRMyPiIXV1207bn98RAxExO8i4sC665MkSWqDbnXKXpSZz8jMadXxR4ALM3MP4MLqmIjYEzgS2As4CPhSRIztUo2SJEmNaery5eFAf/V9P/DyjvPfzswHM/MPwACwT/fLkyRJ6q5uhLIE5kXEVRHxturcTpl5M0D1dcfq/K7AjR33XVydkyRJGtXGdeExnp+ZN0XEjsD8iPjtOm4baziXD7tRCXdvA9h99903TZWSJEkNqr1Tlpk3VV9vA75PuRx5a0TsAlB9va26+WJgt467TwJuWsPvPDMzp2XmtB122KHO8iVJkrqi1lAWEVtFxKOHvgcOAH4NnAP0VTfrA35QfX8OcGREbBYRjwf2AK6os0apLgsWLGDGjBkMDAw0XYokaQSou1O2E/CTiPglJVz9KDPPBz4NTI+IhcD06pjMvA74DnA9cD7w7sxcUXONUi1mz57N0qVLOeWUU5ouRZI0AtQ6piwzfw88fQ3n7wBespb7fBL4ZJ11SXVbsGABixYtAmDRokUMDAwwZcqUZouSJLWaK/pLNZg9e/Yqx3bLJEnrYyiTajDUJVvbsSRJqzOUSTWYPHnyOo8l1WdwcJCZM2dyxx13NF2KNCyGMqkGJ5xwwirHJ510UkOVSL2nv7+fa6+9lv7+/vXfWGoRQ5lUg6lTpzJhwgQAJkyY4CB/qUsGBweZO3cumcncuXPtlmlEMZRJNRgcHOT+++8H4IEHHvCNQeqS/v5+MstGMCtXrrRbphHFUCbVoL+/n5UrVwKwYsUK3xikLpk/fz7Lli0DYNmyZcybN6/hiqQNZyiTajBv3ryHPq1nJhdccEHDFUm9Yfr06YwfPx6A8ePHc8ABBzRckbThDGVSDXbaaad1HkuqR19fHxEBwJgxY+jr61vPPaT2MJRJNbj11lvXeSypHhMnTmTGjBlEBDNmzGD77bdvuiRpgxnKpBoccMABD31ajwgOPPDAhiuSekdfXx9Pe9rT7JJpxDGUSTXo6+tbZVyLbw5S90ycOJHTTz/dLplGHEOZVIPOSygHH3ywbw6SpPUylEk1OfTQQ9lyyy057LDDmi5FkjQCGMqkmvzwhz/kvvvu45xzzmm6FEnSCGAok2rgVi+SpOEylEk1cEV/SdJwGcqkGsyfP5/ly5cDsHz5crd6kSStl6FMqsG+++67yvF+++3XUCWSpJHCUCZJktQChjKpBpdffvkqx5dddllDlUiSRgpDmVSD6dOnM3bsWADGjh3LAQcc0HBFkqS2M5RJNejr6yMzAchMt1mSJK2XoUyqSWcok9Q9CxYsYMaMGQwMDDRdijQshjKpBv39/auEMtcpk7pn9uzZLF26lFNOOaXpUqRhMZRJNVh9XbILLrigoUqk3rJgwQIWLVoEwKJFi+yWaUQxlEk12GabbVY53nbbbZspROoxs2fPXuXYbplGEkOZVINbbrllleObb765oUqk3jLUJVvbsdRmhjKpBqsP7newv9QdkydPXuex1GaGMqkGO+644yrHO+20U0OVSL3lhBNOWOX4pJNOaqgSafgMZVINhhaOHTJmjC81qRumTp36UHds8uTJTJkypdmCpGHwnUKqwepjyBxTJnXPCSecwFZbbWWXTCPOuKYLkEajyZMnrzLA2HEtUvdMnTqVuXPnNl2GNGx2yqQaOK5FkjRchjKpBlOnTmXcuNKIHjdunONaJEnrZSiTarBgwQKWL18OwPLly11VXJK0XoYyqQaf+MQnVjn++Mc/3lAlkqSRwlAm1eDGG29c57EkSaszlEmSJLWAoUyqQUSs81iSpNUZyqQaTJo0aZ3HkiStzlAm1WBwcHCdx5Ikrc5QJtXggAMOeOiSZURw4IEHNlyR1DsWLFjAjBkzXIpGI46hTKpBX18f48ePB2D8+PH09fU1XJHUO2bPns3SpUs55ZRTmi5FGhZDmVSDiRMnMmPGDCKCgw8+mO23377pkqSesGDBgof2nV20aJHdMo0ohjKpJn19fTztaU+zSyZ10ezZs1c5tlumkWRc0wVIo9XEiRM5/fTTmy5D6ilDXbK1HUttZqdMkjRqTJ48eZ3HUpsZyqSaDA4OMnPmTO64446mS5F6xpve9KZVjt/85jc3VIk0fIYyqSZnnHEGv/zlLznjjDOaLkXqGd/4xjdWOT777LMbqkQaPkOZVIPBwUHmz58PwLx58+yWSV3imDKNZIYyqQZnnHEGK1euBGDlypV2y6QuGTNm1be1sWPHNlSJNHyGMqkGF1544SrHP/7xjxuqROotQx+GhqxYsaKhSqThM5RJNcjMdR5LkrQ6Q5lUg5e+9KWrHE+fPr2hSiRJI4WhTKrB29/+9ofGtowZM4a3v/3tDVckSWo7Q5lUg4kTJ7LffvsB8MIXvtC9L6Uu2XrrrVc53mabbZopRNoIhjKpJpttttkqXyXV7+67717l+K677mqmEGkjGMqkGgwODnLRRRcBcNFFF7lOmSRpvQxlUg36+/tZvnw5AMuWLaO/v7/hiiRJbWcok2owb968h5bByEwuuOCChiuSesNb3/rWVY7f+c53NlSJNHyGMqkGEydOXOexpHocddRRqxy/7nWva6gSafgMZVINbrrppnUeS6rPULfMLplGmnFNFyBJ0qZ01FFHPaxjJo0EdsqkGuy7776rHA+tWSapfoODg8ycOdNZzxpxDGVSDVZfm8y1yqTu6e/v59prr3XWs0YcQ5lUg8suu2yV40svvbShSqTeMjg4yHnnnUdm8qMf/chumUYUQ1mLLViwgBkzZjAwMNB0KRqm1bdVcpslqTv6+/tZtmwZ4BqBGnkMZS02e/Zsli5dyimnnNJ0KRqmP/3pT+s8llSP1dcEPP/88xuqRBo+Q1lLLViwgEWLFgGwaNEiu2UjzNDCsWs7llSPcePGrfNYajNDWUvNnj17lWO7ZZK0fkuWLFnnsdRmhrKWGuqSre1YkvRwu+yyyzqPpTYzlLXU5MmT13msdnvUox61zmNJ3RERTZcgbTBDWUtNnz59leMZM2Y0VIk2xurP18EHH9xQJVJvufnmm1c5doszjSSOgGyps88+e5Xjr3zlK26s25A5c+YMe6LF0JT8IQsXLmTWrFkbfP8pU6YM6/aSpJHPTllLLV++fJ3Harfx48c/NOtr++23Z/z48Q1XJElqOztl0npsbMfqne98J4sWLeKrX/2qi8dKXRIRqyxB45gyjSR2yqSajB8/nj322MNAJnXRscceu8rx+9///mYKkTaCoaylnH0pScN3xBFHPNQdiwgOO+ywhiuSNpyhrKVOOOGEVY5POumkhiqRpJFlqFtml0wjjWPKWmrq1KlMnjyZRYsWMXnyZKZMmdJ0SZI0IhxxxBEcccQRTZchDZudshY74YQT2GqrreySSZLUAwxlLTZ16lTmzp1rl0yShmFwcJCZM2dyxx13NF2KNCyGMknSqNLf38+1115Lf39/06VIw9KVUBYRYyPi6og4tzreLiLmR8TC6uu2Hbc9PiIGIuJ3EXFgN+qTJI0Og4ODzJ07l8xk7ty5dss0onSrU/Ze4Dcdxx8BLszMPYALq2MiYk/gSGAv4CDgSxExtks1SpJGuP7+flauXAnAihUr7JZpRKk9lEXEJOBlwFc7Th8ODL1S+oGXd5z/dmY+mJl/AAaAfequUZI0OsyfP/+hbemWL1/OvHnzGq5I2nDd6JSdBnwIWNlxbqfMvBmg+rpjdX5X4MaO2y2uzkmStF777rvvKsf77bdfQ5VIw1drKIuIQ4DbMvOqDb3LGs7lw24U8baIuDIirrz99tsfUY2SJEltUHen7PnAYRGxCPg28OKI+Dfg1ojYBaD6elt1+8XAbh33nwTctPovzcwzM3NaZk7bYYcd6qxfkjSCXH755ascX3bZZQ1VIg1frSv6Z+bxwPEAEbE/8IHMfGNEfAboAz5dff1BdZdzgG9GxOeBxwJ7AFfUWaMkqZ3mzJnDwMDAsO6zxRZbcN99961yPGvWrA2+/5QpU4Z1e2lTamqbpU8D34mIY4AbgFcDZOZ1EfEd4HpgOfDuzFzRUI2SpBFm5513fmgZjIhg5513brgiacN1LZRl5iXAJdX3dwAvWcvtPgl8slt1SZLaaWM7VkcccQR33HEHhx9+OMcdd9wmrkqqjxuSS5JGlZ133pkHHniAvr6+pkuRhsVtliRJo8r48ePZY4892H777ZsuRRoWQ5kkSVILGMokSZJawFAmSZLUAoYySZKkFjCUSZIktYChrMUGBweZOXPmQwshSpKk0ctQ1mL9/f1ce+219Pf3N12KJEmqmaGspQYHB5k7dy6Zydy5c+2WSZI0yhnKWqq/v5/MBGDlypV2yyRJGuUMZS01f/58li1bBsCyZcuYN29ewxVJkqQ6Gcpaavr06YwfPx4oW4YccMABDVckSZLqZChrqb6+PiICgDFjxrixriRJo5yhrKUmTpzIjBkziAhmzJjhxrqSJI1y45ouQGvX19fHokWL7JJJktQDDGUtNnHiRE4//fSmy5AkSV3g5UtJkqQWMJRJkiS1gKFMkiSpBQxlkiRJLWAokyRJagFDmSRJUgu4JEYXzJkzh4GBgWHfb/HixQBMmjRp2PedMmUKs2bNGvb9JElSMwxlLXb//fc3XYIkSeoSQ1kXbGzHauh+c+bM2ZTlSJKkFnJMmSRJUgsYyiRJklrAUCZJktQChjJJkqQWMJRJkiS1gKFMkiSpBQxlkiRJLWAokyRJagFDmSRJUgsYyiRJklrAUCZJktQChjJJkqQWMJRJkiS1gKFMkiSpBQxlkiRJLWAokyRJagFDmSRJUgsYyiRJklrAUCZJktQChjJJkqQWMJRJkiS1wLgNvWFEbAa8Epjceb/MPGXTlyVJktRbNjiUAT8A7gauAh6spxxJkqTeNJxQNikzD6qtEkmSpB42nDFl/xMRT62tEkmSpB42nE7ZC4CjI+IPlMuXAWRmPq2WyiRJknrIcELZjNqqkCRJ6nEbfPkyM/8IbAMcWv2zTXVOkiRJj9AGh7KIeC/w78CO1T//FhEz6ypMkiSplwzn8uUxwLMzcylARPwD8FPg9DoKkyRJ6iXDmX0ZwIqO4xXVOUmSJD1Cw+mUnQ38PCK+Xx2/HDhrk1ckSZLUgzY4lGXm5yPiEsrSGAG8OTOvrqswSZKkXrLeUBYRj8nMeyJiO2BR9c/Qz7bLzDvrK0+SJKk3bEin7JvAIZQ9L7PjfFTHT6ihLkmSpJ6y3lCWmYdUXx9ffzmSJEm9aYPHlEXE84FrMnNpRLwReBZwWmbeUFt1kvQIzJkzh4GBgWHfb/HixQBMmjRp2PedMmUKs2bNGvb9JGk4S2L8C3BfRDwd+BDwR+Bfa6lKkhp0//33c//99zddhqQeM5wlMZZnZkbE4cAXMvOsiOirqzBJeqQ2tmM1dL85c+ZsynIkaZ2GE8rujYjjgTcC+0XEWGB8PWVJkiT1luFcvnwt8CBwTGbeAuwKfKaWqiRJknrMsDpllMuWKyJiKvAk4Fv1lCVJktRbhtMpuwzYLCJ2BS4E3gx8vY6iJEmSes2wNiTPzPuAVwCnZ+YRwF71lCVJktRbhhXKIuK5wBuAH1Xnxm76kiRJknrPcELZscDxwPcz87qIeAJwcS1VSZIk9ZgNHuifmZcCl3Yc/x5w2WpJkqRNYL2hLCJOy8xjI+KHrLohOQCZeVgtlUmSJPWQDemUDW2l9Nk6C5EkSepl6w1lmXlV9fXS9d1WkiRJG2eDB/pHxCERcXVE3BkR90TEvRFxT53FSZIk9YrhrOh/GmWNsl9l5sPGlkmSJGnjDWdJjBuBXxvIJEmSNr3hdMo+BJwXEZdSNiYHIDM/v8mrkiRJ6jHDCWWfBJYAmwOPqqccSZKk3jScULZdZh5QWyWSJEk9bDhjyn4cEYYySZKkGgwnlL0bOD8iHnBJDEmSpE1rg0NZZj46M8dk5uaZ+Zjq+DHruk9EbB4RV0TELyPiuoj4RHV+u4iYHxELq6/bdtzn+IgYiIjfRcSBG/+vJkmSNHJs8JiyiAjgDcDjM/PUiNgN2CUzr1jH3R4EXpyZSyJiPPCTiJhLWe/swsz8dER8BPgI8OGI2BM4EtgLeCzlkunUzFyxcf960l/NmTOHgYGBrj3ewoULAZg1a1bXHnPKlCldfTxJ0qYznIH+XwJWAi8GTqXMxPxn4G/XdodqTbMl1eH46p8EDgf2r873A5cAH67OfzszHwT+EBEDwD7AT4dRp7RGAwMDLPj1L9h9Qncy/qOWlUb0A4v+tyuPd8OSsV15HElSPYYTyp6dmc+KiKsBMvPPEbHepTEiYixwFTAF+OfM/HlE7JSZN1e/5+aI2LG6+a7Azzruvrg6t/rvfBvwNoDdd999GP8Kj4ydlpFv9wkrOGHakvXfcASafeWEpkuQJD0Cwwlly6qAlQARsQOlc7ZO1aXHZ0TENsD3I+Ip67h5rOlXrOF3ngmcCTBt2rSu7TAwMDDA1b+6npVbbteVx4u/lH+1q/7vlq483pj77uzK40iSpIcbTiibA3wf2DEiPgm8CjhhQ++cmXdFxCXAQcCtEbFL1SXbBbitutliYLeOu00CbhpGjbVbueV2PLDnIU2XUYvNrz+36RIkSepZw5l9+e+UrZb+HrgZeHlm/ufQzztnUHac26HqkBERWwAvBX4LnAP0VTfrA35QfX8OcGREbBYRjwf2ANY1kUCSJGlUGE6njMz8LSVUrcmFwLNWO7cL0F9d9hwDfCczz42InwLfiYhjgBuAV1e//7qI+A5wPbAceLczLyVJUi8YVihbj4eNB8vMa4FnruH8HcBL1vRLMvOTlH02JUmSesZwVvRfn64NuJckSRptNmUokyRJ0kbalKFsTctZSJIkaQNsylC2xjFikiRJWr/1hrKIeGpE/CwiboyIM1fbPPyh5Soy05VHJUmSNtKGdMr+BTgZeCqwgLKp+BOrn42vqS5JkqSesiFLYkzIzPOr7z8bEVcB50fEUTjjUpIkaZPYkFAWEbF1Zt4NkJkXR8Qrge8C3dkEUpIkaZTbkMuX/wA8ufNEtSjsS4Dv1VGUJElSr1lvpywzvzn0fURMKKdyaWbeALy1zuIkSZJ6xQYtiRER74yIG4A/AjdGxB8j4l31liZJktQ7NmRJjBOAQ4H9M3P7zNwOeBEwo/qZJEmSHqEN6ZQdBbwiM38/dKL6/jXAm+oqTJIkqZdsyOxLMvOBNZy7PyJWbvqS2mvx4sWMue9uNr/+3KZLqcWY++5g8eLlTZchSVJP2pBO2eKIeNgWStW5mzd9SZIkSb1nQzpls4AfRMRPgKsoC8b+LfB84PAaa2udSZMmceuD43hgz0OaLqUWm19/LpMm7dx0GZIk9aQNCWUPAkcDU4G9gAAuA84CHnZZU5IkScO3IaHsNOCjmfm1zpMRMa362aGbvixJ0mgxZ84cBgYGuvZ4CxcuBGDWrFlde8wpU6Z09fE0Om1IKJtcreC/isy8MiImb/qSJEmjycDAAL+95hq6NThiaLD0Xddc05XHu6Urj6JesCGhbPN1/GyLTVWIJGn02hk4hmi6jFqcRTZdgkaJDQll/xsRb83Mr3SejIhjKAP/JalWXv6S1As2JJQdC3w/It7AX0PYNOBRwBE11SVJDxkYGODq666Gbbr0gNUKjFf/6eruPN5d3XkYSe22IRuS3wo8LyJeBDylOv2jzLyo1sokqdM2sHL/0ble9ZhLNmgbYkmj3Aat6A+QmRcDF9dYiyRJUs/y45kkSVILGMokSZJawFAmSZLUAoYySZKkFjCUSZIktYChTJIkqQUMZZIkSS1gKJMkSWoBQ5kkSVILGMokSZJawFAmSZLUAhu896U00i1evJil945l9pUTmi6lFn+8dyxbLV7cdBmSpI1kp0ySJKkF7JSpZ0yaNIkHlt/MCdOWNF1KLWZfOYHNJ01qugxJ0kayUyZJktQChjJJkqQWMJRJkiS1gKFMkiSpBRzoP0xj7ruTza8/tyuPFQ/cA0Bu/piuPN6Y++4Edu7KY0mSpFUZyoZhypQpXX28hQvvBWCPJ3YrKO3c9X9HSZJUGMqGYdasWY083pw5c7r6uFLbLF68GO6GMZeM0hEXd8HidOFfqdeN0r9wkiRJI4udMkmtN2nSJG6P21m5/8qmS6nFmEvGMGlXF/6Vep2dMkmSpBYwlEmSJLWAoUySJKkFDGWSJEktYCiTJElqAUOZJElSCxjKJEmSWsBQJkmS1AKGMkmSpBYwlEmSJLWAoUySJKkFDGWSJEktYCiTJElqgXFNFyBJGt0WL17MvcBZZNOl1OJmYMnixU2XoVHATpkkSVIL2CmTJNVq0qRJ3DU4yDFE06XU4iySbSZNaroMjQKGMvWUG5aMZfaVE7ryWLfeVxrRO225siuPd8OSsUztyiNJkupgKFPPmDJlSlcf7y8LFwKw+eQ9uvJ4U+n+v6MkadMxlKlnzJo1q5HHmzNnTlcfV5I0MjnQX5IkqQUMZZIkSS1gKJMkSWoBQ5kkSVILGMokSZJawFAmSZLUAoYySZKkFjCUSZIktYCLx0qSpLWaM2cOc+fOHdZ97rvvPjKzporWLCLYcssth32/GTNmdH1x8bWxUyZJktQCdsokSdJazZo1qzWdpNHOTpkkSVILGMokSZJawFAmSZLUAoYySZKkFjCUSZIktYChTJIkqQUMZZIkSS1QayiLiN0i4uKI+E1EXBcR763ObxcR8yNiYfV12477HB8RAxHxu4g4sM76JEmS2qLuTtly4P2Z+WTgOcC7I2JP4CPAhZm5B3BhdUz1syOBvYCDgC9FxNiaa5QkSWpcraEsM2/OzF9U398L/AbYFTgc6K9u1g+8vPr+cODbmflgZv4BGAD2qbNGSZKkNujamLKImAw8E/g5sFNm3gwluAE7VjfbFbix426Lq3Or/663RcSVEXHl7bffXmvdkiRJ3dCVvS8jYgLwXeDYzLwnItZ60zWce9g285l5JnAmwLRp07q7Db2kZtwFYy7p0ufIJdXXCd15OO5iDR8/JfWa2kNZRIynBLJ/z8zvVadvjYhdMvPmiNgFuK06vxjYrePuk4Cb6q5RUrtNmTKlq4+3cOFCAPbYdY/uPOCu3f93lNQ+tYayKC2xs4DfZObnO350DtAHfLr6+oOO89+MiM8DjwX2AK6os0ZJ7Tdr1qxGHm/OnDldfVxJva3uTtnzgaOAX0XENdW5j1LC2Hci4hjgBuDVAJl5XUR8B7ieMnPz3Zm5ouYaJUmSGldrKMvMn7DmcWIAL1nLfT4JfLK2oiRJklrIFf0lSZJaoCuzLyVJve0W4KyHT6avxR3V1+278mjl322bLj2WRjdDmSSpVt2eWXp7NXt2mz26M3t2G5w9q03DUCZJqpWzZ6UN45gySZKkFjCUSZIktYChTJIkqQUMZZIkSS1gKJMkSWoBQ5kkSVILGMokSZJawFAmSZLUAoYySZKkFjCUSZIktYChTJIkqQUMZZIkSS1gKJMkSWqBcU0X0AvmzJnDwMDAsO+3cOFCAGbNmjXs+06ZMmWj7idJkpphp6zFxo4dy9KlS7nnnnuaLkWSJNXMTlkXbGzH6uCDDwbgtttu4+tf//omrEiSJLWNnbKWuuKKK1iyZAkAS5Ys4aqrrmq4IkmSVCdDWUudfPLJqxyfeOKJzRQiSZK6wsuXLTXUJVvbsaT1c5KNpJHETllLTZgwYZ3HkuqzxRZbsMUWWzRdhqQeY6espY477jhOOeWUh44/+MEPNliNNDLZsZI0ktgpa6nLLrtsleNLLrmkmUIkSVJXGMpa6tJLL13l2FAmSdLoZihrqcxc57EkSRpdDGUtteWWW67zWJIkjS6GspZauXLlOo8lSdLoYihrqQMPPHCV44MOOqihSiRJUjcYylqqr6+P8ePHAzB+/Hj6+voarkiSJNXJUNZSEydO5OCDDyYieNnLXsb222/fdEmSJKlGLh7bYn19fSxatMgumSRJPcBQ1mITJ07k9NNPb7oMSZLUBV6+lCRJagFDmSRJUgsYyiRJklrAUCZJktQChjJJkqQWMJRJkiS1gKFMkiSpBQxlkiRJLWAokyRJagFDmSRJUgsYyiRJklrAUCZJktQChjJJkqQWMJRJkiS1gKFMkiSpBQxlkiRJLWAokyRJagFDmSRJUgsYyiRJklrAUCZJktQChjJJkqQWMJRJkiS1gKGsxQYHB5k5cyZ33HFH06VIkqSaGcparL+/n2uvvZb+/v6mS5EkSTUzlLXU4OAgc+fOJTOZO3eu3TJJkkY5Q1lL9ff3k5kArFy50m6ZJEmjnKGspebPn8+yZcsAWLZsGfPmzWu4IkmSVCdDWUtNnz6d8ePHAzB+/HgOOOCAhiuSJEl1MpS1VF9fHxEBwJgxY+jr62u4IkmSVCdDWUtNnDiRGTNmEBHMmDGD7bffvumSJElSjcY1XYDWrq+vj0WLFtklkySpBxjKWmzixImcfvrpTZfR8+bMmcPAwMCw77dw4UIAZs2aNez7TpkyZaPuJ0kauQxlUk222GKLpkuQJI0ghjJpPexYSZK6wYH+kiRJLWAokyRJagFDmSRJUgsYyiRJklrAUCZJktQChjJJkqQWMJRJkiS1gKFMkiSpBQxlkiRJLWAokyRJagFDmSRJUgsYyiRJklrAUCZJktQChjJJkqQWMJRJkiS1QK2hLCK+FhG3RcSvO85tFxHzI2Jh9XXbjp8dHxEDEfG7iDiwztokSZLapO5O2deBg1Y79xHgwszcA7iwOiYi9gSOBPaq7vOliBhbc32SJEmtUGsoy8zLgDtXO3040F993w+8vOP8tzPzwcz8AzAA7FNnfZIkSW3RxJiynTLzZoDq647V+V2BGztut7g69zAR8baIuDIirrz99ttrLVaSJKkb2jTQP9ZwLtd0w8w8MzOnZea0HXbYoeayJEmS6tdEKLs1InYBqL7eVp1fDOzWcbtJwE1drk2SJKkR4xp4zHOAPuDT1dcfdJz/ZkR8HngssAdwRQP1SZJaYM6cOQwMDAz7fgsXLgRg1qxZw77vlClTNup+0qZQayiLiG8B+wMTI2Ix8HFKGPtORBwD3AC8GiAzr4uI7wDXA8uBd2fmijrrkySNPltssUXTJUgbJTLXOGxrxJg2bVpeeeWVTZchSZK0XhFxVWZOW9PP2jTQX5IkqWcZyiRJklrAUCZJktQChjJJkqQWMJRJkiS1gKFMkiSpBQxlkiRJLWAokyRJagFDmSRJUgsYyiRJklrAUCZJktQChjJJkqQWMJRJkiS1gKFMkiSpBQxlkiRJLWAokyRJagFDmSRJUgtEZjZdwyMSEbcDf2y6jhpNBAabLkIbzedv5PK5G9l8/kau0f7cPS4zd1jTD0Z8KBvtIuLKzJzWdB3aOD5/I5fP3cjm8zdy9fJz5+VLSZKkFjCUSZIktYChrP3ObLoAPSI+fyOXz93I5vM3cvXsc+eYMkmSpBawUyZJktQChjJJkqQWMJRJkiS1gKFMkiSNGhERa/p+JDCUjXAR8aKIOLHpOtScoT86EfHs6p99m65ptIqI50TE6U3XoWZ1vOZ2j4jtImJ80zWpiIjIzIyIl0TEM3KEzWY0lI18NwLHR8TxTReiZlR/gA4Gvgo8BTgnIg5suKzR6jfAiyLi800XouZ0vOb+C/gQcElEPKrZqgQPPTcvA74E7Nh0PcNlKBvBImJsZg4ATwWOjYgTmq5J3RcR2wPHAYcCdwIDwC8bLWqUiWJsZt4NPB94vh2z3hURTwE+AbwGWAg8Btiy0aIEQETsAHwceH1mzouIZ0bE9OrvZOsZykagiBgDkJkrImJ8Zv4f8DzgXV7K7B0dYyXuB34BTAfeD7wxM2+JiFdGxJMaK3CUGLocUr3etq6C2UuAZ0TEF5uuT424H/gXYE/g7cDhmXlXROwfEWObLa3nJfBb4MURcTYloJ0GHN5kURtqXNMFaPgycyVARLwG2DMifg/8B/C3wBURsSIzP9VkjarPUEgAtgPuyMz7qk+HHwC2z8w/R8Q+wPHA3zVZ62gwNCYlIt4G7BsR1wMXAQcBF0XEP2Xm+5qsUfXqGKc09NqD0ilbCTw+M1dGxPMpH4r+jzKsRF3Q8dw8Hrg1Mwcj4r+AfYBvZ+YFEXE08MKI6AdWtnmcmaFshIqItwLvpVw3fwVlLNEXKR2zgYh4MDM/12CJqkHHH6CDgU9GxE+AHwHvBLYGvlWdexXw8cy8tsFyR43qj/pRwHuArwGPycyfR8R04OcR8ZfM/HCTNao+1WvuQOD1EXE+MBd4I+XD8JHVeLL3ASdlpoGsizr/HgJzI2Jr4ITM/B5ARLyAEpbfn5krGix1g7jN0ggUEVsAc4CzMvNnETEFeBPwQGZ+KiKeAIzLzAWNFqpNqiOQ7U0ZQ/YtyuWTScDPquOjgQeBGzPz8tU+2WsDdf53qy5HfQA4B5hGea3NoFwmSWArSodyUTPVqi4RMabqgj2TcgnsUuCxwCDwZcpr783AUuBHVVfG11wXRcSzga8AR1CC8pGUcbVvB5ZRJkB9JTPPbazIYTCUjQBrepFXg4wD+HBmLo2I51I+KbwqM+9sok7VIyKeSHmtDkTEjsBlwMWZ+c6IeDTwSuBZwNXAN0bCp8E2Wy2QbVONFXor5XLw7zPzpdXP3kO5FPKlBstVDSJip8y8tfp+T+A7wMcy8wfV0ICXAZsBZ2bm7xsstedFxPOAe4GdgH8AjgE+TJl88UaAakjHiAjLDvRvudXeIPaNiEOqH/UD9wF91fFEyqe1v3S/StXsucAOEbFZZt4GfAF4TURMz8x7KW8Yvwb2pnyK1yPQ8Xp7P3BaFXz/h9KNvCgitoiI1wNvAS5urlLVoZpA85mImFqduoXyt/X9AJl5BaVrGsA7I2Lrjkk36pKI2CsiXpaZ/5OZv6LMPv9oZl4D/AFYDjwhM/8Mf31dt52dshEiIt4FzATuBm6jtGafDxwCPA7YAnhH9T+kRoGIeCzw6Mz8XURsB/wUeF1m/iIi+vjrOIn5EbElsG1m/qnJmkeLiHgH8AbgyMz8U/Xf90DKZJrnUS6LHFe9GWiUiIhtq67KGODxwHszc1Y1TukbwL2Z+cbqttOAuzNzYYMl95SOIRz7UN4Ddwc+l5nnR8RXgD8DFwIfpbwf/qbBcjeKoWwEqGb1fICy7sr9EfEtyqeA91UzTfYA7szMOxotVJtURHycErzfl5nXRVmH7jWUJS+ujYg3AqdS/vhc0GSto01EnEpZZuRmSqfyAMrg7q9SPgA9kJlLm6tQm1pEbAZ8G/g5ZfzYFpQPQudl5nERsS1wBjA+M49orNAeV024+AzluXg+ZXmSfwV+V51/NGUYx3cbK/IR8PJli0WxE/A64InAswEy83WU1vk3qzEvCw1ko09mfgK4Cjg1IvbMzNmUT+vfjoinZua/Uabl39dknSNd56WniBiakX4j8HrKGJV7gPnAXgCZeYeBbPTJzAeBvwdeBLy7uuz1HOAFEXFadfwOYExEPKO5SntX1cHcF/h0Zv4z8BHgSsrEp12rLuarMvO7I/WSsp2yllnLoP7dgWMply7PzcyrqvNfAT6RmYu7Xqi6JiJmU3ZtOD4zr4+I91Gm3x+amb+sbjMiBrG2zWpjNmdShgKMBT4LbA7clpn3Rtm25UTKf/PbGytYtRh6A68ujf0t8GlgbmZ+NiK2Ac4Drs3Md0TEuMxc3mC5PS0iTqI0KF6dZY3Gv6F0za4DvjzShxTYKWuZjjeId0TEFyPio9WPTgMmAIdGxHOq277VQDa6DL05RMTeEXFERDwlM0+gDDL/dNUx+yfKmnRbD93PQLZxOl5vR1PW+/sUZYmDN2fZKeMvEfEW4B+BtxjIRp+OYP6EiHhsZv4v8DbgoIj4YGbeRZltuU/1+jOQdUGsti1SR+frTMqlyg9VxyspS5RsD0xlhLNT1kIRMQt4OeXS1EmU8WPHUi6jfAxYDHwO+ItvxqNPRBxGee6vp1ymvo3SGTse2J9qjFl1WztkG6EapP1AZv66Ov40ZZ2351DWOzqEstzFyqpL9tsqpGkUiohDKYPDByn7x54F/JHSgbksy/qPdsi6pBpGcAnw37naosxR1g18AWVNxr2omhWUpYE2z8xTRvLfRVf0b5mI2BnYmfKm8HbKEheXUpZBeA8wG1hejX/QKBMR4ylr67wjy4rxUyjP+8zqjWF7yvpIgB2yjRFl9fUnApdGxPbVeMx7KOPH7qdcolweESdHxK2Z+S9N1qt6RcQulAkzRwN3Ac8APlj982FgTkR8KzP/0FCJPad6/b0J+G5E3FeNrx1azHcF5T3x0ihryA1SwtlbKOFsRP9d9PJlg6qB/KtsXpuZt1AulTyZ0i07HPgvYEfKzJLbq7WqNEp0XLLcjNIV3ZEy1RvgBspssCcDZOb7M/MXTdQ5WmTmXzLzPyiLS34vIp5KGTO0B6VDskWUfWUPp3xa1+i2kjJ7/ZosuzJcQhmf9MJqzOZhBrLuioixWRblfQXw6momOlXnOjrGAF5PmSXbBxyRmb9rrOhNxFDWrAlV6ici3hgRR0fEC6sxDA8CN2fmX4CnAxcAb8/MZc2Vq02tY92d6ZQxS0nZz/RtEbF/9fzfBuwWEdtUs4+0ESJij4h4bkS8uJq1vAD4IaVLcjdl/9C/o+xt+VbgqJG4zpHWreND0D4R8YIsK/cviYg5ANXf39v56/ikJY0U2oM6xo1tExE7VGH4EOBVEXEylC5YZycsM/8IvGdoSMdI5+XLBlT/4+0OXFONbdkVOIGy6N1+EfEUyhvzdhHxXcpK7QdXXTSNIlUgO5Cyl+lbqtM/pMwA7I+I/6SMcZpZvVloI1Tjwk6ljBOaAPxNlN0xvkjplMwBjs3MV0TEYyh7x7pd2ShUveYOpgwJeXN1+jjgAxHxY8o4srdRQjrptmVd0fEB9eXAu4CMiG9n5tnV8/WDKLuaHL/6fTJz1ARnB/o3oON/vhOA91LehD+WmTdXbx6vBuZl5jejLIexLDNvbrJmbXpVON8S+C7wxcw8NyJmAM+kXLL8I2WJhrsz88rmKh3ZIuIg4GTKPrGXVuc+TumKvSwzfx0Rx1Iulby/mn2nUSoiJlNec0dVS8zsRbl0/VPKuLIVwK8z8/zGiuxR1RWDTwEzKI2K1wD/mJmnRcTjKAs4H56jeBcFQ1mXRcRjMvOejuNXUfYuPCQzz6s+pe9LecO4IDPPbKhUdUlEHEcZv3QnZZuQuyjTu49xttcjE2V7qkHKuKBzI2LzzHyg+tnJwFGU4QGPoszeOj8zb2yqXtWvmkwzh9KNvpeyBuADwM8y81NN1tbLImJzyjZmKyh7OX8YOJ0yue3fMvPkqlM2qie5Gcq6qOqMvIMymHssJfHPiIgPUqZj75eZv4qyz9pzKIsV2iEbRTq6pE+lXEb7JWXZi0OA32XmNRHxQsqnxCNGU1u+KVX3+dPA/pl5R+cf9oi4mNId+0U1uNhLVaNMx2tuB8oWSTdFxIsos5y/CVxB6czsvfryC+qOqkN2CvBCyuzyMyhXD/4nIr4M7EnZZnDUr8vpmLIuqcaO3U9ZC2mAMpB/GkBmfiYilgPzoux6/4uImDeSp/VqzTrGs/wT5bJ1PyV8/QdARLyEslDwRw1km0Zm/igiVgJXRMS0LBtOj68mzdxD2VzcsUOjVPWaO4yyzt/dEfHTaomFiwEi4gWU7XpObLDMnhVly6q3U8Z0/oWyYPPNwNER8WjgscAHeyGQgbMvu6IaJ3QGZWG7uyiD+O8Gjhy6TZZV2j9DWZdlszX9Ho18EfE0yv56BwEXUdr034qygv9jgGcBH8nMH3bMRNIjlJlzKeu9XRkR22bmsijrIO0M3NpsdapT1RX7BGXM4DzgxIj4XESMjYipwCzglCq8+5rrgtX+Oz+XsmPCrtXPxlDG/C2hjC87MzN/3vUiG+Lly5pVg4xPpOxROS8itqTsqTeGsgDetzPz1Gps2cWUQf33rP03aqQZunxSfT+W0orfjrK/4j6U3RneBLyYcgnzwc77aNOpPiD9I+WD0VHA27Ja1V+jx2qvuf0oVyl2pHTLZlHe9OdSxi1tlpmDvua6qwrLN2Tm/0XEh4GXAKdm5uVRFoldWS2LcXsvPTd2ympUDTI+D/hMFcj2oKw39qzMHKSMIzoyIr5BuZw10UA2+lSXT14SESdl5oosG+buDVxS/aG5gHJJ+1FDY5165Q9Qt1Uds+MpyyG81UA2OlWvuX2j7GV5GWUx2NcAp2VZfPnrlHFk21d/i33Ndd8LgF9FxBMy8x+Ac4D3R8SLM3NldZuee24cU1ajzLwzyp5qp0bE7ykdkf/KzB9Xg4r/UH2Kew5wYpZF8DRKdAwwnkZpzx9bnTqVsq/lcyPi74H9KOuQuexFF1SzMLfJzPuarkWbVsdrbiqlI/q4iNi7Wm7o98C+UbbZeirwqiwr+KuLhlYgqK4QrQB+GhHPy8wvVkN3PhQRV2fmn3spjA3x8mUXVJcwz6MM3v700CyvalbYnzPzfxouUTWJiH2BsylLnOxKuWR5RpZNc4+gXLKcm5nnNVimNGpUl6g/RrlE/SrKEIG9KUMGXg9MB2Zn5rmNFdlDVruUvCfwBuCcoXFiEfFR4H3Ac6pLmY/r5QaFoaxLqim/p1P+x7srIo6mrFr82nRftVGrCl5/m5kfrY4nA9cCJ2XmaR2365kxE1KdIuILwG8y88vV8T9S9hF+brUkynbVVQxfczWr1h57UpalfqYAu1HGct4MfB+4qups/i/lQ+sTslpHsFc5pqxLMnM+5dPA5RHxTuAY4M0GslFvDOXyJADV5ZIzKS36d3ac981B2jSWUGY1D83k+4fq3Pxq4Pid4GuuSyZRtg48E/gxcDXwIWAbSlB+XkQ8mzIr9hW9HsjAUNZVqw0yfkeOkg1UtXaZ+V1gcUT8NCKeWF3K3pqy7c/jGi1OGp2+Qxm/eXQ1YHwq8F/ArynLYqhLMnOAshjs0ZTx1HdVEys+SVlA/a2U5+vyzPxZY4W2iJcvGxARWzrIeHRZ06WQjgVKiYgvAo8Gnkzpkj4dOIyySrVbKUkbqVp89IHM/G3HUgrPBr4G/Ax4KXAocDDwYJY1IVWj1caR7UD5WzcFuAX4f5n5p4jYibJUycTM/H1z1baLoUzaRKqZtNsBf8jMX1bnHpVllWqqWV/jKYslzgFebbdUGr6OWZZ/A3wF6BsaCtIRzHah7Gm6nNIt+xzwhsz8TWOF94CO5+bFlCB2a2b+ICIOoOzx+0vKHr/PBj6emUsbLLd1vHwpbQIRsQ9lluWRwLsi4q0AmfmXiBg39D1lGZonAK8xkEkbp3rTfx5lNvO/VcsLRfWzlVUwuLmaxTeGsjjzmw1k9auem0MpO9QE8O6I+CxlB5PvAXtQNhn/iYHs4eyUSRup4xPhtpSp9wsz85KIeC3wIuDKzPzqGu7nxtfSIxQRjwPOB67NzNeu43ZjKQsz39+14npMREwEHpuZ10bEzpSVBt5P6YZ9CFgA3AW8NzOXR8Qu1dpxzoBdjZ0yaSN1fCL8NuUP0N7Vjy6gfCrct3OGZcf9DGTSMA11wiJi94jYreqCvQR4UkR8fG33y7KLhoGsJtWwjGOAt0XE0zPzFsr2VdsBHwVeDZxF2UHhrGpG7C3gDNg1cUV/aSNVA4zfAnyEEsg+HBFXZNm7bR7lQ4/b+EiPUEdX+nDgA8CdEfFTSkdmBvDDiNgiMz/SaKE9qBqicQlwEPCGiCAzfxkRzwcuysxF1fqM3wW+lX/dQklrYCiTNkLVrp9J2TvvauDq6hPgmRHx7sy8KCL+066YtPGGZjBXgewFlCWFDgaOA94ObEXZTulw4IKIOCszFzZXce+IiN2AJ2fmvMz8eUTcSvmQ+rrqb+GfgFdUHc7XAm/Msu+o1sHLl9IwRcTUaq2d7wBLIuJD1YyvM4F/Br5WjTPzE6G0kaqlFL4YEXtVpx4FvJcye/kA4D2Ubco+BzwIPNNA1h3VJcufAOdHxBkRcSqwLfAj4A7glcA9lOfpMsrONRc3Ve9I4kB/aRiqP0b/A/w0M2dGxIHAEcBC4PPVJ/rdMvPGRguVRoGI+BplGZlTMnNhNWj/C8B3M/PiiPgUsBfwPte66q6IeDpwDmWV/q9R9hu9AtiTEszuA05ON30fFjtl0npUrXjgoWUtDgOeFRH/mJkXUKZ5P40yywhK217SRupYRubvKIPCT42Iv6mGA9wFnBIRhwDPA/7eQNZ91VqMhwEvpDR4ng2cBgxQumZvoiyYrWGwUyatRURsA6zIzHsjYhple6TLq4GtO1E+Jc7NzJMjYgZwY2Y6sF/aBKrLlhOq8UqfpWxmfSJwJ2Uf4X2AL2TmuQ2W2fOqNRrnAcdn5r90LN77+HRv52FzoL+0BhHxaMqg1W8B9wJPoQws/mhE/Hdm3lqNo/j36o/QSQ2WK40aVWd6DPB6YIeIWJGZH6iC2YnApzLzYxHxmMy8x7WumpWZV0TES4HzImKzzDyt+tEiWPMWdFo7O2XSWkTEjpQPLodl5pcj4ihKS/5T1XiW51La9xdk5iUNliqNGh1hazvKwP6tgW9Wb/7/DOwMHJXuH9wq1X6jP6aM8bvRILZxDGXSaoba79X3r6VMwb88M79aBbPXUT4FzqBsKP5TPw1Kj1y1ntUXgdmZ+bNqFvMHKHtX/n1m/iIinpSZv22yTq3ZUKBuuo6RzFAmdehYpHI34KbMXNExw/LqzDyjmnW0G3BvZl7aaMHSCNfxmhufmcsi4gTKYsx/X3XHJlBm9V0InJiZdzVZr9au47n0Q+pGcval1KH6g3Iw8N/AlyPiE5RBrP8FPC0iZgE3ZOa5BjLpkel4E98HuC4idsjM2ZRlZz5a7ZqxDWXJmbMMZO02FMQMZBvPgf4Sq7w5bE3ZRLcPuJsyuP9zlL0txwGHUKZ5/7mpWqXRonrNvQR4ATAWuDQi9s/Mz0TEcspEm78AH8vMaxosVeoKL19KlYh4EXAg8ATKFkq3A08CZlFW5383ZVulwcaKlEaRiPgb4ALKOM0BykbWrwL2yczbImIPYLlLK6hXePlS4qG1dj5NWYl6b8oYsvGZeT1l4PFmwJMMZNImtZSyafVPgcHM/ADwa0rHbMfMXGggUy8xlKnnRcQTKCtRz8nMzwDHAK8Gjo6IzasFYWdl5m8aLFMa8arNqYmILatV+/8M7B0Rb+4Yh/SvwE1Af0Rs3lCpUiMcUyYVtwAzI+L8zLwkIlYAnwXGRsS/ZObShuuTRrxqDNnLgHdRtiP7b+C1wE+qXTLuBI4CPlqdt3GgnuL/8Oo5HZ/WnxwRzwFuo2zbchnw+YjYLjMvp4xvucqZRNKmERH7AZ+g7BN7J3BctebYdMosy2dSAttmlG2U7JSppzjQXz0pIg4FPkWZev9UyuzKQcrWSk8A3pGZdzRXoTTyRcSEzFwSEWOrNf8Oo2wo/mjgBOB1mbkoInbKzFur+7wAOBs4wr1k1WvslKmnRLEr8B5gf8qm4lsD12fmQuCrlPEskxorUhoFIuJJwA8i4quUNcceQ+mA9QPHAwdVgWw6cFJEbF2NM7sPONBApl5kp0w9JSL2yszrIuKjlDGVB1O2Svp9RLwY+CmwMjMfbLRQaQSLiD2BM4GvU5aTeRZwXmaeFxFnA08BXkz5YPQPwAcy87xmqpXaw06ZRr2IGFN93YfyyX1f4HHAy4G3VIHsBcA/A08wkEkbLyIeBfwQuDMzv0q5FLmIEswA/g6YX51/F1UgGxrrKfUyZ19q1IqILTPzvsxcGRG7UFbmPy4zL4+ILYBdgaOqlcNfDnwwM69rsGRpxMvMv0TEkcCPIuJdmfml6vV2dERMo4zd/AJwL3BLZj7gXolS4eVLjUrVeJbPAzdQVgq/kjKOZWxmvrS6zXMoHbOdgSsz8799c5A2jSqAzQcuBnYCjqO81l4APAN4d2YuaKxAqYUMZRp1VhvPMgbYE1hAWfLig5TZX8cavqR6RcQzgYuAf8rMUzr2mJ3o7hjSwzmmTKPKGsazfBW4FZhczeb6HPAo4MvNVSn1hsy8Gngp8J6IeF/HByGXm5HWwFCmUSUz/wIcCTynGs+ykjLFfrtqIPH1lC7a+KqjJqlGmXkVcChwSkTs7hABae28fKlRqWM8y6XAY4CjM/OG6mdjgK0y894GS5R6SkQ8JjPvaboOqc3slGlUyswrgRcBLwQuzswbImJctbL4SgOZ1HX3wl+3OZP0cC6JoVErM6+JiJcCcyNiSWb+U9M1Sb1q6JKlly6ltfPypUa9iHg28GNgL2BxNc5MkqRWMZSpJzieRZLUdo4pU69wPIskqdXslEmSJLWAnTJJkqQWMJRJkiS1gKFMkiSpBQxlkka0iFgREdd0/DN5I37H/hFx7mrnDuz4nUsi4nfV99/YZMVLUgcXj5U00t2fmc/Y1L80My8ALgCIiEuAD1Q7RUhSLeyUSRp1ImJRREysvp9WhSoiYquI+FpE/G9EXB0Rhw/z974kIr7fcTw9Ir5Xfb8kIj4XEb+IiAsjYofq/BMj4vyIuCoiLo+IJ22yf1FJo4qhTNJIt0XHZcbvr+e2HwMuysy/peyN+pmI2GoYj3UR8OShwAW8GTi7+n4r4BeZ+SzgUuDj1fkzgZmZuTfwAeBLw3g8ST3Ey5eSRrrhXL48ADgsIj5QHW8O7L6hD5SZGRH/CrwxIs4Gngu8qfrxSuA/qu//DfheREwAngf8Z8e6xZtt6ONJ6i2GMkmj0XL+eiVg847zAbwyM3/XeeOI2GkYv/ts4IfAA8B/ZubytdwuqxruqmPMm6TRx8uXkkajRcDe1fev7Dh/ATBzaLutiHjmcH9xZt4E3AScAHy940djgFdV378e+Em13+ofIuLV1eNFRDx9uI8pqTcYyiSNRp8AvhARlwMrOs6fCowHro2IX1fHG+PfgRsz8/qOc0uBvSLiKuDFwCnV+TcAx0TEL4HrgGFNLpDUO9z7UpKGKSK+CFydmWd1nFuSmRMaLEvSCGcok6RhqDphS4Hpmflgx3lDmaRHxFAmSZLUAo4pkyRJagFDmSRJUgsYyiRJklrAUCZJktQChjJJkqQWMJRJkiS1wP8HLMg4xgZ9YiIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fuel_type = ds.groupby('Fuel Type')['CO2_emission'].median().sort_values(ascending=False).index\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.boxplot(x = 'Fuel Type', y='CO2_emission', data =ds, order=fuel_type, width=0.5)\n",
    "plt.xticks(rotation=45, horizontalalignment='center')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "ds.groupby('Transmission')['CO2_emission'].mean().sort_values(ascending=False).plot(kind='bar')\n",
    "\n",
    "plt.title('Transmission wrt CO2 emission', fontsize=25)\n",
    "plt.xlabel('Transmission type', fontsize=20)\n",
    "plt.ylabel('CO2 emission', fontsize=20)\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.barplot(x = 'Fuel Consumption Comb (mpg)', y='CO2_emission', data =ds[:25])\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "sns.catplot(x='Cylinders', y='Fuel Consumption Comb (mpg)',data = ds)\n",
    "\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "sns.catplot(x='Cylinders', y='CO2_emission',data = ds)\n",
    "\n",
    "plt.show()                                # co2 emission increases with increasing no of cylinders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAHgCAYAAAB0CWMeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAA0BUlEQVR4nO3dfZxcdX33/9cnZBEkKuZGRCNGXaL1BlFWqqhUbhLNo4riXbVqt0rF9qqkFNSLHwZESMXWYunKVSve7s/bYgsF1CjhTtsi4gYQ8C5ZIeIqQgIXco8b8rn+mLO4Gze7s0nOOZk5r+fjMY+d75mZM++d3dl9z3fOnBOZiSRJkso3q+4AkiRJTWHxkiRJqojFS5IkqSIWL0mSpIpYvCRJkipi8ZIkSarI7LoDtGP+/Pm5aNGiumNIkiRNa82aNRszc8Fkl3VE8Vq0aBFDQ0N1x5AkSZpWRPx8a5f5VqMkSVJFLF6SJEkVsXhJkiRVxOIlSZJUEYuXJElSRSxekiRJFbF4SZIkVaS04hURT4+Ia8ed7oqIYyNibkSsjoh1xdfHlpVBkiRpZ1Ja8crMn2bm/pm5P3AAcB9wHnACcElm7gtcUowlSZK6XlVvNR4G/Cwzfw68Ghgslg8Cr6kogyRJUq2qKl5vAr5cnN8rM28BKL4+rqIMkiRJtSq9eEXErsARwFdneLujI2IoIoY2bNhQTjhJkqQKVTHjtQy4OjNvLca3RsTeAMXX2ya7UWaenZl9mdm3YMGkB/iWJEnqKFUUrzfzu7cZAS4A+ovz/cD5FWSQJEmqXanFKyIeCSwBzh23+MPAkohYV1z24TIzSNJkLr74Yg4++GAuu+yyuqNIapDIzLozTKuvry+HhobqjiGpixx66KFs2rSJ2bNnc+mll9YdR1IXiYg1mdk32WXuuV5S41x88cVs2rQJgE2bNjnrJakyFi9JjfOhD31owvi0006rKYmkprF4SWqcsdmurY0lqSwWL0mNM3v27CnHklQWi5ekxjnxxBMnjE866aSakkhqGouXpMY5/PDDH57lmj17NoccckjNiSQ1hcVLUiONzXo52yWpSm7YIKmRDj/8cA4//PC6Y0hqGGe8JEmSKmLxkiRJqojFS5IkqSIWL0mNtHHjRo455hhuv/32uqNIahCLl6RGGhwc5LrrrmNwcLDuKJIaxOIlqXE2btzIqlWryExWrVrlrJekyli8JDXO4OAgmQnA5s2bnfWSVBmLl6TGWb16NaOjowCMjo5y0UUX1ZxIUlNYvCQ1zpIlS+jp6QGgp6eHpUuX1pxIUlNYvCQ1Tn9/PxEBwKxZs+jv7685kaSmsHhJapz58+dz0EEHAXDQQQcxb968mhNJagqLl6RGGh4eBmDdunU1J5HUJBYvSY2zdu1aRkZGABgZGXm4hElS2Sxekhpn5cqVE8annnpqTUkkNY3FS1LjrF+/fsqxJJXF4iWpcRYtWjTlWJLKYvGS1DgrVqyYMD755JNrSiKpaSxekhpn8eLFD89yLVq0iN7e3noDSWoMi5ekRlqxYgV77LGHs12SKjW77gCSVIfFixezatWqumNIahhnvCRJkipi8ZIkSaqIxUuSJKkiFi9JkqSKWLwkNdLatWtZtmyZx2mUVCmLl6RGWrlyJffee6/HaZRUKYuXpMZZu3btw8dnXL9+vbNekipj8ZLUOCtXrpwwdtZLUlUsXpIaZ2y2a2tjSSqLxUtS44wdp3FrY0kqi8VLUuOsWLFiwtjjNUqqisVLUuMsXryYuXPnAjB37lx6e3trTiSpKSxekhrpjjvumPBVkqpg8ZLUOOedd96E8QUXXFBTEklNY/GS1DhnnnnmhPEZZ5xRTxBJjWPxktQ4mTnlWJLKYvGS1DgRMeVYkspi8ZLUOMcee+yE8fHHH19PEEmNY/GS1DhHHnnkw7NcEcERRxxRcyJJTWHxktRIY7NeznZJqlJ0wkalfX19OTQ0VHcMSZKkaUXEmszsm+wyZ7wkSZIqYvGSJEmqiMVLkiSpIhYvSZKkili8JEmSKmLxkiRJqojFS5IkqSIWL0mSpIpYvCRJkipi8ZIkSapIqcUrIvaMiH+PiJ9ExI8j4kURMTciVkfEuuLrY8vMIEmTOf300zn44IP5yEc+UncUSQ1S9ozXPwPfzMxnAM8FfgycAFySmfsClxRjSarUqlWrALjwwgtrTiKpSUorXhHxaOBg4NMAmfnbzLwTeDUwWFxtEHhNWRkkaTKnn376hLGzXpKqUuaM11OBDcBnI+KaiPhUROwB7JWZtwAUXx9XYgZJ+j1js11jnPWSVJUyi9ds4PnAxzPzecC9zOBtxYg4OiKGImJow4YNZWWUJEmqTJnFawQYyczvFeN/p1XEbo2IvQGKr7dNduPMPDsz+zKzb8GCBSXGlCRJqkZpxSszfw38IiKeXiw6DPgRcAHQXyzrB84vK4MkTWbZsmUTxq961atqSiKpaSIzy1t5xP7Ap4BdgRuBt9Mqe+cA+wA3A2/IzDumWk9fX18ODQ2VllNS8xx88MEPn//Od75TYxJJ3SYi1mRm32SXzS7zjjPzWmCyOz6szPuVJEnaGbnnekmN84lPfGLC+NOf/nRNSSQ1jcVLUuN88YtfnDAeHBzcyjUlaceyeEmSJFXE4iVJklQRi5ekxnnLW94yYdzf37+Va0rSjmXxktQ473rXuyaMjzrqqJqSSGoai5ckSVJFLF6SGufzn//8hPGXv/zlmpJIahqLl6TG+eQnPzlh/PGPf7ymJJKaxuIlSZJUEYuXJElSRSxekhrnne9854TxX/3VX9WURFLTWLwkNc7b3va2CeM3v/nNNSWR1DQWL0mNNDbr5WyXpCpFZtadYVp9fX05NDRUdwxJkqRpRcSazOyb7DJnvCRJkipi8ZIkSaqIxUuSJKkiFi9JjXTeeedx8MEHc8EFF9QdRVKDWLwkNdKZZ54JwBlnnFFvEEmNYvGS1DjnnXceY5/ozkxnvSRVxuIlqXHGZrvGOOslqSoWL0mNs+X+Czthf4aSuoPFS1LjRMSUY0kqi8VLUuMce+yxE8bHH398PUEkNY7FS1LjHHnkkRPGRxxxRE1JJDWNxUuSJKkiFi9JjXPcccdNGL/vfe+rKYmkprF4SWqcoaGhCeMrr7yypiSSmsbiJUmSVBGLlyRJUkUsXpIap6+vb8L4hS98YU1JJDWNxUtS43z0ox+dMP6Hf/iHmpJIahqLl6RGmjdvHgALFiyoOYmkJrF4SWqcjRs3cvfddwNw1113cfvtt9ecSFJTWLwkNc7g4ODDB8bevHkzg4ODNSeS1BQWL0mNs3r1akZHRwEYHR3loosuqjmRpKaweElqnCVLltDT0wNAT08PS5curTmRpKaweElqnP7+fiICgFmzZtHf319zIklNYfGq2dq1a1m2bBnDw8N1R5EaY/78+Rx00EEAHHTQQQ9/wlGSymbxqtnKlSu59957OfXUU+uOIjXKz372MwBf9EiqlMWrRmvXrmX9+vUArF+/3n8AUkXWrl3LL37xCwB+8Ytf+NyTVBmLV41Wrlw5Yeysl1QNn3uS6mLxqtHYbNfWxpLK4XNPUl0sXjVatGjRlGNJ5fC5J6kuFq8arVixYsL45JNPrimJ1Cw+9yTVxeJVo8WLF/OkJz0JgCc96Un09vbWnEhqhsWLFzN//nygdZBsn3uSqmLxqtnTnvY0AP/wSxW74447ADxAtqRKWbxqtHHjRq644goArrjiCv8BSBW5+OKL2bx5M9A6SPZll11WcyJJTWHxqtHg4CCZCbT++A8ODtacSGqGD33oQxPGp512Wk1JJDWNxatGq1evZnR0FIDR0VEuuuiimhNJzbBp06Ypx5JUFotXjZYsWUJPTw8APT09LF26tOZEUjPMnj17yrEklcXiVaP+/n4iAoBZs2bR399fcyKpGU488cQJ45NOOqmmJJKaxuJVo/nz57Ns2TIigmXLljFv3ry6I0mNcPjhh08YH3LIITUlkdQ0Fq+a9ff3s99++znbJVXo4osvnjD2U42SqhJjn6rbmfX19eXQ0FDdMSR1iUMPPXTCBvWzZ8/m0ksvrTGRpG4SEWsys2+yy6bcojQiFgJvAl4KPAG4H7gB+DqwKjM37+CsklQ6P9UoqS5bfasxIj4LfAb4LfD3wJuB/wVcDLwC+O+IOLiKkJK0I/mpRkl1meqvzRmZecMky28Azo2IXYF9yoklSeU58cQTOfXUUx8e+6lGSVXZ6ozXVkrX+Mt/m5nDU10nItZHxPURcW1EDBXL5kbE6ohYV3x97LZFl6Rt46caJdVl2vn1iLge2HIL/N8AQ8DKzJzuAIOHZObGceMTgEsy88MRcUIx/t8zyCxJktSR2tmdxCpaG9O/pThdCHwH+DXwuW24z1cDYwclHAResw3rkKRt9vKXv3zCeNmyZTUlkdQ07WxR+uLMfPG48fUR8T+Z+eKIeOs0t03goohI4BOZeTawV2beApCZt0TE47YtuiRtm/vvv3/C+N57760piaSmaad4zYmIP8zM7wFExIHAnOKy6T6D/eLM/FVRrlZHxE/aDRYRRwNHA+yzj9vwS5KkztdO8foL4DMRMQcI4C7gqIjYAzh9qhtm5q+Kr7dFxHnAgcCtEbF3Mdu1N3DbVm57NnA2tHag2u43JEmStLOadhuvzPx+Zj4H2B/YPzP3K5bdm5nnbO12EbFHRDxq7DywlNauKC4Axo6P0w+cv53fgyTNyO677z5hvMcee9SURFLTTFu8ImJeRAwAlwGXRMQ/R0Q7R3Pei9ZOVn8AXAV8PTO/CXwYWBIR64AlxViSKvOtb31rwnjVqlU1JZHUNO281fgVWp9ifF0xfgvwb8DhW70FkJk3As+dZPntwGEziylJO9buu+/O/fff72yXpEq1U7zmZuZp48YrI+I1JeWRpEpsOeslSVVop3hdFhFvAsa253o9rf16SVLpBgYGGB6e8iAZ22RkZASAhQsX7vB19/b2snz58h2+Xkmdr53i9S7gOOALxXgWcG9EHAdkZj66rHCSVJYt9+UlSVWYtnhl5qOqCCJJkylr5mhsvQMDA6WsX5Im086MFxGxH7Bo/PUz89ySMkmSJHWldnYn8RngM7Q+1fiq4vTKknM1xsaNGznmmGO4/fbpjjUuSZI6XTszXi/MzGeWnqShBgcHue666xgcHOS4446rO44kSSrRtDNewHcjwuJVgo0bN7Jq1Soyk1WrVjnrJUlSl2uneA3SKl8/jYjrIuL6iLiu7GBNMDg4SGbrMJSbN29mcHCw5kSSJKlM7RSvzwBvA17B77bvelWZoZpi9erVjI6OAjA6OspFF11UcyJJklSmdorXzZl5QWbelJk/HzuVnqwBlixZQk9PDwA9PT0sXbq05kSSJKlM7RSvn0TElyLizRHx2rFT6ckaoL+/n4gAYNasWfT399ecSJIklamd4rU78CCwFHcnsUPNnz+fZcuWEREsW7aMefPm1R1JkiSVqJ0917+9iiBN1d/fz/r1653tkiSpAbY64xURKyJi7hSXHxoRznxtp/nz5/Oxj33M2S5Jkhpgqhmv64ELI+IB4GpgA7AbsC+wP3Ax8KGyA0qSJHWLrRavzDwfOD8i9gVeDOwN3AV8ATg6M++vJqIkSVJ3aGcbr3XAugqySJIkdbV2PtWoEnmQbEmSmsPiVbPxB8mWJEndzeJVIw+SLUlSs0xbvCLiqRFxYURsjIjbIuL8iHhqFeG6nQfJliSpWdqZ8foScA7weOAJwFeBL5cZqik8SLYkSc3STvGKzPx8Zm4qTl8AsuxgTeBBsiVJapap9lw/t9hz/WURcUJELIqIJ0fE+4CvVxexe3mQbEmSmmWq/XitoTWzFcX4XeMuS+C0skI1xdhBsi+44AIPki1JUgNMtef6p1QZpKk8SLYkSc0x7Z7rI2IX4I+BReOvn5kfLS9Wc4wdJFuSJHW/aYsXcCHwAK2DZm8uN4604w0MDDA8PLzD1zsyMgLAwoULd/i6AXp7e1m+fHkp65Yk1aOd4rUwM/crPYnUYe6/3+PES5Jmpp3itSoilmamO5lSRypr1mhsvQMDA6WsX5LUfdopXlcC50XELGCU1qccMzMfXWoySZKkLtPODlTPAF4EPDIzH52Zj7J07Thr165l2bJlpWyDJEmSdi7tFK91wA05dlBB7VArV67k3nvv5dRTT607iiRJKlk7bzXeAlweEauAB8cWujuJ7bd27VrWr18PwPr16xkeHqa3t7feUJIkqTTtzHjdBFwC7Ao8atxJ22nlypUTxs56SZLU3aad8crMDwJExKNaw7yn9FQNMTbbtbWxJEnqLtPOeEXEsyPiGuAG4IcRsSYinlV+tO63aNGiKceSJKm7tPNW49nAcZn55Mx8MnA88MlyYzXDihUrJoxPPvnkmpJIkqQqtFO89sjMy8YGmXk5sEdpiRpk8eLF7L333gDsvffeblgvSVKXa+dTjTdGxEnA54vxW2ltcK8dKCLqjiBJkkrWzozXO4AFwLnFaT7w9jJDNcXatWu55ZZbAPjVr37lTlQlSepyWy1eEbFbRCzIzP+bmcsz8/mZ+XzgdMCjA+8A7k5CkqRmmWrGawB46STLDwf+qZw4zeLuJCRJapapitdLMvPcLRdm5heBg8uL1BzuTkKSpGaZqnhNtbV3O9uGaRruTkKSpGaZqkDdFhEHbrkwIl4AbCgvUnMsXryYhQsXArBw4UJ3JyFJUpebancS7wXOiYjPAWuKZX3AnwFvKjlXY/T29jIyMsK+++5bdxRJklSyrc54ZeZVwIG03nL88+IUwB9m5veqCNftNm7cyBVXXAHAFVdcwe23315zIkmSVKYpt9XKzNsy8wOZ+bridHJm3lZVuG43ODhIZgKwefNmBgcHa04kSZLK5EbyNVq9ejWjo6MAjI6OctFFF9WcSJIklcniVaMlS5bQ09MDQE9PD0uXLq05kSRJKpPFq0b9/f0PH6Nx1qxZ9Pf315xIkiSVadriFRGLI+KTEXFRRFw6dqoiXLebP38+Bx10EAAHHXQQ8+bNqzmRJEkq01S7kxjzVeBfgU8CD5Ubp3nGDoy9bt26mpNIkqSytVO8NmXmx0tP0kBr165lZGQEgJGREYaHh92JqiRJXaydbbwujIj/FRF7R8TcsVPpyRpg5cqVE8annnpqTUkkSVIV2pnxGtvi+73jliXw1B0fp1nWr18/5ViSJHWXaYtXZj6liiBNtGjRoglla9GiRbVlkSRJ5WvnU409EbE8Iv69OL07InqqCNftVqxYMWF88skn15REkiRVoZ1tvD4OHAD8S3E6oFjWlojYJSKuiYivFeO5EbE6ItYVXx+7LcG7weLFi5kzZw4Ac+bMccN6SZK6XDvF6wWZ2Z+ZlxantwMvmMF9/A3w43HjE4BLMnNf4JJi3EgbN27kgQceAODBBx/0INmSJHW5dorXQxHxtLFBRDyVNvfnFRELgT8GPjVu8auBsaNBDwKvaStpFxocHHx4z/VjY0mS1L3aKV7vBS6LiMsj4tvApcDxba7/TOB9wOZxy/bKzFsAiq+Pm+yGEXF0RAxFxNCGDRvavLvO4kGyJUlqlmmLV2ZeAuwLLC9OT8/My6a7XUS8ErgtM9dsS7DMPDsz+zKzb8GCBduyip2eB8mWJKlZtlq8IuLQ4utrab1d2As8DfjjYtl0XgwcERHrga8Ah0bEF4BbI2LvYt17A7dt13fQwTxItiRJzTLVjNcfFV9fNcnpldOtODP/v8xcmJmLgDcBl2bmW4EL+N1OWfuB87cteuebP38+y5YtIyJYtmyZB8mWJKnLbXUHqpn5geLsqZl50/jLImJ7dqr6YeCciDgKuBl4w3asq+P19/ezfv16Z7skSWqAdg4Z9B/A87dY9u+09ufVlsy8HLi8OH87cFi7t+128+fP52Mf+1jdMSRJUgW2Wrwi4hnAs4DHbLFN16OB3coOtjMZGBhgeHi4lHWPjIwAsHDhwh2+7t7eXpYvX77D1ytJkrbNVDNeT6e1LdeetLbrGnM38M4SMzXK/fffX3cESZJUkam28TofOD8iXpSZ360w006nzFmjsXUPDAyUdh+SJGnn0M4OVG+NiAsjYkNE3BYR5xd7r5ckSdIMtFO8vgScA+wNPAH4KvDlMkNJkiR1o3aKV2Tm5zNzU3H6ApBlB5MkSeo27exO4rKIOIHW3ucT+BPg6xExFyAz7ygxnyRJUtdop3j9SfH1XVssfwetIub2XtohytxtRxnWrVsHlPvhizK4mxFJqs+0xSszt2cv9VLbhoeHWXvD1ewz56G6o7Rl19HWO/UPrP9+zUnad/M9u9QdQZIabdriFRG70DpI9qLx18/Mj5YXS021z5yHWNF3T90xutbKoTl1R5CkRmvnrcYLgQeA64HN5caRJEnqXu0Ur4WZuV/pSSRJkrpcO7uTWBURS0tPIkmS1OXamfG6EjgvImYBo0AAmZmPLjWZJElSl2mneJ0BvAi4PjPdcaokSdI2auetxnXADZYuSZKk7dPOjNctwOURsQp4cGyhu5OQJEmamXaK103FadfiJEmSpG3Qzp7rP1hFEEmSpG7Xzp7rL6N1TMYJMvPQUhJJkiR1qXbeanzPuPO7Aa8DNpUTR5IkqXu181bjmi0W/U9EfLukPJIkSV2rnbca544bzgIOAB5fWiJJkqQu1c5bjWtobeMVtN5ivAk4qsxQkiRJ3aidtxqfUkUQSZKkbjftnusj4g0R8aji/IqIODcinl9+NEmSpO7SziGDTsrMuyPiJcDLgUHg4+XGkiRJ6j7tFK+Hiq9/DHw8M8/HPdhLkiTNWDvF65cR8QngjcA3IuIRbd5OkiRJ47RToN4IfAt4RWbeCcwF3ltmKEmSpG40bfHKzPuA84F7I2IfoAf4SdnBJEmSuk07O1A9BvgAcCuwuVicwH4l5pIkSeo67exA9W+Ap2fm7WWHkSRJ6mbtbOP1C+A3ZQeRJEnqdu3MeN0IXB4RXwceHFuYmR8tLZWkjjIwMMDw8HDdMWZk3bp1ACxfvrzmJDPT29vbcZkl/U47xevm4rQr7r9L0iSGh4e55ofXwJ51J5mBYovVa355Tb05ZuLOugNI2l7tHKvxgwDFYYMyM+8pPZWkzrMnbH7Z5mmvpm0363J3oSh1unaO1fjsiLgGuAH4YUSsiYhnlR9NkiSpu7Tz8uls4LjMfHJmPhk4HvhkubEkSZK6TzvFa4/MvGxskJmXA3uUlkiSJKlLtfWpxog4Cfh8MX4rcFN5kSRJkrpTOzNe7wAWAOcWp/nA28sMJUmS1I22OuMVEbsBj8rMDcDyccv3Au6vIJskSVJXmWrGawB46STLDwf+qZw4kiRJ3Wuq4vWSzDx3y4WZ+UXg4PIiSZIkdaepilds4+0kSZI0iakK1G0RceCWCyPiBcCG8iJJkiR1p6l2J/Fe4JyI+BywpljWB/wZ8KaSc0mSJHWdrc54ZeZVwIG03nL88+IUwB9m5veqCCdJktRNptyBambeBnygoiySJEldzY3kJUmSKmLxkiRJqojFS5IkqSJTHTLoQiC3dnlmHlFKIkmSpC411cb1/1hZCkmSpAbYavHKzG+PnY+I3YF9MvOnlaRSI42MjHDv3buwcmhO3VG61s/v3oU9RkbqjiFJjTXtNl4R8SrgWuCbxXj/iLig5FySJEldZ8r9eBVOobUj1csBMvPaiFhUXiQ11cKFC3lg0y2s6Lun7ihda+XQHHZbuLDuGJLUWO18qnFTZv6m9CSSJEldrp3idUNE/CmwS0TsGxEfA66Y7kYRsVtEXBURP4iIH0bEB4vlcyNidUSsK74+dju/B0mSpI7QTvE6BngW8CDwZeAu4Ng2bvcgcGhmPhfYH3hFRLwQOAG4JDP3BS4pxpIkSV1v2m28MvM+4P3FqW2ZmcDYxjo9xSmBVwMvK5YP0tp27H/PZN2SJEmdaNriFRGXMcmOVDPz0DZuuwuwBugF/k9mfi8i9srMW4p13BIRj5t5bEmSpM7Tzqca3zPu/G7A64BN7aw8Mx8C9o+IPYHzIuLZ7QaLiKOBowH22Wefdm8mSZK002rnrcY1Wyz6n4j49qRX3vo67oyIy4FXALdGxN7FbNfewG1buc3ZwNkAfX19Wz10kSRJUqdoZweqc8ed5kfEy4HHt3G7BcVM19ie7w8HfgJcAPQXV+sHzt/W8JIkSZ2knbcax894bQJuAo5q43Z7A4PFdl6zgHMy82sR8V3gnIg4CrgZeMMMM0uSJHWkrRaviNgnM2/OzKdsy4oz8zrgeZMsvx04bFvWKUmS1MmmmvH6T+D5ABHxH5n5ukoSbYeBgQGGh4frjjEj69atA2D58uU1J2lfb29vR+VV+UZGRuA3MOvydnYNqG12J4ykBzmXOtlUxSvGnX9q2UF2hOHhYa65/kdsfuTcuqO0LX7b+tzAmp/9uuYk7Zl13x11R5AkqWNNVbxyK+d3apsfOZcHnvnKumN0rd1+9LW6I2gntHDhQjbEBja/bHPdUbrarMtnsfCJHuRc6mRTFa/nRsRdtGa+di/OU4wzMx9dejpJkqQustUNMjJzl8x8dGY+KjNnF+fHxpYuSVJtNm7cyDHHHMPtt99edxRpRtwSVpLUcQYHB7nuuusYHBysO4o0IxYvSVJH2bhxI6tWrSIzWbVqlbNe6ijt7EBVqszN9+zCyqE5dcdoy633tV637PXIztmg/OZ7dmFx3SGk7TQ4OEhm6zNfmzdvZnBwkOOOO67mVFJ7LF7aafT29tYdYUZ+W+yDbbdF+9acpH2L6bzHWdrS6tWrGR0dBWB0dJSLLrrI4qWOYfHSTqPTdso6lndgYKDmJFKzLFmyhG984xuMjo7S09PD0qVL644ktc1tvCRJHaW/v5+I1j6+Z82aRX9/f82JpPZZvCRJHWX+/Pk84QlPAOCJT3wi8+bNqzmR1D6LlySp46xfvx6AG2+8sd4g0gxZvCRJHeXMM8+cMD7rrLPqCSJtA4uXJKmjnHvuuRPG55xzTk1JpJmzeEmSJFXE4iVJklQRi5ckqaO89rWvnTB+4xvfWFMSaeYsXpKkjnLsscdOGL/73e+uJ4i0DSxekqSOM3t268ArPT09NSeRZsbiJUnqKFdddRWbNm0CWsdqXLNmTc2JpPZ11bEaR0ZGmHXfb9jtR1+rO0rXmnXf7YyMbKo7hqQGO+WUUyaMTzrpJL7xjW/UE0aaIWe8JEkd5Z577plyLO3MumrGa+HChdz64GweeOYr647StXb70ddYuPDxdceQ1GBz5syZULbmzJlTYxppZpzxkiR1lC3fajzttNPqCSJtA4uXJKmjHHjggey6664APOIRj+CAAw6oOZHUPouXJKnj/Pa3vwXgwQcfrDmJNDMWL0lSRznvvPMmjC+44IKakkgzZ/GSJHWUM888c8L4jDPOqCeItA0sXpKkjpKZU46lnZnFS5LUUSJiyrG0M7N4SZI6ypYHyT7++OPrCSJtA4uXJKmjHHnkkRPGRxxxRE1JpJmzeEmSOspVV101YexBstVJuuqQQZJqdCfMuryDXsuNHXGmk442cyfwxLpD1M+DZKuTWbwkbbfe3t66I8zYunXrANj3ifvWnGQGntiZj/WO5kGy1cksXpK22/Lly+uOMGNjmQcGBmpOopnyINnqZB30voAkSR4kW53N4iVJ6igHHnjgw7Ncc+bM8SDZ6igWL0lSxznllFOYNWuWs13qOG7jJUnqOAceeCCXX3553TGkGbN4qesNDAwwPDy8w9c79qm4sjYs7+3t7ciN1qUxZT33AEZGRgBYuHDhDl+3zz2VyeIlbaPdd9+97ghSY91///11R5C2icVLXc9XrlI9ynzuuTsQdSo3rpckSaqIxUuSJKkiFi9JkqSKWLwkSZIq0nUb18+67w52+9HX6o7RtnjgLgByt0fXnKQ9s+67A3h83TEkSYWBgQFWrVq1w9d73333kZk7fL1ligge+chHlrLuZcuW7ZAPjHRV8ert7a07woytW3c3APs+rVPKzOM78nGWJGln0FXFqxN3G+BHoiVJ22P58uUd+f+vqdzGS5IkqSIWL0mSpIpYvCRJkipi8ZIkSaqIxUuSJKkiFi9JkqSKWLwkSZIqYvGSJEmqSGnFKyKeFBGXRcSPI+KHEfE3xfK5EbE6ItYVXx9bVgZJkqSdSZkzXpuA4zPzD4AXAn8dEc8ETgAuycx9gUuKsSRJUtcrrXhl5i2ZeXVx/m7gx8ATgVcDg8XVBoHXlJVBkiRpZ1LJNl4RsQh4HvA9YK/MvAVa5Qx4XBUZJEmS6lZ68YqIOcB/AMdm5l0zuN3RETEUEUMbNmwoL6AkSVJFSi1eEdFDq3R9MTPPLRbfGhF7F5fvDdw22W0z8+zM7MvMvgULFpQZU5IkqRJlfqoxgE8DP87Mj4676AKgvzjfD5xfVgZJkqSdyewS1/1i4G3A9RFxbbHsRODDwDkRcRRwM/CGEjNIkiTtNEorXpn530Bs5eLDyrpfSZKknZV7rpckSapImW81SpI6wMDAAMPDw3XHmJF169YBsHz58pqTtK+3t7ej8qocFi9Jarjh4WF+cu21PL7uIDMw9nbNnddeW2eMtv267gDaaVi8JEk8Hjhqq5vlant9mqw7gnYSbuMlSZJUEYuXJElSRSxekiRJFbF4SZIkVcTiJUmSVBGLlyRJUkUsXpIkSRWxeEmSJFXE4iVJklQRi5ckSVJFPGSQJDXcyMgId+Nhbcp0C3DPyEjdMbQTcMZLkiSpIs54SVLDLVy4kDs3bvQg2SX6NMmeCxfWHUM7AWe8JEmSKmLxkiRJqojFS5IkqSIWL0mSpIq4cb0kiV/TWbuTuL34Oq/WFO37NbBn3SG0U7B4SVLD9fb21h1hxjasWwfAnvvuW3OS9uxJZz7O2vEsXpLUcMuXL687woyNZR4YGKg5iTQzbuMlSZJUEYuXJElSRSxekiRJFbF4SZIkVcTiJUmSVBGLlyRJUkUsXpIkSRWxeEmSJFXE4iVJklQRi5ckSVJFLF6SJEkVsXhJkiRVxOIlSZJUEYuXJElSRSxekiRJFbF4SZIkVcTiJUmSVJHZdQfoBAMDAwwPD5ey7nXr1gGwfPnyHb7u3t7eUtYrVams55/PPUl1sHjVbPfdd687gtRIPvck1cHi1QZfuUr18fknqZu4jZckSVJFLF6SJEkVsXhJkiRVxOIlSZJUETeulySVwl3xSL/P4iVJ6jjuDkSdyuIlSSqFs0bS73MbL0mSpIpYvCRJHefMM8/k4IMP5qyzzqo7ijQjFi9JUsc599xzATjnnHNqTiLNjMVLktRRzjzzzAljZ73USSxekqSOMjbbNcZZL3USi5ckSVJFLF6SJEkVsXhJkjrKa1/72gnjN77xjTUlkWautOIVEZ+JiNsi4oZxy+ZGxOqIWFd8fWxZ9y9J6k7HHnvshPG73/3ueoJI26DMGa/PAa/YYtkJwCWZuS9wSTGWJGlGxma9nO1Sp4nMLG/lEYuAr2Xms4vxT4GXZeYtEbE3cHlmPn269fT19eXQ0FBpOSVJknaUiFiTmX2TXVb1Nl57ZeYtAMXXx1V8/5IkSbXZaTeuj4ijI2IoIoY2bNhQdxxJkqTtVnXxurV4i5Hi621bu2Jmnp2ZfZnZt2DBgsoCSpIklaXq4nUB0F+c7wfOr/j+JUmSalPm7iS+DHwXeHpEjETEUcCHgSURsQ5YUowlSZIaYXZZK87MN2/losPKuk9JkqSd2U67cb0kSVK3sXhJkiRVxOIlSZJUEYuXJElSRSxekiRJFbF4SZIkVcTiJUmSVBGLlyRJUkUiM+vOMK2I2AD8vO4cJZoPbKw7hLaJP7vO5s+vs/nz61zd/rN7cmZOeqDpjihe3S4ihjKzr+4cmjl/dp3Nn19n8+fXuZr8s/OtRkmSpIpYvCRJkipi8do5nF13AG0zf3adzZ9fZ/Pn17ka+7NzGy9JkqSKOOMlSZJUka4rXhHxUERcGxE3RMRXI+KRJdzHX0bEn+3o9RbrfmtEXBcRP4yIH0TEpyJizx24/j+PiLOK86V9H51s3O/Q2M/guIiYVVzWFxED27jee3Zs0p3bFs/FC3fk7/G4+7g8Irbrk1E+56o37ndj7HRCsfzY8X+zq3jONO15uTURkRFxxrjxeyLilGlu87KIOGgH51gfEfMnWT4nIj4eET+LiGsiYk1EvHNH3ndVZtcdoAT3Z+b+ABHxReAvgY+OXRgRu2TmQ9tzB5n5r9uVcCsi4hXA3wLLMvOXEbEL0A/sBdy5o++vrO+jC4z/HXoc8CXgMcAHMnMIGKoxWycZ/zgOAn8N/F2dgbZ8/vucq83DvxtbOBb4AnBfpWkE8CDw2og4PTPb3b/Wy4B7gCvavZPt+B/8KeBGYN/M3BwRC4B3bMN6atd1M15b+C+gt2jll0XEl4DrI2KXiPhIRHy/eKX7Lni4vX87Is6JiLUR8eGIeEtEXBUR10fE04rrnRIR7ynOP/yKOyLmR8T64vyfR8R/Fq/0b4qIdxczJ9dExJURMXeSvO8H3pOZvwTIzIcy8zOZ+dNinScXmW+IiLMjIorlyyPiR8X38pVi2dzi/q8r7m+/Le9sku/j74vvdW1EvLRYPulj1RSZeRtwNPDuaHlZRHwNICL+aNwr9msi4lHF8veOe7w+WGf+nch3gScCRMTTIuKbxSvW/4qIZ4xbfmXx2J06NhMx/jEvxmdFxJ9veQfFq+GhaM1cfXDc8vXFc+e/gTdscTOfczuJiFgOPAG4LCIuG7f876I1E3llROxVLHtVRHyveN5dPG75KRHxmeKxvbFY59h6jit+jjdExLEVf3udYBOtDd7/dssLJnu8I2IRrYmNvy3+Br40Ij4XEa8fd7vxz+GH/wcXy/6z+Bvww4g4eqpg0frfeyCwIjM3A2Tmhsz8++LyORFxSURcHa3/1a8ulu8REV8vfn9uiIg/2f6Haft1bfGKiNnAMoofMq0f2vsz85nAUcBvMvMFwAuAd0bEU4rrPRf4G+A5wNuAxZl5IK22fcwMYzwb+NPivv8OuC8zn0frn9Bkbzc8C7h6ivWdlZkvyMxnA7sDryyWnwA8LzP3o/VEAPggcE2x7ETg/28j7+ziez0W+ECxbKrHqhEy80Zaz5XHbXHRe4C/Ll65vxS4PyKWAvvS+pnvDxwQEQdXl3bnE61ZpMOAC4pFZwPHZOYBtB7DfymW/zPwz8Xv2q+24a7eX+yQcT/gj7YoPg9k5ksy8ytb3MbnXD12j4lvNf5JZg7Q+rkfkpmHFNfbA7gyM58LfAcYe2vpv4EXFn9PvwK8b9y6nwG8nNZz8AMR0RMRBwBvB/4QeCGtx/R5ZX+THej/AG+JiMdssfz3Hu/MXA/8K/BPmbl/Zv7XNOse/z8Y4B3F34A+YHlEzJvits8CfjBWuibxAHBkZj4fOAQ4o3iR9ArgV5n53OI5/M1pMlaiG4vX7hFxLa23g24GPl0svyozbyrOLwX+rLje94B5tP5ZAnw/M2/JzAeBnwEXFcuvBxbNMMtlmXl3Zm4AfgNc2O66IuI5xR+kn41r6YcUrzquBw6l9csIcB3wxYh4K61XLQAvAT4PkJmXAvMmeTJt6dzi65px+aZ6rJokJln2P8BHi1fVe2bmJlqP11LgGlr/0J9BMx8v+N1z8XZgLrA6IuYABwFfLS77BLB3cf0XAV8tzn9pG+7vjRFxNa3H/lnAM8dd9m/T3djnXKXuL/5Zj5229vP5LTA22zn+MVoIfKv4ubyX3/1cAL6emQ8Wb5fdRutt45cA52XmvZl5D63H/aU79lvqfJl5F60XDMu3uGiqx7td4/8HQ6ts/QC4EngSM/gdj4j3F8/VsRdoAXwoIq4DLqY1u74Xrf+1hxczyy/NzN9sQ+4drhuL1/gn9DGZ+dti+b3jrhO0XnGPXe8pmTlWsB4cd73N48abmXybuE387nHcbYvLZrquHwLPB8jM64uZlFW0/oHtRmtm4PWZ+Rzgk+Pu749pvVI5AFhTzPZNVhSm23fIWL6HxuWb6rFqhIh4Kq3H5LbxyzPzw8Bf0JoJuTJab5kFcPq4x6s3Mz/9eytthrHteJ4M7EprG69ZwJ1b/NP9g2nWM/45Br//PKOYEXoPcFgx4/T1La5375a3Kfic27mN5u/2eTT+MfoYrdnI5wDvYuLPevzf3bHbTPaz0eTOpDXruse4ZVM93uM9/FwtZpx2HXfZw8/BiHgZcDjwomI285op1gnwI+C5UXzIKTP/rniuPrq4/C3AAuCAYvmtwG6ZuZbWc/R64PSIOHmK+6hMNxavdnwL+KuI6AGIiMURscc0t9ma9bR+sACvn+J67Tgd+MeIWDhu2e7F17Ffyo3FrMHrAYpfxCdl5mW0ptv3BObQmpZ/S3GdlwEbi1czM7UjH6uOE60NOP+V1h+d3OKypxX/rP+e1gzrM2g9Xu8ofkZExBOjtYF+YxWvMpfTKkb3AzdFxBug9cc5Ip5bXPVK4HXF+TeNW8XPgWdGxCOKGaTDJrmbR9P6w/6baG3vs6zNeD7ndi53A49q43qPAX5ZnO9v4/rfAV4TEY8sHssjaW0DrC1k5h3AObTK15itPd5b/rzW87v/h68GerZyN48B/m9m3le8YH3hNJmGaf2NXVlsukDxwmisUD8GuC0zRyPiEFov9oiIJ9DaxOcLwD9SvMiqWzd+qrEdn6I1ZX110co3AK/ZxnX9I3BORLwNuHR7QmXmN4p/9KuKX647gRuAb2XmnRHxSVrNfT3w/eJmuwBfKP4hBa332++M1seAP1tMvd5He3+cJrMjH6tOMfYWWQ+tV3CfZ9wnY8c5tniSP0TrFdmqzHwwIv4A+G7r4eIe4K1sMVvWNJl5TfG2wptolZOPR8QKWo/xV4AfUHyiLSKOpzVj9Zvitr+IiHNovb23jtar4y3X/4OIuIbWDNaNtN4GbieXz7l6jD3HxnwzM0+gtf3fqoi4Zdx2XpM5hdbb1b+kVdin3AYuM6+OiM8BVxWLPpWZv/d7pIedAbx73PgUJn+8LwT+PVobsx9Da1b4/Ii4CriErc80fxP4y+K58tNindP5C+AjwHBE3EHrRdz/Li77InBhRAwB1wI/KZY/B/hIRGwGRoG/auN+Suee6yXtFKK1/6b7MzMj4k3AmzPz1XXnkqQdqakzXpJ2PgcAZxWzPHfSofvokaSpOOMlSZJUkaZuXC9JklQ5i5ckSVJFLF6SJEkVsXhJ2ulFxEMx8RAzi7ZhHROO+Vgse/m4dd4TET8tzrdzuB9JmjE/1SipE4ztBX+Hysxv0dphKRFxOa0DZg/t6PuRpDHOeEnqSBGxPiLmF+f7iuJEROwREZ+JiO9HxDXFzh1nst7DIuK8ceMlEXFucf6eiDgjIq6OiEuKna8SEU+LiG9GxJqI+K9ib9yS9HssXpI6we7j3hI8b5rrvh+4NDNfABxCa8/VMznkzqXAH4yVKuDtwGeL83sAV2fm84FvAx8olp9N6/iKB9A6NNK/zOD+JDWIbzVK6gQzeatxKXBERLynGO8G7NPuHRV7zv888NaI+CzwIuDPios3A/9WnP8CcG5xHMeDaB1SZWw1j2j3/iQ1i8VLUqfaxO9m7XcbtzyA12XmT8dfuTh4drs+S+s4dA8AX83MTVu5XhYZ7ixjGzRJ3ce3GiV1qvW0DjME8Lpxy78FHFMceoiIeN5MV5yZvwJ+BawAPjfuolnA64vzfwr8d2beBdwUEW8o7i8i4rkzvU9JzWDxktSpPgj8c0T8F/DQuOWnAT3AdRFxQzHeFl8EfpGZPxq37F7gWRGxBjgUOLVY/hbgqIj4AfBDwIN7S5qUx2qUpElExFnANZn56XHL7snMOTXGktThLF6StIViRuteYElmPjhuucVL0naxeEmSJFXEbbwkSZIqYvGSJEmqiMVLkiSpIhYvSZKkili8JEmSKmLxkiRJqsj/A0mzMhYyNFHXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "sns.boxplot(x = 'Fuel Type', y='Fuel Consumption Comb (mpg)', data =ds)\n",
    "\n",
    "plt.xticks([0,1,2,3,4],['Premium Gasoline','Disel', 'Regular Gasoline','Ethanol', 'Natural Gas'])\n",
    "plt.show()                           # efficiency of fuel "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# DATA PREPROCESSING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2_emission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Automatic of Selective type</td>\n",
       "      <td>Premium gasoline</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>33</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Premium gasoline</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>CVT</td>\n",
       "      <td>Premium gasoline</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>48</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX 4WD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>Automatic of Selective type</td>\n",
       "      <td>Premium gasoline</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>Automatic of Selective type</td>\n",
       "      <td>Premium gasoline</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>27</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Make       Model Vehicle Class  Engine Size(L)  Cylinders  \\\n",
       "0  ACURA         ILX       COMPACT             2.0          4   \n",
       "1  ACURA         ILX       COMPACT             2.4          4   \n",
       "2  ACURA  ILX HYBRID       COMPACT             1.5          4   \n",
       "3  ACURA     MDX 4WD   SUV - SMALL             3.5          6   \n",
       "4  ACURA     RDX AWD   SUV - SMALL             3.5          6   \n",
       "\n",
       "                  Transmission         Fuel Type  \\\n",
       "0  Automatic of Selective type  Premium gasoline   \n",
       "1                       Manual  Premium gasoline   \n",
       "2                          CVT  Premium gasoline   \n",
       "3  Automatic of Selective type  Premium gasoline   \n",
       "4  Automatic of Selective type  Premium gasoline   \n",
       "\n",
       "   Fuel Consumption City (L/100 km)  Fuel Consumption Hwy (L/100 km)  \\\n",
       "0                               9.9                              6.7   \n",
       "1                              11.2                              7.7   \n",
       "2                               6.0                              5.8   \n",
       "3                              12.7                              9.1   \n",
       "4                              12.1                              8.7   \n",
       "\n",
       "   Fuel Consumption Comb (L/100 km)  Fuel Consumption Comb (mpg)  CO2_emission  \n",
       "0                               8.5                           33           196  \n",
       "1                               9.6                           29           221  \n",
       "2                               5.9                           48           136  \n",
       "3                              11.1                           25           255  \n",
       "4                              10.6                           27           244  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DATA PREPROCESSING\n",
    "\n",
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Automatic of Selective type    3127\n",
       "Automatic                      1851\n",
       "Manual                         1185\n",
       "Automated Manual                646\n",
       "CVT                             576\n",
       "Name: Transmission, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds['Transmission'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Regular gasoline    3637\n",
       "Premium gasoline    3202\n",
       "Ethanol              370\n",
       "Diesel               175\n",
       "Natural gas            1\n",
       "Name: Fuel Type, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds['Fuel Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2_emission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2439</th>\n",
       "      <td>CHEVROLET</td>\n",
       "      <td>IMPALA DUAL FUEL</td>\n",
       "      <td>MID-SIZE</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6</td>\n",
       "      <td>Automatic of Selective type</td>\n",
       "      <td>Natural gas</td>\n",
       "      <td>15.2</td>\n",
       "      <td>9.5</td>\n",
       "      <td>12.7</td>\n",
       "      <td>22</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Make             Model Vehicle Class  Engine Size(L)  Cylinders  \\\n",
       "2439  CHEVROLET  IMPALA DUAL FUEL      MID-SIZE             3.6          6   \n",
       "\n",
       "                     Transmission    Fuel Type  \\\n",
       "2439  Automatic of Selective type  Natural gas   \n",
       "\n",
       "      Fuel Consumption City (L/100 km)  Fuel Consumption Hwy (L/100 km)  \\\n",
       "2439                              15.2                              9.5   \n",
       "\n",
       "      Fuel Consumption Comb (L/100 km)  Fuel Consumption Comb (mpg)  \\\n",
       "2439                              12.7                           22   \n",
       "\n",
       "      CO2_emission  \n",
       "2439           213  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping natural gas as there is only one data we have which would not make much difference in modelling\n",
    "\n",
    "ds_N = ds[ds['Fuel Type']== 'Natural gas']\n",
    "\n",
    "ind = ds_N.index\n",
    "\n",
    "ds_N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in ind:\n",
    "    ds.drop(i, axis=0, inplace=True)     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2_emission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Make, Model, Vehicle Class, Engine Size(L), Cylinders, Transmission, Fuel Type, Fuel Consumption City (L/100 km), Fuel Consumption Hwy (L/100 km), Fuel Consumption Comb (L/100 km), Fuel Consumption Comb (mpg), CO2_emission]\n",
       "Index: []"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds[ds['Fuel Type']=='Natural gas']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Fuel_Ethanol</th>\n",
       "      <th>Fuel_Premium gasoline</th>\n",
       "      <th>Fuel_Regular gasoline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fuel_Ethanol  Fuel_Premium gasoline  Fuel_Regular gasoline\n",
       "0             0                      1                      0\n",
       "1             0                      1                      0\n",
       "2             0                      1                      0\n",
       "3             0                      1                      0\n",
       "4             0                      1                      0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating dummy variables of fuel type and transmission (catagorical features)\n",
    "\n",
    "d_v =pd.get_dummies(ds['Fuel Type'], prefix='Fuel', drop_first=True)\n",
    "dv = pd.get_dummies(ds[\"Transmission\"], drop_first=True)\n",
    "d_v.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Automatic</th>\n",
       "      <th>Automatic of Selective type</th>\n",
       "      <th>CVT</th>\n",
       "      <th>Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Automatic  Automatic of Selective type  CVT  Manual\n",
       "0          0                            1    0       0\n",
       "1          0                            0    0       1\n",
       "2          0                            0    1       0\n",
       "3          0                            1    0       0\n",
       "4          0                            1    0       0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Fuel Type</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2_emission</th>\n",
       "      <th>Fuel_Ethanol</th>\n",
       "      <th>Fuel_Premium gasoline</th>\n",
       "      <th>Fuel_Regular gasoline</th>\n",
       "      <th>Automatic</th>\n",
       "      <th>Automatic of Selective type</th>\n",
       "      <th>CVT</th>\n",
       "      <th>Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Automatic of Selective type</td>\n",
       "      <td>Premium gasoline</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>33</td>\n",
       "      <td>196</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>Manual</td>\n",
       "      <td>Premium gasoline</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29</td>\n",
       "      <td>221</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>ILX HYBRID</td>\n",
       "      <td>COMPACT</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>CVT</td>\n",
       "      <td>Premium gasoline</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>48</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>MDX 4WD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>Automatic of Selective type</td>\n",
       "      <td>Premium gasoline</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25</td>\n",
       "      <td>255</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACURA</td>\n",
       "      <td>RDX AWD</td>\n",
       "      <td>SUV - SMALL</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>Automatic of Selective type</td>\n",
       "      <td>Premium gasoline</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>27</td>\n",
       "      <td>244</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Make       Model Vehicle Class  Engine Size(L)  Cylinders  \\\n",
       "0  ACURA         ILX       COMPACT             2.0          4   \n",
       "1  ACURA         ILX       COMPACT             2.4          4   \n",
       "2  ACURA  ILX HYBRID       COMPACT             1.5          4   \n",
       "3  ACURA     MDX 4WD   SUV - SMALL             3.5          6   \n",
       "4  ACURA     RDX AWD   SUV - SMALL             3.5          6   \n",
       "\n",
       "                  Transmission         Fuel Type  \\\n",
       "0  Automatic of Selective type  Premium gasoline   \n",
       "1                       Manual  Premium gasoline   \n",
       "2                          CVT  Premium gasoline   \n",
       "3  Automatic of Selective type  Premium gasoline   \n",
       "4  Automatic of Selective type  Premium gasoline   \n",
       "\n",
       "   Fuel Consumption City (L/100 km)  Fuel Consumption Hwy (L/100 km)  \\\n",
       "0                               9.9                              6.7   \n",
       "1                              11.2                              7.7   \n",
       "2                               6.0                              5.8   \n",
       "3                              12.7                              9.1   \n",
       "4                              12.1                              8.7   \n",
       "\n",
       "   Fuel Consumption Comb (L/100 km)  Fuel Consumption Comb (mpg)  \\\n",
       "0                               8.5                           33   \n",
       "1                               9.6                           29   \n",
       "2                               5.9                           48   \n",
       "3                              11.1                           25   \n",
       "4                              10.6                           27   \n",
       "\n",
       "   CO2_emission  Fuel_Ethanol  Fuel_Premium gasoline  Fuel_Regular gasoline  \\\n",
       "0           196             0                      1                      0   \n",
       "1           221             0                      1                      0   \n",
       "2           136             0                      1                      0   \n",
       "3           255             0                      1                      0   \n",
       "4           244             0                      1                      0   \n",
       "\n",
       "   Automatic  Automatic of Selective type  CVT  Manual  \n",
       "0          0                            1    0       0  \n",
       "1          0                            0    0       1  \n",
       "2          0                            0    1       0  \n",
       "3          0                            1    0       0  \n",
       "4          0                            1    0       0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = [ds, d_v,dv]\n",
    "\n",
    "data = pd.concat(df, axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data.drop(['Fuel Type'], inplace=True, axis=1)\n",
    "data.drop(['Transmission'], inplace=True, axis=1)\n",
    "\n",
    "                         "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "HANDLING OTHER CATAGORICAL FEATURES HAVING MULTIPLE CATAGORIES (MAKE , MODEL, VEHICLE CLASS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_freq = data['Make'].value_counts().to_dict()\n",
    "mod_freq = data['Model'].value_counts().to_dict()\n",
    "veh_freq = data['Vehicle Class'].value_counts().to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Make'] = data['Make'].map(df_freq)\n",
    "data['Model'] = data['Model'].map(mod_freq)\n",
    "data['Vehicle Class'] = data['Vehicle Class'].map(veh_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>CO2_emission</th>\n",
       "      <th>Fuel_Ethanol</th>\n",
       "      <th>Fuel_Premium gasoline</th>\n",
       "      <th>Fuel_Regular gasoline</th>\n",
       "      <th>Automatic</th>\n",
       "      <th>Automatic of Selective type</th>\n",
       "      <th>CVT</th>\n",
       "      <th>Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72</td>\n",
       "      <td>9</td>\n",
       "      <td>1022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>33</td>\n",
       "      <td>196</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72</td>\n",
       "      <td>9</td>\n",
       "      <td>1022</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29</td>\n",
       "      <td>221</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "      <td>1022</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>48</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>1217</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25</td>\n",
       "      <td>255</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>7</td>\n",
       "      <td>1217</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>27</td>\n",
       "      <td>244</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Make  Model  Vehicle Class  Engine Size(L)  Cylinders  \\\n",
       "0    72      9           1022             2.0          4   \n",
       "1    72      9           1022             2.4          4   \n",
       "2    72      2           1022             1.5          4   \n",
       "3    72      1           1217             3.5          6   \n",
       "4    72      7           1217             3.5          6   \n",
       "\n",
       "   Fuel Consumption City (L/100 km)  Fuel Consumption Hwy (L/100 km)  \\\n",
       "0                               9.9                              6.7   \n",
       "1                              11.2                              7.7   \n",
       "2                               6.0                              5.8   \n",
       "3                              12.7                              9.1   \n",
       "4                              12.1                              8.7   \n",
       "\n",
       "   Fuel Consumption Comb (L/100 km)  Fuel Consumption Comb (mpg)  \\\n",
       "0                               8.5                           33   \n",
       "1                               9.6                           29   \n",
       "2                               5.9                           48   \n",
       "3                              11.1                           25   \n",
       "4                              10.6                           27   \n",
       "\n",
       "   CO2_emission  Fuel_Ethanol  Fuel_Premium gasoline  Fuel_Regular gasoline  \\\n",
       "0           196             0                      1                      0   \n",
       "1           221             0                      1                      0   \n",
       "2           136             0                      1                      0   \n",
       "3           255             0                      1                      0   \n",
       "4           244             0                      1                      0   \n",
       "\n",
       "   Automatic  Automatic of Selective type  CVT  Manual  \n",
       "0          0                            1    0       0  \n",
       "1          0                            0    0       1  \n",
       "2          0                            0    1       0  \n",
       "3          0                            1    0       0  \n",
       "4          0                            1    0       0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DIVIDING DATA SET INTO INDEPENDENT AND DEPENDENT VARIABLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('CO2_emission', axis=1)\n",
    "y = data['CO2_emission']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Engine Size(L)</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Fuel Consumption City (L/100 km)</th>\n",
       "      <th>Fuel Consumption Hwy (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (L/100 km)</th>\n",
       "      <th>Fuel Consumption Comb (mpg)</th>\n",
       "      <th>Fuel_Ethanol</th>\n",
       "      <th>Fuel_Premium gasoline</th>\n",
       "      <th>Fuel_Regular gasoline</th>\n",
       "      <th>Automatic</th>\n",
       "      <th>Automatic of Selective type</th>\n",
       "      <th>CVT</th>\n",
       "      <th>Manual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72</td>\n",
       "      <td>9</td>\n",
       "      <td>1022</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72</td>\n",
       "      <td>9</td>\n",
       "      <td>1022</td>\n",
       "      <td>2.4</td>\n",
       "      <td>4</td>\n",
       "      <td>11.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9.6</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "      <td>1022</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.9</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>1217</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>12.7</td>\n",
       "      <td>9.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>7</td>\n",
       "      <td>1217</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6</td>\n",
       "      <td>12.1</td>\n",
       "      <td>8.7</td>\n",
       "      <td>10.6</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Make  Model  Vehicle Class  Engine Size(L)  Cylinders  \\\n",
       "0    72      9           1022             2.0          4   \n",
       "1    72      9           1022             2.4          4   \n",
       "2    72      2           1022             1.5          4   \n",
       "3    72      1           1217             3.5          6   \n",
       "4    72      7           1217             3.5          6   \n",
       "\n",
       "   Fuel Consumption City (L/100 km)  Fuel Consumption Hwy (L/100 km)  \\\n",
       "0                               9.9                              6.7   \n",
       "1                              11.2                              7.7   \n",
       "2                               6.0                              5.8   \n",
       "3                              12.7                              9.1   \n",
       "4                              12.1                              8.7   \n",
       "\n",
       "   Fuel Consumption Comb (L/100 km)  Fuel Consumption Comb (mpg)  \\\n",
       "0                               8.5                           33   \n",
       "1                               9.6                           29   \n",
       "2                               5.9                           48   \n",
       "3                              11.1                           25   \n",
       "4                              10.6                           27   \n",
       "\n",
       "   Fuel_Ethanol  Fuel_Premium gasoline  Fuel_Regular gasoline  Automatic  \\\n",
       "0             0                      1                      0          0   \n",
       "1             0                      1                      0          0   \n",
       "2             0                      1                      0          0   \n",
       "3             0                      1                      0          0   \n",
       "4             0                      1                      0          0   \n",
       "\n",
       "   Automatic of Selective type  CVT  Manual  \n",
       "0                            1    0       0  \n",
       "1                            0    0       1  \n",
       "2                            0    1       0  \n",
       "3                            1    0       0  \n",
       "4                            1    0       0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    196\n",
       "1    221\n",
       "2    136\n",
       "3    255\n",
       "4    244\n",
       "Name: CO2_emission, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7384, 17)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FEATURE SELECTION USING CHI-SQUARE TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranked_feature = SelectKBest(score_func = chi2, k='all')\n",
    "ordered_feature = ranked_feature.fit(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_feat = pd.DataFrame(ordered_feature.scores_ , columns=['score'])\n",
    "top_feat['variables'] = X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>score</th>\n",
       "      <th>variables</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>173723.188600</td>\n",
       "      <td>Vehicle Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>95021.040629</td>\n",
       "      <td>Make</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13236.852695</td>\n",
       "      <td>Fuel Consumption Comb (mpg)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6232.307799</td>\n",
       "      <td>Fuel Consumption City (L/100 km)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4862.577319</td>\n",
       "      <td>Fuel Consumption Comb (L/100 km)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3412.144543</td>\n",
       "      <td>Cylinders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3394.028026</td>\n",
       "      <td>Engine Size(L)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3293.816329</td>\n",
       "      <td>Fuel Consumption Hwy (L/100 km)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2612.972867</td>\n",
       "      <td>Model</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1938.744672</td>\n",
       "      <td>CVT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1022.032185</td>\n",
       "      <td>Automatic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>825.862516</td>\n",
       "      <td>Fuel_Ethanol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>774.942190</td>\n",
       "      <td>Manual</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>763.320532</td>\n",
       "      <td>Fuel_Premium gasoline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>677.576319</td>\n",
       "      <td>Fuel_Regular gasoline</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>497.189839</td>\n",
       "      <td>Automatic of Selective type</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            score                         variables\n",
       "2   173723.188600                     Vehicle Class\n",
       "0    95021.040629                              Make\n",
       "8    13236.852695       Fuel Consumption Comb (mpg)\n",
       "5     6232.307799  Fuel Consumption City (L/100 km)\n",
       "7     4862.577319  Fuel Consumption Comb (L/100 km)\n",
       "4     3412.144543                         Cylinders\n",
       "3     3394.028026                    Engine Size(L)\n",
       "6     3293.816329   Fuel Consumption Hwy (L/100 km)\n",
       "1     2612.972867                             Model\n",
       "14    1938.744672                               CVT\n",
       "12    1022.032185                         Automatic\n",
       "9      825.862516                      Fuel_Ethanol\n",
       "15     774.942190                            Manual\n",
       "10     763.320532             Fuel_Premium gasoline\n",
       "11     677.576319             Fuel_Regular gasoline\n",
       "13     497.189839       Automatic of Selective type"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_feat.sort_values(by='score', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CREATING TRAINING SET AND TESTING SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting of traing testing set into X and y\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train,X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5907, 16)\n",
      "(1477, 16)\n",
      "(5907,)\n",
      "(1477,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5632    368\n",
       "1550    290\n",
       "1128    382\n",
       "6498    211\n",
       "3270    193\n",
       "Name: CO2_emission, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FEATURE SCALING USING STANDARDIZATION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# STANDARDIZATION\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler =StandardScaler()\n",
    "\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.27521116, -0.68670295, -1.80962504, ..., -0.85157221,\n",
       "        -0.29299418, -0.44024503],\n",
       "       [-0.54676246, -0.68670295, -0.42577231, ...,  1.17429853,\n",
       "        -0.29299418, -0.44024503],\n",
       "       [ 1.2100082 , -0.87774305, -0.52091218, ...,  1.17429853,\n",
       "        -0.29299418, -0.44024503],\n",
       "       ...,\n",
       "       [ 0.11826113, -0.30462276, -0.14900176, ..., -0.85157221,\n",
       "         3.4130371 , -0.44024503],\n",
       "       [-0.27521116, -0.30462276,  1.24061702, ..., -0.85157221,\n",
       "         3.4130371 , -0.44024503],\n",
       "       [-1.17299301, -1.06878314, -0.71695798, ..., -0.85157221,\n",
       "        -0.29299418, -0.44024503]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "250.58978873239437"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['CO2_emission'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MODEL IMPLEMENTATION (Approach 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LINEAR REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "250.98357880480785"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.09846243,  -0.09126632,  -0.03196586,   0.25410999,\n",
       "         2.17469937,  24.28719842,  13.07132844,  20.53745833,\n",
       "        -6.35439345, -30.19620392, -15.35456651, -15.09009955,\n",
       "        -0.35425518,  -0.32284677,  -0.17696755,  -0.3645601 ])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([359.06209907, 292.97346311, 377.59960241, ..., 341.77783102,\n",
       "       193.05390931, 177.97061893])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.918260935039378"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.993041824997087"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = [y_pred, y_test.values]\n",
    "result_pred = pd.DataFrame(data=frames)\n",
    "result_pred = result_pred.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>pred_values</th>\n",
       "      <th>real_values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>359.06</td>\n",
       "      <td>368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>292.97</td>\n",
       "      <td>290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>377.60</td>\n",
       "      <td>382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>210.80</td>\n",
       "      <td>211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>192.94</td>\n",
       "      <td>193.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1472</th>\n",
       "      <td>233.49</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1473</th>\n",
       "      <td>262.28</td>\n",
       "      <td>263.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1474</th>\n",
       "      <td>341.78</td>\n",
       "      <td>346.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1475</th>\n",
       "      <td>193.05</td>\n",
       "      <td>193.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1476</th>\n",
       "      <td>177.97</td>\n",
       "      <td>177.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1477 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pred_values  real_values\n",
       "0          359.06        368.0\n",
       "1          292.97        290.0\n",
       "2          377.60        382.0\n",
       "3          210.80        211.0\n",
       "4          192.94        193.0\n",
       "...           ...          ...\n",
       "1472       233.49        235.0\n",
       "1473       262.28        263.0\n",
       "1474       341.78        346.0\n",
       "1475       193.05        193.0\n",
       "1476       177.97        177.0\n",
       "\n",
       "[1477 rows x 2 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_pred = result_pred.rename(columns={0: 'pred_values',1:'real_values'})\n",
    "lin_pred['pred_values'] = lin_pred['pred_values'].map(lambda x: round(x,2))\n",
    "\n",
    "lin_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean diff:  2.979058903182132\n"
     ]
    }
   ],
   "source": [
    "lin_pred['diff'] = abs(lin_pred['pred_values'] - lin_pred['real_values'])\n",
    "\n",
    "print('mean diff: ', (abs(lin_pred['diff']).mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>pred_values</th>\n",
       "      <th>real_values</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>359.06</td>\n",
       "      <td>368.0</td>\n",
       "      <td>8.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>292.97</td>\n",
       "      <td>290.0</td>\n",
       "      <td>2.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>377.60</td>\n",
       "      <td>382.0</td>\n",
       "      <td>4.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>210.80</td>\n",
       "      <td>211.0</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>192.94</td>\n",
       "      <td>193.0</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>249.45</td>\n",
       "      <td>244.0</td>\n",
       "      <td>5.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>213.25</td>\n",
       "      <td>210.0</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>174.25</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>266.72</td>\n",
       "      <td>268.0</td>\n",
       "      <td>1.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>303.68</td>\n",
       "      <td>305.0</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pred_values  real_values  diff\n",
       "0       359.06        368.0  8.94\n",
       "1       292.97        290.0  2.97\n",
       "2       377.60        382.0  4.40\n",
       "3       210.80        211.0  0.20\n",
       "4       192.94        193.0  0.06\n",
       "5       249.45        244.0  5.45\n",
       "6       213.25        210.0  3.25\n",
       "7       174.25        174.0  0.25\n",
       "8       266.72        268.0  1.28\n",
       "9       303.68        305.0  1.32"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_pred.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x16f4521e580>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(y_pred-y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y_pred')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmYAAAHgCAYAAAAPLaHSAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAA7rklEQVR4nO3dfZRc9X3n+c+3iwKX8ENLY+GgQm0IgzWLIiM5PUge7WQxSSxibGjLIcAgDznDMd49eG1ipx3JqxPkGEXaKLaZJ3sOOF6TgAWyLZdlnBkZ87DeMEi45RY0ArTggCWVGKQYemNDB1qt7/5Rt1rV1beqq7vurbpV9/06R0ddv7pV9eubCv7o9/D9mbsLAAAA7dfT7g4AAACghGAGAACQEAQzAACAhCCYAQAAJATBDAAAICEIZgAAAAlxWrs70Ky3v/3tfu6557a7GwAAADPat2/fP7j7wlrPxx7MzOwFSb+UNCHphLv3m9kCSfdKOlfSC5L+wN1fCa7fIOmG4PpPuvvueu9/7rnnamhoKLb+AwAARMXMfl7v+VZNZb7P3Ze7e3/weL2kB9z9AkkPBI9lZhdKukbSUkmXSfqKmWVa1EcAAIC2atcasysl3Rn8fKekgYr2e9z9dXd/XtJzki5uffcAAABarxXBzCX90Mz2mdmNQds73P1FSQr+Pitoz0s6XPHaI0EbAABA12vF4v/V7n7UzM6SdL+ZPVPnWgtpm3aYZxDwbpSkvr6+aHoJAADQZrGPmLn70eDvY5K+q9LU5EtmdrYkBX8fCy4/ImlxxcvPkXQ05D1vd/d+d+9fuLDmxgYAAICOEmswM7Mzzewt5Z8lvV/Sk5J2Sbo+uOx6Sd8Lft4l6RozO8PMzpN0gaTH4uwjAABAUsQ9lfkOSd81s/JnfdPd/5uZ/UTSDjO7QdIhSVdJkrsfMLMdkp6SdELSTe4+EXMfAQAAEiHWYObufy/popD2X0j67Rqv2Sxpc5z9AgAASCKOZAIAAEgIghkAAEBCEMwAAAASgmAGAACQEAQzAACAhCCYAQAAJEQrjmQCAABIrMJwUdt2H9TR0TEt6s1pcM0SDaxoz1HdBDMAAJBaheGiNuwc0dh4qZ59cXRMG3aOSFJbwhnBDAAAdLy5jnpt231wMpSVjY1PaNvugwQzAACA2Wpm1Ovo6Nis2uPG4n8AANDR6o16zWRRb25W7XEjmAEAgI7WzKjX4JolymUzU9py2YwG1yyJpG+zRTADAAAdrZlRr4EVeW1Zu0z53pxMUr43py1rl7ErEwAAYC4G1yyZssZMmt2o18CKfNuCWDWCGQAA6GjlUJWUWmTNIJgBAICOl6RRr2awxgwAACAhCGYAAAAJQTADAABICIIZAABAQhDMAAAAEoJgBgAAkBAEMwAAgIQgmAEAACQEwQwAACAhCGYAAAAJQTADAABICIIZAABAQhDMAAAAEoJgBgAAkBAEMwAAgIQ4rd0dAAAgrQrDRW3bfVBHR8e0qDenwTVLNLAi3+5uJUJa7w3BDACANigMF7Vh54jGxickScXRMW3YOSJJqQgg9aT53jCVCQBAG2zbfXAyeJSNjU9o2+6DbepRcqT53hDMAABog6OjY7NqT5M03xuCGQAAbbCoNzer9jRJ870hmAEA0AaDa5Yol81MactlMxpcs6RNPUqONN8bFv8DANAG5UXsadx5OJM03xtz93b3oSn9/f0+NDTU7m4AAADMyMz2uXt/reeZygQAAEgIghkAAEBCEMwAAAASoiXBzMwyZjZsZvcFjzeZWdHM9gd/PlBx7QYze87MDprZmlb0DwAAIAlatSvzU5KelvTWirYvu/tfVl5kZhdKukbSUkmLJP3IzN7l7lPL/wIAgK6U1jMyy2IfMTOzcyRdLulrDVx+paR73P11d39e0nOSLo6zfwAAIBnKZ2QWR8fkOnVGZmG42O6utUwrpjJvk/RZSSer2j9hZk+Y2dfNbH7Qlpd0uOKaI0HbFGZ2o5kNmdnQ8ePH4+gzAABosTSfkVkWazAzsw9KOubu+6qe+qqk8yUtl/SipC+WXxLyNtMKrbn77e7e7+79CxcujLDHAACgXYo1zsKs1d6N4l5jtlrSFcHi/jdJequZ3eXu68oXmNkdku4LHh6RtLji9edIOhpzHwEAQAJkzDQRUvg+Y2HjNt0p1hEzd9/g7ue4+7kqLep/0N3XmdnZFZd9WNKTwc+7JF1jZmeY2XmSLpD0WJx9BAAAyRAWyuq1d6N2nZX5F2a2XKVpyhckfVyS3P2Ame2Q9JSkE5JuYkcmAADpkO/NhU5b5ntzbehNe7QsmLn7w5IeDn7+aJ3rNkva3JpeAQCApBhcs0Qbdo5M2QCQy2Y0uGZJG3vVWu0aMQMAAJiiXK8szXXMCGYAACAxBlbkUxXEqnFWJgAAQEIQzAAAABKCYAYAAJAQBDMAAICEIJgBAAAkBMEMAAAgIQhmAAAACUEwAwAASAiCGQAAQEJQ+R8AgBTaWBjR9r2HNeGujJmuXblYtw4sa3e3Uo9gBgBAymwsjOiuPYcmH0+4Tz4OC2eF4WKqz69sJaYyAQBIme17DzfcXhguasPOERVHx+SSiqNj2rBzRIXhYsy9TCeCGQAAKTPh3nD7tt0HNTY+MaVtbHxC23YfjKVvaUcwAwAgZTJmDbcfHR0LvbZWO5pDMAMAIGWuXbm44fZFvbnQa2u1ozkEMwAAUubWgWVat6pvcoQsY6Z1q/pCF/4PrlmiXDYzpS2XzWhwzZKW9DVtzGvMM3eK/v5+Hxoaanc3AADoWuzKjI6Z7XP3/lrPUy4DANB1CBKNa+ReDazIc/9ahGAGAOgq5fIO5Z2E5fIOkggXVbhXycMaMwBAV6G8Q+O4V8nDiBkAoKt0UnmHdk+5dtK9SgtGzAAAXaVTyjskoaJ+p9yrNCGYAQC6SqeUd0jCNGKn3Ks0YSoTANBVylOBSd+VmYRpxE65V2lCMAMAdJ1OKO+wqDenYkgIa/U0YifcqzRhKhMAgDZgGhFhGDEDAKANmEZEGIIZAABtwjQiqjGVCQAAkBAEMwAAgIQgmAEAACQEa8wAAGijjYURbd97WBPuypjp2pWLdevAsnZ3C21CMAMAoE02FkZ0155Dk48n3CcfE87SiWAGAMAcNXoIea3rtu89HPq+2/ceJpilFMEMAIA5KB9CXj7vsnwIuaQp4azedRPuoe9dqx3dj8X/AADMQaOHkNe7LmMW+t612tH9CGYAAMxBo4eQ17vu2pWLQ5+r1Y7uRzADAGAOah02Xt0+03XVY2PrVvWxvizFCGYAAMxBI4eQF4aLevX1E9Nem8tmdO4/y+muPYfEajJUakkwM7OMmQ2b2X3B4wVmdr+ZPRv8Pb/i2g1m9pyZHTSzNa3oHwAAszWwIq8ta5cp35uTScr35rRl7bLJhf/lRf+jY+NTXjd/XlZb1i7Tnr9/JfR9a+3URDq0alfmpyQ9LemtweP1kh5w961mtj54/CdmdqGkayQtlbRI0o/M7F3uPhH2pgAAtFO9Q8jDFv1L0rzTT9PAirxuvnd/6OvYkZlusY+Ymdk5ki6X9LWK5isl3Rn8fKekgYr2e9z9dXd/XtJzki6Ou48AAERtps0B7MhEmFZMZd4m6bOSTla0vcPdX5Sk4O+zgva8pMox3CNB2xRmdqOZDZnZ0PHjx2PpNAAAMykMF7V664M6b/0PtHrrgyoMFyefm2nRPzsyESbWYGZmH5R0zN33NfqSkLZpY7rufru797t7/8KFC5vqIwCgdeoFmaSq1efyGrLi6JhcpwrHlp+faXPArQPLtG5V3+QIWcaMHZmIfY3ZaklXmNkHJL1J0lvN7C5JL5nZ2e7+opmdLelYcP0RSZX/VDhH0tGY+wgAaIFGK+UnSb0+1yscW7n2rN6RTbcOLCOIYQrzFi0yNLNLJP2xu3/QzLZJ+kXF4v8F7v5ZM1sq6ZsqrStbJOkBSRfUW/zf39/vQ0ND8f8CAICmrN76oIoh667yvTk9sv7SNvRoZrX6nO2Rxk+GvEClqZ/nt14eb8fQscxsn7v313q+XWdlbpW0w8xukHRI0lWS5O4HzGyHpKcknZB0EzsyAaCzlQ/wDgs4Uu1F8u1SeeB4raGLWqFMkt6Wy8bSL6RDy4KZuz8s6eHg519I+u0a122WtLlV/QIAxKd6KjBMrUXy7dBIf2fCpko0g8r/AIDY1KrlVVZdKb/dZupvI0ZfG5/5IqCGdk1lAgBSoN40ZT5kMXy7RTGtmqQRQHQeghkAIDaLenMdteC/Vn8blbQRQHQepjIBALFp5KDvJAnrb6NLxqrPygTmgmAGAIjNTAd9J01Yf//V+QumhbNcNqPbrl6uF7ZertuuXq58b05HR8e0bffBjiiai+RqWR2zuFDHDAAQl7BdmibpuqBCf9jzuWwm0eET7ZXUOmYAgC5RWfcrrLp9u20sjGj73sOacFfGTNeuXDyl2n69/oft0nRJDz1zvObzldX/gdkimAEA5izpxyxtLIzorj2HJh9PuOuuPYd0155Dyvfm9L5/sVDf2Ves2f9auzTL7TM9D8wWa8wAAHNWb8QoCbbvPVzzueLomO7ac6hu/2uVvii3z/Q8MFsEMwDAnCV9xGhijuuoy/2faVdpp+06RfIxlQkAmLNadb/aNWJUvV6sx6STc8hm5f5XrjULW4M20/PAbBHMAABzNrhmSeiuxHaMGIWtd5uL6v4PrMjXDVozPQ/MBsEMADBnSRoxiuKcyyQeE4V0IZgBAJqSlBGjmda15bI9Ghs/GfqcSfry1csT8Xsg3Vj8DwDoCj0znJ00Nn5S61b1TaviXy4YSyhDEjBiBgDoChMNLPK/dWCZ+t+5IBFTr0AYghkAIBUsGCpLytQrEIapTABAKnT40dBICUbMAACTZjpXshXCzq6Umt/5macaPzoAwQwAIKn2uZKSWhbOwmqRDX7rccmk8WARWfV5luUgVw/V+NEpmMoEAEiqfa5kvfMmo/b57x+YVots/KRPhrKy8nmW5SBXr5hsvjenLWuXsa4MHYERMwCApNrnSs71vMnZKgwX9cpr4w1ff3R0rGZR2XxvTo+svzTK7gEtwYgZAECSlLHwQmC12qM203RktUW9ucQfog7MFsEMACBJunbl4lm1R61emMpUVY8trxmrdVh6uw5RB5pFMAMASCot8F+3qm9yhCxjpnWr+lq28L9emOqRNH9eVqapa8YG1yxRLpuZci0L/dHJzDu8sEt/f78PDQ21uxsAgCZV78isVmvdWFh5DRb6I6nMbJ+799d6nsX/AIC2qgxWb8tlawazWlOdVPJHNyGYAQDapnqUbHRsXCYpbC6HdWNIA9aYAQDaJqzcRa0FNsXRMa3e+qAKw8X4Owa0CSNmAIDYhB3x1P/OBZNTl7Nd5Vxd9R/oNiz+BwDEovqIp7IeSSebfG8KyKJTzbT4n6lMAEAsah3l1Gwokyggi+7FVCYAoCm1ylXEeZQTGwHQrQhmAIA5q95VWbkGLGMWSzgziQKy6FpMZQIA5ixsV+XY+IS27T6oVb8+P/LPM0nXrepj4T+6FiNmAIA5q3eI+Kuvnwh9bl62R2PjJ2e1I9MkqvojFQhmAIA5W9SbUzEknLlKxWLDvDY+u+X/7MBEmjCVCQCYs7BDxKOU7THWkyFVCGYAgDkbWJHXR34zr4xZ5O/dm8tq21UXMXWJVGEqEwAwZ4Xhor6zrxjZ7svy4v5bB5ZF8n5ApyGYAQDmLGxX5mxlzHTSncX9gGIOZmb2Jkk/lnRG8FnfdvdbzGyTpI9JOh5c+jl3/9vgNRsk3SBpQtIn3X13nH0EAExVq2Bs2HNhC/9nI5fNaMvaZYQxIBD3iNnrki5191+ZWVbS35nZfw2e+7K7/2XlxWZ2oaRrJC2VtEjSj8zsXe7e3D/HAAANqVcwVtK050yqWfZipgKzeUbIgGliDWZeOiH9V8HDbPCn3kKEKyXd4+6vS3rezJ6TdLGkR+PsJwCgpF7B2PLPlVwKDWe9uaw2XbFUn9nxeGg4y5hRAgMIEfuuTDPLmNl+Scck3e/ue4OnPmFmT5jZ182sXB46L6ny1NsjQRsAoAVqFYwtjo7VnLYM+9f26Ni4br53v3594bzQ11y7cvFcuwh0tdiDmbtPuPtySedIutjMfkPSVyWdL2m5pBclfTG4PGy/9bT/nzezG81syMyGjh8/HvISAMBcRH04+LPHXtXq8xdMltPImGkduy6Bmlq2K9PdR83sYUmXVa4tM7M7JN0XPDwiqfKfUedIOhryXrdLul2S+vv7oz8hFwBSanDNkinryKLwwi/G9LMtH4js/YBuFuuImZktNLPe4OecpN+R9IyZnV1x2YclPRn8vEvSNWZ2hpmdJ+kCSY/F2UcAwClxFIytNT0KYLq4R8zOlnSnmWVUCoE73P0+M/sbM1uu0jTlC5I+LknufsDMdkh6StIJSTexIxMAWifqgrFS9NOjQDeLe1fmE5JWhLR/tM5rNkvaHGe/AADh9cqiKBhbKZvhrEtgNqj8DwApVBguavDbj2t8ojQyVhwdm/I4CvPnZXXLh5ZSpwyYBYIZAKRIeZQsrPTF+ISrx6STTWaz265eThgD5ohgBgApURgu6uZ799e9ptlQlu/NEcqAJsRexwwAkAyf3rE/1vfPZTOsJwOaxIgZAHS5jYURbd97uOnRsGrZHtOb33SaRl8bn3bYOYC5IZgBQBfbWBjRXXsORfZ+87I9Ghs/SRADYkIwA4AuVRguRhbKMma6duVijlICYkYwA4AuVC6H0YxcNqMta5cxKga0EMEMAFosrLBr1OHn898/0FRNsjxTlUBbEMwAoIUKw8Uph4QXR8e0YeeIJM0YgmYKdJXPN7POf/X5C3T3x97bxDsAmCuCGQC0UNiRR2PjE9q2+2DdYFYr0A39/GU99MxxFUfHZFJTgUySsj0ilAFtRDADgBY6GlJxv157Wa1AV7m4v9lQVl5TBqB9KDALAC20qDc3q/aymYJbs3pzWRb6AwlAMAOAFhpcs0S5bGZKWyMV82cKbs247erl2n/L+wllQAIQzACghQZW5LVl7TLle3MylXY/NjJSFRboosDZlkCysMYMAFpsYEV+VmGovNuyeo1ZszjbEkgeghkAJFj1bsyozJ+X1S0fWspoGZAwBDMASKjCcFGf2fG4Jjza08dvu3o5gQxIKIIZACREZYHY3nlZ/eqfTkQeytat6iOUAQlGMAOAGlpxdFL5M6oLxL7y2niknyOVQhmHkAPJRjADgBDNHJ00m8+4+d79k4+jHRs7hcPIgc5BuQwACFHv6KSofGbH/sjeS9Jk+Y11q/pmXY4DQDIwYgYAIeZ6dNJsTEQ4RJbvzemR9ZdG94YA2oIRMwAIMdejk9qFemRAdyCYAUCIuR6d1A7stAS6B1OZABCiHHTi3pXZrNXnL2CnJdBFCGYAUMNsj06arfnzsk2VxaD8BdB9mMoEgAgUhotavfVBnbf+B1q99UEVhoszvuaWDy1Vj83t8zJmhDKgCzFiBgBNqlXzbOjnL+uhZ47HMhUa9YkAAJKBYAYATapV8+yuPYcmH5fD2reGDum//+zlpovJ5hO6OxRAcwhmANCkRmubjY1P6JGfvRzJZyZxdyiA5rHGDACa1OraZqvPX5C43aEAokEwA4Amve9fLIz8PTNmMknzsj0yO9W2blWf7v7YeyP/PADJwFQmAEjaWBjR9r2HNeGujJmuXbl4xl2PGwsjU9aRRemku57fenks7w0guQhmAFKvOmBNuE8+rhXO4gxlUnKPfgIQL6YyAaTe9r2HZ9UuKdZQJrG4H0grRswApF6tmmDV7YXh4uQRTXHi7EsgvQhmAFIvYxYazjJ2qix/dRHZKJikf3X+Ar3wi7FEn8cJoHUIZgBS79qVi0OnJq9duXjy57Aiss3IE8IAhCCYAUi98gL/ersyo5y+zJjpkfWXRvZ+ALoHwQwAVApn9cpjvC2X1ejYeCSfxTmXAGohmAFAiMqF/r3zogtlEudcAqitbjAzs/8o1T5r190/OcPr3yTpx5LOCD7r2+5+i5ktkHSvpHMlvSDpD9z9leA1GyTdIGlC0ifdfXejvwwAzFVlgVkzSX7qP36vvBZdKMtlM5TCAFDTTHXMhiTtk/QmSe+R9GzwZ7lKwWkmr0u61N0vCl5zmZmtkrRe0gPufoGkB4LHMrMLJV0jaamkyyR9xcwys/uVAGB2ysViy1OM7nX+RTpL61b1Kd+bk6k0UrZl7TIW/AOoqe6ImbvfKUlm9oeS3ufu48Hj/yLphzO9ubu7pF8FD7PBH5d0paRLgvY7JT0s6U+C9nvc/XVJz5vZc5IulvToLH4nAGhIebqyGENdsnq7LiunSSmRAaBSo2vMFkl6i6SXg8dvDtpmFIx47ZP0zyX9Z3ffa2bvcPcXJcndXzSzs4LL85L2VLz8SNBW/Z43SrpRkvr6+hr8FQDglMJwUZ++d79OxvDe9XZdVtdDK46OacPOEUkinAFo+EimrZKGzewbZvYNST+V9OeNvNDdJ9x9uaRzJF1sZr9R53ILaZs2o+Dut7t7v7v3L1y4sJFuAMAUG3Y+EUsok6bWP6sWVg9tbHxC23YfjKk3ADpJQyNm7v5/mdl/lbQyaFrv7v9jNh/k7qNm9rBKa8deMrOzg9GysyUdCy47Iqnyv2jnSDo6m88BgFoKw0Vt2nUg0h2W1XLZnrplN2rVQ4v7mCcAnaGhETMzM0m/I+kid/+epNPN7OIGXrfQzHqDn3PBezwjaZek64PLrpf0veDnXZKuMbMzzOw8SRdIeqzxXwcAwhWGixr81uMxh7KMtqx9d91rFtUolVGrHUC6NLrG7CuSTkq6VNKfSfqlpO9I+pczvO5sSXcG68x6JO1w9/vM7FFJO8zsBkmHJF0lSe5+wMx2SHpK0glJN7l7dGegAEidOBf4l5nU8CL+wTVLpp25SQkNAGWNBrOV7v4eMxuWJHd/xcxOn+lF7v6EpBUh7b+Q9Ns1XrNZ0uYG+wUAksJ3OkqK/ODxavne3KyOVyoHN3ZlAgjTaDAbD0a9XCpNUUqxrZsFgIZUjoYFNWElndrpeMZpPbGGsrmOdA2syBPEAIRqNJj9B0nflXSWmW2W9PuSNsbWKwCYQXnN2PjJoChs1fNj4xOxj5Qx0gUgajMGMzPrkfS8pM+qNP1okgbc/emY+wYANW3adWAylLVKby6rTVcsJYwBiM2MwczdT5rZF939vSrtqASAtotzd2U1M+n5LZe37PMApFejU5k/NLOPSNoZHLMEAC1XucC/lfivHoBWaTSYfVrSmZImzOyfgjZ397fG0y0AmKr6KKNWylNjDECLNFr5/y1xdwQAwrSiDlk91BgD0EqNjpjJzNZK+p9V2vz0/7h7Ia5OAYDUvlGyfG+OGmMA2qKhYGZmX5H0zyVtD5r+VzP7XXe/KbaeAUi9sAO/43bb1csJYgDaptERs/9F0m+UF/6b2Z2SRmLrFQCotQd7m6TrVvURygC0VaPB7KCkPkk/Dx4vlvRELD0CgMCi3lxL1pZRLBZAUvQ0eN0/k/S0mT1sZg+rdMj4QjPbZWa7YusdgFQbXLNEuWwm9s95ZP2lhDIAidDoiNmfxtoLAAhRDkubdh2IraAspTAAJEmj5TL+73rPm9mjwckAABCZ3/3Sw3r22KuxfgalMAAkScPlMmbwpojeB0DKtbJu2erzFzCFCSBRogpmHFgCYE4qj1mad3pGr74Rf3mMjJlW/fp8vfCLMZ23/gfUKwOQGFEFMwCYtY2FEd2959Dkv+ziDGW9uaw2XbFUAyvy0wrXFkfHtGFnqQIQ4QxAOzW0K9PMPmFm8+tdElF/AKREYbg4JZTFad2qPu2/5f2ToSuscO3Y+IS27T7Ygt4AQG2Nlsv4NUk/MbMdZnaZmVUHsY9G3C8AXW7TrgMtWwNx68CyKY9rFa5tZUFbAAjTUDBz942SLpD0V5L+UNKzZvbnZnZ+8PyTsfUQQNcpDBdjK39Rbd2qvmlti2qUyKjVDgCt0uiImYLjmP5H8OeEpPmSvm1mfxFT3wB0qc9//0Dsn5Ex07pVfdNGy6TwwrW5bIbSGQDartFDzD8p6XpJ/yDpa5IG3X3czHokPSvps/F1EUC3eeW1eEfLXth6ed3nK9eaHR0dY1cmgMRodFfm2yWtdfefVza6+0kz+2D03QLQDSpLYZzWI42fjP8zG63kP7AiTxADkDiNVv6veSSTuz8dXXcAdIvqkhStCGVMRwLodNQxAxCLsJIUcTGJ6UgAXYFgBiAWrSw98fwMa8oAoFMQzABEovKMy4xZy2qUZaaVVQSAzkUwA9C06vVkE96643OvXbm4ZZ8FAHEjmAFoWivXk5VlzHTtysWhdcoAoFMRzADMSWUpjNaNj5XMVKcMADoVwQzArFVPXbYSa8oAdLOGj2QCgLJ2TF2WsaYMQDdjxAzArBVbWAqjjDVlANKAYAagYb/7pYf17LFXY3nvC846M/S9ax1EDgDdiKlMAA2JM5RlzHTT+y7QulV9k2vIMmaEMgCpw4gZgLriDGRlE+7asHNEW9YuI4gBSDWCGYAppS8qz5xsRSgrGxuf0LbdBznrEkCqEcyAlKsufVEcHdMf3btfN9+7v+V9aeX5mgCQRKwxA1IurPRFqwvGli3qzbXpkwEgGQhmQIoVhottKX2Ry/Yom7GqtowG1yxpeV8AIEkIZkBKlacw45St8V+YBWeeoW2/f5HyvTmZpHxvTlvWLmN9GYDUi3WNmZktlvTXkn5N0klJt7v7vzezTZI+Jul4cOnn3P1vg9dskHSDpAlJn3T33XH2EUirOKv3m6Tnt16u89b/IPT5o6NjGliRJ4gBQJW4F/+fkPQZd/+pmb1F0j4zuz947svu/peVF5vZhZKukbRU0iJJPzKzd7l7e85+AbpYnAvtr1vVJ6m0ZixsqpS1ZAAQLtapTHd/0d1/Gvz8S0lPS6r3T+QrJd3j7q+7+/OSnpN0cZx9BNJq3umZWN63sijs4JolymWnfg5ryQCgtpatMTOzcyWtkLQ3aPqEmT1hZl83s/lBW17S4YqXHVFIkDOzG81syMyGjh8/Xv00gBlsLIzo1TfiGYiuLBA7sCKvLWuXsZYMABrUkjpmZvZmSd+RdLO7/6OZfVXSF1Talf8FSV+U9O9UWppSbdrOfXe/XdLtktTf39+unf1AR9pYGNFdew7F8t7zQlb7s5YMABoXezAzs6xKoexud98pSe7+UsXzd0i6L3h4RNLiipefI+lo3H0Eut11dzyqR372cqyf0WPSn699d6yfAQDdLtapTDMzSX8l6Wl3/1JF+9kVl31Y0pPBz7skXWNmZ5jZeZIukPRYnH0Eul1coeytZ2SmTFF+6Q+WMzIGAE2Ke8RstaSPShoxs/1B2+ckXWtmy1WapnxB0sclyd0PmNkOSU+ptKPzJnZkAs2JOpRVLu4HAEQr1mDm7n+n8HVjf1vnNZslbY6tU0BKrNx8v1765RuRvueZp2cIZQAQIyr/A10ojlCWzZg2f5hQBgBxasmuTACtEdd6MjNp2+9fxBoyAIgZI2ZAl4grlPVI+jIL+wGgJRgxAzpYYbiobbsP6ujo2PSCfxHozWW16YqlhDIAaBGCGdChCsNFDX7rcY2fjKfGMrsvAaD1mMoEOtDGwohuvnd/LKGsx6Rctkd37zmk1VsfVGG4GPlnAADCEcyADhPnkUrrVvXpjNMyGhs/KZdUHB3Tzffu13nrf6CNhZFYPhMAcArBDOgwd8cUyubPy+qhZ45rbHx6TWeXdNeeQ4QzAIgZwQzoENfd8ajOXf+DWBb557IZ3fKhpTo6Olb3uu17D8fw6QCAMoIZ0AHiPIS8N5fVlrXLNLAir0W9ubrXTng8Gw0AACXsygQSamNhRNv3Ho4tDPWYph08PrhmiTbsHAmdzpSkjIWdsAYAiArBDEigOBf4S6Wpy/IoWaXy4//juyN69Y3p4ezalYtj6xMAgKlMIJHiWuAvSfneXGgoKxtYkdeBP7tM61b1TY6QZcyoawYALcCIGZBAca3kMkmPrL90WnvlCQKLenMaXLNEtw4sI4gBQIsRzIAEqAxGvfOysX1O2OL+wnBxyrqy4uiYNuwslcXgKCYAaC2mMoE2KwwXNfjtx1UMzrt85bXxWD4n22N67Y0TOm/9D6ZU9N+2++C0xf5j4xPatvtgLP0AANTGiBnQZp///gGNT8QzeTkv26Ox8ZN6Wy6rV984MRn6KkfFatUum6mmGQAgeoyYAW0WxwhZebH+U1/4PT2/9XKdecZp08JfeVSsVu2ymWqaAQCix4gZ0CV6c1ltumJp6LqweqNiX756+bTaZblsRoNrlsTWVwBAOIIZ0AaVi/2b1WPSv1lZv5TFot6ciiGftag3NxnkqndlsvAfAFqPYAa0SDmMFUfHZIquJMZJl76zr6j+dy6oGabCKvpXjooNrMgTxAAgAVhjBrRA5c5LKfo6ZTPtohxYkdeWtcuU783JNHORWQBAezBiBrRAnDsvy2aaFmVUDACSj2AGxKRyHVm8kayEXZQA0PmYygRisLEwoj+6d/9k0dhWePX1E5NFYwEAnYlgBkSsMFzU3XsOtSyQlY2OjWvDzhHCGQB0MKYygQhUTlv2mLU8lJWVNwGwlgwAOhPBDGhS9SHgEx5NLOtRafdmj9ms3pOjlACgczGVCTQp7BDwZq0+f4H+fuvlen7r5To5y6DHJgAA6FyMmAFNCquo34zbrl4+ZSqyVtX+M0/P6LU3JqZMm3KUEgB0NkbMgDlaufl+nbv+B5G93/x5WfXmsvqje/dr9dYHJxfxD65Zolw2M+36V6tCmUn6yG9SqwwAOhnBDJiDlZvv10u/fCOS9zrz9Ixuu3q5/mn8pEbHxuUqjcKVd1iWq/bPn5et+z4u6aFnjkfSJwBAexDMgDmIKpRJ0uYPLwtdp1Z5zNLAirzmnT7zygMW/gNAZyOYAbOwsTCi8zf8bWTvZyqFrlqBqrK9kdDFwn8A6GwEM6BBGwsjumvPocjKYUjSdav6JNUOVJXtM4UuFv4DQOdjVyZQx+9+6WE9e+zVyN83Y6ZrVy7WrQPLJJUW+FfWQpOmB62wa0yltWX53pwG1yxh4T8AdDiCGVBD1KHszNMz2vzhZZPhqTBc1OqtD+ro6JgW9eb0kd/M66Fnjk8+rg5a5Z/LJwyEXQMA6GwEMyBEYbgYWSirHh0rv3/l6FdxdEzf2VfUlrXL6gatgRWUwwCAbkYwA6oUhou6+d79kbzXulV9UwJZWb1dmAQvAEgvghkQKB9EHlUl/wvOOjM0lEm1d1hS7gIA0o1dmYBKoezTO/ZHFsrWrerT/Z++pObzjezCBACkT6zBzMwWm9lDZva0mR0ws08F7QvM7H4zezb4e37FazaY2XNmdtDM1sTZP6C8AP/me/frZERVMG67ennNkbKysGOWKHcBAIh7xOyEpM+4+/8kaZWkm8zsQknrJT3g7hdIeiB4rOC5ayQtlXSZpK+Y2fRDAoEIlNeSRXkI+erzFzS0Rqx8zFK+NydTqdzFTAv/AQDdL9Y1Zu7+oqQXg59/aWZPS8pLulLSJcFld0p6WNKfBO33uPvrkp43s+ckXSzp0Tj7ifSIeh1ZmalULHamkbJK7LAEAFRr2eJ/MztX0gpJeyW9IwhtcvcXzeys4LK8pD0VLzsStAFNqy5REZXbrl5OwAIARKIlwczM3izpO5Judvd/NLOal4a0TVv5Y2Y3SrpRkvr6+qLqJrpcWImKZuSyPdqy9t1zCmXlkTsKxQIAKsUezMwsq1Iou9vddwbNL5nZ2cFo2dmSjgXtRyQtrnj5OZKOVr+nu98u6XZJ6u/vj+7gQnS1KEpR9Ej6+62XN/UeYcVlN+wckSTCGQCkXNy7Mk3SX0l62t2/VPHULknXBz9fL+l7Fe3XmNkZZnaepAskPRZnH5EO193x6PSh1zn40tXLm36PesVlAQDpFveI2WpJH5U0Ymb7g7bPSdoqaYeZ3SDpkKSrJMndD5jZDklPqbSj8yZ3j3ZBEFKjMFzU579/QK+8Nh7J+zW643ImFJcFANQS967Mv1P4ujFJ+u0ar9ksaXNsnUIqRHmsklT7aKW5WNSbC90VSnFZAACV/9GVPh1hKJs/LxtZKJMoLgsAqI2zMtE1NhZGtH3vYU14dPtBMj2mWz60NLL3k04t8GdXJgCgGsEMXWFjYUR37TkU+ft+8aqLYglMFJcFAIRhKhNd4Zt7ow1lPaJwLACg9RgxQ8eK63ilPFOLAIA2IZihI8V1vNILTRaPBQCgGQQzdJy41pPlKVcBAGgz1piho8QVyihXAQBIAkbMkFiVa8h6TDoZ4amovbmszKTR18YjKVfBoeQAgCgQzJBI1WvIogplUVbwL+NQcgBAVJjKRCKFHfTdrDhCmcSh5ACA6DBihkSK8kDvuMtfcCg5ACAqBDMk0umn9ej1Eyebfp98b06PrL80gh5NVbmmrMcs9BgoDiUHAMwWwQyJUxguRhLK4tppWb2mLCyUscsTADAXBDMkzud2PhHJ+2xZuyyW6cta698yZjrpzq5MAMCcEcyQKBsLI3ptvPnRsjjPuay1duyku57n5AAAQBPYlYlEubvJ4rH53lzsh4/XWjvGmjIAQLMYMUPbFYaL2rTrgEbHxuf8HvPnZTX8p++PsFe1Da5ZMu2cTtaUAQCiQDBDW1RW9W9WNmO65UNL59yH2VbrL19DpX8AQNQIZmi5wnBRg996XOMRlPOfl+3Rn69996xDUbPV+gdW5AliAIDIscYMLbdp14FIQtnq8xfoqS/83pwCEtX6AQBJxIgZWmZjYUTb9x4Orfs1G1FU8qdaPwAgiQhmaImNhRHd1eSOywvOOlP3f/qSSPqzqDcXur6NnZUAgHZiKhMt0WwokxRZKJNKOytz2cyUNnZWAgDajREzdITeXDbS92NnJQAgiQhmiN3GwkhTr8/2mDZdMftyGDNhZyUAIGkIZohcdX2wo//f7BfUm0lyMZIFAEgVghkiVRguavDbj2t8orTzcs4FZF2cOwkASB0W/yNSn//+gclQ1gx2RwIA0ohghkgUhota/vkf6pXX5n7eZRm7IwEAaUUwQ9PKRyzN5RDyfG9Ot129XPnenCx4vGXtMtaUAQBSiTVmmLMoKvkfHR1jdyQAAAGCGeYkikr+EmvJAACoxFQm5mT73sNNvwdryQAAmIoRM8zJXKcvM2Y66U59MgAAQhDMMCcZszmFsy/+wUWEMQAAamAqE7NWGC5Kmn0omz8vSygDAKAOghlmpVzZf6Yaslb1OJfN6JYPRX/eJQAA3YSpTDSkfP7lTEcsmUpHKVWflxnlerJa7x3nZwIA0AoEM8yoMFzUhp0jGhufmPHacvmLuGqTVfelODqmDTtHNPTzl/WdfcVp7eW+AADQCQhmCFU5+tTT4EJ/k2Ivf7Ft98FpAXFsfCK00O3Y+IS27T5IMAMAdAyCGaapHpVqdPfldav6Yg9BR2tMpdbqY63rAQBIolgX/5vZ183smJk9WdG2ycyKZrY/+POBiuc2mNlzZnbQzNbE2TfUFjYqVU9vLqvbrl6uWweWxdirklonBWSsertB/esBAEiiuEfMviHpP0n666r2L7v7X1Y2mNmFkq6RtFTSIkk/MrN3uXvjCQGzUmux/EwL/Mvmz8tq+E/fH3Mvpxpcs2TaerdcNqOP/GZ+yhqzcjsnCwAAOkmswczdf2xm5zZ4+ZWS7nH31yU9b2bPSbpY0qNx9S/N6i2iNzVWpWz0tfFY+ximPFUaFij737mAXZkAgI7WrjVmnzCzfytpSNJn3P0VSXlJeyquORK0TWNmN0q6UZL6+vpi7mp3qreIvtHSse2aJqy14zOunaAAALRKOwrMflXS+ZKWS3pR0heD9rBFQqEZwd1vd/d+d+9fuHBhLJ3sdrNdRF+NaUIAAKLX8mDm7i+5+4S7n5R0h0rTlVJphGxxxaXnSDra6v6lQWG4qJ4ai+VrLaKfPy+rfG9OJinfm9OWtcsYnQIAIGItn8o0s7Pd/cXg4YcllXds7pL0TTP7kkqL/y+Q9Fir+9ftymvLwkbG6i2iv+VDSwliAADELNZgZmbbJV0i6e1mdkTSLZIuMbPlKk1TviDp45Lk7gfMbIekpySdkHQTOzKjV6sURsZschSMRfQAALSHeYNripKqv7/fh4aG2t2NjnHe+h+ELtwrn3EJAADiY2b73L2/1vPtWPyPNqq1k5JCrAAAtB9HMqVEuZhscXRsWp0ydlgCAJAMBLMUqC4m69JkOMuzhgwAgMQgmKVA2IL/cih7ZP2l7ekUAACYhmDWpSrPway1vaNWkVkAANAeBLMuVD11WQsL/gEASBaCWRfZWBjR9r2HGzpWiQX/AAAkD8GsS1x3x6N65GcvN3Rtqxf8V06rUrAWAIDaCGZdoDBcbDiUmdTSBf/V06rF0TFt2DkiSYQzAACqEMw6UPUI1GtvnGj4ta1eVxa2I3RsfELbdh8kmAEAUIVg1mHCRqAa1Y51ZbV2frIjFACA6TiSqcPUOoR8Jr257OQh5a3EEVAAADSOEbMOUXmk0my0u7L/4Jol00p3sCMUAIBwBLMO0Ehdst5cVmeecVpbdz7W233JrkwAAGZGMEuw2YySffCis3XrwLIW9CrcTLsvCWIAAMyMNWYJVQ46jU5dPvTM8Zh7VF+93ZcAAKAxjJglSOVUYI9ZQxX8y9q9y5HdlwAANI8Rs4QoDBc1+K3HVQwOHZ9NKJPav8uR3ZcAADSPYJYQm3Yd0PjJ2YWxsiTschxcs0S5bGZKWxL6BQBAJ2EqMyFGx8bn9LreXFabrlja9sX17L4EAKB5BLMEKAwXZ/2apASySuy+BACgOQSzNivvvmxUxkw/2/KBGHsEAADahTVmbTbbI5ZmuykAAAB0DoJZm822nETGLKaeAACAdiOYtVmtchK5bPj/aa5duTjO7gAAgDYimLVZrTITW9a+W+tW9U2OkGXMtG5VX1uPXQIAAPFi8X+b1SszMbAiTxADACBFCGYJQJkJAAAgEcza4ro7HtUjP3t58vHq8xfo7o+9t409AgAAScAasxarDmWS9MjPXtZ1dzzaph4BAICkIJi1WHUom6kdAACkB8EMAAAgIVhjFoPCcJHDvAEAwKwxYhax8tmXxdExuaTi6Jg27ByZPKh89fkLQl9Xqx0AAKQHwSxiYWdfjo1PaNvug5Kkuz/23mkhjF2ZAABAYiozcsUaZ19WnolJCAMAAGEYMYtQYbioWkeM1zoTEwAAoIxgFqFtuw/KQ9pNpTMxAQAA6mEqc47Cdl4erTGN6RK7MgEAwIwIZnNQGC5q8NuPa3yiND5WHB3T4Lcf19tyWY2OjU+7Ps80JgAAaABTmXPw+e8fmAxlZeMTrvGJk8plM1Pac9kM05gAAKAhsQYzM/u6mR0zsycr2haY2f1m9mzw9/yK5zaY2XNmdtDM1sTZt2a88tr0UTFJevWNCW1Zu0z53pxMpZGyLWuXMY0JAAAaEvdU5jck/SdJf13Rtl7SA+6+1czWB4//xMwulHSNpKWSFkn6kZm9y90nlCDlQrG1DKzIE8QAAMCcxDpi5u4/llR9OveVku4Mfr5T0kBF+z3u/rq7Py/pOUkXx9m/uSgXigUAAIhaO9aYvcPdX5Sk4O+zgva8pMMV1x0J2qYxsxvNbMjMho4fPx5rZ6vV2nkJAADQrCQt/g+rzRpWFkzufru797t7/8KFC2Pu1lT1CsVmrFZ5WQAAgJm1I5i9ZGZnS1Lw97Gg/YikxRXXnSPpaIv7NqPBNUuU6QkPYNeuXBzaDgAA0Ih2BLNdkq4Pfr5e0vcq2q8xszPM7DxJF0h6rA39q2tgRV5fvOoinXn6qbIYJmndqj7dOrCsfR0DAAAdL9ZdmWa2XdIlkt5uZkck3SJpq6QdZnaDpEOSrpIkdz9gZjskPSXphKSbkrIjM6zK/4E/u6zd3QIAAF3G3EOXcXWM/v5+Hxoaiu39NxZGdPeeQ1MWu+WyGeqTAQCAWTOzfe7eX+v5JC3+T5zCcHFaKJOksfEJymYAAIDIEczq2Lb7YPi2UFE2AwAARI9gVke98FWvbAYAAMBcEMzqqBW+TOJgcgAAEDmCWR2Da5Yol81MaTNJ163qY+E/AACIXNyHmHe0cviqLpVBKAMAAHEgmM1gYEWeIAYAAFqCqUwAAICEIJgBAAAkBMEMAAAgIQhmAAAACUEwAwAASAiCGQAAQEIQzAAAABKCYAYAAJAQBDMAAICEIJgBAAAkBMEMAAAgIQhmAAAACUEwAwAASAiCGQAAQEKYu7e7D00xs+OSfh7R271d0j9E9F6djPtwCveihPtwCveihPtQwn04hXtRMtN9eKe7L6z1ZMcHsyiZ2ZC797e7H+3GfTiFe1HCfTiFe1HCfSjhPpzCvShp9j4wlQkAAJAQBDMAAICEIJhNdXu7O5AQ3IdTuBcl3IdTuBcl3IcS7sMp3IuSpu4Da8wAAAASghEzAACAhCCYAQAAJESqgpmZfd3MjpnZkxVtC8zsfjN7Nvh7fsVzG8zsOTM7aGZr2tPr6NW4D5vMrGhm+4M/H6h4rlvvw2Ize8jMnjazA2b2qaA9Vd+JOvchjd+JN5nZY2b2eHAvPh+0p+07Ues+pO47IUlmljGzYTO7L3icqu9DpZB7kbrvhJm9YGYjwe87FLRF951w99T8kfRbkt4j6cmKtr+QtD74eb2k/zP4+UJJj0s6Q9J5kn4mKdPu3yHG+7BJ0h+HXNvN9+FsSe8Jfn6LpP83+H1T9Z2ocx/S+J0wSW8Ofs5K2itpVQq/E7XuQ+q+E8Hv92lJ35R0X/A4Vd+HGe5F6r4Tkl6Q9Paqtsi+E6kaMXP3H0t6uar5Skl3Bj/fKWmgov0ed3/d3Z+X9Jyki1vRz7jVuA+1dPN9eNHdfxr8/EtJT0vKK2XfiTr3oZauvA+S5CW/Ch5mgz+u9H0nat2HWrryPkiSmZ0j6XJJX6toTtX3oazGvailq+9FiMi+E6kKZjW8w91flEr/AyXprKA9L+lwxXVHVP9/rLrBJ8zsiWCqszwMm4r7YGbnSlqh0shAar8TVfdBSuF3Ipiq2S/pmKT73T2V34ka90FK33fiNkmflXSyoi1134fAbZp+L6T0fSdc0g/NbJ+Z3Ri0RfadIJjVZiFt3Vxb5KuSzpe0XNKLkr4YtHf9fTCzN0v6jqSb3f0f610a0tY19yLkPqTyO+HuE+6+XNI5ki42s9+oc3nX3osa9yFV3wkz+6CkY+6+r9GXhLR1/H2Q6t6LVH0nAqvd/T2Sfk/STWb2W3WunfV9IJhJL5nZ2ZIU/H0saD8iaXHFdedIOtrivrWMu78U/If4pKQ7dGqotavvg5llVQojd7v7zqA5dd+JsPuQ1u9EmbuPSnpY0mVK4XeirPI+pPA7sVrSFWb2gqR7JF1qZncpnd+H0HuRwu+E3P1o8PcxSd9V6XeO7DtBMJN2Sbo++Pl6Sd+raL/GzM4ws/MkXSDpsTb0ryXKX6jAhyWVd2x27X0wM5P0V5KedvcvVTyVqu9ErfuQ0u/EQjPrDX7OSfodSc8ofd+J0PuQtu+Eu29w93Pc/VxJ10h60N3XKWXfB6n2vUjbd8LMzjSzt5R/lvR+lX7nyL4Tp8XR8aQys+2SLpH0djM7IukWSVsl7TCzGyQdknSVJLn7ATPbIekpSSck3eTuE23peMRq3IdLzGy5SkOsL0j6uNTd90GlfwF+VNJIsJZGkj6n9H0nat2Ha1P4nThb0p1mllHpH6473P0+M3tU6fpO1LoPf5PC70SYtP03op6/SNl34h2Svlv696xOk/RNd/9vZvYTRfSd4EgmAACAhGAqEwAAICEIZgAAAAlBMAMAAEgIghkAAEBCEMwAQKVTD8zs3zTx+s9F2R8A6UQwA4CScyXNOZipVGIEAJpCMAPQ1czsC2b2qYrHm83skyGXbpX0r81sv5n9UXBW5DYz+0lwDuDHg9efbWY/Dq570sz+tZltlZQL2u5u0a8GoAtRxwxAV7PSwew73f09ZtYj6VlJF7v7L6quu0TSH7v7B4PHN0o6y91vNbMzJD2iUtHItZLe5O6bgwKs89z9l2b2K3d/c8t+MQBdKVWV/wGkj7u/YGa/MLMVKlXtHq4OZTW8X9K7zez3g8dvU+k4lZ9I+npwvmjB3ffH0W8A6UQwA5AGX5P0h5J+TdLXG3yNSfrf3X33tCfMfkvS5ZL+xsy2uftfR9VRAOnGGjMAafBdSZdJ+peSpgWtwC8lvaXi8W5J/1swMiYze1dwgPE7JR1z9ztUOvz9PcH14+VrAWCuGDED0PXc/Q0ze0jSaJ0DhJ+QdMLMHpf0DUn/XqWdmj+10onFxyUNSLpE0qCZjUv6laR/G7z+dklPmNlP3f26mH4VAF2Oxf8Aul6w6P+nkq5y92fb3R8AqIWpTABdzcwulPScpAcIZQCSjhEzAKliZssk/U1V8+vuvrId/QGASgQzAACAhGAqEwAAICEIZgAAAAlBMAMAAEgIghkAAEBCEMwAAAASgmAGAACQEP8/wNAQs0LFcd8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter( y_test,y_pred)\n",
    "plt.xlabel('y_test')\n",
    "plt.ylabel('y_pred')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAWR0lEQVR4nO3dfdCddX3n8feHRB4U3cIa2PROssRtqgJTnyJaoK6KLum2Y3BXSlofMh0qTBe7UnfbgbVb3Z3NjHY6jnVXLBRd05YKWYtL6ipCg9TdUoHwoBBiJAolt8mSYOuIbScS8t0/rovlTLyT3Llzn/O7H96vmTPnOr/rus71/THD5/7ld66HVBWSpNE7pnUBkjRfGcCS1IgBLEmNGMCS1IgBLEmNLGxdwLCsWrWqbr755tZlSBJAJmqcsyPgJ554onUJknRIczaAJWmmM4AlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYA1bcaWLiPJEb3Gli5rXbbUzJy9H7BGb+f4Di66+o4j2ueGS88eUjXSzOcIWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYDV1jELvXhD85YXYqit/fu8eEPzliNgSWrEAJakRoYWwEk+lWR3kgcH2k5OcmuSh/v3kwbWXZlke5JtSc4faH9Vkgf6dR9LkmHVLEmjNMwR8KeBVQe0XQFsqqoVwKb+M0lOB9YAZ/T7XJVkQb/PJ4BLgBX968DvlKRZaWgBXFVfAf7mgObVwPp+eT1wwUD79VW1t6oeAbYDZyVZDLygqv6qqgr4w4F9JGlWG/Uc8KlVtQugfz+lbx8DdgxsN963jfXLB7ZPKMklSTYn2bxnz55pLVySpttM+RFuonndOkT7hKrqmqpaWVUrFy1aNG3FSdIwjDqAH++nFejfd/ft48DSge2WADv79iUTtEvSrDfqAN4IrO2X1wI3DbSvSXJckuV0P7bd1U9TPJnktf3ZD+8a2EdDMpVHC3lyinTkhnYlXJLPAK8HXphkHPgA8CFgQ5KLgceACwGqakuSDcBDwD7gsqp6uv+qX6U7o+IE4Iv9S0M0lUcLgVeoSUdqaAFcVb94kFXnHWT7dcC6Cdo3A2dOY2mSNCPMlB/hJGneMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDeI4bW7qMJEf0kjQaC1sXoOHaOb6Di66+44j2ueHSs4dUjaRBjoAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaMYAlqREDWJIaaRLASX49yZYkDyb5TJLjk5yc5NYkD/fvJw1sf2WS7Um2JTm/Rc2SNN1GHsBJxoB/C6ysqjOBBcAa4ApgU1WtADb1n0lyer/+DGAVcFWSBaOuW5KmW6spiIXACUkWAs8FdgKrgfX9+vXABf3yauD6qtpbVY8A24GzRluuZpRjFh7xk57Hli5rXbX0I0b+VOSq+k6S3wUeA/4BuKWqbklyalXt6rfZleSUfpcx4KsDXzHet/2IJJcAlwAsW+b/cHPW/n0+6VlzQospiJPoRrXLgR8HnpfkHYfaZYK2mmjDqrqmqlZW1cpFixYdfbGSNEQtpiDeBDxSVXuq6ingRuBs4PEkiwH699399uPA0oH9l9BNWUjSrNYigB8DXpvkuUkCnAdsBTYCa/tt1gI39csbgTVJjkuyHFgB3DXimiVp2rWYA74zyWeBe4F9wH3ANcCJwIYkF9OF9IX99luSbAAe6re/rKqeHnXdkjTdRh7AAFX1AeADBzTvpRsNT7T9OmDdsOuSpFHySjhJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQA1vwwhado+CQNDVuTm/FIIzeFp2iAT9LQcDkClqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGmgRwkh9L8tkk30iyNclPJzk5ya1JHu7fTxrY/sok25NsS3J+i5olabq1GgH/HnBzVb0EeBmwFbgC2FRVK4BN/WeSnA6sAc4AVgFXJVnQpGpJmkYjD+AkLwBeB3wSoKp+WFXfA1YD6/vN1gMX9Murgeuram9VPQJsB84aZc2SNAyTCuAk50ymbZJeBOwB/nuS+5Jcm+R5wKlVtQugfz+l334M2DGw/3jfNlGdlyTZnGTznj17plieJI3GZEfA/3WSbZOxEHgl8ImqegXwd/TTDQeRCdpqog2r6pqqWllVKxctWjTF8iRpNBYeamWSnwbOBhYled/AqhcAU52HHQfGq+rO/vNn6QL48SSLq2pXksXA7oHtlw7svwTYOcVjS9KMcbgR8LHAiXRB/fyB1/eBt03lgFX1f4EdSV7cN50HPARsBNb2bWuBm/rljcCaJMclWQ6sAO6ayrElaSY55Ai4qv4C+Iskn66qv57G4/4acF2SY4FvA79M98dgQ5KLgceAC/satiTZQBfS+4DLqurpaaxFkpo4ZAAPOC7JNcBpg/tU1RunctCquh9YOcGq8w6y/Tpg3VSOJUkz1WQD+H8Avw9cCzj6lKRpMNkA3ldVnxhqJZI0z0z2NLQ/S/JvkizuLxk+OcnJQ61Mkua4yY6Anzk74TcG2oruogpJ0hRMKoCravmwC5Gk+WZSAZzkXRO1V9UfTm85kjR/THYK4tUDy8fTnS52L2AAS9IUTXYK4tcGPyf5R8AfDaUiSZonpno7yr+nuyRYkjRFk50D/jOevQPZAuClwIZhFSVJ88Fk54B/d2B5H/DXVTU+hHokad6Y1BREf1Oeb9DdCe0k4IfDLEqS5oPJPhHjF+huAXkh8AvAnUmmdDtKSVJnslMQ7wdeXVW7AZIsAv6c7mbqkqQpmOxZEMc8E7697x7BvpKkCUx2BHxzki8Bn+k/XwR8YTgl6WDGli5j5/iOw28oaVY43DPhfoLuacW/keRfAefSPSTzr4DrRlCfBuwc38FFV99xRPvccOnZQ6pG0tE63DTCR4EnAarqxqp6X1X9Ot3o96PDLU2S5rbDBfBpVfX1AxurajPd44kkSVN0uAA+/hDrTpjOQiRpvjlcAN+d5N0HNvZPLr5nOCVJ0vxwuLMgLgc+l+TtPBu4K4FjgbcOsS5JmvMOGcBV9ThwdpI3AGf2zf+rqm4bemWSNMdN9n7AXwa+PORaJGle8Wo2SWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJYO5ZiFJDmi19jSZa2r1iwx2YdySvPT/n0+h09D4whYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhoxgCWpEQNYkhppFsBJFiS5L8nn+88nJ7k1ycP9+0kD216ZZHuSbUnOb1WzJE2nliPg9wJbBz5fAWyqqhXApv4zSU4H1gBnAKuAq5IsGHGtkjTtmgRwkiXAzwHXDjSvBtb3y+uBCwbar6+qvVX1CLAdOGtEpUrS0LQaAX8U+E1g/0DbqVW1C6B/P6VvHwN2DGw33rdJ0qw28gBO8vPA7qq6Z7K7TNBWB/nuS5JsTrJ5z549U65RkkahxQj4HOAtSR4FrgfemOSPgceTLAbo33f3248DSwf2XwLsnOiLq+qaqlpZVSsXLVo0rPolaVqMPICr6sqqWlJVp9H9uHZbVb0D2Ais7TdbC9zUL28E1iQ5LslyYAVw14jLlqRpN5NuyP4hYEOSi4HHgAsBqmpLkg3AQ8A+4LKqerpdmZI0PZoGcFXdDtzeL38XOO8g260D1o2sMEkaAa+Ek6RGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqRGDGBJasQAlqbbMQtJckSvsaXLWletBha2LkCac/bv46Kr7ziiXW649OwhFaOZzBGwJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDViAEtSIwawJDUy8gBOsjTJl5NsTbIlyXv79pOT3Jrk4f79pIF9rkyyPcm2JOePumZJGoYWI+B9wL+rqpcCrwUuS3I6cAWwqapWAJv6z/Tr1gBnAKuAq5IsaFC3JE2rkQdwVe2qqnv75SeBrcAYsBpY32+2HrigX14NXF9Ve6vqEWA7cNZIi5akIWg6B5zkNOAVwJ3AqVW1C7qQBk7pNxsDdgzsNt63TfR9lyTZnGTznj17hla3JE2HZgGc5ETgT4HLq+r7h9p0graaaMOquqaqVlbVykWLFk1HmZI0NE0COMlz6ML3uqq6sW9+PMnifv1iYHffPg4sHdh9CbBzVLVK0rC0OAsiwCeBrVX1kYFVG4G1/fJa4KaB9jVJjkuyHFgB3DWqeiVpWBY2OOY5wDuBB5Lc37f9B+BDwIYkFwOPARcCVNWWJBuAh+jOoLisqp4eedWSNM1GHsBV9X+YeF4X4LyD7LMOWDe0oqTWjllI94/DyfvxJUv5zo7HhlSQRqHFCFjSgfbv46Kr7ziiXW649OwhFaNR8VJkSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAJakRgxgSWrEAG5kbOkykhzRS9Lc4kM5G9k5vsOHMErznCNgSWrEAJZmq2MWHvE0VhLGli5rXbl6TkFIs9X+fUc8jQVOZc0kjoAlqREDWJIaMYAlqREDWJIaMYCP0lQuqPCiCkngWRBHbSoXVIC/REtyBCxJzRjA0nwzhQs4vHhjOJyCkOabKVzA4ZTZcDgClqRGDGBJasQAlqRGDGBJasQAHuBTKiSNkmdBDPApFZJGyRGwJDViAEtSIwawpMPz6rmhcA5Y0uF59dxQOAKWNBw+NPSwHAFLGg4fGnpYs2YEnGRVkm1Jtie5onU9koZkHs03z4oRcJIFwMeBNwPjwN1JNlbVQ20rkzTt5tF882wZAZ8FbK+qb1fVD4HrgdWNa5I0U0xh1Lzw2OObj7RTVdP6hcOQ5G3Aqqr6lf7zO4HXVNV7DtjuEuCS/uOLgW1TPOQLgSemuO9sN1/7br/nn1H2/YmqWnVg46yYggAmuunCj/zlqKprgGuO+mDJ5qpaebTfMxvN177b7/lnJvR9tkxBjANLBz4vAXY2qkWSpsVsCeC7gRVJlic5FlgDbGxckyQdlVkxBVFV+5K8B/gSsAD4VFVtGeIhj3oaYxabr3233/NP877Pih/hJGkumi1TEJI05xjAktTIvAzgJJ9KsjvJgwNtJye5NcnD/ftJA+uu7C+B3pbk/DZVH70kS5N8OcnWJFuSvLdvn9N9T3J8kruSfK3v93/q2+d0v5+RZEGS+5J8vv88X/r9aJIHktyfZHPfNrP6XlXz7gW8Dngl8OBA2+8AV/TLVwAf7pdPB74GHAcsB74FLGjdhyn2ezHwyn75+cA3+/7N6b7TnUd+Yr/8HOBO4LVzvd8D/X8f8CfA5/vP86XfjwIvPKBtRvV9Xo6Aq+orwN8c0LwaWN8vrwcuGGi/vqr2VtUjwHa6S6NnnaraVVX39stPAluBMeZ436vzg/7jc/pXMcf7DZBkCfBzwLUDzXO+34cwo/o+LwP4IE6tql3QBRVwSt8+BuwY2G68b5vVkpwGvIJuNDjn+97/M/x+YDdwa1XNi34DHwV+E9g/0DYf+g3dH9lbktzT36YAZljfZ8V5wI1N6jLo2STJicCfApdX1feTibrYbTpB26zse1U9Dbw8yY8Bn0ty5iE2nxP9TvLzwO6quifJ6yezywRts67fA86pqp1JTgFuTfKNQ2zbpO+OgJ/1eJLFAP377r59Tl0GneQ5dOF7XVXd2DfPi74DVNX3gNuBVcz9fp8DvCXJo3R3EHxjkj9m7vcbgKra2b/vBj5HN6Uwo/puAD9rI7C2X14L3DTQvibJcUmWAyuAuxrUd9TSDXU/CWytqo8MrJrTfU+yqB/5kuQE4E3AN5jj/a6qK6tqSVWdRnf5/m1V9Q7meL8BkjwvyfOfWQb+BfAgM63vrX+pbPECPgPsAp6i+8t3MfCPgU3Aw/37yQPbv5/uV9FtwM+2rv8o+n0u3T+rvg7c37/+5VzvO/BTwH19vx8Efrtvn9P9PuC/wet59iyIOd9v4EV0ZzV8DdgCvH8m9t1LkSWpEacgJKkRA1iSGjGAJakRA1iSGjGAJakRA1iSGjGANSMl+SdJrk/yrSQPJflCkp9MckaS25J8s7+l4H/sLzAhyduTfL1/3ZHkZUOsb2WSj01hvy88c1GI5HnAmnH6QL0DWF9Vv9+3vZzuFpqfBn61qm5J8ly6y6o/X1UfT3I23VV+f5vkZ4EPVtVrmnRCmgRHwJqJ3gA89Uz4AlTV/cBPAn9ZVbf0bX8PvIfuvq5U1R1V9bf9Ll+lu57/oJK8o79R+/1Jrk6yoG//QZIP93fR+vMkZyW5Pcm3k7yl3+b1Azc4/+f9d9zf3/j8+UkWJ/lK3/Zgkp/pt300yQv75ff16x5Mcnnfdlq6G+b/Qbqbx9/SXz6tOcgA1kx0JnDPBO1nHNheVd8CTkzyggO2vRj44sEOkOSlwEV0d8x6OfA08PZ+9fOA26vqVcCTwH8B3gy8FfjPE3zdvwcu67/nZ4B/AH4J+FLf9jK6y74Hj/8q4JeB19DdHP7dSV7Rr14BfLyqzgC+B/zrg/VDs5u3o9RsEg5+i8D/357kDXQBfO4hvus84FXA3f0U8gk8e2esHwI398sPAHur6qkkDwCnTfBdfwl8JMl1wI1VNZ7kbuBT/d3n/mc/gh90LvC5qvq7vuYb6cJ7I/DIwPb3HOSYmgMcAWsm2kIXjhO1rxxsSPIi4AfVPeGDJD9F9/SH1VX13UMcI3RzzC/vXy+uqg/2656qZ38c2Q/sBaiq/UwwaKmqDwG/QhfiX03ykuqeuvI64DvAHyV51wTHP5i9A8tPT3RMzQ0GsGai24Djkrz7mYYkr6a7g9W5Sd7Ut50AfIzuOV8kWQbcCLyzqr55mGNsAt7W36z7mYc1/tOpFJvkn1XVA1X1YWAz8JL+u3ZX1R/Q3QL0lQfs9hXggiTP7W+X+Fbgf0/l+Jq9DGDNOP3o863Am/vT0LYAH6S7QfZq4LeSbKObHrgb+G/9rr9Nd7vBqzLwJNyDHOMh4LfoHlnzdeBWuoeWTsXl/Q9pX6Ob//0i3e0f709yH90c7u8dcPx76c7ouIvusVDXVtV9Uzy+ZilPQ5OkRhwBS1IjTu5rTkvyzBMQDnTeYX6kk4bOKQhJasQpCElqxACWpEYMYElqxACWpEb+Hw4TxvOussGAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(y_pred, bins=20,color='red')\n",
    "plt.show()\n",
    "sns.displot(data['CO2_emission'], bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DECISION TREE REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(random_state=42)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "model = DecisionTreeRegressor(random_state = 42)\n",
    "\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([357.        , 290.        , 382.        , ..., 342.66666667,\n",
       "       193.        , 177.        ])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtr_pred = model.predict(X_test)\n",
    "dtr_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.629475553464912"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test,dtr_pred ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9962106914830545"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, dtr_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>357.0</td>\n",
       "      <td>368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>290.0</td>\n",
       "      <td>290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>382.0</td>\n",
       "      <td>382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>211.0</td>\n",
       "      <td>211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>193.0</td>\n",
       "      <td>193.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0      1\n",
       "0  357.0  368.0\n",
       "1  290.0  290.0\n",
       "2  382.0  382.0\n",
       "3  211.0  211.0\n",
       "4  193.0  193.0"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [dtr_pred, y_test.values]\n",
    "result_pred = pd.DataFrame(data=frames)\n",
    "result_pred = result_pred.T\n",
    "result_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean diff:  1.7914691943127963\n"
     ]
    }
   ],
   "source": [
    "dtr_pred = result_pred.rename(columns={0: 'pred_values', 1:'real_values'})\n",
    "dtr_pred['pred_values'] = (dtr_pred['pred_values'].map(lambda x: round(x,2)))\n",
    "\n",
    "dtr_pred['diff'] = abs(dtr_pred['real_values'] -dtr_pred['pred_values'])\n",
    "\n",
    "\n",
    "print('mean diff: ', abs(dtr_pred['diff']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>pred_values</th>\n",
       "      <th>real_values</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>357.0</td>\n",
       "      <td>368.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>290.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>382.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>211.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>193.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>244.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>174.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>267.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>304.6</td>\n",
       "      <td>305.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pred_values  real_values  diff\n",
       "0        357.0        368.0  11.0\n",
       "1        290.0        290.0   0.0\n",
       "2        382.0        382.0   0.0\n",
       "3        211.0        211.0   0.0\n",
       "4        193.0        193.0   0.0\n",
       "5        244.0        244.0   0.0\n",
       "6        210.0        210.0   0.0\n",
       "7        174.0        174.0   0.0\n",
       "8        267.0        268.0   1.0\n",
       "9        304.6        305.0   0.4"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtr_pred.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RANDOM FOREST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_model = RandomForestRegressor()\n",
    "rf_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([359.128     , 290.51619048, 382.96      , ..., 343.2952381 ,\n",
       "       191.2125    , 177.75      ])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_rf_pred = rf_model.predict(X_test)\n",
    "y_rf_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 3.1729\n",
      "MAE: 1.9497\n",
      "R2_score: 0.9971\n"
     ]
    }
   ],
   "source": [
    "print('RMSE: {:0.4f}'.format(np.sqrt(mean_squared_error(y_test,y_rf_pred))))\n",
    "print('MAE: {:0.4f}'.format(mean_absolute_error(y_test,y_rf_pred)))\n",
    "print('R2_score: {:0.4f}'.format(r2_score(y_test,y_rf_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>359.128000</td>\n",
       "      <td>368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>290.516190</td>\n",
       "      <td>290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>382.960000</td>\n",
       "      <td>382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>211.193333</td>\n",
       "      <td>211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>192.660000</td>\n",
       "      <td>193.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0      1\n",
       "0  359.128000  368.0\n",
       "1  290.516190  290.0\n",
       "2  382.960000  382.0\n",
       "3  211.193333  211.0\n",
       "4  192.660000  193.0"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [y_rf_pred, y_test.values]\n",
    "result_pred = pd.DataFrame(data=frames)\n",
    "result_pred = result_pred.T\n",
    "result_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean diff:  1.9495463777928175\n"
     ]
    }
   ],
   "source": [
    "y_rf_pred = result_pred.rename(columns={0: 'pred_values', 1:'real_values'})\n",
    "y_rf_pred['pred_values'] = (y_rf_pred['pred_values'].map(lambda x: round(x,2)))\n",
    "\n",
    "y_rf_pred['diff'] = abs(y_rf_pred['real_values'] -y_rf_pred['pred_values'])\n",
    "\n",
    "\n",
    "print('mean diff: ', abs(y_rf_pred['diff']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>pred_values</th>\n",
       "      <th>real_values</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>359.13</td>\n",
       "      <td>368.0</td>\n",
       "      <td>8.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>290.52</td>\n",
       "      <td>290.0</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>382.96</td>\n",
       "      <td>382.0</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>211.19</td>\n",
       "      <td>211.0</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>192.66</td>\n",
       "      <td>193.0</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>245.38</td>\n",
       "      <td>244.0</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>211.55</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>174.44</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>266.94</td>\n",
       "      <td>268.0</td>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>304.62</td>\n",
       "      <td>305.0</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pred_values  real_values  diff\n",
       "0       359.13        368.0  8.87\n",
       "1       290.52        290.0  0.52\n",
       "2       382.96        382.0  0.96\n",
       "3       211.19        211.0  0.19\n",
       "4       192.66        193.0  0.34\n",
       "5       245.38        244.0  1.38\n",
       "6       211.55        210.0  1.55\n",
       "7       174.44        174.0  0.44\n",
       "8       266.94        268.0  1.06\n",
       "9       304.62        305.0  0.38"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_rf_pred.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SIMPLE VECTOR MACHINE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([363.60358933, 294.16469554, 383.39453728, ..., 344.90703477,\n",
       "       191.74388406, 176.98221956])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "model = LinearSVR()\n",
    "model.fit(X_train, y_train)\n",
    "y_svr_pred = model.predict(X_test)\n",
    "y_svr_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.445718017534262"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_svr_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.991821600663927"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_svr_pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>363.603589</td>\n",
       "      <td>368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>294.164696</td>\n",
       "      <td>290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>383.394537</td>\n",
       "      <td>382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>210.676727</td>\n",
       "      <td>211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>192.440312</td>\n",
       "      <td>193.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0      1\n",
       "0  363.603589  368.0\n",
       "1  294.164696  290.0\n",
       "2  383.394537  382.0\n",
       "3  210.676727  211.0\n",
       "4  192.440312  193.0"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [y_svr_pred, y_test.values]\n",
    "result_pred = pd.DataFrame(data=frames)\n",
    "result_pred = result_pred.T\n",
    "result_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean diff:  2.806222071767095\n"
     ]
    }
   ],
   "source": [
    "y_svr_pred = result_pred.rename(columns={0: 'pred_values', 1:'real_values'})\n",
    "y_svr_pred['pred_values'] = (y_svr_pred['pred_values'].map(lambda x: round(x,2)))\n",
    "\n",
    "y_svr_pred['diff'] = abs(y_svr_pred['real_values'] -y_svr_pred['pred_values'])\n",
    "\n",
    "\n",
    "print('mean diff: ', abs(y_svr_pred['diff']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>pred_values</th>\n",
       "      <th>real_values</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>363.60</td>\n",
       "      <td>368.0</td>\n",
       "      <td>4.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>294.16</td>\n",
       "      <td>290.0</td>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>383.39</td>\n",
       "      <td>382.0</td>\n",
       "      <td>1.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>210.68</td>\n",
       "      <td>211.0</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>192.44</td>\n",
       "      <td>193.0</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>248.70</td>\n",
       "      <td>244.0</td>\n",
       "      <td>4.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>210.99</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>174.22</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>266.64</td>\n",
       "      <td>268.0</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>304.33</td>\n",
       "      <td>305.0</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pred_values  real_values  diff\n",
       "0       363.60        368.0  4.40\n",
       "1       294.16        290.0  4.16\n",
       "2       383.39        382.0  1.39\n",
       "3       210.68        211.0  0.32\n",
       "4       192.44        193.0  0.56\n",
       "5       248.70        244.0  4.70\n",
       "6       210.99        210.0  0.99\n",
       "7       174.22        174.0  0.22\n",
       "8       266.64        268.0  1.36\n",
       "9       304.33        305.0  0.67"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_svr_pred.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  APPROACH 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "models =['LinReg','DT', 'RF','SVR']\n",
    "\n",
    "frame = pd.DataFrame(columns={'models':[],'rmse_train':[], 'mae_train':[],'r2_train':[] ,'rmse_test':[], 'mae_test':[],'r2_test':[]})\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for i in range(len(models)):\n",
    "    if models[i] == 'LinReg':\n",
    "        model = LinearRegression()\n",
    "        model.fit(X_train,y_train)\n",
    "        pred_train = model.predict(X_train)\n",
    "        rmse_train = np.sqrt(mean_squared_error(y_train,pred_train))\n",
    "        mae_train = mean_absolute_error(y_train,pred_train)\n",
    "        r2_train = r2_score(y_train,pred_train)\n",
    "        \n",
    "        pred_test = model.predict(X_test)\n",
    "        rmse_test = np.sqrt(mean_squared_error(y_test,pred_test))\n",
    "        mae_test = mean_absolute_error(y_test,pred_test)\n",
    "        r2_test = r2_score(y_test,pred_test)\n",
    "        frame.loc[frame.shape[0]] = ['Linear Regression', rmse_train, mae_train,r2_train,rmse_test,mae_test,r2_test]\n",
    "        \n",
    "        \n",
    "    elif models[i] =='DT':\n",
    "        model = DecisionTreeRegressor()\n",
    "        model.fit(X_train,y_train)\n",
    "        pred_train = model.predict(X_train)\n",
    "        rmse_train = np.sqrt(mean_squared_error(y_train,pred_train))\n",
    "        mae_train = mean_absolute_error(y_train,pred_train)\n",
    "        r2_train = r2_score(y_train,pred_train)\n",
    "        \n",
    "        pred_test = model.predict(X_test)\n",
    "        rmse_test = np.sqrt(mean_squared_error(y_test,pred_test))\n",
    "        mae_test = mean_absolute_error(y_test,pred_test)\n",
    "        r2_test = r2_score(y_test,pred_test)\n",
    "        frame.loc[frame.shape[0]] = ['Decision Tree Regression',rmse_train, mae_train,r2_train,rmse_test,mae_test,r2_test]\n",
    "        \n",
    "    elif models[i] =='RF':\n",
    "        model = RandomForestRegressor()\n",
    "        model.fit(X_train,y_train)\n",
    "        pred_train = model.predict(X_train)\n",
    "        rmse_train = np.sqrt(mean_squared_error(y_train,pred_train))\n",
    "        mae_train = mean_absolute_error(y_train,pred_train)\n",
    "        r2_train = r2_score(y_train,pred_train)\n",
    "       \n",
    "        pred_test = model.predict(X_test)\n",
    "        rmse_test = np.sqrt(mean_squared_error(y_test,pred_test))\n",
    "        mae_test = mean_absolute_error(y_test,pred_test)\n",
    "        r2_test = r2_score(y_test,pred_test)\n",
    "        frame.loc[frame.shape[0]] = ['Random Forest Regression',rmse_train, mae_train,r2_train,rmse_test,mae_test,r2_test]\n",
    "        \n",
    "    else :\n",
    "        models[i] =='SVM'\n",
    "        model = LinearSVR()\n",
    "        model.fit(X_train,y_train)\n",
    "        pred_train = model.predict(X_train)\n",
    "        rmse_train = np.sqrt(mean_squared_error(y_train,pred_train))\n",
    "        mae_train = mean_absolute_error(y_train,pred_train)\n",
    "        r2_train = r2_score(y_train,pred_train)\n",
    "        \n",
    "        pred_test = model.predict(X_test)\n",
    "        rmse_test = np.sqrt(mean_squared_error(y_test,pred_test))\n",
    "        mae_test = mean_absolute_error(y_test,pred_test)\n",
    "        r2_test = r2_score(y_test,pred_test)\n",
    "        frame.loc[frame.shape[0]] = ['Simple Vector Regression',rmse_train, mae_train,r2_train,rmse_test,mae_test,r2_test]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OVERALL PERFORMANCE OF ALL MODELS IN A DATAFRAME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>models</th>\n",
       "      <th>rmse_train</th>\n",
       "      <th>mae_train</th>\n",
       "      <th>r2_train</th>\n",
       "      <th>rmse_test</th>\n",
       "      <th>mae_test</th>\n",
       "      <th>r2_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>4.962163</td>\n",
       "      <td>2.996013</td>\n",
       "      <td>0.992778</td>\n",
       "      <td>4.918261</td>\n",
       "      <td>2.979052</td>\n",
       "      <td>0.993042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Tree Regression</td>\n",
       "      <td>0.939813</td>\n",
       "      <td>0.318859</td>\n",
       "      <td>0.999741</td>\n",
       "      <td>3.752893</td>\n",
       "      <td>1.796705</td>\n",
       "      <td>0.995949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Random Forest Regression</td>\n",
       "      <td>1.465105</td>\n",
       "      <td>0.866309</td>\n",
       "      <td>0.999370</td>\n",
       "      <td>3.163007</td>\n",
       "      <td>1.941471</td>\n",
       "      <td>0.997122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Simple Vector Regression</td>\n",
       "      <td>5.346308</td>\n",
       "      <td>2.713163</td>\n",
       "      <td>0.991617</td>\n",
       "      <td>5.430713</td>\n",
       "      <td>2.799582</td>\n",
       "      <td>0.991516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     models  rmse_train  mae_train  r2_train  rmse_test  \\\n",
       "0         Linear Regression    4.962163   2.996013  0.992778   4.918261   \n",
       "1  Decision Tree Regression    0.939813   0.318859  0.999741   3.752893   \n",
       "2  Random Forest Regression    1.465105   0.866309  0.999370   3.163007   \n",
       "3  Simple Vector Regression    5.346308   2.713163  0.991617   5.430713   \n",
       "\n",
       "   mae_test   r2_test  \n",
       "0  2.979052  0.993042  \n",
       "1  1.796705  0.995949  \n",
       "2  1.941471  0.997122  \n",
       "3  2.799582  0.991516  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}