Car-listing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Objective: To build a regression model to predict the price of a used car."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview:\n",
    "* Two decision tree based alogrithms: Random Forest and Gradient Boosting were employed to predict the price of a used car (sedan/suv) in the Greater Cleveland area.\n",
    "* Gradient Boosting performs better than Random Forest in this case.\n",
    "* Prices are predicted on the test data within a range of around $1600 on the **Root Mean Square Error(RMSE)** metric."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data: Here are some of the details:\n",
    "* The data has been scraped from the website of Cargurus.\n",
    "* Only sedans and suvs/crossovers are considered.\n",
    "* Vehicles within a 200 miles radius and years 2005-2021 are chosen.\n",
    "* In total, there are about 20k records eqully distributed among the 10 most popular car(non-luxury) brands.\n",
    "* There are over 20 numerical and categorical columns, combined."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This project is divided into 5 stages:\n",
    "* Data collection: This step is already done using Scrapy and the data is available in the csv format.\n",
    "* Data cleaning: The dataset is cleaned so that none of the features have missing values.\n",
    "* Data analysis: All the features are analyzed and the dataset is made ready for the next stage.\n",
    "* Model building: Decision tree based methods: Random Forest and GradientBoosting are employed.\n",
    "* Remarks/Conclusions: Discussion about the results, the limitations and further imrpovements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stage 1: Data collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import norm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Accidents</th>\n",
       "      <th>Body_type</th>\n",
       "      <th>Year</th>\n",
       "      <th>Fuel_economy_combined</th>\n",
       "      <th>Days_on_market</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Color</th>\n",
       "      <th>Drivetrain</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Make</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Model</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Additional_features</th>\n",
       "      <th>Previous_owners</th>\n",
       "      <th>Price_drops</th>\n",
       "      <th>Photos</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Seller_type</th>\n",
       "      <th>Trim</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>271936521</td>\n",
       "      <td>0</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>2017</td>\n",
       "      <td>29 MPG</td>\n",
       "      <td>37</td>\n",
       "      <td>GREAT_PRICE</td>\n",
       "      <td>97.736984</td>\n",
       "      <td>BLACK</td>\n",
       "      <td>FRONT_WHEEL_DRIVE</td>\n",
       "      <td>I4</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>49516</td>\n",
       "      <td>Sonic</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>DEALER</td>\n",
       "      <td>LT Sedan FWD</td>\n",
       "      <td>7495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>273738682</td>\n",
       "      <td>0</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>2009</td>\n",
       "      <td>27 MPG</td>\n",
       "      <td>10</td>\n",
       "      <td>GREAT_PRICE</td>\n",
       "      <td>171.23712</td>\n",
       "      <td>SILVER</td>\n",
       "      <td>FRONT_WHEEL_DRIVE</td>\n",
       "      <td>I4</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>132196</td>\n",
       "      <td>Malibu</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>DEALER</td>\n",
       "      <td>LTZ FWD</td>\n",
       "      <td>3997.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>272560966</td>\n",
       "      <td>2</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>2015</td>\n",
       "      <td>26 MPG</td>\n",
       "      <td>28</td>\n",
       "      <td>GREAT_PRICE</td>\n",
       "      <td>116.38247</td>\n",
       "      <td>BROWN</td>\n",
       "      <td>FRONT_WHEEL_DRIVE</td>\n",
       "      <td>I4</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>20833</td>\n",
       "      <td>Impala</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>DEALER</td>\n",
       "      <td>LTZ 1LZ FWD</td>\n",
       "      <td>15995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>268734916</td>\n",
       "      <td>2</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97</td>\n",
       "      <td>GREAT_PRICE</td>\n",
       "      <td>13.022558</td>\n",
       "      <td>BLACK</td>\n",
       "      <td>REAR_WHEEL_DRIVE</td>\n",
       "      <td>V6</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>71347</td>\n",
       "      <td>Caprice</td>\n",
       "      <td>6-Speed Automatic</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>DEALER</td>\n",
       "      <td>Police Sedan RWD</td>\n",
       "      <td>5995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>269505576</td>\n",
       "      <td>0</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>2017</td>\n",
       "      <td>31 MPG</td>\n",
       "      <td>86</td>\n",
       "      <td>GREAT_PRICE</td>\n",
       "      <td>126.89147</td>\n",
       "      <td>SILVER</td>\n",
       "      <td>FRONT_WHEEL_DRIVE</td>\n",
       "      <td>I4</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>31152</td>\n",
       "      <td>Malibu</td>\n",
       "      <td>6-Speed Automatic</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>DEALER</td>\n",
       "      <td>LT FWD</td>\n",
       "      <td>12925.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id Accidents Body_type  Year Fuel_economy_combined Days_on_market  \\\n",
       "0  271936521         0     Sedan  2017                29 MPG             37   \n",
       "1  273738682         0     Sedan  2009                27 MPG             10   \n",
       "2  272560966         2     Sedan  2015                26 MPG             28   \n",
       "3  268734916         2     Sedan  2012                   NaN             97   \n",
       "4  269505576         0     Sedan  2017                31 MPG             86   \n",
       "\n",
       "        Rating   Distance   Color         Drivetrain Engine       Make  \\\n",
       "0  GREAT_PRICE  97.736984   BLACK  FRONT_WHEEL_DRIVE     I4  Chevrolet   \n",
       "1  GREAT_PRICE  171.23712  SILVER  FRONT_WHEEL_DRIVE     I4  Chevrolet   \n",
       "2  GREAT_PRICE  116.38247   BROWN  FRONT_WHEEL_DRIVE     I4  Chevrolet   \n",
       "3  GREAT_PRICE  13.022558   BLACK   REAR_WHEEL_DRIVE     V6  Chevrolet   \n",
       "4  GREAT_PRICE  126.89147  SILVER  FRONT_WHEEL_DRIVE     I4  Chevrolet   \n",
       "\n",
       "  Mileage    Model       Transmission Additional_features Previous_owners  \\\n",
       "0   49516    Sonic          Automatic                   5               2   \n",
       "1  132196   Malibu          Automatic                   5               2   \n",
       "2   20833   Impala          Automatic                   6               1   \n",
       "3   71347  Caprice  6-Speed Automatic                   1               1   \n",
       "4   31152   Malibu  6-Speed Automatic                   4               1   \n",
       "\n",
       "  Price_drops Photos Seats Seller_type              Trim    Price  \n",
       "0         NaN   True     5      DEALER      LT Sedan FWD   7495.0  \n",
       "1         NaN   True     5      DEALER           LTZ FWD   3997.0  \n",
       "2         NaN   True     5      DEALER       LTZ 1LZ FWD  15995.0  \n",
       "3         NaN   True     5      DEALER  Police Sedan RWD   5995.0  \n",
       "4         NaN   True     5      DEALER            LT FWD  12925.0  "
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"vehicles.csv\")\n",
    "pd.set_option('display.max_columns', None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19084, 23)"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'Accidents', 'Body_type', 'Year', 'Fuel_economy_combined',\n",
       "       'Days_on_market', 'Rating', 'Distance', 'Color', 'Drivetrain', 'Engine',\n",
       "       'Make', 'Mileage', 'Model', 'Transmission', 'Additional_features',\n",
       "       'Previous_owners', 'Price_drops', 'Photos', 'Seats', 'Seller_type',\n",
       "       'Trim', 'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Accidents</th>\n",
       "      <th>Body_type</th>\n",
       "      <th>Year</th>\n",
       "      <th>Fuel_economy_combined</th>\n",
       "      <th>Days_on_market</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Color</th>\n",
       "      <th>Drivetrain</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Make</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Model</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Additional_features</th>\n",
       "      <th>Previous_owners</th>\n",
       "      <th>Price_drops</th>\n",
       "      <th>Photos</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Seller_type</th>\n",
       "      <th>Trim</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19084</td>\n",
       "      <td>19006</td>\n",
       "      <td>19070</td>\n",
       "      <td>19084</td>\n",
       "      <td>17001</td>\n",
       "      <td>19084</td>\n",
       "      <td>18597</td>\n",
       "      <td>19084</td>\n",
       "      <td>19084</td>\n",
       "      <td>18023</td>\n",
       "      <td>18831</td>\n",
       "      <td>19084</td>\n",
       "      <td>19036</td>\n",
       "      <td>19084</td>\n",
       "      <td>18882</td>\n",
       "      <td>19084</td>\n",
       "      <td>18779</td>\n",
       "      <td>2018</td>\n",
       "      <td>17744</td>\n",
       "      <td>18809</td>\n",
       "      <td>19084</td>\n",
       "      <td>18894</td>\n",
       "      <td>18786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>18463</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>38</td>\n",
       "      <td>523</td>\n",
       "      <td>7</td>\n",
       "      <td>2235</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>16796</td>\n",
       "      <td>163</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>756</td>\n",
       "      <td>6524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Id</td>\n",
       "      <td>0</td>\n",
       "      <td>SUV / Crossover</td>\n",
       "      <td>2017</td>\n",
       "      <td>29 MPG</td>\n",
       "      <td>4</td>\n",
       "      <td>FAIR_PRICE</td>\n",
       "      <td>180.90224</td>\n",
       "      <td>BLACK</td>\n",
       "      <td>FRONT_WHEEL_DRIVE</td>\n",
       "      <td>I4</td>\n",
       "      <td>Kia</td>\n",
       "      <td>Mileage</td>\n",
       "      <td>Sorento</td>\n",
       "      <td>6-Speed Automatic</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>DEALER</td>\n",
       "      <td>LX</td>\n",
       "      <td>7995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>22</td>\n",
       "      <td>15723</td>\n",
       "      <td>10327</td>\n",
       "      <td>4866</td>\n",
       "      <td>1502</td>\n",
       "      <td>458</td>\n",
       "      <td>9929</td>\n",
       "      <td>118</td>\n",
       "      <td>3525</td>\n",
       "      <td>9030</td>\n",
       "      <td>13496</td>\n",
       "      <td>2400</td>\n",
       "      <td>22</td>\n",
       "      <td>757</td>\n",
       "      <td>5526</td>\n",
       "      <td>4794</td>\n",
       "      <td>11904</td>\n",
       "      <td>1996</td>\n",
       "      <td>17722</td>\n",
       "      <td>15909</td>\n",
       "      <td>19000</td>\n",
       "      <td>935</td>\n",
       "      <td>152</td>\n",
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      ],
      "text/plain": [
       "           Id Accidents        Body_type   Year Fuel_economy_combined  \\\n",
       "count   19084     19006            19070  19084                 17001   \n",
       "unique  18463         7                3     18                    38   \n",
       "top        Id         0  SUV / Crossover   2017                29 MPG   \n",
       "freq       22     15723            10327   4866                  1502   \n",
       "\n",
       "       Days_on_market      Rating   Distance  Color         Drivetrain Engine  \\\n",
       "count           19084       18597      19084  19084              18023  18831   \n",
       "unique            523           7       2235     15                  6      9   \n",
       "top                 4  FAIR_PRICE  180.90224  BLACK  FRONT_WHEEL_DRIVE     I4   \n",
       "freq              458        9929        118   3525               9030  13496   \n",
       "\n",
       "         Make  Mileage    Model       Transmission Additional_features  \\\n",
       "count   19084    19036    19084              18882               19084   \n",
       "unique     13    16796      163                 32                  25   \n",
       "top       Kia  Mileage  Sorento  6-Speed Automatic                   3   \n",
       "freq     2400       22      757               5526                4794   \n",
       "\n",
       "       Previous_owners Price_drops Photos  Seats Seller_type   Trim   Price  \n",
       "count            18779        2018  17744  18809       19084  18894   18786  \n",
       "unique               9           2      2      7           3    756    6524  \n",
       "top                  1        True   True      5      DEALER     LX  7995.0  \n",
       "freq             11904        1996  17722  15909       19000    935     152  "
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19084 entries, 0 to 19083\n",
      "Data columns (total 23 columns):\n",
      " #   Column                 Non-Null Count  Dtype \n",
      "---  ------                 --------------  ----- \n",
      " 0   Id                     19084 non-null  object\n",
      " 1   Accidents              19006 non-null  object\n",
      " 2   Body_type              19070 non-null  object\n",
      " 3   Year                   19084 non-null  object\n",
      " 4   Fuel_economy_combined  17001 non-null  object\n",
      " 5   Days_on_market         19084 non-null  object\n",
      " 6   Rating                 18597 non-null  object\n",
      " 7   Distance               19084 non-null  object\n",
      " 8   Color                  19084 non-null  object\n",
      " 9   Drivetrain             18023 non-null  object\n",
      " 10  Engine                 18831 non-null  object\n",
      " 11  Make                   19084 non-null  object\n",
      " 12  Mileage                19036 non-null  object\n",
      " 13  Model                  19084 non-null  object\n",
      " 14  Transmission           18882 non-null  object\n",
      " 15  Additional_features    19084 non-null  object\n",
      " 16  Previous_owners        18779 non-null  object\n",
      " 17  Price_drops            2018 non-null   object\n",
      " 18  Photos                 17744 non-null  object\n",
      " 19  Seats                  18809 non-null  object\n",
      " 20  Seller_type            19084 non-null  object\n",
      " 21  Trim                   18894 non-null  object\n",
      " 22  Price                  18786 non-null  object\n",
      "dtypes: object(23)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stage 2: Data cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Id for each vehicle must be unique. Seems like we have a few duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id           22\n",
       "267159932     2\n",
       "266190455     2\n",
       "270969267     2\n",
       "272902133     2\n",
       "             ..\n",
       "274187963     1\n",
       "273021675     1\n",
       "272719579     1\n",
       "268998565     1\n",
       "201945652     1\n",
       "Name: Id, Length: 18463, dtype: int64"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Id\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Also, the labels also got scraped from each page! Let's drop those!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(index = df[df[\"Id\"] == \"Id\"].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                       object\n",
       "Accidents                object\n",
       "Body_type                object\n",
       "Year                     object\n",
       "Fuel_economy_combined    object\n",
       "Days_on_market           object\n",
       "Rating                   object\n",
       "Distance                 object\n",
       "Color                    object\n",
       "Drivetrain               object\n",
       "Engine                   object\n",
       "Make                     object\n",
       "Mileage                  object\n",
       "Model                    object\n",
       "Transmission             object\n",
       "Additional_features      object\n",
       "Previous_owners          object\n",
       "Price_drops              object\n",
       "Photos                   object\n",
       "Seats                    object\n",
       "Seller_type              object\n",
       "Trim                     object\n",
       "Price                    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of them are numerical columns while some are categorical ones. We need to change the types before we could do any feature engineering. Pandas doesn't let us change the dtype to int when there are null values in the column. Let's change all the numerical columns to float type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numerical feature Fuel_economy_combined has MPG at the end. Let's get rid of the MPG from the column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Fuel_economy_combined\"]=df[\"Fuel_economy_combined\"].str.replace(\"MPG\",\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_float = [\"Fuel_economy_combined\",\"Accidents\",\"Year\",\"Days_on_market\",\"Distance\",\"Mileage\",\n",
    "                    \"Additional_features\",\"Previous_owners\",\"Seats\",\"Price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[columns_to_float] = df[columns_to_float].astype(np.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                        object\n",
       "Accidents                float64\n",
       "Body_type                 object\n",
       "Year                     float64\n",
       "Fuel_economy_combined    float64\n",
       "Days_on_market           float64\n",
       "Rating                    object\n",
       "Distance                 float64\n",
       "Color                     object\n",
       "Drivetrain                object\n",
       "Engine                    object\n",
       "Make                      object\n",
       "Mileage                  float64\n",
       "Model                     object\n",
       "Transmission              object\n",
       "Additional_features      float64\n",
       "Previous_owners          float64\n",
       "Price_drops               object\n",
       "Photos                    object\n",
       "Seats                    float64\n",
       "Seller_type               object\n",
       "Trim                      object\n",
       "Price                    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2e6e6ad0>"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.heatmap(df.isnull(),cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"Price_drops\",axis=1,inplace=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Let's look at each of the columns and see how we can impute the missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing = df.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fuel_economy_combined    10.887228\n",
       "Photos                    7.057740\n",
       "Drivetrain                5.644026\n",
       "Rating                    2.637851\n",
       "Price                     1.614126\n",
       "Previous_owners           1.603293\n",
       "Seats                     1.359549\n",
       "Engine                    1.267468\n",
       "Transmission              1.083306\n",
       "Trim                      1.029141\n",
       "Accidents                 0.411656\n",
       "Mileage                   0.259994\n",
       "Body_type                 0.075831\n",
       "Model                     0.000000\n",
       "Make                      0.000000\n",
       "Color                     0.000000\n",
       "Distance                  0.000000\n",
       "Additional_features       0.000000\n",
       "Days_on_market            0.000000\n",
       "Seller_type               0.000000\n",
       "Year                      0.000000\n",
       "Id                        0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing/len(df)*100   # In terms of percentage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Fuel economy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2e669610>"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.distplot(df[\"Fuel_economy_combined\"],fit = norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.238208120593242"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looks more or less normally distributed. We can impute the missing values with the mean\n",
    "df[\"Fuel_economy_combined\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Fuel_economy_combined'].fillna(df[\"Fuel_economy_combined\"].mean(),inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Photos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Photos\"].fillna(0,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Photos\"].replace(\"True\",1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Drivetrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FRONT_WHEEL_DRIVE    8837\n",
       "ALL_WHEEL_DRIVE      7077\n",
       "FOUR_WHEEL_DRIVE     1465\n",
       "REAR_WHEEL_DRIVE       24\n",
       "FOUR_BY_TWO            17\n",
       "Name: Drivetrain, dtype: int64"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Drivetrain\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Four wheel drive is same as all wheel drive. Also, Four by Two is similar to FWD or RWD and switches in between."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Drivetrain\"].replace(\"FOUR_WHEEL_DRIVE\",\"ALL_WHEEL_DRIVE\",inplace = True)\n",
    "df[\"Drivetrain\"].replace(\"FOUR_BY_TWO\",\"FRONT_WHEEL_DRIVE\",inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2e6822d0>"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x = \"Drivetrain\",y =\"Price\",data =df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The averages and the overall distribution for FWD and RWD doesn't deviate much from each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Drivetrain\"].replace(\"REAR_WHEEL_DRIVE\",\"FRONT_WHEEL_DRIVE\",inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FRONT_WHEEL_DRIVE    8878\n",
       "ALL_WHEEL_DRIVE      8542\n",
       "Name: Drivetrain, dtype: int64"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Drivetrain\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1042"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Drivetrain\"].isnull().sum() #check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Body_type        Drivetrain       \n",
       "SUV / Crossover  ALL_WHEEL_DRIVE      7498\n",
       "                 FRONT_WHEEL_DRIVE    1668\n",
       "Sedan            FRONT_WHEEL_DRIVE    7210\n",
       "                 ALL_WHEEL_DRIVE      1044\n",
       "Name: Drivetrain, dtype: int64"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Body_type\")[\"Drivetrain\"].value_counts() # See how does the drivetrain stack up against the bodytype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like more SUVs have AWD while sedans predominantly have FWD.\n",
    "So, let's impute the missing value with AWD when the bodytype is suv and FWD when sedan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Subaru makes only AWD for both sedans as well as suvs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to impute missing values in the drivetrain column based on the kind of bodytype\n",
    "def impute_drive_train(columns):\n",
    "    body_type = columns[0]\n",
    "    make = columns[1]\n",
    "    drive_train = columns[2]\n",
    "    if pd.isnull(drive_train):\n",
    "        if make == \"Subaru\":\n",
    "            return \"ALL_WHEEL_DRIVE\"\n",
    "        else:\n",
    "            if body_type == \"SUV / Crossover\":\n",
    "                return \"ALL_WHEEL_DRIVE\"\n",
    "            else:\n",
    "                return \"FRONT_WHEEL_DRIVE\"\n",
    "    else:\n",
    "        return drive_train    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Drivetrain\"] = df[[\"Body_type\",\"Make\",\"Drivetrain\"]].apply(impute_drive_train,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FAIR_PRICE     9598\n",
       "GOOD_PRICE     6606\n",
       "GREAT_PRICE    1111\n",
       "POOR_PRICE      519\n",
       "OVERPRICED      114\n",
       "OUTLIER          27\n",
       "Name: Rating, dtype: int64"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rating\n",
    "df[\"Rating\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2b886fd0>"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x = \"Rating\",y = \"Price\",data =df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Rating\"].replace(dict.fromkeys([\"FAIR_PRICE\",\"GOOD_PRICE\",\"GREAT_PRICE\"],\"Great_Price\"),inplace = True)\n",
    "df[\"Rating\"].replace(dict.fromkeys([\"POOR_PRICE\",\"OVERPRICED\",\"OUTLIER\"],\"Poor_Price\"),inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Rating\"].fillna(\"Great_Price\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a30b2f4d0>"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x = \"Rating\",y = \"Price\",data =df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **Note:** Vehicles with the rating \"Great\" cost lower as compared with \"Poor\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Previous owners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a30b95750>"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[\"Previous_owners\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    11522\n",
       "2.0     4671\n",
       "3.0     1485\n",
       "4.0      371\n",
       "5.0       86\n",
       "6.0       24\n",
       "7.0        6\n",
       "8.0        1\n",
       "Name: Previous_owners, dtype: int64"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Previous_owners\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Previous_owners\"].fillna(1.0,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The rest of the columns have less than 2% of missing values. Let's drop the rows with the missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a326a9590>"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.heatmap(df.isnull(),cbar=False,cmap='viridis') # Checking one more time for any missing value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stage 3: Data analysis\n",
    "The features are distributed between two categories: **Categorical** and **Numerical**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical features\n",
    "* Engine\n",
    "* Drivetrain : Done already!\n",
    "* Transmission\n",
    "* Trim\n",
    "* Photos\n",
    "* Rating : Done already!\n",
    "* Color\n",
    "* Make\n",
    "* Model\n",
    "* Bodytype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "I4    12493\n",
       "V6     2952\n",
       "H4     1635\n",
       "V8      218\n",
       "I5       75\n",
       "I3       61\n",
       "H6       40\n",
       "I6        6\n",
       "Name: Engine, dtype: int64"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Engine\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combining similar engines into one category:\n",
    "* I3, I4 and I5: base engine\n",
    "* V6, I6 and V8: strong engine\n",
    "* H4 and H6: Hybrid engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Engine\"].replace(dict.fromkeys([\"I3\",\"I5\",\"I4\"],\"I4\"),inplace = True)\n",
    "df[\"Engine\"].replace(dict.fromkeys([\"V6\",\"V8\",\"I6\"],\"V6\"),inplace = True)\n",
    "df[\"Engine\"].replace(dict.fromkeys([\"H4\",\"H6\"],\"H4\"),inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transmission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6-Speed Automatic                     5111\n",
       "Automatic                             5019\n",
       "Continuously Variable Transmission    4039\n",
       "6-Speed Automatic Overdrive            650\n",
       "5-Speed Automatic                      608\n",
       "8-Speed Automatic                      421\n",
       "4-Speed Automatic                      336\n",
       "9-Speed Automatic                      273\n",
       "6-Speed Manual                         199\n",
       "6-Speed Dual Clutch                    173\n",
       "5-Speed Automatic Overdrive            167\n",
       "5-Speed Manual                         121\n",
       "Manual                                 102\n",
       "7-Speed Automatic                       92\n",
       "4-Speed Automatic Overdrive             43\n",
       "7-Speed CVT                             37\n",
       "6-Speed CVT                             18\n",
       "6-Speed Manual Overdrive                15\n",
       "9-Speed Automatic Overdrive             14\n",
       "8-Speed CVT                              9\n",
       "8-Speed Automatic Overdrive              7\n",
       "5-Speed Manual Overdrive                 6\n",
       "1-Speed Automatic                        4\n",
       "1-Speed CVT                              3\n",
       "7-Speed Automatic Overdrive              3\n",
       "7-Speed Dual Clutch                      3\n",
       "2-Speed Automatic                        2\n",
       "4-Speed Manual                           2\n",
       "4-Speed CVT                              1\n",
       "1-Speed Dual Clutch                      1\n",
       "7-Speed Manual                           1\n",
       "Name: Transmission, dtype: int64"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Transmission\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lots of different names for the same kinds of transmission.\n",
    "The three main categories are:\n",
    "1. Automatic\n",
    "2. Manual\n",
    "3. Continuous Variable Transmission(CVT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a mask for each of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_automatic = df[\"Transmission\"].value_counts().index.str.contains(\"Automatic\",case=False,regex=False)\n",
    "mask_manual = df[\"Transmission\"].value_counts().index.str.contains(\"(Manual|Clutch)\",case=False,regex=True)\n",
    "mask_cvt = df[\"Transmission\"].value_counts().index.str.contains(\"(Continuous|CVT)\",case=False,regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "auto_list = df[\"Transmission\"].value_counts().index[mask_automatic].tolist()\n",
    "manual_list = df[\"Transmission\"].value_counts().index[mask_manual].tolist()\n",
    "cvt_list = df[\"Transmission\"].value_counts().index[mask_cvt].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Transmission\"].replace(dict.fromkeys(auto_list,\"Automatic\"),inplace = True)\n",
    "df[\"Transmission\"].replace(dict.fromkeys(manual_list,\"Manual\"),inplace = True)\n",
    "df[\"Transmission\"].replace(dict.fromkeys(cvt_list,\"CVT\"),inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Automatic    12750\n",
       "CVT           4107\n",
       "Manual         623\n",
       "Name: Transmission, dtype: int64"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Transmission\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(df[\"Transmission\"],df[\"Price\"])\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Transmission\"].replace(\"CVT\",\"Automatic\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Automatic    16857\n",
       "Manual         623\n",
       "Name: Transmission, dtype: int64"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Transmission\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(df[\"Transmission\"],df[\"Price\"])\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seller type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DEALER     17423\n",
       "PRIVATE       57\n",
       "Name: Seller_type, dtype: int64"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Seller_type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a311e43d0>"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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77LN64/Pnz9uUBACAxlHYAAAADEdhAwAAMByFDQAAwHBhK2xer1f33XefTp48qTNnzmjixImaNGmScnJyFAgEJEmrV6/W2LFjNWHCBP3xj3+UpGbNBQAAiARhKWxXrlzRggULdNNNN0mSli1bptmzZ2vjxo2yLEvFxcUqKyvTwYMHVVBQoNzcXC1atKjZcwEAACJBWO50vWLFCk2YMEHr1q2TJJWVlWnQoEGSpPT0dB04cEBJSUlKS0uTw+FQ165dVVdXp4qKimbNdbvdIXPU1NTI4/GEYxfRjvGeAQCYpsUL2/bt2+V2uzV48OBgYbMsSw6HQ5IUGxuryspK+Xw+de7cOfi8q9ubM/d6hS0mJkYpKSktvYto53jPAADsEGrBoMUL27Zt2+RwOPTee+/J4/EoKytLFRUVwcerqqqUkJCguLg4VVVV1dseHx8vp9PZ5LkAAACRoMnfYTt9+rR+97vf6dy5c7Isq9F5v/rVr7Rhwwbl5+crJSVFK1asUHp6ukpLSyVJJSUlSk1N1cCBA7V//34FAgGVl5crEAjI7XarX79+TZ4LAAAQCZq0wrZhwwbt2bNHX375pUaPHq2PP/5YCxYsaPIPycrKUnZ2tnJzc5WcnKyMjAy5XC6lpqZq/PjxCgQCwddrzlwAAIBI4LBCLZf9xcSJE7Vx40ZNmTJF+fn5euSRR7Rt27bWyHdDPB4P30dCSEOGDGmwbd++fa2eAwCAUL2lSYdEr3a6qycDREdHt1A0AAAAXE+TDok+/PDD+uEPf6jy8nI98cQTGjZsWLhzAQAA4C+aVNh+9KMf6Z577tGf/vQnJScn66//+q/DnQsAAAB/0aRDolu2bNGWLVv093//91qxYoV+85vfhDsXAAAA/qJJhW3Tpk2aO3euJOn111/Xpk2bwhoKAAAA/6dJhc3pdComJkaS1KFDh+DJBwAAAAi/Jn2HbejQoZo0aZL69++vsrIyPfDAA+HOBQAAgL9oUmH7yU9+ovvvv1+nTp3S6NGj1bdv33DnAgCgTfN6vVq0aJFycnKUmJhodxy0cSEPiRYUFEiSXnnlFb399tv68MMPVVhYqNzc3FYJBwBAW5WXl6djx45p/fr1dkdBOxByhe3222+XJPXo0UMul6tVAgEA0NZ5vV4VFRXJsiwVFRVpypQprLLhhoQsbIMHD5YkFRYW6s0332yVQAAAtHV5eXkKBAKSpLq6Oq1fv15z5syxORXasiadJRofH6+9e/fq5MmTOnXqlE6dOhXuXAAAtFl79+6V3++XJPn9fu3Zs8fmRGjrrnvSgc/n06effqq8vLzgNofDwTF5AAAaMWzYMBUWFsrv9ysqKkrDhw+3OxLauJCFbcOGDXrzzTflcrk0a9Yspaent1YuAADarKlTp6qoqEiS5HK5NGXKFJsToa0LeUh0165dKioq0ubNm1lRAwCgiRITEzVkyBBJ0pAhQzjhADcsZGGLjo5WdHS03G63rly50lqZAABo82prayVJNTU1NidBe9Ckkw4kybKscOYAAKDd8Hq9KikpkSSVlJTI6/XanAhtXcjvsJ04cUJz586VZVnBP1/1yiuvhD0cAABt0bp164KX9QgEAlq3bp2ee+45m1OhLQtZ2H7xi18E/zxhwoSwhwEAoD0oLi5uMKaw4UaELGyDBg1qrRwAALQb3/waEV8rwo1q8nfYAABA0wwdOrTeeNiwYTYlQXtBYQMAoIU9+eSTcjq/+ifW6XRq2rRpNidCW0dhAwCghSUmJgZX1YYPH8512HDDrntrKgAA0HxPPvmkzp07x+oaWgSFDQCAMEhMTNSqVavsjoF2gkOiAAAAhqOwAQAAGI7CBgAAYDi+wwYAsM3u3btVWFhod4ywuHDhgiTpr/7qr2xOEj4PPvigMjIy7I4REShsAACEwdUbvrfnwobWQ2EDANgmIyOj3a7QzJo1S5K0cuVKm5OgPeA7bAAAAIYLywpbXV2dXnjhBZ06dUoul0vLli2TZVl69tln5XA41Lt3b+Xk5MjpdGr16tXat2+foqKiNH/+fPXv319nzpxp8lwAAID2LiyF7d1335Uk/frXv1ZpaWmwsM2ePVt33323FixYoOLiYnXt2lUHDx5UQUGBzp49qxkzZmjbtm1atmxZk+cCAAC0d2EpbMOGDdOQIUMkSeXl5brlllu0b98+DRo0SJKUnp6uAwcOKCkpSWlpaXI4HOratavq6upUUVGhsrKyJs91u93h2AUAAABjhO2kg6ioKGVlZWnPnj1atWqV3n33XTkcDklSbGysKisr5fP51Llz5+Bzrm63LKvJc0MVtpqaGnk8njDtIdor3jMAWkJ1dbUkPlPQMsJ6luiKFSs0b948Pfroo6qpqQlur6qqUkJCguLi4lRVVVVve3x8vJxOZ5PnhhITE6OUlJQW3CNEAt4zAFpCp06dJPGZgqYLVe7DUth+85vf6Pz583ryySfVsWNHORwO/c3f/I1KS0t19913q6SkRPfcc4+6d++ul156SY8//rjOnTunQCAgt9utfv36NXkuWkd7vrjlN109Fb894eKWANC2haWwjRgxQs8995x++MMfyu/3a/78+erZs6eys7OVm5ur5ORkZWRkyOVyKTU1VePHj1cgENCCBQskSVlZWU2eCwAA0N45LMuy7A4RLh6Ph6VohHT15Jiv27dvX6vnAND+cOFcNFeo3sKFcxHRxowZU288btw4m5IAANA4Chsi2syZM+uNp0+fblMSAAAaR2FDxLt68gqrawAAU3Hzd0S8bt26qVu3bqyuAQCMxQobAACA4ShsAAAAhqOwAQAAGI7CBgAAYDgKGwAAgOEobAAAAIajsAEAABiOwgYAAGA4ChsAAIDhKGwAAACGo7ABAAAYjsIGAABgOAobAACA4ShsAAAAhqOwAQAAGI7CBgAAYDgKGwAAgOEobAAAAIajsAEAABiOwgYAAGA4ChsAAIDhKGwAAACGo7ABAAAYjsIGAABgOAobAACA4ShsAAAAhqOwAQAAGI7CBgAAYDgKGwAAgOGiWvoFr1y5ovnz5+vPf/6zamtr9fTTT6tXr1569tln5XA41Lt3b+Xk5MjpdGr16tXat2+foqKiNH/+fPXv319nzpxp8lwAAIBI0OKFbceOHercubNeeuklXbhwQf/wD/+gvn37avbs2br77ru1YMECFRcXq2vXrjp48KAKCgp09uxZzZgxQ9u2bdOyZcuaPBcAACAStHhhy8zMVEZGRnDscrlUVlamQYMGSZLS09N14MABJSUlKS0tTQ6HQ127dlVdXZ0qKiqaNdftdrd0fAAAAOO0eGGLjY2VJPl8Ps2cOVOzZ8/WihUr5HA4go9XVlbK5/Opc+fO9Z5XWVkpy7KaPPd6ha2mpkYej6eldxHtTHV1tSTxXgHQovhsQUtq8cImSWfPntX06dM1adIkjRw5Ui+99FLwsaqqKiUkJCguLk5VVVX1tsfHx8vpdDZ57vXExMQoJSWlhfYK7VWnTp0kifcKgBbFZwuaK1S5b/GzRD///HM99thj+tnPfqaxY8dKkvr166fS0lJJUklJiVJTUzVw4EDt379fgUBA5eXlCgQCcrvdzZoLAAAQCVp8hW3t2rW6ePGiXnvtNb322muSpOeff16LFy9Wbm6ukpOTlZGRIZfLpdTUVI0fP16BQEALFiyQJGVlZSk7O7tJcwEAACKBw7Isy+4Q4eLxeFiKxnXNmjVLkrRy5UqbkwBoT/hsQXOF6i1cOBcAAMBwFDYAAADDUdgAAAAMF5bLekSiV199VSdOnLA7Br6Fq//drn7fBG1Pr169NGPGDLtjhAWfLW0Xny1tn0mfLRS2FnLixAkd+cCjuk5cbqStcdR99Wtw6KPzNifBt+GqrrA7QlidOHFCfyr7g7rH1dkdBc2UYH11EfiaM//P5iT4Nj72ueyOUA+FrQXVdXLrUt8H7Y4BRJSOHxbaHSHsusfVaf7Ai3bHACLK0sMJdkeoh++wAQAAGI7CBgAAYDgKGwAAgOEobAAAAIajsAEAABiOwgYAAGA4ChsAAIDhKGwAAACGo7ABAAAYjsIGAABgOAobAACA4ShsAAAAhqOwAQAAGC7K7gDtRUVFhVzVXnX8sNDuKEBEcVV7VVHRwe4YABBWrLABAAAYjhW2FuJ2u3Xqiyu61PdBu6MAEaXjh4Vyu912xwCAsGKFDQAAwHAUNgAAAMNR2AAAAAxHYQMAADAchQ0AAMBwnCUKAAarqKjQ55UuLT2cYHcUIKKcqXTplooKu2MEscIGAABgOFbYAMBgbrdbsZUfaf7Ai3ZHASLK0sMJijHoGo+ssAEAABiOwgYAAGA4ChsAAIDhwlbYjh49qsmTJ0uSzpw5o4kTJ2rSpEnKyclRIBCQJK1evVpjx47VhAkT9Mc//rHZcwEAACJBWArbv/zLv+iFF15QTU2NJGnZsmWaPXu2Nm7cKMuyVFxcrLKyMh08eFAFBQXKzc3VokWLmj0XAAAgEoTlLNHu3bvr1Vdf1TPPPCNJKisr06BBgyRJ6enpOnDggJKSkpSWliaHw6GuXbuqrq5OFRUVzZrrNujsDUlyVVeo44eFdsdAMzmuXJIkWR062pwE34arukLSbXbHAICwCkthy8jI0KeffhocW5Ylh8MhSYqNjVVlZaV8Pp86d+4cnHN1e3PmXq+w1dTUyOPxtOSuNcrtdqtPco9W+VloWZ9++qUk6Y7bb7U5Cb6deLnd7lb7XW9t1dXVctkdAohQ1dXVxny2tMp12JzO/zvyWlVVpYSEBMXFxamqqqre9vj4+GbNvZ6YmBilpKS00F6ElpOT0yo/By1v1qxZkqSVK1fanARoqFOnTqqxOwQQoTp16tRqPUJSyHLYKmeJ9uvXT6WlpZKkkpISpaamauDAgdq/f78CgYDKy8sVCATkdrubNRcAACAStMoKW1ZWlrKzs5Wbm6vk5GRlZGTI5XIpNTVV48ePVyAQ0IIFC5o9FwAAIBI4LMuy7A4RLh6Pp1WXMtE2cUgUJps1a5Zqzvw/bk0FtLKlhxMU0yO1Vf9tCNVbuHAuAACA4ShsAAAAhqOwAQAAGK5VTjoAAHx7H/tcWno4we4YaKYva7+6pujN0e32q+Lt2sc+l3rbHeJrKGwAYLBevXrZHQHf0sUTJyRJt/bgv2Fb1Ftm/f5R2ADAYDNmzLA7Ar4lzkBHS+I7bAAAAIajsAEAABiOwgYAAGA4ChsAAIDhKGwAAACGo7ABAAAYjsIGAABgOAobAACA4ShsAAAAhqOwAQAAGI7CBgAAYDgKGwAAgOEobAAAAIajsAEAABiOwgYAAGA4ChsAAIDhKGwAAACGi7I7AAAgcu3evVuFhYV2xwiLEydOSJJmzZplc5LwefDBB5WRkWF3jIhAYQMAIAwSExPtjoB2hMIGALBNRkYGKzRAE/AdNgAAAMNR2AAAAAxHYQMAADAchQ0AAMBwFDYAAADDcZYomoRrJbVtXCsJANo2ChsiHtdKAgCYrk0VtkAgoIULF+r48eOKjo7W4sWL1aNHD7tjRQSulQQAgH3a1HfY9u7dq9raWm3evFlz587V8uXL7Y4EAAAQdm2qsB06dEiDBw+WJA0YMEAffPCBzYkAAADCr00dEvX5fIqLiwuOXS6X/H6/oqKuvRs1NTXyeDytFQ8AACAs2lRhi4uLU1VVVXAcCAQaLWuSFBMTo5SUlNaIBgAAcENCLTK1qUOiAwcOVElJiSTpyJEj6tOnj82JAAAAwq9NrbANHz5cBw4c0IQJE2RZlpYuXWp3JAAAgLBrU4XN6XTqxRdftDsGAABAq2pTh0QBAAAiEYUNAADAcBQ2AAAAw1HYAAAADEdhAwAAMByFDQAAwHBt6rIezcWtqQAAQFtRU1PT6GMOy7KsVswCAACAZuKQKAAAgOEobAAAAIajsAEAABiOwgYAAGA4ChsAAIDh2vVlPdC+lZaWavbs2erVq5csy5Lf79eUKVPUv39/jRo1SnfeeWe9+b/85S/lcrkkSU899ZQkae3atcHHH3jgAb399tuKiYkJbtu+fbtWrVqlbt26Bbf16dNH2dnZmjx5si5duqSOHTsqEAjo4sWLmjdvnu67775w7jaAMPr654r01WUWRo4cqUMjRMQAAAeWSURBVLKyMpWVlalz586yLEtffPGF/vEf/1GPPPKItm/fro8++kjf+c53dPDgQf3zP/9z8PU8Ho9efPFFbdq0SZK0bt06rV+/XsXFxYqJiVFFRYVmzZoVnPud73xHHTt21KhRo3Tu3Dnt2rVLt956a/D17r33Xj399NOt+DcCU1DY0Kbdc889+vnPfy5Jqqqq0uTJk7VkyRL16tVL+fn513zO2bNnVV1drStXruiTTz6pV8au5eGHH9a8efOu+diKFSvUs2dPSdJHH32kmTNnUtiANu7rnyu1tbXKzMxU37599bOf/Uzp6emSpC+++EIPP/ywxowZE3zeQw89pJUrV6q6ulqdOnWSJG3dulXjx48Pztm5c6cefPBB/cd//IfGjBkjt9sd/KyaPHmyFi5cGPxMefXVV/XjH/9YEydObJX9htk4JIp2IzY2VuPHj9cbb7wRct7WrVs1dOhQjR49Whs3bmyxn19eXq6EhIQWez0A9vP5fHI6nYqKqr++8fnnnys6OloOhyO4rWPHjnrggQf0zjvvSPqq7JWUlCgzM1PSV6t33bt314QJE/SrX/2q9XYC7QIrbGhXEhMTdeHCBZ04cUKTJ08Obr/zzjv17LPPKhAIaNeuXdq8ebOioqL00EMPadasWbrpppsafc1du3bp6NGjwfEjjzyi0aNHS5KysrIUFRWl8vJyDRgwQMuWLQvfzgFoFf/1X/+lyZMny+FwqEOHDsrOztbbb7+tl156SWvXrlV5ebl69uyplStXNnjuo48+qpdfflmjR4/W3r17dd999wU/XwoKCjRu3DglJycrOjpaR48e1Xe/+92QWX75y1+qsLAwOH7qqaf0/e9/v2V3GG0ChQ3tSnl5ue666y5VVlZe85Do73//e1VVVWnu3LmSpEAgoJ07d2rcuHGNvmZTDon++te/1q5du9SlS5eW2REAtvn6IdGr3n777eAh0d/97nd6+eWX1b179wbPvfPOO3Xx4kWdP39e27dvV1ZWliTpyy+/VElJiSoqKpSfny+fz6cNGzZct7BxSBRXcUgU7YbP51NBQUHw8MO1bN26VYsXL9Ybb7yhN954Q7/4xS9a5LDohAkT1KVLlwYf8gDan/vuu09Dhw5Vdnb2NR8fO3as8vPzdfnyZfXu3VuStGPHDj3yyCN688039cYbb2jLli06cOCAKioqWjM62jBW2NCmXT104XQ6VVdXpxkzZig6OrrBIVFJmjdvno4ePVqvVN11112qqanR4cOHJane/8mOHDlSN998c4NDonFxcVqzZk2DLM8//7xGjRqlH/zgB+rbt29L7yoAg/zkJz/RmDFjtG/fvgaPjRw5UkOGDNHzzz8f3FZQUFDv7NGOHTtqxIgR2rJlS/Cs9Wv55iHRpKQkvfjiiy2zE2hTuPk7AACA4TgkCgAAYDgKGwAAgOEobAAAAIajsAEAABiOwgYAAGA4ChuAdmfdunX68Y9/rMcee0yPP/64Pvjgg2vOKy0t1Zw5cySpRa4ev2HDhht+DQC4Fq7DBqBdOXHihH77299q06ZNcjgc8ng8ysrK0o4dO8L+s9esWaMf/ehHYf85ACIPhQ1Au+J2u1VeXq6tW7cqPT1dKSkp2rp1q44fP67FixdLkjp37qylS5de8/nXmvff//3fevnll9WhQwc9+uijwXvJft2aNWv05ZdfauHChaqsrAxePPXkyZNasWKFMjMzVVxcLJ/PpwsXLmj69OnKyMjQwYMH9fOf/1wul0vdunXTiy++qA4dOoTvLwhAm8QhUQDtitvt1po1a3T48GGNHz9emZmZevfdd5Wdna2cnBzl5+crPT1d//qv/3rN5zc2r6amRhs3brxmWZOkp59+WjfffLMWLlyocePG6a233pL01e3Qxo4dK0mqrq7Wv/3bv+nNN9/U8uXLdeXKFWVnZ2v16tXasGGDbrvttuDzAODrWGED0K6cOXNGcXFxWrZsmSTp2LFjmjZtmi5fvqxFixZJkq5cuaKkpKRrPv/kyZPXnNfY/Gu5++67tWTJEnm9Xh04cED/9E//pJ07d+rv/u7v5HQ6dcsttyghIUGfffaZPvvsM82ePVuSdPny5Rb5Lh2A9ofCBqBdOX78uDZt2qS1a9cqJiZGSUlJio+P12233aYVK1aoa9euOnTokP73f//3ms9PSkq65jyn8/oHJK7e6c/hcGjkyJFasmSJvv/97wcPcZaVlUmSPv/8c/l8Pt1+++26/fbb9dprryk+Pl7FxcXq1KlTS/w1AGhnKGwA2pURI0bo5MmTGjdunDp16iTLsvTMM8/o9ttvV1ZWlurq6iRJS5Ys0Weffdbg+QsXLmzSvGvp2bOn5s2bp5dfflljxozRkCFD9O///u/Bxz///HNNnTpVlZWVysnJkcvl0vPPP69p06bJsizFxsbWu0E4AFzFzd8BIAzOnz+vZ555Rnl5eZKk7du366OPPtK8efNsTgagLWKFDQCaYfXq1SotLW2wfenSperWrZskaffu3Vq9erWWLFnS2vEAtFOssAEAABiOy3oAAAAYjsIGAABgOAobAACA4ShsAAAAhqOwAQAAGI7CBgAAYLj/D1ZuNJfDT8B6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x = \"Seller_type\",y = \"Price\", data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spread of price when buying from a dealer is quite large which is expected. However, regardless of the other factors, a vehicle bought from a private party is almost always lower, interesting!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Photos\n",
    "Having photos on the website could potentially be a variable when it comes to the pricing. Let's see!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a32ae4510>"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MAJfi6NGjPcbvvPOOTUmQKihsSGuFhYXRVbUrr7xShYWF9gYCkBJ40gGsRmFD2nvggQeUlZXF6hoAy3R3d8ccA/GisCHtffjDH9bIkSP14Q9/2O4oAFLE0KFDY46BeFHYkPbq6up04MABrV+/3u4oAFLE8ePHY46BeFHYkNb8fr8aGhpkmqYaGhrk9/vtjgQgBXxwxT43N9emJEgVFDaktbq6OkUiEUlSOBxmlQ2AJY4dO9Zj3N7eblMSpAoKG9JaY2OjQqGQJCkUCmn79u02JwIA4HwUNqS1srIyZWRkSJIyMjI0efJkmxMBAHA+ChvS2uzZs+Vynf0xcLlcmjVrls2JAAA4X8IKm9/v1+c+9zm9+eabOnz4sKZPn64ZM2aouro6es7Q2rVrNXXqVFVWVuq1116TpLjmAn3l9XqVl5cnScrLy5PX67U5EQAA50tIYevu7lZVVZU+9KEPSZKWL1+uBQsW6JlnnpFpmtqxY4daWlq0d+9ebd68WbW1tVq6dGncc4G+8vv9evfddyWdPSmYq0QBAE6UkYhPunLlSlVWVurxxx+XJLW0tGjcuHGSpIkTJ2r37t3Kz89XaWmpDMNQXl6ewuGwOjo64pp7scukOzs75fP5ErGLSBEbN27scZXo6tWrNX36dJtTAUhFvB+hLywvbL/5zW+Um5urCRMmRAubaZoyDEOSlJWVpVOnTikQCGjIkCHRjzu3PZ65FytsmZmZKioqsnoXkUJefvllhcNhSWcL28svv6wf/ehHNqcCkIp4P8LFxCr1lhe2LVu2yDAMvfTSS/L5fFq0aJE6OjqirweDQeXk5Mjj8SgYDPbYnp2dHT0BvDdzgb4qKyvTtm3bon8ocJUoAMCJen0OW1tbm/74xz/q+PHjMk3zgvN++ctf6umnn9aGDRtUVFSklStXauLEidqzZ48kqampSSUlJSouLtauXbsUiUTU3t6uSCSi3NxcjRkzptdzgb669dZbo9/PpmnqlltusTkRAADn69UK29NPP63t27frX//6l26//XYdOXJEVVVVvf4iixYt0pIlS1RbW6uCggKVl5fL7XarpKRE06ZNUyQSiX6+eOYCfbV161YZhhFdYdu2bZsWLlxodywAAHowzFjLZf82ffp0PfPMM5o1a5Y2bNigL3/5y9qyZUt/5OsTn8/HOQOI6aabbtLp06ej48GDB6u+vt7GRABSwQ033HDetp07d/Z7DiSXWL2lV4dEz3W6cxcDDBw40KJogL140gEAIBn0qrB98Ytf1F133aUjR47o7rvvVllZWaJzAf3iP5904Ha7edIBAMCRenUO21e/+lWNHz9eb7zxhgoKCvTxj3880bmAfuH1elVRUaFt27apoqKCJx0AABypVytszz77rJ599ll94Qtf0MqVK/Xcc88lOhfQb2bPnq1rr72W1TUAgGP1qrBt3LhR9957ryRp3bp12rhxY0JDAf3J6/Xq0UcfZXUNAOBYvSpsLpdLmZmZkqQBAwZELz4AAABA4vXqHLZJkyZpxowZuu6669TS0qLPf/7zic4FAACAf+tVYfvOd76jG2+8UW+//bZuv/12XXXVVYnOBQAAgH+LeUh08+bNkqRVq1bp97//vQ4dOqT6+nrV1tb2SzgAAABcZIXtox/9qCRpxIgRcrvd/RIIAAAAPcUsbBMmTJAk1dfX66mnnuqXQAAAAOipV+ewZWdnq7GxUfn5+dG7wufn5yc0GAAAAM66aGELBAI6evSo6urqotsMw9D69esTGgwAAABnxSxsTz/9tJ566im53W7Nnz9fEydO7K9cAAAA+LeYV4n+7ne/U0NDgzZt2sSKGgAAgE1iFraBAwdq4MCBys3NVXd3d39lAgAAwH/o1aOpJMk0zUTmAAAAwAXEPIettbVV9957r0zTjP77nFWrViU8HAAAAC5S2H7yk59E/11ZWZnwMAAAADhfzMI2bty4/soB2Ka1tVXz58/X6tWrVVhYaHccAADO0+tz2IBUVVNTo2AwqJqaGrujAADwX1HYkNZaW1vV1tYmSWpra1Nra6u9gQAA+C8obEhrH1xVY5UNAOBEFDaktXOraxcaAwDgBBQ2pLUrrrgi5hgAACegsCGtfexjH+sxHjZsmE1JAAC4MAob0tq+fft6jPfu3WtTEgAALozChrRmGEbMMQAATkBhQ1qbNGlSzDEAAE5AYUNamzNnTswxAABOQGFD2nO5XD3+CwCA0/AOhbRWV1fXo7CtX7/e5kQAAJwvIYUtHA7r/vvvV2Vlpe666y4dOXJEhw8f1vTp0zVjxgxVV1crEolIktauXaupU6eqsrJSr732miTFNRfoi8bGRoVCIUlSKBTS9u3bbU4EAMD5MhLxSV988UVJ0q9+9Svt2bNHy5cvl2maWrBgga6//npVVVVpx44dysvL0969e7V582YdO3ZMc+fO1ZYtW7R8+fJezwX6oqysTPX19QqFQsrIyNDkyZPtjgQAwHkSUtjKysp0ww03SJLa29t12WWXaefOnRo3bpwkaeLEidq9e7fy8/NVWloqwzCUl5encDisjo4OtbS09Hpubm5uInYBH/D888+rvr7e7hiW6+7ujq6whcNhvfHGG5o/f77Nqax30003qby83O4YAIBLlJDCJkkZGRlatGiRtm/frkcffVQvvvhi9B5XWVlZOnXqlAKBgIYMGRL9mHPbTdPs9dxYha2zs1M+ny9Be5he2tvbdfr0abtjJITb7VY4HFZOTo66u7vV3d1tdyTLtbe387MA2IyfQfRFwgqbJK1cuVL33XefvvKVr6izszO6PRgMKicnRx6PR8FgsMf27OzsHlfrXWxuLJmZmSoqKrJwj9JXUVGRvva1r9kdIyG+853v6PDhw3rqqafk9XrtjgMgRfF+hIuJVeoTUtiee+45nThxQt/85jc1aNAgGYaha665Rnv27NH111+vpqYmjR8/XsOHD9cjjzyib3zjGzp+/LgikYhyc3M1ZsyYXs8F+mrAgAEqLCykrAE2SNXTLf4bTrdAXySksE2ZMkX333+/7rrrLoVCIS1evFgjR47UkiVLVFtbq4KCApWXl8vtdqukpETTpk1TJBJRVVWVJGnRokW9ngsAAJDqDNM0TbtDJIrP52MJGhd17q/e1atX25wEQKqYNGmSwuFwdOx2u7Vjxw4bEyEZxOot3DgXAACLrVu3LuYYiBeFDQAAixUWFkb/7Xa7e4yBS0FhAwAgAUaNGiWXy8XqGixBYQMAIAEGDx6sa6+9ltU1WILCBgAA4HAUNgAAAIejsAEAADgchQ0AAMDhKGwAAAAOR2EDAABwOAobAACAw1HYAAAAHI7CBgAA4HAUNgAAAIejsAEAADgchQ0AAMDhKGwAAAAOR2EDAABwOAobAACAw1HYAAAAHI7CBgAA4HAUNgAAAIejsAEAADgchQ0AAMDhKGwAAAAOR2EDAABwOAobAACAw1HYAAAAHI7CBgAA4HAUNgAAAIejsAEAADgchQ0AAMDhKGwAAAAOl2H1J+zu7tbixYv17rvvqqurS9/+9rdVWFioH/7whzIMQ6NGjVJ1dbVcLpfWrl2rnTt3KiMjQ4sXL9Z1112nw4cP93ouAABAOrC8sG3dulVDhgzRI488on/84x/60pe+pKuuukoLFizQ9ddfr6qqKu3YsUN5eXnau3evNm/erGPHjmnu3LnasmWLli9f3uu5AAAA6cDywlZRUaHy8vLo2O12q6WlRePGjZMkTZw4Ubt371Z+fr5KS0tlGIby8vIUDofV0dER19zc3Fyr4wMAADiO5YUtKytLkhQIBDRv3jwtWLBAK1eulGEY0ddPnTqlQCCgIUOG9Pi4U6dOyTTNXs+9WGHr7OyUz+ezeheRYk6fPi1JfK8AsBS/W2AlywubJB07dkz33HOPZsyYoVtuuUWPPPJI9LVgMKicnBx5PB4Fg8Ee27Ozs+VyuXo992IyMzNVVFRk0V4hVQ0ePFiS+F4BYCl+tyBescq95VeJnjx5Ul//+tf1/e9/X1OnTpUkjRkzRnv27JEkNTU1qaSkRMXFxdq1a5cikYja29sViUSUm5sb11wAAIB0YPkK289//nO99957+tnPfqaf/exnkqT/+7//U01NjWpra1VQUKDy8nK53W6VlJRo2rRpikQiqqqqkiQtWrRIS5Ys6dVcAACAdGCYpmnaHSJRfD4fS9G4qPnz50uSVq9ebXMSAKmE3y2IV6zewo1zAQAAHI7CBgAA4HAUNgAAAIdLyG09AADWWLNmjVpbW+2OgUtw7v/buXPZkHwKCws1d+5cu2NIorABgKO1trbqjZZXNNwTtjsK4pRjnr0JfOfhl21OgktxJOC2O0IPFDaL8Fdw8uKv4OTnpL+CE2G4J6zFxe/ZHQNIK8v+mmN3hB4obBZpbW1V80GfwoO5oW+yMcJnfwz2v3XC5iS4FO7THXZHAICEo7BZKDw4V2euusnuGEBaGXSo3u4IAJBwXCUKAADgcBQ2AAAAh6OwAQAAOByFDQAAwOEobAAAAA5HYQMAAHA4ChsAAIDDUdgAAAAcjsIGAADgcBQ2AAAAh6OwAQAAOBzPEgUAB+vo6NDJU24t+2uO3VGAtHL4lFuXdXTYHSOKFTYAAACHY4UNABwsNzdXWafe0uLi9+yOAqSVZX/NUWZurt0xolhhAwAAcDhW2CzS0dEh92m/Bh2qtzsKkFbcp/3q6BhgdwwASChW2AAAAByOFTaL5Obm6u1/duvMVTfZHQVIK4MO1SvXQeeZAEAisMIGAADgcBQ2AAAAh6OwAQAAOByFDQAAwOEobAAAAA5HYQMAAHA4ChsAAIDDJaywvfrqq5o5c6Yk6fDhw5o+fbpmzJih6upqRSIRSdLatWs1depUVVZW6rXXXot7LgAAQDpISGF74okn9MADD6izs1OStHz5ci1YsEDPPPOMTNPUjh071NLSor1792rz5s2qra3V0qVL454LAACQDhLypIPhw4drzZo1+sEPfiBJamlp0bhx4yRJEydO1O7du5Wfn6/S0lIZhqG8vDyFw2F1dHTENZe7mwNIB0cCbi37a47dMRCnf3UZkqT/GWjanASX4kjArVF2h/gPCSls5eXlOnr0aHRsmqYM4+w3blZWlk6dOqVAIKAhQ4ZE55zbHs/cixW2zs5O+Xw+K3ftgk6fPt0vXwfA+U6fPt1vP+v9LTc3Vx/L/7jCdgdB3P757/dBz/8OszkJLsXH/vfsz59Tfrf0y7NEXa7/f+Q1GAwqJydHHo9HwWCwx/bs7Oy45l5MZmamioqKLNqL2AYPHizpVL98LQA9DR48uN9+1vtbdXW13RFwiebPny9JWr16tc1JkCxilcN+uUp0zJgx2rNnjySpqalJJSUlKi4u1q5duxSJRNTe3q5IJKLc3Ny45gIAAKSDfllhW7RokZYsWaLa2loVFBSovLxcbrdbJSUlmjZtmiKRiKqqquKeCwAAkA4M0zRT9mxIn8/Xb4dJ5s+fr+aDPoUHs/KXbIzuM5Ikc8Agm5PgUrhPd2jsNUUcdoLjcEgU8YrVW/plhS0dFBYW2h0Bl6i1tVWSVFhwuc1JcGku5+cPQMqjsFlk7ty5dkfAJeKvYACA0/FoKgAAAIejsAEAADgchQ0AAMDhKGwAAAAOR2EDAABwOAobAACAw1HYAAAAHI7CBgAA4HAUNgAAAIejsAEAADgchQ0AAMDhKGwAAAAOR2EDAABwOAobAACAw1HYAAAAHI7CBgAA4HAUNgAAAIejsAEAADgchQ0AAMDhKGwAAAAOR2EDAABwOAobAACAw1HYAAAAHI7CBgAA4HAUNgAAAIejsAEAADgchQ0AAMDhMuwOgOTw/PPPq76+3u4YCdHa2ipJmj9/vs1JEuemm25SeXm53TEAAJeIwoa05/V67Y4AAEBMhmmapt0hEsXn86moqMjuGACAC0iH1fvCwkKbkyQOq/fWitVbWGEDACABWL2HlZKqsEUiET344IN6/fXXNXDgQNXU1GjEiBF2xwIAXKLy8nJWaIBeSKqrRBsbG9XV1aVNmzbp3nvv1YoVK+yOBAAAkHBJVdj279+vCRMmSJLGjh2rgwcP2pwIAAAg8ZLqkGggEJDH44mO3W63QqGQMjL++0UWIt8AAARbSURBVG50dnbK5/P1VzwAAICESKrC5vF4FAwGo+NIJHLBsiZJmZmZXCUKAACSQqxFpqQ6JFpcXKympiZJUnNzs0aPHm1zIgAAgMRLqhW2yZMna/fu3aqsrJRpmlq2bJndkQAAABIuqQqby+XSj370I7tjAAAA9KukOiQKAACQjihsAAAADkdhAwAAcDgKGwAAgMNR2AAAAByOwgYAAOBwSXVbj3jxaCoAAJAsOjs7L/iaYZqm2Y9ZAAAAECcOiQIAADgchQ0AAMDhKGwAAAAOR2EDAABwOAobAACAw1HYkNYikYiqqqo0bdo0zZw5U4cPH7Y7EoAU8uqrr2rmzJl2x0AKSOn7sAEX09jYqK6uLm3atEnNzc1asWKFHnvsMbtjAUgBTzzxhLZu3apBgwbZHQUpgBU2pLX9+/drwoQJkqSxY8fq4MGDNicCkCqGDx+uNWvW2B0DKYLChrQWCATk8XiiY7fbrVAoZGMiAKmivLxcGRkcyII1KGxIax6PR8FgMDqORCL8ggUAOA6FDWmtuLhYTU1NkqTm5maNHj3a5kQAAJyPpQSktcmTJ2v37t2qrKyUaZpatmyZ3ZEAADgPD38HAABwOA6JAgAAOByFDQAAwOEobAAAAA5HYQMAAHA4ChsAAIDDcVsPAGlpz549WrBggQoLCyVJnZ2duuWWW/TCCy/owQcf1MiRIy/6Odrb23Xo0CF9/vOfT3RcAGmOwgYgbY0fP14//vGPJUldXV2qqKhQdnZ2rz/+L3/5i9566y0KG4CEo7ABgM4+V9blcsntduunP/2pTp48qTNnzqi2tlZXXHGFVqxYof3790uSvvjFL+qrX/2qHn/8cb3//vv65Cc/qaFDh+qhhx6S2+1WZmamHnroIXm9Xs2fP1+BQEDvv/++vv/97+v666+3eU8BJCMKG4C09Ze//EUzZ86UYRgaMGCAlixZoieffFKf+9zndNttt2nNmjVqaGhQYWGhjh49qmeffVahUEgzZszQ+PHjNWfOHL311luaNGmS7rjjDj388MMqKipSY2OjVqxYoblz5+rkyZP6xS9+Ib/fr7a2Nrt3GUCSorABSFv/eUj0nCeffFLXXHONJOmyyy7TyZMn9eabb6qkpCRa7D7xiU/ozTff7PFxf//731VUVCRJ+vSnP61Vq1Zp1KhRuuuuu/S9731PoVBIM2fO7J8dA5ByuEoUAC5i5MiR0cOh3d3deuWVVzRixAi5XC5FIhFJ0kc+8hEdOnRIkrRv3z5deeWVev311xUMBvX4449rxYoVeuihh2zbBwDJjRU2ALiIG2+8UXv37tW0adPU3d2tiooKXX311TIMQ4899piuvvpq1dTU6KGHHpJpmnK73Vq2bJk+8pGP6Kc//amee+45DRgwQPPmzbN7VwAkKR7+DgAA4HAcEgUAAHA4ChsAAIDDUdgAAAAcjsIGAADgcBQ2AAAAh6OwAQAAOByFDQAAwOEobAAAAA73/wDPluCzxTM/2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x = \"Photos\", y = \"Price\", data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make, Body type, Model and Trim levels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Body type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SUV / Crossover    9306\n",
       "Sedan              8174\n",
       "Name: Body_type, dtype: int64"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Body_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a311d7bd0>"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.boxplot(x = \"Body_type\", y = \"Price\", data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hyundai       1776\n",
       "Chevrolet     1771\n",
       "Honda         1757\n",
       "Kia           1752\n",
       "Ford          1729\n",
       "Toyota        1715\n",
       "Subaru        1675\n",
       "Nissan        1674\n",
       "Mazda         1219\n",
       "Volkswagen     864\n",
       "Jeep           861\n",
       "Mitsubishi     687\n",
       "Name: Make, dtype: int64"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Make'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a306513d0>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.boxplot(x = \"Make\", y = \"Price\", data = df, hue = \"Body_type\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For body type, we have only two categories: sedan and suv/crossover."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Almost all the automakers are represented in both the sedan as well as the suv segment except Jeep which only builds suvs and Volkswagen which is known for building sedans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " For almost all the makes, the respective SUVs tend to be slightly expensive as compared with their sedans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make\n",
       "Jeep          16982.505226\n",
       "Subaru        16306.595224\n",
       "Mazda         16191.030353\n",
       "Toyota        15297.636735\n",
       "Honda         14723.656801\n",
       "Ford          13997.481781\n",
       "Mitsubishi    13961.628821\n",
       "Nissan        13691.411589\n",
       "Chevrolet     13596.844720\n",
       "Kia           13447.441210\n",
       "Hyundai       12887.360360\n",
       "Volkswagen    11639.137731\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "df.groupby(\"Make\")[\"Price\"].mean().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Except for Jeep and Volkswagen, the mean price for the rest of the automakers is hovering in the 13k to 16k range."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since Jeep sells suvs, it's obvious that the average price of their vehicles are the highest.\n",
    "Similarly, Volkawagen is at the bottom because it sells only sedans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each carmaker has a trim level! We need to change it to numeric values so that we can use them to build our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trim levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Trim_copy\"] = df[\"Trim\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The make is: Chevrolet and the trims are: ['LT Sedan FWD', 'LTZ FWD', 'LTZ 1LZ FWD', 'Police Sedan RWD', 'LT FWD', 'LTZ 2LZ FWD', '1LT FWD', 'LS Sedan FWD', 'LS FWD', 'LT Diesel Sedan FWD', '2LT FWD', 'Premier Sedan FWD', 'LS Fleet FWD', '1LT Sedan FWD', 'Limited LT FWD', 'Premier FWD', 'Eco 1SA FWD', 'LTZ Sedan FWD', '2LT Sedan FWD', 'LT Fleet FWD', 'Diesel Sedan FWD', 'LT Fleet Sedan FWD', 'Limited LTZ FWD', 'LTZ 1LZ Sedan FWD', 'Limited LS FWD', '3LT FWD', 'L Sedan FWD', 'L FWD', 'FWD', 'Unmarked Police FWD', 'Eco Sedan FWD', '2LS Sedan FWD', 'RS Sedan FWD', 'LT AWD', 'LS AWD', 'Z71 4WD', '1LT AWD', 'LTZ AWD', '1.5T LT AWD', '2LT AWD', 'LTZ 4WD', 'LT 4WD', '1.5T LS AWD', 'Premier AWD', '1500 LT 4WD', '1.5T LT FWD', 'RS AWD', '1500 LS 4WD', '1500 LT RWD', '1.5T Premier AWD', '2.0T Premier AWD', '1.6T Diesel Premier FWD', '1500 LTZ RWD', 'LS 4WD', '1500 LTZ 4WD', 'LS Fleet 4WD', '2LS', 'LT', '2.0T LT AWD', '2.0T LT FWD', 'LT Cloth FWD', '3SS AWD', 'LT Leather AWD', 'RS FWD', '2LT 4WD', '1500 Premier 4WD', '1.5T Premier FWD', 'Premier 4WD', 'LTZ', '2.0T Premier FWD', '4WD', '1.5T LS FWD', 'Sport AWD', 'LT RWD']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Ford and the trims are: ['SE', 'Titanium AWD', 'SEL AWD', 'Titanium FWD', 'SEL', 'SE FWD', 'SE AWD', 'FWD', 'Limited AWD', 'SEL V6 AWD', 'SHO AWD', 'S', 'SEL FWD', 'Platinum', 'Sport', 'Limited FWD', 'Titanium', 'S FWD', 'Sport AWD', 'Limited', 'Platinum AWD', 'SEL V6', 'SES', 'SE Luxury', 'Sport V6', 'SE V6', 'Sport V6 AWD', 'XLT 4WD', 'Limited EcoBoost AWD', 'Base', 'XLT', 'Limited 4WD', 'EL Limited 4WD', 'XLT AWD', 'Base 4WD', 'Sport 4WD', 'King Ranch 4WD', 'Titanium AWD with Ecoboost', 'SES AWD', 'MAX XLT 4WD', 'Platinum 4WD', 'XLT V6 AWD', 'Limited V6 AWD', 'S AWD', 'Limited AWD with Ecoboost', 'Eddie Bauer V6 4WD', 'MAX Limited 4WD', 'ST AWD', 'AWD', 'XLS AWD', 'XLT FWD', 'Eddie Bauer 4WD', 'Limited AWD w/ Ecoboost', 'Police Interceptor AWD', 'EL XLT 4WD']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Honda and the trims are: ['EX-T', 'EX w/ Navigation', 'LX', 'EX-L', 'LX FWD', 'EX Sedan FWD', 'EX', 'LX-S', 'EX with Leather', '1.5T Sport FWD', 'Sport', 'EX V6 with Nav', 'Si', 'LX-P', 'EX-L V6', 'SE', 'Sport FWD', 'Touring Sedan FWD', 'EX FWD', 'V6 Touring FWD', 'DX-VP', '1.5T LX FWD', '1.5T EX FWD', 'Sedan', 'Sport SE FWD', 'Touring', 'Value Package', '2.0T EX-L FWD', 'Special Edition', '2.0T Sport FWD', 'V6 Touring', 'LX with Honda Sensing', '1.5T Touring FWD', 'Si with Summer Tires', 'EX-L FWD', 'EX with Navigation', 'EX with Honda Sensing', 'EX FWD with Honda Sensing', 'LX Sedan FWD', 'EX V6', 'SI', 'HF', 'EX-L V6 with Nav', '1.5T EX-L FWD', 'EX-L with Navigation', 'V6 EX-L FWD', 'FWD with Navigation', '2.0T Touring FWD', 'FWD with Leather', 'EX-L FWD with Navigation and Honda Sensing', 'GX', 'SI with Summer Tires', 'FWD', 'LX FWD with Honda Sensing', 'EX-L with Nav', 'Si FWD', 'EX-L with Honda Sensing', 'EX-L AWD', 'EX AWD', 'LX AWD', 'EX-L AWD with Navigation', 'LX 4WD', 'Touring AWD', 'Touring 4WD', 'EX-L V6 AWD w/ Navi', 'EX-L 4WD', 'SE AWD', 'EX 4WD', 'EX-L V6 with Navi', 'Sport AWD', 'EX-L AWD with RES', '4 Dr EX-L 4X4', 'EX-L with DVD 4WD', 'Touring with Nav and DVD 4WD', 'EX-L V6 AWD', 'Elite AWD', 'EX-L AWD with Navigation and RES', 'Touring with Navi 4WD', 'EX-L 4WD with Navigation', 'EX-L with Nav AWD', 'EX-L with Nav 4WD', 'SE 4WD', 'EX-P AWD', 'EX-L V6 AWD with Navi', 'Touring 8-Seat AWD', 'Touring FWD', 'EX-L AWD with Honda Sensing', 'Touring with Nav 4WD', 'EX-L AWD with Nav', 'Touring 7-Seat AWD']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Hyundai and the trims are: ['GLS Sedan FWD', 'Sport FWD', 'SE Value Edition Sedan FWD', 'SE Sedan FWD', 'SEL Sedan FWD', 'V6 SE FWD', 'Signature RWD', 'Limited FWD', 'SE FWD', 'Limited Sedan FWD', 'Sport Sedan FWD', 'GLS FWD', '3.8 RWD', '3.8 AWD', 'Value Edition Sedan FWD', 'FWD', 'SEL FWD', 'GL FWD', '5.0 RWD', '2.0T SE FWD', '2.0T Sport FWD with Black Leather Interior', '2.0T Sport FWD', 'Limited FWD with Blue Pearl Interior', '5.0 R-Spec RWD', '2.0T Limited FWD', 'V6 Limited FWD', '2018.5 Sport+ FWD', 'Eco FWD', '2.0T Ultimate AWD', '2.4L AWD', '3.5L Limited FWD', '3.3L SE AWD', '3.5L Limited AWD', '1.6T Limited FWD', '3.3L Limited AWD', '2.4L FWD', 'The Walking Dead Edition AWD', '2.4L SEL Plus AWD', '2.0T FWD', 'Limited AWD', '2.7L GLS AWD', 'GLS AWD', '1.6T Eco AWD', 'SE AWD', 'SEL AWD', 'SE Ultimate FWD', '1.6T Limited AWD', 'Value FWD', '3.3L SE FWD', '3.3L Limited FWD', '2.0L SE AWD', 'GL 2WD', '2.0L SE AWD with Beige Seats', '2.0L SEL Plus AWD', '2.4L SEL AWD', '1.6T Sport AWD with Beige Seats', '1.6T Sport AWD', '2.0L SE Plus AWD', '2.0L SE FWD', '2.0T AWD', '4 Dr SE 4X4', '2.4L GLS AWD', '1.6T Night AWD', '1.6T Eco FWD', 'Limited Ultimate AWD', '2.7L GLS FWD', '2.4L GLS FWD', '2.4L SE AWD', 'SE', '3.5L SE AWD', '2.0L SEL FWD', 'Limited', '1.6T Value AWD', '2.0L SEL AWD', '3.5L SE FWD', '1.6T Sport FWD', 'GLS 4WD', 'GLS', 'Value AWD', '2.0T Ultimate FWD', 'Ultimate AWD', '2.4L SE FWD', '1.6T Value FWD', '2.0L SE FWD with Beige Seats', 'Ultimate FWD', 'SE Ultimate AWD', '2.4L Ultimate AWD', '2.4L Limited FWD', '2.0L SE Plus FWD', '2.4L Sport AWD', '3.5L LX AWD', '4 Dr GLS', '1.6T Night FWD']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Kia and the trims are: ['Premium', 'EX', 'LX', 'LXS FWD', 'S', 'SX Turbo', 'SXL Turbo', 'SX', 'LX FWD', 'Technology FWD', 'S FWD', 'Limited', 'Premium AWD', 'GT2 AWD', 'EX Turbo', 'FE FWD', 'SX V6', 'LX Turbo', 'GT1 AWD', 'V6 Premium', 'Premium FWD', 'SX Turbo FWD', 'LX AWD', 'LX 4WD', 'EX AWD', 'L FWD', 'EX V6 AWD', 'SX Limited AWD', 'EX V6 4WD', 'LX V6 AWD', 'SX AWD', '2.0T EX AWD', 'SX Turbo AWD', 'SX V6 AWD', 'LX V6', 'Touring', 'FE', 'LX V6 4WD', 'EX V6', 'SX 4WD', 'LX V6 FWD', 'L', 'SX Limited V6 AWD', 'GT-Line FWD', 'EX FWD', 'Touring Launch Edition', 'Base', 'S V6 FWD', 'LX V6 4X4', 'EX 4WD', 'EX V6 FWD', 'V6 SX Limited AWD', 'SX Limited', 'S AWD']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Mazda and the trims are: ['i Sport', 'i Grand Touring', 'i Touring', 'Touring Sedan FWD', '2017.5 Touring Sedan FWD', 'Grand Touring', 'Touring FWD', 'Hatchback FWD', 'Sport Sedan FWD', 'Sedan FWD', 'i SV', 'Sport', 'i Sport Sport', 'Grand Touring Sedan FWD', 's Sport', 'Touring', 's Grand Touring', 'Grand Touring FWD', 'Premium Sedan FWD', 'Select Sedan FWD', 'Grand Touring Reserve Sedan FWD', '2017.5 Grand Touring Sedan FWD', 'i Touring with R Production', 's Touring', 'i Touring Value', 'i Touring Plus', '2017.5 Sport Sedan FWD', 'Select Sedan AWD', 's', 's 4dr Sedan', 'i 4dr Sedan', 'i Sport - Grand Touring', 's Sport - Touring', 'i Sport - Sport', 'Grand Touring Reserve FWD', 'Preferred Sedan FWD', 'Signature Sedan FWD', 'Signature FWD', 'Sport AWD', 'Touring AWD', 'Grand Touring AWD', 'Signature AWD', 's AWD', 's Grand Touring 4WD', 'Sport FWD', 'Grand Select AWD', 'Grand Select', 'Signature Diesel AWD', 's Grand Touring AWD', 'Sport 4WD', 'i Grand Touring 4WD', 'Premium AWD', 'Touring 4WD', 'Grand Touring 4WD', 's Touring AWD', 's 4WD']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Mitsubishi and the trims are: ['SE', 'ES FWD', 'ES', 'MR', 'SE FWD', 'SE AWC', 'ES AWD', 'SE AWD', 'GSR', 'AWD Final Edition', 'SEL AWD', 'GT', 'Ralliart', 'GTS', 'IX', 'RF FWD', 'FE', 'Sport V6', 'DE', 'Limited AWD', 'GT AWD', 'SE S-AWC AWD', 'ES AWC AWD', 'LS AWD', 'LE AWD', 'SEL FWD', 'SE 4WD', '2.4 ES AWD', 'SEL S-AWC AWD', '2.4 ES', 'LE S-AWC AWD', 'LS', 'XLS AWD', 'GT S-AWC AWD', 'LE', 'SEL', 'XLS', 'LS 4WD', 'Limited 4WD', 'LE FWD', 'ES 4WD']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Nissan and the trims are: ['2.5 SR FWD', '2.5 S', 'SV FWD', 'S Plus FWD', 'SV', 'S', 'SR Turbo FWD', '2.5 SL', 'SL', '1.6 SL', '2.5 SV', '3.5 SL', 'S Plus', '2.0 SR', '2.5 SR', '2.5 SR AWD', '2.5', '2017.5 SV FWD', 'SR', 'FE+ S', 'SR FWD', '2.5 SL FWD', '2.0 S', '2017.5 2.5 SV', 'SL FWD', '2018.5 SV', 'Platinum FWD', '2.0', 'S FWD', 'SR Turbo', '2.5 S FWD', '1.6 S', '2017.5 2.5 SR', '2017.5 2.5 S', '1.6 SV', '1.6 S Plus', '3.5 SR', 'FWD', '2017.5 2.5 SL', 'S 1.8', '2.5 SV FWD', '2.5 S AWD', '2.5 SV AWD', 'FE+ 2.0 S', '2.5 Platinum FWD', 'NISMO FWD', 'NISMO', '2.5 Platinum AWD', 'X 4WD', 'SV AWD', 'Platinum 4WD', 'LE 4X4', 'S AWD', 'SL AWD', 'S Krom Edition AWD', '2017.5 SL', '2017.5 Platinum', '2017.5 Platinum AWD', 'Platinum', 'SL 4WD', 'SV 4WD', 'LE AWD', '2017.5 SL AWD', '2017.5 SV AWD', '2018.5 SL AWD', '2017.5 S AWD', 'Platinum AWD', 'S 4WD', '2018.5 SV AWD', 'Silver Edition 4WD', 'Pro-4X', 'SV with SL AWD', 'SE 4X4', 'SL 4X4', 'SE 4WD', '2018.5 S FWD', 'Platinum Edition AWD', 'Off Road', 'S 4X4', 'SE AWD', 'NISMO AWD', 'Titanium 4WD', 'LE V6 4WD', '2018.5 S AWD', 'Base', '2017.5 S', '2017.5 S FWD']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Subaru and the trims are: ['Sedan', 'Premium Package', 'Limited', '2.5i', '2.0i Premium', '2.0i Sedan AWD', '2.5i Premium', '3.6R Limited', '2.0i Sport', '2.5i Limited', 'Premium', '3.6R Limited AWD', '2.5i Special Edition', '2.0i', '2.5i Premium AWD', 'Base', 'AWD', '2.0i Sport Sedan AWD', '2.5i Sport AWD', '2.0i Premium Sedan AWD', 'Limited AWD', '2.5i Limited AWD', 'Premium AWD', '2.5 i Limited', '2.0i Limited', 'Limited with Wing Spoiler', '2.5i Sport', '2.5 i Special Edition', 'Limited with Low Profile Spoiler', '2.5 i', '2.5 RS', 'Limited Sedan AWD', 'Limited AWD with Low Profile Spoiler', '2.0i Limited Sedan AWD', 'Turbo AWD', 'Base AWD', '2.5i AWD', '2.5GT Limited', 'Type RA AWD', 'Limited AWD with Wing Spoiler', 'Sedan AWD', '2.5 RS Sport', '2.5i Premium Package', '2.5 GT Limited', 'S209 AWD', '2.5i Limited Sedan AWD', '3.6R Premium', 'Limited 7-Passenger AWD', '2.0i Premium AWD', '2.5X Premium', '2.5i Touring', 'Touring 7-Passenger AWD', '2.5 X', '2.0XT Touring', '5-Passenger', '2.5 X Limited', '2.5X Limited', '2.0i Limited AWD', '2.0XT Premium', '2.5i Touring AWD', '2.5 X Premium', 'Premium 8-Passenger AWD', '4 Dr Limited 7-Passenger AWD', 'LTD 5-Passenger', 'Touring AWD', '2.0i Base AWD', '2.5 X Premium Package', 'Premium 7-Passenger AWD', '2.5X', '2.5XT Touring', '2.5 X L.L. Bean Edition', '8-Passenger AWD', 'Sports 2.5X', '2.5 X Sports', '5-Passenger Special Edition', 'Limited 8-Passenger AWD', 'LTD 7-Passenger', 'Limited 5 Passenger', 'Onyx Edition XT AWD', 'Limited 5-Passenger', 'Touring', '2.5 XT Limited']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Toyota and the trims are: ['Limited', 'LE', 'SE', 'XSE FWD', 'Sedan', 'LE FWD', 'L', 'XL', 'FWD', 'XLE V6', 'S', 'CE', 'XLE', 'SE FWD', 'Base', 'XLE FWD', 'XSE', 'Limited FWD', 'S Premium', 'LE Eco', 'XLE Touring FWD', 'XSE V6', 'L Sedan FWD', 'Touring FWD', 'XLS', 'S Plus', 'LE Premium', 'LE V6', 'XLE Premium', 'Touring', 'SE V6', 'LE Plus', 'XLE Premium FWD', 'XSE V6 FWD', '50th Anniversary Edition', 'LE Sedan FWD', 'SE Sport', 'SE Sport Limited Edition', 'XLE Touring', 'XLE Plus', 'S Special Edition', 'L FWD', 'SR5 4WD', 'AWD', 'Limited V6 4WD', 'V6 AWD', 'LE AWD', 'XLE AWD', 'Limited Platinum AWD', 'Base 4WD', 'SE AWD', 'Base V6 4WD', 'TRD Off-Road Premium 4WD', 'Limited AWD', 'SR5 Premium 4WD', 'Sport V6 AWD', '4WD', 'TRD Pro 4WD', 'Limited V6 AWD', 'Limited 4WD', 'SR5 V6 4WD', 'LE i4', 'V6 XLE AWD', 'SR5 V6', 'Limited Platinum', 'LE Plus AWD', 'Platinum FFV 4WD', 'Sport 4WD', 'XLE V6 AWD', 'V6 Limited AWD', 'SR5 FFV 4WD', 'Adventure AWD', 'V6 4x4 SR5', 'Sport', 'SE V6 AWD', 'SR5', 'Plus V6 AWD', 'TRD Off-Road 4WD', 'Trail Premium 4WD', 'Limited FFV 4WD', 'Platinum 4WD', 'XSE AWD', 'V6 4x2 SR5', 'I4 AWD', 'LE i4 FWD', 'Sport AWD', '4 Dr Limited V8 4WD', 'LE V6 AWD', 'Base AWD', 'Sport V6 4WD', 'SE V6 4WD', 'Limited V6', 'SR5 5.7L 4WD', 'TRD Off-Road AWD', 'Base V6', 'Sport V6', '2WD', 'Limited Nightshade 4WD']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Volkswagen and the trims are: ['TDI SEL Premium', 'TDI', '1.4T S FWD', 'TDI SE', 'S', '2.0T Luxury FWD', 'SE 1.8', 'TDI with Premium', '1.8T S', '1.4T SE FWD', 'SE', '1.4T Wolfsburg Edition FWD', '2.0T Sport FWD', 'SEL Premium', '2.0T SEL FWD', 'SE with Sunroof and Nav', 'SE with Sunroof', '1.8T Sport FWD', '1.8T SE', '1.8T SEL FWD', 'Wolfsburg Edition', '1.4T R-Line FWD', '2.0T GLI FWD', 'TDI SE with Sunroof', '2.0T SE FWD', 'SE with Connectivity', 'SEL V6 Premium', 'Base', 'S 1.8', 'VR6 4Motion', '1.8T SE with Technology Pkg', '2.0T Wolfsburg FWD', 'TDI with Premium and Nav', '1.8T SE with Technology', 'TDI SE with Sunroof and Nav', 'SEL', 'GLI Autobahn', 'TDI S', '1.8T SEL Premium', 'Wolfsburg Edition 1.8', 'Komfort', 'Limited Edition', '2.0T R-Line FWD', 'R-Line', 'GLI', '2.0T SEL Premium 4Motion AWD with R-Line', '1.8T SEL', 'TDI SEL', '1.4T S FWD with Technology', '2.0T Lux Plus FWD', '1.8T SEL Premium FWD', '2.0T S FWD', 'VR6 Executive 4Motion AWD', '1.8T R-Line', 'V6 SEL Premium', '2.0T Lux FWD', 'SEL Premium FWD', '2.0T Sport Plus FWD', '2.0T GLI SE FWD', 'S with Appearance', '2.0T SEL 4Motion AWD', '2.0T GLI SEL FWD', 'GLI Autobahn with Nav', '2.0T Trend FWD', 'SE with Connectivity and Sunroof', 'TDI SE with Connectivity', 'SEL with Sunroof', 'TDI Value Edition', '2.0T SEL Premium FWD', '2.0T Executive FWD', '1.4T SE FWD with Connectivity', '1.8T SE Sport FWD', '1.4T SEL FWD']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Jeep and the trims are: ['Trailhawk 4WD', 'Altitude 4WD', 'Limited 4WD', 'X High Altitude 4WD', 'SRT 4WD', 'Laredo 4WD', 'Laredo E 4WD', 'Latitude 4WD', 'Latitude FWD', 'Overland 4WD', 'Sport Altitude 4WD', 'High Altitude Edition 4WD', 'Sport 4WD', 'High Altitude FWD', 'X 4WD', 'Sport', 'Sterling Edition 4WD', 'Sport FWD', 'X Sport', 'Sahara 4WD', '70th Anniversary 4WD', 'Latitude', 'Summit 4WD', 'High Altitude 4WD', 'Trailhawk L Plus 4WD', 'Trackhawk 4WD', 'SE', 'Sport SE FWD', 'Latitude Plus 4WD', 'X', 'High Altitude Edition', 'North Edition 4WD', 'Upland 4WD', 'Base 4WD', 'Limited FWD', 'Laredo', 'Rubicon 4WD', 'X High Altitude', 'Altitude', 'Latitude X 4WD', 'Trailhawk Elite 4WD', 'Sport Jet 4WD', 'Limited 75th Anniversary 4WD', 'X Sport SE 4WD', 'Limited', 'Sport 75th Anniversary', 'Limited 4X4', 'Laredo X 4WD', '75th Anniversary Edition 4WD', 'Latitude 75th Anniversary 4WD', 'Limited RWD', 'Sahara', 'Overland Summit 4WD', 'Deserthawk 4WD', 'Altitude Edition 4WD', 'Laredo 75th Anniversary 4WD', 'Limited Jet 4WD', 'Renegade 4WD', 'Rubicon', 'Unlimited', 'X Latitude', 'Limited X 4WD', 'Backcountry 4WD', 'Base 4X4']\n",
      "-------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Look for various trims across make\n",
    "for make in df[\"Make\"].unique():\n",
    "    print(\"The make is: {} and the trims are: {}\".format(make,df[df[\"Make\"] == make][\"Trim\"].unique().tolist()))\n",
    "    print(\"-\"*115)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 levels of trims for each make"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **Masks for each trim**: Let's build a mask for each trim so that we can filter out the records trimwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_1 = df[\"Trim_copy\"].value_counts().index.str.contains(\n",
    "    \"(LX|SportSi|Gx|L|LE|Sedan|Base|LS|RS|FWD|XLT|value|ES|SR|Latitude)\",\n",
    "    case=False,regex=True)   # Mask for base trim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_2 = df[\"Trim_copy\"].value_counts().index.str.contains(\n",
    "    \"(EX|Navigation|RES|SE|S|LT|Hybrid|SEL|ST|Signature|SX|SV|SL|Altitude)\",\n",
    "    case=False,regex=True) # Mask for mid trim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_3 = df[\"Trim_copy\"].value_counts().index.str.contains(\n",
    "    \"(Touring|EX-T|Elite|XLE|XSE|Limited|Edition|Premier|Titanium|Luxury|Premium|Ultimate|Grand|GT|Platinum|GLI|2.0T|Trailhawk|Overland)\",\n",
    "    case=False,regex=True)  # Mask for high trim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "738"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[\"Trim_copy\"].value_counts())   #Omega  # This sample space is divided into 8 parts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "510"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[\"Trim_copy\"].value_counts().index[mask_1])  # Mask1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "486"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[\"Trim_copy\"].value_counts().index[mask_2]) # Mask2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "261"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[\"Trim_copy\"].value_counts().index[mask_3]) # Mask3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some of the values are shared among the 3 masks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"venn.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here, we're going to replace the trim levels with the respective numbers:\n",
    "* Values that belong to 100: 1\n",
    "* Values that belong to 010: 3\n",
    "* Values that belong to 001: 5\n",
    "* Values that belong to 110: 2 : Mean of 100 and 010\n",
    "* Values that belong to 101: 3 : Mean of 100 and 001\n",
    "* Values that belong to 001: 4 : Mean of 010 and 001\n",
    "* Values that belong to 111: 3 : Mean of 100 and 101 and 001\n",
    "* Values that belong to 000: 5 : These are usually the models which have fancy names and are mostly higher trim."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_111 = df[\"Trim_copy\"].value_counts().index[(mask_1)&(mask_2)&(mask_3)].tolist()        # 111\n",
    "list_110 = df[\"Trim_copy\"].value_counts().index[(~mask_3)&((mask_1)&(mask_2))].tolist()     # 110\n",
    "list_101 = df[\"Trim_copy\"].value_counts().index[(~mask_2)&((mask_1)&(mask_3))].tolist()     # 101\n",
    "list_011 = df[\"Trim_copy\"].value_counts().index[(~mask_1)&((mask_3)&(mask_2))].tolist()     # 011\n",
    "list_100 = df[\"Trim_copy\"].value_counts().index[(mask_1)&(~((mask_2)|(mask_3)))].tolist()   # 100\n",
    "list_010 = df[\"Trim_copy\"].value_counts().index[(mask_2)&(~((mask_1)|(mask_3)))].tolist()   # 010\n",
    "list_001 = df[\"Trim_copy\"].value_counts().index[(mask_3)&(~((mask_2)|(mask_1)))].tolist()   # 001\n",
    "list_000 = df[\"Trim_copy\"].value_counts().index[~((mask_1)|(mask_2)|(mask_3))].tolist()     # 000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Trim_copy\"].replace(dict.fromkeys(list_100,1),inplace = True)\n",
    "df[\"Trim_copy\"].replace(dict.fromkeys(list_010,3),inplace = True)\n",
    "df[\"Trim_copy\"].replace(dict.fromkeys(list_001,5),inplace = True)\n",
    "df[\"Trim_copy\"].replace(dict.fromkeys(list_110,13),inplace = True)\n",
    "df[\"Trim_copy\"].replace(dict.fromkeys(list_101,15),inplace = True)\n",
    "df[\"Trim_copy\"].replace(dict.fromkeys(list_011,35),inplace = True)\n",
    "df[\"Trim_copy\"].replace(dict.fromkeys(list_111,135),inplace = True)\n",
    "df[\"Trim_copy\"].replace(dict.fromkeys(list_000,0),inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a32c94490>"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x = \"Trim_copy\", y = \"Price\", data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13     5789\n",
       "3      3794\n",
       "1      2631\n",
       "5      2045\n",
       "15     1978\n",
       "135     626\n",
       "0       480\n",
       "35      137\n",
       "Name: Trim_copy, dtype: int64"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Trim_copy\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjusting for the intersecting trims.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Trim_copy\"] = df[\"Trim\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Trim_copy\"].replace(dict.fromkeys(list_100,1),inplace = True)\n",
    "df[\"Trim_copy\"].replace(dict.fromkeys(list_010,1),inplace = True)\n",
    "df[\"Trim_copy\"].replace(dict.fromkeys(list_001,5),inplace = True)\n",
    "df[\"Trim_copy\"].replace(dict.fromkeys(list_110,1),inplace = True)\n",
    "df[\"Trim_copy\"].replace(dict.fromkeys(list_101,5),inplace = True)\n",
    "df[\"Trim_copy\"].replace(dict.fromkeys(list_011,5),inplace = True)\n",
    "df[\"Trim_copy\"].replace(dict.fromkeys(list_111,3),inplace = True)\n",
    "df[\"Trim_copy\"].replace(dict.fromkeys(list_000,1),inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a31055910>"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x = \"Trim_copy\", y = \"Price\", data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Trim_copy\n",
       "5    17310.021154\n",
       "3    15477.827476\n",
       "1    13342.938239\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Trim_copy\")[\"Price\"].mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model\n",
    "Usually there are three levels of size for each bodytype: sedan as well as suv.\n",
    "And, for the same trim, the Price usually goes up with the level. Let's see!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Model_copy\"] = df[\"Model\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The make is: Chevrolet and the models are: ['Sonic', 'Malibu', 'Impala', 'Caprice', 'Cruze', 'Cruze Limited', 'Impala Limited', 'Cobalt', 'Volt', 'Aveo', 'Equinox', 'Tahoe', 'Traverse', 'Trax', 'Blazer', 'Suburban', 'Trailblazer', 'Captiva Sport', 'Trailblazer EXT', 'Tahoe Hybrid']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Ford and the models are: ['Fusion', 'Focus', 'Fusion Hybrid', 'Taurus', 'Fiesta', 'Fusion Energi', 'Escape', 'Explorer', 'Edge', 'Flex', 'Expedition', 'EcoSport', 'Taurus X']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Honda and the models are: ['Civic', 'Accord', 'Insight', 'Accord Hybrid', 'Civic Hybrid', 'CR-V', 'HR-V', 'Pilot', 'Crosstour', 'Element', 'Accord Crosstour', 'Passport']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Hyundai and the models are: ['Accent', 'Sonata', 'Elantra', 'Equus', 'Sonata Hybrid Plug-In ', 'Sonata Hybrid', 'Genesis', 'Azera', 'Santa Fe', 'Santa Fe Sport', 'Tucson', 'Kona', 'Santa Fe XL', 'Veracruz', 'Palisade']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Kia and the models are: ['Cadenza', 'Optima', 'Forte', 'Spectra', 'Rio', 'Optima Hybrid', 'Stinger', 'K900', 'Sportage', 'Sorento', 'Soul', 'Telluride', 'Niro', 'Seltos', 'Rondo', 'Borrego']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Mazda and the models are: ['MAZDA6', 'MAZDA3', 'CX-5', 'CX-9', 'CX-3', 'CX-7', 'Tribute', 'CX-30']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Mitsubishi and the models are: ['Mirage G4', 'Lancer', 'Lancer Evolution', 'Galant', 'Outlander', 'Outlander Sport', 'Eclipse Cross', 'Endeavor', 'Outlander Hybrid Plug-in ', 'Montero']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Nissan and the models are: ['Altima', 'Sentra', 'Versa', 'Maxima', 'Altima Hybrid', 'Xterra', 'Juke', 'Pathfinder', 'Armada', 'Rogue', 'Murano', 'Rogue Select', 'Rogue Sport', 'Kicks', 'Pathfinder Hybrid', 'Murano CrossCabriolet', 'Rogue Hybrid']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Subaru and the models are: ['WRX', 'Impreza WRX', 'Legacy', 'Impreza', 'WRX STI', 'Impreza WRX STI', 'Outback', 'Ascent', 'Forester', 'Crosstrek', 'XV Crosstrek', 'Tribeca', 'B9 Tribeca', 'XV Crosstrek Hybrid', 'Crosstrek Hybrid']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Toyota and the models are: ['Avalon', 'Corolla', 'Camry', 'Yaris iA', 'Camry Hybrid', 'Yaris', 'Avalon Hybrid', 'Corolla Hybrid', '4Runner', 'Land Cruiser', 'RAV4', 'Venza', 'Highlander', 'RAV4 Hybrid', 'FJ Cruiser', 'C-HR', 'Highlander Hybrid', 'Sequoia']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Volkswagen and the models are: ['Passat', 'Jetta', 'CC', 'Arteon', 'Jetta Hybrid']\n",
      "-------------------------------------------------------------------------------------------------------------------\n",
      "The make is: Jeep and the models are: ['Cherokee', 'Grand Cherokee', 'Compass', 'Renegade', 'Patriot', 'Liberty', 'Wrangler Unlimited', 'Wrangler', 'Commander']\n",
      "-------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for make in df[\"Make\"].unique():\n",
    "    print(\"The make is: {} and the models are: {}\".format(make,df[df[\"Make\"] == make][\"Model_copy\"].unique().tolist()))\n",
    "    print(\"-\"*115)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_compact = df[\"Model_copy\"].value_counts().index.str.contains(\n",
    "    \"(Sonic|Cruze|Equinox|Volt|Trailblazer|Trax|Fiesta|Edge|EcoSport|Escape|Civic|HR-V|Element|Accent|Elantra|Kona|Tucson|Forte|Soul|Rio|Niro|Sportage|Seltos|Mazda3|CX-3|CX-30|Mirage|Lancer|Eclipse|Galant|Sentra|Versa|Rogue|Xterra|Juke|Kicks|Impreza|Crosstrek|Corolla|Yaris|C-HR|Jetta|Renegade|Compass)\",\n",
    "    case=False,regex=True)   # Mask for compact size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_mid = df[\"Model_copy\"].value_counts().index.str.contains(\n",
    "    \"(Malibu|Traverse|Tahoe|Fusion|Explorer|Accord|Insight|CR-V|Passport|Sonata|Santa Fe|Optima|Sorento|Mazda6|CX-5|CX-7|Tribute|Outlander|Altima|Murano|Legacy|Forester|Outback|WRX|Tribeca|Camry|Rav-4|Highlander|4Runner|Passat|CC|Wrangler|Cherokee)\",\n",
    "    case=False,regex=True)   # Mask for mid size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_full = df[\"Model_copy\"].value_counts().index.str.contains(\n",
    "    \"(Impala|Suburban|Taurus|Flex|Expedition|Pilot|Palisade|Cadenza|Stinger|Telluride|CX-9|Endeavor|Montero|Maxima|Pathfinder|Armada|Ascent|Avalon|Land Cruiser|Sequoia|Cruiser|Arteon|Grand Cherokee)\",\n",
    "    case=False,regex=True)   # Mask for full size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We'll use the same idea of venn diagram dealing with the intersecting samples for the category \"Model\"\n",
    "* Values that belong to 100: 1\n",
    "* Values that belong to 010: 3\n",
    "* Values that belong to 001: 5\n",
    "* Values that belong to 110: 3 : More inclined towards 010\n",
    "* Values that belong to 101: 3 : Mean of 100 and 001\n",
    "* Values that belong to 001: 4 : Mean of 010 and 001\n",
    "* Values that belong to 111: 3 : Mean of 100 and 101 and 001\n",
    "* Values that belong to 000: 2 : The distribution is more like the mix of 100 and 010"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_list_111 = df[\"Model_copy\"].value_counts().index[(mask_compact)&(mask_mid)&(mask_full)].tolist()        # 111\n",
    "model_list_110 = df[\"Model_copy\"].value_counts().index[(~mask_full)&((mask_compact)&(mask_mid))].tolist()     # 110\n",
    "model_list_101 = df[\"Model_copy\"].value_counts().index[(~mask_mid)&((mask_compact)&(mask_full))].tolist()     # 101\n",
    "model_list_011 = df[\"Model_copy\"].value_counts().index[(~mask_compact)&((mask_full)&(mask_mid))].tolist()     # 011\n",
    "model_list_100 = df[\"Model_copy\"].value_counts().index[(mask_compact)&(~((mask_mid)|(mask_full)))].tolist()   # 100\n",
    "model_list_010 = df[\"Model_copy\"].value_counts().index[(mask_mid)&(~((mask_compact)|(mask_full)))].tolist()   # 010\n",
    "model_list_001 = df[\"Model_copy\"].value_counts().index[(mask_full)&(~((mask_mid)|(mask_compact)))].tolist()   # 001\n",
    "model_list_000 = df[\"Model_copy\"].value_counts().index[~((mask_compact)|(mask_mid)|(mask_full))].tolist()     # 000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[\"Model_copy\"].value_counts().index[(~mask_mid)&((mask_compact)&(mask_full))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[\"Model_copy\"].value_counts().index[(mask_compact)&(mask_mid)&(mask_full)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Model_copy\"].replace(dict.fromkeys(model_list_100,1),inplace = True)\n",
    "df[\"Model_copy\"].replace(dict.fromkeys(model_list_010,3),inplace = True)\n",
    "df[\"Model_copy\"].replace(dict.fromkeys(model_list_001,5),inplace = True)\n",
    "df[\"Model_copy\"].replace(dict.fromkeys(model_list_110,13),inplace = True)\n",
    "df[\"Model_copy\"].replace(dict.fromkeys(model_list_101,15),inplace = True)\n",
    "df[\"Model_copy\"].replace(dict.fromkeys(model_list_011,35),inplace = True)\n",
    "df[\"Model_copy\"].replace(dict.fromkeys(model_list_111,135),inplace = True)\n",
    "df[\"Model_copy\"].replace(dict.fromkeys(model_list_000,0),inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a3104a990>"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x=\"Model_copy\",y = \"Price\", data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, no common value for all three sizes as well as 1 and 5, that's good!\n",
    "### Adjusting for the values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Model_copy\"] = df[\"Model\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Model_copy\"].replace(dict.fromkeys(model_list_100,1),inplace = True)\n",
    "df[\"Model_copy\"].replace(dict.fromkeys(model_list_010,3),inplace = True)\n",
    "df[\"Model_copy\"].replace(dict.fromkeys(model_list_001,5),inplace = True)\n",
    "df[\"Model_copy\"].replace(dict.fromkeys(model_list_110,1),inplace = True)\n",
    "df[\"Model_copy\"].replace(dict.fromkeys(model_list_101,15),inplace = True)\n",
    "df[\"Model_copy\"].replace(dict.fromkeys(model_list_011,5),inplace = True)\n",
    "df[\"Model_copy\"].replace(dict.fromkeys(model_list_111,135),inplace = True)\n",
    "df[\"Model_copy\"].replace(dict.fromkeys(model_list_000,1),inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a36862450>"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x=\"Model_copy\",y = \"Price\", data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BLACK      18.529748\n",
       "SILVER     16.744851\n",
       "WHITE      15.331808\n",
       "GRAY       14.073227\n",
       "BLUE       11.195652\n",
       "RED        10.783753\n",
       "UNKNOWN     9.914188\n",
       "BROWN       1.315789\n",
       "GREEN       0.972540\n",
       "GOLD        0.411899\n",
       "ORANGE      0.314645\n",
       "TEAL        0.274600\n",
       "PURPLE      0.097254\n",
       "YELLOW      0.040046\n",
       "Name: Color, dtype: float64"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Color\"].value_counts()/len(df)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RYWGhPB6PNm7cqAEDBuiyyy7Thg0bJEmfffaZLrjgAqWmpqpJkyYBbWuVMwf8mMTUuE1m8jGfO3euTpw4oTlz5lgdSsBMXUgDABA7IlI5vummm/TII49o/Pjxstlseuqpp5SQkKDs7GxVVlYqIyNDl1xyiS666CJt2rRJ48aNk8fj0VNPPSVJevzxxwPeNtpMHfBjatwmM/mYb9++XYWFhZKkgoIC7dy504jqcfVCGitWrGBBCgDAOWGe4yAtWLDA77btNddcY8R0UabGbTKTj/ndd9/tS44lqXv37lq0aJGFEQXO5K4sAIDoqStPZPnoIOXm5voN+MnJybE4osCYGrfJTD7mZybGUlX12BR2u13z588nMQYAnBOS4yBlZmb6BgImJSVp2LBhFkcUGFPjNpnJx7xbt25+bRbcAQA0FiTHQXI4HH7zqJoy4IeBStFn6rkiSVOmTPFrT5061aJIAACILpLjc3BmwmOK6oFKNpuNgUpRVN2l37Su/b179/ZVj7t3727EYDwAAMKB5DhINafkMmmKLofDoX79+hlVwTSZ0+n0S45NOlekqupxSkoKVWMAQKPCbBVBuu6663Tq1Clfu1mzZnrnnXfC+hyID9dff71Onjzpazdv3lxvv/22hREBAIBqzFYRJjVX5WtolT40Xu3atfNrt2/f3qJIAABAoEiOg1RSUlJvG6h26NAhv/bBgwctigQAAASK5DhIVI4RqGHDhslms0mSbDabhg8fbnFEQGS4XC5lZ2erqKjI6lAAIGQkx0GqXtShrjZQzeFw+L48NWnShIGQiFtOp1N5eXnGDToFgNqQHAeJxREQKLvdrpEjRzJ9HuKay+XSmjVr5PF4tHr1aqrHAIxHchwkFkdAMJg+D/HO6XT6zf1O9RiA6UiOg8TiCAiG3W7X/PnzqRojbuXm5vq6l1VUVCgnJ8fiiAAgNCTH58DUxREYNBN9HPPo45hHV2Zmpq9vfVJSkoYNG2ZxRAAQGpLjc9C7d2+99dZbxlWNGTQTfRzz6OOYR5fD4VBCQtVHSUJCAl2IABiP5LiRYNBM9HHMo49jHn12u10jRoxg4CmAuEFy3EgwaCb6OObRxzG3BgNPAcQTkuNGgkEz0ccxjz6OOQAgVCTHjQSDZqKPYx59mZmZfqsScsyjg37eAOIJyXEjwaCZ6OOYR9/o0aPl8XgkSR6PR2PGjLE4ovhHP28A8YbkuJFg0Ez0ccyjb+XKlX6V4xUrVlgcUXBMnIaOft4A4g3JcSPCoJno45hHV25url/l2LQ+xyZ2T6CfN4B4Q3J8Dkys7kis1mYFjnl0mdzP29TuCSYfcwCoDcnxOTCxugM0Bib38za1e4LD4fCL26RjDgC1ITkOkqnVHaAxMLmfN90TACA2kBwHydTqDqxhahcck40ePVrNmzc3bqYKU7snOJ1Ov0GQXBMBmI7kOEhUdxAMuuBE38qVK3Xy5EnjZqowtUtIbm6uKisrJUmVlZVcEwEYj+Q4SKZWdxB9dMGJPpOPud1u15AhQyRJQ4cONaZLCNdEAPGG5DhIplZ3EH10wYk+jnn0cU0EEG9IjoNk8oAfRBddcKLP5GPucrn0z3/+U5K0YcMGY6reXBMBxBuS43PAwg4IRGZmpt9AJW43R57Jt/hNrnpzTQQQT0iOzwELOyAQo0eP9lutzbTZE0xk8i1+k6veXBMBxBOSYyBCVq5c6dc2bfYEE5l8i9/kOw1MWQggnpAcAxGSm5vr1zapEmgyU2/xm3yngSkLAcQTkuNGhOpOdF1xxRV+7SuvvNKiSBoXU2/xm3qnweVyafXq1fJ4PFq1ahXXFwDGIzk+B6YmmVR3gNhV887CunXrLIokOE6n06+vNNcXAKYjOT4Hixcv1ldffaXFixdbHUrATF4cwVQffPCBX3vz5s0WRQITtGvXzq/dvn17iyIJTk5Ojl93EFOSegCoC8lxkFwul68vaU5OjjFJpsnTRJnK5GnFEH2HDh3yax88eNCiSIJjalIPAHUhOQ7S4sWL/ZJMU6rHJk8TZSqTpxVD9A0bNsxvtorhw4dbHFFgTE3qAaAuJMdBWr9+vV+75owEsYoqZvTZ7XYNHDhQkjRo0CDjBoht375dN9xwg3bu3Gl1KEExdUyAw+Hwe4+a8mXK1KQeAOoSseTY5XLpqquu0o4dO1RQUKDx48fL4XBo5syZvsrrwoULddNNN2ncuHH64osvJCmoba1QHU9d7VjlcDh8H2BUMaPnm2++kSTjEkxJmjt3rk6cOKE5c+ZYHUpQTB14arfbNXLkSNlsNo0aNcqYL1NnXltsNhvXFgDGi0hyfPr0ac2YMUPNmjWTJM2ePVsPPPCAnE6nb8BGXl6etm7dquXLl+vZZ5/V448/HvS2Vqj+EKirHavsdruvL2C7du2M+eA12fbt27Vnzx5J0p49e4xKkLdv367CwkJJVV9YTYnd9IGnps7RDADxJCLJ8Zw5czRu3DhfMpaXl+e7vTx06FBt3rxZn3zyiTIyMmSz2dS5c2dVVlaqqKgoqG2tUJ3w19WOVS6XS3v37pUk7d2717ikwURz5871a5tUgTU1dtMHnpo4R7PT6fSrHJt2zAGgpqRw7/Ctt95Senq6hgwZopdeeklS1fQ+1RfPFi1a6Pjx4yopKVGbNm18v1f9eDDbNvQBUlZWpvz8/LC+vhMnTpzVDvdzREJ1JV6q+ns899xzVKcirLryWq2goMCIc0UyN/a1a9f6DTxds2YNfWAjbO3ataqsrJQkVVZWcswBGC/syfGbb74pm82mDz74QPn5+Zo6dapflbK0tFStWrVSamqqSktL/R5v2bKlb3R/INs2JDk5WX369AnTK6vSpUsX363y6na4nyMSPv3007Pav/71ry2KpnFITU1VSUmJX9uEc0WSunXr5pcgd+/e3YjYs7KytGrVKlVUVCgpKUkjRowwIm6TccwBmKquok/Yu1X8+c9/1tKlS7VkyRL16dNHc+bM0dChQ7VlyxZJ0oYNGzRgwABddtll2rhxo9xut/bu3Su326309HT17ds34G2t0KNHD792z549LYkjWKb2lTZZdQWzrnYsmzJlil976tSpFkUSHKbPiz6OOYB4E5Wp3KZOnaoFCxZo7NixOn36tEaNGqX+/ftrwIABGjt2rCZPnqwZM2YEva0VPv74Y7/2Rx99ZFEkwbnqqqv82ldffbU1gTQi3//+9/3aGRkZFkUSvLS0NL/2md2aYpndbteIESNks9k0cuRIo/rumopjDiDeBNyt4j//+Y8KCgr0ne98Rx06dAio8rhkyRLf/y9duvSsn0+ePFmTJ0/2e6xHjx4Bb2uFxMTEetux6vrrr/db1vWGG26wMBrEupqDqpxOpyZNmmRRNMFxOBwqKCigghlFHHMA8SSgyvHSpUs1c+ZMPffcc3r//fcbdV/VM/s+19aOVStXrvQbUb5ixQqLI4p/mzdv9mtv2rTJokiCV3MFxTO/WMU6E2d8MB3HHEA8CSg5/vvf/65XXnlFLVu21J133qnPP/880nEhzHJzc/1mq2D56MirmSiYlDi0a9fOr109LSNQG1NXUwQaA1NXDbVSQMlxdVJVXXls2rRp5CJCRGRmZvq1WT468vbv319vO5YdPHjQr33gwAGLIoEJTF1NEWgMTF011EoBJcfXXnutbr31VhUWFuquu+5SVlZWpONCmF155ZV+7ZqDxYAz1awUd+jQwaJIEOtMXU0RaAxMXzXUKgElxz/+8Y/1q1/9SlOnTlV2drYmTpwY6bhilqkD8l544QW/9sKFCy2KpPHo2LGjX7tTp04WRRK8Q4cO+bVrVpIRGSbe/jR1NUWgMTB91VCrBJQcv/7663r99dc1evRozZkzR++8806k44pZSUlJ9bZj1ZkLl9TWRvjVTHBcLpdFkQSPOw3WMPH2Z22rKQKIDbm5uX6rhjLeKDABJcfLli3Tgw8+KElatGiRli1bFtGgYlnNLiUjRoywKJLgsAhI9JFgWsPE6qtk7u3Pbt26+bW7d+9uUSQAasrMzPQV8ZKSkhhvFKCAkuOEhAQlJydLkpo0adKoE6v+/fv7tS+++GKLIgmOyQtSmKqsrKzediwzeRo6E6uvkrm3P01dTRFoDFjB8twElBwPHz5cDodDTz/9tG677bZG/c3j+eef92v/5je/sSiS4Nx77731thF+H3zwgV+7ZsIZy0ydhs7U6qtk7u3P3r17+6rH3bt3V8+ePS2OCEA1VrA8NwElx/fdd5+mT5+uiy++WI8++qjuvvvuSMcVs06ePOnXPnHihEWRBMdut/sGiHXs2JE3SBRUVlbW245lpk5DZ2r1VTL79ueUKVOUkpJC1RiIQQ6HQ/369aNqHIR6k+Ply5dLkp555hmtXLlSX3/9tVasWKFnn302KsEhfFwulw4fPuz7f5MqaqYydWYTk5lafZXMvv2Zlpamnj17qk2bNlaHAqAGVrAMXr3JcXWlsXv37urRo4fff41VzZXDarZjldPp9FUuKyoqjKqomerqq6/2a9dciCWWmRp7Zmam3zLpJlVf7Xa7hgwZIkkaOnSoUR9kpvbzBoDa1JscV1+oV6xYoeuvv97vv8ZqwoQJfu277rrLokiCk5OT47d89Lp16yyOKP7VfJ/ccMMNFkUSPFNjHz16tN95PmbMGIsjCk71oE2TBm+a3M8bAGoTUJ/jli1bau3atdqxY4e++eYbffPNN5GOK2a9+uqrfu2XX37ZokiCY+oAK5OtXLnSr71ixQqLIgne22+/7dd+6623LIokOCtXrvSrHJt0zF0ul2/Q5qZNm4xJMk3u5w0AtWkwOS4pKdHu3bv16quvatasWZoxY4ZmzpwZjdhiUs2BSfv27bMokuDUjNOUuE1Ws7+rSdX69evX+7Vzc3OtCSRIubm5fpVjk/ocL1682C/JXLx4scURBcbkft4AUJt6k+OlS5fqhz/8oY4dO6a77rpLS5Ys0ZIlS/SnP/0pWvEhTKoH+tTVRvjV7I/evn17iyIJnqkzbZg844OpX0hM7ucNALWpN0N677339P777+u1114jITbc4MGD/dpXXHGFRZE0HocOHfJrHzx40KJIgmfqTBsOh8Ov+mrSjA+mfiFxOBx+1XqTjjkA1Kbe5Lhp06Zq2rSp0tPTdfr06WjFFNMGDhzo1x40aJBFkQSnvLy83jbC76KLLvJrm7KaoiR973vf82sPGDDAokiCV52omcbUuztffPGFX/urr76yKBIACI+Ar76mfuCEW8uWLettx6qtW7f6tbds2WJRJI3Hl19+6deumUTEsj179tTbjlVOp9N3rTJtcFinTp3qbceqZ555xq89d+5ciyIBgPBIqu+H27dv14MPPiiPx+P7/2o1L4iNRc0lgDdt2qTs7GyLokEsq7maYs12LKuZDO/evduiSIJT2yDISZMmWRRNcGrOTmHKbBXVg/HqagOAaepNjn/zm9/4/n/cuHERD8YEqampfkmOKZXj5s2b+8XdvHlzC6NBrOvSpYtfgty1a1cLowlcu3btVFhY6GubNAhy2LBh+vvf/+5rDx8+3MJoAmez2fzuLFYPzgMAU9WbHNfsXwtzB1nR5zj60tLSVFxc7Nc2RY8ePfyS4549e1oYTeBMfX9KVQuYnJkcm7aASTWSYwCmM2PEB0JWs884fcgj78zEuLZ2LPv444/92jX7rMeqK6+80q/9/e9/36JIgmfqojEkwwDiTb2VY8SP6umt6moDZ6qeK7iudqyqueyySXdIas5rnJOTY0R/6ZrJMckygIasXbtWq1atanC76qJSQ3deR40apaysrLDEJlE5BlCLkpKSetux6sMPP/Rr1xxAG8v69+/v1zZl6j9T52cGEPuKi4stuetqRjkIMFBCQoJfhd6UeWulqoGnZybEqampFkYTOJO7D9Wc+u/zzz+3KJLgJCUl+c1QYcpdBgDWycrKCqjS+9BDD0mS5s2bF+mQ/JjzaQ0YxuSuLDUX/TFlEaCaKz/W7IMcy0yd+q/meW3SFxIAqA3JMRAh6enpfm273W5RJMHr0KGDX7tjx44WRRKc5OTketuIPJJjAKYjOQYi5MiRI35tk2arqDkF2oEDByyKJDgffPCBX9ukPsemYkAeGihP3EUAACAASURBVAuXy6Xs7GxjFujBuSM5BiLE5G4VNavcplS9Te5WUbNPuil91BmQh8bC6XQqLy/PqGXpcW7MuPoiZImJifW2gTPt37+/3nasMnkqt06dOtXbjlU1V9tMSUmxKBIgclwul9asWSOPx6PVq1dTPY5zJMeNhKnz1gLBqNmNYtOmTRZFEjyXy1VvO1bVnMnElJlNgGA4nU7f3T+32031OM6RHDcSGRkZfu0hQ4ZYFAlMYGr3hJpdV0y6xW9qVxaTl+wGApWbm+ubsrCiokI5OTkWR4RIIjkGgBiwb9++etuxqma3ipptIB5kZmb67rgmJSVp2LBhFkeESCI5DlKTJk3qbccq00fxmzhK2ORR/DVXmqt5/sQqk/u/mjogz9T5mYFgOBwO33syISFBDofD4ogQSWZcfWMIiyNYw8RRwiav1mZq7BdddJFf25QlmCXpqquu8mtfffXV1gQC4Cx2u10jRoyQzWbTyJEjz5rHHvGF5LiROH78eL3tWMYo4ehr3bq1X7tNmzYWRRIcU5dglqQLL7zQr923b1+LIglOzQViTJllAwiWw+FQv379qBo3AiTHjcTWrVv92lu2bLEokuAxSjj6an4BMWXmhHbt2vm127dvb1EkwVu0aJFf+4UXXrAokuD07t273jYQL+x2u+bPn0/VuBEgOUbMY5QwAlVzJT9T5meW5DvH62rHqo8++sivXfOLOACYhuQYMS8zM9O3aEliYiKjhFEnFruJPlbIAxBvIpIcV1ZW6pFHHtG4ceN06623qrCwUAUFBRo/frwcDodmzpzpu02+cOFC3XTTTRo3bpy++OILSQpqW8Q/h8PhGxDm8XiM6e/VpUsXv3bXrl0tiqTxOHHiRL1thJ+pFW8AqEtElknLzc2VJP3lL3/Rli1bNHv2bHk8Hj3wwAMaNGiQZsyYoXXr1qlz587aunWrli9frn379mny5Ml68803NXv27IC3jbbU1FSVlJT4tYHa9O3bV3v27PG1+/fvb2E0wUlLS1NxcbGvbUofO5vN5jezhknT5zVr1kynTp3ya5vA5GMOALWJSOU4KytLv/71ryVJe/fuVdu2bZWXl6eBAwdKkoYOHarNmzfrk08+UUZGhmw2mzp37qzKykoVFRUFtW20nZkY19ZG+DmdTr/5JU0ZkLdmzRq/9vvvv29RJMEzde7apk2b1tuOZWcmxrW1Y5Wp0/4BQF0iUjmWqlaQmTp1qtasWaPnn39eubm5vopCixYtdPz4cZWUlPhNEVX9uMfjCXjb+ipaZWVlys/PDyrujz76KOiZHO6///5aHx80aJAuv/zyoPYVTcEeG6usXbvWb0DemjVrNHz4cIujOjemHPOaidnJkyeNiL2srOystglx18XU2E2NG0Bsqe4aF+1rSsSSY0maM2eOsrOzdcstt/h9aJWWlqpVq1ZKTU1VaWmp3+MtW7b0WxmqoW3rk5ycrD59+gQV8549e4JeVauu7Tt37hz080dTLMd2pqysLK1YscL3pWnEiBHGxF6TqXFL5sZuStxJSUl+/XWTkpKMib0mU+MGEFuq86tIXVPqSrojkhy/8847OnDggH72s5+pefPmstls6t+/v7Zs2aJBgwZpw4YNGjx4sLp166Z58+Zp4sSJ2r9/v9xut9LT09W3b9+Atw23rKwsZWVl1fnzxYsX6/XXX/e1x48frzvuuCPsceBbo0eP1t///ndJVbdsx4wZY3FEQPgxsA0AYkNE+hyPHDlS//d//6dbb71VEydO1LRp0zRjxgwtWLBAY8eO1enTpzVq1Cj1799fAwYM0NixYzV58mTNmDFDkjR16tSAt422CRMm+LVNSYxrDpIxadDMypUrffHabDatWLHC4ogQqy699FK/9mWXXWZRJMFLS0vza5syCJLp8wDEm4hUjlNSUvTb3/72rMeXLl161mOTJ0/W5MmT/R7r0aNHwNtawW63y+Vyafz48VaH4rN27VqtWrUqqN956KGH6vzZqFGj6q2gR1Nubq7fVG45OTmaNGmSxVGZK5BzpVWrVjp27Jhfu67zJZbOlUOHDvm1ay4KEsvOnB1EOnuVQqs0dL60bNlSR44c8WubcK4AQF1YBOQcdO7cWRdddJExVWPp7KpUzXYsy8zMVFJS1fe4pKQkFgGJgs6dO9fbjlVnTp1XWxvh17Fjx3rbsczlcik7OztmvogAiA0RHZCH6Gmor7TL5dKtt94qqSrBXLhwoTG3bR0Oh29atISEhJhZBMTUan1D50q1W265RceOHdOQIUP06KOPRjyucIjVecjP5VyR6j5folmBDeR8GTdunI4cOaKsrCxlZ2dHJa5wcDqdysvLk9Pp5G5UIxbo+7P67k5DxSXukJiPynEjYbfbfcnwNddcY0xiLFXFPmLECNlsNo0cOdKo2E3WuXNnpaSk6N5777U6lICZPKitU6dO9bZjWceOHZWSknLWmIxY5nK5tGbNGnk8Hq1evZrqMRpUXFx8VvcnxCcqx41I+/btderUqZipvFYL5Fv74cOH1aRJE23fvj0mqq9SwxW1//7v/1ZlZaWvnZiYqHnz5kUjtLBo0qSJevXqFVNfRho6V1JSUvzmaE5JSTGm+ipVfXGVqgaevvzyy5EOK2xi8VxpiNPplNvtliS53W6qx41YoO/P6muJSddxnBsqx42IiR9g1Y4dO6YmTZqoSZMmVocSsJq3l6dOnWpRJI1H+/bt623HuupqcSwMPI53ubm5fosL5eTkWBwRgFhB5RiWC+Rbu4nf2DMzMzVnzhxJVVXjoUOHWhyR+QI5VxwOh4qKinTttdcaVwls27at2rZty1zeUZCZmalVq1apoqKCgb4A/FA5BiKoa9eukqgaR1P79u2VkpISc92HEFscDodvNdZYGugLwHokx0AEpaWl6aKLLqJqHEUmdx9C9DDQF0Bd6FYBAGiUHA6HCgoKqBoD8ENyDABolOx2u+bPn291GABiDN0qAAAAAC8qxwAAAAYKZJ0AVvYLHpVjAACAOMXKfsGjcgwAQAyhGohAxes6AVajcgwAgGGoBgKRQ+UYAIAYQjUQsBaVYwAAAMCL5BgAAADwIjkGAAAAvEiOAQAAAC+SYwAAAMCL2SoAAAAQFi+++KJ27NgRln3t3LlT0rezs4SiV69euueeewLaluQYAABEhcvl0uzZszVt2jSlp6dbHQ4iYMeOHdqR/291a9Uh5H21UrIk6fSeoyHtp/DYgaC2JzkGAABR4XQ6lZeXJ6fTqUmTJlkdDiKkW6sOemTwj60Ow2f2h0uD2p4+xwAAIOJcLpfWrFkjj8ej1atXq6ioyOqQgFqRHAMAgIhzOp1yu92SJLfbLafTaXFEQO1IjgEAQMTl5uaqoqJCklRRUaGcnByLIwJqR3IMAAAiLjMzU0lJVUOdkpKSNGzYMIsjAmpHcgwAACLO4XAoIaEq7UhISJDD4bA4IqB2zFYBAIg7a9eu1apVq+rdpri4WJKUlpbW4P5GjRqlrKyssMTWWNntdo0YMUIrVqzQyJEjmcoNMYvkGADQKAWTHCM8HA6HCgoKqBojppEcAwDiTlZWVoOV3upVt+bNmxeNkKCq6vH8+fOtDgOoF32OAQAAAC+SYwAAAMCL5BgAAADwIjkGAAAAvEiOAQAAAC+SYwAAAMCL5BgAAADwIjkGAAAAvMK+CMjp06c1bdo07dmzR+Xl5br33nvVu3dvPfzww7LZbDr//PM1c+ZMJSQkaOHChVq/fr2SkpI0bdo0XXzxxSooKAh4WwAAACCcwp4c/+1vf1ObNm00b948FRcX6/rrr9eFF16oBx54QIMGDdKMGTO0bt06de7cWVu3btXy5cu1b98+TZ48WW+++aZmz54d8LYAAABAOIU9Ob7mmms0atQoXzsxMVF5eXkaOHCgJGno0KHatGmTevTooYyMDNlsNnXu3FmVlZUqKioKatv09PRwhw8AAGC5F198UTt27Ah5Pzt37pT07XLpoerVq5fuueeesOwrVoU9OW7RooUkqaSkRD//+c/1wAMPaM6cObLZbL6fHz9+XCUlJWrTpo3f7x0/flwejyfgbRtKjsvKypSfnx/ul6gTJ05IUkT2HUmmxi2ZG7upcUvmxm5q3JK5sRN39Jkcu6mifcy//PJL7S3Yq26p54W0n5aelpKk8l1lIcdUWLJLJ06cqPcYnDhxQk1CfqbwayjuM4U9OZakffv26f7775fD4dAPfvADzZs3z/ez0tJStWrVSqmpqSotLfV7vGXLlkpISAh424YkJyerT58+YXpV30pJSZGkiOw7kkyNWzI3dlPjlsyN3dS4JXNjJ+7oMzl2U0X7mKekpKhb6nl65HsPRuX5AjH7k2fUNKX+3ColJUWni49GMarApKSknBV3Xcly2GerOHz4sCZMmKCHHnpIN910kySpb9++2rJliyRpw4YNGjBggC677DJt3LhRbrdbe/fuldvtVnp6elDbAgAAAOEU9srxiy++qGPHjumFF17QCy+8IEl69NFH9cQTT+jZZ59Vz549NWrUKCUmJmrAgAEaO3as3G63ZsyYIUmaOnWqpk+fHtC2AAAAQDiFPTl+7LHH9Nhjj531+NKlS896bPLkyZo8ebLfYz169Ah4WwAAACCcWAQEAAAA8CI5BgAAALxIjgEAAACviEzlBpiOydcR78J1jkuc56iydu1arVq1qt5tiouLJUlpaWkN7m/UqFHKysoKS2xAMBpNcswHAYKxY8cOff31F0pv0/C29Un03ps5uP+LkGMqOtLwNrF6nnOOx54dO3boy21fS+GYFjMpUZL05aGDoe+rqCj0fcQo3p/BJceAVRpNcrxjxw7tyP9a3VqH/oZsZavKeE7vPRDyvgqPFoe8D0RGehvpmuFWR/Gt99c1vM2OHTuU//UXSg1DvuOpyne062BoiX1J/OY65ktPV9K1I62Owk/Fe6utDiFiqr6Q/D8l2ruEvC93UtVqtP93+ERI+6l07Qk5lmpZWVkNVnqrk/kzFwcDYk2jSY4lqVvrND2aEVsfBE9ubPiDgFv8CEZquvTd0VZH8a1/rbQ6gsiiGohgJNq7qMW1sTMtael7C6wOAYg5jSo5NtWOHTu0PT9P3Vo3DWk/LW2VkqTyvf8v5JgKj5aHvA/gTKZ+CayqBuZL9oaXtG9QkkeS9OXh3aHtx3U89FgAoJEiOTZEt9ZNNeXK0G/FhcvczeG7FQdI1UnmV1LbEC9LTdySpC9dX4ce1OGKwLazt1TiDwaF/nxhUvnuFqtDAGICd3ZwLkiOAcSOtkmy/SgMHabDxPNXOkwDJtuxY4f+/fUOdWzTLeR9NUtoJUk6tv90SPvZf6QwoO2Ki4vlOu7S7E+eCen5wqnw+C7Zi+1WhxFxJMcAACBudWzTTT8ZPs3qMHxeXveU1SGgASTHAAAAMSYtLU0tSlL0yPcetDoUn9mfPKOmaclWhxFxrJAHAAAAeFE5RsQwEAIAAJiG5BgRUzUQ4kt1aB36vpJtVf8e3fdlSPs5cDT0WAAAQPwiOUZEdWgt3XZV7JxmS/4R4NRcAACgUYqdrAUAAABGKy4uluvYIc3+cKnVofgUHDsge4o74O0ZkAcAAAB4UTkGACAKiouLVek6rNL3Flgdik+la4+KE9taHQbiSFpamlJPJOiRwT+2OhSf2R8uVZO0wAdAUTkGAAAAvKgcA7UoLi5W0RHp/XVWR/KtoiNSk+Riq8MALBeuaSLDOUWk1PA0kWlpadpXmawW104Oy/OFQ+l7C5SWlmJ1GEBMITkGgEaouLhYchWp4r3VVofiz1Wk4qQm9W6yY8cOfbnt37KltwvpqTxJTSVJXx0K/Uunp+hQyPsAEBsaTXJcXFws19FiPbkxtj4ICo4Wy968qdVhoIa0tDSdLtula4ZbHcm33l9XFVd9iouLdbxI+tfKKAUVgONFUnETKt4IL1t6OzW99marw/Apf2+51SEACJNGkxwDiG3FxcXS4Qp5/lpkdSjfOlyh4oT6E/uqCuxxVb67JUpBBcB1XMWJ9cedlpam3RWnlXTtyCgFFZiK91Y3+CUQACKp0STHaWlpSj1ZrkczYuuD4MmNq9WEDwKESVpamkpO79J3R1sdybf+tbLhijcAALGi0STHJqvqElKmuZv3WB2KT+HRMtmbc6sc4ZOWlqbd7gOy/Sjd6lB8PH8tajCxT0tL0+7KUiX+YFCUompY5btb+EICAOeIqdwAAAAALyrHBkhLS1OLk4c15couVofiM3fzHjWlMgUAAOIMyTEAAKiXqXNLm66wZJdmf/JMSPs4Wn5MktS6aauwxNNbvUPeT6wjOQYAAPXasWOHvt62Q2n2biHtJyGpKkE7cPh0yDEVuwpD3kcs69WrV1j2c3zncUlSu/NCmxdcknqrd9jiimUkxwAAoEFp9m4a/oNHrQ7DZ927T1odQkSFqyJeXaWfN29eWPYXiMJjBzT7w6Uh7+doWYkkqXVyasjx9OrSOuDtSY4BAAAQFuGsLB/b6ZIkte0S2pirXl1aBxUXyTEAAADCIpx9wK2oekskx4ig4uJiHToiLflHhdWh+Bw4IrmbMT8zAACoHckxAACIS1VFGpdeXveU1aH47D9SoMpku9VhoB4kx4iYtLQ0JZzarduuip3TbMk/KtSa+ZkBWKTStUel7y0IeT/uE1UzECSktAw5HrU9P+R4gHgSO1kLAABxLJwDlXbu3C9J6tm2Q2g7ant+XE/NlZaWpsSyVP1k+DSrQ/F5ed1TapXWxOowUA+SY6AORUek99eFto+Tp6r+bd4sPPG07xj6fgBYIx4GKgGNAckxUItwVVKqV4Nq37FnyPtq3zG8lScAAHA2kmOgFiZPvg4ErKhIFe+tDn0/J09W/du8eej7KiqS2rUPfT8AcI4ilhx//vnnmj9/vpYsWaKCggI9/PDDstlsOv/88zVz5kwlJCRo4cKFWr9+vZKSkjRt2jRdfPHFQW0L4GwlRdK/Voa+n3JvvtM0xHynpEgSuU7MCW//16o7JD3DkdS2a88dEgCWikhy/Pvf/15/+9vf1NxbRZg9e7YeeOABDRo0SDNmzNC6devUuXNnbd26VcuXL9e+ffs0efJkvfnmm0FtG6zCo8V6cmPoVZKjZVVZQ+vk0KskhUeL1atziAMqAK9IJDzntQ+xS0h7uoPEIvq/IhjFxcUqdrliasnmYleBmiYyJRrCLyLJcbdu3bRgwQJNmTJFkpSXl6eBAwdKkoYOHapNmzapR48eysjIkM1mU+fOnVVZWamioqKgtk1PT683jrKyMuXn50uqGrHaqXs3nQ7D6yvec0ySlNIh9CpJpzYtlZaW5ouzNidOnNDeo+Wau3lPSM91tKxSktQ6OTGk/UhS4dFydW5zosG4Y9GJE/XHHe7nkhS157vqqqt01VVXhWVfCxculCRNmDAhLPtr6BicOHFCOlwhz1+LQnuiE+6qf1MSQtuPJB2u0InmAZznruOqfHdL6M93oqzq35Tk0PbjOq4TKfF7nnNtif4xLy8vj8rzBKu8vDyAz6HYmxkins+VcLIq9ogkx6NGjdLu3bt9bY/HI5vNJklq0aKFjh8/rpKSErVp08a3TfXjwWzbUHKcnJysPn36SJLv33CIdpXkoosuUkpKSsj7Oe6tBLbrHPrgsN6dq6qB9R3XlJQUHT0a8lOFXUpKSljPh4aeSwrv+Rct0Y49XOf5zqPeW/z20M9z2Rs+z8MVt3RG94S2XUPbUduG4w6naJ8rKSkpUmlZVJ4rGPF8benQoYOUmK7hP3g0Ks8XiHXvPqkObZs0+Dl07Fg4ymLhFc/nSjhFOva6ku6oDMhLSPi2glNaWqpWrVopNTVVpaWlfo+3bNkyqG0bCwaHoTEw9Tyne0L0FRcXy+M6qPL3llsdio/HdVDFcT7EvdhVGHK3ipMnqiomzVNahyWeDm3psoXwi8pbuW/fvtqyZYsGDRqkDRs2aPDgwerWrZvmzZuniRMnav/+/XK73UpPTw9qWwAAEHnhm96yqltih7ZtQ95Xh7a9GM+AiIhKcjx16lRNnz5dzz77rHr27KlRo0YpMTFRAwYM0NixY+V2uzVjxoygtwUAND5paWnaUyE1vfZmq0PxKX9vudLieGl6U+/sAOciYslx165d9frrr0uSevTooaVLl561zeTJkzV58mS/x4LZFgAAAAinMAznBgAAAOIDyTEAAADgRXIMAAAAeMX5xDMAAKAx23+kUC+veyrk/ZScqpqGLrVZaNPQ7T9SqFYdG/csG2vXrtWqVasa3K567vfqgZx1GTVqlLKyssISm0RyDAAA4lQ4p3o77J2GrnPH0Kaha9WRKegCZdUMMCTHAAAgLrFIT2zKysoKa6U33OhzDAAAAHhROUZEHTgqLflHRcj7KTlV9W9qs9Djad0p5HAAAECcIjlGxISzT5XL2ym/S6eeIe2ndafwxgUAAOILyTEihr5eAADANPQ5BgAAALxIjgEAAAAvkmMAAADAiz7HAADjeIoOqfy95aHt42SpJMnWvEVY4lE7axYsiBWBrHoW6IpnUvhXPQMCRXIMADBKuGac2bmzWJLUs13X0HfWLo2ZcAJg1YpnQDBIjgEARgnXTDjMghNesb7qGRAo+hwDAAAAXiTHAAAAgBfdKgAAiCHhHNjGoDYgeCTHAAAYhoFtQOSQHAMAEEMY2AZYiz7HAAAgKrZv364bbrjB1y0EiEVUjgEAQFTMnTtXJ06c0Jw5c7Ro0SKrwzEe/dMjg8oxAACIuO3bt6uwsFCSVFBQQPU4StLS0uijHiQqxwAAIOLmzp3r16Z6HDr6p0cGlWMAABBx1VXjagUFBRZFAtSP5BgAAERct27d/Nrdu3e3KBKgfnSrABqZQAZwSAzigNnCOVBJ4jwPhylTpmjSpEm+9tSpUy2M5ltcE1ETyTEsx2jb2MQADsQ7zvHo6t27t7p166bCwkJ1795dPXv2tDqkoHC+NB4kxzACF6XwYQAHGgPO89g0ZcoUTZkyJWaqxhLnCs5GcgzLcWECgMahd+/eeuutt6wOA6gXyTEAY9AFBwAQaSTHAOJKLHbBMXnAD19IADQ2JMc18EEAxK5474ITi4l9IEyNGwBqQ3J8DvgggMRUUQicyUm9ybEDwLkgOa6BDwKEE1+kAAAwi83j8XisDiJS8vPz1adPH6vDiIpgqpiBzC1JFRMAAMSzuvJEKseNCFVMAACA+pEcxwm6gwAAAIQuweoAAAAAgFhhVOXY7XZr1qxZ2rZtm5o2baonnnhC3bt3tzosAAAAxAmjKsdr165VeXm5XnvtNT344IN6+umnrQ4JAAAAccSoyvEnn3yiIUOGSJIuvfRSffXVV/VuX1ZWpvz8/GiEBgAAgDhgVHJcUlKi1NRUXzsxMVEVFRVKSqr9ZSQnJzeaqdwAAAAQuLoKqEZ1q0hNTVVpaamv7Xa760yMAQAAgGAZlRxfdtll2rBhgyTps88+0wUXXGBxRAAAAIgnRpVdR4wYoU2bNmncuHHyeDx66qmnrA4JAAAAccSo5DghIUG/+tWvrA4DAAAAccqobhUAAABAJJEcAwAAAF4kxwAAAIAXyTEAAADgRXIMAAAAeJEcAwAAAF4kxwAAAICXUfMcB6usrKzOdbMBAADQeJWVldX6uM3j8XiiHAsAAAAQk+hWAQAAAHiRHAMAAABeJMcAAACAF8kxAAAA4EVyDAAAAHiRHAMAAABecT3PcUO2bNmiBx54QL1795bH41FFRYWefPJJ/f73v9eYMWM0dOjQs35nxYoVmjZtmlatWqUOHTr4Hl+7dq1effVVSdKpU6c0ceJEXXPNNXrrrbe0c+dOZWdnS5JeffVVrVy5Ui+99JJatWoVdMwvvfSSNm/erISEBNlsNv3iF79Qbm6u2rZtq/Hjx+v73/++Nm3a5Nu+pKREI0eO1Jo1a9SiRQvf4z/60Y/029/+VhMmTFCnTp2UkPDt96SpU6eqtLTUd2wkqbS0VF27dtX8+fPVtGnToGKu7zjn5eWpTZs28ng8OnLkiH7yk5/oxhtvlCR9+OGHeuGFF+TxeHT69GmNGjVKd955pz7++GO99NJL+v3vfy9JWrRokf74xz9q8+bNSkpK0ocffqglS5bo9ttv1/333693331XnTp1kiTNnz9fPXv21A033BBw/Lfffruys7N18cUXq7y8XFdccYXuu+8+TZw4UZL04x//WNu2bdNf/vIX9erVS1LV3ImjR49WTk6OFixYoLZt26p58+Z68803VVZWpu3bt6tfv36+mMaPH1/r36F///5BHetAnfk3kb79+/7iF7/QjTfe6Iut2iuvvKIXXnhB7733ntq3b6/Kyko1a9ZM2dnZ6tu3b0RirCveht6ru3fv1i9/+Uu9/vrrvseWLVumw4cPa/Lkyerfv7+++93v+u1//vz5fu/numL4y1/+oueee87v93r27Knp06frtdde8/29zny+YcOGaeXKlUpOTtahQ4c0YcIE/fSnP1XHjh3rPT9LS0v13HPPKT8/XwkJCWrRooWmTp2qHj16BHROPvbYY3ryySfVt29fPfLII5L8z8v67Nq1S/PmzdP+/fvVrFkzNWvWTA899JDef/993zkgSUeOHNGYMWN07733+n535syZ+vzzz/XOO+9IkvLy8jRp0iS98847at26tSTpT3/6kz799FP95je/qTeOc1FX7Oeff36d1xSbzabbbrtNs2bN8r2HpdrPu9tvv11jxow559jmzp2rI0eO6PTp07rwwguVnZ2tl19+OejjKkm33XZbvX/fzZs3a9GiRSovL1dSUpK6dOmiRx99VC1bttRtt92mkydPqnnz5r79TZw4UVdffXWtsde8ZpSVlekHP/iB8vLyznoPVn8OLViwoM7X9dZbb+n555/XeeedJ0kqLy/XHXfcoTFjxjT4t6iWlpam559/Pui/QbDnds1YJemCCy7Q9OnTRA3cfQAAElVJREFU6zyOvXv31qhRo+q8LoTq6aefVl5eng4dOqRTp07pvPPOU1pamjZu3Fjr9TsxMVGSdM8990iSXnzxRd/Pz7xGhcPPf/5z9e/fX3fffbekqs+XG264Qb1791ZhYaHatGnj2/aHP/yhbr755rNyF0m+z87x48f7PR6Oa+OFF15Y72to1MmxJA0ePNj3Ybdx40bNnTtXaWlpdW6/fPly/fjHP9brr7/uO8E//fRTvfLKK1q0aJFatGih4uJijR071u9NLEl/+MMftHHjRi1evFgpKSlBx7p9+3bl5ORo2bJlstlsys/P19SpUzVixIg6fyc1NVWZmZlatWqVLyH86quv1Lp1a/3Xf/2XJGnx4sVnvSm2bNnid2wk6cEHH1ROTo6uueaaoGOv6zg/9NBDvovqkSNHdO211+qGG27Q9u3bNWfOHC1atEjt27dXRUWFZs2apT/+8Y+64447tG3bNrndbiUkJGjjxo0aPHiwPv30Uw0cOFBbt27VkCFDJElNmjTRI488opdfflk2my3ouCUpIyNDH3/8sS6++GJ98sknysjI0Pr16zVx4kSVlZVp3759Db7RJOm6667Tdddd50velixZ4vfz2v4OkVTX37d3795nxVbtzjvv9F2oduzYofvvv19//etfoxJ3sO/VurRu3brO13euUlNT9cgjj+jNN9+s88vjgQMHdNddd+l//ud/lJWVpS1bttR7fk6fPl3f/e539dhjj0mSvv76a91///167bXXgjon33vvPQ0fPlwDBw4M6LWcPHlS9957r37961/7vkR88cUX+tWvfqWBAwf6nQPl5eUaM2aMbrnlFtntdp08eVKffvqpLrjgAm3ZskWDBg1Sv379dNNNN+mJJ57QvHnzVFhYqGXLlum11147p2N9rrHPmDGjzmvKT3/60zr3eeZ5V1paqttuu009evRQnz59gort1KlTuu+++/TEE0/okksukSS9/fbbevDBB9W/f/+gj2u1uv6+X3/9tebNm6cXX3zR98XvlVde0R/+8Af94he/kCTNmTPHLwFtyJnHory8XNdcc02D1766XpckXXvttb7C0ZEjR/TDH/5Qo0ePDuj5z8W5nts1Y62ptuO4e/fugK4L5+rhhx+WJL8C3O7du7V///46r2/79u3TiRMndPr0ae3atcsv2Q+nWbNm6cYbb9SwYcPUu3dvzZkzR2PHjtW///1vv8/8cxWua2N96FZxhmPHjqlLly51/nzXrl06evSofvazn+mvf/2rTp8+LakqYb7jjjt8ldm0tDQtX77c783y4osv6sMPP9RLL710TomxJKWnp2vv3r164403dODAAfXp00dvvPFGg793yy23+FUb3nzzTY0dOzao5y4vL9fBgwd9lZ9Q1HWcDx8+rKZNm8pms2nZsmX62c9+5vsWn5SUpIcfflivvfaamjRpor59+2rbtm06duyY3G63xowZo/Xr10uSPvroI19yPHjwYLVu3Vp//vOfzzneK6+8Uh9//LEk6R//+IduvvlmHT9+XMePH9e//vWvgJOOWFb99w3mbkavXr3Ur18/ffLJJxGMrHYNvVejrXv37hoyZEidH9x79+7VT37yEz388MPKysryPV7X+VlUVKR///vfuu2223yPXXjhhcrMzNTq1auDOicfffRRTZ8+XaWlpQG9ltzcXA0ePNivun7xxRfrT3/601nbFhcXq6KiwvflaOXKlbriiit0/fXX+72me+65R//5z3+0YcMGzZo1S7NmzTqnO2ehxF7fNSVQLVq00NixY/X+++8HHdv69et1+eWX+xJjSbr++utVXFysXbt2+W0b6HGV6v77Llu2TPfee6/fHZE777zTlxiHqqSkRAkJCUpKCrzGVvN1nen48eNq1qzZORcxAhHKuX0uGrouRNsbb7yh4cOH67rrrpPT6YzY86Snp2v69Ol67LHHtHXrVu3atUs/+clPwrLvcF4b69PoK8cffvihbrvtNpWXl2vbtm1atGiR3n777Vq3feONN3TjjTeqZcuWuvTSS7VmzRqNGTNGBw8ePOsb2JlJ5Lvvvqvu3bvr2LFjCmVBwvT0dP3ud7/T0qVL9b//+79q1qxZQBe6Sy65REePHtW+fftkt9u1efNm3204SZowYYLvdn5CQoKve0j1sXG5XEpISNAtt9yiK6644pxir+s4V1c29u7dq169eum3v/2tpKovIjfddJPfPlJTU3Xy5Em53W7fG+Cbb77RlVdeqe9///t68cUXVVZWpuPHj6tLly7avXu3pKpvsTfffLMyMjLOKfa+fftq586d8ng8+uijj/TLX/5SV1xxhTZv3qxt27ZpyJAhWrZsmaZOneq7teZ2u4N+nrr+DpFS19939uzZfheefv36+aoUNdntdhUXF0c0zprxBvJerU31h+7Ro0f9Xl/79u31zDPPnHNce/fulSQ98MADuummm3wX5jP9/Oc/V7NmzeRyuc76WW3n5+7du2ut6px33nnau3evrr/++gbPyWrf+c53dN3/b+/+Y6Ku/wCOP7kDRnrYkOqQyIJzk4JWS82mTRSWM5QCEgPrcrFl2TLlbO52wWYIcgi2ckv6sVu1q5FifxSYaxb+YLNWWlEShtCiIzHKZe6OHx3nff847jOOO04OIePr6/GX3K/P6/N+vz9v35/3+/X5fLKzMZvNykxLMF1dXcyePVv5e8OGDdjtdnp6epg/fz4NDQ0cOHCA7u5utFotZWVlaDQawDNRUFpaik6nY9u2bfz+++9otVrUajWVlZXo9XpycnJ8Zj4nUrDYtVpt0D5lrGJjY2lpaQk5NpvN5hObV0JCAt3d3TQ3N4dcrjB6/Q4vC5vNhslkwu1243K5qK2tBfDpswBeffVVZs6cOeo+eI/BsLAwIiIiKCkp4eDBg36fGz7Afeedd0bdr4aGBpqbmwkLC+O6665j586dQcvQu32vtLS0oLP+I11J2/bG6vXII4+QnZ0NBC5Hr2D9wmRob28P2H9funSJhoYG9u7dS3h4OCtXrmTTpk1ERUVNShzp6ekcOnQIo9GorHYDVFVVKSmRAMXFxcydO3fMvzuRfWMw1/zgePgyzc8//0x+fj6LFi3y+5zL5aK+vp6bb76ZxsZG/v77b9577z0yMzOJj4/3m6o/efIkN9xwAwC33347e/bsoaqqitLSUsrLy8cVa2dnJxqNhoqKCgB++OEH1q9fz8qVK5VtjWb16tV8/PHHJCQkkJ6e7rPEM9pyvrds/vrrLwoLC0lISBhX3MN/C3zL2bvEcvToUaqrq5WOS6vV8ttvv/nks9rtdiIjI1GpVCxevJjdu3czbdo0HnvsMaKjo4mOjqapqcnvzDAmJgaTyYTRaOSee+4JOXaVSkVycjLHjh3jxhtvJDIykiVLlnDkyBFOnz7NE088QW1trc/Smjf3LxRXK61iZP0GS6sY6ezZsyxfvnwyw1SM9ViNiorin3/+8Xmtt7dXKdvxplWM9rveE+HIyEgqKirYsmWLshTrtWPHDiV37o477vBZVQrUPm+66SZl0D1cZ2cnOp1uTG1yuPXr11NQUMCxY8cuu59xcXGcOnVK+bumpgbwrEC5XC5l6fnUqVMYDAYlPaujo4MzZ85gNpsBlBWgzZs3A5CUlERSUhI5OTmXjWG8gsWuVquD9iljdfbsWeLi4kKOTavV8v333/u9/ssvvzBnzhwyMzPHVa4QuH5nzZpFV1cXycnJ3HLLLVitVr9+6UrSKrwaGxv9jovBwUHl36O1FwieqjDW7YdivG37crEGKsfe3l4geL8wGUbrv5uamnA4HGzZsgXwTODU19eTl5c3abFkZ2fT39/vs3pxpWkVE903jkbSKoYJNsA8evQoqampWK1WLBYL+/fv5/z585w+fZrc3FwsFotyMJw/fx6TyURfXx/gaawqlYqioiJaW1t9UhxC8dNPP7Ft2zYGBgYASExMJDo6ekwd+0MPPcRnn31GfX19yAdoTEwMVVVVFBcX09PTM67YhwtUzmlpaWRkZFBSUgJAQUEBNTU1/PHHHwA4nU7Ky8vJz88HPEv6PT09tLW1KRcf3H///VgsloBnhunp6SQmJoY00zjc4sWLeeONN5TfnjdvHj/++COAz8UFU9Hw+vWW91i0tbXR3t7O3XffPYnRBRbsWI2NjcXhcNDe3g54TmyPHz/OnXfeeUXb1Ol0tLa2KsfAwMAAX3/9tc/JQUpKCqtWrfKZGQHPxTuzZs3CaDSyefNm+vv7fd4f2T7j4uKYPXu2zxJ6S0sLjY2NyvZCaZNqtRqz2aycWAeTkZHBF198wXfffae81tnZyblz53xmBFNTU3nqqacwGAxcunSJuro6ioqKsFgsWCwW3n33XT788EO/gdNkChb7hg0bgvYpY2G326mrqxvXdRcZGRkcP37cZ4BcV1fHzJkzfWbCxlOugeo3Pz+fmpoanz77yy+/DDnuy0lJSeHQoUPK3ydOnPC73ibQfl0N423bV2q0fuHftH//fsrKypR29Morr0xqasVkmei+cTTX/Myxd5lGpVLhcDgwGo189dVXlJeXK1dSJyYm4nA4/M6wVq9ezfvvv8/27dtZs2YNhYWFhIeH09/fj8FgIDk5WakQ8JxBVldX8/jjj5OamhqwAwlm+fLldHR0kJeXx7Rp03C73WzdupXW1lblMxcuXPC5E0NhYSGrVq3i+uuvJzExkT///JPExESf3x2+nA+euzOMzAecM2cOer2esrKykK8OhtHLebhnn32W3Nxcjhw5wtKlSykqKqKoqAiXy8Xg4CAPPPCAzxLabbfdhtvtVjq1JUuW8Nprr42aU/Tiiy+O+z+HRYsWUVxcrCz7RUZGEh0dPaF3aghUD8EutpxI3vp9++23/ZblwDPzCZ4l0k8++UTJNdy9e3dIOYdXYqzH6q5du6ioqMBkMqFSqXA6nWRkZHDfffcB/mkVAAaDwe8OFiNpNBqMRiNPP/00UVFROJ1O9Hq9chW41zPPPMPhw4cD/saKFStoamripZdeUpZkvUa2z8rKSnbu3EleXh5qtZoZM2awZ88e5dgMtU0mJSWxbt26y6brTJ8+nZqaGnbt2kV1dTWDg4OEh4ezfft2v5nPvLw8Dh48iNVq5cCBA3z00UfKe/Hx8SQnJ/Ppp5+SlZUVdJsTJVjs995772X7lE2bNimragsXLmTp0qU+7c7lcrFx40aSkpLGFdvrr7/Ojh07uHDhAi6Xi7lz5/Lyyy/71clYy3W4kfWbmprK1q1bMRqNOJ1O+vr6iI+P580331S+MzId4MEHH2Tt2rUh7VdOTg6tra08/PDDTJ8+nYiICEpLSwN+1rtftbW1PtsNJFhdDPfWW2+NOTVgPG3bG+vItAqNRqPMPAcqx5Gzo8H6hYkUqP9+4YUXaG5u9pl1nzdvHgMDA3zzzTcAPneEyMrKmrAc4ZFGplUsWLCA559/PuDYBTx36KqrqwM89We1Wie8bwwkzH0lSbBCCCGEEEL8H5G0CiGEEEIIIYbI4FgIIYQQQoghMjgWQgghhBBiiAyOhRBCCCGEGCKDYyGEEEIIIYZc87dyE0KIqezMmTNUVVXR19dHb28vaWlpbNy4MeBjeI1GI5mZmVd0E34hhPh/JzPHQggxRV28eBGDwYDJZMJqtbJv3z7a2tr44IMPrnZoQggxZcnMsRBCTFGff/45CxcuVB5zq1arqaysJCIiArPZzMmTJwHPo2/XrVunfM/pdGIymbDZbLhcLp588kkyMzPR6/XExMRw8eJFLBaL3wNOhBDiWiCDYyGEmKJ6enp8Hj0MnqdIHT58mK6uLvbt28fg4CBr165VnhAIsHfvXuWx4Xa7ndzcXOX9rKysf+3JjEII8V8kaRVCCDFFxcfHc+7cOZ/XbDYbLS0tzJ8/n7CwMCIiIrjrrrvo6OhQPtPR0cGCBQsAz2NwdTodNpsNwO/x8kIIca2RwbEQQkxRy5Yto6mpiV9//RXwpEuYzWZmzJihpFQ4nU6+/fZbbr31VuV7Op2OEydOAGC322lrayMhIQEg4IV8QghxLZG0CiGEmKI0Gg1ms5ni4mLcbjcOh4Nly5ah1+vp7u7m0Ucfxel0smLFClJSUpTvrVmzhpKSEgoKChgYGOC5554jNjb2Ku6JEEL8d4S53W731Q5CCCGEEEKI/wJJqxBCCCGEEGKIDI6FEEIIIYQYIoNjIYQQQgghhsjgWAghhBBCiCEyOBZCCCGEEGKIDI6FEEIIIYQYIoNjIYQQQgghhvwPpmEvZKCEhxIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(df[\"Color\"],df[\"Price\"])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Color doesn't seem to affect the sales much here in our case. Let's drop it! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"Color\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now on to the numerical features\n",
    "* Accidents\n",
    "* Year\n",
    "* Fuel economy\n",
    "* Distance\n",
    "* Days on market\n",
    "* Mileage\n",
    "* Additional features (eg: heated seats, push-button start, etc.)\n",
    "* Previous owners\n",
    "* Seats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll analyze all of these features and see which ones affect the price the most."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a368e41d0>"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,10))\n",
    "sns.heatmap(df.corr(),annot=True,linewidths=1,cmap = \"coolwarm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price                    1.000000\n",
       "Year                     0.678290\n",
       "Additional_features      0.447112\n",
       "Trim_copy                0.274199\n",
       "Seats                    0.240168\n",
       "Model_copy               0.229179\n",
       "Distance                 0.111819\n",
       "Photos                   0.035993\n",
       "Days_on_market          -0.012846\n",
       "Fuel_economy_combined   -0.130001\n",
       "Accidents               -0.158132\n",
       "Previous_owners         -0.441762\n",
       "Mileage                 -0.670072\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()[\"Price\"].sort_values(ascending = False) # Understand each of these blocks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Photos, Distance and Days on market don't seem to correlate much(+ve or -ve) with the target variable, Price. Let's drop these three! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"Distance\",axis=1,inplace=True)\n",
    "df.drop(\"Days_on_market\",axis=1,inplace=True)\n",
    "df.drop(\"Photos\",axis=1,inplace=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Price\n",
    "### This is our target variable. \n",
    "It has positive strong correlation with some features while strongly negative with some. We'll drop the columns such as Distance and days on market since they don't really correlate with the Price column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a37669590>"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.distplot(df[\"Price\"],fit=norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18666    73495.0\n",
       "12462    69995.0\n",
       "6723     60777.0\n",
       "9442     58800.0\n",
       "18185    57750.0\n",
       "          ...   \n",
       "140       1987.0\n",
       "7564      1800.0\n",
       "13252     1800.0\n",
       "3670      1595.0\n",
       "13076     1000.0\n",
       "Name: Price, Length: 17480, dtype: float64"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Price\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "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>Id</th>\n",
       "      <th>Accidents</th>\n",
       "      <th>Body_type</th>\n",
       "      <th>Year</th>\n",
       "      <th>Fuel_economy_combined</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Drivetrain</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Make</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Model</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Additional_features</th>\n",
       "      <th>Previous_owners</th>\n",
       "      <th>Seats</th>\n",
       "      <th>Seller_type</th>\n",
       "      <th>Trim</th>\n",
       "      <th>Price</th>\n",
       "      <th>Trim_copy</th>\n",
       "      <th>Model_copy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3793</th>\n",
       "      <td>271119024</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>Great_Price</td>\n",
       "      <td>ALL_WHEEL_DRIVE</td>\n",
       "      <td>V6</td>\n",
       "      <td>Kia</td>\n",
       "      <td>22805.0</td>\n",
       "      <td>Stinger</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>DEALER</td>\n",
       "      <td>GT2 AWD</td>\n",
       "      <td>33800.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3963</th>\n",
       "      <td>271322853</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>Great_Price</td>\n",
       "      <td>ALL_WHEEL_DRIVE</td>\n",
       "      <td>I4</td>\n",
       "      <td>Kia</td>\n",
       "      <td>10781.0</td>\n",
       "      <td>Stinger</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>DEALER</td>\n",
       "      <td>Premium AWD</td>\n",
       "      <td>30750.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4309</th>\n",
       "      <td>272889566</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>Great_Price</td>\n",
       "      <td>ALL_WHEEL_DRIVE</td>\n",
       "      <td>V6</td>\n",
       "      <td>Kia</td>\n",
       "      <td>10812.0</td>\n",
       "      <td>Stinger</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>DEALER</td>\n",
       "      <td>GT2 AWD</td>\n",
       "      <td>35900.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4979</th>\n",
       "      <td>272349541</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>Poor_Price</td>\n",
       "      <td>FRONT_WHEEL_DRIVE</td>\n",
       "      <td>I4</td>\n",
       "      <td>Mazda</td>\n",
       "      <td>2595.0</td>\n",
       "      <td>MAZDA6</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>DEALER</td>\n",
       "      <td>Signature FWD</td>\n",
       "      <td>31144.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5131</th>\n",
       "      <td>268162851</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>Poor_Price</td>\n",
       "      <td>ALL_WHEEL_DRIVE</td>\n",
       "      <td>I4</td>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>15264.0</td>\n",
       "      <td>Lancer Evolution</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>DEALER</td>\n",
       "      <td>GSR</td>\n",
       "      <td>34999.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18996</th>\n",
       "      <td>269445941</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SUV / Crossover</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>Great_Price</td>\n",
       "      <td>ALL_WHEEL_DRIVE</td>\n",
       "      <td>V6</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>3992.0</td>\n",
       "      <td>4Runner</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>DEALER</td>\n",
       "      <td>TRD Pro 4WD</td>\n",
       "      <td>47500.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19022</th>\n",
       "      <td>274403219</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SUV / Crossover</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>Great_Price</td>\n",
       "      <td>ALL_WHEEL_DRIVE</td>\n",
       "      <td>V6</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>14793.0</td>\n",
       "      <td>4Runner</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>DEALER</td>\n",
       "      <td>Limited Nightshade 4WD</td>\n",
       "      <td>39997.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19045</th>\n",
       "      <td>272700761</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SUV / Crossover</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>27.238208</td>\n",
       "      <td>Great_Price</td>\n",
       "      <td>ALL_WHEEL_DRIVE</td>\n",
       "      <td>V6</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>25183.0</td>\n",
       "      <td>4Runner</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>DEALER</td>\n",
       "      <td>Limited 4WD</td>\n",
       "      <td>34187.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19080</th>\n",
       "      <td>270767782</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SUV / Crossover</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>Great_Price</td>\n",
       "      <td>ALL_WHEEL_DRIVE</td>\n",
       "      <td>V6</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>31792.0</td>\n",
       "      <td>4Runner</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>DEALER</td>\n",
       "      <td>SR5 4WD</td>\n",
       "      <td>30855.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19082</th>\n",
       "      <td>271451784</td>\n",
       "      <td>0.0</td>\n",
       "      <td>SUV / Crossover</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>Great_Price</td>\n",
       "      <td>ALL_WHEEL_DRIVE</td>\n",
       "      <td>I4</td>\n",
       "      <td>Toyota</td>\n",
       "      <td>8847.0</td>\n",
       "      <td>RAV4 Hybrid</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>DEALER</td>\n",
       "      <td>XSE AWD</td>\n",
       "      <td>33500.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>299 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Id  Accidents        Body_type    Year  Fuel_economy_combined  \\\n",
       "3793   271119024        1.0            Sedan  2018.0              21.000000   \n",
       "3963   271322853        0.0            Sedan  2019.0              25.000000   \n",
       "4309   272889566        0.0            Sedan  2018.0              21.000000   \n",
       "4979   272349541        0.0            Sedan  2019.0              27.000000   \n",
       "5131   268162851        0.0            Sedan  2011.0              20.000000   \n",
       "...          ...        ...              ...     ...                    ...   \n",
       "18996  269445941        0.0  SUV / Crossover  2020.0              17.000000   \n",
       "19022  274403219        0.0  SUV / Crossover  2019.0              18.000000   \n",
       "19045  272700761        0.0  SUV / Crossover  2017.0              27.238208   \n",
       "19080  270767782        0.0  SUV / Crossover  2017.0              18.000000   \n",
       "19082  271451784        0.0  SUV / Crossover  2019.0              40.000000   \n",
       "\n",
       "            Rating         Drivetrain Engine        Make  Mileage  \\\n",
       "3793   Great_Price    ALL_WHEEL_DRIVE     V6         Kia  22805.0   \n",
       "3963   Great_Price    ALL_WHEEL_DRIVE     I4         Kia  10781.0   \n",
       "4309   Great_Price    ALL_WHEEL_DRIVE     V6         Kia  10812.0   \n",
       "4979    Poor_Price  FRONT_WHEEL_DRIVE     I4       Mazda   2595.0   \n",
       "5131    Poor_Price    ALL_WHEEL_DRIVE     I4  Mitsubishi  15264.0   \n",
       "...            ...                ...    ...         ...      ...   \n",
       "18996  Great_Price    ALL_WHEEL_DRIVE     V6      Toyota   3992.0   \n",
       "19022  Great_Price    ALL_WHEEL_DRIVE     V6      Toyota  14793.0   \n",
       "19045  Great_Price    ALL_WHEEL_DRIVE     V6      Toyota  25183.0   \n",
       "19080  Great_Price    ALL_WHEEL_DRIVE     V6      Toyota  31792.0   \n",
       "19082  Great_Price    ALL_WHEEL_DRIVE     I4      Toyota   8847.0   \n",
       "\n",
       "                  Model Transmission  Additional_features  Previous_owners  \\\n",
       "3793            Stinger    Automatic                  7.0              1.0   \n",
       "3963            Stinger    Automatic                  8.0              1.0   \n",
       "4309            Stinger    Automatic                  7.0              1.0   \n",
       "4979             MAZDA6    Automatic                  7.0              1.0   \n",
       "5131   Lancer Evolution       Manual                  0.0              1.0   \n",
       "...                 ...          ...                  ...              ...   \n",
       "18996           4Runner    Automatic                  9.0              1.0   \n",
       "19022           4Runner    Automatic                  8.0              1.0   \n",
       "19045           4Runner    Automatic                  6.0              1.0   \n",
       "19080           4Runner    Automatic                  3.0              2.0   \n",
       "19082       RAV4 Hybrid    Automatic                  3.0              1.0   \n",
       "\n",
       "       Seats Seller_type                    Trim    Price  Trim_copy  \\\n",
       "3793     5.0      DEALER                 GT2 AWD  33800.0          5   \n",
       "3963     5.0      DEALER             Premium AWD  30750.0          5   \n",
       "4309     5.0      DEALER                 GT2 AWD  35900.0          5   \n",
       "4979     5.0      DEALER           Signature FWD  31144.0          1   \n",
       "5131     5.0      DEALER                     GSR  34999.0          1   \n",
       "...      ...         ...                     ...      ...        ...   \n",
       "18996    5.0      DEALER             TRD Pro 4WD  47500.0          1   \n",
       "19022    7.0      DEALER  Limited Nightshade 4WD  39997.0          3   \n",
       "19045    7.0      DEALER             Limited 4WD  34187.0          5   \n",
       "19080    7.0      DEALER                 SR5 4WD  30855.0          1   \n",
       "19082    5.0      DEALER                 XSE AWD  33500.0          5   \n",
       "\n",
       "       Model_copy  \n",
       "3793            5  \n",
       "3963            5  \n",
       "4309            5  \n",
       "4979            3  \n",
       "5131            1  \n",
       "...           ...  \n",
       "18996           3  \n",
       "19022           3  \n",
       "19045           3  \n",
       "19080           3  \n",
       "19082           1  \n",
       "\n",
       "[299 rows x 20 columns]"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"Price\"] > 30000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(index = df[df[\"Price\"] > 30000].index, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Year\n",
    "We expect the price to go up with the year as newer cars are going to be more expesive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a31154050>"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x = \"Year\", y = \"Price\", data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional features\n",
    "More the features in a vehicle, the expensive it gets!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2c1b1a10>"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x = \"Additional_features\", y = \"Price\", data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do see an upward trend in the Price as the number of features increase."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a37642e50>"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BYEOnS3W3aFZGwO4yjDf/PbfdJQAADEFgAwB0eX6/X2cajmvTvtV2l2K8Mw3HdZ3fYXcZuEbcdAAAAGA4VtgAAF2e1+vVudOW7rxpot2lGG/TvtXyer12l4FrxAobAACA4QhsAAAAhmt3YDt48KD+7//+T0ePHpVlWZGsCQAAAH+jXdewrVq1Shs2bNDnn3+ue++9V59++qmKiooiXRsAAADUzhW2//mf/9Hvf/97eTwe/exnP9Pu3bsjXRcAAAC+0K7AdukUqMNx8bktCQkJkasIAAAArbTrlOg999yjBx54QLW1tXrooYc0atSoSNcFAACAL7QrsP3jP/6jbrvtNu3fv1/9+vXTd7/73UjXBQAAgC+065RoaWmpSktLNWbMGC1atEhvvvlmpOuKWSdPnlRubq5OnTpldykAAMAQ7Qpsr776qh577DFJ0osvvqhXX301okXFshUrVmjPnj1asWKF3aUAAABDtOuUqNPpVGJioiSpW7duoZsPEF4nT57U+vXrZVmW1q9fr6lTp6pnz552lwUAQESVl5errKysw/34/X5JCturt8aOHaucnJyw9NVR7Qpsd911lyZPnqwhQ4aosrJSP/jBDyJdV0xasWKFgsGgJKmlpUUrVqzQo48+anNVAAB0DZcuJ4rGd6W2K7D9+te/1p133qlPPvlE9957r2666aZI1xWTNmzYoAsXLkiSLly4oD/96U8ENgBA1MvJyQnLSlZeXp4kqaSkpMN9meaK17CtWbNGkvTcc89p/fr12rdvn8rKyrR48eKrdrx7925NmTJFklRZWamRI0dqypQpmjJlSmjZc+nSpRo3bpwmTpyoPXv2SJJqamo0adIkTZ48WXPmzAmtOF2ubbQZOXJkq89ZWVk2VQIAAExyxRW23r17S5L69u2ruLi4dnf6u9/9TuvWrdN1110nSfrwww/185//XNOmTQu1qays1I4dO7RmzRodOXJEubm5Wrt2rYqLi5Wfn68RI0aoqKhIGzduVEpKymXbAgAAxIIrBrZLKz5lZWV65ZVX2t1pamqqlixZoieeeEKStHfvXn3yySfauHGj+vbtq8LCQu3cuVOZmZlyOBxKSUlRS0uL/H6/KisrNXz4cEkXV5gqKirk8/ku2zbazlH/+c9/bvV5y5YtKiwstKkaAF+X3+/XMUkvy7K7FOMdkRT84kJxAF+tXdeweTwevfXWW/L5fHI6L55F9fl8X9k+Oztbhw4dCn0eMmSIxo8fr5tvvlnLli3T888/L4/Ho+Tk5FCbHj16qK6uTpZlhe5CvbQtEAhctu3VAltjY6Oqqqrac4hGyMjIUEVFhVpaWhQXF6d/+Id/6FL1t0dDQ0P7niUDSRfHq6NzoKGhIUzVxIZwjHlTU1OYqokNTU1NzPNOFo55bqJL8yAaj+2qgS0QCOjQoUOtngvmcDj0H//xH+3+j4wePVpJSUmhv8+bN0933XWX6uvrQ23q6+vl8XhCgfDStqSkJLnd7su2vZrExESlp6e3u0675efn691331VLS4vi4+OVn58fdY/1cLlcOm93EV2Iy+Xq8Bx2uVzS6TAVFAPCMea9e/dW96NHNV08AulqXpal5N69wzLPT+tcmKqKfuGY5yZyuVyS1GWP7UpB84qLHatWrdKPfvQjnT17Vg899JBWrlyplStXXlNYk6Tp06eHbhTYtm2bBg8erGHDhmnr1q0KBoOqra1VMBiU1+vVoEGDtH37dkkXTwlmZGR8Zdto06tXL40ZM0YOh0NjxoyJurAGAAC+niuusP3xj39UeXm5AoGAnnjiia991+LcuXM1b948devWTb169dK8efPkdruVkZGhCRMmKBgMqqioSJJUUFCg2bNna/HixerXr5+ys7MVFxd32bbRaOrUqTp48KCmTp1qdykAAMAQVwxsCQkJSkhIkNfrVXNz8zV13KdPH5WWlkqSBg8erNWrV7dpk5ubq9zc3FbbfD6fVq1a1a620ahXr15asmSJ3WUAAHBFJSUlqq6utruMVvbv3y/pr89jM0laWlqH6mrXTQeSZFnc7QQAAC6qrq7W3t275Ulod5SIOKvl4rNba6oqba6ktbqmCx3u44qjXF1drccee0yWZYX+fslzzz3X4f84AADoujwJ8Rp+4zfsLsN4O451/M6vKwa2f/3Xfw39feLEiR3+jwEAAODaXTGwXXqALQAAAOxjzonnLq68vDz0jtSO8H/xxO9wPbZk7NixYXmhLgAAsA+BzTCnTp2SFL7ABgAAuj4CW5jk5OSEZSXr0i2/JSUlHe4LAABEBwIbOpXf79eJujjNf89tdynGq6mL0zd5KTYAQFd5NRUAAADsxwobOpXX65Xr7AHNygjYXYrx5r/nVneuZQQAiBU2AAAA4xHYAAAADEdgAwAAMByBDQAAwHAENgAAAMMR2AAAAAxHYAMAADAcgQ0AAMBwBDYAAADDEdgAAAAMR2ADAAAwHIENAADAcAQ2AAAAwxHYAAAADEdgAwAAMByBDQAAwHAENgAAAMMR2AAAAAxHYAMAADAcgQ0AAMBw8XYXAAAAuh6/36+6pgvacey03aUYr67pgvx+f4f6YIUNAADAcKywAQCAa+b1elV37IiG3/gNu0sx3o5jp+X1ejvUBytsAAAAhiOwAQAAGI5TogCAqHCm4bg27Vttdxkh55vrJUndu/WwuZLWzjQc17fU0+4ycI0IbACALi8tLc3uEtrYv//iXYHf6p9qcyWtfUs9jRwvXBmBDQDQ5eXl5dldQhuXaiopKbG5EkQDrmEDAAAwHCtsABABRyW9LMvuMkICX/zptrWKto5KSra7CKALILABQJiZeH3Qif37JUl9BgywuZLWkmXmeAGmIbABQJhxPRWAcOMaNgAAAMNFLLDt3r1bU6ZMkSTV1NRo0qRJmjx5subMmaNgMChJWrp0qcaNG6eJEydqz54919wWAAAgFkQksP3ud7/TrFmz1NjYKEkqLi5Wfn6+/vCHP8iyLG3cuFGVlZXasWOH1qxZo8WLF+upp5665rYAAACxICLXsKWmpmrJkiV64oknJEmVlZUaPny4JCkrK0sVFRXy+XzKzMyUw+FQSkqKWlpa5Pf7r6ltR1+kCnt8GojT/PfMuVft8yaHJOn6BHPu6JMujtNAu4sAABghIoEtOztbhw4dCn22LEsOx8X/U+zRo4fq6uoUCASUnPzXm7kvbb+WtlcLbI2NjaqqqgrnoUVcQ0ODJHW5utvL6/WqT7+bFLS7kL9x5rPPJEmeG75tcyWt9bnh4nh1dC5cmlNon4aGhqj89xft3y0mivYx57vl2nT0u6VT7hJ1Ov965rW+vl5JSUlyu92qr69vtd3j8VxT26tJTExUenp6mI6ic7hcLknqcnW319y5c+0uoY1ov3vO5XJJp+2uoutwuVxR+e8v2r9bTBTtY37p+NA+7fluuVKg65S7RAcNGqTt27dLkrZs2aKMjAwNGzZMW7duVTAYVG1trYLBoLxe7zW1BQAAiAWdssJWUFCg2bNna/HixerXr5+ys7MVFxenjIwMTZgwQcFgUEVFRdfcFgAAIBZELLD16dNHpaWlkiSfz6dVq1a1aZObm6vc3NxW266lbUeVlJSouro6rH121P4vnkZu4oM309LSjKwLAIBoF9NvOqiurtb7H3yooMuc06uOlov/k+w8cNTmSlpzNvjtLgEAgJgV04FNkoIur84PusfuMozX/cM/2l0CAAAxi1dTAQAAGI7ABgAAYDgCGwAAgOEIbAAAAIYjsAEAABiOwAYAAGA4AhsAAIDhCGwAAACGI7ABAAAYjsAGAABguJh/NRUQ7fx+v3RGcm7m97OrOiP5r+O9uQDMwzc4AACA4VhhA6Kc1+tVzbkaBe8I2l2K8ZybnfJ6vXaXAQBtsMIGAABgOAIbAACA4QhsAAAAhiOwAQAAGI7ABgAAYDgCGwAAgOEIbAAAAIYjsAEAABiOwAYAAGA4AhsAAIDhCGwAAACGI7ABAAAYjsAGAABgOAIbAACA4eLtLsBOfr9fzoZT6v7hH+0uxXjOhlPy+xPsLgMAgJgU04ENAAB8fXVNF7Tj2Gm7ywhpbAlKkhLjzDqBWNd0ocN9xHRg83q9+uR0k84PusfuUozX/cM/yuv12l0GAMAQaWlpdpfQxv79+yVJfQcMsLmStjo6XjEd2AAAwNeTl5dndwltXKqppKTE5krCz6w1QwAAALRBYAMAADBczJ8SdTb4jbpL1NF8TpJkdbvO5kpaczb4JfW2uwwAAGJSTAc2ky+YHNDftHDU28jxAgAgFsR0YOOCScSMM5Jzs0FXQJz/4s/utlbR1hlJ37K7CABoK6YDGxALTFwZDa0kf8uwW++/ZeZ4AQCBDYhyrCQDQNdn0DkSAAAAXA6BDQAAwHAENgAAAMN16jVs9957rzwejySpT58+mjBhgp555hnFxcUpMzNTv/nNbxQMBjV37lx99NFHSkhI0Pz589W3b1/t2rWrTVsAAIBY0GmBrbGxUZK0cuXK0LYf//jHWrJkib797W/rF7/4hSorK3X48GE1NTXptdde065du7Rw4UItW7ZMc+bMadN28ODBnVU+AACAbTotsO3bt0/nzp3TtGnTdOHCBeXm5qqpqUmpqamSpMzMTG3btk0nTpzQyJEjJUlDhw7V3r17FQgELtuWwAYAAGJBpwW27t27a/r06Ro/frwOHjyohx56SElJSaH9PXr00GeffaZAICC32x3aHhcX12bbpbZX09jYqKqqqvAeSIQ1NDRIUperuytjzDsfY975GPPOx5h3vmge804LbD6fT3379pXD4ZDP55PH49GZM2dC++vr65WUlKTz58+rvr4+tD0YDMrtdrfadqnt1SQmJio9PT28BxJhLpdLkrpc3V0ZY975GPPOx5h3Psa883X1Mb9S0Oy0u0Rff/11LVy4UJJ07NgxnTt3Ti6XS59++qksy9LWrVuVkZGhYcOGacuWLZKkXbt2aeDAgXK73erWrVubtgAAALGg01bYxo0bpyeffFKTJk2Sw+HQggUL5HQ69fjjj6ulpUWZmZn63ve+p7//+79XRUWFJk6cKMuytGDBAknSU0891aYtAABALOi0wJaQkKDnnnuuzfbS0tJWn51Op55++uk27YYOHdqmLQAAQCzgwbkAAACGI7ABAAAYjsAGAABgOAIbAACA4QhsAAAAhiOwAQAAGI7ABgAAYDgCGwAAgOEIbAAAAIYjsAEAABiOwAYAAGA4AhsAAIDhCGwAAACGI7ABAAAYjsAGAABguHi7C4gW5eXlKisr63A/+/fvlyTl5eV1uC9JGjt2rHJycsLSFwDEAr7POx9jfnUENsP07NnT7hIAAGHA93nni+YxJ7CFSU5OjjEpHIgUfgtGLOD7vPMx5ldHYAPQ6aL5t2AAiAQCG4B247dgALAHd4kCAAAYjsAGAABgOAIbAACA4QhsAAAAhiOwAQAAGI7ABgAAYDgCGwAAgOF4DhsAGIy3SwCQCGwAEBN4uwTQtRHYAMBgvF0CgMQ1bAAAAMYjsAEAABiOwAYAAGA4AhsAAIDhCGwAAACG4y5RdFk8nwoAECsIbIh5PJ8KAGA6Ahu6LJ5PBQCIFVzDBgAAYDgCGwAAgOEIbAAAAIYjsAEAABiOwAYAAGC4LnWXaDAY1Ny5c/XRRx8pISFB8+fPV9++fe0uCwAAIKK61ArbW2+9paamJr322mt67LHHtHDhQrtLAgAAiLguFdh27typkSNHSpKGDh2qvXv32lwRAABA5HWpU6KBQEButzv0OS4uThcuXFB8/OUPo7GxUVVVVZ1VHgAAQER0qcDmdrtVX18f+hwMBr8yrElSYmKi0tPTO6M0AACADrnSIlOXOiU6bNgwbdmyRZK0a9cuDRw40OaKAAAAIq9LrbCNHj1aFRUVmjhxoizL0oIFC+wuCQAAIOK6VGBzOp16+umn7S4DAACgU3WpwHatuOkAAAB0FY2NjV+5z2FZltWJtQAAAOAadambDgAAAGIRgQ0AAMBwBDYAAADDEdgAAAAMR2ADAAAwXFQ/1qOrOHXqlH7605/qlVdeUf/+/UPb3377bT3//POKj4/Xfffdp/vvv9/GKqPHvffeK4/HI0nq06ePigKQkHgAAAcVSURBVIuLQ/tKS0u1evVqxcfH6+GHH9add95pV5lR5cUXX9Tbb7+t5uZmTZo0SePHjw/tY56H3xtvvKH/+q//kvTXxxtVVFQoKSlJEvM8EpqbmzVz5kwdPnxYTqdT8+bN4/s8wpqamvTkk0/qs88+k9vtVlFRkb7zne+E9kfdPLdgq6amJuvXv/61dffdd1vV1dWtto8aNco6c+aM1djYaP30pz+1jh8/bmOl0eH8+fPWj3/848vuO378uHXPPfdYjY2N1tmzZ0N/R8e8++671i9/+UurpaXFCgQCVklJSWgf8zzy5s6da61evTr0mXkeGRs2bLDy8vIsy7KsrVu3Wr/5zW9C+5jnkbFy5Upr1qxZlmVZ1oEDB6xp06aF9kXjPOeUqM0WLVqkiRMn6oYbbmi1/cCBA0pNTdX111+vhIQE3XrrrXrvvfdsqjJ67Nu3T+fOndO0adP04IMPateuXaF9e/bs0S233KKEhAR5PB6lpqZq3759NlYbHbZu3aqBAwdqxowZ+tWvfqU77rgjtI95HlkffPCBqqurNWHChNA25nlk+Hw+tbS0KBgMKhAIKD7+ryewmOeRUV1draysLElSv379dODAgdC+aJznnBK10RtvvCGv16uRI0fqpZdearUvEAiETttJUo8ePRQIBDq7xKjTvXt3TZ8+XePHj9fBgwf10EMPqby8XPHx8Yx5hJw+fVq1tbVavny5Dh06pIcffljl5eVyOByMeYS9+OKLmjFjRqttjHlkuFwuHT58WGPGjNHp06e1fPny0D7GPDLS09O1adMmjRo1Srt379axY8fU0tKiuLi4qBxzVthstHbtWr3zzjuaMmWKqqqqVFBQoBMnTkiS3G636uvrQ23r6+tbTT58PT6fTz/60Y/kcDjk8/mUnJzMmEdYcnKyMjMzlZCQoH79+ikxMVF+v18SYx5JZ8+e1ccff6zbbrut1XbGPDJ+//vfKzMzU//7v/+r//7v/9bMmTNDrxlizCPjvvvuk9vt1oMPPqhNmzZp8ODBiouLkxSdY05gs9F//ud/atWqVVq5cqXS09O1aNEiffOb35Qk9e/fXzU1NTpz5oyampr03nvv6ZZbbrG54q7v9ddf18KFCyVJx44dUyAQCI35kCFDtHPnTjU2Nqqurk4HDhzQwIED7Sw3Ktx6663685//LMuydOzYMZ07d07JycmSmOeR9Je//EW33357m+3M88hISkoKBYLrr79eFy5cUEtLiyTmeaR88MEHuvXWW7Vy5UqNGjVK3/72t0P7onGe8y5RQ0yZMkVz587Vhx9+qIaGBk2YMCF0V5FlWbrvvvv0wAMP2F1ml3fprqLa2lo5HA49/vjj2r17t1JTU3XXXXeptLRUr732mizL0i9/+UtlZ2fbXXJU+Od//mdt375dlmXpkUce0ZkzZ5jnEfZv//Zvio+P189+9jNJ0r//+78zzyOovr5ehYWFOnHihJqbm/Xggw9KEvM8gvx+vx599FGdO3dOHo9HzzzzjMrKyqJ2nhPYAAAADMcpUQAAAMMR2AAAAAxHYAMAADAcgQ0AAMBwBDYAAADD8aYDADHvpZde0jvvvCOn0ymHw6FHHnlEN998c7t/vra2Vvv27dMPfvCDCFYJIJYR2ADEtOrqar399tt69dVX5XA4Qm8dWbduXbv7ePfdd/Xxxx8T2ABEDIENQEzzer2qra3V66+/rqysLKWnp+v111/XRx99pPnz50u6+HqtBQsWyOVyqaioSEePHtXp06eVlZWl3NxcvfTSSzp//rxuueUWHT16VG+++aacTqeGDRumgoICm48QQDTgwbkAYl5lZaVWrVqlbdu2qXv37nrkkUf08ssva8GCBUpLS9OaNWt06NAhjR8/Xtu2bdP48ePV2NiorKwsbd++XW+88YY+/vhjPf7447rvvvs0e/ZsDR06VH/4wx90//33Kz6e340BdAzfIgBiWk1Njdxut4qLiyVdfD/hL37xC50/f15PPfWUJKm5uVk+n0/Jycn64IMP9O6778rtdqupqalNf8XFxXrllVf0L//yLxo6dKj4nRhAOBDYAMS0jz76SK+++qqWL1+uxMRE+Xw+eTwe3XjjjVq0aJFSUlK0c+dOnThxQm+88YY8Ho+efvpp1dTUqLS0VJZlyel0KhgMSpJKS0v11FNPKTExUdOnT9f777+v4cOH23yUALo6AhuAmHb33XfrwIEDGj9+vFwulyzL0hNPPKHevXuroKBALS0tkqRnnnlG/fv316OPPqqdO3fquuuuU9++fXX8+HENHDhQy5Yt0+DBg/Xd735X48aN0ze+8Q3deOON+t73vmfzEQKIBlzDBgAAYDgenAsAAGA4AhsAAIDhCGwAAACGI7ABAAAYjsAGAABgOAIbAACA4QhsAAAAhiOwAQAAGO7/B8ICtg7Gnlk1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x = \"Seats\", y = \"Price\", data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of seats is ambiguous. Sedans and small/mid level suvs can accommodate 5 people. So, some brands write 4 while others write 5 in their seating description. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, 6 and higher can again be up for debate. Let's see!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Seats_testing\"] = df[\"Seats\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Seats_testing\"] = df[\"Seats_testing\"].replace(5.0, 4.0)\n",
    "df[\"Seats_testing\"] = df[\"Seats_testing\"].replace(7.0, 6.0)\n",
    "df[\"Seats_testing\"] = df[\"Seats_testing\"].replace(9.0, 8.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2c1b10d0>"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x = \"Seats_testing\", y = \"Price\", data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seats_testing\n",
       "4.0    13713.115658\n",
       "6.0    15327.497470\n",
       "8.0    17147.673387\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Seats_testing\")[\"Price\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"Seats\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fuel economy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Body_type\n",
       "SUV / Crossover    24.760239\n",
       "Sedan              30.205635\n",
       "Name: Fuel_economy_combined, dtype: float64"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"Body_type\")[\"Fuel_economy_combined\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a37373390>"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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reUZ+uxyDLLMsRPtyG/SdTifPPPMM8+bNIyUlhYKCgmZ5ysrKmDZtGjZb41OWdXV1PPjggyxYsIB7772XsrIyoHEf3Tlz5jBv3jw2b97czrciuouC0ppWx/MviO0bSKnVzvlvn9wVQrQPt0F/27Zt2O120tPTeeSRR1izZk2T4zt27ODuu++mpOS7KXTvvfceMTExvPvuu8yePZtXX32V+vp6Vq9ezRtvvMHGjRtJT0+nuLi4/e9IdGkWm4MSi73VOfoXyHIMQnQMt0E/KyuL8ePHAxAfH09OTk7TArRa0tLS6NWrV4vnTJgwgZ07d3L06FGioqIICgrCaDQyZswY9u7d2573IrqBY99Ow7x0zZ1LyQweITqG24ezLBYLZvN3D9HodDocDgd6feOpY8eObfGcgIDGNVT8/f2prq5uknYh3WJpex62zWZDKUVeXt7l3Y2Xqaur63Zt8/mxxv9zVVdFYVGNK700WEN1UdOhwzB/PbvzT5MUXt8k3a43U1jUfC/di8vojm3TWaRtWuYt7eI26JvNZqxWq+u10+l0BfzLOcdqtRIYGNisHKvV2uRNoCUmU2NvMDY21l01vVJeXl63a5t/nT6MhvOMGNgf/UVP24b0CSGy94AmeeOirBwvsTS7x9PlNfSNaP4B78VldMe26SzSNi3rSe3S1puX2+GdxMREMjIyAMjOziYmJsbtBRMTE/nss88AyMjIYMyYMURHR1NQUEBFRQV2u529e/eSkJBwufcgeogTpVYignyaBPzWjOwbwPESK3X1DZ1QMyG8g9ue/tSpU8nMzCQ5ORmlFKtWrSItLY2oqCgmT57c4jnz589n+fLlzJ8/H4PBwLp16zAYDDz22GPcc889KKWYM2cO4eHh7X5Doms7XmJlwCW7ZbVmZL9AnAoOFVXzgwG93J8ghHDLbdDXarWsXLmySVp0dHSzfNu3b3d97+vry8svv9wsz6RJk5g0adLV1FP0AEopjpdYmTby8t7sL57BI0FfiPYhD2eJTlNqtVNd5yCyt99l5R/Q2w9/o05m8AjRjmRpZdFhLl33fv/pCgAiAk1cznO2Wq1GNkoXop1J0Bcd5tJ177MKGp/MDgv04VzV5T1pG9s3kL/uO4PTqdBqW1+2QQhxeSToi05TYrGj02gIbyHoOxqcnC6vaXbOoBA/LDYHp8prGBji31lVFaLHkqAvOk2JxUawvxFdCz322non+46WNUuPiWh8lmPHNyUS9IVoB/JBrug0pRY7Ia2sod+aQSF+DAzx498Hz3VQrYTwLhL0Rae4sC9ua1sktkaj0TA1NpydR0uxtLAZuhDiykjQF52iqrYeh7P1fXHbMnVkOPYGJxmHZVVWIb4vCfqiU5RY7ABXPLwDMGZgb3r7GWSIR4h2IEFfdIoSS+Nsnavp6et1WpJGhLE9/zz1Dc72rpoQXkWCvugUpRYbBp2GQJ+rmzA2bWQ4lbX1fHmi+QwfIcTlk6AvWlVZY+d0eU2Tr8oa+1WVVfzth7ht7YvblvHDQjHqtXywv/k6+kKIyyfz9EWrLn2iFmBCTB+C/K58XL6wso5hYWb3GVvhb9IzJzGSLVmnuCOh31WXI4S3k56+aLFHf7q8Bls7rWNfXVdPdZ2DvkGXt6Ryax6aPAytRsMbmSfapV5CeCPp6YsWe/QACVHts5xxUWUdAH2DfL5XORFBPtx58yD+J+MYMeEBRAR+v/KE8EbS0xcdrtAV9L9fTx/ggYnR+Bl1Mn1TiKskQV90uLOVtfTyM+Br1H3vsnr7G5l/QxR5hVWcLGu+QJsQom0S9EWHK6yoa5de/gVzr43E36Tno9wilLqclfmFEBe4HdN3Op2sWLGCQ4cOYTQaSU1NZeDAga7jmzdvZtOmTej1eh544AGSkpJ44YUXyM/PB6C4uJjAwEA2b95MamoqX331Ff7+jaslvvrqqwQEBHTQrYmuwO5wUmKxcU1kULuV6WfUkzQ8lL8fKOTIeQvDwuVnSIjL5Tbob9u2DbvdTnp6OtnZ2axZs4b169cDjQF948aNbN26FZvNxoIFCxg7dixPPvkkAPX19SxYsIDnn38egNzcXF5//XWCg4M78JZEV3Kuqg4F9PueH+Je6vpBwXx+pISPD54jOsyMVqNpsia/XW92fR9g0l/VNFMheiK3wztZWVmMHz8egPj4eHJyclzHDhw4QEJCAkajkYCAAKKiolw9fIC3336bsWPHMnz4cJxOJwUFBTzzzDMkJyezZcuWDrgd0dWcrawF2udD3IvpdVqmxIZzpqLWtZ1ibb2TjMMlZBwuYdvBs67vq2V1TiFc3Pb0LRYLZvN3D9XodDocDgd6vR6LxdJkeMbf3x+LxQKA3W5n06ZNruBeU1PDwoUL+Y//+A8aGhpYtGgRo0ePZsSIEa1e22azoZQiLy/vqm+wJ6urq2uXtrHrzRQWNX/SdUQfY7P00mAN1UUFl13u0cIyjDoNtVUl1FVrWi23pbTWrnehvuEGRYBJy2d5hQRra5qU4aivd31/JXX2Bu31c9PTeEu7uA36ZrMZq9Xqeu10OtHr9S0es1qtrjeBnTt3ct1117le+/r6smjRInx9G3t8N954I/n5+W0GfZOpcXGu2NjYK70vr5CXl9cubXO6vIa+Ec0/EPX186NvRN8maSF9QojsPeCyy620a+jf249+fb97iralcltKa+16F9f3pio9Hx88h94/uEkZhUWFru+vpM7eoL1+bnqantQubb15uR3eSUxMJCMjA4Ds7GxiYmJcx+Li4sjKysJms1FdXc3Ro0ddx7/44gsmTJjgynvixAkWLFhAQ0MD9fX1fPXVV4waNeqqb0p0fQ1ORVFV3fd+KKstYwb2RquBPcdLO+waQvQkbnv6U6dOJTMzk+TkZJRSrFq1irS0NKKiopg8eTIpKSksWLAApRRLly519c6PHz/O7NmzXeVER0czY8YM5s6di8FgYNasWQwbNqzj7kx43NFiC/UNiv692nc8/2IBPgZG9Qviq5MV2Bzts2yEED2Z26Cv1WpZuXJlk7To6GjX93PnzmXu3LnNztuwYUOztHvvvZd77733auopuqELyyBHf4+F1i7HDYOD+fpMJRmHS65qvX4hvImsvSM6zN4T5YQFmAj0MXTodQb38Sc0wMQ/vi7kzpsGtXv5lTX2FmcAyVRQ0R1J0Bcdoq6+gf2nK7l2YO8Ov5ZGo+G6gb35Z04R56rqCG/nhdhaW5DuapeZFsKTZBkG0SG+KijH7nAytIOHdi6Ij+qNXqthr+ysJUSbpKcvOsTnR0rQaTUMDvHvlOuZTXpuHBLCVyfLmT4q4qrLaWkop732FRCiK5CgLzrE50dKGNUvEJPh+6+sebmmjwrn8yMl5BVV0+cqL9vSUE577SsgRFcgwzui3VXU2Pn6TOeM518sIao3Qb4GGeIRog0S9EW723m0FKXgukGdu7CeTqshMao3R85bqLLJkIwQLZGgL1rkaHDyZuYJNn15ko8PFnHwbOVlr13/l31nCPY3Etu385c8vm5QbzQaOFBY50q7sPrmxV+VNfZOr5sQXYGM6YtmKmvreXd3AafKa+nlayDnTCVOBTcOCWbcsD5tnnuytIZteef45S1D0es6v0/Ry8/IqH5B5BZVMbO+AZNBR229k31Hmw75yHRL4a0k6Ismyqx2/vjZUewNTp66LRY/o54Gp+Lj3CJ2HCnh2fdz+ePCMfi08gHt/+48gU6jYeGNA3E4nZ1b+W+NG9qHr89UknWynJuj236TEsLbyPCOaOLDnEJsjgbunxjN2KGNAVOn1XDrNX350egIPj1UzNL0bJzOpkM9lTV2Dp+rYtOXp5g4PBSH0+mxqY4Dgv2IMOv54mgpTtlOUYgmJOgLl2MlFnLPVjExJpSIFp5qHTcslCVJ0XyYU8Taf+U3OVZtc/D77Uex2BwMCzWTcbgEe4PnAm58P1/KrHbyv91gRQjRSIK+AMCpFP/8upAgXwPjhoa2mm/edQNIuXEgr2Uc438yjrl6/IeKqvn00Hkie/syINivs6rdquhgI8H+Rv6ddw5Hg2eGmYToimRMXwCQfbKCsxV1zL12AEZ9630BjUbDszNGUlhZywv/zOP9/WeZNjKc328/gq9Rxx0J/dFoNJ1Y85ZpNRp+NDqCt3ef5O8HChnYSU8GC9HVSU9foJQi45ti+vXy4QeRQW7z63VaXku5lnU//QGlFhvr/n2YayID+WXS0HbfC/f7iO0byLAwMxt3FVBdV+/p6gjRJUjQFxw4U8n5ahs3Dg657F66TqthzphItj96CxvvuZ51c3+A2dS1/nDUaDTcHtcPu8PJv3KKPF0dIboECfqC97PPYtJriYu88jVmfAw6xg8LRa/tmj9KoQEm5oyJZN+pCjIOF3u6OkJ4XNfqmolOV26180l+MYkDe7U5lt+dpdw4kLzCKv6VW4ROq3FNRRXCG0nQ93JbvzqNvcHJ9YNCPF2VDqPTavjpmAE0OBX/+LqQglIrvf0M9O/li0ajob7ByZHzFvIKqzhbUcuxYivB/kaGhHbOXgBCdCa3Qd/pdLJixQoOHTqE0WgkNTWVgQMHuo5v3ryZTZs2odfreeCBB0hKSqKiooLp06cTExMDwJQpU7jzzjtbzCs8RynFe3tOMqpfIBFB7bvbVFej02qYd90AtuedZ/fxMh545yvMJj1OpbA5nDQ4mz9T8IPIIGb+oL8HaitEx3Eb9Ldt24bdbic9PZ3s7GzWrFnD+vXrASguLmbjxo1s3boVm83GggULGDt2LAcPHuT222/n6aefdpXTWl6jUdY/6UwXbxLy9elKjhZbWTYtxsO16hx6rZZpoyK4ZXgYFns9JdV29FoNPgYdw8LNjOwbiNXmIPNoKV+dLOeT/PMcL7EyIPgHl30N2U9XdHVug35WVhbjx48HID4+npycHNexAwcOkJCQgNFoxGg0EhUVRX5+Pjk5OeTm5rJw4UKCg4N56qmn+Prrr1vMGxcX13F3J5q5eJOQv+47jVGn5aboPuQXVXu4Zp3HqNcye2R/Ins3f4jsdHkNvf2MTB4RzojwQP6UeYw3Mo9fdo9f9tMVXZ3boG+xWDCbvxvb1Ol0OBwO9Ho9FouFgIDvls/19/fHYrEwZMgQRo8ezc0338z7779PamoqkydPbjFvW2w2G0op8vLyrubeery6urorbhu73kxhUSGOBsX+UxUMCTaCw0ZhUWGzvCP6GJullwZrqC4qaLVcd+e3lt5a3pau19K1Li3DUV/v+v5q70MLxEf4sOtYGVH+DYSbDVddt9au5wlX83PjDbylXdwGfbPZjNVqdb12Op3o9foWj1mtVgICAoiLi8PXt/EhnalTp/Lyyy8za9asFvO2xWQyARAbG3sFt+Q98vLyrrhtTpfX0DeiMeDbG0oZO7wfvn5+9I3o2yxvS+khfUKI7D2g1XLdnd9aemt5W7peS9e6tIzCokLX99/nPqaFNJBbfJjs8w3cNTTqquvW2vU84Wp+brxBT2qXtt683M7RS0xMJCMjA4Ds7GzXh7MAcXFxZGVlYbPZqK6u5ujRo8TExPDUU0/x0UcfAbBz505GjRrVal7hGV+dLKeXn4HBfWR5grb4GHT8ZEwkh89ZOFlqdX+CEF2c257+1KlTyczMJDk5GaUUq1atIi0tjaioKCZPnkxKSgoLFixAKcXSpUsxmUw88sgjPPHEE7z33nv4+vqSmppKaGhoi3lF56usrefIeQu3DA9D2wXWyenqZv6gH5v3nubTw8UsukneJEX35jboa7VaVq5c2SQtOjra9f3cuXOZO3duk+MDBgxg48aNzcpqKa/ofF+dLEcBiVFX/gSuN/Ix6EgY0Iudx0qxORow6VveQEaI7qBnPoIpWuVwOtlzvIyhoWZCzPKX1uUaERFAg1Nx9Hzbkw+E6Ook6HuZzG9Kqayt58YhPfcJ3I4wMMQfH4PWq6a2ip5Jgr6X+cu+0/TyNTCib9szp0RTOq2GYWEBHCqqli0YRbcmQd+LfHOumqyCCm4YHCwf4F6FEREBVNscnK2o9XRVhLhqEvS9yMZdBRh0GsYMCvZ0VbqlmPAANCBDPKJbk6DvJc5V1bF57ymmxIZ3uc1Ougt/k56oYD/yi2SzdXE9hU8AABpWSURBVNF9SdD3Er/7f9/Q4FTcNXaQp6vSrY2ICOBsRR1VtbL9ouieJOj3AJU1dk6X1zT5qqyxu44fL7GS/uUpFlwfRf9eXWcP2+4oJqLxA/AjMnVTdFPyd34P0NLKjhev6rju40OY9FqWTBqGzdHgiSr2GOGBPphNer45L+P6onuSoN+NXLpWu11v5nR5Dbb61gP5nuNl/P1AIQ9OGkpogInT5TWdUdUeS6vRMDTMzDfnZOqm6J4k6Hcjl/boG1eSVCS0spxCRY2dhzbtY2CIH4snRreYR1y5YWFmsk9VcOS8hahgWYtHdC8ypt9DKaV49M8HKLHY+P38BJmx046iwxr3l9hzvMzDNRHiyknQ76He3n2SbXnneOzWWOIiZWG19hToYyAi0IcvT0jQF92PdP96GKUU/847x6eHirk9ri93yxTNDjEszMyu46XU2B34GeXXSHQf0tPvQewOJ3/dd4ZPDxUz4wd9+V1yAhpZbqFDDA03U9+g2C1DPKKbkS5KF3XpTB2g1Vk6SikOnq3kgwOFVNbWc8vwUH49fTg6rQT8jjIoxB+TXstHOUUkDQ/zdHWEuGwS9LuolubeXzpLRynFN+ereXd3ATlnqwgPNDHv2iEM6uMvPfwOZtBpmTYynL/uO8OvfziCYH+jp6skxGWR4Z1uqqDUytbcStIyT1BYVceMuL4sSRrGINnzttP89NoB2BxO3t1d0GY+JfP5RRciPf1uprK2ng/2n+VgYRX+Bi2z4vtx99jB5J6VRcA625BQfybEhPLWzgLunTDEtY2iUood35SQe7aSUqudBqcirc91RPb283CNhbiMnr7T6eSZZ55h3rx5pKSkUFDQtFezefNmfvzjHzN37lw++eQTAM6ePctdd91FSkoKCxcu5NixYwCkpaVx2223kZKSQkpKiitdXJ6Mw8X87v8d5sh5C1NHhpOS0JsbBodg1MsfbJ7y83GDKa628cH+QgAanIr/23+Wf+UWATCqXyAaDfz3tsPS4xddgtue/rZt27Db7aSnp5Odnc2aNWtYv349AMXFxWzcuJGtW7dis9lYsGABY8eO5Xe/+x0LFy5kypQp7Nixg5deeolXXnmF3Nxc1q5dy+jRozv8xnoSp1J8sP8su4+XMaC3L3OvHUCI2URhUaGnq+b1xg/rQ0y4mTUf5vPF0RLOlNey53gZE2NCmTYyHI1GQ3igD38/UMhHuUX8cHRfT1dZeDm3QT8rK4vx48cDEB8fT05OjuvYgQMHSEhIwGg0YjQaiYqKIj8/n+XLlxMQ0LgaYUNDAyZT4wbcubm5bNiwgeLiYm655RYWL17c5rVtNhtKKfLy8q76Brsru95MYVEhDU7Fx99Uc6TMzqy4cPr7OrBbyii0gKO+nsKiQkb0MTZ7AygN1lBd1Hys+UK5F2vp/NbSO6rc1vK2dL2WrnVpGRfapiPvo7EMC/clBvK/+8rIyC+iyuZkbJQfcSGKonONvf2BfopBwT4889cDhKsyfDz8l1ldXZ1X/k654y3t4jboWywWzGaz67VOp8PhcKDX67FYLK7gDuDv74/FYiE4uHFnpmPHjrF27Vr+8Ic/AHDbbbexYMECzGYzS5Ys4ZNPPiEpKanVa194s4iNjb26u+vGTpfXEBrawP/uKuBImZ0fXdOX+5OGsu9khStP49o7ffH186NvRNMeZEifECJ7D2ix3L4RTYcZWjq/tfSOKre1vC1dr6VrXVrGhbbpyPu4UEZsLPxkYmPaqTIrO74pbZZ32RA9v3x3H1+UmPhl0tBmxztTXl6eV/5OudOT2qWtNy+3XQ6z2YzVanW9djqd6PX6Fo9ZrVbXm8CuXbv45S9/yYsvvsiQIUNQSnHnnXcSHByM0Whk4sSJHDx48KpvqqdTSvGXfWc4ct7CnMT+jBvax9NVEpehtamyPxjQixuHBLMl67SM7QuPchv0ExMTycjIACA7O5uYmBjXsbi4OLKysrDZbFRXV3P06FFiYmLYtWsXL7zwAq+//jrXXHMN0PgXw+23347VakUpxe7du2Vsvw1vfnGC7FMVTB0ZzpiBsqdtT3BHQn+Ol1jZf7rS01URXszt8M7UqVPJzMwkOTkZpRSrVq0iLS2NqKgoJk+eTEpKCgsWLEApxdKlSzGZTKxatYr6+noee+wxAAYPHszKlStZunQpixYtwmg0ctNNNzFx4sQOv8Hu6J9fF/Knz0+QGNWLW2JCPV0d0U5+OLovT/8tl7/tO0P8AFkET3iG26Cv1WpZuXJlk7To6O/WZp87dy5z585tcvz9999vsazZs2cze/bsq6mn1zhZWsPyLQcY2TeQ2Qn95cnaHiTI18CkEWH8/cBZnrotFr1OptqKzic/dV2I3eFkyXtfodHAc7NGotfKf09PMzuhPyUWO58fKXGfWYgOIFGlC/nNR/kcOF3Jiz+Jo2+QbGDeEyWNCCXQR8//ZZ/1dFWEl5Kg30Vszz/H/+w4TsqNA+UBnh7MpNfxo2v68lFuETV2h/sThGhnEvQ9rLLGzr6T5SxN38/QMDN3jR3odrNz0b3NTuhPjb2Bfx885+mqCC8kQd/DKmrreXjzfmrtDcyI68fuY+VkHC7B3iBzuXuq6wcF0y/Ih7/tO+PpqggvJEHfw17LOMbxEisz4/sRGmDydHVEJ9BqNcyI70fGNyWUWmyero7wMhL0PejvB87y7u6T3DA4mMSo3p6ujrhCjgYnp8trmnxd7rDcHQn9aXAq/n5AFs0TnUvW0/eQ/KIqlv35ANf0D+S2OPngtjuqrXey72jTPXIv3d2sNSMiAhkREcDfss9w582DOqB2QrRMevoeUFhZy91pXxLgo+f5WaNlPr6XmhXfn30nKzhRYnWfWYh2ItGmk1XW1HPnG3uoqnPwxl3X0UfG8b3WHQn9Meq0/OGTI56uivAiEvQ7kcXm4Of/+yUnSmrYkDKG0f2DPF0l4UERQT4sumkgW746TV6hbHcpOocE/U5SUWPnZ6/v5quTFfzXvHhulqWSBbBk0lACTHpWf5jv6aoILyFBvxOcr6ojecMu8s5Wsf5nifLBrXDp5WfkPycPI+NwMTu+KfZ0dVwqa+zNZiZV1tg9XS3RDmT2TgeorLFTbWt8xD7nTCVP/i0Hq83By8nxTBsV4eHaia4m5aaBvLXzBI/+eT9v33MDw8ID3J7T0aptDjION10UbkJMH4L8jB6qkWgv0tPvANU2B58eKmbNh/n88p19KAX3jY9mdKSM4YvvXOhNF1fbeGH2aBwNip/8cSc7ZQVO0YEk6HeA4yVW/ifjGH8/UEh0mD+/uCWaiCAfT1dLdDEXetMZh0s4XV7HXTcPQquB/3jrS1L/fpAzFbWeriIADqeTytp6jpy3YLHJInHdnQzvtKMDpyv40+fH+fuBQow6LT9JjCQhqpdshCIuS4jZxOIJ0ewtKCMt8wRpmSeIjwri2kHBjIvuw3WDg/Ex6DqlLjV2B1kF5Xx1spzjFz1HoNNqGN0/iFtiQkm+foAsAd4NSdC/TEopausbqK5zUGtvwGJzUGyxca6yjv2nK9h9vIxjxVbMJj1zEvszNCwAs0maV1yZQF8Dj0wbTmJUb3YfL+NQUTVZBcd47bNjGHQahkcEcE3/XsRFBjGqXyDDwgLwNbbfG0FRZR1pXxznnV0nsdgchPgbuSUmlCA/A4lRvSiutrPrWCkvb/+GVz45wtTYcBbdNJCbokOkc9NNuI1KTqeTFStWcOjQIYxGI6mpqQwcONB1fPPmzWzatAm9Xs8DDzxAUlISZWVlPProo9TV1REWFsbq1avx9fVtMW9XYbU5OFFq5XiJlePFjf+erqilosZORU095TV26ltZ+dJs0hMXGcSMuH7cek0EBq2G3cfLO/kORE/Sy8/I9FERTB8VQXVdPb4GLYfPW8gvrOaD/Wd5b89JADTAwBA/YsIDGB4RwIDefkQE+dA3yIeIIB8CfAxtXkcpxdnKOjKPlPCPA4VkHinBqRS3DA9jWJiZqGA/VzCfENOHyN5+AJwqq+Ht3QVs/vIU/8otYmiYmTmJkUyJDWNomLnT3wDOlNdw+LyFwopaCivrKKyso8HpxKjTodNp6OVroLefkX69fIkK9iMq2I8gv7bbpqdyG/S3bduG3W4nPT2d7Oxs1qxZw/r16wEoLi5m48aNbN26FZvNxoIFCxg7diyvvvoqt99+Oz/+8Y/ZsGED6enp3HbbbS3mNRo7bjaAUooGp8Le4KTeobDaHZRZ7RRbbJwosXK02MKxYivHiq0UVdU1OTcswOT6xRkeHoCfUUdlrQOTQYtRp8Wo1xJg0nPDkBDOVNSi/faH/KuCistef0WIyxHgYyAhqhd6nY6RfYNQSlFmtVNYWYevUUtRpY1D56r5f/nnaXA27Zj4GnT4mXT4GnT4G3WYfQw4bLVoPymj1t7AybIaauyNi8RF9vbl5+OH8LMbotBoaDZ752IDgv14/NZYlk6J4c97T/HenlOs/Vc+a/+VT1iAiZF9AxndP4iIIB+C/Y34m/ToNBp02gtfoP32tbZJusaVT6vVoNU0biNqczR+rlBqafz9PV1Ww6nyGk6V1XKqvIaKmvom9TPqtQT66NGgod7ppLrWQYNq2jaBPnqiQvwYGOzPgGA/DPYqSvUlhAeaMOi06LQa178aDTQ4FQ6nwvntvw2XfDmVQqfVoNdp0Gsb44Re1/jaoNVi0GvRX1Smp7gN+llZWYwfPx6A+Ph4cnJyXMcOHDhAQkICRqMRo9FIVFQU+fn5ZGVlsXjxYgAmTJjASy+9xIABA1rMGxcX1+43dbTYwk//uJPyGjuqjWXpA3z0DAk1c/PQEIb08WdIqJnBffwx6DTsuaSnnhDVi30nK5qVERHkQ2FlXbN0ITqKRqMhxGwixGxq0vu2O5ycq6rjwJkKMg6XUFVbT1VtPTaHE3uDkwCTnjqHk3qlw1+roY/ZRFxkEFHBfoyIaPxL4UIP/XJXC/Ux6EgaEYZOq6Wytp78oiqOFVs5UWplx5GSZm9C7cWo0xLZ25fIYD/iIoMI9NVTUeOgt5+BYD8jvkYdiQN7u35nlVLU1TupqLUTEeRDja3xDe9kWQ15hVV8fLCI+gbF73d2zswpjQYMWi0X/iC6+A8jDRr0Wg0vzYtn6sjwdr+226BvsVgwm82u1zqdDofDgV6vx2KxEBDw3Zxif39/LBZLk3R/f3+qq6tbzdsWm82GyWQiLy/vim/snZ9EXuEZDqACKiqoBxICLzlcYW2e1lp6K3mri6zkFTVPv9pyEwIDAWuLeVu7VovXu4J766hy27vNXG3TStnd5T6uptwhehgy0gBc7vCFAyjHcu6Sjs6Vtlkg3BLuA3hqppoeUMC3exRU1LTcvtQ0/hOqAwK+/eqKysjLK3OfrQU2W+v7NLgN+mazGav1u0/vnU4ner2+xWNWq5WAgABXuo+PD1arlcDAwFbztiU+Pt5d9YQQQlwBt/P0ExMTycjIACA7O5uYmBjXsbi4OLKysrDZbFRXV3P06FFiYmJITEzks88+AyAjI4MxY8a0mlcIIUTn0SjV1qj3d7N3Dh8+jFKKVatWkZGRQVRUFJMnT2bz5s2kp6ejlGLx4sVMnz6dkpISli9fjtVqpXfv3qxbtw4/P78W8wohhOg8boO+EEKInkOWYRBCCC8iQV8IIbyIBH0hhPAiXXJxmP379/Pb3/6WjRs3UlBQwGOPPYZGo2HYsGE8++yzaL1wI/H6+nqeeOIJzpw5g91u54EHHmDo0KHSNkBDQwNPPfUUx48fR6fTsXr1apRS0jbfKi0t5cc//jFvvPEGer1e2uVbs2fPdk0bj4yMZN68ebzwwgvodDrGjRvHkiVLPFzDDqK6mA0bNqjbb79d/fSnP1VKKbV48WK1a9cupZRSTz/9tPr44489WT2P2bJli0pNTVVKKVVWVqYmTpwobfOtf//73+qxxx5TSim1a9cudf/990vbfMtut6tf/OIXatq0aerIkSPSLt+qq6tTs2bNapI2c+ZMVVBQoJxOp/r5z3+ucnJyPFS7jtXl3uKjoqL4/e9/73qdm5vL9ddfDzQu6fDFF194qmoe9cMf/pCHHnrI9Vqn00nbfGvKlCk8//zzAJw9e5Y+ffpI23xr7dq1JCcnExYWBsjv0wX5+fnU1tZy9913s2jRIr788kvsdjtRUVFoNBrGjRvHzp07PV3NDtHlgv706dNdT/xC45oZF9YDubCkgzfy9/fHbDZjsVj4z//8T371q19J21xEr9ezfPlynn/+eaZPny5tA/zlL38hODjYtXYWyO/TBT4+Ptxzzz386U9/4rnnnuPxxx/H1/e7vQF6ctt0uaB/qYvHGy8s6eCtCgsLWbRoEbNmzWLGjBnSNpdYu3YtH330EU8//XSTtUe8tW22bt3KF198QUpKCnl5eSxfvpyysu/WcvHWdgEYPHgwM2fORKPRMHjwYAICAqio+G5BxZ7cNl0+6I8cOZLdu3cDjUs6XHvttR6ukWeUlJRw9913s2zZMn7yk58A0jYX/O1vf+O1114DwNfXF41Gw+jRo72+bd555x3efvttNm7cSGxsLGvXrmXChAle3y4AW7ZsYc2aNQCcO3eO2tpa/Pz8OHnyJEopPv/88x7bNl3yidzTp0/z8MMPs3nzZo4fP87TTz9NfX09Q4YMITU1FZ2uc7aM60pSU1P58MMPGTJkiCvtySefJDU11evbpqamhscff5ySkhIcDgf33nsv0dHR8nNzkZSUFFasWIFWq5V2Aex2O48//jhnz55Fo9Hw6KOPotVqWbVqFQ0NDYwbN46lS5d6upodoksGfSGEEB2jyw/vCCGEaD8S9IUQwotI0BdCCC8iQV8IIbyIBH0hhPAiEvRFhzh9+jSJiYmkpKS4vl555ZUrOn/u3LkdWMOeYdKkSc02wc7IyCA9Pf17lbt06VLXfH7Rs3TJVTZFzzB06FA2btzo6Wp4nQkTJni6CqILk6AvOs3u3bvZtGkT//Vf/wXA2LFjyczMpLCw0LV0gslkci2e5s66dev48ssvUUpx1113ceutt7J//35eeOEFlFKEh4fz29/+lmPHjvH888+j0+lc5TudTh555BEiIiI4deoU11xzDc899xxVVVUsW7YMi8VCQ0MDDz30EDfddBMzZszg2muv5fDhwwwePJiQkBD27t2L0Whkw4YNpKSk8PzzzzNs2DA+++wzPv30U5599tkW6/3JJ5+4/uoZOXIkzz33HDt37uS///u/MZlM9OrVi1WrVpGXl8eGDRswGAwUFRWRnJzMrl27yM/PZ9GiRSxYsACAZ555hjNnzhASEsLatWv55z//ybFjx0hOTm7xHqurq3nyyScpLy8H4KmnnmL48OG88847/PnPfyY0NJTS0tLv+98tuioPre4perhTp06phIQEtXDhQtfX//3f/6lf/epXrjw333yzUkqphx56SH366adKKaW++OIL9fDDD6tTp065ltduyaeffuoqq66uTs2cOVNVVlaqGTNmqCNHjiillHr77bdVTk6OuuOOO9TBgweVUo3LMD/44IPq1KlT6vrrr1fV1dXK4XCoW265RZ0/f16tWbNGvfnmm0oppYqKilRSUpJqaGhQSUlJau/evUoppaZPn+6q789+9jN18OBBtWXLFrV27VqllFIPPvhgq8vy1tfXq6SkJFVSUqKUUur3v/+9On36tEpKSlJFRUVKKaXefPNNtWbNGrVr1y71ox/9SNntdrVv3z41YcIEZbPZ1MmTJ9XMmTOVUkolJSWpffv2KaWUWrt2rXrrrbfU1q1b1W9+85tW7/HFF19U77zzjlJKqePHj6vk5GRVVVWlpk2bpmw2m7Lb7er22293LcEsehbp6YsOc+nwzqVjxOrbh8EPHz7Ma6+9xuuvv45SCoPB4Lbsw4cPk5ubS0pKCgAOh4OzZ89SWlpKdHQ0AD/72c8AOH/+PLGxsQBcd911rFu3DmhcxttsNgMQGhqKzWbj6NGjzJgxA4Dw8HDMZrNrkbJRo0YBEBgY6LpGYGAgNpuNH/3oR9xxxx3cc889FBUVufJeqry8nMDAQEJCQgBYsmQJZWVlmM1mwsPDXXV86aWXuOWWWxg2bBgGg4GAgACioqIwGo0EBQW5xvENBgPx8fEAJCYmkpmZyTXXXOO6Xkv3ePjwYXbt2sWHH34IQFVVFceOHWPo0KEYjUYA4uLi3P4fiO5Jgr7oNCaTieLiYgDOnDlDZWUlAEOGDOHuu+8mMTGRo0eP8uWXX7ota8iQIdxwww2uoZpXX32VyMhIwsLCOHHiBIMGDWLDhg0MHjyYsLAw8vPzGTFiBF9++SWDBg0CcC0xfLHo6Gj27t3LyJEjOXfuHFVVVfTq1avV/Bf4+vpyww038MILLzBr1qxW84WEhFBVVUVFRQW9evUiNTWVGTNmYLFYOH/+PGFhYezZs6fNOl6svr6evLw8YmNj2bt3L8OGDWtyvKXzhwwZwsyZM5kxYwalpaX8+c9/ZsCAARw5coS6ujoMBgN5eXnMnDmzzWuL7kmCvug0o0ePJiAggJ/+9KdER0cTGRkJwPLly1mxYgU2m426ujqefPJJt2VNmjSJPXv2sGDBAmpqapgyZQpms5nnnnuOJ554Aq1WS2hoKHfddRf9+/fn+eefRymFTqdj1apVrZa7ePFinnjiCT766CPq6upYuXJlk/0d2jJ37lzmz5/PihUrWs2j1Wp59tlnWbx4MVqtlpEjRxIXF0dqaioPPvggGo2GoKAgVq9ezTfffOP2mgaDwbWtaL9+/XjkkUf44IMP2jzn/vvv58knn2Tz5s1YLBaWLFlCcHAwDz30EMnJyQQHBzdZW170LLLgmhDt5MCBA7z99tu8+OKLnq6KEK2Snr7o0l555ZUW54uvWrWKAQMGeKBGLXv77bfZunUrL7/8MtD4BvCb3/ymWb5bb73VNetGCE+Qnr4QQngReSJXCCG8iAR9IYTwIhL0hRDCi0jQF0IILyJBXwghvMj/BzP1tSMTvqEWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[\"Fuel_economy_combined\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "sns.jointplot(df[\"Fuel_economy_combined\"],df[\"Price\"])\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is interesting! Contrary to my initial thought that vehicles with low fuel economy must be cheaper to buy,  the opposite is true! Actually, the bigger cars with additional features tend to have low fuel economy and are usually expensive."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** The vehicles with the Fuel economy over 40 MPG are all hybrid models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accidents and Previous owners\n",
    "With the number of the accidents and previous owners, the value of the vehicle depreciates quickly. Let's see!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a37e2f0d0>"
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x = \"Accidents\", y = \"Price\", data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a37cb94d0>"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(x = \"Previous_owners\", y = \"Price\", data = df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mileage\n",
    "The value of the vehicle should go down with mileage!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "sns.jointplot(df[\"Mileage\"],df[\"Price\"])\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a38578ed0>"
      ]
     },
     "execution_count": 327,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df[\"Mileage\"],fit = norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The distribution of the Mileage looks skewed. Let's do a log transformation and see if it makes it better.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a386db610>"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.log(df[\"Mileage\"]),fit = norm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Mileage\"] = df[\"Mileage\"].apply(np.log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The column ID is of no use in building the model. Also, I'm not so much interested in a particular brand as I am in the body type i.e. I'm interested in predicting the price of a sedan vs an suv regardless of the carmaker. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"Id\",axis=1,inplace=True)\n",
    "df.drop(\"Make\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"Model\",\"Trim\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get dummies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [],
   "source": [
    "body = pd.get_dummies(df[\"Body_type\"],drop_first=True)\n",
    "rating = pd.get_dummies(df[\"Rating\"],drop_first=True)\n",
    "drivetrain = pd.get_dummies(df[\"Drivetrain\"],drop_first=True)\n",
    "engine = pd.get_dummies(df[\"Engine\"],drop_first=True)\n",
    "transmission = pd.get_dummies(df[\"Transmission\"],drop_first=True)\n",
    "seller = pd.get_dummies(df[\"Seller_type\"],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"Body_type\",\"Rating\",\"Drivetrain\",\"Engine\",\"Transmission\",\"Seller_type\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df,body,rating,drivetrain,engine,transmission,seller],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "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>Accidents</th>\n",
       "      <th>Year</th>\n",
       "      <th>Fuel_economy_combined</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Additional_features</th>\n",
       "      <th>Previous_owners</th>\n",
       "      <th>Price</th>\n",
       "      <th>Trim_copy</th>\n",
       "      <th>Model_copy</th>\n",
       "      <th>Seats_testing</th>\n",
       "      <th>Sedan</th>\n",
       "      <th>Poor_Price</th>\n",
       "      <th>FRONT_WHEEL_DRIVE</th>\n",
       "      <th>I4</th>\n",
       "      <th>V6</th>\n",
       "      <th>Manual</th>\n",
       "      <th>PRIVATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>10.810051</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7495.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>11.792041</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3997.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>9.944294</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15995.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>27.238208</td>\n",
       "      <td>11.175311</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5995.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>10.346634</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12925.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Accidents    Year  Fuel_economy_combined    Mileage  Additional_features  \\\n",
       "0        0.0  2017.0              29.000000  10.810051                  5.0   \n",
       "1        0.0  2009.0              27.000000  11.792041                  5.0   \n",
       "2        2.0  2015.0              26.000000   9.944294                  6.0   \n",
       "3        2.0  2012.0              27.238208  11.175311                  1.0   \n",
       "4        0.0  2017.0              31.000000  10.346634                  4.0   \n",
       "\n",
       "   Previous_owners    Price  Trim_copy  Model_copy  Seats_testing  Sedan  \\\n",
       "0              2.0   7495.0          1           1            4.0      1   \n",
       "1              2.0   3997.0          1           3            4.0      1   \n",
       "2              1.0  15995.0          1           5            4.0      1   \n",
       "3              1.0   5995.0          1           1            4.0      1   \n",
       "4              1.0  12925.0          1           3            4.0      1   \n",
       "\n",
       "   Poor_Price  FRONT_WHEEL_DRIVE  I4  V6  Manual  PRIVATE  \n",
       "0           0                  1   1   0       0        0  \n",
       "1           0                  1   1   0       0        0  \n",
       "2           0                  1   1   0       0        0  \n",
       "3           0                  1   0   1       0        0  \n",
       "4           0                  1   1   0       0        0  "
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stage 4: Building the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_predict,cross_val_score,cross_validate\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"Price\",axis=1)   # Features\n",
    "y = df[\"Price\"]               # Price(target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20,random_state= 10)             # Train-test split                                                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_reg_pred = lin_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_lin_reg = np.sqrt(mean_squared_error(y_test,lin_reg_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2399.159135308122"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_lin_reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.064662175504573"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Percentage_of_error_lin_reg = (error_lin_reg/y_test.mean())*100\n",
    "Percentage_of_error_lin_reg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear regresssion performs poorly with the RMSE of ~$2400."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision tree based algorithms: \n",
    "The idea behind using decision tree based algorithms is to efficiently tackle the categorical columns that make the relationship between the features and the target variable, Price highly non-linear. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random Forest:** It takes in a large number of decision trees and they're all built at the same time and the results are aggregated at the end to give the final output. The trees have high variance in the beginning which decreases over time due to the randomness in both the sampling and the features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Gradient Boosting:** It also takes in a large number of decision trees however, it updates the results along the way. The trees are all weak learners which learn from the mistakes of the previous trees and subsequently improve upon it.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random forest regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grid Search\n",
    "param_grid_rf = {    \n",
    "    'max_depth': [5,6,8],    \n",
    "    'min_samples_leaf': [4,8],\n",
    "    'min_samples_split': [8,16],\n",
    "    'n_estimators': [ 500, 1000, 2000]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search_rf = GridSearchCV(estimator=rf, param_grid=param_grid_rf, cv = 5, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n",
       "                                             criterion='mse', max_depth=None,\n",
       "                                             max_features='auto',\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             max_samples=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             n_estimators=100, n_jobs=None,\n",
       "                                             oob_score=False, random_state=None,\n",
       "                                             verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'max_depth': [5, 6, 8], 'min_samples_leaf': [4, 8],\n",
       "                         'min_samples_split': [8, 16],\n",
       "                         'n_estimators': [500, 1000, 2000]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=8, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=4,\n",
       "                      min_samples_split=8, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=2000, n_jobs=None, oob_score=False,\n",
       "                      random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_rf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest = RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
    "                      max_depth=8, max_features='auto', max_leaf_nodes=None,\n",
    "                      max_samples=None, min_impurity_decrease=0.0,\n",
    "                      min_impurity_split=None, min_samples_leaf=4,\n",
    "                      min_samples_split=8, min_weight_fraction_leaf=0.0,\n",
    "                      n_estimators=2000, n_jobs=None, oob_score=False,\n",
    "                      random_state=None, verbose=0, warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=8, max_features='auto', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=4,\n",
       "                      min_samples_split=8, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=2000, n_jobs=None, oob_score=False,\n",
       "                      random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_forest.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_forest_pred = random_forest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_rf = np.sqrt(mean_squared_error(y_test,random_forest_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1999.348339089767"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "Percentage_of_error_rf = (error_rf/y_test.mean())*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.87437416148955"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Percentage_of_error_rf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random forest seems to be performing better than Linear regression with RMSE of ~$2000 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient boosting regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grid Search\n",
    "param_grid_grb = {   \n",
    "    'learning_rate': [0.1,0.3,0.5],\n",
    "    'max_depth': [2, 3],    \n",
    "    'min_samples_leaf': [8,16],\n",
    "    'min_samples_split': [8, 16, 32],\n",
    "    'n_estimators': [500, 1000, 2000]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "grb = GradientBoostingRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search_grb = GridSearchCV(estimator=grb, param_grid= param_grid_grb ,cv = 5, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0,\n",
       "                                                 criterion='friedman_mse',\n",
       "                                                 init=None, learning_rate=0.1,\n",
       "                                                 loss='ls', max_depth=3,\n",
       "                                                 max_features=None,\n",
       "                                                 max_leaf_nodes=None,\n",
       "                                                 min_impurity_decrease=0.0,\n",
       "                                                 min_impurity_split=None,\n",
       "                                                 min_samples_leaf=1,\n",
       "                                                 min_samples_split=2,\n",
       "                                                 min_weight_fraction_leaf=0.0,\n",
       "                                                 n_estimators=100,\n",
       "                                                 n_iter_n...\n",
       "                                                 random_state=None,\n",
       "                                                 subsample=1.0, tol=0.0001,\n",
       "                                                 validation_fraction=0.1,\n",
       "                                                 verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'learning_rate': [0.1, 0.3, 0.5], 'max_depth': [2, 3],\n",
       "                         'min_samples_leaf': [8, 16],\n",
       "                         'min_samples_split': [8, 16, 32],\n",
       "                         'n_estimators': [500, 1000, 2000]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_grb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\n",
       "                          init=None, learning_rate=0.1, loss='ls', max_depth=3,\n",
       "                          max_features=None, max_leaf_nodes=None,\n",
       "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                          min_samples_leaf=16, min_samples_split=32,\n",
       "                          min_weight_fraction_leaf=0.0, n_estimators=2000,\n",
       "                          n_iter_no_change=None, presort='deprecated',\n",
       "                          random_state=None, subsample=1.0, tol=0.0001,\n",
       "                          validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_grb.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "gradient_boost = GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\n",
    "                          init=None, learning_rate=0.1, loss='ls', max_depth=3,\n",
    "                          max_features=None, max_leaf_nodes=None,\n",
    "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "                          min_samples_leaf=16, min_samples_split=32,\n",
    "                          min_weight_fraction_leaf=0.0, n_estimators=2000,\n",
    "                          n_iter_no_change=None, presort='deprecated',\n",
    "                          random_state=None, subsample=1.0, tol=0.0001,\n",
    "                          validation_fraction=0.1, verbose=0, warm_start=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',\n",
       "                          init=None, learning_rate=0.1, loss='ls', max_depth=3,\n",
       "                          max_features=None, max_leaf_nodes=None,\n",
       "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                          min_samples_leaf=16, min_samples_split=32,\n",
       "                          min_weight_fraction_leaf=0.0, n_estimators=2000,\n",
       "                          n_iter_no_change=None, presort='deprecated',\n",
       "                          random_state=None, subsample=1.0, tol=0.0001,\n",
       "                          validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gradient_boost.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "gradient_boost_pred = gradient_boost.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_grb = np.sqrt(mean_squared_error(y_test,gradient_boost_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1593.7396741955058"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_grb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "Percentage_of_error_grb = (error_grb/y_test.mean())*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.335900455953784"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Percentage_of_error_grb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gradient boost seems to be working better than the random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of the distribution is: 13959.474097788127\n",
      "The standard deviation is:       5407.951396573754\n",
      "The root mean squared error is:  1593.7396741955058\n",
      "The error percentage is:         11.335900455953784\n"
     ]
    }
   ],
   "source": [
    "print(\"The mean of the distribution is: {}\".format(y_train.mean()))\n",
    "print(\"The standard deviation is:       {}\".format(y_train.std()))\n",
    "print(\"The root mean squared error is:  {}\".format(error_grb))\n",
    "print(\"The error percentage is:         {}\".format(Percentage_of_error_grb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stage 5: Conclusions and Future directions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Based on my own experience, the car prices usually vary in the range of $1500 and the model seems to capture that really well. There could still be room for improvement due to the following reasons:\n",
    "* Vehicles with some fancy additional features could be more costly since we just counted # of features.\n",
    "* Prices seem to blow much quickly as we go higher on the trims ladder.  \n",
    "* Similarly, a large car from another brand could be more expensive than other brands.\n",
    "* Some brands hold their value more than other brands and that could very well have been the factor behind the error.\n",
    "* It probably would've done better had we access to more data at our disposal."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **Remark:** At this point, I feel like it would lead to overfitting if we tried to match the prices too closely. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Future directions:\n",
    "As far as the future directions go, I have a few of objectives in mind:\n",
    "* Extend the model and enable it to predict the prices of other vehicles in addition to sedan/suv eg trucks.\n",
    "* Make the model more robust enough to predict the prices for brand-specific vehicles.\n",
    "* Incorporate the luxury car segment to the model.\n",
    "* **Last:** Bring the model to production stage by deploying it on a webpage/app!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Suggestions and inputs are welcome!"
   ]
  }
 ],
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