Car_price_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data come from the Kaggle dataset Car Features and MSRP of Kaggle. It describes almost 12 000 car models sold in the USA between 1990 and 2018 with the market price and some features. This study is intended to conduct an analysis leading to the construction of a model that will best predict the price of a car based on the information collected in this database.\n",
    "\n",
    "This is a regression problem that will be solved using 3 models: Linear Regression, Support Vector Machine and Random Forest. \n",
    "Statistics such as mean squared error, mean absolute error and R^2 will be used to evaluate the models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main focus will be on the modeling process and the selection of appropriate hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data description:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make                Car Make\n",
    "# Model               Car Model\n",
    "# Year                Car Year Productuin\n",
    "# Engine Fuel Type    Engine Fuel Type\n",
    "# Engine HP           Engine Horse Power (HP)\n",
    "# Engine Cylinders    Engine Cylinders\n",
    "# Transmission Type   Transmission Type\n",
    "# Driven_Wheels       Driven Wheels\n",
    "# Number of Doors     Number of Doors\n",
    "# Market Category     Market Category\n",
    "# Vehicle Size        Size of Vehicle\n",
    "# Vehicle Style       Type of Vehicle\n",
    "# highway MPG         Highway MPG\n",
    "# city mpg            City MPG\n",
    "# Popularity          Popularity (Twitter)\n",
    "# MSRP                Manufacturer Suggested Retail Price\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Development environment preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (7, 5)\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "import os\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from sklearn import feature_selection\n",
    "from sklearn import linear_model\n",
    "import sklearn.metrics as metrics\n",
    "import statsmodels.api as sm\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "path=os.getcwd()\n",
    "pd.options.mode.chained_assignment = None  # default='warn'\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data structure and data cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start the analysis by reviewing the data structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year</th>\n",
       "      <th>Engine Fuel Type</th>\n",
       "      <th>Engine HP</th>\n",
       "      <th>Engine Cylinders</th>\n",
       "      <th>Transmission Type</th>\n",
       "      <th>Driven_Wheels</th>\n",
       "      <th>Number of Doors</th>\n",
       "      <th>Market Category</th>\n",
       "      <th>Vehicle Size</th>\n",
       "      <th>Vehicle Style</th>\n",
       "      <th>highway MPG</th>\n",
       "      <th>city mpg</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>MSRP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series M</td>\n",
       "      <td>2011</td>\n",
       "      <td>premium unleaded (required)</td>\n",
       "      <td>335.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Factory Tuner,Luxury,High-Performance</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>3916</td>\n",
       "      <td>46135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>premium unleaded (required)</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Luxury,Performance</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Convertible</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>3916</td>\n",
       "      <td>40650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>premium unleaded (required)</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Luxury,High-Performance</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>3916</td>\n",
       "      <td>36350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>premium unleaded (required)</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Luxury,Performance</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>3916</td>\n",
       "      <td>29450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>premium unleaded (required)</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>MANUAL</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Luxury</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Convertible</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>3916</td>\n",
       "      <td>34500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "  Make       Model  Year             Engine Fuel Type  Engine HP  \\\n",
       "0  BMW  1 Series M  2011  premium unleaded (required)      335.0   \n",
       "1  BMW    1 Series  2011  premium unleaded (required)      300.0   \n",
       "2  BMW    1 Series  2011  premium unleaded (required)      300.0   \n",
       "3  BMW    1 Series  2011  premium unleaded (required)      230.0   \n",
       "4  BMW    1 Series  2011  premium unleaded (required)      230.0   \n",
       "\n",
       "   Engine Cylinders Transmission Type     Driven_Wheels  Number of Doors  \\\n",
       "0               6.0            MANUAL  rear wheel drive              2.0   \n",
       "1               6.0            MANUAL  rear wheel drive              2.0   \n",
       "2               6.0            MANUAL  rear wheel drive              2.0   \n",
       "3               6.0            MANUAL  rear wheel drive              2.0   \n",
       "4               6.0            MANUAL  rear wheel drive              2.0   \n",
       "\n",
       "                         Market Category Vehicle Size Vehicle Style  \\\n",
       "0  Factory Tuner,Luxury,High-Performance      Compact         Coupe   \n",
       "1                     Luxury,Performance      Compact   Convertible   \n",
       "2                Luxury,High-Performance      Compact         Coupe   \n",
       "3                     Luxury,Performance      Compact         Coupe   \n",
       "4                                 Luxury      Compact   Convertible   \n",
       "\n",
       "   highway MPG  city mpg  Popularity   MSRP  \n",
       "0           26        19        3916  46135  \n",
       "1           28        19        3916  40650  \n",
       "2           28        20        3916  36350  \n",
       "3           28        18        3916  29450  \n",
       "4           28        18        3916  34500  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11914 entries, 0 to 11913\n",
      "Data columns (total 16 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Make               11914 non-null  object \n",
      " 1   Model              11914 non-null  object \n",
      " 2   Year               11914 non-null  int64  \n",
      " 3   Engine Fuel Type   11911 non-null  object \n",
      " 4   Engine HP          11845 non-null  float64\n",
      " 5   Engine Cylinders   11884 non-null  float64\n",
      " 6   Transmission Type  11914 non-null  object \n",
      " 7   Driven_Wheels      11914 non-null  object \n",
      " 8   Number of Doors    11908 non-null  float64\n",
      " 9   Market Category    8172 non-null   object \n",
      " 10  Vehicle Size       11914 non-null  object \n",
      " 11  Vehicle Style      11914 non-null  object \n",
      " 12  highway MPG        11914 non-null  int64  \n",
      " 13  city mpg           11914 non-null  int64  \n",
      " 14  Popularity         11914 non-null  int64  \n",
      " 15  MSRP               11914 non-null  int64  \n",
      "dtypes: float64(3), int64(5), object(8)\n",
      "memory usage: 1.5+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=pd.read_csv(path+'\\\\data\\\\data.csv')\n",
    "display(data.head())\n",
    "display(data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year</th>\n",
       "      <th>Engine Fuel Type</th>\n",
       "      <th>Engine HP</th>\n",
       "      <th>Engine Cylinders</th>\n",
       "      <th>Transmission Type</th>\n",
       "      <th>Driven_Wheels</th>\n",
       "      <th>Number of Doors</th>\n",
       "      <th>Market Category</th>\n",
       "      <th>Vehicle Size</th>\n",
       "      <th>Vehicle Style</th>\n",
       "      <th>highway MPG</th>\n",
       "      <th>city mpg</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>MSRP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11914</td>\n",
       "      <td>11914</td>\n",
       "      <td>11914.000000</td>\n",
       "      <td>11911</td>\n",
       "      <td>11845.00000</td>\n",
       "      <td>11884.000000</td>\n",
       "      <td>11914</td>\n",
       "      <td>11914</td>\n",
       "      <td>11908.000000</td>\n",
       "      <td>8172</td>\n",
       "      <td>11914</td>\n",
       "      <td>11914</td>\n",
       "      <td>11914.000000</td>\n",
       "      <td>11914.000000</td>\n",
       "      <td>11914.000000</td>\n",
       "      <td>1.191400e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>48</td>\n",
       "      <td>915</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Silverado 1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>regular unleaded</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AUTOMATIC</td>\n",
       "      <td>front wheel drive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Crossover</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1123</td>\n",
       "      <td>156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7172</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8266</td>\n",
       "      <td>4787</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1110</td>\n",
       "      <td>4764</td>\n",
       "      <td>3048</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010.384338</td>\n",
       "      <td>NaN</td>\n",
       "      <td>249.38607</td>\n",
       "      <td>5.628829</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.436093</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.637485</td>\n",
       "      <td>19.733255</td>\n",
       "      <td>1554.911197</td>\n",
       "      <td>4.059474e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.579740</td>\n",
       "      <td>NaN</td>\n",
       "      <td>109.19187</td>\n",
       "      <td>1.780559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.881315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.863001</td>\n",
       "      <td>8.987798</td>\n",
       "      <td>1441.855347</td>\n",
       "      <td>6.010910e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1990.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>170.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>549.000000</td>\n",
       "      <td>2.100000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>227.00000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1385.000000</td>\n",
       "      <td>2.999500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.00000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>4.223125e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1001.00000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>5657.000000</td>\n",
       "      <td>2.065902e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Make           Model          Year  Engine Fuel Type  \\\n",
       "count       11914           11914  11914.000000             11911   \n",
       "unique         48             915           NaN                10   \n",
       "top     Chevrolet  Silverado 1500           NaN  regular unleaded   \n",
       "freq         1123             156           NaN              7172   \n",
       "mean          NaN             NaN   2010.384338               NaN   \n",
       "std           NaN             NaN      7.579740               NaN   \n",
       "min           NaN             NaN   1990.000000               NaN   \n",
       "25%           NaN             NaN   2007.000000               NaN   \n",
       "50%           NaN             NaN   2015.000000               NaN   \n",
       "75%           NaN             NaN   2016.000000               NaN   \n",
       "max           NaN             NaN   2017.000000               NaN   \n",
       "\n",
       "          Engine HP  Engine Cylinders Transmission Type      Driven_Wheels  \\\n",
       "count   11845.00000      11884.000000             11914              11914   \n",
       "unique          NaN               NaN                 5                  4   \n",
       "top             NaN               NaN         AUTOMATIC  front wheel drive   \n",
       "freq            NaN               NaN              8266               4787   \n",
       "mean      249.38607          5.628829               NaN                NaN   \n",
       "std       109.19187          1.780559               NaN                NaN   \n",
       "min        55.00000          0.000000               NaN                NaN   \n",
       "25%       170.00000          4.000000               NaN                NaN   \n",
       "50%       227.00000          6.000000               NaN                NaN   \n",
       "75%       300.00000          6.000000               NaN                NaN   \n",
       "max      1001.00000         16.000000               NaN                NaN   \n",
       "\n",
       "        Number of Doors Market Category Vehicle Size Vehicle Style  \\\n",
       "count      11908.000000            8172        11914         11914   \n",
       "unique              NaN              71            3            16   \n",
       "top                 NaN       Crossover      Compact         Sedan   \n",
       "freq                NaN            1110         4764          3048   \n",
       "mean           3.436093             NaN          NaN           NaN   \n",
       "std            0.881315             NaN          NaN           NaN   \n",
       "min            2.000000             NaN          NaN           NaN   \n",
       "25%            2.000000             NaN          NaN           NaN   \n",
       "50%            4.000000             NaN          NaN           NaN   \n",
       "75%            4.000000             NaN          NaN           NaN   \n",
       "max            4.000000             NaN          NaN           NaN   \n",
       "\n",
       "         highway MPG      city mpg    Popularity          MSRP  \n",
       "count   11914.000000  11914.000000  11914.000000  1.191400e+04  \n",
       "unique           NaN           NaN           NaN           NaN  \n",
       "top              NaN           NaN           NaN           NaN  \n",
       "freq             NaN           NaN           NaN           NaN  \n",
       "mean       26.637485     19.733255   1554.911197  4.059474e+04  \n",
       "std         8.863001      8.987798   1441.855347  6.010910e+04  \n",
       "min        12.000000      7.000000      2.000000  2.000000e+03  \n",
       "25%        22.000000     16.000000    549.000000  2.100000e+04  \n",
       "50%        26.000000     18.000000   1385.000000  2.999500e+04  \n",
       "75%        30.000000     22.000000   2009.000000  4.223125e+04  \n",
       "max       354.000000    137.000000   5657.000000  2.065902e+06  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_copy= data.copy()\n",
    "display(df_copy.describe(include='all'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The database contains 11,914 observations and 16 variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "715"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for any duplicates in the data\n",
    "df_copy.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.drop_duplicates(keep=False,inplace=True)\n",
    "#removing the duplicates in the data\n",
    "df_copy.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns with null values:\n",
      " Make                    0\n",
      "Model                   0\n",
      "Year                    0\n",
      "Engine Fuel Type        3\n",
      "Engine HP              69\n",
      "Engine Cylinders       30\n",
      "Transmission Type       0\n",
      "Driven_Wheels           0\n",
      "Number of Doors         6\n",
      "Market Category      3092\n",
      "Vehicle Size            0\n",
      "Vehicle Style           0\n",
      "highway MPG             0\n",
      "city mpg                0\n",
      "Popularity              0\n",
      "MSRP                    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('Columns with null values:\\n', df_copy.isnull().sum())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a 5 variables with a missing values in data: Engine Fuel Type, Engine HP, Engine Cylinders, Number of Doors, Market Category. \n",
    "We start data cleaning with numerical variables, so first Engine HP.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df_copy['Engine HP'], bins=10, color='c', edgecolor='k', alpha=0.65)\n",
    "\n",
    "plt.axvline(df_copy['Engine HP'].mean(), color='k', linestyle='dashed', linewidth=1)\n",
    "plt.axvline(df_copy['Engine HP'].median(), color='r', linestyle='dashed', linewidth=1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I think that missing values in Engine HP can fill a median value of all observations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy['Engine HP'].fillna(df_copy['Engine HP'].median(),inplace=True)\n",
    "df_copy['Engine HP'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next is a Engine Cylinders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df_copy['Engine Cylinders'], bins=10, color='c', edgecolor='k', alpha=0.65)\n",
    "plt.axvline(df_copy['Engine Cylinders'].mean(), color='k', linestyle='dashed', linewidth=1)\n",
    "plt.axvline(df_copy['Engine Cylinders'].median(), color='r', linestyle='dashed', linewidth=1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0     4138\n",
       "4.0     4000\n",
       "8.0     1920\n",
       "12.0     226\n",
       "5.0      117\n",
       "10.0      62\n",
       "0.0       56\n",
       "3.0       30\n",
       "16.0       3\n",
       "Name: Engine Cylinders, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy['Engine Cylinders'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, we have a 9 unique value. It's not exactly a continuous variable. In addition we see a doubt, because it is impossible that car have 0 cylinders, so we should correct it. Therefore we put missing values and values 0 to the group with most frequent value(4.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0     4138\n",
       "4.0     4086\n",
       "8.0     1920\n",
       "12.0     226\n",
       "5.0      117\n",
       "10.0      62\n",
       "3.0       30\n",
       "16.0       3\n",
       "Name: Engine Cylinders, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy['Engine Cylinders'].fillna(4.0,inplace=True)\n",
    "df_copy['Engine Cylinders'].replace(0.0,4.0,inplace=True)\n",
    "df_copy['Engine Cylinders'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last continuous variable with missing values is number of doors. Let's go over this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "4.0    7598\n",
       "2.0    2655\n",
       "3.0     323\n",
       "Name: Number of Doors, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df_copy['Number of Doors'])\n",
    "plt.show()\n",
    "display(df_copy['Number of Doors'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, let's replace missing values to 4 - most frequent value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copy['Number of Doors'].fillna(4,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I take care about categorical variables with missing values. Start with Engine Fuel Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "regular unleaded                                6234\n",
       "premium unleaded (required)                     1905\n",
       "premium unleaded (recommended)                  1267\n",
       "flex-fuel (unleaded/E85)                         875\n",
       "diesel                                           146\n",
       "electric                                          66\n",
       "flex-fuel (premium unleaded required/E85)         52\n",
       "flex-fuel (premium unleaded recommended/E85)      26\n",
       "flex-fuel (unleaded/natural gas)                   6\n",
       "natural gas                                        2\n",
       "Name: Engine Fuel Type, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy['Engine Fuel Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y='Engine Fuel Type',x='MSRP',data=df_copy)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df_copy[df_copy['MSRP']<np.quantile(df_copy[\"MSRP\"],0.9)],y='Engine Fuel Type',x='MSRP')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I think it's worth create new group \"other\" for observations \"flex-fuel (premium unleaded recommended/E85)\", \"flex-fuel (unleaded/natural gas)\", \"natural gas\" and for missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "regular unleaded                             6234\n",
       "premium unleaded (required)                  1905\n",
       "premium unleaded (recommended)               1267\n",
       "flex-fuel (unleaded/E85)                      875\n",
       "diesel                                        146\n",
       "electric                                       66\n",
       "flex-fuel (premium unleaded required/E85)      52\n",
       "other                                          37\n",
       "Name: Engine Fuel Type, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy['Engine Fuel Type'].replace(\"flex-fuel (premium unleaded recommended/E85)\",\"other\",inplace=True)\n",
    "df_copy['Engine Fuel Type'].replace(\"flex-fuel (unleaded/natural gas)\",\"other\",inplace=True)\n",
    "df_copy['Engine Fuel Type'].replace(\"natural gas\",\"other\",inplace=True)\n",
    "df_copy['Engine Fuel Type'].fillna('other',inplace=True)\n",
    "df_copy['Engine Fuel Type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last variable, which I should cleaninng is a Market Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Crossover,Exotic,Luxury,High-Performance                   1\n",
       "Flex Fuel,Factory Tuner,Luxury,High-Performance            1\n",
       "Exotic,Luxury,High-Performance,Hybrid                      1\n",
       "Crossover,Exotic,Luxury,Performance                        1\n",
       "Performance,Hybrid                                         1\n",
       "Flex Fuel,Hybrid                                           2\n",
       "Flex Fuel,Performance,Hybrid                               2\n",
       "Crossover,Luxury,Performance,Hybrid                        2\n",
       "Factory Tuner,Luxury                                       2\n",
       "Hatchback,Luxury,Hybrid                                    3\n",
       "Exotic,Factory Tuner,Luxury,Performance                    3\n",
       "Crossover,Factory Tuner,Performance                        4\n",
       "Crossover,Factory Tuner,Luxury,Performance                 5\n",
       "Crossover,Hatchback,Factory Tuner,Performance              6\n",
       "Crossover,Flex Fuel,Luxury,Performance                     6\n",
       "Crossover,Hatchback,Performance                            6\n",
       "Crossover,Flex Fuel,Performance                            6\n",
       "Hatchback,Flex Fuel                                        7\n",
       "Crossover,Diesel                                           7\n",
       "Crossover,Hatchback,Luxury                                 7\n",
       "Hatchback,Factory Tuner,Luxury,Performance                 9\n",
       "Crossover,Luxury,High-Performance                          9\n",
       "Exotic,Performance                                        10\n",
       "Crossover,Flex Fuel,Luxury                                10\n",
       "Exotic,Flex Fuel,Luxury,High-Performance                  11\n",
       "Luxury,Performance,Hybrid                                 11\n",
       "Luxury,High-Performance,Hybrid                            12\n",
       "Exotic,Luxury                                             12\n",
       "Hatchback,Factory Tuner,High-Performance                  13\n",
       "Exotic,Flex Fuel,Factory Tuner,Luxury,High-Performance    13\n",
       "Name: Market Category, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Crossover                                       1040\n",
       "Flex Fuel                                        838\n",
       "Luxury                                           785\n",
       "Luxury,Performance                               645\n",
       "Hatchback                                        513\n",
       "Performance                                      445\n",
       "Crossover,Luxury                                 404\n",
       "Luxury,High-Performance                          334\n",
       "Exotic,High-Performance                          247\n",
       "Factory Tuner,Luxury,High-Performance            215\n",
       "High-Performance                                 197\n",
       "Hatchback,Performance                            144\n",
       "Hybrid                                           119\n",
       "Crossover,Luxury,Performance                     111\n",
       "Factory Tuner,High-Performance                   102\n",
       "Flex Fuel,Performance                             87\n",
       "Diesel                                            84\n",
       "Factory Tuner,Performance                         76\n",
       "Exotic,Luxury,High-Performance                    75\n",
       "Crossover,Hatchback                               72\n",
       "Crossover,Performance                             69\n",
       "Crossover,Flex Fuel                               64\n",
       "Hatchback,Hybrid                                  56\n",
       "Luxury,Hybrid                                     52\n",
       "Exotic,Factory Tuner,Luxury,High-Performance      50\n",
       "Hatchback,Luxury                                  44\n",
       "Diesel,Luxury                                     43\n",
       "Crossover,Hybrid                                  42\n",
       "Flex Fuel,Luxury                                  39\n",
       "Exotic,Luxury,Performance                         36\n",
       "Name: Market Category, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_copy['Market Category'].value_counts(ascending=True).head(30))\n",
    "display(df_copy['Market Category'].value_counts(ascending=False).head(30))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see this variable has many categories, many of which are single observations and it would be useful to group them. I suggest to group the observations by the first word in the category description, because I think it is the main key in the category, by which you can find the car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Crossover           1956\n",
       "Luxury              1839\n",
       "Flex Fuel           1044\n",
       "Hatchback            857\n",
       "Exotic               479\n",
       "Performance          446\n",
       "Factory Tuner        426\n",
       "High-Performance     197\n",
       "Diesel               127\n",
       "Hybrid               119\n",
       "Name: Market Category, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_copy['Market Category']=df_copy['Market Category'].str.split(\",\").str.get(0)\n",
    "display(df_copy['Market Category'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition we have 3092 missing value, which we replace \"Other\" group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Other               3092\n",
       "Crossover           1956\n",
       "Luxury              1839\n",
       "Flex Fuel           1044\n",
       "Hatchback            857\n",
       "Exotic               479\n",
       "Performance          446\n",
       "Factory Tuner        426\n",
       "High-Performance     197\n",
       "Diesel               127\n",
       "Hybrid               119\n",
       "Name: Market Category, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_copy['Market Category'].fillna('Other',inplace=True)\n",
    "display(df_copy['Market Category'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Make                 0\n",
       "Model                0\n",
       "Year                 0\n",
       "Engine Fuel Type     0\n",
       "Engine HP            0\n",
       "Engine Cylinders     0\n",
       "Transmission Type    0\n",
       "Driven_Wheels        0\n",
       "Number of Doors      0\n",
       "Market Category      0\n",
       "Vehicle Size         0\n",
       "Vehicle Style        0\n",
       "highway MPG          0\n",
       "city mpg             0\n",
       "Popularity           0\n",
       "MSRP                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking whether all missing values were managed\n",
    "df_copy.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Make</th>\n",
       "      <th>Model</th>\n",
       "      <th>Year</th>\n",
       "      <th>Engine Fuel Type</th>\n",
       "      <th>Engine HP</th>\n",
       "      <th>Engine Cylinders</th>\n",
       "      <th>Transmission Type</th>\n",
       "      <th>Driven_Wheels</th>\n",
       "      <th>Number of Doors</th>\n",
       "      <th>Market Category</th>\n",
       "      <th>Vehicle Size</th>\n",
       "      <th>Vehicle Style</th>\n",
       "      <th>highway MPG</th>\n",
       "      <th>city mpg</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>MSRP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10582</td>\n",
       "      <td>10582</td>\n",
       "      <td>10582.000000</td>\n",
       "      <td>10582</td>\n",
       "      <td>10582.000000</td>\n",
       "      <td>10582.000000</td>\n",
       "      <td>10582</td>\n",
       "      <td>10582</td>\n",
       "      <td>10582.000000</td>\n",
       "      <td>10582</td>\n",
       "      <td>10582</td>\n",
       "      <td>10582</td>\n",
       "      <td>10582.000000</td>\n",
       "      <td>10582.000000</td>\n",
       "      <td>10582.000000</td>\n",
       "      <td>1.058200e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>48</td>\n",
       "      <td>897</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Silverado 1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>regular unleaded</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AUTOMATIC</td>\n",
       "      <td>front wheel drive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Sedan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1051</td>\n",
       "      <td>156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7624</td>\n",
       "      <td>3946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3092</td>\n",
       "      <td>4063</td>\n",
       "      <td>2657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010.942544</td>\n",
       "      <td>NaN</td>\n",
       "      <td>256.940465</td>\n",
       "      <td>5.725477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.467681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.533737</td>\n",
       "      <td>19.695143</td>\n",
       "      <td>1564.109998</td>\n",
       "      <td>4.309634e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.968729</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.628275</td>\n",
       "      <td>1.767122</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.866500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.093646</td>\n",
       "      <td>9.359977</td>\n",
       "      <td>1450.877905</td>\n",
       "      <td>6.289803e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1990.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>549.000000</td>\n",
       "      <td>2.199500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1385.000000</td>\n",
       "      <td>3.130000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>305.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>4.380000e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1001.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>5657.000000</td>\n",
       "      <td>2.065902e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Make           Model          Year  Engine Fuel Type  \\\n",
       "count       10582           10582  10582.000000             10582   \n",
       "unique         48             897           NaN                 8   \n",
       "top     Chevrolet  Silverado 1500           NaN  regular unleaded   \n",
       "freq         1051             156           NaN              6234   \n",
       "mean          NaN             NaN   2010.942544               NaN   \n",
       "std           NaN             NaN      6.968729               NaN   \n",
       "min           NaN             NaN   1990.000000               NaN   \n",
       "25%           NaN             NaN   2007.000000               NaN   \n",
       "50%           NaN             NaN   2015.000000               NaN   \n",
       "75%           NaN             NaN   2016.000000               NaN   \n",
       "max           NaN             NaN   2017.000000               NaN   \n",
       "\n",
       "           Engine HP  Engine Cylinders Transmission Type      Driven_Wheels  \\\n",
       "count   10582.000000      10582.000000             10582              10582   \n",
       "unique           NaN               NaN                 5                  4   \n",
       "top              NaN               NaN         AUTOMATIC  front wheel drive   \n",
       "freq             NaN               NaN              7624               3946   \n",
       "mean      256.940465          5.725477               NaN                NaN   \n",
       "std       110.628275          1.767122               NaN                NaN   \n",
       "min        55.000000          3.000000               NaN                NaN   \n",
       "25%       175.000000          4.000000               NaN                NaN   \n",
       "50%       240.000000          6.000000               NaN                NaN   \n",
       "75%       305.000000          6.000000               NaN                NaN   \n",
       "max      1001.000000         16.000000               NaN                NaN   \n",
       "\n",
       "        Number of Doors Market Category Vehicle Size Vehicle Style  \\\n",
       "count      10582.000000           10582        10582         10582   \n",
       "unique              NaN              11            3            16   \n",
       "top                 NaN           Other      Compact         Sedan   \n",
       "freq                NaN            3092         4063          2657   \n",
       "mean           3.467681             NaN          NaN           NaN   \n",
       "std            0.866500             NaN          NaN           NaN   \n",
       "min            2.000000             NaN          NaN           NaN   \n",
       "25%            2.000000             NaN          NaN           NaN   \n",
       "50%            4.000000             NaN          NaN           NaN   \n",
       "75%            4.000000             NaN          NaN           NaN   \n",
       "max            4.000000             NaN          NaN           NaN   \n",
       "\n",
       "         highway MPG      city mpg    Popularity          MSRP  \n",
       "count   10582.000000  10582.000000  10582.000000  1.058200e+04  \n",
       "unique           NaN           NaN           NaN           NaN  \n",
       "top              NaN           NaN           NaN           NaN  \n",
       "freq             NaN           NaN           NaN           NaN  \n",
       "mean       26.533737     19.695143   1564.109998  4.309634e+04  \n",
       "std         9.093646      9.359977   1450.877905  6.289803e+04  \n",
       "min        12.000000      7.000000      2.000000  2.000000e+03  \n",
       "25%        22.000000     15.000000    549.000000  2.199500e+04  \n",
       "50%        25.000000     18.000000   1385.000000  3.130000e+04  \n",
       "75%        30.000000     22.000000   2009.000000  4.380000e+04  \n",
       "max       354.000000    137.000000   5657.000000  2.065902e+06  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10582 entries, 0 to 11913\n",
      "Data columns (total 16 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Make               10582 non-null  object \n",
      " 1   Model              10582 non-null  object \n",
      " 2   Year               10582 non-null  int64  \n",
      " 3   Engine Fuel Type   10582 non-null  object \n",
      " 4   Engine HP          10582 non-null  float64\n",
      " 5   Engine Cylinders   10582 non-null  float64\n",
      " 6   Transmission Type  10582 non-null  object \n",
      " 7   Driven_Wheels      10582 non-null  object \n",
      " 8   Number of Doors    10582 non-null  float64\n",
      " 9   Market Category    10582 non-null  object \n",
      " 10  Vehicle Size       10582 non-null  object \n",
      " 11  Vehicle Style      10582 non-null  object \n",
      " 12  highway MPG        10582 non-null  int64  \n",
      " 13  city mpg           10582 non-null  int64  \n",
      " 14  Popularity         10582 non-null  int64  \n",
      " 15  MSRP               10582 non-null  int64  \n",
      "dtypes: float64(3), int64(5), object(8)\n",
      "memory usage: 1.7+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_copy.describe(include='all'))\n",
    "display(df_copy.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last variable worth looking at more closely is the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.058200e+04\n",
       "mean     4.309634e+04\n",
       "std      6.289803e+04\n",
       "min      2.000000e+03\n",
       "25%      2.199500e+04\n",
       "50%      3.130000e+04\n",
       "75%      4.380000e+04\n",
       "max      2.065902e+06\n",
       "Name: MSRP, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "MSRP   \n",
       "1500000    2\n",
       "548800     2\n",
       "2065902    1\n",
       "1705769    1\n",
       "1382750    1\n",
       "1380000    1\n",
       "643330     1\n",
       "535500     1\n",
       "506500     1\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check main statistics \n",
    "display(df_copy['MSRP'].describe())\n",
    "display(df_copy[df_copy['MSRP']>500000][['MSRP']].value_counts())\n",
    "#Create a boxplot for Price in each dataframe with and without car at price above 500000 \n",
    "fig,axs=plt.subplots(2,1)\n",
    "sns.boxplot(x='MSRP',data=df_copy,ax=axs[0])\n",
    "sns.boxplot(x='MSRP',data=df_copy[df_copy['MSRP']<500000],ax=axs[1])\n",
    "axs[0].set(title='Boxplot of car prices with all data')\n",
    "axs[0].xaxis.label.set_visible(False)\n",
    "axs[1].set(title='Boxplot of car prices without car at price above 500000 ',xlabel='Price')\n",
    "plt.tight_layout(pad=1.15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that there are middle class and high class cars in the base. I think there should be a separation between the middle class segment and the upscale cars, which are outlier observations in this base. These cases should be considered separately, and I believe that for the purposes of this study they should not be taken into account, so I remove them from the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10571 entries, 0 to 11913\n",
      "Data columns (total 16 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Make               10571 non-null  object \n",
      " 1   Model              10571 non-null  object \n",
      " 2   Year               10571 non-null  int64  \n",
      " 3   Engine Fuel Type   10571 non-null  object \n",
      " 4   Engine HP          10571 non-null  float64\n",
      " 5   Engine Cylinders   10571 non-null  float64\n",
      " 6   Transmission Type  10571 non-null  object \n",
      " 7   Driven_Wheels      10571 non-null  object \n",
      " 8   Number of Doors    10571 non-null  float64\n",
      " 9   Market Category    10571 non-null  object \n",
      " 10  Vehicle Size       10571 non-null  object \n",
      " 11  Vehicle Style      10571 non-null  object \n",
      " 12  highway MPG        10571 non-null  int64  \n",
      " 13  city mpg           10571 non-null  int64  \n",
      " 14  Popularity         10571 non-null  int64  \n",
      " 15  MSRP               10571 non-null  int64  \n",
      "dtypes: float64(3), int64(5), object(8)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_copy.drop(df_copy[df_copy['MSRP'] >= 500000].index,inplace=True)\n",
    "df_copy.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will recode the categorical variables into binary variables before splitting the set into training and test sets. This will be used later in the modeling process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10571 entries, 0 to 11913\n",
      "Data columns (total 57 columns):\n",
      " #   Column                                                      Non-Null Count  Dtype  \n",
      "---  ------                                                      --------------  -----  \n",
      " 0   Make                                                        10571 non-null  object \n",
      " 1   Model                                                       10571 non-null  object \n",
      " 2   Year                                                        10571 non-null  int64  \n",
      " 3   Engine Fuel Type                                            10571 non-null  object \n",
      " 4   Engine HP                                                   10571 non-null  float64\n",
      " 5   Engine Cylinders                                            10571 non-null  float64\n",
      " 6   Transmission Type                                           10571 non-null  object \n",
      " 7   Driven_Wheels                                               10571 non-null  object \n",
      " 8   Number of Doors                                             10571 non-null  float64\n",
      " 9   Market Category                                             10571 non-null  object \n",
      " 10  Vehicle Size                                                10571 non-null  object \n",
      " 11  Vehicle Style                                               10571 non-null  object \n",
      " 12  highway MPG                                                 10571 non-null  int64  \n",
      " 13  city mpg                                                    10571 non-null  int64  \n",
      " 14  Popularity                                                  10571 non-null  int64  \n",
      " 15  MSRP                                                        10571 non-null  int64  \n",
      " 16  Engine Fuel Type_electric                                   10571 non-null  uint8  \n",
      " 17  Engine Fuel Type_flex-fuel (premium unleaded required/E85)  10571 non-null  uint8  \n",
      " 18  Engine Fuel Type_flex-fuel (unleaded/E85)                   10571 non-null  uint8  \n",
      " 19  Engine Fuel Type_other                                      10571 non-null  uint8  \n",
      " 20  Engine Fuel Type_premium unleaded (recommended)             10571 non-null  uint8  \n",
      " 21  Engine Fuel Type_premium unleaded (required)                10571 non-null  uint8  \n",
      " 22  Engine Fuel Type_regular unleaded                           10571 non-null  uint8  \n",
      " 23  Transmission Type_AUTOMATIC                                 10571 non-null  uint8  \n",
      " 24  Transmission Type_DIRECT_DRIVE                              10571 non-null  uint8  \n",
      " 25  Transmission Type_MANUAL                                    10571 non-null  uint8  \n",
      " 26  Transmission Type_UNKNOWN                                   10571 non-null  uint8  \n",
      " 27  Driven_Wheels_four wheel drive                              10571 non-null  uint8  \n",
      " 28  Driven_Wheels_front wheel drive                             10571 non-null  uint8  \n",
      " 29  Driven_Wheels_rear wheel drive                              10571 non-null  uint8  \n",
      " 30  Market Category_Diesel                                      10571 non-null  uint8  \n",
      " 31  Market Category_Exotic                                      10571 non-null  uint8  \n",
      " 32  Market Category_Factory Tuner                               10571 non-null  uint8  \n",
      " 33  Market Category_Flex Fuel                                   10571 non-null  uint8  \n",
      " 34  Market Category_Hatchback                                   10571 non-null  uint8  \n",
      " 35  Market Category_High-Performance                            10571 non-null  uint8  \n",
      " 36  Market Category_Hybrid                                      10571 non-null  uint8  \n",
      " 37  Market Category_Luxury                                      10571 non-null  uint8  \n",
      " 38  Market Category_Other                                       10571 non-null  uint8  \n",
      " 39  Market Category_Performance                                 10571 non-null  uint8  \n",
      " 40  Vehicle Size_Large                                          10571 non-null  uint8  \n",
      " 41  Vehicle Size_Midsize                                        10571 non-null  uint8  \n",
      " 42  Vehicle Style_2dr SUV                                       10571 non-null  uint8  \n",
      " 43  Vehicle Style_4dr Hatchback                                 10571 non-null  uint8  \n",
      " 44  Vehicle Style_4dr SUV                                       10571 non-null  uint8  \n",
      " 45  Vehicle Style_Cargo Minivan                                 10571 non-null  uint8  \n",
      " 46  Vehicle Style_Cargo Van                                     10571 non-null  uint8  \n",
      " 47  Vehicle Style_Convertible                                   10571 non-null  uint8  \n",
      " 48  Vehicle Style_Convertible SUV                               10571 non-null  uint8  \n",
      " 49  Vehicle Style_Coupe                                         10571 non-null  uint8  \n",
      " 50  Vehicle Style_Crew Cab Pickup                               10571 non-null  uint8  \n",
      " 51  Vehicle Style_Extended Cab Pickup                           10571 non-null  uint8  \n",
      " 52  Vehicle Style_Passenger Minivan                             10571 non-null  uint8  \n",
      " 53  Vehicle Style_Passenger Van                                 10571 non-null  uint8  \n",
      " 54  Vehicle Style_Regular Cab Pickup                            10571 non-null  uint8  \n",
      " 55  Vehicle Style_Sedan                                         10571 non-null  uint8  \n",
      " 56  Vehicle Style_Wagon                                         10571 non-null  uint8  \n",
      "dtypes: float64(3), int64(5), object(8), uint8(41)\n",
      "memory usage: 1.8+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Divide features into target, categorical, numerical\n",
    "target=['MSRP']\n",
    "cat_features=df_copy.select_dtypes(include='object').columns.values.tolist()\n",
    "cat_features.remove('Make')\n",
    "cat_features.remove('Model')\n",
    "num_features=df_copy.select_dtypes(exclude='object').columns.values.tolist()\n",
    "num_features.remove('MSRP')\n",
    "\n",
    "# One hot encoding\n",
    "dummLev = pd.get_dummies(df_copy[cat_features], drop_first=True)\n",
    "\n",
    "# Combination of numeric, categorical on hot encoding and target variables\n",
    "df_new = pd.concat([df_copy, dummLev], axis=1)\n",
    "display(df_new.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.Exploratory and Data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we move on to exploratory analysis, it is useful to separate some of the data that will be used for final validation at the end from our collection. The set will not be used in subsequent stages of the project and will constitute a test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Divide dataset to train and test and save them\n",
    "train_set, test_set = train_test_split(df_new,test_size=0.2,random_state=42)\n",
    "train_set.to_csv(path+'\\\\data\\\\train_set.csv')\n",
    "test_set.to_csv(path+'\\\\data\\\\test_set.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1 Car prirce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by looking at the distribution of car prices in this database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array of the 5% of the most expensive car brands:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Make         \n",
       "Aston Martin     68\n",
       "Bentley          64\n",
       "Ferrari          56\n",
       "Mercedes-Benz    44\n",
       "Lamborghini      39\n",
       "Porsche          34\n",
       "Rolls-Royce      28\n",
       "Audi             23\n",
       "Maserati         17\n",
       "BMW              15\n",
       "Land Rover       12\n",
       "Maybach          11\n",
       "McLaren           4\n",
       "Lexus             3\n",
       "Nissan            2\n",
       "Tesla             1\n",
       "Dodge             1\n",
       "Ford              1\n",
       "Acura             1\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1)\n",
    "#Two histplots for two price range \n",
    "sns.histplot(data=train_set[train_set['MSRP']<np.quantile(train_set[\"MSRP\"],0.95)],x='MSRP',kde=True,ax=axs[0])\n",
    "sns.histplot(data=train_set[train_set['MSRP']>=np.quantile(train_set[\"MSRP\"],0.95)],x='MSRP',kde=True,ax=axs[1])\n",
    "axs[0].set(title=\"Distribution of car prices below the 95th percentile\")\n",
    "axs[0].xaxis.label.set_visible(False)\n",
    "axs[1].set(title=\"Price distribution of the 5% most expensive cars\",xlabel='Price')\n",
    "plt.tight_layout(pad=1.15)\n",
    "plt.show()\n",
    "print('Array of the 5% of the most expensive car brands:')\n",
    "display(train_set[train_set['MSRP']>=np.quantile(train_set[\"MSRP\"],0.95)][['Make']].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I had to determine two distributions to better present the price ranges the cars fall into. Also, I presented the 5% most expensive car brands in our database. \n",
    "These are, for example, the cars of Aston Martin, Bentley, Ferrari, Mercedes Benz, or Lamborghini. It's also worth looking at those cars priced under 10000, as they stand out in this distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=train_set[train_set['MSRP']<=10000],x='MSRP',kde=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Engine HP</th>\n",
       "      <th>Engine Cylinders</th>\n",
       "      <th>Number of Doors</th>\n",
       "      <th>highway MPG</th>\n",
       "      <th>city mpg</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>MSRP</th>\n",
       "      <th>Engine Fuel Type_electric</th>\n",
       "      <th>Engine Fuel Type_flex-fuel (premium unleaded required/E85)</th>\n",
       "      <th>...</th>\n",
       "      <th>Vehicle Style_Convertible</th>\n",
       "      <th>Vehicle Style_Convertible SUV</th>\n",
       "      <th>Vehicle Style_Coupe</th>\n",
       "      <th>Vehicle Style_Crew Cab Pickup</th>\n",
       "      <th>Vehicle Style_Extended Cab Pickup</th>\n",
       "      <th>Vehicle Style_Passenger Minivan</th>\n",
       "      <th>Vehicle Style_Passenger Van</th>\n",
       "      <th>Vehicle Style_Regular Cab Pickup</th>\n",
       "      <th>Vehicle Style_Sedan</th>\n",
       "      <th>Vehicle Style_Wagon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>895.000000</td>\n",
       "      <td>895.000000</td>\n",
       "      <td>895.000000</td>\n",
       "      <td>895.000000</td>\n",
       "      <td>895.000000</td>\n",
       "      <td>895.000000</td>\n",
       "      <td>895.000000</td>\n",
       "      <td>895.000000</td>\n",
       "      <td>895.0</td>\n",
       "      <td>895.0</td>\n",
       "      <td>...</td>\n",
       "      <td>895.000000</td>\n",
       "      <td>895.000000</td>\n",
       "      <td>895.000000</td>\n",
       "      <td>895.0</td>\n",
       "      <td>895.000000</td>\n",
       "      <td>895.000000</td>\n",
       "      <td>895.000000</td>\n",
       "      <td>895.000000</td>\n",
       "      <td>895.000000</td>\n",
       "      <td>895.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1995.518436</td>\n",
       "      <td>169.237989</td>\n",
       "      <td>5.486034</td>\n",
       "      <td>2.941899</td>\n",
       "      <td>23.392179</td>\n",
       "      <td>17.395531</td>\n",
       "      <td>1474.658101</td>\n",
       "      <td>2410.608939</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049162</td>\n",
       "      <td>0.010056</td>\n",
       "      <td>0.139665</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.115084</td>\n",
       "      <td>0.052514</td>\n",
       "      <td>0.007821</td>\n",
       "      <td>0.098324</td>\n",
       "      <td>0.244693</td>\n",
       "      <td>0.056983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.964262</td>\n",
       "      <td>55.280496</td>\n",
       "      <td>1.466088</td>\n",
       "      <td>0.952436</td>\n",
       "      <td>5.025588</td>\n",
       "      <td>3.939416</td>\n",
       "      <td>1525.617035</td>\n",
       "      <td>618.609801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.216327</td>\n",
       "      <td>0.099829</td>\n",
       "      <td>0.346833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.319301</td>\n",
       "      <td>0.223186</td>\n",
       "      <td>0.088140</td>\n",
       "      <td>0.297919</td>\n",
       "      <td>0.430145</td>\n",
       "      <td>0.231940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1990.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1993.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>535.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1996.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>870.000000</td>\n",
       "      <td>2065.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1998.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>2649.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>402.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>5657.000000</td>\n",
       "      <td>4976.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year   Engine HP  Engine Cylinders  Number of Doors  \\\n",
       "count   895.000000  895.000000        895.000000       895.000000   \n",
       "mean   1995.518436  169.237989          5.486034         2.941899   \n",
       "std       2.964262   55.280496          1.466088         0.952436   \n",
       "min    1990.000000   55.000000          3.000000         2.000000   \n",
       "25%    1993.000000  133.000000          4.000000         2.000000   \n",
       "50%    1996.000000  162.000000          6.000000         3.000000   \n",
       "75%    1998.000000  203.000000          6.000000         4.000000   \n",
       "max    2000.000000  402.000000         12.000000         4.000000   \n",
       "\n",
       "       highway MPG    city mpg   Popularity         MSRP  \\\n",
       "count   895.000000  895.000000   895.000000   895.000000   \n",
       "mean     23.392179   17.395531  1474.658101  2410.608939   \n",
       "std       5.025588    3.939416  1525.617035   618.609801   \n",
       "min      14.000000   10.000000    26.000000  2000.000000   \n",
       "25%      20.000000   15.000000   535.000000  2000.000000   \n",
       "50%      23.000000   17.000000   870.000000  2065.000000   \n",
       "75%      26.000000   19.000000  2009.000000  2649.000000   \n",
       "max      42.000000   34.000000  5657.000000  4976.000000   \n",
       "\n",
       "       Engine Fuel Type_electric  \\\n",
       "count                      895.0   \n",
       "mean                         0.0   \n",
       "std                          0.0   \n",
       "min                          0.0   \n",
       "25%                          0.0   \n",
       "50%                          0.0   \n",
       "75%                          0.0   \n",
       "max                          0.0   \n",
       "\n",
       "       Engine Fuel Type_flex-fuel (premium unleaded required/E85)  ...  \\\n",
       "count                                              895.0           ...   \n",
       "mean                                                 0.0           ...   \n",
       "std                                                  0.0           ...   \n",
       "min                                                  0.0           ...   \n",
       "25%                                                  0.0           ...   \n",
       "50%                                                  0.0           ...   \n",
       "75%                                                  0.0           ...   \n",
       "max                                                  0.0           ...   \n",
       "\n",
       "       Vehicle Style_Convertible  Vehicle Style_Convertible SUV  \\\n",
       "count                 895.000000                     895.000000   \n",
       "mean                    0.049162                       0.010056   \n",
       "std                     0.216327                       0.099829   \n",
       "min                     0.000000                       0.000000   \n",
       "25%                     0.000000                       0.000000   \n",
       "50%                     0.000000                       0.000000   \n",
       "75%                     0.000000                       0.000000   \n",
       "max                     1.000000                       1.000000   \n",
       "\n",
       "       Vehicle Style_Coupe  Vehicle Style_Crew Cab Pickup  \\\n",
       "count           895.000000                          895.0   \n",
       "mean              0.139665                            0.0   \n",
       "std               0.346833                            0.0   \n",
       "min               0.000000                            0.0   \n",
       "25%               0.000000                            0.0   \n",
       "50%               0.000000                            0.0   \n",
       "75%               0.000000                            0.0   \n",
       "max               1.000000                            0.0   \n",
       "\n",
       "       Vehicle Style_Extended Cab Pickup  Vehicle Style_Passenger Minivan  \\\n",
       "count                         895.000000                       895.000000   \n",
       "mean                            0.115084                         0.052514   \n",
       "std                             0.319301                         0.223186   \n",
       "min                             0.000000                         0.000000   \n",
       "25%                             0.000000                         0.000000   \n",
       "50%                             0.000000                         0.000000   \n",
       "75%                             0.000000                         0.000000   \n",
       "max                             1.000000                         1.000000   \n",
       "\n",
       "       Vehicle Style_Passenger Van  Vehicle Style_Regular Cab Pickup  \\\n",
       "count                   895.000000                        895.000000   \n",
       "mean                      0.007821                          0.098324   \n",
       "std                       0.088140                          0.297919   \n",
       "min                       0.000000                          0.000000   \n",
       "25%                       0.000000                          0.000000   \n",
       "50%                       0.000000                          0.000000   \n",
       "75%                       0.000000                          0.000000   \n",
       "max                       1.000000                          1.000000   \n",
       "\n",
       "       Vehicle Style_Sedan  Vehicle Style_Wagon  \n",
       "count           895.000000           895.000000  \n",
       "mean              0.244693             0.056983  \n",
       "std               0.430145             0.231940  \n",
       "min               0.000000             0.000000  \n",
       "25%               0.000000             0.000000  \n",
       "50%               0.000000             0.000000  \n",
       "75%               0.000000             0.000000  \n",
       "max               1.000000             1.000000  \n",
       "\n",
       "[8 rows x 49 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set[train_set['MSRP']<5000].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is interesting that there are so many cars in the database worth less than 5000. As you can see from the statistics, such a low price is probably caused by the year of production of these cars, which are simply old (less than 2000). Now, let's look on most frequent car models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2 Car brands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a percentage proportion of car brands\n",
    "make_percentage=train_set['Make'].value_counts()*100/sum(train_set['Make'].value_counts())\n",
    "#Create barplot\n",
    "sns.barplot(x=make_percentage[:10],y=make_percentage.index[:10],palette='mako')\n",
    "plt.xlabel('Percentage')\n",
    "plt.ylabel('Car Makes')\n",
    "plt.title('Proportion of Car Makes(in %)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to this barplot, we can see that the brand that has sold the most models is Chevrolet (almost 10%). In addition 10 most recurrent car brands in that dataset represent around 50% of total number of cars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s2EHXrl0JCMi9T/PgwYOZOnVqgT7d3NxYtmwZu3fv5syZM/z444+4udn3e42fnz9+fv5MnTrLrustq3r1anLpUprldUTEHE4mXHZgRLncPKoR1LjuLbdj4fidUWmPogQHd2Lv3j2YTCY8PDwIDg4BoGHDRlYTtIeHY/8sFBdvWesJ4Yoq3czZ2jlipRQ5OTm0bduW5ORkvvvuOxo1asQdd9xhOXyWv27hmUN6ejqDBg0iMTGRtm3bMmzYsAodgxC2pNWGo9FoANBo3NBqc6/HGDt2gtX6o0dbL7cXrTbc8uU3f7zW6lkblxCVQaVLzr6+vjRq1IjvvvsOgN9++43Lly9z3333odFo6N+/P/PmzWPgwIEAPPLII+zevdtyMcmXX35Ju3btAHB3dycnJ4f4+Hg0Gg1jx46lXbt27Nixg5wc5z4fKUQef/8AQkJC0Wg0hISEWs5TBwXdTcOGjQrU9fDwcOj5ZsiN99FHHy0Sr7V61sYlRGVQ6ZIzwKJFi1izZg1hYWHMnTuXyMhIvLy8AOjTpw+ZmZk8+uijADRv3pwxY8YwbNgwevbsybVr15g0aRIAoaGhjB49mpo1a9KiRQt69epFnz59CAgI4Pz5844anhC3TasNp2nT5kVml2PHTqBatWoMGjQEcPysOc8zzzxjNd7CihuXEK5Oo6rQb4XMZjOff/45Z86cYfr06XZd95Ur12/r+aLWztkCLn/O2RkeGXlfJTvnbG0srhB/SSR+x5L4K56bm4Y6dXyLXV7pLggryUsvvURSUhKffvqpo0O5bSEhoY4OQTgZ2SeEqLyqVHL+6KOPHB1Cmcn9k0Vhsk8IUXlVynPOQgghhCuT5CyEEEI4mSp1WFs4Vs6NVMvFXOXtByhTX7lt65Y7BiGEqEiSnIVdNG4cZLO+rl7N3W39/PzL0LquTWMRQoiKIMlZ2MWQISMdHYIQQrgMOecshBBCOBlJzkIIIYSTkcPaokyio1eRkGAodnlxz0O+etUI3Pp8cePGQXIoXAhRZUlyFmWSkGAg/uRRGvh6Wl1+o5h2KdezAfDOKP7xkRf+riOEEFWVJGdRZg18PRn14O39LOnTX3OTcknt8uoIIURVJeechRBCCCcjyVkIIYRwMpKchRBCCCcjydnF7Nv3Pfv2fe/oMCq9Xbt2yXYWQjiMJGcXo9fHotfHOjSGquC7776T7SyEcBhJzi7ivffeYcSIwRw79ifnzp1zdDhVjtGYyoIFszEajSWWCSGELbh8ck5MTKRZs2bMnDmzQPnRo0dp1qwZGzZsKFf/w4YN4+DBg+XqwxZ+++1ny7/T0q46MJKqSaeL4cSJY+h0MSWWCSGELbh8cgbw9/dHr9eTk/O/O1Jt376d2rVrOzAq23nvvXeKlH366QoHRFI1GY2p6PWxKKXQ62MxGo1Wy4QQwlYqxU1IatSoQfPmzTl06BDt27cHYP/+/QQHBwPw2WefodPpyMzMxNPTkyVLluDl5cX48eMtfZw5c4aJEycybNgwpk2bxpEjRwgMDCQ1NffZwSaTidmzZ3Py5EkuX75Ms2bNePfdd/Hx8anw8eWfNefZu3cXyclJFb7u4hgM8dSg6O05beF6Vg4XDfFERMypkP5L4+xZA7Vq+QG5M2SlFABKmf+eKasiZSNGjHJUuEKISqZSzJwBevXqxbfffgvA77//TrNmzfD09OT69evs3LmTNWvWsHXrVkJDQ4mOjqZRo0bodDp0Oh0vvfQS9913H0OHDmXNmjUAfP3110yfPp2EhAQAfv31Vzw9PVm3bh07duwgLS2N77+Xq3mrgri4fZhMJiD3S1pcnN5qmRBC2EqlmDkDdOvWjffffx+z2czXX39Nr1692L59O76+vixZsoRt27YRHx+PXq+nRYsWlnbHjh1j4cKFrFmzBm9vb3788UcGDx4MwN13382DDz4IQNu2bfH39yc6OprTp08THx9PRkaGQ8aaZ+rUWQ5bd0TEHG4k/VUhfft6uVP3zrsdOr7Fi+dZHtwRHNyJvXv3YDKZ8PDwIDg4BFBWyoQQwjYqzcw579D2zz//zA8//GA5pJ2UlMTgwYNJS0ujc+fODBgwwHI4MiUlhZdffpkFCxbQsGFDADQajWU5gIdH7veXXbt28dprr+Hj48PAgQNp27ZtgXoVqU2bfxUp69y5u13WLUCrDUej0QCg0bih1YZbLRNCCFupNMkZcg9tL1myhFatWlmSavXq1QkKCmLkyJHcf//97Ny5k5ycHLKzsy3nmNu1a2fpo0OHDmzZsgWz2cy5c+f45ZdfADhw4AC9evUiPDycWrVqcfDgwQIXoFWkV155o0jZqFGj7bJuAf7+AYSEhKLRaAgJCcXf399qmRBC2EqlOawN0LVrV6ZNm8bEiRMtZZ6enpjNZnr37o1SirZt23Ly5Em++eYbfv31VzIzM4mJyb3gJzg4mFdffZWTJ0/Sq1cvAgMDadq0KQBPPPEEr732Gtu2bcPT05OHHnqIxMREu42tTZt/WS4Mq1nTz27rFbm02nDOnUssMEO2ViaEELbg8sm5UaNG7N69G8g9tP3f//7XsmzhwoUADB061GrbsLAwq+Xz5s2zWr5ly5byhFouebNnR17BXJX5+wfw5puzb1kmhBC24PLJuaoJCQl1dAhVQo8ePbh2LdPRYQghqihJzi6mU6cujg6hSujevTuXLqU5OgwhRBVVqS4IE0IIISoDSc5CCCGEk5HD2qLMLlzP5tNfL99Wm6Tr2QAltrtwPZu7yxOYEEK4OEnOokwaNw4qcbmnp7vlDlv51b5qBMDHz7/YtneXon8hhKjMJDmLMhkyZGSJy+vVqykXVAkhRBnJOWchhBDCyUhyFkIIIZyMJGchhBDCycg5ZyFsIDp6FQkJhjK1vfr3RXJ+JVwkd7uKuyCvLBo3DrrlNQZCCNuS5CyEDSQkGDh+6hge/t633dZkvAnAZVOqrcMqt7zYhBD2JclZCBvx8PcmILTRbbdLjc19ullZ2la0vNiEEPYl55yFEEIIJyPJWQghhHAykpyFy9u373v27fve0WEIUYDsl6I85JyzcHl6fSwgj9MUzkWvjwVkvxRlI8nZRWzZson16z8HoH79BtSsWZOXX34Nf39/xwYmhCgTozGVjz5ayrhxk+RzLIpwmcPa169fZ86cOfTt2xetVsuwYcP4448/St0+MjKSyMhIALRabZGyZs2a2T5oG8pLzADJyRc4deokOl2MAyMSQpSHThfDiRPH5HMsrHKJ5Gw2m3nhhRfw8/Nj06ZN6HQ6xo8fzwsvvEBq6u3/NlSn01VAlBVny5ZNVsv1+j0YjUa7xiKEKD+jMRW9PhalFHp9rHyORREucVj74MGDJCUl8fLLL+Pmlvt9on379kRERGA2m5k+fTonT57k8uXLNGvWjHfffRcfHx8++eQTvvzySwICAqhVqxatW7cGcmfJx48ft7quixcv8uabb5KWlkZycjIDBgxg4sSJ3Lx5kzlz5vDzzz/j6enJuHHj6N27t13Gn3/WnJ/JZEKni2HEiFF2icNZXb1qxGg0EhExx2Z93u4dtgyGeMweJput31mYb5gwGOJtum1Lw5Z3OHMET093DIb4Yg9X63QxKKUAUMosn2NRhEvMnP/880+aN29uScx5unTpwunTp/H09GTdunXs2LGDtLQ0vv/+ew4fPkxMTAwbN25k5cqVXLhwoVTr2rp1K3379uXLL79ky5YtrF69mpSUFNasWUNGRgZff/01K1euZNmyZWRlZVXEcEtNKUVcnN6hMQghbl9c3D5MptwvcyaTST7HogiXmDm7ubnh7W39toht27bF39+f6OhoTp8+TXx8PBkZGfz444906dKFGjVqANCzZ0/MZvMt1zVq1Ch++OEHPv30U06ePEl2djaZmZkcOnSIJ598Ejc3N+rVq8e2bdtsOsay0Gg0BAeHODoMh/Pz88fPz5+pU2fZrM/bfR51RMQcTl05Y7P1Ows3Hw+C6txt021bGq7+PPB69Wry6quvFbs8OLgTe/fuwWQy4eHhIZ9jUYRLzJxbtWrFn3/+aTkMlOfdd99l586dvPbaa/j4+DBw4EDatm2LUgqNRlOgvodH6b6HLFy4kDVr1tCwYUNefPFFAgICUErh4eGBRqOx1DMYDHabOQ8a9LTVcg8PD7TacLvEIISwHa023PL3RKNxk8+xKMIlkvPDDz9MnTp1+PDDD8nJyT0Ppdfr2bBhA3q9nl69ehEeHk6tWrU4ePAgOTk5dOjQgT179pCWlsbNmzfZsWNHqda1f/9+Ro0aRa9evThz5gwXL17EbDbTtm1btm/fjlKKK1euMHToULsl57Cw/lbLQ0K6yk8whHBB/v4BhISEotFoCAkJlc+xKMIlDmtrNBo++ugjIiIi6Nu3Lx4eHgQEBLBixQrc3d157bXX2LZtG56enjz00EMkJibyxBNPMGLECAYNGkStWrVo2LBhqdY1ZswY3njjDXx8fGjQoAGtWrUiMTGRZ555hnnz5tGvXz8AZsyYga+vb0UOu4BBg54u8jtn+bYthOvSasM5dy5RPsfCKpdIzgC1a9dm0aJFVpdt2bLFavmQIUMYMmRIkfK8K7UnTJhQpKxv37707dvXan9z5869rZhtKSysf7EzaCGE6/H3D+DNN2c7OgzhpFwmOQtRnJCQUEeHIEQRsl+K8pDkLFye3LtYOCPZL0V5uMQFYUIIIURVIslZCCGEcDJyWFsIGzEZb5Iam1imdkCZ2lY0k/Em1HF0FEJUPZKchbCBxo2Dytz2qocRyL3Tma3Y7N7Udco3NiFE2UhyFsIGhgwZ6egQCnD1218KUdXJOWchhBDCyUhyFkIIIZyMJGchhBDCycg5Z1HhoqNXkZBgKFcfV68aAdtdNNW4cZDTnScWQog8kpxFhUtIMHD6+FHqupd9d7vy94PpNZculTueyzmmcvchhBAVSZKzsIu67h4M9K9d5vYbjCkA5eqjcF9CCOGs5JyzEEII4WQkOQshhBBORpKzi9i373v27fve0WEIJyP7hRCVk5xzdhF6fSwgj6ETBen1sYDsF0JUNjJzFkIIIZyMSyfnxMREmjVrxv79+wuUd+vWjcOHDzNt2jQHRSaE4xmNqSxYMBuj0ejoUKo8W74XhfsyGOIZO3bkLe8lIPuDbZR2e5eXSydnAE9PT2bMmMH169cLlAcEBDB//nwHRSWE4+l0MZw4cQydLsbRoVR5tnwvCvcVFRVJZmYmUVEf2C2Gqqy027u8XD45169fn+DgYN5+++0C5T/++CPDhg0DYOXKlfTr14/+/fszc+ZMAI4dO8aTTz7JwIEDefrpp4mPjwfgs88+44knnqBv374MGDCA06dPA7mz8ffff59BgwbRp08fjhw5Yr9BCnGbUlJS0OtjUUqh18fKbMmBjMZUm70Xhfs6cuQw58/nPgf83LnEYmdztoyhKjMY4ku1vW2hUlwQNmXKFMLCwti/fz8dO3YssCwnJ4fly5ej1+txd3dn2rRpXLx4kdWrV/Pss8/Sq1cvNm7cyG+//UbdunXZuXMna9aswcfHh6VLlxIdHc2MGTMA8Pf3Z/369axZs4bly5cTGRlptzFevWrEaDQSETHHbussj/zPEzYY4vE2mx0c0f9kmM2kGuJL3JY2ex5yBTMY4vH39y9SvnbtWpRSAChlRqeLYcSIUXaOTkDujNVW70XhvpYte6/A8qioD1iwYEmFxlCVRUVFFnptfXvbgsvPnAF8fX156623rB7ednd358EHH2TQoEF8+OGHPPvss9xxxx106dKFt956izfffJOaNWsSFhaGr68vS5YsYdu2bSxZsoQ9e/aQkZFh6SskJASA++67T755CqcWGxuL6e9bnppMJuLi9A6OqOqKi9tns/eicF8ZGekFlp87l1jhMVRlebPmPMVtb1uoFDNngE6dOlk9vA3w0Ucf8dtvv7F3716ef/55Fi9eTM+ePXnwwQfZs2cPq1atIjY2lvHjxzNs2DCGDh1K586dqVu3LkePHrX04+3tDYBGo7HbuPL4+fnj5+fP1Kmz7L7usqhXryaXLqUBEBExh2t/nXRwRP9T3c2NBkF3l7gt88fvzIqb/YeGhvLdd99hMpnw8PAgODjEzpGJPMHBndi7d49N3ovCfXl5eRdI0IGBjSo8hqqsYcNGBRJ0cdvbFirFzDnPlClT2LdvH8nJyZaylJQUevfuTdOmTZk4cSIdO3bk+PHjTJo0icOHD/PUU08xceJE/vzzTw4fPkxQUBAjR47k/vvvZ+fOneTkOP+hTSEKe+aZZyxfIjUaN7TacAdHVHVpteE2ey8K9zV+/CsFlo8d+3KFx1CVjR07odBr69vbFipVcs47vJ2dnW0pq127NoMHD2bQoEEMHDiQrKwswsPDGTt2LB9//DEDBgxg0aJFzJ49m44dO2I2m+nduzcDBgzgnnvuITGx4g5bCFFRateuTUhIKBqNhpCQUKvnpYV9+PsH2Oy9KNxXq1b307Bh7uwtMLARjRsHVXgMVVlQ0N2l2t624NKHtRs1asTu3bsLlHXq1Injx48DMHDgQABGjhzJyJEjC9Rr3rw5MTFFf1KwcuVKq+vKv5527drRrl278oQuRIXTasM5dy5RZklOwJbvReG+xo6dQETE7FvO4mR/sI3Sbu/ycunkXJWEhIQ6OgThhEraL/z9A3jzzdl2i0UUz5bvReG+goLuJipqlV1jqMpKu73LS5Kzi5B7JwtrZL8QonKqVOechRBCiMpAkrMQQgjhZCQ5CyGEEE5GzjkLu7icY2KDMaXs7f++u1F5+sgfS61y9yKEEBVHkrOocLb4LaC6agSglp9/ufuqhW1iEkKIiiLJWVS4IUNGOjoEIYRwKXLOWQghhHAykpyFEEIIJyOHtYXTiY5eVa6HmF/9+/y0XznOT+c9z7lx4yA5LC+EsDtJzsLpJCQYOHnyL2pUq12m9ukZV3L/n2YuVxzpmeW/MlwIIcpCkrNwSjWq1ab1fb3L1Pb3k9sByty+cD9CCGFvcs5ZCCGEcDKSnIUQQggnI8lZFGvfvu/Zt+97R4chymDXrl3y3gnhwiQ5i2Lp9bHo9bEOjUGUzXfffSfvnRAuTJKzEEII4WTkam0XceTI7yxaNL9AWaNGjXF3d8NkyuHKlUtMmPA6mzevZ9y4Sfj7+zsmUOGUjhz5ncWLF/D669Px9fUlImI2b745h1q1avHBB4sxmXLw8PDg5Zdfs+w7BkM8ERGzmTDh3+h0MQX2K6MxlY8+WsqQIc+yevX/ARRoK4Qon0o/czaZTHz88cf06tWL3r178/jjjxMVFYVSisjISJo1a8avv/5aoM38+fNp1qyZ5XVycjKvvfYaffr0oV+/fowZM4azZ8/adRzLlr1fpCwxMQGDIZ5z585y48YNli17lxMnjqHTxdg1NuH8li17H6UUy5a9S1RUJJmZmURFfYBOF8OpU39hMJzh1KmTBfadvHrLlr1XZL/S6WI4ceIYUVEfcOrUySJthRDlU+mT85w5c/j9999Zt24d27dvJyYmhgMHDrB27VoAGjRowLfffmupr5Ti0KFDltcZGRkMGzaMtm3bsnXrVjZv3kyfPn149tlnyc7OtssYjhz5nYyM9FvWy8hIRymFXh+L0Wis6LCEi8i//6Snp3P+fCIA584lEhu7q0DdvXv3YDQaMRjiLfXS0wvuV0ZjKnp9LEopS538bYUQ5VepD2tfuHCBzZs3s3fvXmrVyn2Cr6+vLzNnzuSvv/4iJSWF7t27s2vXLqZMmQLATz/9RJs2bTh69CgA27Zto3bt2gwePNjSb79+/fDy8iIrKwtPT88KH4e1WXNJlDKj08UwYsSocq336lUjRqORiIg5t9027/aXZWEwxKPMFb9dbyUrO/PvQ7u3P35HO3vWQK1afkDJ+4/ZXPAuajk5JnS6GI4d+7NI3bz9ChRKqSLL89qWd78TQlTymfPvv/9OkyZN8PPzK1DepEkTHn/8cQACAgK46667+P333wHYvn07vXv/785SR48epWXLlkX67tmzJzVq1KjA6P+nNLPm/EwmE3Fx+gqKRria29l/lFLExekLzIjz5O1XcXH7MJlMxbYVQpRfpZ45A2g0Gsu/v/nmGz7++GPMZjNeXl6EhoYC0KtXL7799ltatmzJr7/+yowZMyxt3Nzc8PLysnfYBVSvXuO2/sB6eHgQHBxS7vX6+fnj5+fP1KmzbrttvXo1uXQprUzrjYiYw/lEx9/X2suzGg0bBZZp/I62ePE8y5GL29l/NBoNwcEhHDv2Z5EE/b/9SrF3754iCTqvrRCi/Eo1c05PT2fOnDmMGDECo9HIzJkzSU+/vdmcI7Rq1YpTp05x/fp1IHe2q9Pp+Pjjj0lNTbXUe/TRR9m1axc//vgjDz/8MG5ubgX6OHLkSJG+p02bxsmTJyt+EMD48ZNuq75G44ZWG14xwQiXU9L+k39fB3B390CrDWfs2AlF6ubtV1pteIEvvYXbCiHKr1TJed68edSqVYsrV67g7e3N9evXmTlzZkXHVm4NGzakX79+TJ48mWvXrgG5h+ZiY2ML/FEKCAggMDCQpUuXFjikDbkJ/dy5c3z11VeWspiYGH788UeCgoLsMo5WrVpTvfqtD6FXr14DjUZDSEio/KRFWOTff2rUqEHDho0ACAxsRGho9wJ1O3fuir+/P0FBd1vq1ahRcL/y9w8gJCQUjUZjqZO/rRCi/EqVnI8ePcorr7yCh4cH1apVY/HixZYLppzd7Nmzeeihhxg+fDhhYWH06NGDP/74g//7v/8rUK9nz55cuHCBBx98sEC5j48Pq1atYvfu3fTp04e+ffuyY8cO/t//+392PdxtbfbTqFFjgoLuJjDwLnx8fBg//lWaNm0usxdRxPjxk9BoNIwf/ypjx06gWrVqjB37MlptOE2a/IOgoHto0uS+AvtOXr3x418psl9pteE0bdqcsWNfpkmT+4q0FUKUj0ZZu+yykIEDB7Jhwwb69+/Ppk2byM7OZsCAAWzdutUeMVYKV65cx2y+5aa2KM85W1vJuzdzp05dbrutLc45O8MjIxs2qu2S55x///1Hrl3LLNN75wycYf8vD4nfsVwhfjc3DXXq+Ba7vFQXhLVt25ZFixZx48YN9Ho90dHRPPLIIzYLUjgnV/3DLqB79+5O/8dJCFG8Uh3Wfu2116hevTo1a9bkvffeo1mzZpbfBQshhBDCtko1czaZTIwfP57x48dbyn777TfatGlTUXEJIYQQVVapZs7jxo2z/KYxJyeH9957jzFjxlRoYEIIIURVVaqZ83333cerr77KhAkTmDx5Mn5+fmzcuLGiYxNVWHpmiuXCrttum3EFoMzt88cAtcvVhxBClEWpkvObb77JW2+9Rf/+/XnzzTcZMmRIRcclqrDGjcv3+/GrV3MPCPn5+Ze5j9x7g9cudyxCCFEWJSbnlStXWv7dsGFDAgIC+OWXX8jKygLg2WefrdjoRJU0ZMhIR4fgEj/FEEJUXiUm5xMnThR4HRISYrVcCCGEELZTYnKOiIiwVxxCCCGE+Fupzjn/+uuvrFixgoyMDJRSmM1mEhMTiY2NreDwRGUUHb2KhARDufq4etUIlO+8cuPGQU5xCF0IIQorVXKePn06Wq2Wb7/9lqeeeopdu3bRo0ePio5NVFIJCQb+OnEU32plvzd5WmbudQ+Z166Uqf31v9sLIYQzKlVy1mg0jB49mtTUVO69917CwsIID5eb3Iuy863mxcP/qF/m9j/9lQxQ5j7y2gshhDMq1U1IatTIfdxc48aNOXnyJD4+PkWeAyuEEEII2yjVzPn+++9n0qRJTJw4kTFjxhAfH4+HR6maCiGEEOI2lZhhjUYjkHv7zjNnzhAQEMC4ceM4cuQIM2bMsEd8QgghRJVTYnJu3749Go3G8jr/o5+jo6M5evRoxUUmCijPs5VF5SL7ghCVX4nJuX///vz6669069aN8PBw/vGPf9grLlGIXh8LyB9kIfuCEFVBicl54cKFZGZm8t133zF//nwyMjLo168fYWFh1KpVy14xCuDYsT8BGDNmJMuXr3JsMMKpGAzxLFgwi7p16wNmLl26RL169bh06RJ+fgEkJ19gxIgX+OKL/2A2K5QyYzKZGDfuFdq1a2/px2hM5aOPljJkyLNER69k3LhJ+Pv7F1leuFyIqsRen4NbXnJdrVo1tFotK1euZOnSpVy/fp3hw4czadKkCguqIiQmJtKqVSu0Wm2B/5KSksrUV7du3Sogylu7cSPTIesVzisqKpIbN26QmJhAYmIiN2/etPw/OfkCAP/5zyfcvHmT7Owsy+NfV6yILNCPThfDiRPHiIr6gBMnjqHTxVhdXrhciKrEXp+D2/o9VEpKCikpKaSmppKW5noPBahfvz46na7Af3feeaejw7qlMWNGFHo90jGBCKdjMMRz/nziLevlv14kj8lk4uDBH4Dc2YBeH4tSivPnE1FKodfHWi4Kzb88f7kQVYk9Pwe3/D1UUlISmzdvRqfT4e7uTr9+/fjyyy+54447Kiwoe7p8+TLTpk3j/PnzeHh48Morr9C5c2ciIyP57bffSEpKYujQobRp04Zp06YB0Lx5c7vGeOPGjUKvM4mImGPXGG5X7iMXc6wuMxji0eRYX2YvWdk5GAzxxW7HkuJ3NIMh3nI4LSoqsuTKt7BiRSTt2rVHp4spksCVMqPTxTBixKgCy/OXC1GV2PNzUGJyHjZsGGfOnKF3794sXryYf/7znxUShL0kJyej1Wotr8PCwjh8+DDt27fn2Wef5ezZszz99NNs2rQJgKysLLZv326pO2XKFDp27MiyZcs4ePCgI4YgRAGlmTWXJO8Qd1zcPsu/8y+Li9MzYsSoAsvzlwtRldjzc1Bicj506BDe3t589dVXrF+/3lKulEKj0fDLL79USFAVJe+wdn7t2rVj3rx5ANx111088MAD/Pe//wWgdevWQO7h/OTkZDp27AjAwIEDiYlx7Hm3qVNnOXT9t1LS85AjIuZw4ewpO0dUkJenOw3uurvY7ejMz3POP9tv2LBRuRJ03s2EgoM7sXfvngIJ2sPDg+DgkCLL85cLUZXY83NQYnLetWtXha3YWRQ9lKfI+fuQq4+PD5B7b/H89dzd3e0X4N9x5D+07eNTza7rF85r7NgJzJw5ucztR4+eAIBWG275iVYejcYNrTa8yPL85UJUJfb8HJR4QVhgYGCJ/1UG7du3txwVOHv2LL/88gtt2rQpUCcgIICGDRtaHpG5detWu8a4fPnqQq9X2XX9wnkFBd1Nw4aNblkv/82E8nh4eFh+SuXvH0BISCgajYaGDRuh0WgICQm1nNvOvzx/uRBViT0/B1X+6RXTpk3jhx9+ICwsjHHjxjFv3jzq1y/6pKNFixbx4Ycf0r9/fxISEhwQaS6ZNYvCxo6dgI+PD40aNaZRo0Z4e3tb/l+/fgMAhg9/Hm9vbzw9vSyHsvNmzXm02nCaNm3O2LEv07Rp8yKzgrzlMmsWVZm9PgcaZe03FsLmrly5jtlc+k1d+Jynq92ysTTnnB39yMgGdzVxyXPOpdkXnDn+0pD4HUvir3hubhrq1PEtdrk8WspFuEpSFhVP9gUhKr8qf1hbCCGEcDaSnIUQQggnI8lZCCGEcDJyzlk4xPXMLMtFXWWRlpkFUOY+rv/dXgghnJEkZ2F3jRsHlbuPaleNAPj5+Ts0DiGEqAiSnIXdDRky0tEhCCGEU5NzzkIIIYSTkeQshBBCOBk5rC0qxPLlyzl+/ORtt7tajnPJjRsHySFzIUSlIMlZVIjTp08T/9cp7qx1e7fXTL12BQCfm7f35K+ka2W/8lsIIZyNJGdRYe6sVZ/RwU/fVpsVcZ8DlLmdEEJUBnLOWQghhHAykpyFEEIIJyPJWQghhHAykpxdxL5931ue4ytck7yHQojSkgvCXIReHwvIs3xdmV4fC8h7KIS4NZk5u4hjx/7k2LE/GTFisKNDETZkMMQzduxIEhIMZe7DaExlwYLZGI3Gctc7cuR3Ro58ij/+OFLmeOytpG1oi+1bnNJu9/L2/8cfv1fYGAqvK28sKSkpFTo2cWsun5wTExNp1aoVWq0WrVbL448/ztSpU7l8+XKp+4iMjCQyMrICoxTCuqioSDIzM4mK+qDMfeh0MZw4cQydLqbc9ZYtex+lFMuWvVvmeOytpG1oi+1bnNJu9/L2v2zZ+xU2hsLryhvL2rVrK3Rs4tZcPjkD1K9fH51Oh06n45tvvqFu3bq8/PLLjg7LZgrPlmX2XDkYDPGcP58IwLlziWWaGRmNqej1sSil0Otji53plKbekSO/k5GRDkB6erpLzJ5L2oa22L7FKe12t0X/6em574mtx2BtXXp9LAkJ8ezcubPCxiZKp9Kdc9ZoNEyYMIGOHTty7NgxYmNj2bx5M+7u7nTs2JHXX38dd3d3PvnkE7788ksCAgKoVasWrVu3BmD79u188MEHVK9enRYtWpCTk8PChQv5/fffiYiI4MaNGwQEBDBnzhzuuusuh40zImKOw9ZdGmfPGqiGt93Wl3YznQuGKzbbLp6e7mRn59ikrzwGQzz+/v6W11FRBY/WREV9wIIFS26rT50uBqUUAEqZ0eliGDFiVJnqLVv2fqHX7/LRR//vtuKxt5K2oS22b3FKu91t0X9+thyDtXUpZSYqKhKz2Wx5beuxidKpFDPnwry8vAgKCuLo0aPs3r2bmJgYNm7ciMFg4IsvvuDw4cOWspUrV3LhwgUg7zzLAlavXs369eu5evUqAFlZWUyfPp0lS5awceNGnn32WWbMmOHIIYpKIG9Wl+fcucRiahYvLm4fJpMJAJPJRFycvsz18mbNefJmbM6spG1oi+1bnNJud1v0n58tx2BtXSaTiXPnEit0bKJ0Kt3MOY9Go2HNmjVotVqqVasGQHh4OJs2beLGjRt06dKFGjVqANCzZ0/MZjM//fQTDz74IHfccQcA/fv3Z+fOncTHx3P27FlefPFFS//Xr1+3/6DymTp1lkPXfyuLF8/j+jmj3dZX07sGdes3sNl2qVevJpcupdmkrzyFZ/UNGzYqkEACAxvddp/BwZ3Yu3cPJpMJDw8PgoNDylyvevUaBRJ03ufDmZW0DW2xfYtT2u1ui/7zs+UYrK3Lw8ODO+5owMWLFypsbKJ0KuXMOSsrizNnznDnnXcWWWYymdBoNAUOGXl45H5HcXNzsxzOyc9sNtOoUSPLee0NGzawdu3aihuAqBLGjp1Q6PXtXyeh1Yaj0WgA0Gjc0GrDy1xv/PhJhV6/etvx2FtJ29AW27c4pd3utug/P1uOwdq6NBo3xo6dgJubm+W1rccmSqfSJWez2UxkZCQPPPAAgwYNYtu2bdy4cQOTyURMTAzt27enQ4cO7Nmzh7S0NG7evMmOHTsAeOihhzh8+DDJyckopdi+fTsajYZ7772Xq1ev8tNPPwEQExPDa6+9ZrcxrV69rsTXwjUFBd1Nw4a5M6HAwEY0bhx02334+wcQEhKKRqMhJCS0wDnt263XqlVrqlfPnS3XqFGDli1b3XY89lbSNrTF9i1Oabe7LfrPO4Jh6zFYW1dISCiNG9/No48+WmFjE6VTKQ5rJycno9Vqgdzk3KJFC9599138/Pw4evQo4eHhmEwmOnXqxNChQ/Hw8GDEiBEMGjSIWrVq0bBhQwBq167N9OnTee655/Dy8qJRo0bUqlULLy8vli5dyvz587l58ya+vr68/fbbjhyyqCTGjp1ARMTscs2ItNpwzp1LvOUMpzT1xo+fxOLFC1xi1pynpG1oi+1bnNJu9/L3P5DIyHcrZAxF15U7lmeeeYZTp87IrNmBNMraJYFVVGpqKmvWrOGll17Czc2NefPmERQUxLBhw8rd95Ur1zGbS7+pC5/zzDtf6eznmvPknXO25yMjvevXdIlzzvZ4DysifnuS+B1L4q94bm4a6tTxLXZ5pZg524q/vz/Xrl2jb9++uLu707JlS5588klHhwVASEioo0MQ5STvoRCitCQ556PRaJg+fbqjw7BK7sfs+uQ9FEKUVqW7IEwIIYRwdZKchRBCCCcjh7VFhUm6lmy5wOt22gBland3/Zq31UYIIZyVJGdRIe69994y3Zs6wDu3jbff7SXau+vXrJDfgAohhCNIchYVYsyYMU7/UwYhhHBWcs5ZCCGEcDKSnIUQQggnI4e1hcNFR6+65UPkr141AuDn53/L/ho3DmLIkJHlD0wIIRxEkrNwuIQEA3/9dQJf3+JvZZeWlnv+OjMzo8S+HP0oTyGEsAVJzsIp+Pr68q9//avY5T///DNAiXXy1xNCCFcm55yFEEIIJyPJWQghhHAykpyFEEIIJyPJWdyWffu+Z9++7x0dhsPJdhBCVCS5IEzcFr0+FpDHH+r1sYBsByFExZCZs4tYtux9RowYzLhxozAajY4OR/zNaExlwYLZRd6TwuUGQzxjxozgzTdfY+7c6SQkxFuW59VNSIhn7txpzJw5hblzpxf7PltbZ/4yozGVV155pUgfeXUMBoPVmPPXmzt3WokxlFZx2+dWbd544w2n3c9LO6bc7TjdJttRVD0Vkpyffvpptm3bVqAsIyODdu3akZKSUqT+hg0bmDJlCgDdunUjMTGxIsJyaT/+eACA9PTr6HQxDo5G5NHpYjhx4liR96RweVRUJDdu3ODcubOcOnWSqKhIy/K8ulFRkZw69RcGwxlOnTpZ7PtsbZ35y3S6GI4fP16kj/+t5wOrMeevd+rUXyXGUN7tc6s2f/zxh9Pu56UdU+52PGmT7SiqngpJzuHh4WzZsqVA2XfffUe7du2oXbt2RayyUlu27P0Cr3fv/k6+iTsBkykbvT4WpRR6fazlPTEaUwuU//HH75w/X/AL57lziSil2Lt3j6XuuXMF6+j1e6zOyAuvs2DZHr7/fneRPvLXOX8+sUjM+fvfu3dPiTGUlrVYK6KNPZU2vpSUFMupD4C9e8u+HUXVVCHnnHv16sU777yD0WjE398fgM2bNzN8+HD+/e9/c/z4cTQaDaNGjaJ///5W+zhz5gxjxozhnXfewcfHh5kzZ2IymfD29iYiIoI1a9bQpEkTnnnmGdatW8eqVav4+uuvyc7O5tFHH2Xnzp2sW7cOnU5HZmYmnp6eLFmyhHvvvZeDBw8yb9483N3dadOmDadOnWLNmjUYDAZmz849XOXj48OMGTP45z//yZQpU/D19eWPP/7g4sWLjB8/nvDw8IrYdFblzZrz0+liGDFilN1iyHP1qhGj0UhExJwS63l6upf6kZEGQzwajS2ig6ysLAyG+FvGdyu3ij8vZqUUAEqZLe+JThdToLzwl6v8cnJMxS4zmUxF3ufCfefOyJSlzGQyWf6dv4/8dfLkjzl//zk5OUXal2VfsxbrrfopSxt7Km18a9euxWT633ubk1P27SiqpgqZOdeoUYPu3bvzzTffAHDx4kXOnDnDwYMHCQgIYOvWraxevZrIyEiOHTtWpP2FCxd46aWXWLBgAW3atGH16tU8++yzbNiwgSeffJLffvuNLl268MMPPwDwww8/cPXqVS5fvszPP//Mgw8+yM2bN9m5cydr1qxh69athIaGEh0dTXZ2Nm+88QaLFi1i06ZNeHj87/vJ5MmTef3119m4cSNvvfUWr7zySoGY1q5dy8cff8w777xTEZvttsTF6R0dQpWXmZlp+QNsMpks70lc3L4C5enp6cX2oVTRpJl/WeH3uXDfcXH6AmVFE7AqUidP/pjz95+/D2sxlJa1WCuijT2VNr7Y2FibbUdRNVXY1doDBw5k6dKlPPXUU2zZsoV+/fqh1+tZsGABALVr16Z79+78+OOPRe6pPHHiRO6//34efvhhALp06cLcuXPR6/V069aNrl27YjKZmDFjBjk5OZw+fZrevXtz6NAhDh8+TGhoKL6+vixZsoRt27YRHx+PXq+nRYsWnDhxgjp16tC8eXMABg0axPz580lPT+fIkSNMnTrVEkdGRgapqakAdOzYEY1GQ9OmTZ3i8FRwcIhD1uvn54+fnz9Tp84qsV69ejVL/TzniIg5XLhw3hbh4eXlRYMGDW8Z363cKv7cmJO4fj0Nk8mEh4eH5T0JDu7E3r17LOXe3t7FJmjN34cMrCVojUZT5H0u3HfucmUp02g0Bfr6Xx//q5Mnf8z5+9+zZ6elD2sxlJb1WG3fxp5KG19oaChff/21TbajqJoq7Grttm3bcunSJZKSkti8eTPh4eFWv9XnP4SWZ9q0aZw9e5bY2FgAevbsycaNG2ndujWrVq1i1qxZeHt706JFC7Zs2cK9995Lu3btOHToEPv376dz584kJSUxePBg0tLS6Ny5MwMGDEAphbu7O2azucg6zWYzXl5e6HQ6y39fffWV5bC8t7c38L8/pvb0yCMdipRptfY7rC6sq1u3rmV/0GjcLO+JVhteoHz8+EnF9uHu7lHg6E1+Hh4eRd7nwn1rteEFyjw8PHB3dy/SR/46efLHnL9/a+3LwlqsFdHGnkob3zPPPFPgfXV3L/t2FFVThf6Uqn///nz88cf4+fnRuHFj2rdvz/r164HcCyZ27drFI488UqRd69atmT17NnPnziUjI4NJkyZx+PBhnnrqKSZOnMiff/4J5M6oly1bxiOPPMIjjzzCrl27qF69OrVr1+bw4cMEBQUxcuRI7r//fnbu3ElOTg733nsv165d4/jx4wCWC9dq1qzJ3XffjU6nA2D//v0MGTKkIjdPqRX+496tWw/LlwbhOB4enoSEhKLRaAgJCbW8J/7+AQXKW7ZsTcOGjQq0DQxshEajoXPnrpa6gYEF64SEdC3yPhfu29/fv1BZV7p06Vakj/x1GjZsVCTm/P137ty1xBhKy1qsFdHGnkobX+3atQkJCbW87ty57NtRVE0VehOSgQMH0q1bN+bPnw/A+PHjmT17NmFhYeTk5DB27FhatmxpSZT5tW3blnbt2vH+++8zduxYpk2bxrJly/D09GT27NlA7qGj2bNn88gjj+Dn50edOnUIDQ0Fcg9Df/755/Tu3RulFG3btuXkyZN4eXnxzjvvMHnyZNzc3Ljnnnvw8fEBYNGiRcyePZtPPvkET09P3nvvPYfMlK155JEO/PjjAWrU8JVv4E5Eqw3n3LlEqzPQ/OVjx05gwYJZ1KlTDx8fH0aOfJ7PPlv19/LcK7WHDh3JqlX/h8mUU+KM1do6C5Ypzp1LwGQyW60zZMizREevLLF/g+EMoCn3vlbc9rlVm+TkJKfdz0s7ptztGG/5txC3Q6OKuxqlkjKbzSxevJiXXnqJ6tWrs3LlSi5evGj5nXVFuXLlOmZz6Tf17Zyztae8q6Ar4pyzrR4Zaa9zznDr7eAozrr/lJbE71gSf8Vzc9NQp07xz7CvcrfvdHNzw9/fn0GDBuHp6UlgYKBlZi9uLf+huqpMtoMQoiJVueQMMHr0aEaPHu3oMFyS3Es6l2wHIURFkntrCyGEEE5GkrMQQgjhZKrkYW3hfK5fv2656MuatLTciztKqpPXjxBCuDpJzsLhGjcOumWdatWMQO4dymzRnxBCODNJzsLhhgwZ6egQhBDCqcg5ZyGEEMLJSHIWQgghnIwkZyGEEMLJyDlnYXPR0atISkokO7voE8dux9WrRqB0F4GVVePGQXLOWwjhdCQ5C5tLSDBw5vRx6tetXq5+Uq5kAOChqZh75CZfzqiQfoUQorwkOYsKUb9udYb0b1GuPqI3HQUodz+36l8IIZyNnHMWQgghnIwkZyGEEMLJSHJ2Efv2fc++fd87OgxRicg+JYTzknPOLkKvjwXkUYXCdvT6WED2KSGckcychRBCCCfjNDPn69evs2TJEg4dOoS7uzu1atViypQpXL9+nQ8//JA1a9bYJY4pU6bwyCOPMHDgwGLrTJ06lZdeeonAwEC7xCRERbh+PY3ExLOMGDGYevXqkZaWxrRpc2ncOAijMZUPPliMyZQDKEBDVtZNkpLO4+npRZ06dTEaUyz1AYzGVD76aCnjxk0CFO+8M4/z58/x+uvTCQwMtCzz9/e3Go/BEM/8+TMBWLJkCTVr1iuwPDemJZhMJnJycrh8ObnA+vPH0K9fOB9+uIQJE/7NunXRXLyYVKRucW3efHOOPDxFOJxTzJzNZjMvvPACfn5+bNq0CZ1Ox/jx43nhhRcwGo2ODq+IgwcPopRydBhClMv58+cs/7506RI3btwgKuoDAHS6GE6d+guD4QwGQzwGwxmSks4DkJ2dxYUL5wvUz2tz4sQxdLoYdLoYzp1LRCnFsmXvFlhWnKioSG7evMnNmzdZtGhRkeW5MZ3EYDhDYmJCkfXnj2HZsvfIzMxk2bL3MBjOWK1bXJvi6glhT04xcz548CBJSUm8/PLLuLnlfl9o3749ERERpKenk5KSwgsvvEBCQgL33HMPH3zwAV5eXmzatInVq1djNptp2bIls2bNYt26dRgMBmbMmAHAwoULadCgAWlpafz2228kJSUxdOhQOnTowMyZMzEajVSvXp1p06bRunXrAnFZ63/16tUkJyczevRooqOjCQgIsPv2EqK8jhz5HbPZXKT83LlE/vjjMHv37ilVP+fOJZKQYKBWrVro9bEopdDr92AymSx10tPT+f773X8vi0WrDS8yezYY4jl/PjHfawMJCYYCs3K9PrbY9efN9vNiyMhIt6zbWt081tpYqyeEvTlFcv7zzz9p3ry5JTHn6dKlCwcPHuT8+fNERUURGBjIk08+SVxcHIGBgXz55Zd88cUXeHt7s2TJEj799FOeeuop+vfvz5tvvombmxvfffcd69at44svviArK4vt27cDMGjQIEaPHk2PHj347bffmDhxIt9++61l3SdPnrTa/7hx4/jiiy9YsWKFXRPz1atGjEYjERFz7LbOsjIY4qnmXfQPv7NJz8jmcmq81W3q6ele7tuPOtKt4j9x4lixy5Yte4+cnNKPPSrqA5o1a2E5mmQymYocWcrrTykzOl0MI0aMKtRHpNV+FyxYAuTObvMnfGv1dLqYWx7Ryt9nXr/W2hSuJ4S9OUVydnNzw9vbu9jlzZs356677gKgSZMmpKamkpiYiMFg4MknnwQgOzubf/7zn9SuXZvmzZtz8OBBPD09ueeee6hXL/fcVd7MOD09nYSEBHr06AFAmzZt8PPz4/Tp05Z1Hjx40Gr/QlQG1mbNefLPNkvj3LlErly5YkmeJSVIk8lEXJy+SHLOP2vO32+euLh9xfabVy8ubl+xCdxanyW1KVxPCHtziuTcqlUr1q5di1IKjUZjKX/33XcJDg7Gw+N/YWo0GpRS5OTk0KtXL6ZPnw7k/kHJ+3au1WrZvn07np6ehIWFWdr6+PgA1v945PWZp6T+HcHPzx8/P3+mTp3lsBhKKyJiDunXzjo6jFuqUd2T+g3usrpN69WryaVLFXNPb3u4Vfwvvvic5TBuYTVq1CAjI6PU11UEBjaiWbMW7N2bezg77zNqjYeHB8HBIUXKGzZsVCRBBwY2svw7OLgTe/bstNpvXr3g4E6WGEqKNb/i2hSuJ4S9OcUFYQ8//DB16tThww8/tCRAvV7Phg0bSElJsdqmXbt27NixgytXrqCUYvbs2axevRqA7t27c+jQIfbv389jjz1WpK2vry+NGjXiu+++A+C3337j8uXL3HfffaXq393d3aGJWojyGj9+UgnLXsHd3b3UfY0d+zJabbjli7WHh0eBL9mApT+Nxg2tNtxKHxOs9ptHqw0v8CXdWr38MZQUa37FtSlcTwh7c4rkrNFo+Oijj0hISKBv376EhYXxf//3f6xYsYI6depYbdO8eXNeeuklRowYQZ8+fTCbzYwePRrInSE/9NBD3H///dSoUcNq+0WLFrFmzRrCwsKYO3cukZGReHl5lar/0NBQRo8ezdmzzj87FMKaVq1aF7nGA3JnjC1b3k/nzl1L1U9gYCMaNw7C3z+AkJBQNBoNISFd6dr1UUudGjVq0KVLt7+XhVr9KVVQ0N00bNgo3+ugAhdk5fVf3Prz19FoNFSvXsOybmt1C/ebv421ekLYm1Mc1gaoXbu21Z9PQO4sNs/ChQst/37iiSd44oknrLaJiIgo8HrChILfzJs0aWL1t9Ol6X/atGlMmzbN6nqFcBUNGwaSmJj7BTPvd875Z6EGw5lb/s658Oz23LnEv2fGiuPHj3L+/DnGj3+VwMDAfMusGzt2guV3zq+//nqR5bkxxRf4nbO1mfC5c4mW3yyPH/+K5XfOxc2GC7eRWbNwBholP9i1iytXrmM2l35TFz5nmHcPZFe41WLeOWdXeGRkjVpV85wzOPc+VRW2vzOT+Cuem5uGOnV8i13uNDNnUTJn/AMqXJvsU0I4L6c45yyEEEKI/5HkLIQQQjgZOawtKkTy5QzLOePy9AGUu5+S+r+nVoV0LYQQ5SLJWdhc48ZBNrn9ZW1lBKBGLf/yB2XFPbWQn8wIIZySJGdhc0OGjHSJqyWFEMJZyTlnIYQQwslIchZCCCGcjCRnIYQQwsnIOWdhN9HRq0hIMJSq7tWrRiD3aVy3q3HjIIYMGXnb7YQQwllIchZ2k5Bg4MTpU3jXqX/LujevXAHASOmfjpTbLrlMsQkhhDOR5CzsyrtOfYL6PnPLeoatawFKVddaOyGEcGVyzlkIIYRwMpKchRBCCCcjyVncln37vrc8alAUT7aTEKI85JyzuC16fSwgjxu8Fb0+FpDtJIQoG5k5CyGEEE7G5WfOiYmJDB8+nN27dxcob9asGcePHy9X35GRkQBMmDChXP3Ywn/+s5Jdu76xvPby8mbixDfYvHk948ZNwt/f33HBCSGEsCmZObuI/IkZICvrJsuWvcuJE8fQ6WIcFJUQQoiK4PIz55KYzWYWLFjAgQMH0Gg09OvXj9GjR3Pw4EGWL1+Oj48Pp06dolmzZixevBgvLy8++eQTvvzySwICAqhVqxatW7cG4LPPPkOn05GZmYmnpydLlizh3nvvtcs4/vOflVbLMzLSgdzzm1ptuMyehRCikqgUyTk5ORmtVluk/PPPPycpKYnNmzeTlZXFsGHDaNq0KdWqVePXX3/l66+/pn79+jz55JPs27ePevXqERMTw8aNG9FoNAwePJjWrVtz/fp1du7cyZo1a/Dx8WHp0qVER0czY8YMu4yv8Ky5MKXM6HQxjBgxqsJjuXrViNFoJCJiTon1rD3P2WCIx+TlU5HhYcpIx2C8csv4bqW8z6M2GOLly5IQoswqRXKuX78+Op2uQFmzZs04ePAgAwYMwN3dnWrVqhEWFsaBAwfo1q0b9913Hw0aNACgSZMmXL16lTNnztClSxdq1KgBQM+ePTGbzfj6+rJkyRK2bdtGfHw8er2eFi1a2H2cxTGZTMTF6e2SnIUQQlS8SpGci2M2mwu8VkqRk5M7G/L29raUazQalFKW/+fx8PAgKyuLpKQkhg0bxtChQ+ncuTN169bl6NGj9hlEKXh4eBAcHGKXdfn5+ePn58/UqbNKrFevXk0uXUorUBYRMQfD1bRiWtiGR/UaBN3Z4Jbx3Yq1+G9HeWfuQoiqrVJfENa+fXs2bdpETk4OmZmZbNmyhXbt2hVbv0OHDuzZs4e0tDRu3rzJjh07ADh8+DBBQUGMHDmS+++/n507d1qSvD10796zxOUajRtabbidohFCCFHRKnVyHjx4MA0aNECr1dK/f3+6du3KY489Vmz9Fi1aMGLECAYNGsTQoUNp2LAhAB07dsRsNtO7d28GDBjAPffcQ2Jior2GwfDhz1otr169BhqNhpCQUDm/KYQQlYjLH9Zu1KhRkd84A5bfOE+fPr3Isnbt2hWYQS9cuNDy7yFDhjBkyJAibVautH7FtL10796zyO+cx49/lc2b18usWQghKhmXT85VxfDhz1qdQbdq1cqucYSEhNp1fa5KtpMQojwkOYvbIveKLh3ZTkKI8qjU55yFEEIIVyTJWQghhHAykpyFEEIIJyPnnIVd3bySjGHr2lLVA0pVt0g7v5plik0IIZyFJGdhN40bB5W67lVyb/Lid7uJ1q/mba1HCCGckSRnO3Fz09iljTMpHP+wYdZvpuKsKtv2dzUSv2NJ/BXrVvFpVP6bSQshhBDC4eSCMCGEEMLJSHIWQgghnIwkZyGEEMLJSHIWQgghnIwkZyGEEMLJSHIWQgghnIwkZyGEEMLJSHIWQgghnIwkZyGEEMLJSHJ2Qlu2bKF379706NGD6Ohoh8by4Ycf0qdPH/r06cM777wDQFxcHGFhYfTo0YP33nvPUvfo0aMMHDiQxx9/nGnTpmEymQA4f/48Q4YMoWfPnrz44oukp6cDcO3aNUaPHk2vXr0YMmQIly5dqrBxvP3220yZMsXl4t+9ezcDBw6kV69ezJs3z+XiB9DpdJZ96O2333aJMVy/fp2+ffuSmJhol3izsrJ4/fXX6dWrFwMGDODUqVM2jX/dunX07duXsLAwpk6dSlZWlkvFn+ezzz5j2LBhltfOGr9NKOFULly4oLp27apSU1NVenq6CgsLUydPnnRILPv371eDBw9WN2/eVFlZWWr48OFqy5YtqkuXLiohIUFlZ2er5557TsXGxiqllOrTp4/69ddflVJKTZ06VUVHRyullBo9erTaunWrUkqpDz/8UL3zzjtKKaXmzJmjli9frpRSauPGjWrixIkVMo64uDjVrl07NXnyZJWZmeky8SckJKhOnTqppKQklZWVpZ5++mkVGxvrMvErpVRGRoZq27atunLlisrOzlaDBg1Su3btcuox/Pbbb6pv376qZcuW6uzZs3bZZz755BM1Y8YMpZRSP/74o3riiSdsFv/p06fVY489ptLS0pTZbFZvvPGGWrlypcvEn+fkyZMqJCREDR061FLmjPHbiiRnJ7NhwwY1depUy+sPP/xQRUZGOiSWEydOWHZ8pXJ37MjISDV8+HBL2caNG9WUKVNUYmKi6t69u6X80KFDatiwYSorK0s9+OCDKjs7Wyml1Pnz51W3bt2UUkp17dpVnT9/XimlVHZ2tnrwwQdVVlaWTceQmpqqnnjiCbVy5Uo1efJkdfDgQZeJ/9NPP1ULFiywvL5w4YJLxa+UUmlpaepf//qXSkxMVJmZmap///5OP4Y333xTHTp0SHXt2lWdPXvWLvEOHTpUHTp0yNJX9+7d1blz52wSf2Jiotq3b59l+SeffKLmz5/vMvErpdTNmzfV008/rb766itLcnbW+G1FDms7meTkZOrVq2d5Xb9+fS5evOiQWO677z7atGkDQHx8PF9//TUajcZqfIXjrlevHhcvXiQ1NRVfX188PDwKlEPBsXp4eODr60tKSopNxzBz5kxeeeUVatWqVWSdzh6/wWAgJyeHsWPHotVqWbt2rUvFD+Dr68vEiRPp1asXXbp0ITAw0OnHMH/+fB5++GHLa3vEa62vCxcu2CT+wMBAOnbsCEBKSgrR0dF0797dZeIHWLJkCeHh4dx1112WMmeN31YkOTsZs9mMRvO/R4kppQq8doSTJ0/y3HPP8cYbb3DXXXdZja+4uK3FX9x4lFK4udlul/zqq6+488476dChg6WsuDidMf6cnBwOHDjAggULWLduHb///jtnz551mfgBjh07RkxMDHv27EGv1+Pm5kZ8fLxLjcEe+0zhNhUxjosXLzJixAjCw8Np166dy8S/f/9+kpKSCA8PL1DuKvGXlSRnJ9OgQYMCF7VcunSJ+vXrOyyen3/+mZEjR/Lvf/+bAQMGFBtf4fLLly9Tv359ateuTVpaGjk5OQXqQ+4M5PLlywCYTCbS09Px9/e3Wezbt29n//79aLVaPvjgA3bv3s1XX33lMvHXrVuXDh06ULt2bXx8fHj00UeJi4tzmfgB9u3bR4cOHahTpw5eXl4MHDiQgwcPutQY7LHP33HHHSQnJxfpy1ZOnTrFU089xYABAxg/frzVcTlr/Fu3buXkyZNotVqmT5/OkSNHmDRpksvEX1aSnJ1McHAwBw4cICUlhczMTL777js6d+7skFiSkpIYP348ixcvpk+fPgA88MADnDlzxnLIdevWrXTu3JnAwEC8vb35+eefgdwrdDt37oynpycPP/ww27dvB2DTpk2W8XTp0oVNmzYBuYn04YcfxtPT02bxr1y5kq1bt6LT6Xj55Zfp1q0bn3zyicvE37VrV/bt28e1a9fIyclBr9fTs2dPl4kfoHnz5sTFxZGRkYFSit27d7vUPgT22ee7dOmCTqcD4KeffsLb25uGDRvaJP7r168zatQoJk6cyHPPPWcpd5X4IyIi+Prrr9HpdMybN49WrVrx/vvvu0z8ZVbRJ7XF7du8ebPq06eP6tGjh1qxYoXD4njrrbdUmzZtVL9+/Sz/rV27VsXFxamwsDDVo0cPNX/+fGU2m5VSSh09elSFh4erxx9/XL366qvq5s2bSqncCzeGDh2qevXqpZ577jllNBqVUrkXa40ZM0b17t1bDR48uMCVmbYWExOjJk+erJRSLhX/V199ZdkX5syZo3JyclwqfqWUWr58uXr88cdV37591dSpU9WNGzdcYgz5L0iq6Hhv3Lih3njjDdW7d2/Vv39/deTIEZvFv3LlStWyZcsCn+P333/fZeLP74cffihwtbYzx19eGqWUcuzXAyGEEELkJ4e1hRBCCCcjyVkIIYRwMpKchRBCCCcjyVkIIYRwMpKchRBCCCfj4egAhBCuKzExkccee4ymTZtaypRSDB8+nEGDBhWou2vXLg4cOMD06dPtHaYQLkd+SiWEKLPExETCwsL49ddfLWUXL16kb9++rFmzhubNmzswOiFcl8ychRA2dccddxAUFMT+/fuZO3cumZmZ+Pr6MmDAAL799luWL1/OpUuXmDVrFqdPn8bNzY2nnnqK4cOHk5aWxvz58zlx4gTZ2dl06NCBN954w/IQAyGqCjnnLISwqV9//ZWEhARu3LjBX3/9xZo1a1izZk2BOnPmzOHuu+/mm2++Yd26dXz55ZcYDAYWLFhAy5Yt2bBhA5s2bSI1NZWVK1c6aCRCOI58HRVClMuNGzfQarVA7pO0AgICWLRoEVeuXKFZs2b4+voWaRMXF8frr78OQM2aNdm6dSsAsbGxHD58mPXr11v6FqIqkuQshCgXHx8fy0MD8tuwYQPVq1e32sbDw6PAI/rOnj1LQEAAZrOZpUuX0qRJEwCuXbvm8EemCuEIclhbCGF3HTp0ICYmBoC0tDRGjBhBfHw8nTp1YtWqVSilyMrK4sUXX+Szzz5zcLRC2J8kZyGE3c2cOZPTp08TFhbG008/zZgxY2jVqhXTpk0jIyODsLAwwsLCaNq0Kc8//7yjwxXC7uSnVEIIIYSTkZmzEEII4WQkOQshhBBORpKzEEII4WQkOQshhBBORpKzEEII4WQkOQshhBBORpKzEEII4WQkOQshhBBO5v8DxWwxra7ccuUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frequent_make_cars=train_set[train_set['Make'].isin(make_percentage.index[:10])]\n",
    "ax=sns.boxplot(data=frequent_make_cars,y='Make',x='MSRP')\n",
    "ax.set(xlabel='Price', title='Price of the 10 most common car makes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most of them presents a similar statistics of price and differences between them are little. Only Cadillac stands out, and on average it's significantly more expensive than the other popular brands."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.3 Year of car production"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another interesting variable is the year of car production. Let's look at its distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=train_set, x='Year',bins=20)\n",
    "plt.title('Car year distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The database includes most new cars whose year of manufacture exceeds 2015. Let's see how the year of production affects the price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=train_set,x='Year',y='MSRP',ci=False)\n",
    "plt.ylabel('Price')\n",
    "plt.title('Price depending on year of car production')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the graph, the price variation can be divided into 3 periods of car production. The first is between 1990-2000, the next 2001-2007 and last above 2008. So let's create three boxplots according to these divisions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#showfliers=False -> no outliers in the graph\n",
    "sns.boxplot(data=train_set[train_set['Year']<=2000],x='Year',y='MSRP',showfliers=False)\n",
    "plt.ylabel('Price')\n",
    "plt.title('Price of cars manufactured between 1990 and 2000')\n",
    "\n",
    "plt.show()\n",
    "                           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=train_set[(train_set['Year']>2000) & (train_set['Year']<2008)],x='Year',y='MSRP',showfliers=False)\n",
    "plt.ylabel('Price')\n",
    "plt.title('Price of cars manufactured between 2001 and 2007')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=train_set[train_set['Year']>=2008],x='Year',y='MSRP',showfliers=False)\n",
    "plt.ylabel('Price')\n",
    "plt.title('Price of cars manufactured between 2008 and 2017')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graphs were created without including outliers for better clarity. It can be deduced that the biggest changes can be seen in the first graph between 1990 and 2000, where for old cars every year matters. In the case of the second figure, between 2001 and 2007, the price of cars oscillates between 20,000 dollars and 30,000 dollars. The last graph for cars made in 2008 and newer does not show significant differences compared to the previous graph. The price of cars is between 30 and 40 thousand. A special year is 2014, from which the most cars come and the boxplot for this year shows the highest values. After this year there is a decrease"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.4 Transmission type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_to_pie=train_set.groupby('Transmission Type')['Transmission Type'].count()\n",
    "plt.pie(data_to_pie,labels=data_to_pie.keys(),autopct=\"%.1f%%\",)\n",
    "plt.title(\"Pie chart of transmission type\")\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>MSRP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission Type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AUTOMATED_MANUAL</th>\n",
       "      <td>51400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUTOMATIC</th>\n",
       "      <td>33395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DIRECT_DRIVE</th>\n",
       "      <td>35282.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MANUAL</th>\n",
       "      <td>19776.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNKNOWN</th>\n",
       "      <td>2362.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      MSRP\n",
       "Transmission Type         \n",
       "AUTOMATED_MANUAL   51400.0\n",
       "AUTOMATIC          33395.0\n",
       "DIRECT_DRIVE       35282.5\n",
       "MANUAL             19776.0\n",
       "UNKNOWN             2362.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.groupby('Transmission Type')[['MSRP']].median()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most popular transmission type is automatic (it states 72% of data in our set) and it is a second most expensive type of cars (median price is 33395).However, the most expensive cars are those with automated manual transmissions (the median is 51400) and account for 4.8% of all cars in database"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the database there are many other variables that can be represented graphically, however, they are in my opinion a secondary issue and it is worth moving on to the next stage of modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Feature selection "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many methods for selecting explanatory variables that can help you make the right decisions. I chose to primarily examine the correlations between variables and use a statistic called mutual information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1. Correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8456 entries, 7446 to 8210\n",
      "Data columns (total 14 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Year               8456 non-null   int64  \n",
      " 1   Engine HP          8456 non-null   float64\n",
      " 2   Engine Cylinders   8456 non-null   float64\n",
      " 3   Number of Doors    8456 non-null   float64\n",
      " 4   highway MPG        8456 non-null   int64  \n",
      " 5   city mpg           8456 non-null   int64  \n",
      " 6   Popularity         8456 non-null   int64  \n",
      " 7   Engine Fuel Type   8456 non-null   int32  \n",
      " 8   Transmission Type  8456 non-null   int32  \n",
      " 9   Driven_Wheels      8456 non-null   int32  \n",
      " 10  Market Category    8456 non-null   int32  \n",
      " 11  Vehicle Size       8456 non-null   int32  \n",
      " 12  Vehicle Style      8456 non-null   int32  \n",
      " 13  MSRP               8456 non-null   int64  \n",
      "dtypes: float64(3), int32(6), int64(5)\n",
      "memory usage: 792.8 KB\n"
     ]
    }
   ],
   "source": [
    "data_for_selection=pd.concat([train_set[num_features],train_set[cat_features],train_set[target]],axis=1)\n",
    "le = preprocessing.LabelEncoder()\n",
    "for feature in cat_features:\n",
    "    # Let's match the data (create a map and apply it to the variable right away)\n",
    "    data_for_selection[feature] = le.fit_transform(train_set[feature])\n",
    "data_for_selection.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's calculate correlation using spearman method\n",
    "corr_matrix=data_for_selection.corr('spearman')\n",
    "#Create a heatmap without no repetition\n",
    "mask = np.triu(np.ones_like(corr_matrix, dtype=bool))\n",
    "sns.set_theme(style=\"white\")\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "cmap=sns.diverging_palette(230, 20, as_cmap=True)\n",
    "sns.heatmap(corr_matrix, mask=mask, cmap=cmap, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5},annot=True,annot_kws={\"size\":10})\n",
    "plt.title('Spearman Correlation matrix')\n",
    "plt.savefig(path+'\\\\img\\\\corr_matrix.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very High correlation(0.95) appear between highway mpg and city mpg. Very High correlation appear between highway mpg and city mpg. When look at relation between dependent variable and them, it can believe that mpg variables give the same information, so maybe should delete it.\n",
    "Also there is a high correlation between Engine cylinders and Engine HP variables, which is intuitive because more cylinders translate into more power in the car. \n",
    "\n",
    "On the other hand, Highway mpg / Engine Cylinders have a strong negative correlation with highway(-0.75) and city MPG(-0.83) because lower MPG figures mean higher fuel consumption.\n",
    "Looking at the dependent variable, it is worth noting specifically the 4 variables: Year, Engine HP, Engine Cylinders, Engine Fuel Type. They have the highest correlation/anti-correlation and are likely to have the highest predictive value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5.2 Mutual Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sorted list with variable and its mutual information'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[(1.144021178187737, 'Engine HP'),\n",
       " (0.40837961877973106, 'Year'),\n",
       " (0.3555775718099523, 'Engine Cylinders'),\n",
       " (0.21242685961374952, 'Driven_Wheels'),\n",
       " (0.18287454889424248, 'highway MPG'),\n",
       " (0.17911340340008097, 'city mpg'),\n",
       " (0.1754452663142636, 'Vehicle Size'),\n",
       " (0.14243360965322194, 'Transmission Type'),\n",
       " (0.1285490625657011, 'Market Category'),\n",
       " (0.12171051036174796, 'Engine Fuel Type'),\n",
       " (0.11481642090890531, 'Vehicle Style'),\n",
       " (0.1082092964744108, 'Number of Doors'),\n",
       " (0.10378507344510601, 'Popularity')]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#List for results\n",
    "mut_info=[]\n",
    "#Calculation for categorical and continuous variables\n",
    "for feature in cat_features:\n",
    "    mut_info.append(feature_selection.mutual_info_regression(data_for_selection[feature].values.reshape(-1,1),data_for_selection['MSRP'].values)[0])\n",
    "for feature in num_features:\n",
    "    mut_info.append(feature_selection.mutual_info_regression(data_for_selection[feature].values.reshape(-1,1),data_for_selection['MSRP'].values)[0])\n",
    "    \n",
    "mut_info_list = list(zip(mut_info, cat_features+num_features))\n",
    "mut_info_list.sort(reverse=True)\n",
    "display('Sorted list with variable and its mutual information',mut_info_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to mutual information, it can be confirmed that Engine HP and Year are the two most significant variables for the price modeling process. Equally important is Engine Cylinders. On the other hand, the variables Number of Doors and Popularity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's perform a transformation of two variables into one before moving on to modeling. Let's scale highway mpg and city mpg in Average total mpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>highway MPG</th>\n",
       "      <th>city mpg</th>\n",
       "      <th>average total mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7446</th>\n",
       "      <td>32</td>\n",
       "      <td>26</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>37</td>\n",
       "      <td>25</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1691</th>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11273</th>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       highway MPG  city mpg  average total mpg\n",
       "7446            32        26               29.0\n",
       "358             37        25               31.0\n",
       "1691            20        15               17.5\n",
       "11273           26        19               22.5\n",
       "849             31        21               26.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>highway MPG</th>\n",
       "      <th>city mpg</th>\n",
       "      <th>average total mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1357</th>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7829</th>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10856</th>\n",
       "      <td>24</td>\n",
       "      <td>19</td>\n",
       "      <td>21.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11387</th>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466</th>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       highway MPG  city mpg  average total mpg\n",
       "1357            30        21               25.5\n",
       "7829            26        18               22.0\n",
       "10856           24        19               21.5\n",
       "11387           21        12               16.5\n",
       "1466            22        20               21.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_set['average total mpg']=train_set[['highway MPG','city mpg']].mean(axis=1)\n",
    "display(train_set[['highway MPG','city mpg','average total mpg']].head())\n",
    "\n",
    "test_set['average total mpg']=test_set[['highway MPG','city mpg']].mean(axis=1)\n",
    "display(test_set[['highway MPG','city mpg','average total mpg']].head())\n",
    "\n",
    "num_features.append('average total mpg')\n",
    "num_features.remove('highway MPG')\n",
    "num_features.remove('city mpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is a good idea to standardize the numerical variables before you start modeling, since many of them have different measures and different magnitudes. Also, it will be easier to compare models if the variables are standardized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set[num_features] = train_set[num_features].apply(lambda x: (x-x.mean())/x.std())\n",
    "train_set[['MSRP']] = train_set[['MSRP']].apply(lambda x: (x-x.mean())/x.std())\n",
    "\n",
    "test_set[num_features] = test_set[num_features].apply(lambda x: (x-x.mean())/x.std())\n",
    "test_set[['MSRP']] = test_set[['MSRP']].apply(lambda x: (x-x.mean())/x.std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start the modeling process with a simple linear regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1 Linear regression "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear regression is a basic and commonly used type of predictive analysis. It is a linear approach to modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In modeling, it is worthwhile to take care of the correct transformation of categorical data. In the linear regression and SVM, we will use the previously created binary variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Engine HP</th>\n",
       "      <th>Engine Cylinders</th>\n",
       "      <th>Number of Doors</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>average total mpg</th>\n",
       "      <th>Engine Fuel Type_electric</th>\n",
       "      <th>Engine Fuel Type_flex-fuel (premium unleaded required/E85)</th>\n",
       "      <th>Engine Fuel Type_flex-fuel (unleaded/E85)</th>\n",
       "      <th>Engine Fuel Type_other</th>\n",
       "      <th>...</th>\n",
       "      <th>Vehicle Style_Convertible SUV</th>\n",
       "      <th>Vehicle Style_Coupe</th>\n",
       "      <th>Vehicle Style_Crew Cab Pickup</th>\n",
       "      <th>Vehicle Style_Extended Cab Pickup</th>\n",
       "      <th>Vehicle Style_Passenger Minivan</th>\n",
       "      <th>Vehicle Style_Passenger Van</th>\n",
       "      <th>Vehicle Style_Regular Cab Pickup</th>\n",
       "      <th>Vehicle Style_Sedan</th>\n",
       "      <th>Vehicle Style_Wagon</th>\n",
       "      <th>MSRP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7446</th>\n",
       "      <td>-2.148111</td>\n",
       "      <td>-1.488833</td>\n",
       "      <td>-0.979467</td>\n",
       "      <td>-1.688503</td>\n",
       "      <td>0.319613</td>\n",
       "      <td>0.662229</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.801058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>0.579000</td>\n",
       "      <td>-0.664850</td>\n",
       "      <td>-0.979467</td>\n",
       "      <td>0.616070</td>\n",
       "      <td>-0.673231</td>\n",
       "      <td>0.887277</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>-0.345202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1691</th>\n",
       "      <td>-1.861047</td>\n",
       "      <td>-0.972712</td>\n",
       "      <td>0.157051</td>\n",
       "      <td>-1.688503</td>\n",
       "      <td>-0.673231</td>\n",
       "      <td>-0.631796</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.786954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11273</th>\n",
       "      <td>0.291936</td>\n",
       "      <td>0.095750</td>\n",
       "      <td>0.157051</td>\n",
       "      <td>0.616070</td>\n",
       "      <td>0.319613</td>\n",
       "      <td>-0.069176</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.181316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>849</th>\n",
       "      <td>0.004872</td>\n",
       "      <td>-0.429426</td>\n",
       "      <td>-0.979467</td>\n",
       "      <td>0.616070</td>\n",
       "      <td>-0.817520</td>\n",
       "      <td>0.324657</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.240659</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Year  Engine HP  Engine Cylinders  Number of Doors  Popularity  \\\n",
       "7446  -2.148111  -1.488833         -0.979467        -1.688503    0.319613   \n",
       "358    0.579000  -0.664850         -0.979467         0.616070   -0.673231   \n",
       "1691  -1.861047  -0.972712          0.157051        -1.688503   -0.673231   \n",
       "11273  0.291936   0.095750          0.157051         0.616070    0.319613   \n",
       "849    0.004872  -0.429426         -0.979467         0.616070   -0.817520   \n",
       "\n",
       "       average total mpg  Engine Fuel Type_electric  \\\n",
       "7446            0.662229                          0   \n",
       "358             0.887277                          0   \n",
       "1691           -0.631796                          0   \n",
       "11273          -0.069176                          0   \n",
       "849             0.324657                          0   \n",
       "\n",
       "       Engine Fuel Type_flex-fuel (premium unleaded required/E85)  \\\n",
       "7446                                                   0            \n",
       "358                                                    0            \n",
       "1691                                                   0            \n",
       "11273                                                  0            \n",
       "849                                                    0            \n",
       "\n",
       "       Engine Fuel Type_flex-fuel (unleaded/E85)  Engine Fuel Type_other  ...  \\\n",
       "7446                                           0                       0  ...   \n",
       "358                                            0                       0  ...   \n",
       "1691                                           0                       0  ...   \n",
       "11273                                          0                       0  ...   \n",
       "849                                            0                       0  ...   \n",
       "\n",
       "       Vehicle Style_Convertible SUV  Vehicle Style_Coupe  \\\n",
       "7446                               0                    1   \n",
       "358                                0                    0   \n",
       "1691                               0                    0   \n",
       "11273                              0                    0   \n",
       "849                                0                    0   \n",
       "\n",
       "       Vehicle Style_Crew Cab Pickup  Vehicle Style_Extended Cab Pickup  \\\n",
       "7446                               0                                  0   \n",
       "358                                0                                  0   \n",
       "1691                               0                                  0   \n",
       "11273                              0                                  0   \n",
       "849                                0                                  0   \n",
       "\n",
       "       Vehicle Style_Passenger Minivan  Vehicle Style_Passenger Van  \\\n",
       "7446                                 0                            0   \n",
       "358                                  0                            0   \n",
       "1691                                 0                            0   \n",
       "11273                                0                            0   \n",
       "849                                  0                            0   \n",
       "\n",
       "       Vehicle Style_Regular Cab Pickup  Vehicle Style_Sedan  \\\n",
       "7446                                  0                    0   \n",
       "358                                   0                    1   \n",
       "1691                                  1                    0   \n",
       "11273                                 0                    0   \n",
       "849                                   0                    0   \n",
       "\n",
       "       Vehicle Style_Wagon      MSRP  \n",
       "7446                     0 -0.801058  \n",
       "358                      0 -0.345202  \n",
       "1691                     0 -0.786954  \n",
       "11273                    1 -0.181316  \n",
       "849                      1 -0.240659  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['Year',\n",
       " 'Engine HP',\n",
       " 'Engine Cylinders',\n",
       " 'Number of Doors',\n",
       " 'Popularity',\n",
       " 'average total mpg',\n",
       " 'Engine Fuel Type_electric',\n",
       " 'Engine Fuel Type_flex-fuel (premium unleaded required/E85)',\n",
       " 'Engine Fuel Type_flex-fuel (unleaded/E85)',\n",
       " 'Engine Fuel Type_other',\n",
       " 'Engine Fuel Type_premium unleaded (recommended)',\n",
       " 'Engine Fuel Type_premium unleaded (required)',\n",
       " 'Engine Fuel Type_regular unleaded',\n",
       " 'Transmission Type_AUTOMATIC',\n",
       " 'Transmission Type_DIRECT_DRIVE',\n",
       " 'Transmission Type_MANUAL',\n",
       " 'Transmission Type_UNKNOWN',\n",
       " 'Driven_Wheels_four wheel drive',\n",
       " 'Driven_Wheels_front wheel drive',\n",
       " 'Driven_Wheels_rear wheel drive',\n",
       " 'Market Category_Diesel',\n",
       " 'Market Category_Exotic',\n",
       " 'Market Category_Factory Tuner',\n",
       " 'Market Category_Flex Fuel',\n",
       " 'Market Category_Hatchback',\n",
       " 'Market Category_High-Performance',\n",
       " 'Market Category_Hybrid',\n",
       " 'Market Category_Luxury',\n",
       " 'Market Category_Other',\n",
       " 'Market Category_Performance',\n",
       " 'Vehicle Size_Large',\n",
       " 'Vehicle Size_Midsize',\n",
       " 'Vehicle Style_2dr SUV',\n",
       " 'Vehicle Style_4dr Hatchback',\n",
       " 'Vehicle Style_4dr SUV',\n",
       " 'Vehicle Style_Cargo Minivan',\n",
       " 'Vehicle Style_Cargo Van',\n",
       " 'Vehicle Style_Convertible',\n",
       " 'Vehicle Style_Convertible SUV',\n",
       " 'Vehicle Style_Coupe',\n",
       " 'Vehicle Style_Crew Cab Pickup',\n",
       " 'Vehicle Style_Extended Cab Pickup',\n",
       " 'Vehicle Style_Passenger Minivan',\n",
       " 'Vehicle Style_Passenger Van',\n",
       " 'Vehicle Style_Regular Cab Pickup',\n",
       " 'Vehicle Style_Sedan',\n",
       " 'Vehicle Style_Wagon']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_linreg=pd.concat([train_set[num_features],train_set[dummLev.columns.tolist()],train_set[target]],axis=1)\n",
    "display(train_linreg.head())\n",
    "ind_features=num_features+dummLev.columns.tolist()\n",
    "ind_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>MSRP</td>       <th>  R-squared:         </th> <td>   0.807</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.806</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   749.9</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 03 May 2021</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:57:17</td>     <th>  Log-Likelihood:    </th> <td> -5034.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  8456</td>      <th>  AIC:               </th> <td>1.016e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  8408</td>      <th>  BIC:               </th> <td>1.050e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    47</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                               <td></td>                                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>                                                      <td>    0.1887</td> <td>    0.093</td> <td>    2.021</td> <td> 0.043</td> <td>    0.006</td> <td>    0.372</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Year</th>                                                       <td>    0.0773</td> <td>    0.008</td> <td>    9.833</td> <td> 0.000</td> <td>    0.062</td> <td>    0.093</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Engine HP</th>                                                  <td>    0.4011</td> <td>    0.013</td> <td>   29.801</td> <td> 0.000</td> <td>    0.375</td> <td>    0.428</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Engine Cylinders</th>                                           <td>    0.1509</td> <td>    0.011</td> <td>   13.556</td> <td> 0.000</td> <td>    0.129</td> <td>    0.173</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Number of Doors</th>                                            <td>   -0.0386</td> <td>    0.020</td> <td>   -1.886</td> <td> 0.059</td> <td>   -0.079</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Popularity</th>                                                 <td>   -0.0115</td> <td>    0.005</td> <td>   -2.208</td> <td> 0.027</td> <td>   -0.022</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>average total mpg</th>                                          <td>    0.1078</td> <td>    0.014</td> <td>    7.839</td> <td> 0.000</td> <td>    0.081</td> <td>    0.135</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Engine Fuel Type_electric</th>                                  <td>   -1.2541</td> <td>    0.342</td> <td>   -3.662</td> <td> 0.000</td> <td>   -1.925</td> <td>   -0.583</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Engine Fuel Type_flex-fuel (premium unleaded required/E85)</th> <td>   -0.0167</td> <td>    0.094</td> <td>   -0.177</td> <td> 0.859</td> <td>   -0.201</td> <td>    0.168</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Engine Fuel Type_flex-fuel (unleaded/E85)</th>                  <td>   -0.2986</td> <td>    0.066</td> <td>   -4.515</td> <td> 0.000</td> <td>   -0.428</td> <td>   -0.169</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Engine Fuel Type_other</th>                                     <td>   -0.0806</td> <td>    0.102</td> <td>   -0.792</td> <td> 0.428</td> <td>   -0.280</td> <td>    0.119</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Engine Fuel Type_premium unleaded (recommended)</th>            <td>   -0.1867</td> <td>    0.060</td> <td>   -3.136</td> <td> 0.002</td> <td>   -0.303</td> <td>   -0.070</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Engine Fuel Type_premium unleaded (required)</th>               <td>    0.0577</td> <td>    0.060</td> <td>    0.961</td> <td> 0.337</td> <td>   -0.060</td> <td>    0.175</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Engine Fuel Type_regular unleaded</th>                          <td>   -0.2190</td> <td>    0.058</td> <td>   -3.795</td> <td> 0.000</td> <td>   -0.332</td> <td>   -0.106</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Transmission Type_AUTOMATIC</th>                                <td>   -0.1040</td> <td>    0.026</td> <td>   -4.028</td> <td> 0.000</td> <td>   -0.155</td> <td>   -0.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Transmission Type_DIRECT_DRIVE</th>                             <td>   -0.1600</td> <td>    0.315</td> <td>   -0.507</td> <td> 0.612</td> <td>   -0.778</td> <td>    0.458</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Transmission Type_MANUAL</th>                                   <td>   -0.1793</td> <td>    0.027</td> <td>   -6.760</td> <td> 0.000</td> <td>   -0.231</td> <td>   -0.127</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Transmission Type_UNKNOWN</th>                                  <td>   -0.4209</td> <td>    0.145</td> <td>   -2.909</td> <td> 0.004</td> <td>   -0.705</td> <td>   -0.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Driven_Wheels_four wheel drive</th>                             <td>   -0.1068</td> <td>    0.024</td> <td>   -4.400</td> <td> 0.000</td> <td>   -0.154</td> <td>   -0.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Driven_Wheels_front wheel drive</th>                            <td>   -0.0193</td> <td>    0.016</td> <td>   -1.208</td> <td> 0.227</td> <td>   -0.051</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Driven_Wheels_rear wheel drive</th>                             <td>   -0.1767</td> <td>    0.018</td> <td>  -10.062</td> <td> 0.000</td> <td>   -0.211</td> <td>   -0.142</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Market Category_Diesel</th>                                     <td>    0.0139</td> <td>    0.064</td> <td>    0.218</td> <td> 0.827</td> <td>   -0.111</td> <td>    0.139</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Market Category_Exotic</th>                                     <td>    2.3159</td> <td>    0.045</td> <td>   51.591</td> <td> 0.000</td> <td>    2.228</td> <td>    2.404</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Market Category_Factory Tuner</th>                              <td>   -0.2077</td> <td>    0.040</td> <td>   -5.176</td> <td> 0.000</td> <td>   -0.286</td> <td>   -0.129</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Market Category_Flex Fuel</th>                                  <td>    0.0499</td> <td>    0.037</td> <td>    1.356</td> <td> 0.175</td> <td>   -0.022</td> <td>    0.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Market Category_Hatchback</th>                                  <td>    0.0659</td> <td>    0.057</td> <td>    1.153</td> <td> 0.249</td> <td>   -0.046</td> <td>    0.178</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Market Category_High-Performance</th>                           <td>   -0.3163</td> <td>    0.050</td> <td>   -6.355</td> <td> 0.000</td> <td>   -0.414</td> <td>   -0.219</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Market Category_Hybrid</th>                                     <td>    0.0285</td> <td>    0.053</td> <td>    0.541</td> <td> 0.588</td> <td>   -0.075</td> <td>    0.132</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Market Category_Luxury</th>                                     <td>    0.0633</td> <td>    0.028</td> <td>    2.252</td> <td> 0.024</td> <td>    0.008</td> <td>    0.118</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Market Category_Other</th>                                      <td>    0.0683</td> <td>    0.024</td> <td>    2.831</td> <td> 0.005</td> <td>    0.021</td> <td>    0.116</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Market Category_Performance</th>                                <td>   -0.0630</td> <td>    0.036</td> <td>   -1.767</td> <td> 0.077</td> <td>   -0.133</td> <td>    0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vehicle Size_Large</th>                                         <td>   -0.0713</td> <td>    0.018</td> <td>   -3.888</td> <td> 0.000</td> <td>   -0.107</td> <td>   -0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vehicle Size_Midsize</th>                                       <td>   -0.1381</td> <td>    0.014</td> <td>   -9.832</td> <td> 0.000</td> <td>   -0.166</td> <td>   -0.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vehicle Style_2dr SUV</th>                                      <td>    0.1845</td> <td>    0.095</td> <td>    1.949</td> <td> 0.051</td> <td>   -0.001</td> <td>    0.370</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vehicle Style_4dr Hatchback</th>                                <td>    0.0995</td> <td>    0.058</td> <td>    1.730</td> <td> 0.084</td> <td>   -0.013</td> <td>    0.212</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vehicle Style_4dr SUV</th>                                      <td>    0.1903</td> <td>    0.078</td> <td>    2.429</td> <td> 0.015</td> <td>    0.037</td> <td>    0.344</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vehicle Style_Cargo Minivan</th>                                <td>    0.1829</td> <td>    0.104</td> <td>    1.757</td> <td> 0.079</td> <td>   -0.021</td> <td>    0.387</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vehicle Style_Cargo Van</th>                                    <td>    0.0607</td> <td>    0.093</td> <td>    0.655</td> <td> 0.513</td> <td>   -0.121</td> <td>    0.243</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vehicle Style_Convertible</th>                                  <td>    0.1879</td> <td>    0.070</td> <td>    2.689</td> <td> 0.007</td> <td>    0.051</td> <td>    0.325</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vehicle Style_Convertible SUV</th>                              <td>    0.3712</td> <td>    0.113</td> <td>    3.280</td> <td> 0.001</td> <td>    0.149</td> <td>    0.593</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vehicle Style_Coupe</th>                                        <td>    0.0042</td> <td>    0.069</td> <td>    0.060</td> <td> 0.952</td> <td>   -0.131</td> <td>    0.139</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vehicle Style_Crew Cab Pickup</th>                              <td>   -0.0276</td> <td>    0.082</td> <td>   -0.336</td> <td> 0.737</td> <td>   -0.189</td> <td>    0.133</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vehicle Style_Extended Cab Pickup</th>                          <td>    0.0097</td> <td>    0.078</td> <td>    0.125</td> <td> 0.901</td> <td>   -0.143</td> <td>    0.162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vehicle Style_Passenger Minivan</th>                            <td>    0.1047</td> <td>    0.083</td> <td>    1.266</td> <td> 0.205</td> <td>   -0.057</td> <td>    0.267</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vehicle Style_Passenger Van</th>                                <td>    0.1769</td> <td>    0.086</td> <td>    2.048</td> <td> 0.041</td> <td>    0.008</td> <td>    0.346</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vehicle Style_Regular Cab Pickup</th>                           <td>    0.0013</td> <td>    0.072</td> <td>    0.018</td> <td> 0.985</td> <td>   -0.140</td> <td>    0.143</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vehicle Style_Sedan</th>                                        <td>    0.1309</td> <td>    0.082</td> <td>    1.600</td> <td> 0.110</td> <td>   -0.029</td> <td>    0.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Vehicle Style_Wagon</th>                                        <td>    0.1641</td> <td>    0.082</td> <td>    1.993</td> <td> 0.046</td> <td>    0.003</td> <td>    0.326</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>6395.510</td> <th>  Durbin-Watson:     </th>  <td>   2.027</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>561414.518</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 2.955</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>42.478</td>  <th>  Cond. No.          </th>  <td>    160.</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   MSRP   R-squared:                       0.807\n",
       "Model:                            OLS   Adj. R-squared:                  0.806\n",
       "Method:                 Least Squares   F-statistic:                     749.9\n",
       "Date:                Mon, 03 May 2021   Prob (F-statistic):               0.00\n",
       "Time:                        15:57:17   Log-Likelihood:                -5034.2\n",
       "No. Observations:                8456   AIC:                         1.016e+04\n",
       "Df Residuals:                    8408   BIC:                         1.050e+04\n",
       "Df Model:                          47                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================================================================\n",
       "                                                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------------------------------------------\n",
       "const                                                          0.1887      0.093      2.021      0.043       0.006       0.372\n",
       "Year                                                           0.0773      0.008      9.833      0.000       0.062       0.093\n",
       "Engine HP                                                      0.4011      0.013     29.801      0.000       0.375       0.428\n",
       "Engine Cylinders                                               0.1509      0.011     13.556      0.000       0.129       0.173\n",
       "Number of Doors                                               -0.0386      0.020     -1.886      0.059      -0.079       0.002\n",
       "Popularity                                                    -0.0115      0.005     -2.208      0.027      -0.022      -0.001\n",
       "average total mpg                                              0.1078      0.014      7.839      0.000       0.081       0.135\n",
       "Engine Fuel Type_electric                                     -1.2541      0.342     -3.662      0.000      -1.925      -0.583\n",
       "Engine Fuel Type_flex-fuel (premium unleaded required/E85)    -0.0167      0.094     -0.177      0.859      -0.201       0.168\n",
       "Engine Fuel Type_flex-fuel (unleaded/E85)                     -0.2986      0.066     -4.515      0.000      -0.428      -0.169\n",
       "Engine Fuel Type_other                                        -0.0806      0.102     -0.792      0.428      -0.280       0.119\n",
       "Engine Fuel Type_premium unleaded (recommended)               -0.1867      0.060     -3.136      0.002      -0.303      -0.070\n",
       "Engine Fuel Type_premium unleaded (required)                   0.0577      0.060      0.961      0.337      -0.060       0.175\n",
       "Engine Fuel Type_regular unleaded                             -0.2190      0.058     -3.795      0.000      -0.332      -0.106\n",
       "Transmission Type_AUTOMATIC                                   -0.1040      0.026     -4.028      0.000      -0.155      -0.053\n",
       "Transmission Type_DIRECT_DRIVE                                -0.1600      0.315     -0.507      0.612      -0.778       0.458\n",
       "Transmission Type_MANUAL                                      -0.1793      0.027     -6.760      0.000      -0.231      -0.127\n",
       "Transmission Type_UNKNOWN                                     -0.4209      0.145     -2.909      0.004      -0.705      -0.137\n",
       "Driven_Wheels_four wheel drive                                -0.1068      0.024     -4.400      0.000      -0.154      -0.059\n",
       "Driven_Wheels_front wheel drive                               -0.0193      0.016     -1.208      0.227      -0.051       0.012\n",
       "Driven_Wheels_rear wheel drive                                -0.1767      0.018    -10.062      0.000      -0.211      -0.142\n",
       "Market Category_Diesel                                         0.0139      0.064      0.218      0.827      -0.111       0.139\n",
       "Market Category_Exotic                                         2.3159      0.045     51.591      0.000       2.228       2.404\n",
       "Market Category_Factory Tuner                                 -0.2077      0.040     -5.176      0.000      -0.286      -0.129\n",
       "Market Category_Flex Fuel                                      0.0499      0.037      1.356      0.175      -0.022       0.122\n",
       "Market Category_Hatchback                                      0.0659      0.057      1.153      0.249      -0.046       0.178\n",
       "Market Category_High-Performance                              -0.3163      0.050     -6.355      0.000      -0.414      -0.219\n",
       "Market Category_Hybrid                                         0.0285      0.053      0.541      0.588      -0.075       0.132\n",
       "Market Category_Luxury                                         0.0633      0.028      2.252      0.024       0.008       0.118\n",
       "Market Category_Other                                          0.0683      0.024      2.831      0.005       0.021       0.116\n",
       "Market Category_Performance                                   -0.0630      0.036     -1.767      0.077      -0.133       0.007\n",
       "Vehicle Size_Large                                            -0.0713      0.018     -3.888      0.000      -0.107      -0.035\n",
       "Vehicle Size_Midsize                                          -0.1381      0.014     -9.832      0.000      -0.166      -0.111\n",
       "Vehicle Style_2dr SUV                                          0.1845      0.095      1.949      0.051      -0.001       0.370\n",
       "Vehicle Style_4dr Hatchback                                    0.0995      0.058      1.730      0.084      -0.013       0.212\n",
       "Vehicle Style_4dr SUV                                          0.1903      0.078      2.429      0.015       0.037       0.344\n",
       "Vehicle Style_Cargo Minivan                                    0.1829      0.104      1.757      0.079      -0.021       0.387\n",
       "Vehicle Style_Cargo Van                                        0.0607      0.093      0.655      0.513      -0.121       0.243\n",
       "Vehicle Style_Convertible                                      0.1879      0.070      2.689      0.007       0.051       0.325\n",
       "Vehicle Style_Convertible SUV                                  0.3712      0.113      3.280      0.001       0.149       0.593\n",
       "Vehicle Style_Coupe                                            0.0042      0.069      0.060      0.952      -0.131       0.139\n",
       "Vehicle Style_Crew Cab Pickup                                 -0.0276      0.082     -0.336      0.737      -0.189       0.133\n",
       "Vehicle Style_Extended Cab Pickup                              0.0097      0.078      0.125      0.901      -0.143       0.162\n",
       "Vehicle Style_Passenger Minivan                                0.1047      0.083      1.266      0.205      -0.057       0.267\n",
       "Vehicle Style_Passenger Van                                    0.1769      0.086      2.048      0.041       0.008       0.346\n",
       "Vehicle Style_Regular Cab Pickup                               0.0013      0.072      0.018      0.985      -0.140       0.143\n",
       "Vehicle Style_Sedan                                            0.1309      0.082      1.600      0.110      -0.029       0.291\n",
       "Vehicle Style_Wagon                                            0.1641      0.082      1.993      0.046       0.003       0.326\n",
       "==============================================================================\n",
       "Omnibus:                     6395.510   Durbin-Watson:                   2.027\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           561414.518\n",
       "Skew:                           2.955   Prob(JB):                         0.00\n",
       "Kurtosis:                      42.478   Cond. No.                         160.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create first linear model with constant\n",
    "mod=sm.OLS(train_linreg[['MSRP']],sm.add_constant(train_linreg[ind_features]))\n",
    "results=mod.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to linear regression, we obtained an R^2 coefficient of 0.8, that is, we were able to explain 80% of the variation in the dependent variable. But what is more important in Ordinary least squared it is possible to interpret the individual coefficients standing by the variables. In the case of standardized variables, interpretation is a little more difficult but not impossible. For example, according to the model, a 1 standard deviation change in year will equal a 0.0773 standard deviation change in price with the other variables unchanged. Also, an increase in Engine Cylinders by 1 standard deviation will increase the price by 0.4011 standard deviation ceteris paribus.Let's see how the model performs on the test data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Engine HP</th>\n",
       "      <th>Engine Cylinders</th>\n",
       "      <th>Number of Doors</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>average total mpg</th>\n",
       "      <th>Engine Fuel Type_electric</th>\n",
       "      <th>Engine Fuel Type_flex-fuel (premium unleaded required/E85)</th>\n",
       "      <th>Engine Fuel Type_flex-fuel (unleaded/E85)</th>\n",
       "      <th>Engine Fuel Type_other</th>\n",
       "      <th>...</th>\n",
       "      <th>Vehicle Style_Convertible SUV</th>\n",
       "      <th>Vehicle Style_Coupe</th>\n",
       "      <th>Vehicle Style_Crew Cab Pickup</th>\n",
       "      <th>Vehicle Style_Extended Cab Pickup</th>\n",
       "      <th>Vehicle Style_Passenger Minivan</th>\n",
       "      <th>Vehicle Style_Passenger Van</th>\n",
       "      <th>Vehicle Style_Regular Cab Pickup</th>\n",
       "      <th>Vehicle Style_Sedan</th>\n",
       "      <th>Vehicle Style_Wagon</th>\n",
       "      <th>MSRP</th>\n",
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       "  </thead>\n",
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       "      <th>1357</th>\n",
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       "      <td>-0.987532</td>\n",
       "      <td>-1.726039</td>\n",
       "      <td>0.312467</td>\n",
       "      <td>-0.180844</td>\n",
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       "      <td>0.202364</td>\n",
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       "<p>2115 rows × 48 columns</p>\n",
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      ],
      "text/plain": [
       "           Year  Engine HP  Engine Cylinders  Number of Doors  Popularity  \\\n",
       "1357  -1.123355  -1.071038         -0.987532         0.603618   -1.069400   \n",
       "7829   0.593540   0.863685          0.177998        -1.726039   -0.955116   \n",
       "10856 -2.124877  -1.128222         -0.987532        -1.726039    0.312467   \n",
       "11387  0.736614   3.741942          2.509060        -1.726039    0.202364   \n",
       "1466  -0.264908   1.263972          1.343529         0.603618   -0.381602   \n",
       "...         ...        ...               ...              ...         ...   \n",
       "2934   0.593540  -1.071038         -0.987532         0.603618    0.327798   \n",
       "347    0.879689   0.758847          0.177998        -1.726039    0.312467   \n",
       "11275  0.307391  -0.680281         -0.987532         0.603618    0.327798   \n",
       "1168   0.879689   0.549173          0.177998         0.603618   -0.704944   \n",
       "10522 -0.264908  -0.642159          0.177998         0.603618   -0.941178   \n",
       "\n",
       "       average total mpg  Engine Fuel Type_electric  \\\n",
       "1357            0.252260                          0   \n",
       "7829           -0.126706                          0   \n",
       "10856          -0.180844                          0   \n",
       "11387          -0.722225                          0   \n",
       "1466           -0.234982                          0   \n",
       "...                  ...                        ...   \n",
       "2934            1.280883                          0   \n",
       "347            -0.289120                          0   \n",
       "11275          -0.018430                          0   \n",
       "1168           -0.180844                          0   \n",
       "10522          -0.289120                          0   \n",
       "\n",
       "       Engine Fuel Type_flex-fuel (premium unleaded required/E85)  \\\n",
       "1357                                                   0            \n",
       "7829                                                   0            \n",
       "10856                                                  0            \n",
       "11387                                                  0            \n",
       "1466                                                   0            \n",
       "...                                                  ...            \n",
       "2934                                                   0            \n",
       "347                                                    0            \n",
       "11275                                                  0            \n",
       "1168                                                   0            \n",
       "10522                                                  0            \n",
       "\n",
       "       Engine Fuel Type_flex-fuel (unleaded/E85)  Engine Fuel Type_other  ...  \\\n",
       "1357                                           0                       0  ...   \n",
       "7829                                           0                       0  ...   \n",
       "10856                                          0                       0  ...   \n",
       "11387                                          0                       0  ...   \n",
       "1466                                           0                       0  ...   \n",
       "...                                          ...                     ...  ...   \n",
       "2934                                           0                       0  ...   \n",
       "347                                            0                       0  ...   \n",
       "11275                                          0                       0  ...   \n",
       "1168                                           0                       0  ...   \n",
       "10522                                          0                       0  ...   \n",
       "\n",
       "       Vehicle Style_Convertible SUV  Vehicle Style_Coupe  \\\n",
       "1357                               0                    0   \n",
       "7829                               0                    0   \n",
       "10856                              0                    0   \n",
       "11387                              0                    1   \n",
       "1466                               0                    0   \n",
       "...                              ...                  ...   \n",
       "2934                               0                    0   \n",
       "347                                0                    0   \n",
       "11275                              0                    0   \n",
       "1168                               0                    0   \n",
       "10522                              0                    0   \n",
       "\n",
       "       Vehicle Style_Crew Cab Pickup  Vehicle Style_Extended Cab Pickup  \\\n",
       "1357                               0                                  0   \n",
       "7829                               0                                  0   \n",
       "10856                              0                                  0   \n",
       "11387                              0                                  0   \n",
       "1466                               0                                  0   \n",
       "...                              ...                                ...   \n",
       "2934                               0                                  0   \n",
       "347                                0                                  0   \n",
       "11275                              0                                  0   \n",
       "1168                               0                                  0   \n",
       "10522                              0                                  0   \n",
       "\n",
       "       Vehicle Style_Passenger Minivan  Vehicle Style_Passenger Van  \\\n",
       "1357                                 0                            0   \n",
       "7829                                 0                            0   \n",
       "10856                                0                            0   \n",
       "11387                                0                            0   \n",
       "1466                                 0                            0   \n",
       "...                                ...                          ...   \n",
       "2934                                 0                            0   \n",
       "347                                  0                            0   \n",
       "11275                                0                            0   \n",
       "1168                                 0                            0   \n",
       "10522                                0                            0   \n",
       "\n",
       "       Vehicle Style_Regular Cab Pickup  Vehicle Style_Sedan  \\\n",
       "1357                                  0                    1   \n",
       "7829                                  0                    0   \n",
       "10856                                 1                    0   \n",
       "11387                                 0                    0   \n",
       "1466                                  0                    0   \n",
       "...                                 ...                  ...   \n",
       "2934                                  0                    1   \n",
       "347                                   0                    0   \n",
       "11275                                 0                    0   \n",
       "1168                                  0                    0   \n",
       "10522                                 0                    0   \n",
       "\n",
       "       Vehicle Style_Wagon      MSRP  \n",
       "1357                     0 -0.488455  \n",
       "7829                     0  0.497232  \n",
       "10856                    0 -0.845936  \n",
       "11387                    0  1.525021  \n",
       "1466                     0  0.120335  \n",
       "...                    ...       ...  \n",
       "2934                     0 -0.384545  \n",
       "347                      0  0.120335  \n",
       "11275                    1 -0.155339  \n",
       "1168                     0  0.112497  \n",
       "10522                    0 -0.337629  \n",
       "\n",
       "[2115 rows x 48 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_linreg=pd.concat([test_set[num_features],test_set[dummLev.columns.tolist()],test_set[target]],axis=1)\n",
    "test_linreg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 score:  0.7789903580880139\n",
      "Mean squared error: 0.22\n",
      "Mean absolute error: 0.24\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Training Accuracy %</th>\n",
       "      <th>Testing Accuracy %</th>\n",
       "      <th>r2 score</th>\n",
       "      <th>MSE</th>\n",
       "      <th>MAE</th>\n",
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       "      <td>Linear Regression</td>\n",
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      ],
      "text/plain": [
       "               Model  Training Accuracy %  Testing Accuracy %  r2 score  \\\n",
       "0  Linear Regression             0.807389             0.77899   0.77899   \n",
       "\n",
       "        MSE       MAE  \n",
       "0  0.220905  0.235512  "
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "preds=results.predict(sm.add_constant(test_linreg[ind_features]))\n",
    "\n",
    "#Some statistics to evaluate a linear model\n",
    "print('R^2 score: ',metrics.r2_score(test_linreg['MSRP'],preds))\n",
    "print(\"Mean squared error: %.2f\" % metrics.mean_squared_error(test_linreg[\"MSRP\"], preds))\n",
    "print(\"Mean absolute error: %.2f\" % metrics.mean_absolute_error(test_linreg['MSRP'], preds))   \n",
    "\n",
    "#Create Dataframe with statistics to compare\n",
    "results_df = pd.DataFrame(data=[[\"Linear Regression\",results.rsquared,metrics.r2_score(test_linreg[\"MSRP\"],preds) ,metrics.r2_score(test_linreg[\"MSRP\"],preds),metrics.mean_squared_error(test_linreg[\"MSRP\"],preds),metrics.mean_absolute_error(test_linreg[\"MSRP\"],preds)]], \n",
    "                          columns=['Model', 'Training Accuracy %', 'Testing Accuracy %','r2 score','MSE','MAE'])\n",
    "\n",
    "display(results_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the test data, the model achieved an R^2 of 0.78, which is a good baseline result that can only be improved. Let us propose regularizations to the linear model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Regularization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a form of regression, that constrains/ regularizes or shrinks the coefficient estimates towards zero. In other words, this technique discourages learning a more complex or flexible model, so as to avoid the risk of overfitting.\n",
    "\n",
    "We will use two regularization methods: Ridge and Lasso. In ridge regression, the cost function is altered by adding a penalty equivalent to square of the magnitude of the coefficients. So, ridge regression shrinks the coefficients and it helps to reduce the model complexity and multi-collinearity.\n",
    "\n",
    "Lasso regression is regularization can lead to zero coefficients i.e. some of the features are completely neglected for the evaluation of output. So Lasso regression not only helps in reducing over-fitting but it can help us in feature selection. \n",
    "\n",
    "Source: https://towardsdatascience.com/ridge-and-lasso-regression-a-complete-guide-with-python-scikit-learn-e20e34bcbf0b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the models that follow, I will use pre-made wrappers that are based on cross-validation. This will help to get better results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Wrapper for linear model and regularization\n",
    "def CVTestLinearReg(nFolds = 5, randomState=42, method = \"linear\", debug=False, features=ind_features, \n",
    "                    saveModels = False, *args, **kwargs):\n",
    "    df=train_linreg.copy()\n",
    "    # Let's prepare a cross-validation\n",
    "    kf = KFold(n_splits=nFolds, shuffle=True, random_state=randomState)\n",
    "    # Let's record the results on the training set and the valid set:\n",
    "    validResults = []\n",
    "    trainResults = []\n",
    "    predictions = []\n",
    "    indices = []\n",
    "    # Let's prepare a list of models to keep\n",
    "    models = []\n",
    "    for train, valid in kf.split(df.index.values):\n",
    "        algorithm = {\n",
    "        \"linear\":linear_model.LinearRegression,\n",
    "        \"lasso\":linear_model.Lasso,\n",
    "        \"ridge\":linear_model.Ridge,\n",
    "        \"elastic\":linear_model.ElasticNet\n",
    "        }\n",
    "        # Preparing the estimator\n",
    "        clf = algorithm[method](*args, **kwargs)\n",
    "        if debug:\n",
    "            print(clf)\n",
    "        #Training the model\n",
    "        clf.fit(df.iloc[train][features], df.iloc[train]['MSRP'])\n",
    "\n",
    "    \n",
    "        predsTrain = clf.predict(df.iloc[train][features])\n",
    "        preds = clf.predict(df.iloc[valid][features])\n",
    "        \n",
    "        predictions.append(preds.tolist().copy())\n",
    "        \n",
    "        indices.append(df.iloc[valid].index.tolist().copy())\n",
    "        \n",
    "        #Statistics to evaluate model\n",
    "        trainScore = metrics.mean_squared_error(df['MSRP'].iloc[train], predsTrain)\n",
    "        validScore = metrics.mean_squared_error(df['MSRP'].iloc[valid], preds)\n",
    "        \n",
    "        trainResults.append(trainScore)\n",
    "        validResults.append(validScore)\n",
    "        \n",
    "        if debug:\n",
    "            print(\"Train MSE:\", trainScore,\n",
    "                  \"Valid MSE:\", validScore)\n",
    "        if saveModels:\n",
    "            models.append(clf)\n",
    "        \n",
    "    return trainResults, validResults, predictions, indices, models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression()\n",
      "Train MSE: 0.19026160816146778 Valid MSE: 0.20408511021738865\n",
      "LinearRegression()\n",
      "Train MSE: 0.18566606854325735 Valid MSE: 0.22119964355001817\n",
      "LinearRegression()\n",
      "Train MSE: 0.1920361204946759 Valid MSE: 0.1959006472151046\n",
      "LinearRegression()\n",
      "Train MSE: 0.2006321422475699 Valid MSE: 0.16159324891640084\n",
      "LinearRegression()\n",
      "Train MSE: 0.1931721196078896 Valid MSE: 0.19106528291704988\n",
      "Average Train MSE:  0.1923536118109721 Average Valid MSE: 0.1947687865631924\n",
      "R^2 score:  0.7794330012244985\n",
      "Mean squared error: 0.22\n",
      "Root mean squared error: 0.47\n",
      "Mean absolute error: 0.24\n"
     ]
    }
   ],
   "source": [
    "trainResults, validResults, predictions, indices, models = CVTestLinearReg(method=\"linear\",debug=True,saveModels=True)\n",
    "print('Average Train MSE: ',np.mean(trainResults), 'Average Valid MSE:',np.mean(validResults))\n",
    "preds=models[3].predict(test_linreg[ind_features])\n",
    "print('R^2 score: ',metrics.r2_score(test_linreg['MSRP'],preds))\n",
    "print(\"Mean squared error: %.2f\" % metrics.mean_squared_error(test_linreg[\"MSRP\"], preds))\n",
    "print(\"Root mean squared error: %.2f\" % np.sqrt(metrics.mean_squared_error(test_linreg[\"MSRP\"], preds)))\n",
    "print(\"Mean absolute error: %.2f\" % metrics.mean_absolute_error(test_linreg['MSRP'], preds))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the basic model, we obtained the same results. Let's check Lasso regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha: 0.001 Average Train MSE 0.1945536345120582 Average Valid MSE:  0.1963936067676872\n",
      "Alpha: 0.01 Average Train MSE 0.2115210241965531 Average Valid MSE:  0.2127248247084766\n",
      "Alpha: 0.1 Average Train MSE 0.4662414887072523 Average Valid MSE:  0.46795770821493676\n",
      "Alpha: 0.25 Average Train MSE 0.5216300001930037 Average Valid MSE:  0.5225799738656309\n",
      "Alpha: 0.5 Average Train MSE 0.7091658283891962 Average Valid MSE:  0.7100188869103077\n",
      "Alpha: 1 Average Train MSE 0.9998693294522271 Average Valid MSE:  0.9999833304222514\n",
      "Alpha: 2 Average Train MSE 0.9998693294522271 Average Valid MSE:  0.9999833304222514\n",
      "Alpha: 5 Average Train MSE 0.9998693294522271 Average Valid MSE:  0.9999833304222514\n"
     ]
    }
   ],
   "source": [
    "for alpha in [0.001, 0.01, 0.1, 0.25, 0.5, 1, 2, 5]:\n",
    "    trainResults, validResults, predictions, indices, models = CVTestLinearReg(method=\"lasso\", alpha=alpha, random_state=42)\n",
    "    print(\"Alpha:\", alpha, \"Average Train MSE\", np.mean(trainResults), \"Average Valid MSE: \", np.mean(validResults))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, the results didn't improve, they just got worse. Let's check out the Ridge method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha: 0.1 Average Train MSE 0.19235520798805575 Average Valid MSE 0.19475793021505475\n",
      "Alpha: 0.5 Average Train MSE 0.19237464725815392 Average Valid MSE 0.19474384620246635\n",
      "Alpha: 2 Average Train MSE 0.19248741817830167 Average Valid MSE 0.19475541467130714\n",
      "Alpha: 5 Average Train MSE 0.19280295482098134 Average Valid MSE 0.19493847034616346\n",
      "Alpha: 10 Average Train MSE 0.1934681397787021 Average Valid MSE 0.19548469585734113\n",
      "Alpha: 20 Average Train MSE 0.19502106593752488 Average Valid MSE 0.19692633703132345\n",
      "Alpha: 100 Average Train MSE 0.2108709723802677 Average Valid MSE 0.21265062461489942\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for alpha in [0.1, 0.5, 2, 5, 10, 20, 100]:\n",
    "    trainResults, validResults, predictions, indices, models = CVTestLinearReg(method=\"ridge\", alpha=alpha, random_state=42)\n",
    "    print(\"Alpha:\", alpha, \"Average Train MSE\", np.mean(trainResults), \"Average Valid MSE\", np.mean(validResults))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately, again no improvement in performance was obtained. Let's move on to the next model which is SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.2 Support Vector Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "“Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems. In the SVM algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_svm=pd.concat([train_set[num_features],train_set[dummLev.columns.tolist()],train_set[target]],axis=1)\n",
    "ind_features=num_features+dummLev.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's prepare a simple wrapper with cross-validation SVM\n",
    "def CVTestSVM(nFolds = 5, randomState=42, debug=False, features=ind_features, *args, **kwargs):\n",
    "    kf = KFold(n_splits=nFolds, shuffle=True, random_state=randomState)\n",
    "    df=train_svm.copy()\n",
    "    # Lists for Results:\n",
    "    validMSE = []\n",
    "    trainMSE = []\n",
    "    trainR2=[]\n",
    "    validR2=[]\n",
    "    predictions = []\n",
    "    indices = []\n",
    "    \n",
    "    for train, valid in kf.split(df.index.values):\n",
    "        # Prepare estimator\n",
    "        clf = SVR(max_iter=-1, tol=0.001, cache_size=500, *args, **kwargs)\n",
    "        # Displaying the function call\n",
    "        if debug:\n",
    "            print(clf)\n",
    "\n",
    "        # Training the model\n",
    "        clf.fit(df.iloc[train][features], df.iloc[train]['MSRP'])\n",
    "\n",
    "        # Prepare predictions for the training and test set\n",
    "        predsTrain = clf.predict(df.iloc[train][features])\n",
    "        preds = clf.predict(df.iloc[valid][features])\n",
    "        \n",
    "        predictions.append(preds.tolist().copy())\n",
    "        \n",
    "        indices.append(df.iloc[valid].index.tolist().copy())\n",
    "        \n",
    "        #Calculating statistics for model evaluation\n",
    "        trainScoreMSE = metrics.mean_squared_error(df['MSRP'].iloc[train], predsTrain)\n",
    "        validScoreMSE = metrics.mean_squared_error(df['MSRP'].iloc[valid], preds)\n",
    "        trainScoreR2 = metrics.r2_score(df['MSRP'].iloc[train], predsTrain)\n",
    "        validScoreR2 = metrics.r2_score(df['MSRP'].iloc[valid], preds)\n",
    "        \n",
    "        trainMSE.append(trainScoreMSE)\n",
    "        validMSE.append(validScoreMSE)\n",
    "        trainR2.append(trainScoreR2)\n",
    "        validR2.append(validScoreR2)\n",
    "        if debug:\n",
    "            print(\"Train MSE:\", trainScoreMSE,\n",
    "                  \"Valid MSE:\", validScoreMSE,\n",
    "                  \"Train R2:\", trainScoreR2,\n",
    "                  \"Valid R2:\", validScoreR2)\n",
    "       \n",
    "    return trainMSE, validMSE,trainR2,validR2, predictions, indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVR(cache_size=500, kernel='linear')\n",
      "Train MSE: 0.21348954620070756 Valid MSE: 0.23225978684559237 Train R2: 0.7835197136548667 Valid R2: 0.7797175391354726\n",
      "SVR(cache_size=500, kernel='linear')\n",
      "Train MSE: 0.20709194436952666 Valid MSE: 0.2434311011926446 Train R2: 0.7897025466180206 Valid R2: 0.7704164360641279\n",
      "SVR(cache_size=500, kernel='linear')\n",
      "Train MSE: 0.21603938748914178 Valid MSE: 0.22099687821377897 Train R2: 0.784077921224913 Valid R2: 0.7783866835551945\n",
      "SVR(cache_size=500, kernel='linear')\n",
      "Train MSE: 0.2269323779263101 Valid MSE: 0.1742277985482027 Train R2: 0.7801241501659115 Valid R2: 0.7997900221192177\n",
      "SVR(cache_size=500, kernel='linear')\n",
      "Train MSE: 0.21740770236105053 Valid MSE: 0.21463350279808355 Train R2: 0.7816683519894073 Valid R2: 0.7888055061639649\n",
      "Average Train MSE:  0.2161921916693473 Average Valid MSE: 0.21710981351966044\n",
      "Average Train R2:  0.7838185367306238 Average Valid R2: 0.7834232374075956\n"
     ]
    }
   ],
   "source": [
    "# train a SVR linear model (with default hyperparameter C)\n",
    "trainMSE, validMSE,trainR2,validR2, predictions, indices = CVTestSVM(debug=True,kernel='linear')\n",
    "print('Average Train MSE: ',np.mean(trainMSE), 'Average Valid MSE:',np.mean(validMSE))\n",
    "print('Average Train R2: ',np.mean(trainR2), 'Average Valid R2:',np.mean(validR2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM with linear kernel gave worse results than simple linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVR(cache_size=500, degree=2, kernel='poly')\n",
      "Train MSE: 0.09951282965399427 Valid MSE: 0.10743637838225796 Train R2: 0.8990931113870166 Valid R2: 0.898103971686886\n",
      "SVR(cache_size=500, degree=2, kernel='poly')\n",
      "Train MSE: 0.0958467379032955 Valid MSE: 0.12359317159443195 Train R2: 0.9026696815397757 Valid R2: 0.8834374051886972\n",
      "SVR(cache_size=500, degree=2, kernel='poly')\n",
      "Train MSE: 0.10141860665955925 Valid MSE: 0.1222302128047053 Train R2: 0.8986364633277553 Valid R2: 0.8774288440255615\n",
      "SVR(cache_size=500, degree=2, kernel='poly')\n",
      "Train MSE: 0.10431041777791494 Valid MSE: 0.08906535542300546 Train R2: 0.898933144908412 Valid R2: 0.8976525388728337\n",
      "SVR(cache_size=500, degree=2, kernel='poly')\n",
      "Train MSE: 0.09979475661033806 Valid MSE: 0.09549983660010888 Train R2: 0.8997811326970973 Valid R2: 0.9060303289596029\n",
      "Average Train MSE:  0.10017666972102042 Average Valid MSE: 0.10756499096090191\n",
      "Average Train R2:  0.8998227067720113 Average Valid R2: 0.8925306177467162\n"
     ]
    }
   ],
   "source": [
    "# SVM with polynomial kernel, degree two\n",
    "trainMSE, validMSE,trainR2,validR2, predictions, indices = CVTestSVM(debug=True,degree=2, kernel=\"poly\")\n",
    "print('Average Train MSE: ',np.mean(trainMSE), 'Average Valid MSE:',np.mean(validMSE))\n",
    "print('Average Train R2: ',np.mean(trainR2), 'Average Valid R2:',np.mean(validR2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM with polynomial kernel(second degree) give better results than simple linear regression. Let's check RBF kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVR(cache_size=500)\n",
      "Train MSE: 0.0718195623130399 Valid MSE: 0.08670731006414151 Train R2: 0.9271743291819434 Valid R2: 0.9177640697286502\n",
      "SVR(cache_size=500)\n",
      "Train MSE: 0.06679846759296534 Valid MSE: 0.10359108011652705 Train R2: 0.9321675806010425 Valid R2: 0.9023016810563672\n",
      "SVR(cache_size=500)\n",
      "Train MSE: 0.074599562944936 Valid MSE: 0.0910266754889137 Train R2: 0.9254409443852311 Valid R2: 0.9087194190112976\n",
      "SVR(cache_size=500)\n",
      "Train MSE: 0.07061308551039099 Valid MSE: 0.06869634213231984 Train R2: 0.9315826488583042 Valid R2: 0.9210591349175703\n",
      "SVR(cache_size=500)\n",
      "Train MSE: 0.07242940821835528 Valid MSE: 0.063917749746407 Train R2: 0.9272627791517539 Valid R2: 0.9371063854018632\n",
      "Average Train MSE:  0.07125201731593751 Average Valid MSE: 0.08278783150966182\n",
      "Average Train R2:  0.928725656435655 Average Valid R2: 0.9173901380231497\n"
     ]
    }
   ],
   "source": [
    "# SVM with rbf kernel\n",
    "trainMSE, validMSE,trainR2,validR2, predictions, indices = CVTestSVM(debug=True, kernel=\"rbf\")\n",
    "print('Average Train MSE: ',np.mean(trainMSE), 'Average Valid MSE:',np.mean(validMSE))\n",
    "print('Average Train R2: ',np.mean(trainR2), 'Average Valid R2:',np.mean(validR2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM with RBF kernel give best results from all SVM estimator.  Finally, let's check the effect of the size of the parameter C on estimate. Let's see if we can further improve the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01 Average Train MSE:  0.5235733170152462 Average Valid MSE: 0.5262352832869786\n",
      "0.01 Average Train R2:  0.4763595283963237 Average Valid R2: 0.4746387642955991\n",
      "----------------------------------------------------\n",
      "0.1 Average Train MSE:  0.16762401265850146 Average Valid MSE: 0.17397205074347422\n",
      "0.1 Average Train R2:  0.8323679573422977 Average Valid R2: 0.8265881657837106\n",
      "----------------------------------------------------\n",
      "0.25 Average Train MSE:  0.12152576561762647 Average Valid MSE: 0.12941010317701834\n",
      "0.25 Average Train R2:  0.8784763719005152 Average Valid R2: 0.8710647410726136\n",
      "----------------------------------------------------\n",
      "0.5 Average Train MSE:  0.09232089560802001 Average Valid MSE: 0.10203466775934537\n",
      "0.5 Average Train R2:  0.9076698597816015 Average Valid R2: 0.8983351750105439\n",
      "----------------------------------------------------\n",
      "1 Average Train MSE:  0.07125201731593751 Average Valid MSE: 0.08278783150966182\n",
      "1 Average Train R2:  0.928725656435655 Average Valid R2: 0.9173901380231497\n",
      "----------------------------------------------------\n",
      "2 Average Train MSE:  0.05504208877640898 Average Valid MSE: 0.068181489926838\n",
      "2 Average Train R2:  0.944933973879824 Average Valid R2: 0.9318649765108933\n",
      "----------------------------------------------------\n",
      "5 Average Train MSE:  0.038296135334212264 Average Valid MSE: 0.05264204817669391\n",
      "5 Average Train R2:  0.9616902157540468 Average Valid R2: 0.9473610662046088\n",
      "----------------------------------------------------\n",
      "10 Average Train MSE:  0.02751282482805791 Average Valid MSE: 0.0441811822810048\n",
      "10 Average Train R2:  0.9724769049159481 Average Valid R2: 0.9558327561996783\n",
      "----------------------------------------------------\n",
      "25 Average Train MSE:  0.018205653743627336 Average Valid MSE: 0.03476890848997015\n",
      "25 Average Train R2:  0.9817955108108782 Average Valid R2: 0.9651561712153196\n",
      "----------------------------------------------------\n",
      "50 Average Train MSE:  0.014479227060062225 Average Valid MSE: 0.03393273725317298\n",
      "50 Average Train R2:  0.985523095931215 Average Valid R2: 0.9659421513499009\n",
      "----------------------------------------------------\n",
      "100 Average Train MSE:  0.011814730918081311 Average Valid MSE: 0.036358690333978885\n",
      "100 Average Train R2:  0.988184546858826 Average Valid R2: 0.9635828579649001\n",
      "----------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for c in [0.01, 0.1, 0.25, 0.5, 1, 2, 5, 10, 25, 50, 100]:\n",
    "    trainMSE, validMSE,trainR2,validR2, predictions, indices= CVTestSVM(debug=False, kernel=\"rbf\", C=c)\n",
    "    print(c,'Average Train MSE: ',np.mean(trainMSE), 'Average Valid MSE:',np.mean(validMSE))\n",
    "    print(c,'Average Train R2: ',np.mean(trainR2), 'Average Valid R2:',np.mean(validR2))\n",
    "    print('----------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the C parameter increased, the MSE error decreased. However, C cannot be taken too large because it will lead to overfitting, so it was decided to choose an SVM model with RBF kernel and parameter C=2. Let's evaluate best estimator on test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_svm=pd.concat([test_set[num_features],test_set[dummLev.columns.tolist()],test_set[target]],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 score:  0.9246888198234645\n",
      "Mean squared error: 0.08\n",
      "Mean absolute error: 0.12\n"
     ]
    },
    {
     "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>Model</th>\n",
       "      <th>Training Accuracy %</th>\n",
       "      <th>Testing Accuracy %</th>\n",
       "      <th>r2 score</th>\n",
       "      <th>MSE</th>\n",
       "      <th>MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>0.807389</td>\n",
       "      <td>0.778990</td>\n",
       "      <td>0.778990</td>\n",
       "      <td>0.220905</td>\n",
       "      <td>0.235512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVR(kernel=RBF,C=2)</td>\n",
       "      <td>0.948425</td>\n",
       "      <td>0.924689</td>\n",
       "      <td>0.924689</td>\n",
       "      <td>0.075276</td>\n",
       "      <td>0.117617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Training Accuracy %  Testing Accuracy %  r2 score  \\\n",
       "0    Linear Regression             0.807389            0.778990  0.778990   \n",
       "1  SVR(kernel=RBF,C=2)             0.948425            0.924689  0.924689   \n",
       "\n",
       "        MSE       MAE  \n",
       "0  0.220905  0.235512  \n",
       "1  0.075276  0.117617  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svr_best= SVR(max_iter=-1, tol=0.001, cache_size=500, kernel='rbf',C=2)\n",
    "svr_best.fit(train_svm[ind_features],train_svm['MSRP'])\n",
    "preds=svr_best.predict(test_svm[ind_features])\n",
    "\n",
    "print('R^2 score: ',metrics.r2_score(test_svm['MSRP'],preds))\n",
    "print(\"Mean squared error: %.2f\" % metrics.mean_squared_error(test_svm[\"MSRP\"], preds))\n",
    "print(\"Mean absolute error: %.2f\" % metrics.mean_absolute_error(test_svm['MSRP'], preds))   \n",
    "\n",
    "#Create Dataframe with statistics to compare\n",
    "results_df=results_df.append(pd.DataFrame(data=[[\"SVR(kernel=RBF,C=2)\",svr_best.score(train_svm[ind_features],train_svm['MSRP']),svr_best.score(test_svm[ind_features],test_svm['MSRP']),metrics.r2_score(test_svm[\"MSRP\"],preds),metrics.mean_squared_error(test_svm[\"MSRP\"],preds),metrics.mean_absolute_error(test_svm[\"MSRP\"],preds)]], \n",
    "                          columns=['Model', 'Training Accuracy %', 'Testing Accuracy %','r2 score','MSE','MAE']),ignore_index=True)\n",
    "\n",
    "display(results_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that using a more sophisticated model such as SVM and simple tuning of hyperparameters produced much better results than linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's move to the last algorithm, which will be used in this project - Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.3 Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"A Random Forest is an ensemble technique capable of performing both regression and classification tasks with the use of multiple decision trees and a technique called Bootstrap and Aggregation, commonly known as bagging. The basic idea behind this is to combine multiple decision trees in determining the final output rather than relying on individual decision trees.\n",
    "Random Forest has multiple decision trees as base learning models. We randomly perform row sampling and feature sampling from the dataset forming sample datasets for every model.\"\n",
    "\n",
    "Source : https://www.geeksforgeeks.org/random-forest-regression-in-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_forest=pd.concat([train_set[num_features],train_set[dummLev.columns.tolist()],train_set[target]],axis=1)\n",
    "ind_features=num_features+dummLev.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's prepare a simple wrapper for Random Forest\n",
    "def CVTestRFRegressor(nFolds = 5, randomState=42, debug=False, features=ind_features, *args, **kwargs):\n",
    "    kf = KFold(n_splits=nFolds, shuffle=True, random_state=randomState)\n",
    "    df=train_svm.copy()\n",
    "    # Lists for Results:\n",
    "    validMSE = []\n",
    "    trainMSE = []\n",
    "    trainR2=[]\n",
    "    validR2=[]\n",
    "    predictions = []\n",
    "    indices = []\n",
    "    \n",
    "    for train, valid in kf.split(df.index.values):\n",
    "        # Prepare estimator\n",
    "        clf = RandomForestRegressor(*args, **kwargs, random_state=randomState, n_jobs=-1)\n",
    "        # Displaying the function call\n",
    "        if debug:\n",
    "            print(clf)\n",
    "\n",
    "      # Training the model\n",
    "        clf.fit(df.iloc[train][features], df.iloc[train]['MSRP'])\n",
    "\n",
    "        # Prepare predictions for the training and test set\n",
    "        predsTrain = clf.predict(df.iloc[train][features])\n",
    "        preds = clf.predict(df.iloc[valid][features])\n",
    "        \n",
    "        predictions.append(preds.tolist().copy())\n",
    "        \n",
    "        indices.append(df.iloc[valid].index.tolist().copy())\n",
    "         #Calculating statistics for model evaluation       \n",
    "        trainScoreMSE = metrics.mean_squared_error(df['MSRP'].iloc[train], predsTrain)\n",
    "        validScoreMSE = metrics.mean_squared_error(df['MSRP'].iloc[valid], preds)\n",
    "        trainScoreR2 = metrics.r2_score(df['MSRP'].iloc[train], predsTrain)\n",
    "        validScoreR2 = metrics.r2_score(df['MSRP'].iloc[valid], preds)\n",
    "        \n",
    "        trainMSE.append(trainScoreMSE)\n",
    "        validMSE.append(validScoreMSE)\n",
    "        trainR2.append(trainScoreR2)\n",
    "        validR2.append(validScoreR2)\n",
    "        if debug:\n",
    "            print(\"Train MSE:\", trainScoreMSE,\n",
    "                  \"Valid MSE:\", validScoreMSE,\n",
    "                  \"Train R2:\", trainScoreR2,\n",
    "                  \"Valid R2:\", validScoreR2)\n",
    "       \n",
    "    return trainMSE, validMSE,trainR2,validR2, predictions, indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor(n_jobs=-1, random_state=42)\n",
      "Train MSE: 0.005389444539321818 Valid MSE: 0.02500623894490224 Train R2: 0.9945350556133706 Valid R2: 0.9762832992892932\n",
      "RandomForestRegressor(n_jobs=-1, random_state=42)\n",
      "Train MSE: 0.005658083916314111 Valid MSE: 0.02299834215899977 Train R2: 0.9942543364386052 Valid R2: 0.9783099146673896\n",
      "RandomForestRegressor(n_jobs=-1, random_state=42)\n",
      "Train MSE: 0.005223390799921744 Valid MSE: 0.022506844806074473 Train R2: 0.9947794454850023 Valid R2: 0.9774303756664029\n",
      "RandomForestRegressor(n_jobs=-1, random_state=42)\n",
      "Train MSE: 0.005563084193108269 Valid MSE: 0.01665722892186587 Train R2: 0.9946099015229309 Valid R2: 0.9808587179440299\n",
      "RandomForestRegressor(n_jobs=-1, random_state=42)\n",
      "Train MSE: 0.005047222748873912 Valid MSE: 0.018611772049134355 Train R2: 0.9949313274154007 Valid R2: 0.9816864388547654\n",
      "Average Train MSE:  0.005376245239507971 Average Valid MSE: 0.021156085376195344\n",
      "Average Train R2:  0.994622013295062 Average Valid R2: 0.9789137492843762\n"
     ]
    }
   ],
   "source": [
    "trainMSE, validMSE,trainR2,validR2, predictions, indices = CVTestRFRegressor(debug=True)\n",
    "print('Average Train MSE: ',np.mean(trainMSE), 'Average Valid MSE:',np.mean(validMSE))\n",
    "print('Average Train R2: ',np.mean(trainR2), 'Average Valid R2:',np.mean(validR2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without tuning hyperparameters, RandomForest gives the best results for this dataset. Let's tune to further improve the model and also combat the problem of overfitting. Let's start with n_estimators, which is the number of trees  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 Average Train MSE:  0.006827385115430124 Average Valid MSE: 0.02505098875105659\n",
      "10 Average Train R2:  0.9931675966360196 Average Valid R2: 0.9750942858420734\n",
      "--------------------------------------------------------\n",
      "25 Average Train MSE:  0.005985827057092894 Average Valid MSE: 0.022261495570626205\n",
      "25 Average Train R2:  0.9940111524063859 Average Valid R2: 0.9778377824165003\n",
      "--------------------------------------------------------\n",
      "50 Average Train MSE:  0.005603542738931072 Average Valid MSE: 0.02121466006404654\n",
      "50 Average Train R2:  0.9943953259882145 Average Valid R2: 0.9788557153534267\n",
      "--------------------------------------------------------\n",
      "100 Average Train MSE:  0.005376245239507971 Average Valid MSE: 0.021156085376195344\n",
      "100 Average Train R2:  0.994622013295062 Average Valid R2: 0.9789137492843762\n",
      "--------------------------------------------------------\n",
      "200 Average Train MSE:  0.005250552284431285 Average Valid MSE: 0.020926896994908257\n",
      "200 Average Train R2:  0.9947461902228127 Average Valid R2: 0.9791522695952505\n",
      "--------------------------------------------------------\n",
      "500 Average Train MSE:  0.00522240162713203 Average Valid MSE: 0.020970796831964927\n",
      "500 Average Train R2:  0.9947737035984143 Average Valid R2: 0.9791069147757474\n",
      "--------------------------------------------------------\n",
      "1000 Average Train MSE:  0.005243265073558035 Average Valid MSE: 0.020996108051717567\n",
      "1000 Average Train R2:  0.9947523556715355 Average Valid R2: 0.9790869113345166\n",
      "--------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for k in [10, 25, 50, 100, 200, 500, 1000]:\n",
    "    trainMSE, validMSE,trainR2,validR2, predictions, indices = CVTestRFRegressor(debug=False,n_estimators=k)\n",
    "    print(k,'Average Train MSE: ',np.mean(trainMSE), 'Average Valid MSE:',np.mean(validMSE))\n",
    "    print(k,'Average Train R2: ',np.mean(trainR2), 'Average Valid R2:',np.mean(validR2))\n",
    "    print('--------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the results, a model with 200 trees will be the best choice. Now let's see how the tree depth will affect the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 Average Train MSE:  0.24011998518512828 Average Valid MSE: 0.24568634705483955\n",
      "2 Average Train R2:  0.7599035171344097 Average Valid R2: 0.7538953568091289\n",
      "--------------------------------------------------------\n",
      "4 Average Train MSE:  0.0820808803351516 Average Valid MSE: 0.09068930913300237\n",
      "4 Average Train R2:  0.9179105446789322 Average Valid R2: 0.9093722752377612\n",
      "--------------------------------------------------------\n",
      "6 Average Train MSE:  0.03645382834875858 Average Valid MSE: 0.045689041946245776\n",
      "6 Average Train R2:  0.9635421541073497 Average Valid R2: 0.9544245545290282\n",
      "--------------------------------------------------------\n",
      "8 Average Train MSE:  0.018013218037784908 Average Valid MSE: 0.0298837026888966\n",
      "8 Average Train R2:  0.9819801435518626 Average Valid R2: 0.9701730549098798\n",
      "--------------------------------------------------------\n",
      "10 Average Train MSE:  0.010425768994931372 Average Valid MSE: 0.023829113930688078\n",
      "10 Average Train R2:  0.9895681008783874 Average Valid R2: 0.9762369062212528\n",
      "--------------------------------------------------------\n",
      "12 Average Train MSE:  0.007455231030150668 Average Valid MSE: 0.021578284366346442\n",
      "12 Average Train R2:  0.9925398825775632 Average Valid R2: 0.9784880697163579\n",
      "--------------------------------------------------------\n",
      "14 Average Train MSE:  0.006216268268663812 Average Valid MSE: 0.020875996821999913\n",
      "14 Average Train R2:  0.993779607682389 Average Valid R2: 0.9791948891004489\n",
      "--------------------------------------------------------\n",
      "16 Average Train MSE:  0.005651615659305508 Average Valid MSE: 0.020738430300977072\n",
      "16 Average Train R2:  0.9943445722493742 Average Valid R2: 0.9793360506829993\n",
      "--------------------------------------------------------\n",
      "18 Average Train MSE:  0.005381878783828967 Average Valid MSE: 0.02073525950240907\n",
      "18 Average Train R2:  0.9946147217219636 Average Valid R2: 0.9793343336051681\n",
      "--------------------------------------------------------\n",
      "20 Average Train MSE:  0.005312934501945917 Average Valid MSE: 0.020761533620641575\n",
      "20 Average Train R2:  0.99468362576742 Average Valid R2: 0.9793110822135956\n",
      "--------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for k in range(2,22,2):\n",
    "    trainMSE, validMSE,trainR2,validR2, predictions, indices = CVTestRFRegressor(debug=False,n_estimators=200,max_depth=k)\n",
    "    print(k,'Average Train MSE: ',np.mean(trainMSE), 'Average Valid MSE:',np.mean(validMSE))\n",
    "    print(k,'Average Train R2: ',np.mean(trainR2), 'Average Valid R2:',np.mean(validR2))\n",
    "    print('--------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, a maximum depth of 16. Next parameter is a max features in one three."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 Average Train MSE:  0.01424256667797116 Average Valid MSE: 0.0357939475628864\n",
      "2 Average Train R2:  0.9857456959963834 Average Valid R2: 0.9643665514888091\n",
      "--------------------------------------------------------\n",
      "4 Average Train MSE:  0.00895085000710858 Average Valid MSE: 0.029067700128166424\n",
      "4 Average Train R2:  0.9910469234001497 Average Valid R2: 0.9709739354373642\n",
      "--------------------------------------------------------\n",
      "6 Average Train MSE:  0.007307604766900032 Average Valid MSE: 0.025864435623548426\n",
      "6 Average Train R2:  0.9926903765025168 Average Valid R2: 0.9741109322089206\n",
      "--------------------------------------------------------\n",
      "8 Average Train MSE:  0.006616728478138556 Average Valid MSE: 0.023982735146301157\n",
      "8 Average Train R2:  0.9933801793647616 Average Valid R2: 0.9760440184479208\n",
      "--------------------------------------------------------\n",
      "10 Average Train MSE:  0.006310465713039599 Average Valid MSE: 0.022622513819461936\n",
      "10 Average Train R2:  0.9936863532304882 Average Valid R2: 0.9773335522693285\n",
      "--------------------------------------------------------\n",
      "12 Average Train MSE:  0.00594104464046713 Average Valid MSE: 0.021151986992969492\n",
      "12 Average Train R2:  0.9940556489600171 Average Valid R2: 0.978796887853586\n",
      "--------------------------------------------------------\n",
      "14 Average Train MSE:  0.005831773415172758 Average Valid MSE: 0.021076753058132305\n",
      "14 Average Train R2:  0.9941655246152721 Average Valid R2: 0.9788556507883566\n",
      "--------------------------------------------------------\n",
      "16 Average Train MSE:  0.005604247496579581 Average Valid MSE: 0.020451669233820177\n",
      "16 Average Train R2:  0.9943916336642543 Average Valid R2: 0.9794406978937156\n",
      "--------------------------------------------------------\n",
      "18 Average Train MSE:  0.005569846466971691 Average Valid MSE: 0.020190152867946117\n",
      "18 Average Train R2:  0.994426605433139 Average Valid R2: 0.9797260040795164\n",
      "--------------------------------------------------------\n",
      "20 Average Train MSE:  0.005513647374891488 Average Valid MSE: 0.019628706388428577\n",
      "20 Average Train R2:  0.994482364026992 Average Valid R2: 0.9802849175560789\n",
      "--------------------------------------------------------\n",
      "22 Average Train MSE:  0.005502496073015015 Average Valid MSE: 0.020186434735396965\n",
      "22 Average Train R2:  0.9944931774461848 Average Valid R2: 0.9797783480601554\n",
      "--------------------------------------------------------\n",
      "24 Average Train MSE:  0.005475677552042215 Average Valid MSE: 0.01978870643598949\n",
      "24 Average Train R2:  0.9945208403429519 Average Valid R2: 0.9801510073076696\n",
      "--------------------------------------------------------\n",
      "26 Average Train MSE:  0.005469390110965622 Average Valid MSE: 0.019626785934657436\n",
      "26 Average Train R2:  0.9945265057001581 Average Valid R2: 0.98032915811102\n",
      "--------------------------------------------------------\n",
      "28 Average Train MSE:  0.005424960692415331 Average Valid MSE: 0.019652645318527544\n",
      "28 Average Train R2:  0.9945707285633733 Average Valid R2: 0.9802758578649442\n",
      "--------------------------------------------------------\n",
      "30 Average Train MSE:  0.00543692346960741 Average Valid MSE: 0.019565049526445565\n",
      "30 Average Train R2:  0.9945595023434336 Average Valid R2: 0.9803912912149055\n",
      "--------------------------------------------------------\n",
      "32 Average Train MSE:  0.005473832657548118 Average Valid MSE: 0.019737560878669395\n",
      "32 Average Train R2:  0.9945225614170509 Average Valid R2: 0.9802468381215433\n",
      "--------------------------------------------------------\n",
      "34 Average Train MSE:  0.005443492023513499 Average Valid MSE: 0.01940120530608511\n",
      "34 Average Train R2:  0.9945526550235095 Average Valid R2: 0.9806031604185279\n",
      "--------------------------------------------------------\n",
      "36 Average Train MSE:  0.005472676860790492 Average Valid MSE: 0.019578181740120357\n",
      "36 Average Train R2:  0.994523692489152 Average Valid R2: 0.9804185657268059\n",
      "--------------------------------------------------------\n",
      "38 Average Train MSE:  0.005472238232773481 Average Valid MSE: 0.019677048733454692\n",
      "38 Average Train R2:  0.9945245348915763 Average Valid R2: 0.980335345550807\n",
      "--------------------------------------------------------\n",
      "40 Average Train MSE:  0.0055327237826594275 Average Valid MSE: 0.01969679072692821\n",
      "40 Average Train R2:  0.9944638805395641 Average Valid R2: 0.9803379917926434\n",
      "--------------------------------------------------------\n",
      "42 Average Train MSE:  0.005527341482812388 Average Valid MSE: 0.020251558299387053\n",
      "42 Average Train R2:  0.9944692300550614 Average Valid R2: 0.979797785016973\n",
      "--------------------------------------------------------\n",
      "44 Average Train MSE:  0.005574241850072297 Average Valid MSE: 0.020224230298678834\n",
      "44 Average Train R2:  0.9944219386200206 Average Valid R2: 0.9798287897749445\n",
      "--------------------------------------------------------\n",
      "46 Average Train MSE:  0.005631645957038795 Average Valid MSE: 0.020703325105854077\n",
      "46 Average Train R2:  0.9943644828495829 Average Valid R2: 0.9793640732982984\n",
      "--------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for k in range(2,len(ind_features)+1,2):\n",
    "    trainMSE, validMSE,trainR2,validR2, predictions, indices = CVTestRFRegressor(debug=False,n_estimators=200,max_depth=16,max_features=k)\n",
    "    print(k,'Average Train MSE: ',np.mean(trainMSE), 'Average Valid MSE:',np.mean(validMSE))\n",
    "    print(k,'Average Train R2: ',np.mean(trainR2), 'Average Valid R2:',np.mean(validR2))\n",
    "    print('--------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best result was found for max_features=20.In the next two loops we will check the operation of min_samples_split and min_samples_leaf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 Average Train MSE:  0.005513647374891488 Average Valid MSE: 0.01962870638842858\n",
      "2 Average Train R2:  0.994482364026992 Average Valid R2: 0.9802849175560789\n",
      "--------------------------------------------------------\n",
      "4 Average Train MSE:  0.00728650624819725 Average Valid MSE: 0.020273992502764796\n",
      "4 Average Train R2:  0.9927069317651045 Average Valid R2: 0.9796786947194663\n",
      "--------------------------------------------------------\n",
      "6 Average Train MSE:  0.00962364982824865 Average Valid MSE: 0.022027490137435515\n",
      "6 Average Train R2:  0.9903695859236734 Average Valid R2: 0.9779580430059971\n",
      "--------------------------------------------------------\n",
      "8 Average Train MSE:  0.012267442561782794 Average Valid MSE: 0.02402335456494059\n",
      "8 Average Train R2:  0.9877252157020102 Average Valid R2: 0.9760256862264095\n",
      "--------------------------------------------------------\n",
      "10 Average Train MSE:  0.014755885640443663 Average Valid MSE: 0.02604403572238202\n",
      "10 Average Train R2:  0.9852371986644066 Average Valid R2: 0.9740196197951164\n",
      "--------------------------------------------------------\n",
      "15 Average Train MSE:  0.022055189981359857 Average Valid MSE: 0.0327266401400154\n",
      "15 Average Train R2:  0.9779379082565349 Average Valid R2: 0.9674125451944656\n",
      "--------------------------------------------------------\n",
      "20 Average Train MSE:  0.02901440643123676 Average Valid MSE: 0.038578827220359733\n",
      "20 Average Train R2:  0.9709810714561206 Average Valid R2: 0.9615765248114977\n",
      "--------------------------------------------------------\n",
      "30 Average Train MSE:  0.03861536554818351 Average Valid MSE: 0.048022540372733645\n",
      "30 Average Train R2:  0.9613774897526343 Average Valid R2: 0.9522140040357406\n",
      "--------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for k in [2, 4, 6, 8, 10, 15, 20, 30]:\n",
    "    trainMSE, validMSE,trainR2,validR2, predictions, indices = CVTestRFRegressor(debug=False,n_estimators=200,max_depth=16,max_features=20,min_samples_split=k)\n",
    "    print(k,'Average Train MSE: ',np.mean(trainMSE), 'Average Valid MSE:',np.mean(validMSE))\n",
    "    print(k,'Average Train R2: ',np.mean(trainR2), 'Average Valid R2:',np.mean(validR2))\n",
    "    print('--------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we must have at least 2 for the split then after the split we can only test values 1 and 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Average Train MSE:  0.005513647374891488 Average Valid MSE: 0.019628706388428577\n",
      "1 Average Train R2:  0.994482364026992 Average Valid R2: 0.9802849175560789\n",
      "--------------------------------------------------------\n",
      "2 Average Train MSE:  0.010344088600005037 Average Valid MSE: 0.023167009147595178\n",
      "2 Average Train R2:  0.9896478452718858 Average Valid R2: 0.9768312502801054\n",
      "--------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for k in [1,2]:\n",
    "    trainMSE, validMSE,trainR2,validR2, predictions, indices = CVTestRFRegressor(debug=False,n_estimators=200,max_depth=16,max_features=20,min_samples_split=2,min_samples_leaf=k)\n",
    "    print(k,'Average Train MSE: ',np.mean(trainMSE), 'Average Valid MSE:',np.mean(validMSE))\n",
    "    print(k,'Average Train R2: ',np.mean(trainR2), 'Average Valid R2:',np.mean(validR2))\n",
    "    print('--------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we obtained the best result for 200 trees,\n",
    "depth 16, maximum number of variables 20, observations before split 2 and min in leaf after split 1.  Let's check it for the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_forest=pd.concat([test_set[num_features],test_set[dummLev.columns.tolist()],test_set[target]],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 score:  0.9507759534970475\n",
      "Mean squared error: 0.05\n",
      "Mean absolute error: 0.10\n"
     ]
    },
    {
     "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>Model</th>\n",
       "      <th>Training Accuracy %</th>\n",
       "      <th>Testing Accuracy %</th>\n",
       "      <th>r2 score</th>\n",
       "      <th>MSE</th>\n",
       "      <th>MAE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>0.807389</td>\n",
       "      <td>0.778990</td>\n",
       "      <td>0.778990</td>\n",
       "      <td>0.220905</td>\n",
       "      <td>0.235512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVR(kernel=RBF,C=2)</td>\n",
       "      <td>0.948425</td>\n",
       "      <td>0.924689</td>\n",
       "      <td>0.924689</td>\n",
       "      <td>0.075276</td>\n",
       "      <td>0.117617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RandomForest(n_estimators=200,max_depth=16,max...</td>\n",
       "      <td>0.994650</td>\n",
       "      <td>0.950776</td>\n",
       "      <td>0.950776</td>\n",
       "      <td>0.049201</td>\n",
       "      <td>0.098726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Model  Training Accuracy %  \\\n",
       "0                                  Linear Regression             0.807389   \n",
       "1                                SVR(kernel=RBF,C=2)             0.948425   \n",
       "2  RandomForest(n_estimators=200,max_depth=16,max...             0.994650   \n",
       "\n",
       "   Testing Accuracy %  r2 score       MSE       MAE  \n",
       "0            0.778990  0.778990  0.220905  0.235512  \n",
       "1            0.924689  0.924689  0.075276  0.117617  \n",
       "2            0.950776  0.950776  0.049201  0.098726  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forest_best= RandomForestRegressor(n_estimators=200,max_depth=16,max_features=20,min_samples_split=2,min_samples_leaf=1,random_state=42)\n",
    "forest_best.fit(train_forest[ind_features],train_forest['MSRP'])\n",
    "preds=forest_best.predict(test_forest[ind_features])\n",
    "\n",
    "print('R^2 score: ',metrics.r2_score(test_forest['MSRP'],preds))\n",
    "print(\"Mean squared error: %.2f\" % metrics.mean_squared_error(test_forest[\"MSRP\"], preds))\n",
    "print(\"Mean absolute error: %.2f\" % metrics.mean_absolute_error(test_forest['MSRP'], preds))   \n",
    "\n",
    "#Create Dataframe with statistics to compare\n",
    "results_df=results_df.append(pd.DataFrame(data=[[\"RandomForest(n_estimators=200,max_depth=16,max_features=20,min_samples_split=2,min_samples_leaf=1)\",forest_best.score(train_forest[ind_features],train_forest['MSRP']),forest_best.score(test_forest[ind_features],test_forest['MSRP']),metrics.r2_score(test_forest[\"MSRP\"],preds),metrics.mean_squared_error(test_forest[\"MSRP\"],preds),metrics.mean_absolute_error(test_forest[\"MSRP\"],preds)]], \n",
    "                          columns=['Model', 'Training Accuracy %', 'Testing Accuracy %','r2 score','MSE','MAE']),ignore_index=True)\n",
    "\n",
    "display(results_df)\n",
    "#Save Results to \n",
    "results_df.to_csv(path+'\\\\data\\\\Results_eval_models.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest on the test set scored 0.95 which is an improvement over the SVM model. \n",
    "In the case of Random Forest model, there is an additional method that helps to develop the result of the analysis, which is feature importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_feature_importance(importance,names,model_type):\n",
    "\n",
    "    #Create arrays from feature importance and feature names\n",
    "    feature_importance = np.array(importance)\n",
    "    feature_names = np.array(names)\n",
    "\n",
    "    #Create a DataFrame using a Dictionary\n",
    "    data={'feature_names':feature_names,'feature_importance':feature_importance}\n",
    "    fi_df = pd.DataFrame(data)\n",
    "\n",
    "    #Sort the DataFrame in order decreasing feature importance\n",
    "    fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True)\n",
    "\n",
    "    #Define size of bar plot\n",
    "    plt.figure(figsize=(10,8))\n",
    "    #Plot Searborn bar chart\n",
    "    sns.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names'])\n",
    "    #Add chart labels\n",
    "    plt.title(model_type + '-FEATURE IMPORTANCE')\n",
    "    plt.xlabel('FEATURE IMPORTANCE')\n",
    "    plt.ylabel('FEATURE NAMES')\n",
    "    #Save figure to png file\n",
    "    plt.savefig(path+'\\\\img\\\\RandomForest-FeatureImportance.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importance(forest_best.feature_importances_,ind_features,'RANDOM FOREST')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph is moderately clear due to the large number of variables. However, you can see that the most important variable is the binary variable that determines whether a car is exotic. Another key variable is Engine HP. Together these variables comprise 50% of the importance. Besides these, Engine Cylinders, average total mpg, Year and popularity were found to be the important variables in modeling the price of cars\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In summary, the project presents an analysis of car data. The main objective of the study was to perform modeling and create the best estimator that would predict car prices. For this task 3 models were used: linear regression, Support vector machine and Random Forest. Hyperparameter tuning was performed for each of them. Finally, 3 evaluation statistics were calculated for each model: r square, mean squared error and mean absolute error. It turned out that among the estimated models, the best results were obtained by Random Forest, for which R^2 was as high as 0.948.\n",
    "\n",
    "This is a good result, however I am convinced that better results can be achieved. Among other things, it would be worthwhile to take an interest in boosted decision trees from the xgboost library, which improve the results of classical decision tree-based models.\n",
    "Probably more accurate feature generation process and data manipulation would also improve the results"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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