Data Preprocessing and Data Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "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",
      "Make                 11914 non-null object\n",
      "Model                11914 non-null object\n",
      "Year                 11914 non-null int64\n",
      "Engine Fuel Type     11911 non-null object\n",
      "Engine HP            11845 non-null float64\n",
      "Engine Cylinders     11884 non-null float64\n",
      "Transmission Type    11914 non-null object\n",
      "Driven_Wheels        11914 non-null object\n",
      "Number of Doors      11908 non-null float64\n",
      "Market Category      8172 non-null object\n",
      "Vehicle Size         11914 non-null object\n",
      "Vehicle Style        11914 non-null object\n",
      "highway MPG          11914 non-null int64\n",
      "city mpg             11914 non-null int64\n",
      "Popularity           11914 non-null int64\n",
      "MSRP                 11914 non-null int64\n",
      "dtypes: float64(3), int64(5), object(8)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "###Data Preprocessing\n",
    "cp = pd.read_csv(\"data.csv\")\n",
    "cp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "</table>\n",
       "</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  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AUTOMATIC           8266\n",
       "MANUAL              2935\n",
       "AUTOMATED_MANUAL     626\n",
       "DIRECT_DRIVE          68\n",
       "UNKNOWN               19\n",
       "Name: Transmission Type, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp['Transmission Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AUTOMATIC           8266\n",
       "MANUAL              2935\n",
       "AUTOMATED_MANUAL     626\n",
       "DIRECT_DRIVE          68\n",
       "Name: Transmission Type, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove the rows that contain 'UNKNOWN'\n",
    "cp = cp[cp['Transmission Type'] != 'UNKNOWN']\n",
    "cp['Transmission Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 11895 entries, 0 to 11913\n",
      "Data columns (total 16 columns):\n",
      "Make                 11895 non-null object\n",
      "Model                11895 non-null object\n",
      "Year                 11895 non-null int64\n",
      "Engine Fuel Type     11892 non-null object\n",
      "Engine HP            11826 non-null float64\n",
      "Engine Cylinders     11865 non-null float64\n",
      "Transmission Type    11895 non-null object\n",
      "Driven_Wheels        11895 non-null object\n",
      "Number of Doors      11889 non-null float64\n",
      "Market Category      8169 non-null object\n",
      "Vehicle Size         11895 non-null object\n",
      "Vehicle Style        11895 non-null object\n",
      "highway MPG          11895 non-null int64\n",
      "city mpg             11895 non-null int64\n",
      "Popularity           11895 non-null int64\n",
      "MSRP                 11895 non-null int64\n",
      "dtypes: float64(3), int64(5), object(8)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "cp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    8266\n",
       "1    2935\n",
       "3     626\n",
       "4      68\n",
       "Name: Transmission Type, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###Converting Transmission Type to numeric value\n",
    "cp = cp.replace({'AUTOMATIC':'2'})\n",
    "cp = cp.replace({'MANUAL':'1'})\n",
    "cp = cp.replace({'AUTOMATED_MANUAL':'3'})\n",
    "cp = cp.replace({'DIRECT_DRIVE':'4'})\n",
    "cp['Transmission Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 11895 entries, 0 to 11913\n",
      "Data columns (total 16 columns):\n",
      "Make                 11895 non-null object\n",
      "Model                11895 non-null object\n",
      "Year                 11895 non-null int64\n",
      "Engine Fuel Type     11892 non-null object\n",
      "Engine HP            11826 non-null float64\n",
      "Engine Cylinders     11865 non-null float64\n",
      "Transmission Type    11895 non-null object\n",
      "Driven_Wheels        11895 non-null object\n",
      "Number of Doors      11889 non-null float64\n",
      "Market Category      8169 non-null object\n",
      "Vehicle Size         11895 non-null object\n",
      "Vehicle Style        11895 non-null object\n",
      "highway MPG          11895 non-null int64\n",
      "city mpg             11895 non-null int64\n",
      "Popularity           11895 non-null int64\n",
      "MSRP                 11895 non-null int64\n",
      "dtypes: float64(3), int64(5), object(8)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "cp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 11895 entries, 0 to 11913\n",
      "Data columns (total 16 columns):\n",
      "Make                 11895 non-null object\n",
      "Model                11895 non-null object\n",
      "Year                 11895 non-null int64\n",
      "Engine Fuel Type     11892 non-null object\n",
      "Engine HP            11826 non-null float64\n",
      "Engine Cylinders     11865 non-null float64\n",
      "Transmission Type    11895 non-null int64\n",
      "Driven_Wheels        11895 non-null object\n",
      "Number of Doors      11889 non-null float64\n",
      "Market Category      8169 non-null object\n",
      "Vehicle Size         11895 non-null object\n",
      "Vehicle Style        11895 non-null object\n",
      "highway MPG          11895 non-null int64\n",
      "city mpg             11895 non-null int64\n",
      "Popularity           11895 non-null int64\n",
      "MSRP                 11895 non-null int64\n",
      "dtypes: float64(3), int64(6), object(7)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "###Converting Transmission Type datatype to numeric \n",
    "cp[\"Transmission Type\"] = pd.to_numeric(cp[\"Transmission Type\"])\n",
    "cp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Vehicle Size</th>\n",
       "      <th>Vehicle Style</th>\n",
       "      <th>highway MPG</th>\n",
       "      <th>city mpg</th>\n",
       "      <th>MSRP</th>\n",
       "      <th>Age</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>1</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>46135</td>\n",
       "      <td>6</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>1</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Convertible</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>40650</td>\n",
       "      <td>6</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>1</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>36350</td>\n",
       "      <td>6</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>1</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>29450</td>\n",
       "      <td>6</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>1</td>\n",
       "      <td>rear wheel drive</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Compact</td>\n",
       "      <td>Convertible</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>34500</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</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                  1  rear wheel drive              2.0   \n",
       "1               6.0                  1  rear wheel drive              2.0   \n",
       "2               6.0                  1  rear wheel drive              2.0   \n",
       "3               6.0                  1  rear wheel drive              2.0   \n",
       "4               6.0                  1  rear wheel drive              2.0   \n",
       "\n",
       "  Vehicle Size Vehicle Style  highway MPG  city mpg   MSRP  Age  \n",
       "0      Compact         Coupe           26        19  46135    6  \n",
       "1      Compact   Convertible           28        19  40650    6  \n",
       "2      Compact         Coupe           28        20  36350    6  \n",
       "3      Compact         Coupe           28        18  29450    6  \n",
       "4      Compact   Convertible           28        18  34500    6  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###Drop column Popularity and Market Category as Both of them does not really affect MSRP\n",
    "cp = cp.drop('Popularity',axis=1)\n",
    "cp = cp.drop('Market Category',axis=1)\n",
    "\n",
    "###Calculate The age of the car from 2017 since the dataset published in 2017\n",
    "cp['Age']=2017-cp['Year']\n",
    "cp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Check null values for Age\n",
    "cp['Age'].isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Removing Duplicates\n",
    "cp.drop_duplicates(keep=False,inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10562 entries, 0 to 11913\n",
      "Data columns (total 15 columns):\n",
      "Make                 10562 non-null object\n",
      "Model                10562 non-null object\n",
      "Year                 10562 non-null int64\n",
      "Engine Fuel Type     10559 non-null object\n",
      "Engine HP            10493 non-null float64\n",
      "Engine Cylinders     10532 non-null float64\n",
      "Transmission Type    10562 non-null int64\n",
      "Driven_Wheels        10562 non-null object\n",
      "Number of Doors      10556 non-null float64\n",
      "Vehicle Size         10562 non-null object\n",
      "Vehicle Style        10562 non-null object\n",
      "highway MPG          10562 non-null int64\n",
      "city mpg             10562 non-null int64\n",
      "MSRP                 10562 non-null int64\n",
      "Age                  10562 non-null int64\n",
      "dtypes: float64(3), int64(6), object(6)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "cp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         10562\n",
       "unique           48\n",
       "top       Chevrolet\n",
       "freq           1051\n",
       "Name: Make, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###Checking Unique values\n",
    "cp['Make'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count              10562\n",
       "unique               897\n",
       "top       Silverado 1500\n",
       "freq                 156\n",
       "Name: Model, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp['Model'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                10559\n",
       "unique                  10\n",
       "top       regular unleaded\n",
       "freq                  6214\n",
       "Name: Engine Fuel Type, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp['Engine Fuel Type'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['rear wheel drive', 'all wheel drive', 'front wheel drive',\n",
       "       'four wheel drive'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp['Driven_Wheels'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    3935\n",
       "3    3535\n",
       "1    3092\n",
       "Name: Driven_Wheels, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###Converting Driven_Wheels to numeric value\n",
    "cp = cp.replace({'rear wheel drive':'1'})\n",
    "cp = cp.replace({'all wheel drive':'3'})\n",
    "cp = cp.replace({'front wheel drive':'2'})\n",
    "cp = cp.replace({'four wheel drive':'3'})\n",
    "cp[\"Driven_Wheels\"] = pd.to_numeric(cp[\"Driven_Wheels\"])\n",
    "cp['Driven_Wheels'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['premium unleaded (required)', 'regular unleaded',\n",
       "       'premium unleaded (recommended)', 'flex-fuel (unleaded/E85)',\n",
       "       'diesel', 'electric',\n",
       "       'flex-fuel (premium unleaded recommended/E85)', 'natural gas',\n",
       "       'flex-fuel (premium unleaded required/E85)',\n",
       "       'flex-fuel (unleaded/natural gas)', nan], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp['Engine Fuel Type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0     6214\n",
       "1.0     1905\n",
       "3.0     1267\n",
       "4.0      875\n",
       "5.0      146\n",
       "6.0       66\n",
       "9.0       52\n",
       "7.0       26\n",
       "10.0       6\n",
       "8.0        2\n",
       "Name: Engine Fuel Type, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###Converting Engine Fuel Type to numeric\n",
    "cp = cp.replace({'premium unleaded (required)':'1'})\n",
    "cp = cp.replace({'regular unleaded':'2'})\n",
    "cp = cp.replace({'premium unleaded (recommended)':'3'})\n",
    "cp = cp.replace({'flex-fuel (unleaded/E85)':'4'})\n",
    "cp = cp.replace({'diesel':'5'})\n",
    "cp = cp.replace({'electric':'6'})\n",
    "cp = cp.replace({'flex-fuel (premium unleaded recommended/E85)':'7'})\n",
    "cp = cp.replace({'natural gas':'8'})\n",
    "cp = cp.replace({'flex-fuel (premium unleaded required/E85)':'9'})\n",
    "cp = cp.replace({'flex-fuel (unleaded/natural gas)':'10'})\n",
    "cp[\"Engine Fuel Type\"] = pd.to_numeric(cp[\"Engine Fuel Type\"])\n",
    "cp['Engine Fuel Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##check null values\n",
    "cp['Engine Fuel Type'].isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Drop null values\n",
    "cp=cp.dropna()\n",
    "cp['Engine Fuel Type'].isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Compact', 'Midsize', 'Large'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp['Vehicle Size'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    3998\n",
       "2    3869\n",
       "3    2593\n",
       "Name: Vehicle Size, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###Converting Vehicle Size to numeric\n",
    "cp = cp.replace({'Compact':'1'})\n",
    "cp = cp.replace({'Midsize':'2'})\n",
    "cp = cp.replace({'Large':'3'})\n",
    "cp[\"Vehicle Size\"] = pd.to_numeric(cp[\"Vehicle Size\"])\n",
    "cp['Vehicle Size'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Coupe', 'Convertible', 'Wagon', 'Sedan', '4dr Hatchback',\n",
       "       '2dr Hatchback', '4dr SUV', 'Passenger Minivan', 'Cargo Minivan',\n",
       "       'Crew Cab Pickup', 'Regular Cab Pickup', 'Extended Cab Pickup',\n",
       "       '2dr SUV', 'Cargo Van', 'Convertible SUV', 'Passenger Van'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp['Vehicle Style'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Vehicle Size</th>\n",
       "      <th>Vehicle Style</th>\n",
       "      <th>highway MPG</th>\n",
       "      <th>city mpg</th>\n",
       "      <th>MSRP</th>\n",
       "      <th>Age</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>1.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>46135</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Convertible</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>40650</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>36350</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Coupe</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>29450</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BMW</td>\n",
       "      <td>1 Series</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Convertible</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>34500</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Make       Model  Year  Engine Fuel Type  Engine HP  Engine Cylinders  \\\n",
       "0  BMW  1 Series M  2011               1.0      335.0               6.0   \n",
       "1  BMW    1 Series  2011               1.0      300.0               6.0   \n",
       "2  BMW    1 Series  2011               1.0      300.0               6.0   \n",
       "3  BMW    1 Series  2011               1.0      230.0               6.0   \n",
       "4  BMW    1 Series  2011               1.0      230.0               6.0   \n",
       "\n",
       "   Transmission Type  Driven_Wheels  Number of Doors  Vehicle Size  \\\n",
       "0                  1              1              2.0             1   \n",
       "1                  1              1              2.0             1   \n",
       "2                  1              1              2.0             1   \n",
       "3                  1              1              2.0             1   \n",
       "4                  1              1              2.0             1   \n",
       "\n",
       "  Vehicle Style  highway MPG  city mpg   MSRP  Age  \n",
       "0         Coupe           26        19  46135    6  \n",
       "1   Convertible           28        19  40650    6  \n",
       "2         Coupe           28        20  36350    6  \n",
       "3         Coupe           28        18  29450    6  \n",
       "4   Convertible           28        18  34500    6  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Engine 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>Vehicle Size</th>\n",
       "      <th>highway MPG</th>\n",
       "      <th>city mpg</th>\n",
       "      <th>MSRP</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>335.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>46135</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>40650</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>36350</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>29450</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>34500</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Engine Fuel Type  Engine HP  Engine Cylinders  Transmission Type  \\\n",
       "0               1.0      335.0               6.0                  1   \n",
       "1               1.0      300.0               6.0                  1   \n",
       "2               1.0      300.0               6.0                  1   \n",
       "3               1.0      230.0               6.0                  1   \n",
       "4               1.0      230.0               6.0                  1   \n",
       "\n",
       "   Driven_Wheels  Number of Doors  Vehicle Size  highway MPG  city mpg   MSRP  \\\n",
       "0              1              2.0             1           26        19  46135   \n",
       "1              1              2.0             1           28        19  40650   \n",
       "2              1              2.0             1           28        20  36350   \n",
       "3              1              2.0             1           28        18  29450   \n",
       "4              1              2.0             1           28        18  34500   \n",
       "\n",
       "   Age  \n",
       "0    6  \n",
       "1    6  \n",
       "2    6  \n",
       "3    6  \n",
       "4    6  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###Drop Unnecessary columns\n",
    "cp = cp.drop('Make',axis=1)\n",
    "cp = cp.drop('Model',axis=1)\n",
    "cp = cp.drop('Year',axis=1)\n",
    "cp = cp.drop('Vehicle Style',axis=1)\n",
    "cp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10460 entries, 0 to 11913\n",
      "Data columns (total 11 columns):\n",
      "Engine Fuel Type     10460 non-null float64\n",
      "Engine HP            10460 non-null float64\n",
      "Engine Cylinders     10460 non-null float64\n",
      "Transmission Type    10460 non-null int64\n",
      "Driven_Wheels        10460 non-null int64\n",
      "Number of Doors      10460 non-null float64\n",
      "Vehicle Size         10460 non-null int64\n",
      "highway MPG          10460 non-null int64\n",
      "city mpg             10460 non-null int64\n",
      "MSRP                 10460 non-null int64\n",
      "Age                  10460 non-null int64\n",
      "dtypes: float64(4), int64(7)\n",
      "memory usage: 980.6 KB\n"
     ]
    }
   ],
   "source": [
    "cp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cp = cp[['Engine Fuel Type','Engine HP','Engine Cylinders','Transmission Type','Driven_Wheels',\n",
    "                  'Number of Doors','Vehicle Size','highway MPG','city mpg','Age','MSRP']]\n",
    "#check boxplot of continous features\n",
    "cp.boxplot(column=['Engine HP','Engine Cylinders','highway MPG','city mpg','Age','MSRP'])\n",
    "plt.xticks(rotation = 90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 8720 entries, 0 to 11913\n",
      "Data columns (total 11 columns):\n",
      "Engine Fuel Type     8720 non-null float64\n",
      "Engine HP            8720 non-null float64\n",
      "Engine Cylinders     8720 non-null float64\n",
      "Transmission Type    8720 non-null int64\n",
      "Driven_Wheels        8720 non-null int64\n",
      "Number of Doors      8720 non-null float64\n",
      "Vehicle Size         8720 non-null int64\n",
      "highway MPG          8720 non-null int64\n",
      "city mpg             8720 non-null int64\n",
      "Age                  8720 non-null int64\n",
      "MSRP                 8720 non-null int64\n",
      "dtypes: float64(4), int64(7)\n",
      "memory usage: 817.5 KB\n"
     ]
    }
   ],
   "source": [
    "###Check outliers for MSRP\n",
    "Q1 = cp['Engine HP'].quantile(0.25)\n",
    "Q3 = cp['Engine HP'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "cp = cp[~((cp['Engine HP'] <= (Q1 - (1.5 * IQR))) | (cp['Engine HP'] >= (Q3 + (1.5 * IQR))))]\n",
    "\n",
    "Q1 = cp['highway MPG'].quantile(0.25)\n",
    "Q3 = cp['highway MPG'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "cp = cp[~((cp['highway MPG'] <= (Q1 - (1.5 * IQR))) | (cp['highway MPG'] >= (Q3 + (1.5 * IQR))))]\n",
    "\n",
    "Q1 = cp['city mpg'].quantile(0.25)\n",
    "Q3 = cp['city mpg'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "cp = cp[~((cp['city mpg'] <= (Q1 - (1.5 * IQR))) | (cp['city mpg'] >= (Q3 + (1.5 * IQR))))]\n",
    "\n",
    "Q1 = cp['Engine Cylinders'].quantile(0.25)\n",
    "Q3 = cp['Engine Cylinders'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "cp = cp[~((cp['Engine Cylinders'] <= (Q1 - (1.5 * IQR))) | (cp['Engine Cylinders'] >= (Q3 + (1.5 * IQR))))]\n",
    "\n",
    "Q1 = cp['Age'].quantile(0.25)\n",
    "Q3 = cp['Age'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "cp = cp[~((cp['Age'] <= (Q1 - (1.5 * IQR))) | (cp['Age'] >= (Q3 + (1.5 * IQR))))]\n",
    "\n",
    "Q1 = cp['MSRP'].quantile(0.25)\n",
    "Q3 = cp['MSRP'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "cp = cp[~((cp['MSRP'] <= (Q1 - (1.5 * IQR))) | (cp['MSRP'] >= (Q3 + (1.5 * IQR))))]\n",
    "    \n",
    "\n",
    "cp.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp.to_csv('cp.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###Data Visualization\n",
    "#Generate Correlation\n",
    "corr=cp.corr()\n",
    "\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Engine 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>Vehicle Size</th>\n",
       "      <th>highway MPG</th>\n",
       "      <th>city mpg</th>\n",
       "      <th>Age</th>\n",
       "      <th>MSRP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Engine Fuel Type</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.101449</td>\n",
       "      <td>0.104000</td>\n",
       "      <td>0.115849</td>\n",
       "      <td>0.011921</td>\n",
       "      <td>0.007221</td>\n",
       "      <td>0.191534</td>\n",
       "      <td>-0.033940</td>\n",
       "      <td>-0.057399</td>\n",
       "      <td>-0.170706</td>\n",
       "      <td>0.087630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engine HP</th>\n",
       "      <td>0.101449</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.715015</td>\n",
       "      <td>0.229980</td>\n",
       "      <td>-0.042120</td>\n",
       "      <td>0.032308</td>\n",
       "      <td>0.549343</td>\n",
       "      <td>-0.432033</td>\n",
       "      <td>-0.536901</td>\n",
       "      <td>-0.356089</td>\n",
       "      <td>0.732723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engine Cylinders</th>\n",
       "      <td>0.104000</td>\n",
       "      <td>0.715015</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.137804</td>\n",
       "      <td>-0.110877</td>\n",
       "      <td>-0.018630</td>\n",
       "      <td>0.611437</td>\n",
       "      <td>-0.704158</td>\n",
       "      <td>-0.761367</td>\n",
       "      <td>0.105773</td>\n",
       "      <td>0.350003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transmission Type</th>\n",
       "      <td>0.115849</td>\n",
       "      <td>0.229980</td>\n",
       "      <td>0.137804</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.163885</td>\n",
       "      <td>0.332564</td>\n",
       "      <td>0.267023</td>\n",
       "      <td>-0.031441</td>\n",
       "      <td>-0.049494</td>\n",
       "      <td>-0.306338</td>\n",
       "      <td>0.354757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Driven_Wheels</th>\n",
       "      <td>0.011921</td>\n",
       "      <td>-0.042120</td>\n",
       "      <td>-0.110877</td>\n",
       "      <td>0.163885</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.292844</td>\n",
       "      <td>0.017002</td>\n",
       "      <td>-0.004052</td>\n",
       "      <td>0.053450</td>\n",
       "      <td>-0.131267</td>\n",
       "      <td>0.114429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Number of Doors</th>\n",
       "      <td>0.007221</td>\n",
       "      <td>0.032308</td>\n",
       "      <td>-0.018630</td>\n",
       "      <td>0.332564</td>\n",
       "      <td>0.292844</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.177700</td>\n",
       "      <td>0.057149</td>\n",
       "      <td>0.076010</td>\n",
       "      <td>-0.271186</td>\n",
       "      <td>0.153067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vehicle Size</th>\n",
       "      <td>0.191534</td>\n",
       "      <td>0.549343</td>\n",
       "      <td>0.611437</td>\n",
       "      <td>0.267023</td>\n",
       "      <td>0.017002</td>\n",
       "      <td>0.177700</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.407583</td>\n",
       "      <td>-0.475607</td>\n",
       "      <td>-0.075504</td>\n",
       "      <td>0.346508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highway MPG</th>\n",
       "      <td>-0.033940</td>\n",
       "      <td>-0.432033</td>\n",
       "      <td>-0.704158</td>\n",
       "      <td>-0.031441</td>\n",
       "      <td>-0.004052</td>\n",
       "      <td>0.057149</td>\n",
       "      <td>-0.407583</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.935562</td>\n",
       "      <td>-0.356116</td>\n",
       "      <td>-0.094828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city mpg</th>\n",
       "      <td>-0.057399</td>\n",
       "      <td>-0.536901</td>\n",
       "      <td>-0.761367</td>\n",
       "      <td>-0.049494</td>\n",
       "      <td>0.053450</td>\n",
       "      <td>0.076010</td>\n",
       "      <td>-0.475607</td>\n",
       "      <td>0.935562</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.316868</td>\n",
       "      <td>-0.171587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.170706</td>\n",
       "      <td>-0.356089</td>\n",
       "      <td>0.105773</td>\n",
       "      <td>-0.306338</td>\n",
       "      <td>-0.131267</td>\n",
       "      <td>-0.271186</td>\n",
       "      <td>-0.075504</td>\n",
       "      <td>-0.356116</td>\n",
       "      <td>-0.316868</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.610561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSRP</th>\n",
       "      <td>0.087630</td>\n",
       "      <td>0.732723</td>\n",
       "      <td>0.350003</td>\n",
       "      <td>0.354757</td>\n",
       "      <td>0.114429</td>\n",
       "      <td>0.153067</td>\n",
       "      <td>0.346508</td>\n",
       "      <td>-0.094828</td>\n",
       "      <td>-0.171587</td>\n",
       "      <td>-0.610561</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Engine Fuel Type  Engine HP  Engine Cylinders  \\\n",
       "Engine Fuel Type           1.000000   0.101449          0.104000   \n",
       "Engine HP                  0.101449   1.000000          0.715015   \n",
       "Engine Cylinders           0.104000   0.715015          1.000000   \n",
       "Transmission Type          0.115849   0.229980          0.137804   \n",
       "Driven_Wheels              0.011921  -0.042120         -0.110877   \n",
       "Number of Doors            0.007221   0.032308         -0.018630   \n",
       "Vehicle Size               0.191534   0.549343          0.611437   \n",
       "highway MPG               -0.033940  -0.432033         -0.704158   \n",
       "city mpg                  -0.057399  -0.536901         -0.761367   \n",
       "Age                       -0.170706  -0.356089          0.105773   \n",
       "MSRP                       0.087630   0.732723          0.350003   \n",
       "\n",
       "                   Transmission Type  Driven_Wheels  Number of Doors  \\\n",
       "Engine Fuel Type            0.115849       0.011921         0.007221   \n",
       "Engine HP                   0.229980      -0.042120         0.032308   \n",
       "Engine Cylinders            0.137804      -0.110877        -0.018630   \n",
       "Transmission Type           1.000000       0.163885         0.332564   \n",
       "Driven_Wheels               0.163885       1.000000         0.292844   \n",
       "Number of Doors             0.332564       0.292844         1.000000   \n",
       "Vehicle Size                0.267023       0.017002         0.177700   \n",
       "highway MPG                -0.031441      -0.004052         0.057149   \n",
       "city mpg                   -0.049494       0.053450         0.076010   \n",
       "Age                        -0.306338      -0.131267        -0.271186   \n",
       "MSRP                        0.354757       0.114429         0.153067   \n",
       "\n",
       "                   Vehicle Size  highway MPG  city mpg       Age      MSRP  \n",
       "Engine Fuel Type       0.191534    -0.033940 -0.057399 -0.170706  0.087630  \n",
       "Engine HP              0.549343    -0.432033 -0.536901 -0.356089  0.732723  \n",
       "Engine Cylinders       0.611437    -0.704158 -0.761367  0.105773  0.350003  \n",
       "Transmission Type      0.267023    -0.031441 -0.049494 -0.306338  0.354757  \n",
       "Driven_Wheels          0.017002    -0.004052  0.053450 -0.131267  0.114429  \n",
       "Number of Doors        0.177700     0.057149  0.076010 -0.271186  0.153067  \n",
       "Vehicle Size           1.000000    -0.407583 -0.475607 -0.075504  0.346508  \n",
       "highway MPG           -0.407583     1.000000  0.935562 -0.356116 -0.094828  \n",
       "city mpg              -0.475607     0.935562  1.000000 -0.316868 -0.171587  \n",
       "Age                   -0.075504    -0.356116 -0.316868  1.000000 -0.610561  \n",
       "MSRP                   0.346508    -0.094828 -0.171587 -0.610561  1.000000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see Engine HP is high correlated to MSRP and als Age negatively correlated with MSRP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x12a6a5f0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x180 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###Plot against MSRP\n",
    "sns.pairplot(data=cp,\n",
    "                  y_vars=['MSRP'],\n",
    "                  x_vars=['Engine Fuel Type', 'Engine HP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x13cad130>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x180 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=cp,\n",
    "                  y_vars=['MSRP'],\n",
    "                  x_vars=['Engine Cylinders','Transmission Type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x13d0ed90>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9sr0jzda2dMm5bm1Ls70jXeuuVZWGaHmnvJcFcOKUAqzwpciPgbnAapxK2/9XVW/z1GufMHXqVF21ahUAf92+i5/818vdltle8eXDGBmguf+WnSne/zDVLWPaAQMSDO0XnJqEmz7qIK82eVsKztCIpUTEYnj/wgOq5pqvWAbf376L0xat6DbaWjJ/GgcESAYhXPdbWybXrbp8czxafL/tlRz2ZOKYA0xW1V0iMgRYBtS1gi7GEjjn2HF8/8Hdiex/fsoRBGxgCcCAZJTF8z69uwx8sDKqAqAKb2/p6FaYYPxQnybiALIVzE/ZgJmfIFz3W9dT8nqKPSnolKruAlDVrSLi37miB2yFfD5XsrDhzU0fEbR7I2vbdGTzRK3dWrkjmycesJwjWVsLyhkcRfe9B9Zyn4dqyvuKSgtVIgHUWrbCnc++xeUzJxZG0Hc++xZXfPmwWnetqmTyFe43DwmwetrjIBFZ6r7+q8vnpXvzAyISEZG/iMjD7udxIvKciKwXkftFJO62N7if33C/H1t0jB+47a+JyN8XtZ/ktr0hIpf19uSbGywGNSeZ44afzVm0gkHNSZp9bLP0gtrwy8de583NbWzemebNzW388rHX0YDZ2isVx7V9/MSNV1ioEg+ggu4cQV/18DrmLFrBVQ+v45xjxwVuBK1a/n7zIoY9jaBndfn8773/CS4FXgH6u59/DlyrqveJyC3A14CF7t/tqnqwiJzubjdHRCYCpwOHAQcAvxeRQ9xj/Qr4PNAKrBSRpaq6bm871pa2ufGJ10ue6Dc+8TpXfPkwBgTHJIZlCef/3Xi+teSFwtTy2tMmBcpzDpW9534ejcZiFRZLBSxGHZwRdKd5A5yH5/cfXOuk4QwQ0Qr3W9Wreqvq05W+E5Hjejq4iIwCvgT8G/BtERFgBnCGu8mdwJU4CnqW+x7gAeAmd/tZwH2qmgbeEpE3gE+7272hqhvc37rP3XavFbQIfO0z4/nOb3f/I685dVLgnui2Kv262KBzdp49OYjrkaglXH/6ZC69b03hel5/+mRfR6ukMk61m1NaRtNIpFDt5icnHw4BGiSAI4dlZzgBk8NK95uX89yjghaRCHAaMBJYpqovichM4IdAEpjSw/GvA/4F6Od+HgLsUNWc+7nVPTbu3/cAVDUnIh+6248EVhQds3if97q0H13hPOYD8wHGjBmz+wuFJ1/5W7cq0OccO66H06ovIiJsb8/yvQdWlzjPBiXjte5aVYlaQv9EtGQ02j8R9YWCriSDmbzNwGSc8UObiFjC4KY4A5NxskGL9QQsqZAYSmp/faqJVcX7rSdj6+3AeTiK8gYRuQPHzPELVd2jcnYV+SZVXV3cXGZT7eG73rZ3b1RdpKpTVXXqsGHDCu3RiDB76mhat3eweWea1u0dzJ46mmjAYlArOc+CFimQtZV5i1cxb/FK5ixawbzFK5m3eJUvzrOSDDbGI5x//LgSGTz/+HEk48ELs4lFpGx8cNBivqt5v/Vkg54KHKGqtogkgC3Awar6t7049nHAySLyRSCBY4O+DhgoIlF3FD0KeN/dvhUYDbSKSBQYAGwrau+keJ9K7XuHwpa2DJc/9FLJk25AIlgVle06dJ55IZOzy55nxscFWNXWUMggOLlSbnpyfYnP56Yn1wcuiqOSszrfBwo6o+r4+lU1JSKv76VyRlV/APwAQESmA99V1TNF5LfAbOA+4BzgIXeXpe7nP7nfP6mq6kaL3Csiv8RxEk4A/owzgp4gIuOAv+I4Ejtt23tFpg7DsrwQi1plp5axgIXZRSrUmPSzkzAsMgiObbZc8eLLZ06sUY/6hlikwv3WB2F2h4rIWvf1YtHnF0Vkba9/zeH7OA7DN3BMJ7e77bcDQ9z2bwOXAajqy8ASHOffMuAbqpp3R+AXA4/iRIkscbfda6r5pPMzERGuPW1SydTy2tMmEQmY7S8WES6eMaEkjOviGRN8PYUOiwzCbht0MUG0QccjwsIuppyFZx7pKb9KTyPoquTfUNXlwHL3/QZ2R2EUb5MCTq2w/7/hRIJ0bX8EeMRrv6r5pPMzqWyenz3yasnU8mePvMr1p0+uddeqSqV0o34O4wqLDAIk3FJQFxUVyF04t4VEwIoXp3I2N3Yx5dz45Hp+7MGU01OY3TvFn93l3scD73Zx/tUl8Yhwy9yWbhWVg5ZJLGIJm9vSXPCb3ZfM7/HBXqg4GvVxGFcyVl4GkwGMg05lKq87wL+r8XtN3i5vyvnRl3pvyukpzO5h4DI3vG4E8DywCmdF4SJVva7Xv+gj2jN57nr27ZIwu9ue2cDFMw6uddeqigj8/JQjAp8DoWKaRx9PoaMRGFGmjmI0eEEc5Coorn/1oLj8TDUXTPVk4hinqi+57+cBj6vq2SLSD/hfnKiMuiVmCc9u2MqS1a2FtlGDkvzziRNq2Ks+oEIOhCsD5j1H4JpTJ3VbeFT7/HWVyWTh/Q9TXFSUaXChm2mQ4CQaBOozX7cXYpbj8+m6ktBLncmeFHS26P0JuJnsVHWniPg3dmkvibt5W7um4QxaEiGxyq+YDFbqKycHwu1/3FDyILr9jxt8HcaVytkF5QxutRuf2829kohVsEEHrHhxXpVELFKyYCoRi5Avv0xjj/SkoN8TkUtwYpGPxImiQESSQN0HaqZyNu9s2VlSTPUv72xlcFOwVtjZdv0pLi8kYhZXnDwR2xbyqhwwMMnhJ08k4eMHbs5Wjh0/hPOPH19iZgtatRuAVNZm9VtbuPf8aagqIsKT6zYy+LARte5aVVGEt7fsZMqBQ0r0ypDmob0+Vk8K+mvAT4ETgTmqusNtnwbc0etf8xnJmMXYYf053U2Y3vlETwbMq2xZFfLwBus0AdjenuvmcBsxwL8G3aZ4hLnHHNitEnlTQFcSHjl2CGfctqLkXP0cBumFZEwY10WveHX87vEWVdVNqnqhqs5S1ceK2p9SVS+Z7XxFKlu+mGrKQ/VdP2Pb8MxrH3DHuUfx5Hc+yx3nHsUzr32AHazTJJ21C8oZnOt54d2rSWf9e6LpnF02NDDt49WPXsnmlf9+4a8lcvjfL/zV11XXvdCRKS+HVa/q3VPOZ1U9ude/6CPCUkw1FhG+NGlkt1Fa0EYu2To0F4RFBsFxEp44cf8SOfSahtPPVFMOezJxHIOTMe4/gefwtT+894TFq1w8cinO2nd2wLL2NVYwF/g58VBYZBAgHhUGNsVLnGcDm+LEo8E618Z4hPPcBFid53mexwRYPVkh98dJLXo4cD1Ocvwtqvr0nnJF1wudK5u6VoEO2sqmWESY6Y6gZ1zzNPMWr2TmpJHEAnZjZCqYC/ycLCksGd4AdmVsXn1/BxP2a2b/AQkm7NfMq+/vYFfgTIrKVjcB1u7q5RlPycl6WkmYx4ncWCYiDcBXgeUi8lNVvdFT731EKrMHr3KAVjZl8xqKUK56NBeEZXYDzsPowKH9SpxnQXwYVbM2Zk8mDlzF/CUc5TwWuAH4Xa9/yYc0Jyxaxg0t8SovnNtCcyJYI+hKiitoCXliFcwFXhYI7Csa4xYzJ48qMcssnNtCY8BmcQC5Cg+joBXIqGZtzJ6chHfimDf+B/hJ0arCQLAzZZPNZkuW2X7w4S52pmIMSPa8f71Qj7X6vNAQs7jj3Km0bk8VbH+jBiVo8PFCiI6szdubP+oWiz+kaQiDat25KhO1hOmH7lfyMFow+4jA2dvjVUyA1dMI+iygHTgE+KbszmkggKpq/0o71gPJmMX+A5PkbacUi62w/8AkDT5e2OAFq8IS6IDdF+RtJZ3TkuT3t8xt8fVMIR6xysbMxgOYzS6dt/nFstdKFkz9Ytlr3PDVYGVVjEel7IpJL87QnmzQwZOSIiyBre25bv/IUQP96/X3giVCImZ1WXpqBS4PbzavZeNP/WxrtxXytl1ybfK2jY+fKZ6xpHxWxaDJYSq7h6x9vaRHG3SQ2ZUpv1Dl/vnTGBQgJ6Gt8I17/9JtyvXbC46pYa+qTz3a2jN5m0fWvs/sqWNK7LJnBcwuC05WxZvOmML29mzhYTSoKUbA9HPFrH1VTzcadOrR6++FTL58rb6gVY6O1mHJq3iFRURBy0kOEBXnAVRsgrr+9Mm+TgfrhWrKYagVdFiqWUQqOAktHysuL3SWvOqandDPYVzZvPL821u59/xp2KpYbqjn5wOWQAggr3DpfWtKZqyX3rcmcDO5asphsDRRL4kILJh9RMkigQWzj8DH97MnYpaUPU8/h595oVLJKz/nekjEd4d6Tl+wnDNuW0HLuKGBWywFkM3bDGtu4NazWrh//jRuPauFYc0NgZvJVVMOQz2CTuXKe5WvC1itPhEY2ly6xHZoczxwtj9bK8Sf+rjkVVjKQAE0RC1++MVDuyWyD1rUVDXlMNQKOlqhVl/Q4jJtpdty50wueJECEakQ7+3jJ5FI+VSwPu6yZ2yloJzBUVrfWvJC4Ewc1ZTDPnt0ichoEXlKRF4RkZdF5FK3fbCIPC4i692/g9x2EZEbROQNEVkrIkcWHescd/v1InJOUXuLiLzo7nODSO/+A50VVbrmQQhaRRXbVi64+3nmLV7JnEUrmLd4JRfc/bynlU1+xqpgyvGzrV2LypHdP38al8+cyJ3PvoWPB/2eydoVnNUBy3tbTTnsyxF0DviOqj7v1jBcLSKPA+cCT6jq1SJyGXAZ8H3gH4AJ7utoYCFwtIgMBq4ApuKsJ1ktIktVdbu7zXxgBfAIcBLOqse9QlVpiAqL530aSzpjUvNowO6OfIUpl5+rXXshXcFkdb2PTVZhKqZQjzMcL1RTDvtMQavqRmCj+36niLwCjARmAdPdze4EluMo6FnAXepoxxUiMtCtJD4dp1jtNgBXyZ8kIsuB/qr6J7f9LuAr9EpBO0Hl29rTBdvs4KZY4EYvYbkxGqJWWZOVn2dEtk1BOYPz4Pz+g2t9vbjGK5WqywdMDIlHysthXyz1rgoiMhaYgpNTej9XeaOqG0VkuLvZSJzc0520um17am8t017u9+fjjLQZM2ZMoT2dt7ly6TounH4QjUTIuJ+vD9jS01i0fI6KmI8VlxdiEeHWs1q44De7V4beelaLL8LsKslg3laGNTeUjLZuWf5m4GY3UGrOKa4uH7TamJEILJ53FO9t250PevTgJBEPC5T7XEGLSDPwIPDPqvrRHszE5b5QD+3dG1UXAYsApk6dWtgmZlV40gVwfpnpkqPi1rktte5S1cnbkMuXLpvO5W38EMVVSQYTUYt/OekThfSUnfbKRMBi8cGZ4ZSLDw5aFAcq7MrkS+63m888kv6J3tfZ7lMFLSIxHOV8j6p2pij9QERGuKPnEUDneshWYHTR7qOA99326V3al7vto8psv9fEI8Itc1u6FRkN2iquXF65vkso1/VPvM5PTj681l2rKpm8XXZJu5c8vPuKnK28tvHDbgtVDhzcWOuuVZ28Xd7n4+el+F6wVbnpyfUl99tNT67np7N6f7/1mYJ2IypuB15R1V8WfbUUOAe42v37UFH7xSJyH46T8ENXiT8K/Kwz2gP4AvADVd0mIjtFZBqO6eRsoFdFBDK2TTRCicDk7DyZgHmVQcs6oipMOOqWaubh3Vc0xCyO6pKT/Na5Lb5OkeqVdM7msgdf4sLpB5WYc/zsxPWCreXvNy/BB305gj4OJ13piyKyxm37IY5iXiIiXwPeBU51v3sE+CLwBrALmAfgKuKrgJXudj/tdBgCFwGLgSSOc3CvHYTgOGjOu3N1txFX0Bw0tobDEVUpYb+f49ozOeWCLgm7Lrh7deBig8FJOVDOpOjnXCle0Creb30ZxfFHKheZPaHM9gp8o8Kxfg38ukz7KpyCAp6ox+xnXqg0sgzaeYpVPu+1+HgwmnGXP3d1EgZt+TM4Nuibzzwy8Dboat5voV9JGIZKI5WSJQXtPG0bbv/jhhJld/sfN/g6SqChgpPQz6GBXmnP5Ln7T++UlLy67ZkNfGPGwQytdeeqSDXvt+BJQS+oWFE5YNWuo5Zw7WmTSs7z2tMm+Xrq74UBSYtLTjiEqx5ex5xFK7jq4XVccsIhDEj6V8ztCgVG/Ww390rUEp7dsJXPX/sMM655ms9f+wzPbtgaODms5v0W6hF0zqZ8EctnlP2lAAAPoklEQVTjxte6a1XFRolFSyuqxKIWdsCchIlonDGDKakx2ZywSETjte5aRcKSkxxgWFO8bCmoYU3+vT5eaIgJA5tKk5MNbIrTEDMKuldELeHEifuXJEsP4shSVbj5qTc4pWV0YUHOzU+9wZUBC7OLRi2aiNOeTiMoIkJTLE7Ux+aCaIWc5NEAxkHH41E+Mayp5AE6rClOPB48NTQgGSW5X3OhELCXeoQQcgWdyub52SOvltgsf/bIq4EL+0nEhG+ecEi3eO+Ehye634lGLQ4YWD8l2Rui5WPxGwJmZuskHo8yMoAKuZi2tM3vX97IjIlO0QVb4ZEX3ufEw0YwsJfh7cH+T/WAVSHsx8/Zz7ywM5XnqVc+4N7zp6HqjCwfer6VWVNGMjhgOYfrjY6szaq3tpRcmyfXbeTEAFZUCQt5W7ny4Ve58uFXS9pnfHL/Xh8r1Ao66qYF7OpBD5qJI2oJ969u5Zrfry+0jRqU5B9bRu1hL8O+IB6x+MSIASULVRbMPoJ4AE0cYaGa0WGhVtCWCI3xSIkxvzEeCVwZ+E6vctdKFkF7ENUjqlpWBoOW8jZMVPN+C7WCHpSM8WEqy7b2bKGtsSHKoGTvk5r4mUQFr3IQbdD1RipXIaNiwPwgYSIRr3C/xY2C7hU7UjkeWPkus6eO2R1mt/Jdzjv+YIbFPOQG9CltaZuN29s5ZL/+Be/5m5s+ojHWnwHBy8lTV1Qquxa0RURhIp2FAckIjfs1F+63WNRpp5f+61Ar6Ewuz61/eJtb//B2SfvZx46rTYf6CMuCSCTKnEW77ZxBrdph28rW9gyZXJ54NMKQprivnb6WVT6JfRCvTVgYmIjy7o6Obvmgxwzsfbx3qBV0LFo+BjVoiewtrLKJ0q+a9alad62q2Lby2gc7Of+uVQVld9vZU/nEfv18q6QFKXttvKSmNPiDeDzKmIFJkrHIx473DrWCjljwqzOmsK09W1LyKmgO9FhUmHfcuG7RKkFb0r6lPV1QzuCsyDv/rlX87uvHMrxfosa9K48l5a9N0BzVYSMajRCPRsCdyUWj3kymoVbQmZzNkOY4g5sayKsSEUFEyeSC5UHvyOTLFrG86YwpEKA46FQ2XzYzXCqbr3XXKtKRLX9trjNOwrrFtpW3t7TzzrZdhYHfgYMbGTu0qdczuVAr6ETUwgZSGRtbwRJIxCOByyAVq1BMNWimnHjE4upTDidiRbAEhjQ3cPUph/s6pjgetfin48Zw2AH9ydvKyEFJ/um4MYHMZgeQy9lsakuTzdvEIhbDmxt8vRTfC9t3pdnani4peXXtaZMY0BhlSHPvZnKhVtARC1q3pbslbzlwcEOtu1ZVIhZl4zJ9rLc8EbOEdE75+j1/Lsk3HPOp/RmgKS6MHdaf04scuAvnttDkISTL7+RyNm9vay9xnu0anGPs4KZAKelU1i7ca+CY2r615AV/JeyvB3am7IJyBucfedHdq7l//jT61086hx7J5Gwm7FeapKapwaIjGyxTTipns6M9VXKeb276iCE+zpYWFhkE2LYrw+adpSPLBbOPoH8ixvD+/vQReCFvK1fM/CQTDxhQkMN173/oqVJ7qBV0WFI9NjdY/O2jLK1FI5dRg5Ps3z9YC3KaGyyG9EuWhBPeelYLzQ3+HZ2FRQbBqR5TLve1n4v6emFAY4T9BzaWyOHCuS0MSPbeURhqBV1pzXzQlkDvSisH9I/RWBT2MzBp0ZZWfBrc4IldWeWC33Sp7/eb1fz2wmMYUOO+VSIsMgjOyPL++UcDUpBD0MCVXmvbw6xoQC9nRf4dWuwDBiYtFs5tKal8sHBuCwN9XIHDC80NwoataeYsWsFnFyxnzqIVbNiaprkhWEogl7fLj0Z9XN8vLDIIMLQpwrZduRI53LYrx5Cm4KzaBaeqdzk59FtVb9+zo8NmdZlUj4MbR9AUoJHljo7KT/Qgnacl5Uejfo4pDosMgpFD8SCHda+gReQk4HogAvyHql69t/vmKuRt/ZyHvK1+Jix2TpHyy6Z9rJ9DI4Ng5NCLHNa1ghaRCPAr4PNAK7BSRJaq6rq92T8s9r+wnKcqZZdN+7mqd1iuDYTnXKsph/Vu6Po08IaqblDVDHAfMGtvdx6SjJe1/w1J+jcsywthOs9yVb39fJ5huTYQnnOtphxKPScGF5HZwEmqep77+SzgaFW9uMt284H5AGPGjGl55513Ct+lUjm2dmQKXuUhyTiJRF1PLMpizrNATYZrRgYdwnKu1ZLDev/PlDvJbk8cVV0ELAKYOnVqyfeJRJSRARSQrpjzrC1GBh3Ccq7VOs96N3G0AqOLPo8C3q9RXwwGg6Gq1LuCXglMEJFxIhIHTgeW1rhPBoPBUBXqeq6hqjkRuRh4FCfM7teq+nKNu2UwGAxVoa4VNICqPgI8Uut+GAwGQ7Wp6ygOL4jIZuCdMl8NBbbs4+7UgrCf5xZVPWlfd6YYI4NAeM71Y8lh6BR0JURklapOrXU/+hpznv6lHvvslbCc68c9z3p3EhoMBkNgMQraYDAYfIpR0LtZVOsO7CPMefqXeuyzV8Jyrh/rPI0N2mAwGHyKGUEbDAaDTwm9ghaRX4vIJhF5qdZ96UtEZLSIPCUir4jIyyJyaa371BeISEJE/iwiL7jn+ZNa96knzLUJJiISEZG/iMjDno8RdhOHiBwPtAF3qerhte5PXyEiI4ARqvq8iPQDVgNf2dvc2fWCOGUrmlS1TURiwB+BS1V1RY27VhFzbfx7bT4OIvJtYCrQX1VnejlG6EfQqvoMsK3W/ehrVHWjqj7vvt8JvAKMrG2vqo86tLkfY+7L16MQc22Ch4iMAr4E/MfHOU7oFXQYEZGxwBTgudr2pG9wp5ZrgE3A46paN+dprk1guA74F+BjVSw2CjpkiEgz8CDwz6r6Ua370xeoal5VJ+Okn/20iNSF6cpcm2AgIjOBTaq6+uMeyyjoEOHa/R4E7lHV39W6P32Nqu4AlgM1zb2xN5hrEyiOA04WkbdxyvDNEJG7vRzIKOiQ4DpobgdeUdVf1ro/fYWIDBORge77JHAi8Oqe96ot5toEC1X9gaqOUtWxODnqn1TVuV6OFXoFLSL/CfwJ+ISItIrI12rdpz7iOOAsnKf5Gvf1xVp3qg8YATwlImtxCjo8rqqew5z2EebaGMoS+jA7g8Fg8CuhH0EbDAaDXzEK2mAwGHyKUdAGg8HgU4yCNhgMBp9iFLTBYDD4FKOgDYYAISIqItcUff6uiFxZpWMvFpHZ1ThWD79zqpvZ76ku7WNFpMPNEPeKmxnvnL7uTy0xCrqPEZG8G9f6sptm8dsiUvb/LiIHiMgD+6BPl4rIdUWfbxWR3xd9vkREbnBviKqkYRWRc0Xkpmocy7BH0sA/isjQWnekGBGJ9GLzrwFfV9XPlfnuTVWdoqqfxFkE8i0RmVeF/kU/7jH6AqOg+54OVZ2sqocBnwe+CFzRdSMRiarq+6ra5yMU4Fng2KLPk4EBRTfRscD/7oN+GKpPDqfM0re6ftF1BCwibe7f6SLytIgsEZHXReRqETnTHaG+KCIHFR3mRBH5g7vdTHf/iIgsEJGVIrJWRC4oOu5TInIv8GKZ/nzVPf5LIvJzt+3HwGeAW0RkwZ5OVFU3AN8GvunuO1hE/p/bhxUickQP7VeKyCIReQy4S0QOc895jbvthL38n/cdqmpeffgC2rp8Hg9sBQQ4F/gt8F/Ak8BY4CV3u+eAw4r2Ww60AE3Ar3FWYv0FmOV+fy7wO2AZsB74xR76FAV2AElgAPAUcBsw2f3+HWC0259X3O9eBh4Dku42B7m/tRr4A3Co2z4MJ6fESvd1XFH/bnLfnwq8BLwAPFPraxSkF05u8/7A2+61/S5wpfvdYmB2V9kEprvyMAJoAP4K/MT97lLguqL9l+EM7CYArUACmA/8yN2mAVgFjHOP2w6MK9PPA4B3XXmJuvL/lSJZn1pmn8L9UdQ2EGcQBHAjcIX7fgawpof2K135TRZtd6b7Pt7ZXsuXGUHvY9R56lvAcLfpGOAcVZ3RZdP7gNOgkND9AHWyY/0rztr+o4DPAQtEpMndZzIwB/gUMEdERlfoQw5YAxwFTMN5GKwAjhWRA3BWmL7nbj4B+JU6M4AdwClu+yLgElVtwVECN7vt1wPXuv07hfL5cH8M/L2qTgJOrvjPMnhCnUx4d+GOLPeSlerkpU4Db+I8jMEZ+Y4t2m6Jqtqquh7YABwKfAE4200j+hwwBEduAP6sqm+V+b2jgOWqutmVx3uA43vR306k6P1ngN8AqOqTwBARGbCHdoClqtrhvv8T8EMR+T5wYFF7zfCl3SUEFAvV46parmDAEuBxHHPIaTgjbXBuhpNF5Lvu5wQwxn3/hKp+CCAi64ADgfcoz//imDKSOIK5HvghsBnHBNLJW6q6xn2/GhjrpsU8Fvitk+cHcEZO4CTAmVjU3l+cKiFdf3uxiCzBGfUbqs91wPPAHUVtOVyzppugKV70XbrovV302aZUT3TNDaE48nyJqj5a/IWITMcZQZdDKrT3lik4s7xKx9Q9tENR/1T1XhF5DifR/qMicp6r0GuGGUHvY0RkPJDHSVgOFQRYVf8KbHXtZXNwRtTgCNsp6ti1J6vqGFXtFNDimyzPnh/AnXboY3AU9CvARLrbn8sd0wJ2FPVhsjpOG9zvjilqH6lOlZDic7sQ+BGOGWWNiAzZQz8NHnAf+ktwHG6dvI1jJgOYhVPRpLecKiKWa5ceD7wGPApcJE7KVETkkKJZXSWeAz4rIkNd38dXgad70xFxihv8O45pAuAZ4Ez3u+nAFnc2Uam96/HGAxtU9QZgKXBEb/rTFxgFvQ8RkWHALTi22L3JUnUfTlWGAara6WR5FLjEHQEhIlM8dudZHPPGMFXd5PZnM86N++yednSF+y0ROdXtg4jIJPfrx4CLO7cVkcld9xeRg1T1OVX9MbAFR1Ebqs81QHE0x204SvHPwNFUHt3uiddwFOn/ABeqagrHjLUOeN6N+rmVHmbnqroR+AGO/+MF4HlVfWgvfv+gzjA7nAfQjaraOUu4EpgqTra8q4FzemjvyhzgJddUcyiOmai21NoIHvQXzqhzDY6T7QUce63lfncuruPM/TyWIicIsB/OtPSKorYkzg3wIo6j7eEKx3oYmN5D314G7i76fCWOkylaoT/FDqdxOA6jF3Buzh+77UOB+4G1bvstXfuHY9bo7P/1uFkVzcu8zKv0ZdKNGgwGg08xJg6DwWDwKSaKI+C4XumGLs1n6W6btsFg8CnGxGEwGAw+xZg4DAaDwacYBW0wGAw+xShog8Fg8ClGQRsMBoNPMQraYDAYfMr/B4GXy1S59lQCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x180 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=cp,\n",
    "                  y_vars=['MSRP'],\n",
    "                  x_vars=['Driven_Wheels', 'Number of Doors'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x13d5d450>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x180 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=cp,\n",
    "                  y_vars=['MSRP'],\n",
    "                  x_vars=['Vehicle Size','highway MPG',])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x13dd3710>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x180 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=cp,\n",
    "                  y_vars=['MSRP'],\n",
    "                  x_vars=['city mpg','Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13e21d70>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###Distributions\n",
    "cp['MSRP'].plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13e78ed0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cp['Engine HP'].plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1405e670>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cp['highway MPG'].plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15076d30>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fwJ6mIzy/eX/QUUT6NdA/zX7tnCtxzv2Hc64GQr/oAZxzpb29wMwSgbuAy4AS4HozK+nR7Cag0Tk3GbgTuMN7bRLwW+AfnHMzgPOBtpP5D5PYVHvwKPWtx6J6vKLLRdNHMj53CD99uYLOTl0USSLbQIvF93rZ9lY/r5kHVDjntjvnjgMPAkt6tFkC3OfdfwRYZKGJ84uB9c65dQDOuQPOuY4BZpUY9u7gdgwcWSQmGF++aAqb9rbwxHqNXUhkO2GxMLMCMzsDSDezuWZ2unc7n1CX1IkUAlXdHld723pt45xrB5qBXGAK4MzsOTNbbWb/MuD/Iolpm2tip1gAXDV7NNNHZXHHM+W0HNXBs0Su/o4sLgF+BIwBfgz8p3f7CvDNfl7b26m1PY+1+2qTBJwH3OD9/BszW/S+DzC72czKzKysrq6unzgSCzbtbaEwJ53sIbExCS8hwfje1aexr+Uotz+xOeg4In06YbFwzt3nnLsA+JRz7oJut6ucc3/q572rgbHdHo8Beh5rv9vGG6fIBhq87a865+qdc4eBpwnNvOqZ727nXKlzrjQ/P7+fOBIL1u5uYs7YnKBjDKozxg/jH8+fzCOrqvnNWzuDjiPSq/66oT7h3S0ys6/0vPXz3iuBYjObYGYpwHXAsh5tlgE3evevAV5yzjngOWCWmQ3xisiHAP3ZFedqW46yp+kIc8fFVrEA+NJFxVw0fQS3Lduk2VESkfrrhsrwfg4FMnu59ckbg1hK6Bf/FuBh59wmM7vdzK7ymt0D5JpZBaGurVu81zYS6vZaCawFVjvnnjrJ/zaJMWuqmgBislgkJSbw39fPZWZhNksfWM3KnQ1BRxJ5Dwv9IR/9SktLXVlZWdAxxEfff6ace17fzobbLiEtOTHoOL440HqMa3/xFnWtx3j4c+cwPUYG8iVymdmqvk6B6G6gJ+X9wMyyzCzZzF40s/puXVQiYbFmdyMlo7JitlAA5A5N5Tc3zSMjJYmb7y/joGZISYQY6HkWi51zLcCVhAafpwD/7FsqkR7aOzpZX93M3HHDgo7iuzHDhvDTj89lT+MRzZCSiDHQYtE1T/Fy4PfOOXWoSliV7zvIkbaOmByv6E1p0XD+4UOT+MOqalbvbgw6jsiAi8UTZlYOlAIvmlk+cNS/WCLv9fb2AwDMmzA84CTh84ULJpM3NIXvP1NOrIwtSvQa6BLltwDnAKXOuTbgEO9fukPEN29VHmBCXgajstODjhI2GalJfHFRMSt2NPCWVyxFgnIyazxPBz5mZp8kdE7EYn8iibxXe0cny3c0cM6k3KCjhN21pWMZnpHC/76xM+goEueSBtLIzO4HJhE656FrQT8H/ManXCLv2ri3hdZj7ZwzMf6KRVpyItfPG8vPXqmkquEwY4f3tySbiD8GVCwIjVWUOHWcSgDerKwH4Ow4LBYAf3d2ET9/pZKHy6r46uKpQceRODXQbqiNQIGfQUT68kZFPVNGDiU/MzXoKIEoyE5j/uQ8Hlu7RwPdEpiBFos8YLO3ZPiyrpufwUQAWo62sXx7AxdMGxF0lEBdPaeQqoYjmkYrgRloN9RtfoYQ6ctr79TR3um4aPrIoKME6pLTCvjWYxt4dM0ezhgfP9OHJXIMdOrsq8BOINm7vxJY7WMuEQBe2LyfYUOSOT0Oztw+kaGpSVxcUsBT62to7+gMOo7EoYGuDfVZQpc9/aW3qRB4zK9QIhCaMvvy1joumDaCxITerpMVX66YWUDj4TZWaEVaCcBAxyy+AMwHWgCcc9uA+O5EFt+V7Wqk+UgbF8d5F1SXhVPySU1K4LmN+4KOInFooMXimHPueNcD74JEmpYhvnpxy35SEhNYMEVXQQQYkpLEh6bk89ym/XR26usn4TXQYvGqmX0TSDezi4E/AE/4F0sEXthSy9mTchmaOtB5GLHvkhkF7Gs5yvo9zUFHkTgz0GJxC1AHbAA+R+ia2P/qVyiRyrpWdtQf4qLp6u3sbtH0ESQlGM9tUleUhNdAZ0N1EhrQ/kfn3DXOuV/pbG7x0wvedagXabziPXKGpHD2xFye27hPJ+hJWJ2wWFjIbWZWD5QDW82szsxuDU88iVcvbqll+qgsCnPiZ5XZgbrktAK21x+iorY16CgSR/o7svgSoVlQZzrncp1zw4GzgPlm9mXf00lcajp8nLJdDeqC6sPiktDR1rOaFSVh1F+x+CRwvXNuR9cG59x24BPecyKD7tV36uh0xP0SH30ZmZXG6eNyeEbFQsKov2KR7Jyr77nROVfHXy+1KjKoXiqvJTcjhdlj4uMSqqfi8pmj2FzTws76Q0FHkTjRX7E4forPiZyS9o5OXn2njvOn6qztE7l85igAntpQE3ASiRf9FYvZZtbSy+0gMDMcASW+rKlqoulwGxeqC+qERuekM3dcDk+tV7GQ8DhhsXDOJTrnsnq5ZTrn1A0lg+6l8lqSEowFU/KCjhLxrlBXlITRyVyDW8R3b1TUc/q4YWSl6W+R/lymrigJIxULiRjNh9vYuKeZcybF5+VTT1ZhTjpzxubwtIqFhIGvxcLMLjWzrWZWYWa39PJ8qpk95D2/3MyKejw/zsxazexrfuaUyLB8xwE6HZyrYjFgV8wcxaa9LVTW6QQ98ZdvxcLMEoG7gMuAEuB6Myvp0ewmoNE5Nxm4E7ijx/N3As/4lVEiy5uVB0hLTmBunF/o6GQsmTuaxATj4bKqoKNIjPPzyGIeUOGc2+4tb/4gsKRHmyXAfd79R4BFZmYAZnY1sB3Y5GNGiSBvVtZzZtFwUpLUOzpQIzLTuGDqCP64ag9tuoKe+MjPb2Uh0P3PnWpvW69tnHPtQDOQa2YZwNeB7/iYTyJIfesx3tnfqvGKU/CxM8dS33qMl8trg44iMczPYtHbGVU9l8nsq813gDudcyfsiDWzm82szMzK6urqTjGmRIJVuxoBOGvC8ICTRJ8LpuYzIjOV+9/eFXQUiWF+FotqYGy3x2OAvX218a6+lw00EFqs8AdmtpPQYobfNLOlPT/AOXe3c67UOVean6+rqUWz1bsaSUlMYMbo7KCjRJ2kxARuPLeIv2yrZ/PelqDjSIzys1isBIrNbIKZpQDXAct6tFkG3OjdvwZ4yYUscM4VOeeKgJ8A/9c591Mfs0rAVu1q5LTCLNKSE4OOEpU+cdZ4MlIS+cWrlUFHkRjlW7HwxiCWAs8BW4CHnXObzOx2M7vKa3YPoTGKCuArhK7IJ3HmWHsH6/c0c8Z4zYI6VdlDkvm7c4p4Yv1e1lc3BR1HYpCvFzd2zj1N6BKs3bfd2u3+UeDaft7jNl/CScTYuKeF4+2dnDFe4xUfxBcumMQjq6r4zhOb+cPnziFBCzHKINIcRQncam9w+/TxWpL8g8hMS+brl05j1a5GfvZKRdBxJMb4emQhMhCrdjUybvgQRmSmBR0l6l1zxhj+sq2eH/35HdJTkvj7+UV4py6JfCAqFhIo5xyrdjcyX+dXDAoz4wfXzOLw8Q6+++RmHl5ZxdxxOTQePs6uA4epajhMghlnTczlq4unMH1UVtCRJUqoG0oCVdN8lLqDxzhdg9uDJi05kbv/7gx+eM0sstKTeKm8lsq6Q4wZls7fnjmWJXNHs3p3I1ff9QYvb9WJfDIwOrKQQK2rCs3c0SVUB1dCgnFt6ViuLR3b6/NfXDSFT/3PCpb+bjWPL53P5BGZYU4o0UZHFhKotdVNpCQmMG2UflmFU35mKvd+6kxSkhL4l0fW09HZc3EFkfdSsZBAratqYvroLFKTdDJeuI3MSuPWD5ewencTf1pdHXQciXAqFhKYjk7Hhupm5ozREh9BuXpOIbPGZPOTF7ZxrL0j6DgSwVQsJDCVda0cOt7B7LEarwiKmfHPl0xlT9MRfr98d9BxJIKpWEhg1nYNbqtYBOq8yXnMKxrOz1+t5Gibji6kdyoWEph1VU1kpiUxITcj6Chxzcz44kXF7G85xh90xT3pg4qFBGZddROzx+RoDaMIcO6kXErHD+Nnr1Rq7EJ6pWIhgTja1kF5zUFmj9XgdiToOrqoaT7KH8o0M0reT8VCArFpbwvtnU4n40WQ8ybncfq4HH7+SiXH23U9b3kvFQsJRNeZ23M0uB0xzIx/WlTMnqYjPLJKRxfyXioWEoi1VU2Myk5jRJZWmo0kH5qSz+yxOdz1cgVtHTq6kL9SsZBAdA1uS2QxM77kHV389u1dQceRCKJiIWHXeCi0XLbOr4hM50/NZ+GUfH7w7FZ21B8KOo5ECBULCbt11V0n42kmVCQyM37w0VkkJxqf/+0qmo+0BR1JIoCKhYTduqpmzGBmoYpFpCrITuNnN5xBZV0rH/vlW+w6oCOMeKdiIWG3encjxSOGkpmWHHQUOYHzivO491NnUtN8lMV3vsb3nymn4dDxoGNJQFQsJKw6Oh2rdzdyxvjhQUeRAVhQnM8zX1zA5TNH8cvXKllwx0vc8Ww5jSoacUfFQsLqnf0HOXi0nVJdRjVqjM5J586PzeHPX1rIhdNH8otXK7nkJ6+xaldD0NEkjFQsJKzKdjUCcGaRjiyiTfHITP7f9XN5Yul5pKckcv2vlrN8+4GgY0mYqFhIWK3a2UB+Zipjh6cHHUVO0WmF2Tz6j/MZOyydz9xXxva61qAjSRioWEhYle1qpHT8MMy00mw0G56Rwm9uOovEROOfHlyjlWrjgIqFhM3+lqNUNx7hDI1XxITCnHR+8NFZbNzTwl0vVwYdR3zma7Ews0vNbKuZVZjZLb08n2pmD3nPLzezIm/7xWa2ysw2eD8v9DOnhEfZztB4RanGK2LG4hkFLJkzml+8UqnuqBjnW7Ews0TgLuAyoAS43sxKejS7CWh0zk0G7gTu8LbXAx92zs0EbgTu9yunhM9b2+vJSElkxuisoKPIIPrWFdNJTU7g1sc34ZwLOo74xM8ji3lAhXNuu3PuOPAgsKRHmyXAfd79R4BFZmbOuTXOub3e9k1Ampml+phVwuDNigPMmzCc5ET1fsaSEZlpfG3xVF6vqOe5TfuCjiM+8fNbWwh0v6Bvtbet1zbOuXagGcjt0eajwBrn3DGfckoY1DQfYXv9IeZPzgs6ivjghrPGMa0gk+8+uYWjbRrsjkV+Foveprv0PEY9YRszm0Goa+pzvX6A2c1mVmZmZXV1daccVPz3ZkVoPv45k3r+LSCxICkxgduumsGepiP84lUNdsciP4tFNTC22+MxwN6+2phZEpANNHiPxwCPAp90zvX6r885d7dzrtQ5V5qfnz/I8WUwvVFZz/CMFKYXaLwiVp09MZcrZ43i569UUt14OOg4Msj8LBYrgWIzm2BmKcB1wLIebZYRGsAGuAZ4yTnnzCwHeAr4hnPuDR8zShg453iz4gDnTMwlIUHnV8Syb14+nQQz/v2pLUFHkUHmW7HwxiCWAs8BW4CHnXObzOx2M7vKa3YPkGtmFcBXgK7ptUuBycC/mdla7zbCr6ziry01B9nXcpQPTdHRX6wbnZPOFy6YxDMb9/FGRX3QcWQQWaxMdSstLXVlZWVBx5Be/PSlbfzoz++w4luLGJGpa27HuqNtHSy+8zVSkxJ4+osLNPstwpnZKudcaX/t9H9RfPdieS2zx2SrUMSJtORE/u3KErbVtnL/W7qOd6xQsRBfHWg9xtqqJi6cNjLoKBJGF00fwcIp+dz5wjvUHdSs91igYiG+enlrHc7BoukacoonZsa3P1zCsbZObvnjep3ZHQNULMRXz2yoYXR2mpb4iEOT8ofyzcun8WJ5Lfe8viPoOPIBqViIb5qPtPHatjqumDVKS5LHqRvPLWJxyUjueLactyp1oaRopmIhvvnzpn20dTiumDU66CgSEDPjh9fMpig3g5t/U8amvc1BR5JTpGIhvnlqQw1jhqUze0x20FEkQNlDkrnv7+cxNC2JG369nLKdunZ3NFKxEF/Utx7j9W316oISIHSy3kM3n8OwISnc8Ovl/PbtXRr0jjIqFuKLx9bsob3Tcc3pY4KOIhFiXO4Q/vj5c5k3YTj/+thGbrqvjH3NR4OOJQOkYiGDzjnHQyurmDsuh+KRmUHHkQgyPCOF+z49j29/uIQ3Kuq5+Mev8sDy3XR26igj0qlYyKBbV93MttpW/razTh8aAAAKJElEQVR0bP+NJe4kJBifnj+B5760kNMKs/nmoxu4/ldvs6P+UNDR5ARULGTQPVxWRVpyAlfOGhV0FIlgRXkZPPDZs/j+R2ayuaaFy//rL7y8tTboWNIHFQsZVC1H23h8zR6umDmazLTkoONIhDMzrps3jue//CEm5mfw2fvKeHHL/qBjSS9ULGRQPbyyikPHO/jUuUVBR5EoUpCdxoM3n03J6CyWPrCGjXt0PkakUbGQQdPR6fjNW7soHT+MmTq3Qk5SZloyv76xlGFDkln6wGoOHWsPOpJ0o2Ihg+al8lp2Nxzm0/MnBB1FotSIzDTu/Ngcdjcc5ntPbQ46jnSjYiGD5levbWd0dhqXzNBy5HLqzpqYy2cWTOT3K6pYtasx6DjiUbGQQbF8+wFW7Gzg5oUTSdKV0eQD+uKiYgqy0vi3xzbS3tEZdBxBxUIGyU9friBvaArXzRsXdBSJARmpSfzblSVsrmnhd8t3Bx1HULGQQbBmdyN/2VbPZxdMJC05Meg4EiMun1nA/Mm5/Pj5d2g8dDzoOHFPxUI+EOccP3xuK8OGJHPD2eODjiMxxMy49coZtB5r58fPvxN0nLinYiEfyCtb63iz8gBfXFTM0NSkoONIjJlakMknzhrH75bvYktNS9Bx4pqKhZyyto5O/uOZLRTlDuHjZ+moQvzx5YunkJWezHee2KRlzQOkYiGn7O7XtvPO/la+efl0UpL0T0n8kTMkha9ePIW3tzfw7MZ9QceJW/qGyympqG3lv17YxhUzR7F4RkHQcSTGXT9vHNMKMvnuk5tpPtIWdJy4pGIhJ+3w8XaWPrCaIamJ3HbVjKDjSBxISkzgPz4yk/0Hj/Gvj21Ud1QAVCzkpLR3dPK1P6xj6/6D/ORjc8jPTA06ksSJueOG8eWLinli3V5++/auoOPEHV+LhZldamZbzazCzG7p5flUM3vIe365mRV1e+4b3vatZnaJnzllYI62dfClh9by9IZ9fOvy6Zw/dUTQkSTOfP78yVw0fQS3LtvEn1ZXBx0nrvhWLMwsEbgLuAwoAa43s5IezW4CGp1zk4E7gTu815YA1wEzgEuBn3nvJwFZvbuRj/zsTZ5cX8M3LpvGZxZMDDqSxKHEBOOnHz+dsyfk8pWH1/G9Jzdrddow8XNi/Dygwjm3HcDMHgSWAN2XklwC3ObdfwT4qZmZt/1B59wxYIeZVXjv95aPecXT0eloOHSc3Q2HWLO7iT9v3s+KHQ3kDU3l3k+VcuE0LRQowUlLTuS+v5/H7U9u4tev7+DRNXv46BljmD85j+kFmeQOTSUxwYKOGXP8LBaFQFW3x9XAWX21cc61m1kzkOttf7vHawv9CFm+r4WlD6zBy/Dudve+O++522tb95623Z7vvr2XcblTfq9e3/P9bfvL/Z62znHoeAcdnX99dmJ+Bl+/dBqfPGc8GTrxTiJASlIC37t6Jh85fQw/e7mSe1/fwd2vbQcgwSA7PZmUpASSE0O3k60dob9ZB9j25N7aF+dPzedbV/TsuBlcfn7ze9uHPX9V9tVmIK/FzG4GbgYYN+7UFrBLS0pk6sjMXhN13e3+D6d7MDuJtu99X+vz9d23W7et72nbyxuf8nu9p23oUWZaEnlDUxmVncacsTmMyEpDJBKdPm4Yv76xlNZj7ayramJ7XSu1B4/RdLiNto5Ojnd00tbh6DyZ2VMn1TQyZmWNDMN31M9iUQ2M7fZ4DLC3jzbVZpYEZAMNA3wtzrm7gbsBSktLT+n/WlFeBnfdcPqpvFREIsTQ1CTmT85j/uS8oKPELD9nQ60Eis1sgpmlEBqwXtajzTLgRu/+NcBLLtRPsgy4zpstNQEoBlb4mFVERE7AtyMLbwxiKfAckAjc65zbZGa3A2XOuWXAPcD93gB2A6GCgtfuYUKD4e3AF5xzHX5lFRGRE7NYOROytLTUlZWVBR1DRCSqmNkq51xpf+10BreIiPRLxUJERPqlYiEiIv1SsRARkX6pWIiISL9iZjaUmdUBH2Td4jygfpDiDCblOjnKdXKU6+TEYq7xzrn8/hrFTLH4oMysbCDTx8JNuU6Ocp0c5To58ZxL3VAiItIvFQsREemXisVf3R10gD4o18lRrpOjXCcnbnNpzEJERPqlIwsREelX3BcLM7vUzLaaWYWZ3RJ0ni5mttPMNpjZWjMLbIVEM7vXzGrNbGO3bcPN7Hkz2+b9HBYhuW4zsz3ePltrZpcHkGusmb1sZlvMbJOZfdHbHug+O0GuQPeZmaWZ2QozW+fl+o63fYKZLff210PeZQ4iIdf/mtmObvtrTjhzdcuXaGZrzOxJ77H/+8s5F7c3QkunVwITgRRgHVASdC4v204gLwJyLAROBzZ22/YD4Bbv/i3AHRGS6zbgawHvr1HA6d79TOAdoCTofXaCXIHuM0IXaxzq3U8GlgNnAw8D13nbfwF8PkJy/S9wTZD/xrxMXwEeAJ70Hvu+v+L9yGIeUOGc2+6cOw48CCwJOFNEcc69RuhaI90tAe7z7t8HXB3WUPSZK3DOuRrn3Grv/kFgC6Hrxwe6z06QK1AupNV7mOzdHHAh8Ii3PYj91VeuwJnZGOAK4NfeYyMM+yvei0UhUNXtcTUR8AXyOODPZrbKu9Z4JBnpnKuB0C8hYETAebpbambrvW6qsHePdWdmRcBcQn+VRsw+65ELAt5nXpfKWqAWeJ7Q0X6Tc67daxLI97JnLudc1/76d29/3WlmqeHOBfwE+Beg03ucSxj2V7wXC+tlW0T89QDMd86dDlwGfMHMFgYdKAr8HJgEzAFqgP8MKoiZDQX+CHzJOdcSVI6eeskV+D5zznU45+YAYwgd7U/vrVl4U70/l5mdBnwDmAacCQwHvh7OTGZ2JVDrnFvVfXMvTQd9f8V7sagGxnZ7PAbYG1CW93DO7fV+1gKPEvoSRYr9ZjYKwPtZG3AeAJxz+70veCfwKwLaZ2aWTOgX8u+cc3/yNge+z3rLFSn7zMvSBLxCaGwgx8y6Lvsc6PeyW65Lve4855w7BvwP4d9f84GrzGwnoW7zCwkdafi+v+K9WKwEir2ZBCmErgG+LOBMmFmGmWV23QcWAxtP/KqwWgbc6N2/EXg8wCzv6vpl7PkbAthnXv/xPcAW59yPuz0V6D7rK1fQ+8zM8s0sx7ufDlxEaDzlZeAar1kQ+6u3XOXdCr4RGhcI6/5yzn3DOTfGOVdE6PfVS865GwjH/gp6VD/oG3A5oZkhlcC3gs7jZZpIaGbWOmBTkLmA3xPqnmgjdCR2E6E+0heBbd7P4RGS635gA7Ce0C/nUQHkOo9QF8B6YK13uzzofXaCXIHuM2AWsMb7/I3Ard72icAKoAL4A5AaIble8vbXRuC3eDOmgrgB5/PX2VC+7y+dwS0iIv2K924oEREZABULERHpl4qFiIj0S8VCRET6pWIhIiL9UrEQEZF+qViIiEi/VCxERKRf/x9pnG3uNT8c6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cp['city mpg'].plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12ae5a50>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cp['Age'].plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1930.478 187830.48    2322.363    678.801   1652.321   1067.855\n",
      "   2049.612   7317.287   5947.257  51796.025]\n",
      "[[335.  26.  19.   6.]\n",
      " [300.  28.  19.   6.]\n",
      " [300.  28.  20.   6.]\n",
      " [230.  28.  18.   6.]\n",
      " [230.  28.  18.   6.]]\n"
     ]
    }
   ],
   "source": [
    "# Feature Selection with Univariate Statistical Tests (Chi-squared for regression)\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "array = cp.values\n",
    "X = array[:,0:10]\n",
    "Y = array[:,10]\n",
    "# feature extraction\n",
    "test = SelectKBest(score_func=chi2, k=4)\n",
    "fit = test.fit(X, Y)\n",
    "# summarize scores\n",
    "np.set_printoptions(precision=3)\n",
    "print(fit.scores_)\n",
    "features = fit.transform(X)\n",
    "# summarize selected features\n",
    "print(features[0:5,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With Feature Selection, we choose the best 4 features which are Engine HP, highway MPG, city mpg and Age as they have relationship with MSRP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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       "      <th></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>Vehicle Size</th>\n",
       "      <th>highway MPG</th>\n",
       "      <th>city mpg</th>\n",
       "      <th>Age</th>\n",
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      "text/plain": [
       "   Engine Fuel Type  Engine HP  Engine Cylinders  Transmission Type  \\\n",
       "0               1.0      335.0               6.0                  1   \n",
       "1               1.0      300.0               6.0                  1   \n",
       "2               1.0      300.0               6.0                  1   \n",
       "3               1.0      230.0               6.0                  1   \n",
       "4               1.0      230.0               6.0                  1   \n",
       "\n",
       "   Driven_Wheels  Number of Doors  Vehicle Size  highway MPG  city mpg  Age  \\\n",
       "0              1              2.0             1           26        19    6   \n",
       "1              1              2.0             1           28        19    6   \n",
       "2              1              2.0             1           28        20    6   \n",
       "3              1              2.0             1           28        18    6   \n",
       "4              1              2.0             1           28        18    6   \n",
       "\n",
       "    MSRP  \n",
       "0  46135  \n",
       "1  40650  \n",
       "2  36350  \n",
       "3  29450  \n",
       "4  34500  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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