cars_MLR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OUR PROBLEM/TASK STATEMENT-\n",
    "\n",
    "A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts.\n",
    "\n",
    "They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting the pricing of cars in the American market, since those may be very different from the Chinese market. The company wants to know:\n",
    "\n",
    "Which variables are significant in predicting the price of a car\n",
    "How well those variables describe the price of a car\n",
    "Based on various market surveys, the consulting firm has gathered a large data set of different types of cars across the America market."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# THE BUSINESS GOAL-\n",
    "\n",
    "We are required to model the price of cars with the available independent variables. It will be used by the management to understand how exactly the prices vary with the independent variables. They can accordingly manipulate the design of the cars, the business strategy etc. to meet certain price levels. Further, the model will be a good way for management to understand the pricing dynamics of a new market."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IMPORTING PYTHON LIBRARIES AND OUR DATA SET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab as pl\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>...</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero giulia</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero stelvio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>alfa-romero Quadrifoglio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100 ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_ID  symboling                   CarName fueltype aspiration doornumber  \\\n",
       "0       1          3        alfa-romero giulia      gas        std        two   \n",
       "1       2          3       alfa-romero stelvio      gas        std        two   \n",
       "2       3          1  alfa-romero Quadrifoglio      gas        std        two   \n",
       "3       4          2               audi 100 ls      gas        std       four   \n",
       "4       5          2                audi 100ls      gas        std       four   \n",
       "\n",
       "       carbody drivewheel enginelocation  wheelbase  ...  enginesize  \\\n",
       "0  convertible        rwd          front       88.6  ...         130   \n",
       "1  convertible        rwd          front       88.6  ...         130   \n",
       "2    hatchback        rwd          front       94.5  ...         152   \n",
       "3        sedan        fwd          front       99.8  ...         109   \n",
       "4        sedan        4wd          front       99.4  ...         136   \n",
       "\n",
       "   fuelsystem  boreratio  stroke compressionratio horsepower  peakrpm citympg  \\\n",
       "0        mpfi       3.47    2.68              9.0        111     5000      21   \n",
       "1        mpfi       3.47    2.68              9.0        111     5000      21   \n",
       "2        mpfi       2.68    3.47              9.0        154     5000      19   \n",
       "3        mpfi       3.19    3.40             10.0        102     5500      24   \n",
       "4        mpfi       3.19    3.40              8.0        115     5500      18   \n",
       "\n",
       "   highwaympg    price  \n",
       "0          27  13495.0  \n",
       "1          27  16500.0  \n",
       "2          26  16500.0  \n",
       "3          30  13950.0  \n",
       "4          22  17450.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('cars.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(205, 26)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "car_ID                int64\n",
       "symboling             int64\n",
       "CarName              object\n",
       "fueltype             object\n",
       "aspiration           object\n",
       "doornumber           object\n",
       "carbody              object\n",
       "drivewheel           object\n",
       "enginelocation       object\n",
       "wheelbase           float64\n",
       "carlength           float64\n",
       "carwidth            float64\n",
       "carheight           float64\n",
       "curbweight            int64\n",
       "enginetype           object\n",
       "cylindernumber       object\n",
       "enginesize            int64\n",
       "fuelsystem           object\n",
       "boreratio           float64\n",
       "stroke              float64\n",
       "compressionratio    float64\n",
       "horsepower            int64\n",
       "peakrpm               int64\n",
       "citympg               int64\n",
       "highwaympg            int64\n",
       "price               float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "toyota corona            6\n",
       "toyota corolla           6\n",
       "peugeot 504              6\n",
       "subaru dl                4\n",
       "toyota mark ii           3\n",
       "                        ..\n",
       "dodge colt (sw)          1\n",
       "audi 5000s (diesel)      1\n",
       "audi fox                 1\n",
       "chevrolet monte carlo    1\n",
       "volvo 246                1\n",
       "Name: CarName, Length: 147, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CarName'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## REMOVING NULL VALUES AND REPEATED VALUES FROM OUR DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(205, 26)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()\n",
    "df.dropna(axis='columns', how='any')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NO NULL VALUES WERE FOUND IN THE DATASET AS THE DF.SHAPE OUTPUT REMAINS THE SAME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(109, 26)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(subset =\"CarName\", \n",
    "                     keep = False, inplace = True) \n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# WE HAVE REMOVED 96 CAR ENTRIES THAT WERE FEATURED IN THE DATASET MORE THAN ONCE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .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>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>...</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero giulia</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero stelvio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>alfa-romero Quadrifoglio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100 ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>audi fox</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.5</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>15250.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_ID  symboling                   CarName fueltype aspiration doornumber  \\\n",
       "0       1          3        alfa-romero giulia      gas        std        two   \n",
       "1       2          3       alfa-romero stelvio      gas        std        two   \n",
       "2       3          1  alfa-romero Quadrifoglio      gas        std        two   \n",
       "3       4          2               audi 100 ls      gas        std       four   \n",
       "5       6          2                  audi fox      gas        std        two   \n",
       "\n",
       "       carbody drivewheel enginelocation  wheelbase  ...  enginesize  \\\n",
       "0  convertible        rwd          front       88.6  ...         130   \n",
       "1  convertible        rwd          front       88.6  ...         130   \n",
       "2    hatchback        rwd          front       94.5  ...         152   \n",
       "3        sedan        fwd          front       99.8  ...         109   \n",
       "5        sedan        fwd          front       99.8  ...         136   \n",
       "\n",
       "   fuelsystem  boreratio  stroke compressionratio horsepower  peakrpm citympg  \\\n",
       "0        mpfi       3.47    2.68              9.0        111     5000      21   \n",
       "1        mpfi       3.47    2.68              9.0        111     5000      21   \n",
       "2        mpfi       2.68    3.47              9.0        154     5000      19   \n",
       "3        mpfi       3.19    3.40             10.0        102     5500      24   \n",
       "5        mpfi       3.19    3.40              8.5        110     5500      19   \n",
       "\n",
       "   highwaympg    price  \n",
       "0          27  13495.0  \n",
       "1          27  16500.0  \n",
       "2          26  16500.0  \n",
       "3          30  13950.0  \n",
       "5          25  15250.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OUT OF THE 26 COLUMNS, WE MUST SELECT 3 TO SERVE AS FEATURES OR PARAMETERS FOR OUR PRICE ESTIMATION REGRESSION MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# THE THREE PARAMETERS THAT WE ARE SELECTING (x1, x2, x3) WILL BE 'ENGINESIZE', 'HORSEPOWER' AND A NEW COLUMN 'MILEAGE' WHICH WILL BE THE AVERAGE OF 'CITYMPG' AND 'HIGHWAYMPG'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CREATING A NEW COLUMNS FOR MILEAGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['mileage'] = (df['citympg'] + df['highwaympg'])/2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CREATING OUR NEW DATA SET DF_S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>enginesize</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>mileage</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>130</td>\n",
       "      <td>111</td>\n",
       "      <td>24.0</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>130</td>\n",
       "      <td>111</td>\n",
       "      <td>24.0</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>152</td>\n",
       "      <td>154</td>\n",
       "      <td>22.5</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>109</td>\n",
       "      <td>102</td>\n",
       "      <td>27.0</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>136</td>\n",
       "      <td>110</td>\n",
       "      <td>22.0</td>\n",
       "      <td>15250.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   enginesize  horsepower  mileage    price\n",
       "0         130         111     24.0  13495.0\n",
       "1         130         111     24.0  16500.0\n",
       "2         152         154     22.5  16500.0\n",
       "3         109         102     27.0  13950.0\n",
       "5         136         110     22.0  15250.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_s = df[['enginesize', 'horsepower', 'mileage', 'price']]\n",
    "df_s.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TO UNDERSTAND THE RELATIONSHIP BETWEEN PARAMETERS AND THE PRICE, WE WILL PLOT EACH PARAMETER VS PRICE ON A SCATTER PLOT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SCATTER PLOT - ENGINE SIZE VS PRICE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 44,
     "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": [
    "plt.xlabel('Engine Size')\n",
    "plt.ylabel('Price of car')\n",
    "plt.title('Enginesize vs price')\n",
    "plt.scatter(df_s.enginesize, df_s.price, color = 'red')\n",
    "plt.grid(True)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SCATTER PLOT FOR HORSEPOWER VS PRICE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 43,
     "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": [
    "plt.xlabel('Horsepower')\n",
    "plt.ylabel('Price of car')\n",
    "plt.title('Hprsepower vs Price')\n",
    "plt.grid(True)\n",
    "plt.scatter(df_s.horsepower, df_s.price, color='green')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SCATTER PLOT MILEAGE VS PRICE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "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": [
    "plt.xlabel('Mileage')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Mileage vs Price')\n",
    "plt.grid(True)\n",
    "plt.scatter(df_s.mileage, df_s.price, color='blue')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "### FROM THE ABOVE SCATTER PLOTS, WE GATHER THAT NOT ALL PARAMETERS SHARE THE SAME KIND OF RELATIONSHIP WITH PRICE. EXPENSIVE VEHICLES TEND TO HAVE LOW MILEAGES AND THE HIGHEST MILEAGE VEHICLES ARE RATHER QUITE CHEAP. WE WILL KEEP THIS IN MIND FOR OUR REGRESSION EQUATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SPLITTING OUR DATA SET INTO TRAIN SET (80%) AND TEST SET (20%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "msk = np.random.rand(len(df_s)) < 0.8\n",
    "train = df_s[msk]\n",
    "test = df_s[~msk]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TRAINING DATA VISUALIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": [
    "plt.scatter(train.enginesize, train.price,  color='red')\n",
    "\n",
    "plt.scatter(train.horsepower, train.price,  color='green')\n",
    "\n",
    "plt.scatter(train.mileage, train.price,  color='blue')\n",
    "plt.xlabel(\"PARAMETERS\")\n",
    "plt.ylabel(\"PRICE\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## USING SK LEARN TO FIT A REGRESSION INTO OUR TRAIN DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[122.48498358  65.78019439  25.42157357]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "from sklearn.metrics import r2_score\n",
    "regr = linear_model.LinearRegression()\n",
    "x = np.asanyarray(train[['enginesize','horsepower','mileage']])\n",
    "y = np.asanyarray(train[['price']])\n",
    "regr.fit (x, y)\n",
    "\n",
    "print ('Coefficients: ', regr.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TESTING OUR MODEL AND EVALUATING ITS VARIANCE SCORE, R2 SCORE, RESIDUAL SUM OF SQUARES, MEAN ABSOLUTE ERROR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Residual sum of squares: 11628720.70\n",
      "Mean absolute error: 2502.88\n",
      "Residual sum of squares (MSE): 11628720.70\n",
      "R2-score: 0.68\n",
      "Variance score: 0.79\n"
     ]
    }
   ],
   "source": [
    "y_hat= regr.predict(test[['enginesize','horsepower', 'mileage']])\n",
    "x = np.asanyarray(test[['enginesize','horsepower','mileage']])\n",
    "y = np.asanyarray(test[['price']])\n",
    "print(\"Residual sum of squares: %.2f\"\n",
    "      % np.mean((y_hat - y) ** 2))\n",
    "\n",
    "print(\"Mean absolute error: %.2f\" % np.mean(np.absolute(y_hat - y)))\n",
    "print(\"Residual sum of squares (MSE): %.2f\" % np.mean((y_hat - y) ** 2))\n",
    "print(\"R2-score: %.2f\" % r2_score(y_hat , y) )\n",
    "print('Variance score: %.2f' % regr.score(x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### THE MODEL SCORED 0.79 ON THE VARIANCE SCORE AND 0.68 ON THE R2 SCORE"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}