LinearRegression_Cars4U_finalv2_Soumiya.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case Study \"Cars4U Project\" : Factors Influencing Price of Used Cars using Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Context: \n",
    "    \n",
    "- There is a huge demand for used cars in the Indian Market today. As sales of new cars have slowed down in the recent past, the pre-owned car market has continued to grow over the past years and is larger than the new car market now. Cars4U is a budding tech start-up that aims to find footholes in this market.\n",
    "- There is a slowdown in new car sales and that could mean that the demand is shifting towards the pre-owned market. \n",
    "- Unlike new cars, where price and supply are fairly deterministic and managed by OEMs (Original Equipment Manufacturer / except for dealership level discounts which come into play only in the last stage of the customer journey), used cars are very different beasts with huge uncertainty in both pricing and supply.\n",
    "- In this case study, we have come up with a pricing model that can effectively predict the price of used cars and can help the business in devising profitable strategies using differential pricing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem:\n",
    "\n",
    "**key questions:**\n",
    "\n",
    "- What are the factors present in dataset, contribute in the price of used cars.\n",
    "- Does car brand matter in the used cars market?\n",
    "- In which places (Locations) used cars are sold more?\n",
    "- How Fuel type, Transmission, Power, number of seats matter in determining the price of used cars?\n",
    "- How ownership affects the sale of used cars? Do certain level of ownerships are preferred in used\n",
    "  cars market?\n",
    "- How does Year of Manufacture affect the used car price?\n",
    "- What is the correlation between each column?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attribute Information:\n",
    " \n",
    "\n",
    "- S.No. : Serial Number\n",
    "- Name : Name of the car which includes Brand name and Model name\n",
    "- Location : The location in which the car is being sold or is available for purchase Cities\n",
    "- Year : Manufacturing year of the car\n",
    "- Kilometers_driven : The total kilometers driven in the car by the previous owner(s) in KM.\n",
    "- Fuel_Type : The type of fuel used by the car. (Petrol, Diesel, Electric, CNG, LPG)\n",
    "- Transmission : The type of transmission used by the car. (Automatic / Manual)\n",
    "- Owner : Type of ownership\n",
    "- Mileage : The standard mileage offered by the car company in kmpl or km/kg\n",
    "- Engine : The displacement volume of the engine in CC.\n",
    "- Power : The maximum power of the engine in bhp.\n",
    "- Seats : The number of seats in the car.\n",
    "- New_Price : The price of a new car of the same model in INR Lakhs.(1 Lakh = 100, 000)\n",
    "- Price : The price of the used car in INR Lakhs (1 Lakh = 100, 000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# Removes the limit from the number of displayed columns and rows, so we can see the entire dataframe when printed.\n",
    "pd.set_option('display.max_columns', None)\n",
    "from matplotlib.pyplot import figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Imports for Data Preprocessing\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "#Import for Model Building\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split # Sklearn package's randomized data splitting function\n",
    "\n",
    "# To check model performance\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and explore data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 7253 rows and 14 columns.\n"
     ]
    }
   ],
   "source": [
    "#Import \"used_cars_data\" dataset\n",
    "df = pd.read_csv(\"used_cars_data.csv\")\n",
    "print(f'There are {df.shape[0]} rows and {df.shape[1]} columns.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>S.No.</th>\n",
       "      <th>Name</th>\n",
       "      <th>Location</th>\n",
       "      <th>Year</th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner_Type</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>Seats</th>\n",
       "      <th>New_Price</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Maruti Wagon R LXI CNG</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010</td>\n",
       "      <td>72000</td>\n",
       "      <td>CNG</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>26.6 km/kg</td>\n",
       "      <td>998 CC</td>\n",
       "      <td>58.16 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2015</td>\n",
       "      <td>41000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>19.67 kmpl</td>\n",
       "      <td>1582 CC</td>\n",
       "      <td>126.2 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Honda Jazz V</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011</td>\n",
       "      <td>46000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>18.2 kmpl</td>\n",
       "      <td>1199 CC</td>\n",
       "      <td>88.7 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.61 Lakh</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Maruti Ertiga VDI</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012</td>\n",
       "      <td>87000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Manual</td>\n",
       "      <td>First</td>\n",
       "      <td>20.77 kmpl</td>\n",
       "      <td>1248 CC</td>\n",
       "      <td>88.76 bhp</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
       "      <td>Coimbatore</td>\n",
       "      <td>2013</td>\n",
       "      <td>40670</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Second</td>\n",
       "      <td>15.2 kmpl</td>\n",
       "      <td>1968 CC</td>\n",
       "      <td>140.8 bhp</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   S.No.                              Name    Location  Year  \\\n",
       "0      0            Maruti Wagon R LXI CNG      Mumbai  2010   \n",
       "1      1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015   \n",
       "2      2                      Honda Jazz V     Chennai  2011   \n",
       "3      3                 Maruti Ertiga VDI     Chennai  2012   \n",
       "4      4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013   \n",
       "\n",
       "   Kilometers_Driven Fuel_Type Transmission Owner_Type     Mileage   Engine  \\\n",
       "0              72000       CNG       Manual      First  26.6 km/kg   998 CC   \n",
       "1              41000    Diesel       Manual      First  19.67 kmpl  1582 CC   \n",
       "2              46000    Petrol       Manual      First   18.2 kmpl  1199 CC   \n",
       "3              87000    Diesel       Manual      First  20.77 kmpl  1248 CC   \n",
       "4              40670    Diesel    Automatic     Second   15.2 kmpl  1968 CC   \n",
       "\n",
       "       Power  Seats  New_Price  Price  \n",
       "0  58.16 bhp    5.0        NaN   1.75  \n",
       "1  126.2 bhp    5.0        NaN  12.50  \n",
       "2   88.7 bhp    5.0  8.61 Lakh   4.50  \n",
       "3  88.76 bhp    7.0        NaN   6.00  \n",
       "4  140.8 bhp    5.0        NaN  17.74  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print the first 10 rows of the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- There are 14 columns and 7253 rows.\n",
    "- S.No. column is like index, we can drop this column later.\n",
    "- MIleage column has units -kmpl and km/kg\n",
    "- Engine column has unit CC\n",
    "- Power column has unit bhp\n",
    "- New_Price column has units Lakh and Cr.\n",
    "- Name column has car brand name as well as model, we will need to extract these from the column\n",
    "  later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7253 entries, 0 to 7252\n",
      "Data columns (total 14 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   S.No.              7253 non-null   int64  \n",
      " 1   Name               7253 non-null   object \n",
      " 2   Location           7253 non-null   object \n",
      " 3   Year               7253 non-null   int64  \n",
      " 4   Kilometers_Driven  7253 non-null   int64  \n",
      " 5   Fuel_Type          7253 non-null   object \n",
      " 6   Transmission       7253 non-null   object \n",
      " 7   Owner_Type         7253 non-null   object \n",
      " 8   Mileage            7251 non-null   object \n",
      " 9   Engine             7207 non-null   object \n",
      " 10  Power              7207 non-null   object \n",
      " 11  Seats              7200 non-null   float64\n",
      " 12  New_Price          1006 non-null   object \n",
      " 13  Price              6019 non-null   float64\n",
      "dtypes: float64(2), int64(3), object(9)\n",
      "memory usage: 793.4+ KB\n"
     ]
    }
   ],
   "source": [
    "#Check Datatypes of the columns and null counts\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Once we remove units from Mileage, Engine, Power, New_Price, these columns can be converted into float datatype.\n",
    "\n",
    "- Columns Mileage, Engine, Power, Seats, New_Price and Price contain null values.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's remove the units from the columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove units from Mileage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Mileage']\n"
     ]
    }
   ],
   "source": [
    "#Now lets take care of the units of Milege column\n",
    "\n",
    "#This function lists the columns that have mileage units (km/kg or kmpl)\n",
    "milege_cols = []\n",
    "for colname in df.columns[df.dtypes == 'object']:  # only need to consider string columns with unit\n",
    "    if (df[colname].str.endswith('km/kg').any() or df[colname].str.endswith('kmpl').any()):  # using `.str` so we can use an element-wise string method\n",
    "        milege_cols.append(colname)\n",
    "print(milege_cols)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since km/kg=kmpl (1 litre=1 kilogram) we can just remove the unit as everything in same unit, no need of conversion.\n",
    "def mileage_to_num(mileage_val):\n",
    "    if isinstance(mileage_val, str):\n",
    "        return (mileage_val.replace('kmpl', '').replace('km/kg', '').replace(' km/kg', '').replace(' kmpl', ''))\n",
    "    else:\n",
    "        return np.nan\n",
    "\n",
    "for colname in milege_cols:\n",
    "    df[colname] = df[colname].apply(mileage_to_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove units from Power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Power']\n"
     ]
    }
   ],
   "source": [
    "#Now lets take care of the units of Power column\n",
    "#This function lists the columns that have power units (bhp)\n",
    "\n",
    "power_cols = []\n",
    "for colname in df.columns[df.dtypes == 'object']:  # only need to consider string columns with unit\n",
    "    if df[colname].str.endswith('bhp').any():  # using `.str` so we can use an element-wise string method\n",
    "        power_cols.append(colname)\n",
    "print(power_cols)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We can just remove the unit (bhp), since there is no other unit, no need of conversion.\n",
    "def power_to_num(power_val):\n",
    "    if isinstance(power_val, str):\n",
    "        return (power_val.replace('bhp', ''))\n",
    "    else:\n",
    "        return np.nan\n",
    "\n",
    "for colname in power_cols:\n",
    "    df[colname] = df[colname].apply(power_to_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove units from New_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['New_Price']\n"
     ]
    }
   ],
   "source": [
    "#Now lets take care of the units of Price column\n",
    "#This function lists the columns that have price units (Lakh or Cr)\n",
    "\n",
    "price_cols = []\n",
    "for colname in df.columns[df.dtypes == 'object']:  # only need to consider string columns\n",
    "    if (df[colname].str.endswith('Lakh').any() or df[colname].str.endswith('Cr').any() or df[colname].str.endswith('Lakhs').any()):  # using `.str` so we can use an element-wise string method\n",
    "        price_cols.append(colname)\n",
    "print(price_cols)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert Cr to Lakh, so that all the prices are in Lakh, later we remove the unit after conversion.\n",
    "def price_to_num(price_val):\n",
    "    if isinstance(price_val, str):\n",
    "        multiplier=1\n",
    "        if price_val.endswith('Cr'):\n",
    "            multilpier=100\n",
    "        return float(price_val.replace('lakh', '').replace('Lakh', '').replace('Cr', '')) * multiplier #return price in Lakh with float datatype\n",
    "    else:\n",
    "        return np.nan\n",
    "\n",
    "for colname in price_cols:\n",
    "    df[colname] = df[colname].apply(price_to_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove units from Engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Engine']\n"
     ]
    }
   ],
   "source": [
    "#Now lets take care of the units of Engine column\n",
    "#This function lists the columns that have Engine displacement volume units (CC)\n",
    "engine_cols = []\n",
    "for colname in df.columns[df.dtypes == 'object']:  # only need to consider string columns\n",
    "    if (df[colname].str.endswith('CC').any()):  # using `.str` so I can use an element-wise string method\n",
    "        engine_cols.append(colname)\n",
    "print(engine_cols)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove the unit 'CC', since all values are in same unit, no need of conversion.\n",
    "def engine_to_num(engine_val):\n",
    "    if isinstance(engine_val, str):\n",
    "        return (engine_val.replace('CC', '').replace(' CC',''))\n",
    "    else:\n",
    "        return np.nan\n",
    "\n",
    "for colname in engine_cols:\n",
    "    df[colname] = df[colname].apply(engine_to_num)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert datatypes of Engine, Mileage, Power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert Engine displacement volume column to float\n",
    "df['Engine']=df['Engine'].astype(float)\n",
    "#Convert Mileage to float data type\n",
    "df['Mileage']=df['Mileage'].astype(float)\n",
    "\n",
    "#Replace na with np.nan in Power column and convert to float\n",
    "df['Power'].fillna((np.nan), inplace=True)\n",
    "df['Power']=df['Power'].replace('null ',np.nan)\n",
    "df['Power']=df['Power'].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting Company and Model of the car from the Name column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "company=[]\n",
    "def getcompany(name):\n",
    "    company.append(name.split(' ')[0])\n",
    "\n",
    "    \n",
    "for name in df['Name']:\n",
    "    getcompany(name)\n",
    "    \n",
    "    \n",
    "df['Company']=company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Company name extracted and added as a new column 'Company' in the dataframe 'df'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=[]\n",
    "def getmodel(name):\n",
    "    \n",
    "    model.append(name.split(' ')[1])\n",
    "\n",
    "    \n",
    "for name in df['Name']:\n",
    "    getmodel(name)\n",
    "    \n",
    "\n",
    "df['Model']=model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Model name extracted and added as a new column 'Model' in the dataframe 'df'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Treating missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of df (7253, 16)\n",
      " \n",
      "Null value info of dataframe \n",
      "\n",
      "New_Price            6247\n",
      "Price                1234\n",
      "Power                 175\n",
      "Seats                  53\n",
      "Engine                 46\n",
      "Mileage                 2\n",
      "Model                   0\n",
      "Company                 0\n",
      "Owner_Type              0\n",
      "Transmission            0\n",
      "Fuel_Type               0\n",
      "Kilometers_Driven       0\n",
      "Year                    0\n",
      "Location                0\n",
      "Name                    0\n",
      "S.No.                   0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(f'Shape of df {df.shape}')\n",
    "print(' ')\n",
    "print(f'Null value info of dataframe \\n\\n{df.isnull().sum().sort_values(ascending=False)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- There are missing values in columns New_Price, Price, Power, Seats, Engine and Mileage, we will need to treat these missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets replace the missing value for Mileage, since this column is approximately normal \n",
    "print(df['Mileage'].isnull().sum())\n",
    "df['Mileage'].fillna(df['Mileage'].mean(), inplace=True)  # median imputation\n",
    "df['Mileage'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets replace the missing value for Engine, since this column is highly right skewed, we can either have nan or replace the missing values with median as imputing with mean will change the data.\n",
    "print(df['Engine'].isnull().sum())\n",
    "df['Engine'].fillna(df['Engine'].median(), inplace=True)  # median imputation\n",
    "df['Engine'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets replace the missing value for Seats, since this column is highly right skewed, we can either have nan or replace the missing values with median as imputing with mean will change the data.\n",
    "print(df['Seats'].isnull().sum())\n",
    "df['Seats'].fillna(df['Seats'].median(), inplace=True)  # median imputation\n",
    "df['Seats'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "175\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets replace the missing value for Power, since this column is highly right skewed, we can either have nan or replace the missing values with median as imputing with mean will change the data.\n",
    "print(df['Power'].isnull().sum())\n",
    "df['Power'].fillna(df['Power'].median(), inplace=True)  # median imputation\n",
    "df['Power'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median Price of New Cars by Company\n",
      "  \n",
      "Company\n",
      "Mercedes-Benz    64.140\n",
      "BMW              55.070\n",
      "Jaguar           53.720\n",
      "Audi             53.140\n",
      "Volvo            45.670\n",
      "Mini             42.120\n",
      "Isuzu            33.680\n",
      "Mitsubishi       33.210\n",
      "Skoda            31.365\n",
      "Toyota           23.250\n",
      "Jeep             22.950\n",
      "Ford             11.200\n",
      "Honda            11.150\n",
      "Mahindra         11.145\n",
      "Volkswagen       11.105\n",
      "Hyundai           9.740\n",
      "Nissan            9.510\n",
      "Fiat              8.955\n",
      "Maruti            7.870\n",
      "Tata              7.630\n",
      "Renault           4.930\n",
      "Datsun            4.370\n",
      "Bentley           3.750\n",
      "Land              1.950\n",
      "Porsche           1.360\n",
      "Ambassador          NaN\n",
      "Chevrolet           NaN\n",
      "Force               NaN\n",
      "Hindustan           NaN\n",
      "ISUZU               NaN\n",
      "Lamborghini         NaN\n",
      "OpelCorsa           NaN\n",
      "Smart               NaN\n",
      "Name: New_Price, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Median Price of New Cars by Company')\n",
    "print('  ')\n",
    "print(df.groupby('Company')['New_Price'].median().sort_values(ascending=False))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Since median price for certain car companies is not present, we don't want to impute the values for New_Price for these, lets remove these rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_names = df[ (df['Company'] == 'Ambassador') | (df['Company'] == 'Chevrolet')|(df['Company'] == 'Force') |(df['Company'] == 'Hindustan')|(df['Company'] == 'ISUZU')|(df['Company'] == 'Lamborghini')|(df['Company'] == 'OpelCorsa')|(df['Company'] == 'Smart')].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(index_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "\n",
    "- We will remove these 162 rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(index_names, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7091, 16)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fill missing values for Price and New_Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['New_Price'] = df.groupby(['Company'])['New_Price'].transform(lambda x: x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Price'] = df.groupby(['Company'])['Price'].transform(lambda x: x.fillna(x.median()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Model                0\n",
       "Company              0\n",
       "Price                0\n",
       "New_Price            0\n",
       "Seats                0\n",
       "Power                0\n",
       "Engine               0\n",
       "Mileage              0\n",
       "Owner_Type           0\n",
       "Transmission         0\n",
       "Fuel_Type            0\n",
       "Kilometers_Driven    0\n",
       "Year                 0\n",
       "Location             0\n",
       "Name                 0\n",
       "S.No.                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- There are no missing values anymore, lets take care of the datatypes now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7091 entries, 0 to 7252\n",
      "Data columns (total 16 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   S.No.              7091 non-null   int64  \n",
      " 1   Name               7091 non-null   object \n",
      " 2   Location           7091 non-null   object \n",
      " 3   Year               7091 non-null   int64  \n",
      " 4   Kilometers_Driven  7091 non-null   int64  \n",
      " 5   Fuel_Type          7091 non-null   object \n",
      " 6   Transmission       7091 non-null   object \n",
      " 7   Owner_Type         7091 non-null   object \n",
      " 8   Mileage            7091 non-null   float64\n",
      " 9   Engine             7091 non-null   float64\n",
      " 10  Power              7091 non-null   float64\n",
      " 11  Seats              7091 non-null   float64\n",
      " 12  New_Price          7091 non-null   float64\n",
      " 13  Price              7091 non-null   float64\n",
      " 14  Company            7091 non-null   object \n",
      " 15  Model              7091 non-null   object \n",
      "dtypes: float64(6), int64(3), object(7)\n",
      "memory usage: 941.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data type Conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7091 entries, 0 to 7252\n",
      "Data columns (total 16 columns):\n",
      " #   Column             Non-Null Count  Dtype   \n",
      "---  ------             --------------  -----   \n",
      " 0   S.No.              7091 non-null   int64   \n",
      " 1   Name               7091 non-null   object  \n",
      " 2   Location           7091 non-null   category\n",
      " 3   Year               7091 non-null   int64   \n",
      " 4   Kilometers_Driven  7091 non-null   int64   \n",
      " 5   Fuel_Type          7091 non-null   category\n",
      " 6   Transmission       7091 non-null   category\n",
      " 7   Owner_Type         7091 non-null   category\n",
      " 8   Mileage            7091 non-null   float64 \n",
      " 9   Engine             7091 non-null   float64 \n",
      " 10  Power              7091 non-null   float64 \n",
      " 11  Seats              7091 non-null   category\n",
      " 12  New_Price          7091 non-null   float64 \n",
      " 13  Price              7091 non-null   float64 \n",
      " 14  Company            7091 non-null   category\n",
      " 15  Model              7091 non-null   category\n",
      "dtypes: category(7), float64(5), int64(3), object(1)\n",
      "memory usage: 623.1+ KB\n"
     ]
    }
   ],
   "source": [
    "cat_vars = ['Location','Fuel_Type','Transmission','Owner_Type',\n",
    "            'Company','Model','Seats']\n",
    "for colname in cat_vars:\n",
    "    df[colname] = df[colname].astype('category')\n",
    "    \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binning- Let's put the Years in bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "category = pd.cut(df.Year,bins=[1995,2000,2005,2010,2015,2020],labels=['1995-2000','2001-2005','2006-2010','2011-2015','2016-2020'])\n",
    "df.insert(5,'Year_By',category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7091 entries, 0 to 7252\n",
      "Data columns (total 17 columns):\n",
      " #   Column             Non-Null Count  Dtype   \n",
      "---  ------             --------------  -----   \n",
      " 0   S.No.              7091 non-null   int64   \n",
      " 1   Name               7091 non-null   object  \n",
      " 2   Location           7091 non-null   category\n",
      " 3   Year               7091 non-null   int64   \n",
      " 4   Kilometers_Driven  7091 non-null   int64   \n",
      " 5   Year_By            7091 non-null   category\n",
      " 6   Fuel_Type          7091 non-null   category\n",
      " 7   Transmission       7091 non-null   category\n",
      " 8   Owner_Type         7091 non-null   category\n",
      " 9   Mileage            7091 non-null   float64 \n",
      " 10  Engine             7091 non-null   float64 \n",
      " 11  Power              7091 non-null   float64 \n",
      " 12  Seats              7091 non-null   category\n",
      " 13  New_Price          7091 non-null   float64 \n",
      " 14  Price              7091 non-null   float64 \n",
      " 15  Company            7091 non-null   category\n",
      " 16  Model              7091 non-null   category\n",
      "dtypes: category(8), float64(5), int64(3), object(1)\n",
      "memory usage: 630.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Since we have converted datatypes, lets remove the columns we won't be using."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['S.No.','Name','Year'],inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7091 entries, 0 to 7252\n",
      "Data columns (total 14 columns):\n",
      " #   Column             Non-Null Count  Dtype   \n",
      "---  ------             --------------  -----   \n",
      " 0   Location           7091 non-null   category\n",
      " 1   Kilometers_Driven  7091 non-null   int64   \n",
      " 2   Year_By            7091 non-null   category\n",
      " 3   Fuel_Type          7091 non-null   category\n",
      " 4   Transmission       7091 non-null   category\n",
      " 5   Owner_Type         7091 non-null   category\n",
      " 6   Mileage            7091 non-null   float64 \n",
      " 7   Engine             7091 non-null   float64 \n",
      " 8   Power              7091 non-null   float64 \n",
      " 9   Seats              7091 non-null   category\n",
      " 10  New_Price          7091 non-null   float64 \n",
      " 11  Price              7091 non-null   float64 \n",
      " 12  Company            7091 non-null   category\n",
      " 13  Model              7091 non-null   category\n",
      "dtypes: category(8), float64(5), int64(1)\n",
      "memory usage: 464.0 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA- Univariate Categorical variables.\n",
    "#### Analyze following variables:\n",
    "    - Location           \n",
    "    - Year_By\n",
    "    - Fuel_Type\n",
    "    - Transmission\n",
    "    - Owner_Type\n",
    "    - Company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 11 unique Locations.\n",
      " \n",
      "Mumbai        933\n",
      "Hyderabad     863\n",
      "Coimbatore    755\n",
      "Kochi         754\n",
      "Pune          737\n",
      "Delhi         656\n",
      "Kolkata       633\n",
      "Chennai       572\n",
      "Jaipur        485\n",
      "Bangalore     431\n",
      "Ahmedabad     272\n",
      "Name: Location, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Univariate analysis- Location\n",
    "figure(figsize=(20,10), dpi=80)\n",
    "print(f'There are {df.Location.nunique()} unique Locations.')\n",
    "print(' ')\n",
    "print(f'{df.Location.value_counts(ascending=False)}')\n",
    "\n",
    "sns.countplot(x=df['Location']); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Used car sales are highest in Mumbai, followed by Hyderabad.\n",
    "- Used car sales are least in Ahmedabad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Univariate analysis- Year_By\n",
    "figure(figsize=(20,10), dpi=80)\n",
    "sns.countplot(x=df['Year_By']); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 5 unique Fuel Types.\n",
      " \n",
      "Diesel      3772\n",
      "Petrol      3243\n",
      "CNG           62\n",
      "LPG           12\n",
      "Electric       2\n",
      "Name: Fuel_Type, dtype: int64\n"
     ]
    },
    {
     "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": [
    "#Univariate analysis- Fuel_Types\n",
    "print(f'There are {df.Fuel_Type.nunique()} unique Fuel Types.')\n",
    "print(' ')\n",
    "print(f'{df.Fuel_Type.value_counts(ascending=False)}')\n",
    "sns.countplot(x=df['Fuel_Type']); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Diesel cars are the most preferred cars in the used car market, followed by Petrol cars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 unique Transmission Types.\n",
      " \n",
      "Manual       5055\n",
      "Automatic    2036\n",
      "Name: Transmission, dtype: int64\n"
     ]
    },
    {
     "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": [
    "#Univariate analysis Transmission\n",
    "print(f'There are {df.Transmission.nunique()} unique Transmission Types.')\n",
    "print(' ')\n",
    "print(f'{df.Transmission.value_counts(ascending=False)}')\n",
    "sns.countplot(x=df['Transmission']); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Manual Transmission cars are the most preferred cars in the used car market, followed by Automatic Transmission cars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 4 unique Owner Type.\n",
      " \n",
      "First             5837\n",
      "Second            1116\n",
      "Third              126\n",
      "Fourth & Above      12\n",
      "Name: Owner_Type, dtype: int64\n"
     ]
    },
    {
     "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": [
    "#Univariate analysis- Owner_Type\n",
    "print(f'There are {df.Owner_Type.nunique()} unique Owner Type.')\n",
    "print(' ')\n",
    "print(f'{df.Owner_Type.value_counts(ascending=False)}')\n",
    "\n",
    "sns.countplot(x=df['Owner_Type']); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Car resale from First owners are most preferred in the used cars market."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 25 unique Car Companies.\n",
      " \n",
      "Maruti           1444\n",
      "Hyundai          1340\n",
      "Honda             743\n",
      "Toyota            507\n",
      "Mercedes-Benz     380\n",
      "Volkswagen        374\n",
      "Ford              351\n",
      "Mahindra          331\n",
      "BMW               312\n",
      "Audi              285\n",
      "Tata              228\n",
      "Skoda             202\n",
      "Renault           170\n",
      "Nissan            117\n",
      "Land               67\n",
      "Jaguar             48\n",
      "Fiat               38\n",
      "Mitsubishi         36\n",
      "Mini               31\n",
      "Volvo              28\n",
      "Jeep               19\n",
      "Porsche            19\n",
      "Datsun             17\n",
      "Isuzu               2\n",
      "Bentley             2\n",
      "Name: Company, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAucAAAToCAYAAAC4gW4nAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAMTQAADE0B0s6tTgAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzde7hmd13f/c/HDMmEAIGEg+i2RgqoHCQgR+shHHriARFMeUQpRqAIXog2YqsPFKkFrRWjpoooRw2iYsBSzhU5IxAgBBIwyKERZrBgoAqBBEz8PX/Mvemd6exkb7L3vn8z83pd133NvdZvrXV/78k/bxZr7+kYIwAAwOp9zaoHAAAADhDnAAAwCXEOAACTEOcAADAJcQ4AAJMQ5wAAMAlxDgAAk9iz6gHYPscdd9y42c1utuoxAADYwP79+788xjhuo3VxfgS52c1uln379q16DAAANtD2b65p3WMtAAAwCXEOAACTEOcAADAJz5wfQf7hC1/M3/zWC1c9BtvoZo97+KpHAAB2kTvnAAAwCXEOAACTEOcAADAJcQ4AAJMQ5wAAMAlxDgAAkxDnAAAwCXEOAACTEOcAADAJcQ4AAJMQ5wAAMAlxDgAAkxDnAAAwCXEOAACTEOc7oO0N217W9jlf5fmntX334v1d2/7+9k4IAMCMxPnO+IEk5yf5/rY3uC4XGmO8e4zxQ9szFgAAMxPnO+NRSX4pyVuSPDRJ2p7R9tz1A9o+oO0bl7af1vYjbd+U5AFL+79yFx0AgCObON9mbW+f5BuSvCbJc3Mg1K/tnAcm+d4kpya5T5Lb7uSMAADMSZxvv0cl+b0xxlVJXpnkVm2/9VrOuXeSPxpjXLY473mb+aC2Z7bdt/76wpeuuG6TAwCwUuJ8G7W9XpKHJ3lE20uSfCTJ9ZM8MsmVSY5ZOnzv8qlfzeeNMc4aY6ytv044bu+1nwQAwLTE+fZ6UJKPjTG+foxxyhjjlCT/JMkjkvxVkju13dt2T5IfXDrvz5I8tO0JbY9JcsYuzw0AwAT2rHqAI8yjklzt1x6OMS5q+8kkN0vy2iQXJbkkyXuTnLQ45hVt75XkfUn2J3lTkrXdGxsAgBl0jLHqGdgmX3eTk8b7fuHsVY/BNrrZ4x6+6hEAgG3Udv8YY8ObsB5rAQCASYhzAACYhDgHAIBJiHMAAJiEOAcAgEmIcwAAmIQ4BwCASYhzAACYhDgHAIBJiHMAAJiEOAcAgEmIcwAAmIQ4BwCASYhzAACYRMcYq56BbbK2tjb27du36jEAANhA2/1jjLWN1t05BwCASYhzAACYhDgHAIBJiHMAAJiEOAcAgEmIcwAAmIQ4BwCASYhzAACYhDgHAIBJ7Fn1AGyff/jCZfnUb/3KqsdgRW7xuJ9a9QgAwHXkzjkAAExCnAMAwCTEOQAATEKcAwDAJMQ5AABMQpwDAMAkxDkAAExCnAMAwCTEOQAATEKcAwDAJMQ5AABMQpwDAMAkxDkAAExCnAMAwCTE+TZoe0nbi9te0PYv2r6o7QltT2s72v7aQcf/3mL/HRbHfHhp7bi2V7R90tK+f9329bv5nQAA2H3ifPucPsY4NcntktwoyRmL/R9K8n1tj02StjdK8h1J9i/W355kre3aYvteSS5Icu+la5+W5A07OTwAAKsnzrffcUlOSPK/F9uXJXl9kgcttn8gyUuSXJkkY4wv5UCgn7ZYPy3Jc5J843rQR5wDABwVxPn2ObftBUk+lWQkefHS2vOSPHLx/pGL7WVvyP+5U35akjcleVeSe7T9hiRfm+S8nRkbAIBZiPPts/5Yy8lJ/meSX1pfGGO8NQfuhP/zJFeOMT500LlvSHJa2+OSfMMY48NJ3pgDoX7vJG8bY3z54A9se2bbfeuvy770pZ34XgAA7BJxvs3GGFfmwGMr/+Kgpd9L8sIkzz/Eae/Mgbvj/+/ifXLg7vm9F69DPtIyxjhrjLG2/rrBccdtwzcAAGBV9qx6gCPUfXLgB0GXrT/K8kcHHzzG+Pu2b0vy5CTPWOz7UNtTktw6yW/v2KQAAEzDnfPtc+7iVyl+IMm3JvmJ5cUxxqfHGP95jHHZBue/IcltcuCO+brzktwkybt3YmAAAObizvk2GGOcssHSJ5LcdTPnjDF+MckvHrTvB7ZhPAAADhPunAMAwCTEOQAATEKcAwDAJMQ5AABMQpwDAMAkxDkAAExCnAMAwCTEOQAATEKcAwDAJMQ5AABMQpwDAMAkxDkAAExCnAMAwCTEOQAATEKcAwDAJDrGWPUMbJO1tbWxb9++VY8BAMAG2u4fY6xttO7OOQAATEKcAwDAJMQ5AABMQpwDAMAkxDkAAExCnAMAwCTEOQAATEKcAwDAJMT5EeSqyz676hEAALgOxDkAAExCnAMAwCTEOQAATEKcAwDAJMQ5AABMQpwDAMAkxDkAAExCnAMAwCTEOQAATEKcAwDAJMQ5AABMQpwDAMAkxDkAAExCnAMAwCTEOQAATGLPqgeYRdtLklyxeB2X5L1J/s0Y4wtf5fVOTXLbMcaLl/aNJDccY1x23ScGAOBI48751Z0+xjg1ye2S3CjJGdfhWqcmeeh2DAUAwNFBnB/acUlOSPK/k6TtE9ue1/b8tq9q+w2L/U9t+6K2L2/7wbavb3tS25sn+fkk92t7QdtnHfwBbW/T9pVt39X2fW1/bLH/p9v+9tJxN257aduTduOLAwCwOuL86s5te0GSTyUZSV7c9geT3DbJvcYYd0nyB0l+Y+mceyT54THG7ZJ8OsmPjjE+neQpSV43xjh1jPHY5Q9pe0ySFyX5qTHG3ZLcK8lj294lybOTfF/bExeHPyrJy8YYn92h7wwAwCQ8c351p48xLmq7J8lvJ/mlJN+Q5K5J3tM2SY5JctXSOa9eCue3J7njJj7nm5PcPskfLq6ZJDdMcrsxxvltX5LkjLZnJ3lckn91qIu0PTPJmV+5wN7rbepLAgAwJ3F+CGOMKxeB/MtJPpjkaWOM521w+BVL76/K5v5Om+TSxfPth3J2kv+W5KNJPjXGeO8Gc56V5Kz17Vve+PpjE58NAMCkPNaysfsk+VCS/57kx9af+W57vbZ33sT5n0ty4gZrH0ryxbaPWN/R9tbrnzHGuDjJJUl+K1d/hAYAgCOYOL+6cxc/wPmBJN+a5CfGGOckeWGSN7Z9X5ILktx7E9f6syQnLH7Y82o/EDrGuDLJA5M8tO37F5/3nCTHLx327By4C3/udf5WAAAcFjqGJyFm1PaZSf56jPGfNnvOLW98/fHXf/vFHZwKAIDrou3+McbaRuvunE+m7de1vTgHfk/6r616HgAAdo8fCJ3MGOOTSb5l1XMAALD73DkHAIBJiHMAAJiEOAcAgEmIcwAAmIQ4BwCASYhzAACYhDgHAIBJiHMAAJiEOAcAgEmIcwAAmIQ4BwCASYhzAACYhDg/ghxzg5NWPQIAANeBOAcAgEmIcwAAmIQ4BwCASYhzAACYhDgHAIBJiHMAAJiEOAcAgEmIcwAAmIQ4BwCASexZ9QBsn7//wmdy/rMeeMi1uzz25bs8DQAAW+XOOQAATEKcAwDAJMQ5AABMQpwDAMAkxDkAAExCnAMAwCTEOQAATEKcAwDAJMQ5AABMQpwDAMAkxDkAAExCnAMAwCTEOQAATEKcAwDAJMT5IbS9pO3Fbd/X9sNtX9b2OzZx3hltb7sbMwIAcOQR5xs7fYxxpzHGbZI8L8mr2t7jWs45I4k4BwDgqyLON2GM8bIkz0zyxLb3bfv2tu9te1HbH0mSto9OctckZ7e9oO39296z7XsW2xe1fdzi2Be0ffz69ds+o+1TF++f2vZFbV/e9oNtX9/2pF3/0gAA7Lo9qx7gMPKuJN+X5Pwk3znGuGoRzee3fc0Y4zltH57kGWOMVyRJ25cl+ZUxxosW2zfZ5GfdI8ndxhifbfuHSX40yS9u9xcCAGAu4nzzuvjz5CTPXTxbfmWSmya5fZK/PsQ5b0jy5La3TvL6McZbN/lZrx5jfHbx/u1J7njIgdozk5y5vn2D4/3nBAA4nHmsZfPuluSiJM9K8qYkdxxjnJrkL5PsPdQJY4xfS/KAHAj3X2j7zMXSlUmOWTr04POvWHp/VTb4H1FjjLPGGGvrr+sfJ84BAA5n4nwT2j4oyeOSnJXkJkn+aowx2n53kjstHfq5JCcunffNY4yPjTGeneQXktxzsfTRHHh0JW1PTnL/nf8WAADMzq3WjZ3b9ktJTkjywST3H2O8o+3PJHnm4s8PJnnn0jm/k+RX2v50kv8vyf3b3jvJl3PgDvhPLY777cX1L8yBUF++BgAAR6mOMVY9A9vkFjc5frz6F+93yLW7PPbluzwNAAAHa7t/jLG20brHWgAAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4hwAACYhzgEAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4hwAACYhzgEAYBLiHAAAJtExxqpnYJusra2Nffv2rXoMAAA20Hb/GGNto3V3zgEAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4hwAACYhzgEAYBLiHAAAJiHOAQBgEntWPQDb50tfuDSve879v+rz7/foV23jNAAAbJU75wAAMAlxDgAAkxDnAAAwCXEOAACTEOcAADAJcQ4AAJMQ5wAAMAlxDgAAkxDnAAAwCXEOAACTEOcAADAJcQ4AAJMQ5wAAMAlxDgAAk9iz6gGOdG0vSXLF4pUk70jy6SQfGGP80bWce0aSPx9j/OVOzggAwBzE+e44fYxx0Vdx3hlJLk0izgEAjgIea1mBti9o+/jF+/u2fXvb97a9qO2PLPY/Osldk5zd9oK291/lzAAA7Dx3znfHuW3XH2v5jwetnZ/kO8cYV7U9Kcn5bV8zxnhO24cnecYY4xW7Oi0AACshznfH1R5rafugpbWTkzy37W2TXJnkpklun+Svr+2ibc9Mcub69gnH+88JAHA481jL6j0ryZuS3HGMcWoOPF++dzMnjjHOGmOsrb+OP+6YnZwTAIAdJs5X7yZJ/mqMMdp+d5I7La19LsmJqxkLAIDdJs5X72eS/HLbd+TAb2d559La7yR5ih8IBQA4OnhIeYeNMU45xL4zlt7/aZLbbHDuK5L4YVAAgKOEO+cAADAJcQ4AAJMQ5wAAMAlxDgAAkxDnAAAwCXEOAACTEOcAADAJcQ4AAJMQ5wAAMAlxDgAAkxDnAAAwCXEOAACTEOcAADAJcQ4AAJMQ5wAAMImOMVY9A9tkbW1t7Nu3b9VjAACwgbb7xxhrG627cw4AAJMQ5wAAMAlxDgAAkxDnAAAwCXEOAACTEOcAADAJcQ4AAJMQ5wAAMIk9qx6A7XP5Fy/Nuc//F6seg8PU6T/ymlWPAABHPXfOAQBgEuIcAAAmIc4BAGAS4hwAACYhzgEAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4hwAACYhzgEAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4nybtb2k7cVtL1i8nnUdr3da23dv13wAAMxrz6oHOEKdPsa4aKsntd0zxrhyJwYCAGB+7pzvgra3aPsnbS9se1HbxyytXdL2SW3fkOR3F/ue1vYjbd+U5AGrmhsAgN3lzvnOOLftFYv3/zHJDyS5eIzx4LY3T/KetheMMc5bHPOPktxnjDHaPjDJ9yY5NcnlSf5kt4cHAGA13DnfGaePMU5dvP4kyf2S/GaSjDE+neSlSe67dPzzxxhj8f7eSf5ojHHZGOOqJM/b6EPantl23/rriiuu2plvAwDArhDnu2dcw/ZlS++76QuOcdYYY239tXfvMddpQAAAVkuc747XJXlMkrS9WZIHJ3n9Bsf+WZKHtj2h7TFJztiVCQEAWDnPnO+OJyR5Vtv358D/IHr60vPmVzPGeEXbeyV5X5L9Sd6UZG3XJgUAYGXE+TYbY5xyiH2fyoG75Zs9/klJnrS06ynbNB4AABPzWAsAAExCnAMAwCTEOQAATEKcAwDAJMQ5AABMQpwDAMAkxDkAAExCnAMAwCTEOQAATEKcAwDAJMQ5AABMQpwDAMAkxDkAAExCnAMAwCQ6xlj1DGyTtbW1sW/fvlWPAQDABtruH2OsbbTuzjkAAExCnAMAwCTEOQAATEKcAwDAJMQ5AABMQpwDAMAkxDkAAExCnAMAwCTEOQAATGLPqgdg+3zxi5fmub/3z1c9BsCWPeoRr131CABTcOccAAAmIc4BAGAS4hwAACYhzgEAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4hwAACYhzgEAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4nwT2l7S9g4H7Xtj2wds42ds6/UAADj8iHMAAJiEOL+O2t6i7Z+0vbDtRW0fs7R2Sdufa/vnbf9n2ycvrd2u7Tvbnt/295PsXVo7s+272r637Xlt77HLXwsAgBXYs+oBDiPntr1iafvWiz/PTnLxGOPBbW+e5D1tLxhjnLdYv/EY4zva3izJR9o+f4yxP8k5Sc4eY/xu23smedvStc8ZY5yVJIu15ya52mM1AAAcecT55p0+xrhofaPtGxdv75fkTkkyxvh025cmuW+S9Tj//cXa37T9WJJvavv5HIjtcxZr72h74dJn3bntk5KcnOTKJLdre+wY48vLA7U9M8mZ69vHH+8/JwDA4UzNbY9xDdvLd9uvyv/5Oz/4nCRJ22OTvCTJaWOM97S9UZK/S3JskqvF+eLu+lnr2yedtPeQ1wQA4PDgmfPr7nVJHpMki0dXHpzk9dd0whjjc0kuSvJDi/PunuSOi+W9Sa6X5BOL7R/f/pEBAJiROL/unpDk29q+P8kbkjx96Xnza/KIJI9ve34OxP07k6+E+1OSnNf2zUm+tDNjAwAwm47hSYgjxUkn7R2//Gvfs+oxALbsUY947apHANgVbfePMdY2WnfnHAAAJiHOAQBgEuIcAAAmIc4BAGAS4hwAACYhzgEAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4hwAACYhzgEAYBLiHAAAJiHOAQBgEh1jrHoGtsna2trYt2/fqscAAGADbfePMdY2WnfnHAAAJiHOAQBgEuIcAAAmIc4BAGAS4hwAACYhzgEAYBLiHAAAJiHOAQBgEuIcAAAmsWfVA7B9Lrv80jzjD/75qscAAI5iT3zYa1c9wmHNnXMAAJiEOAcAgEmIcwAAmIQ4BwCASYhzAACYhDgHAIBJiHMAAJiEOAcAgEmIcwAAmIQ4BwCASYhzAACYhDgHAIBJiHMAAJiEOAcAgEkc1XHe9pK2dzho3xvbPmCXPn9Tn9X2grbH78ZMAACszp5VD8C1G2OcuuoZAADYeUf1nfNrcFLbT7W9/vqOtn/Q9nGL96PtDZbWLm17yuL9JW1/ru2ft/2fbZ+8dNzt2r6z7fltfz/J3qW1M9u+q+17257X9h5La1f7PAAAjkziPDl38djIBW0vSHLXJJ9N8rokP5gkbb82yf2SnLPJa954jPEdSe6e5Kfbfv1i/zlJnjnGuEuS/5rkbkvnnDPGuNsY485JnpDkudf1iwEAcHjxWEty+hjjovWNtm9cvP31JL+d5DlJfjTJi8YYl23ymr+fJGOMv2n7sSTf1PbzSe6QReCPMd7R9sKlc+7c9klJTk5yZZLbtT12jPHljT6k7ZlJzlzf3nu8/5wAAIczNbeBMcZ5ba9o+z1J/k2S+ywtX5XkmKXtvbm6Kw46dv3veRzqs9oem+QlSU4bY7yn7Y2S/F2SY5NsGOdjjLOSnLW+feOT9x7y+gAAHB481nLNfj3JC5N8YIzxl0v7P5rkHknS9iFJTri2C40xPpfkoiQ/tDjv7knuuFjem+R6ST6x2P7x7RgeAIDDizi/ZucmuUGS3zho/08m+c22b0tylySf2eT1HpHk8W3PT/KYJO9MvhLuT0lyXts3J/nSNswOAMBhpmN4EmIji7vbL0zyLWOMf1j1PNfmxifvHU/+je9Z9RgAwFHsiQ977apHmFrb/WOMtY3WPXO+gbbPSfLPkjz6cAhzAAAOf+J8A2OMR696BgAAji6eOQcAgEmIcwAAmIQ4BwCASYhzAACYhDgHAIBJiHMAAJiEOAcAgEmIcwAAmIQ4BwCASYhzAACYhDgHAIBJiHMAAJiEOAcAgEl0jLHqGdgma2trY9++faseAwCADbTdP8ZY22jdnXMAAJiEOAcAgEmIcwAAmIQ4BwCASYhzAACYhDgHAIBJiHMAAJiEOAcAgEmI8yPI311+6apHAADgOhDnAAAwCXEOAACTEOcAADAJcQ4AAJMQ5wAAMAlxDgAAkxDnAAAwCXEOAACTEOcAADAJcQ4AAJMQ5wAAMAlxDgAAkxDnAAAwCXEOAACTEOcAADAJcX4dtb2k7R1WPQcAAIc/cQ4AAJMQ59uk7ZPb/kXbCxavb1zsH21vsHTcpW1PaXvq0rEXtP1c25+7pnN2+zsBALC79qx6gCPETZI8McktxxiXt71+kn+4phPGGBckOTVJ2t47ybOTPGcrH9r2zCRnrm8fe33/OQEADmfunG+PzyX5cJIXtv3RJCeNMa7YzIltb5/k+Um+b4yxfysfOsY4a4yxtv46du8xWx4cAIB5iPPtcVWSeyb5tSQ3T/KOtt+1tLZczXvX37T9uiQvS/LIMcZFB13vkOcAAHDkEufbo0luMcZ4yxjjPyV5a5I7L9Y+muQeSdL2IUlOWLy/YZJXJvm5McbrD7reIc8BAODIJs63x4lJXtr2wrbvT3K9JL+7WPvJJL/Z9m1J7pLkM4v9D0nyLUl+eumHQh97LecAAHAE6xhj1TOwTW548t7x+c9s6lF3AABWoO3+McbaRuvunAMAwCTEOQAATEKcAwDAJMQ5AABMQpwDAMAkxDkAAExCnAMAwCTEOQAATEKcAwDAJMQ5AABMQpwDAMAkxDkAAExCnAMAwCTEOQAATEKcH0FOPP6mqx4BAIDrQJwDAMAkxDkAAExCnAMAwCTEOQAATEKcAwDAJMQ5AABMQpwDAMAkxDkAAExiz6oHYPtcesX/zr982aO27XqvftBzt+1aAABcO3fOAQBgEuIcAAAmIc4BAGAS4hwAACYhzgEAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4hwAACYhzgEAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4nyT2l7S9g6rngMAgCOXOD+MtN2z6hkAANg54nyL2p7Z9l1t39v2vLb3WFr7/rYXL9ae3Ha0vcFi7SvvF9uXtj1l8f6XF9e8oO2b2t5msf+UxXFPafuWJD++q18WAIBdJc637pwxxt3GGHdO8oQkz02StjdP8jtJHrhYu2wL1/ylxTVPTfJbSX51ae3kJB8ZY3zXGONXD306AABHAo9JbN2d2z4pB6L5yiS3a3tsknsmOX+M8eHFcc/P1SP7mvyztj+e5IY58D+YbrS0dkWSPzjUSW3PTHLm+vae619vK98DAIDJiPOt+ZokL0ly2hjjPW1vlOTvkhybpEnGNZx7VZJjlrb3Jknbf5Tk7CR3H2N8rO23JXn90nFfGGMc8rpjjLOSnPWVC970hGv6fAAAJuexlq27XpJPLN4vPwP+jiTf3vbWi+0fPui8jya5R5K0fUiSExb7T0zy5ST/q22TPH4nhgYAYH7ifPP2JPlikqckOa/tm5N8aX1xjPGpJI9N8sq2f54D8f33i3OS5CeT/GbbtyW5S5LPLM67MMkfJ/lAkjcm+fhufBkAAObTDZ6YYEnbWya5OMnXjjEuv4bjbjjG+Pzi/Y8kedQY4zt3aczsvekJ497P/YFtu96rH/TcbbsWAABJ2/1jjLWN1j1zfi0WP3T5o0meeE1hvvCEtv8qB/5eP5vk3+z0fAAAHDnE+bU4+Icur+XYpyd5+s5OBADAkcoz5wAAMAlxDgAAkxDnAAAwCXEOAACTEOcAADAJcQ4AAJMQ5wAAMAlxDgAAkxDnAAAwCXEOAACTEOcAADAJcQ4AAJPoGGPVM7BN1tbWxr59+1Y9BgAAG2i7f4yxttG6O+cAADAJcQ4AAJMQ5wAAMAlxDgAAkxDnAAAwCXEOAACTEOcAADAJcQ4AAJMQ5wAAMIk9qx6A7XPp5Z/P/f/kaRuuv+rBT97FaQAA2Cp3zgEAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4hwAACYhzgEAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4hwAACYhzgEAYBLiHAAAJiHOd0DbS9reYdVzAABweBHnAAAwCXG+g9p+bdsXtz2v7fvb/vzS2m3avrLtu9q+r+2PLa2Ntk9t+7a2f9n2Yav5BgAA7KY9qx7gCPe7SZ4+xnhz2z1JXtH2wUn+e5IXJfnXY4yL214/yTvavmOMcf7i3DHG+Cdtb5XkvLZvHWN8YjVfAwCA3SDOd84xSe6T5BZt1/fdIMm3JPlQktsn+cOltRsmuV2S9Th/TpKMMT7W9q1JvisHgv4r2p6Z5Mz17T3XP24nvgcAALtEnO+cf0gyktxtjPH3ywttb5/k0jHGqVu43vi/doxxVpKz1rf3nnzi/3UMAACHD8+c75yR5C1JfmZ9R9uva7uWA3fOv9j2EUtrt2570tL5j1zsPyXJdyZ56y7MDADAConznbEnyRVJfijJt7a9sO2FSV6S5OQxxpVJHpjkoYsfFP1ADjzGcvzSNb7U9m1J/keSH/e8OQDAkc9jLdus7S1z4Pnx/WOMy5P84KGOG2N8OMkDruFSzxxj/JcdGBEAgEm5c76NFj+g+cYkT1yEOQAAbJo759vo4B/QvA7X6bUfBQDAkcadcwAAmIQ4BwCASYhzAACYhDgHAIBJiHMAAJiEOAcAgEmIcwAAmIQ4BwCASYhzAACYhDgHAIBJiHMAAJiEOAcAgEl0jLHqGdgma2trY9++faseAwCADbTdP8ZY22jdnXMAAJiEOAcAgEmIcwAAmIQ4BwCASYhzAACYhDgHAIBJiHMAAJiEOAcAgEmIcwAAmMSeVQ/A9vnM5V/I//PS39i1z3vlQx6/a58FAHA0cOccAAAmIc4BAGAS4hwAACYhzgEAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4s652zEAACAASURBVBwAACYhzgEAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4hwAACYhzrdJ20va3mGHrj3a3mAnrg0AwDzEOQAATEKc76C2v9z2XW0vaPumtrdZ7D+l7aVtf77te9p+pO39l857SNuL27697X9Y3TcAAGA3ifOd9UtjjLuNMU5N8ltJfnVp7eQk7xljfHuSx6+vtb15kmcnedAY415JvrTRxdue2Xbf+uvKK67YsS8CAMDOE+c7658t7n5flOQpSU5dWvvCGONli/dvT/KPF+/vmeT8McaHFtu/s9HFxxhnjTHW1l979u7d7vkBANhFe1Y9wJGq7T9KcnaSu48xPtb225K8fumQ5dvcVyU5Zv3UXRoRAIDJuHO+c05M8uUk/6ttc+DRlc14e5I7t73tYvvROzEcAADzced8e72u7ZVL269O8oEkH0/yp5u5wBjj020fk+TlbT+T5NztHxMAgBmJ820yxjhlE4c9bXHsJUluunTuZVl6nGWM8dIkL10676xtGRIAgKl5rAUAACYhzgEAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4hwAACYhzgEAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4hwAACYhzgEAYBIdY6x6BrbJ2tra2Ldv36rHAABgA233jzHWNlp35xwAACYhzgEAYBLiHAAAJrHpOG/7LTs5CAAAHO22cuf8NW3/tO2D2nbHJgIAgKPUVuL8VkmeleQnknys7b9re/LOjAUAAEefTcf5GOMfxhgvGWPcJ8lDkzw+ySfaPrvt1+3YhAAAcJTY0g+Etv3HbX8lyUuTvDLJdyb5cJLX7MBsAABwVNmz2QPbvibJbZM8M8kdxxh/u1g6v+0jdmI4tuYzl38xD3jJC1Y9BpN6xfefseoRAIBrsek4T/KcJC8dY/zDwQtjjDts30gAAHB02nScjzHObfs1i+fL9yzt//iOTAYAAEeZrTzWckaSs5P8fZL1u+cjyc23fywAADj6bOWxlv+Q5O5jjIt3ahgAADiabeW3tfyNMAcAgJ2zlTh/advHtz2p7fXXXzs2GQAAHGW28ljLf178eXYOPGvexZ/HbPdQAABwNNrKb2vZ0j9YBAAAbM1W7pwnSdruSXLs+vYY44vbOhEAABylNn03vO3d216Y5Iokn196AQAA22Ard87PTvLoJM9K8t1JnpDk8p0YCgAAjkZbeY78emOMdybZM8b4/Bjj6Um+d4fmAgCAo85W4vzKxZ+faXtq25sm+cYdmAkAAI5KW3ms5Q/bnpzkF5K8eXHuU3ZkKgAAOApt5Vcp/uri7f9YRPreMYYfCAUAgG2ypd9d3vb0ts/KgR8O/ac7M9L2a3tJ20+3vd7Svvu0HW2fcS3nvqDt4zdYe2zbf/tVzHNa23dv9TwAAI5sm75z3va/JLl3khcudv37tncfY/zMjky2/T6eAz/A+pLF9iOTXKdAHmM867oOdbC2e8YYV177kQAAHGm2cuf8QUm+e4zx62OMX09yWpIH78hUO+N5ORDkaXtiknsmec1i+45t39L2/LYfbPuzB517u7ava/uXbV/a9tjFeU9dv/Pe9oy2r237B20vbPvutrdav0Dbp7X9SNs3JXnA0v7T2l7Q9uy2b0/y4LY/2Padbd+7WLv/Tv7FAAAwh63E+SeTfGlp+8uLfYeLNye5VduvT/KwJH+c5KrF2iVJ7jfGuEuSb0/y0LZ3XTr31CQPTPKtSW6R5Ps3+Ix7JPmZMcYdk7wuyb9PkrYPzIG79qcmuU+S2x503rclefEY415jjD9O8tok9xxj3DnJ9yV5zvIjOQAAHJm2EucXJHlV24e1fViSlyd5W9v7H0Z3ds9J8sM5cAf9eUv7j8+BAL4wyTty4FdEnrq0/tIxxuVjjKuSnJfkH29w/beOMf5q8f7tS8fdO8kfjTEuW1zjeQed95djjLcubX9Tkle3vSjJf0tyyF9b2fbMtvvWX1deccU1fnkAAOa2lV+luB6rj1na908Wr5HkVds11A56QZLzcyCGP9x2ff8vJPlUkjuPMa5s+9Ike5fOW67eq7Lx39tGx/UQxy677KDtP0zyxDHGf0uStp89aJ4kyRjjrCRnrW8ff/JJ41o+BwCAiW3lVyneeycH2Q1jjE8unie/+KClmyS5aBHm35wDv4nm9dv40X+W5Oltfy0HAv6Mazn+JjnwqE3aPnyxDQDAEW4rd87T9tZJbrV83hjjcLhj/hVjjOcfYvfTkpzT9odyIIq3M8wzxnhF23sleV+S/UnelGTtGk75iSR/0nZ/Djwe8/HtnAcAgDl1jM09CdH2V5I8PMmH8n9+kHKMMe6zQ7OxRceffNK47++cde0HclR6xfefseoRAOCo13b/GGPDm7RbuXP+oCTfNMb44nUfCwAAONhWflvLX+XAr08EAAB2wFbunP9Ukpe3/dMs/VaSMcYzt30qAAA4Cm0lzn82yS1z4FcqfuWZ822fCAAAjlJbifO7JLnt2OxPkAIAAFuylWfOL05ywk4NAgAAR7ut3Dn/fJL3tH1trv7M+b/b9qkAAOAotJU4/9DiBQAA7IBNx/kY4z/u5CAAAHC02/Qz521v2Pa/tv2Lth9s++ttb7iTwwEAwNFkKz8Q+swkxyZ5WJIfTHK9xT4AAGAbbOWZ828bY9xpafvH2r5vuwcCAICj1VbunB+z/BhL2xts8XwAAOAabOXO+e8leUfb38+Bfxn0B5L87o5MBQAAR6Fe2z/42fZGSU4aY1zS9l8muW+SJvlokheOMT6382OyGWtra2Pfvn2rHgMAgA203T/GWNtofTOPpfyXJN+eJGOMV48xnjjG+Kkc+EeJ/vP2jAkAAGwmzr97jPGSg3eOMc5J8t3bPxIAABydNhPnV13D2jU/EwMAAGzaZuJ8z+K586tpe2IO/K5zAABgG2wmzv8gyTltb7K+Y/H++Un+cKcGAwCAo81m4vzpSf42ySfavrfte5N8Igd+IPQ/7eRwAABwNLnW33M+xrgqyQ+3/fkkd1nsPn+M8dEdnQwAAI4ym/5HiBYxLsgBAGCHbOaxFgAAYBds+s458/vM5ZfnAee+eNVjAAAcFl5x+kNXPcL/xZ1zAACYhDgHAIBJiHMAAJiEOAcAgEmIcwAAmIQ4BwCASYhzAACYhDgHAIBJiHMAAJiEOAcAgEmIcwAAmIQ4BwCASYhzAACYhDgHAIBJiPNNaHtJ20+3vd7Svvu0HW2fsc2f9ZNtb760/di2/3Y7PwMAgDmJ8837eJLvXdp+ZJJ3b+UCbfds4rCfTPKVOB9jPGuM8atb+RwAAA5P4nzznpcDQZ62Jya5Z5LXLLbv2PYtbc9v+8G2P7t+UtsXtD277WuSvG+xb7S9wdIxl7Y9pe1TknxdknPbXtD21LZP3e678wAAzEmcb96bk9yq7dcneViSP05y1WLtkiT3G2PcJcm3J3lo27sunfudSU4fY9z+mj5gjPHzST65OPbUMcYF2/wdAACYmDjfmnOS/HAO3EF/3tL+45M8p+2FSd6R5BuTnLq0/uIxxmXbPUzbM9vuW39decUV2/0RAADsInG+NS9I8oQkV4wxPry0/xeSfCrJnccYd0ryxiR7l9YPDvOrkhyztL03X4UxxlljjLX11569X9VlAACYhDjfgjHGJ5P8bJJ/f9DSTZLsG2Nc2fabk/zTa7nUR5PcI0naPiTJCUtrn0ty4vZMDADA4WQzvz2EJWOM5x9i99OSnNP2h3Lg+fPXX8tlfjLJb7b9dJI3JPnM0trZSZ7f9otJzrjOAwMAcNjoGGPVM7BNjj/55HHf3/6tVY8BAHBYeMXpD931z2y7f4yxttG6x1oAAGAS4hwAACYhzgEAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4hwAACYhzgEAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4hwAACbRMcaqZ2CbrK2tjX379q16DAAANtB2/xhjbaN1d84BAGAS4hwAACYhzgEAYBLiHAAAJiHOAQBgEuIcAAAmIc4BAGAS4hwAACYhzgEAYBJ7Vj0A2+czl1+RB537qlWPwVHiZafff9UjAMARx51zAACYhDgHAIBJiHMAAJiEOAcAgEmIcwAAmIQ4BwCASYhzAACYhDgHAIBJiHMAAJiEOAcAgEmIcwAAmIQ4BwCASYhzAACYhDgHAIBJiHMAAJjEjsd520vafrrt9Zb23aftaPuMnf78a5jpDtt0rVPaXtn2gsXrL9o+eTuuDQDA0WW37px/PMn3Lm0/Msm7t3KBtl/TdtY7/X87xjh1jHFqkrsneXzb2696KAAADi+7FbvPy4EgT9sTk9wzyWvWF9s+se15bc9v+6q237DY/9S257R9aZILktyy7be2fW3b9y9ej10c+7VtX7y4zvvb/vzS9b+r7YWLtd9I0qW127R9Zdt3tX1f2x9b7D++7R+1/eBi///Y5He94eL6n1tc54Ztn70017PW/1+Etm9s+0tt39L2o22ftTTXu5fuxn+y7Ru2/LcOAMBhZc8ufc6bk/x4269P8v+3d+9xm5V1vfg/XxhgOGmAIOKQpOYBFdEwQ9Q8tStLTdFK8YDaNn9lapOaWdvM8rf3b2eYh8xUEEPTFDqghQdUUhI1xOHoaauEg9vwkCngiMD398e9Hn0c5xlmhmfmvubh/X691ut1r+taa93fdbEe789cXvfzPCzJ25NclyRV9bgkd0hydHdfV1VPSPKqJI+Yzn1gknt29xVVtSrJB5L8QXe/bTr/FtNxb0zyku7+4HTcO6vqkUn+OclbkxzX3WdV1S8n+c3p3F2T/E2SJ3T3p6pqryQfqaqPJLlNkv26+/Dp2P03c38/UlXrkuw63cv/7u4vTn1/luSD3f3fq6qSvC7JM5K8bOq/XZIHJNk9ySVVdXR3n9PdR03ve6tp/P5k4zetqrVJ1i7sr9prr82UCADA6HZUOE+SU5I8KckvJTlu2jLtH5Xk47Psml0zBffJO7v7iun1HZOsWgjmSdLdX62qvZM8KMktp2skyT5J7pTkc0mu7u6zpuPfVlWvXXS9uyR566Lz9k1yeJIPJ7lTVb06yb9kFvKX8o1pSctCiH9fVf1bd58+3d9PVdXvTMfumeSaRee+tbuvS/LtKeDfLsk507X2SfJPSV7c3e/b+E27+4QkJyzs73nALXozNQIAMLgdGc5PTnJeks9092cXheFK8ifdfdIS5125BdfeJUknuVd3f3dxR1XdfTPnVZKvLgTrH+qsOjyz0P+QJP+7qo5M8kdJ7j8d8oQk31p8Tnd/varem+Rnk5w+vccvdffnl6hhw6LX12X6bzLN/p+a5LTuPmUz9wAAwAqxw75g2d1fSvJ7SX53o67Tk/zGwrKRqtqtqu6xxGU+neSaqnrMQkNV3aK7v5XkQ0mev6j9kKpak+RTSfasqvtP7Y9OcvNF17u6qp646LzbV9X+07k9zX4/J7OQfWh3P3Phy5/dfeHGBVbVHkmOma69cH/Pn8J2qmq/qrr9DQxXkvxVksu6+yVbcCwAACvADv3tJ939hu4+Z6O2U5K8KclZVXV+Zl/8fOAS51+b2Vr0p01f8LwgybFT93FJ7jy1X5jktCQHdPd3kjw2yV9U1ccy+20qly263sOS/PL0Zc2Lk7w+s6Und0vy4ek9zktySndfsMSt/cjClzen+s9P8pdT37OTXJtk3XStM5MctrlxqqrbZPYF2qMXfSn09Zs7BwCAnV91W6a8Uux5wC36v/3VX8+7DG4i/vHRD513CQCw06mqy7t7zVL9o/7ecAAAuMkRzgEAYBDCOQAADEI4BwCAQQjnAAAwCOEcAAAGIZwDAMAghHMAABiEcA4AAIMQzgEAYBDCOQAADEI4BwCAQQjnAAAwCOEcAAAGUd097xpYJmvWrOn169fPuwwAAJZQVZd395ql+s2cAwDAIIRzAAAYhHAOAACDEM4BAGAQwjkAAAxCOAcAgEEI5wAAMAjhHAAABrFq3gWwfL727WvyyNPO3qpz/v7Y+26nagAA2FpmzgEAYBDCOQAADEI4BwCAQQjnAAAwCOEcAAAGIZwDAMAghHMAABiEcA4AAIMQzgEAYBDCOQAADEI4BwCAQQjnAAAwCOEcAAAGIZwDAMAghHMAABiEcL6dVNWlVXVFVe22qO1BVdVV9dKqenhV/ekWXOeQqvrA9q0WAIARCOfb12VJHr5o/ylJzk2S7j69u597Qxfo7i919wO3U30AAAxEON++TsoskKeqbp7kp5K8a9o/vqpOnV4/oKrWVdWrq+r8qrq4qo6a+g6rqq/OqX4AAHYg4Xz7+mCS21bVrZM8Nsnbk1y3xLF3SXJSd989ySuTvGTHlAgAwCiE8+3vlCRPymwG/aTNHPfp7j53en1Oktvd0IWram1VrV/Yrt1w9Y2vFgCAuRHOt7+TkzwzyYbu/uxmjtuw6PV1SVbd0IW7+4TuXrOwrVq9142rFACAubrBAMiN091fqqrfS/KpedcCAMDYhPMdoLvfMO8aAAAYn3C+nXT3YUu0v2jR7slT21lJjlp0zEVJDpteX5rkFtujRgAAxmLNOQAADEI4BwCAQQjnAAAwCOEcAAAGIZwDAMAghHMAABiEcA4AAIMQzgEAYBDCOQAADEI4BwCAQQjnAAAwCOEcAAAGIZwDAMAghHMAABhEdfe8a2CZrFmzptevXz/vMgAAWEJVXd7da5bqN3MOAACDEM4BAGAQwjkAAAxCOAcAgEEI5wAAMAjhHAAABiGcAwDAIIRzAAAYhHAOAACDWDXvAlg+X//2tfnl0y7ZpnPfduzhy1wNAABby8w5AAAMQjgHAIBBCOcAADAI4RwAAAYhnAMAwCCEcwAAGIRwDgAAgxDOAQBgEMI5AAAMQjgHAIBBCOcAADAI4RwAAAYhnAMAwCCEcwAAGMSKDOdVdWlVXVFVuy1qe1BVdVW9tKoeXlV/OrUfVlVPW4b37KraZ4m+dVW152bOPayqvrqZ/s2eDwDAyrAiw/nksiQPX7T/lCTnJkl3n97dz53aD0tyo8P55nT3kd397XmdDwDAzmElh/OTMgvkqaqbJ/mpJO+a9o+vqlOn416T5PBpdvr0qtqlql5VVZ+qqvOr6uNVtXrj2e2q2qeqeqP3fE5V/WtVfaaqHrvo2J6O3+S1Fx334qnt/1TVQzc+f7kHCACAsayadwHb0QeT/FZV3TrJw5K8Pcl1mzju6Ule2t1HJUlV3SPJg5Mc3t3XT8H+mi18z+7uY6rqtkk+VlVnd/cXF/XffTPXPiDJx7v7hVX1c0lenuSft+qOAQDYqa3kmfMkOSXJkzKbQT9pC8/5fJLdkpxUVU9Kslt3X7+F574+Sbr780nOTnK/rbj2Vd39j9Prc5Lc7oberKrWVtX6he3aDVdtYZkAAIxopYfzk5M8M8mG7v7slpzQ3f+V5C5J/ibJnZJcUFW3T3Jtkl0XHbp6E6f/0OW28NpJsmHRoddt9F5L1XpCd69Z2Fat3nsLSgIAYFQrOpx395eS/F6S393MYd9McvOFnao6MMne3f2eJC9IcmmSw5N8OcmqqrrjdOgTN3GthTXuhyW5b2az59+zmWsDAMCKXnOeJOnuN9zAIRck+XRVXZTZspMXJXnd9GsYd0ny4SRndPe1VfXMJGdU1fokZ2ziWt+pqn9NcmCS39povXmSHLqpaye59bbdHQAAK0l1b/wLR9hZ7XXAwf2Lr33/Np37tmNN4AMAbG9VdXl3r1mqf0UvawEAgJ2JcA4AAIMQzgEAYBDCOQAADEI4BwCAQQjnAAAwCOEcAAAGIZwDAMAghHMAABiEcA4AAIMQzgEAYBDCOQAADEI4BwCAQQjnAAAwiOruedfAMlmzZk2vX79+3mUAALCEqrq8u9cs1W/mHAAABiGcAwDAIIRzAAAYhHAOAACDEM4BAGAQwjkAAAxCOAcAgEEI5wAAMAjhHAAABrFq3gWwfL757evyor//0rzL2GYveuQh8y4BAGCuzJwDAMAghHMAABiEcA4AAIMQzgEAYBDCOQAADEI4BwCAQQjnAAAwCOEcAAAGIZwDAMAghHMAABiEcA4AAIMQzgEAYBDCOQAADEI4BwCAQQjnAAAwCOH8BlTVpVX1qapatajt3Kp6QFW9uKp+ZZ71AQCwcgjnW2aPJE/duLG7X9jdfzuHegAAWIGE8y3zh0n+R1Xttbixqk6uqmdMrx9WVRdU1bqquqiqHjG1/0FVfXJqX1dVt5na3zTNwF9QVe+sqoOm9gdMx726qs6vqour6qgdfL8AAMyBcL5lzkvywSS/vZlj/iTJ07v7yCRHJPmXqtovyXOS3HNqv0+S/5iOf3Z3H9XdRyQ5O8kLF13rLklO6u67J3llkpds6g2ram1VrV/Yrtlw1Y24RQAA5k0433J/kOTZVXXAEv3vS/LnVfW8JEd09zeSfDPJZ5O8qap+Pcn+3b1hOv64aeb8wiS/luTIRdf6dHefO70+J8ntNvWG3X1Cd69Z2HZfvfeNu0MAAOZKON9C3f35JG/JLKRvqn9tkicnuTrJG6vqed19XZKfSvLnSQ5K8pGqul9V3TfJM5L8fHffLcnaJKsXXW7DotfXJVkVAABWPKFv6/xxkkuSfHfjjqq6U3dfnOTiqro2yX+rqn2T7NvdH0ryoaq6S5J7JPlCZrPqX6+q3ZP8+g67AwAAhiWcb4Xu/kpVvSLJizfR/T+r6g5Jrsls9vz/SXLzJKdW1d5JOrMlLm9MclWSxyf5VJL1ST6c5Ge3/x0AADCy6u5518AyudkBt+q1r//4vMvYZi965CHzLgEAYLuqqsu7e81S/dacAwDAIIRzAAAYhHAOAACDEM4BAGAQwjkAAAxCOAcAgEEI5wAAMAjhHAAABiGcAwDAIIRzAAAYhHAOAACDEM4BAGAQwjkAAAxCOAcAgEFUd8+7BpbJmjVrev369fMuAwCAJVTV5d29Zql+M+cAADAI4RwAAAYhnAMAwCCEcwAAGIRwDgAAgxDOAQBgEMI5AAAMQjgHAIBBrJp3ASyfq799fd582leW7D/u2AN3YDUAAGwtM+cAADAI4RwAAAYhnAMAwCCEcwAAGIRwDgAAgxDOAQBgEMI5AAAMQjgHAIBBCOcAADAI4RwAAAYhnAMAwCCEcwAAGIRwDgAAgxDOAQBgEMI5AAAMQjhPUlWXVtWnqmpdVV1SVb+5A96zq2qf7f0+AADsPFbNu4CBPLq7L6qqQ5NcWFUf6u4LbuikqtolSbr7+u1eIQAAK5qZ84109xeTfCbJHarqeVV1cVVdWFVvrqqbJ0lVvaiqTqmqv0uyLsmtqupV0+z7+VX18apaPR37C1X1b1P7uqq696K3+82q+mhVfaGqnrzQWFU/XlX/tOi839iBQwAAwJyYOd9IVd0tyZ2S3CrJk5Mc3d3fqKrXJvl/kywseXlgknt29xVVdY8kD05yeHdfP4X4a6rqDklOTHL/7v5MVe2WZK9Fb7ehu+9dVXdO8rGqOiVJJ/mbJE/o7k9V1V5JPlJVH+nu87b/CAAAMC/C+fedWlUbklyd5ClJjk7y5u7+xtT/l0neuuj4d3b3FdPrzyfZLclJVfWBJP80hfSfSfLP3f2ZJOnu7yb5r0XXePPU/smqujbJwUluluQuSd5aVQvH7Zvk8CQ/EM6ram2StQv7e+51sxtx+wAAzJtw/n2P7u6LFnaq6j6ZzWIvtnj/yu81dv9XVd0lyU9nNqP+P6vq/lvwnhsWvb4us/8eleSr3X3kDZ3c3SckOWFhf/8DDtm4XgAAdiLWnC/tvUl+tar2nfafluTMTR1YVQcm2bu735PkBUkuzWym+91Jfn5a3pKq2m1h3fpmfDrJ1VX1xEXXv31V7X9jbgYAgPGZOV9Cd58xrT8/p6o6yQVJlvpi5qFJXjetKd8lyYeTnNHd362qpyZ5y9R3XZJfT/KxzbzvtVX1sCQvq6rnJNk1yVeSHLdc9wYAwJiq20qIlWL/Aw7pV772/CX7jzv2wB1YDQAAG6uqy7t7zVL9lrUAAMAghHMAABiEcA4AAIMQzgEAYBDCOQAADEI4BwCAQQjnAAAwCOEcAAAGIZwDAMAghHMAABiEcA4AAIMQzgEAYBDCOQAADEI4BwCAQayadwEsn7323CXHHXvgvMsAAGAbmTkHAIBBCOcAADAI4RwAAAYhnAMAwCCEcwAAGIRwDgAAgxDOAQBgEMI5AAAMQjgHAIBB+AuhK8h3rr4+Z/7NV+ZdxnAe8jh/NRUA2DmYOQcAgEEI5wAAMAjhHAAABiGcAwDAIIRzAAAYhHAOAACDEM4BAGAQwjkAAAxCOAcAgEEI5wAAMAjhHAAABiGcAwDAIIRzAAAYhHAOAACDEM4nVXVpVX2qqtZV1aer6vlzquP4qjp1en1YVT1tHnUAALDjCec/6NHdfWSSByZ5flX95JzrOSyJcA4AcBMhnG9Cd38pyaeT3CZJquoJVfXRqjqvqv6lqu46tR9fVe+uqrdU1YVVdW5V3XbqO7iqPlBVH6+qi6vqFVVVU9+LquqlC+9XVc+oqpM3Ucprkhw+zeafvp1vGwCAORPON6Gq7pTkFknOqqpjkvxqkvt39z2T/EGSNy86/N5Jnt/dd0tyZpLfndq/keRh3f0TSY5Ictskx25lKU9Pckl3H9ndD9/mGwIAYKewat4FDObUquokd0zy2939lap6bpK7J/noNPGdJAdW1e7T67O7+9+n1+ck+a3p9S5J/r+qum+SSnJQknVJTl2uYqtqbZK1C/t773mz5bo0AABzIJz/oEd390VV9ZAk76iq92cWrE/q7hdufPAU1jcsarou3x/TtUkOSHLv7t5QVSckWT31XZtk10Xnrc426O4TkpywsH/g/of0tlwHAIAxWNayCd19ZpK/TPInSd6R5IlVdWiSVNUuVXXUFlxmvyRfnoL5LZM8ZlHf55IcQbkYdQAAGGVJREFUNV1rryy93OWbSW6+rfcBAMDORThf2h8nuW+Sq5K8IMk/VtX5SS5K8itbcP4rktynqtYlOSmz9egLTktyRZJLkvxdZstdNuWCJJ+uqot8IRQAYOWrbishVooD9z+k3/Kq8+ddxnAe8rgD510CAECSpKou7+41S/WbOQcAgEEI5wAAMAjhHAAABiGcAwDAIIRzAAAYhHAOAACDEM4BAGAQwjkAAAxCOAcAgEEI5wAAMAjhHAAABiGcAwDAIIRzAAAYhHAOAACDWDXvAlg+e+y1Sx7yuAPnXQYAANvIzDkAAAxCOAcAgEEI5wAAMAjhHAAABiGcAwDAIIRzAAAYhHAOAACDEM4BAGAQwjkAAAzCXwhdQb571fX5xOuvmHcZK8I9fu2geZcAANwEmTkHAIBBCOcAADAI4RwAAAYhnAMAwCCEcwAAGIRwDgAAgxDOAQBgEMI5AAAMQjgHAIBBCOcAADAI4RwAAAYhnAMAwCCEcwAAGIRwDgAAgxDOAQBgEML5VqiqR1XVx6tqXVV9sqreV1W7VNWlVXXXG3ntk6vqGctVKwAAO59V8y5gZ1FVByd5TZJ7dfe/T233TNJzLQwAgBXDzPmWu1WSa5N8baGhu8/r7h8I51X1zKo6u6oOrKpdq+qlVXXRtL2yqnafjrv1NPN+QVX9Y5JbLLrG46rqo1X1iWmW/qE76B4BAJgj4XzLnZ/knCSXVdXfV9Vzq+rWi/p3qaqXJfnpJD/T3V9J8rQkPzFtRya5XZJnTce/IskHu/uIJL89nbfg3Ul+qrvvkeSXkry+qnbbuKCqWltV6xe2q79z1bLeMAAAO5ZwvoW6+/ruPjbJfZK8K8kxSS6uqttPh5yUZHWSx3T3t6e2hyQ5sbu/093XJnnd1JYkD0zy+unan0/yvkVv92NJzqiqi5L8Q2az6rfZRE0ndPeahW2vPfZexjsGAGBHE863Und/qrv/qrt/KclHkjx86jors+B+0KLDKz+8Jn1L1qi/Nclruvuu3X1kkiszC/4AAKxgwvkWmtaIH7Nof7/MZrg/NzWdnOQlSd5fVQuz3O9NcnxV7V5Vq5I8NcmZU9/7kzxlutZhSR686O32S3Lp1Pf4aR8AgBXOb2vZcquSvLCqfizJ1dP+G7v7H6vq5UnS3W+rqquSvKeqHpbktZmtMz9vusZZma01T2Zrz/+6qh6T5DP5fmhf6Pv7qro80zr37XpnAAAMoTb6ZSPsxG653yH9rj9dN+8yVoR7/NpBN3wQAMBWqqrLu3vNUv2WtQAAwCCEcwAAGIRwDgAAgxDOAQBgEMI5AAAMQjgHAIBBCOcAADAI4RwAAAYhnAMAwCCEcwAAGIRwDgAAgxDOAQBgEMI5AAAMQjgHAIBBrJp3ASyf3fbeJff4tYPmXQYAANvIzDkAAAxCOAcAgEEI5wAAMAjhHAAABiGcAwDAIIRzAAAYhHAOAACDEM4BAGAQ/gjRCnLdldfni3/25XmXcZNz6O8cPO8SAIAVwsw5AAAMQjgHAIBBCOcAADAI4RwAAAYhnAMAwCCEcwAAGIRwDgAAgxDOAQBgEMI5AAAMQjgHAIBBCOcAADAI4RwAAAYhnAMAwCCEcwAAGIRwDgAAg1g17wJWkqpaN73cPckdklw07X+6u39lM+c9KMku3X3mdi4RAICBCefLqLuPTJKqOizJuQv7W+BBmf23EM4BAG7ChPMdpKpuneRNSW6WZHWS9yRZm+Qnkvza7JD6uSRvT/LSJKcnOWA69rwkT+vuDXMoHQCAHUQ433G+nuRh3X1lVa1K8o4kj+juf6iq1ydZ1d3PT5Kq2iXJY7v761VVSf4qydOT/Pm8igcAYPsTznecXZL8aVXdJ0klOSjJkUn+YRPHVpLnVNVDk+ya5OaZrWP/wYOq1mY2+54k2Xf1vtuhbAAAdhS/rWXHeW5mS1ru3d1HJHlbZktWNuUJSY5Ocr/uvltmM+Y/dGx3n9Ddaxa2vffYZzuVDgDAjiCc7zj7Jfm/3b2hqm6V5NGL+r6Z2ez44mO/1t3fqqqbJXniDqwTAIA5saxlx/nzJG+rqk8kWZ/kvYv6Tkty2vSrGN+e5NVJfrGqLk5yeZIPZrYMBgCAFUw43w66+9Ikt9io7QtJ7rXE8Z/LbP35Yg/eLsUBADAsy1oAAGAQwjkAAAxCOAcAgEEI5wAAMAjhHAAABiGcAwDAIIRzAAAYhHAOAACDEM4BAGAQwjkAAAxCOAcAgEEI5wAAMAjhHAAABiGcAwDAIFbNuwCWz6777JJDf+fgeZcBAMA2MnMOAACDEM4BAGAQwjkAAAxCOAcAgEEI5wAAMAjhHAAABiGcAwDAIIRzAAAYhHAOAACD8BdCV5Drr7w2Xz7hknmXATulg9cePu8SAMDMOQAAjEI4BwCAQQjnAAAwCOEcAAAGIZwDAMAghHMAABiEcA4AAIMQzgEAYBDCOQAADEI4BwCAQQjnAAAwCOEcAAAGIZwDAMAghHMAABiEcL6Eqlo3bZdU1bWL9v92O7zX/lX1nOW+LgAAO5dV8y5gVN19ZJJU1WFJzl3Y3072T/KcJC/dju8BAMDgzJxvo6r6vaq6aNpOqaqbVdVeVfUfVXXIouP+tKpeMr2+d1V9pKouqKqPVtXR02GvSXLANDP/0enY51XVxxbaquqoHX+XAADsSML5NqiqhyU5Lsl9ktwtyXeT/HF3X53k5CRPm47bM8kTk7ymqlYnOS3J73f3EUmel+S06ZinJ/ladx/Z3fee3uYN3f2T04z97yR5/Q67QQAA5kI43zYPSfKm7v5md3eSv5zakuQvkjylqnZL8rgkZ3f3F5PcOcmV3f2+JOnuf0nyn0nuusR7HFVVH6yqi5K8Ksldq2rXxQdU1dqqWr+wXfmdq5b7PgEA2IGsOd82laQ3ausk6e7LpqUpj0zym5nNei91TjbVNs2yvy3J/bp7XVXtn+RrSXZLct33Tuw+IckJC/uH/MjBm7o+AAA7CTPn2+a9SR5bVftM+09Lcuai/pcn+bMku3f3B6a2S5LsW1X3T5Kqul+S/ZJcnOSbSfZeNDO+V2b/cFo/7f/W9roRAADGYeZ8G3T3O6rqrkk+UlVJsi7fnyFPd59dVVdmtsRloW1DVR2b5OVVtVeSbyc5tru/neTbVfX2JBdV1Te7+95V9eIk51bVZUnesePuDgCAeanZkmmW0/TrF89J8uPdfeWOet9DfuTgPu+F799RbwcrysFrD593CQDcBFTV5d29Zql+y1qW2fRrE89O8twdGcwBANj5WdayzLr795P8/rzrAABg52PmHAAABiGcAwDAIIRzAAAYhHAOAACDEM4BAGAQwjkAAAxCOAcAgEEI5wAAMAjhHAAABiGcAwDAIIRzAAAYhHAOAACDWDXvAlg+u+yzKgevPXzeZQAAsI3MnAMAwCCEcwAAGIRwDgAAgxDOAQBgEMI5AAAMQjgHAIBBCOcAADAI4RwAAAYhnAMAwCD8hdAV5Porr8l/vPxf510GK9gtn3XMvEsAgBXNzDkAAAxCOAcAgEEI5wAAMAjhHAAABiGcAwDAIIRzAAAYhHAOAACDEM4BAGAQwjkAAAxCOAcAgEEI5wAAMAjhHAAABiGcAwDAIIRzAAAYhHAOAACD2CnCeVWdUVXP2ET7+VX1yM2cd/LCeVX1oqp66fasEwAAboydIpwnOTHJkxc3VNVRSQ5O8s65VAQAAMtsZwnnpyc5tKruvqjtKUn+Osn1VfXSqrpo2l5ZVbtv7mJVdXhVXVhVP19Ve1bV31bVJdNM/HumY95SVY+dXj+zqr5TVXtP+x+qqvtV1aqqendVnVtVF1fVm6tqr+mY3avqtVX1mar616p6dVWduqiG51TVx6rqvKr656o6dGp/UVX9TVW9Y6rp/VW1/3IOJgAAY9opwnl3X5PkTZlmz6tqdZJfTXJSkqcl+YlpOzLJ7ZI8a6lrVdWDkrw9yRO6+4wkP5dkv+4+vLvvPl03Sc5M8jPT6wcn+XiS+1XVPknukuQjSa5L8rjuPirJXZN8M8lvTOf8epIfTXL4dP49F9XwuCR3SHJ0d98zyVuSvGpRmfdO8qTuPjzJFdO1NnUva6tq/cJ25XeuXuq2AQDYCewU4XxyYpLjplnxRyX5ZHd/MslDkpzY3d/p7muTvG5q25SfySwE/1x3r5vazk9yp2lm+1eSfHdqf2+Sh1TVrknunOSE6bo/neSc7v5ukkry21X1iSQXJPmFzP6BkCQPTHJKd1/b3RsyC+ALfmm61seral2S5yW5zaL+M7r769PrczL7B8cP6e4TunvNwrbPHnstNXYAAOwEdppw3t0XJ/lckodltqTlxKmrkvTGhy9xmc9Ox//kout+PrPZ7XclOSbJRVW1X3dfluQ7SR6f5Nwk70vygMxC9ZnT6Y/LLKzfv7vvluSlSVZvpq4s6vuT7j5y2u7W3Ucu6t+w6PV1SVYtcR0AAFaQnSacT05M8oIk90rytqntvUmOn9Z4r0ry1Hw/PG/s0syWmPxhVT0xSapqTZLu7tOTPCez4HzodPyZSf4oyZnd/Z+ZBeVHLbr+fkm+1t3fqqp9kxy/6L0+kOTx07r01Ul+ZVHf6Ul+Y2EteVXtVlX32NrBAABgZdnZZmTfmuRlSf62u6+c2l6b2bKP86b9s5K8YqkLdPeXpnXn75rWj38hyf+qqsrsHyundPcF0+HvTfL0fD+Mvy/JU7v7wmn/r5M8oqouSXJ5kg8lufXU95okd09ycZL1U317TjWcUlUHJDmrqjqz/w4nJvnEVo8IAAArRnUvtfKCG6uq9p1m1ffIbLb87d39+u31fof8yEH9iT/6++11ecgtn3XMvEsAgJ1aVV3e3WuW6t/ZZs53NmdOwXx1ZrPvJ8+3HAAARiacb0fdfe951wAAwM5jZ/tCKAAArFjCOQAADEI4BwCAQQjnAAAwCOEcAAAGIZwDAMAghHMAABiEcA4AAIMQzgEAYBDCOQAADEI4BwCAQQjnAAAwiFXzLoDls8s+u+eWzzpm3mUAALCNzJwDAMAghHMAABiEcA4AAIMQzgEAYBDCOQAADEI4BwCAQQjnAAAwCOEcAAAG4Y8QrSDXX7UhV7zqn7f4+IOe8dDtWA0AAFvLzDkAAAxCOAcAgEEI5wAAMAjhHAAABiGcAwDAIIRzAAAYhHAOAACDEM4BAGAQwjkAAAxCOAcAgEEI5wAAMAjhHAAABiGcAwDAIIRzAAAYhHAOAACDEM63QVWdUVXP2ET7+VX1yM2cd/KmzgMAgEQ431YnJnny4oaqOirJwUneOZeKAADY6Qnn2+b0JIdW1d0XtT0lyV8nub6qXlpVF03bK6tq98UnV9VeVfW1qjp4UdsfVdUJ0+ujquqcqrqgqj5WVcfsiJsCAGC+hPNt0N3XJHlTptnzqlqd5FeTnJTkaUl+YtqOTHK7JM/a6Pyrk5yW5PHT+ZXkiUneMAX5v0vyou4+IsnaJKdW1d7b/84AAJgn4XzbnZjkuClMPyrJJ7v7k0kekuTE7v5Od1+b5HVT28bekOT46fWDknytuy9Mcsck13T3u5Oku89OckWSIza+QFWtrar1C9tVG769vHcIAMAOJZxvo+6+OMnnkjwssyUtJ05dlaQ3PnwT55+TZNdprfrxmc26L3X+Utc4obvXLGx7r95zW24FAIBBCOc3zolJXpDkXkneNrW9N8nxVbV7Va1K8tQkZy5x/huSPDPJLyR5y9T2qSR7VNWDkqSq7pPkoCQXbpc7AABgGKvmXcBO7q1JXpbkb7v7yqnttZmtMz9v2j8rySuWOP+UJJclOa27/zOZrWevqmOTvGJaZ74hyWO6+6rtcwsAAIxCOL8RuvtbSfbZqO26JM+Zto2PP36j/f+bZLdNHPdvSY5ezloBABifZS0AADAI4RwAAAYhnAMAwCCEcwAAGIRwDgAAgxDOAQBgEMI5AAAMQjgHAIBBCOcAADAI4RwAAAYhnAMAwCCEcwAAGIRwDgAAgxDOAQBgEKvmXQDLZ5e9V+egZzx03mUAALCNzJwDAMAghHMAABiEcA4AAIMQzgEAYBDV3fOugWVSVdcm+fK861gB9kly5byLWAGM4/IwjsvDON54xnB5GMflsTOP44HdvcdSnX5by8ry5e5eM+8idnZVtd443njGcXkYx+VhHG88Y7g8jOPyWMnjaFkLAAAMQjgHAIBBCOcrywnzLmCFMI7LwzguD+O4PIzjjWcMl4dxXB4rdhx9IRQAAAZh5hwAAAYhnAMAwCCE8xWiqn68qj5cVZ+pqo9V1eHzrmlEVbW6qv5hGqd1VfWuqjps6jto2v9sVV1UVfdddN6SfTdlVfWHVdVVdddpf8nn0DP6w6pqj6p61fRcXVxVb5rajeNWqKqfraqPV9Unpp/PJ03tfqY3o6peUVWXLv4Zntq36fm7KT6bmxrDzX3OTP2ey40s9Swu6v/DjftW9LPY3bYVsCV5f5Ljp9ePTnLOvGsacUuyOslD8/3vWzwjyXum1ycledH0+l5J/j3Jqhvqu6luSe6Z5IxpLO46tS35HHpGNzmGL0vyikXP462M41aPYSX5WpIjpv3DkmxIsq+f6Rscu/snWZPk0oWf4al9m56/m+Kzuakx3NznzLTvudzCZ3Hq+6HPmpX+LM69ANsy/EdMDkryjUU/3JXZXwo9bN61jb4lOSrJ/5leX5nZX+1a6PtYkgfcUN9NcUuyR5JzkvzYwv+Ybu459Ixucgz3nsZkn43ajePWjeNCOL//tH9EksuT7O5neovH8HuBaFufv5v6s5lNhMpFfd/7nJn2PZdbOI6b+qyZ2lf0s2hZy8pwaJIvdfe1SdKzp/GyJD8616p2Ds9M8o6qOiDJLt39lUV9lyb50c317bAqx/PiJG/q7i8satvcc+gZ/WG3yyxU/kFVnVtVH6qqB8c4bpVpDH45yd9V1b8nOTvJkzKbOfczvfW29fnzbC7tmUnekSQ+a7bapj5rkhX+LArnK8fGvxOz5lLFTqSqXpDkx5P8/tS0uTE0vpOqOjqz/7v11ZvoNoZbbrckt01ySXcfldn/9f3WJKtiHLdYVa1K8ntJHtHdt0ny4CRvnLqN47bZ1nEzphvZxOdMYgy3yA181iQreByF85Xhi0nWTB9SqarK7F+Ol821qoFV1XOSPCrJz3f31d39tan9wEWH3SbJZZvr21H1Duank9wpyReq6tLM1gm+O7OlLUs9h57RH/bvSa5P8uYk6e7zk3whs2fLOG65I5Mc0t3/miTd/W9JvpTZ8hY/01tvc8/YtvbdJG38OZMkPmu2yiY/a6rq57PCn0XhfAXo7iuSfCLJ46emY5Nc2t2Xzq2ogVXV2iSPTfIz3f2NRV1vT/Kb0zH3SnJwZv8X+Q313aR09//q7kO6+7DuPizJ+iQ/291vzBLPoWf0h3X3V5O8L8nPJklV3SazdZUfinHcGgsfxHdMkqq6fWZLhj4TP9NbbXPP2Lb27bDiB7KZz5nEc7lFNvNZc8aKfxbnvejdtjxbkjtm9qWJzyQ5N8ld5l3TiFtm//LuJJ9Lsm7aPjr13TLJe5J8NsnFSX560XlL9t3Ut/zgl3SWfA49o5scu9smOSvJhdOz+EjjuE3j+NhpDM9PckGSX53a/Uxvftz+IrPAc21mX5hb+HL8Nj1/N8Vnc1NjuLnPGc/l1j2LGx3zvc+alf4sLvyaHwAAYM4sawEAgEEI5wAAMAjhHAAABiGcAwDAIIRzAAAYhHAOwIpRVc+uqoPmXQfAthLOAVhJnp1EOAd2WsI5ANtdVR1dVR+qqvOr6oKqekRVHVVV50z7H6uqY6ZjD6uqry46d5+q6kX7XVW/W1UfraovVNWTp/YXJjkkyalVta6qjtzR9wlwY/kjRABsV1W1f5JLkjyquz9cVbskuUVmf7nvv3f3u6vqvpn96fLbJzkwybndfYvp/H2SfKu7a9rvJM/u7pdX1Z2TfCzJft19bVVdmuQXu/uiHXybAMvCzDkA29vRSS7p7g8nSXdfn9mfKb+mu989tZ2d5IokR2zhNd88nffJzP7k98HLXTTAPAjnAMxDJdnU/3XbmYXtXRe1rd7EcRsWvb4uyarlKw1gfoRzALa3Dye5c1XdJ0mmZS1fTrJHVT1oartPZl/kvHDqW1VVd5zOf+JWvNc3k9x8uQoH2NHMNACwXXX3f1bVI5P8WVXtm9ns+P9IcmySV1TV3pnNhD+mu69Kkqp6ZpIzqmp9kjO24u1ekeQNVXV1kuO7e91y3gvA9uYLoQAAMAjLWgAAYBDCOQAADEI4BwCAQQjnAAAwCOEcAAAGIZwDAMAghHMAABiEcA4AAIMQzgEAYBD/P/4aot/f05X9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x1600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Univariate analysis- Car Company\n",
    "figure(figsize=(10, 20), dpi=80)\n",
    "print(f'There are {df.Company.nunique()} unique Car Companies.')\n",
    "print(' ')\n",
    "print(f'{df.Company.value_counts(ascending=False)}')\n",
    "sns.countplot(y=df['Company']); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Maruti is the most sold car in the used car market followed by Hyundai and Honda.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 9 unique Seat Number categories\n",
      " \n",
      "5.0     5966\n",
      "7.0      784\n",
      "8.0      163\n",
      "4.0      115\n",
      "6.0       38\n",
      "2.0       16\n",
      "10.0       6\n",
      "9.0        2\n",
      "0.0        1\n",
      "Name: Seats, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Univariate analysis- Seats\n",
    "figure(figsize=(10, 10), dpi=80)\n",
    "print(f'There are {df.Seats.nunique()} unique Seat Number categories')\n",
    "print(' ')\n",
    "print(f'{df.Seats.value_counts(ascending=False)}')\n",
    "sns.countplot(x=df['Seats']); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Most used cars sold are 5 seater, followed by 7 seater car."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA- Univariate Continous Variables.\n",
    "\n",
    "#### Analyze following variables:\n",
    "    - Mileage           \n",
    "    - Engine\n",
    "    - Power\n",
    "    - Seats\n",
    "    - New_Price\n",
    "    - Price\n",
    "    - Kilometers_Driven\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Kilometers_Driven</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>New_Price</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.091000e+03</td>\n",
       "      <td>7091.000000</td>\n",
       "      <td>7091.000000</td>\n",
       "      <td>7091.000000</td>\n",
       "      <td>7091.000000</td>\n",
       "      <td>7091.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.857065e+04</td>\n",
       "      <td>18.147073</td>\n",
       "      <td>1620.012974</td>\n",
       "      <td>112.703595</td>\n",
       "      <td>18.470192</td>\n",
       "      <td>9.373224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.520718e+04</td>\n",
       "      <td>4.571676</td>\n",
       "      <td>594.137881</td>\n",
       "      <td>52.925682</td>\n",
       "      <td>17.781365</td>\n",
       "      <td>10.621793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.710000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>34.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.379650e+04</td>\n",
       "      <td>15.200000</td>\n",
       "      <td>1198.000000</td>\n",
       "      <td>78.900000</td>\n",
       "      <td>7.870000</td>\n",
       "      <td>3.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.300000e+04</td>\n",
       "      <td>18.160000</td>\n",
       "      <td>1493.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>11.105000</td>\n",
       "      <td>5.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.300000e+04</td>\n",
       "      <td>21.100000</td>\n",
       "      <td>1968.000000</td>\n",
       "      <td>138.030000</td>\n",
       "      <td>23.250000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.500000e+06</td>\n",
       "      <td>33.540000</td>\n",
       "      <td>5998.000000</td>\n",
       "      <td>616.000000</td>\n",
       "      <td>99.920000</td>\n",
       "      <td>160.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Kilometers_Driven      Mileage       Engine        Power    New_Price  \\\n",
       "count       7.091000e+03  7091.000000  7091.000000  7091.000000  7091.000000   \n",
       "mean        5.857065e+04    18.147073  1620.012974   112.703595    18.470192   \n",
       "std         8.520718e+04     4.571676   594.137881    52.925682    17.781365   \n",
       "min         1.710000e+02     0.000000    72.000000    34.200000     1.000000   \n",
       "25%         3.379650e+04    15.200000  1198.000000    78.900000     7.870000   \n",
       "50%         5.300000e+04    18.160000  1493.000000    94.000000    11.105000   \n",
       "75%         7.300000e+04    21.100000  1968.000000   138.030000    23.250000   \n",
       "max         6.500000e+06    33.540000  5998.000000   616.000000    99.920000   \n",
       "\n",
       "             Price  \n",
       "count  7091.000000  \n",
       "mean      9.373224  \n",
       "std      10.621793  \n",
       "min       0.440000  \n",
       "25%       3.850000  \n",
       "50%       5.450000  \n",
       "75%      10.000000  \n",
       "max     160.000000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kilometers_Driven    61.276928\n",
       "Mileage              -0.446711\n",
       "Engine                1.401902\n",
       "Power                 1.945145\n",
       "Seats                 1.905787\n",
       "New_Price             1.744816\n",
       "Price                 3.265235\n",
       "dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Skewness of the dataframe\n",
    "df.skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Mileage is approximately normal.\n",
    "- Engine, Power, Seats, Price and New_Price is highly positively skewed.\n",
    "- We will need to treat highly skewed \"Kilometers_Driven\" column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEGCAYAAABrQF4qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3de3xV5Z3v8c8vO1dCEkISkpAA4Q4REBHwbq2gRW1Fp1pRe6odz+hM68z0dOZ0bM+ZXmw703Yuta/W2jrV1rZWRaeeYQpqFax3kYDcEyBgCIGEXElCQu7P+WMv2hhy2ZDL2tn5vl+vvLL2Ws/a+5fF3r/98KznYs45REQkckX5HYCIiAwvJXoRkQinRC8iEuGU6EVEIpwSvYhIhIv2O4Ce0tPTXV5ent9hiIiMKlu3bq12zmX0dizsEn1eXh4FBQV+hyEiMqqY2eG+jqnpRkQkwinRi4hEOCV6EZEIp0QvIhLhlOhFRCKcEr2ISIRTohcRiXBK9CIiEU6JXkQkwoXdyFiRcPabzaVn7Lvjoqk+RCISOtXoRUQinBK9iEiEU6IXEYlwSvQiIhFOiV5EJMIp0YuIRDglehGRCKdELyIS4UIaMGVmq4AfAAHgZ8657/Q4fiXwELAIWOOce87bvxh4BEgGOoFvO+eeGbrwRUYXDbgSPwxYozezAPAwcB2QD9xuZvk9ipUCdwO/6bG/GfiMc+48YBXwkJlNGGzQIiISulBq9MuBYufcIQAzexpYDew9XcA5V+Id6+p+onNuf7ftY2ZWCWQAJwYduYiIhCSUNvoc4Ei3x2XevrNiZsuBWOBgL8fuNbMCMyuoqqo626cWEZF+hJLorZd97mxexMyygV8Bn3XOdfU87px71Dm31Dm3NCMj42yeWkREBhBKoi8DpnR7nAscC/UFzCwZWA/8X+fcu2cXnoiIDFYoiX4LMNvMpptZLLAGWBfKk3vlnwd+6Zx79tzDFBGRczVgonfOdQD3Ay8BhcBa59weM3vQzG4EMLNlZlYG3Ar81Mz2eKd/CrgSuNvMtns/i4flLxERkV6F1I/eObcB2NBj31e7bW8h2KTT87xfA78eZIwiIjIIGhkrIhLhlOhFRCKcEr2ISIRTohcRiXBK9CIiEU6JXkQkwinRi4hEOCV6EZEIp0QvIhLhlOhFRCKcEr2ISIRTohcRiXBK9CIiEU6JXkQkwinRi4hEOCV6EZEIp0QvIhLhlOhFRCKcEr2ISIRTohcRiXBK9CIiEU6JXkQkwinRi4hEuJASvZmtMrN9ZlZsZg/0cvxKM9tmZh1mdkuPY3eZ2QHv566hClxEREIzYKI3swDwMHAdkA/cbmb5PYqVAncDv+lx7kTga8BFwHLga2aWOviwRUQkVKHU6JcDxc65Q865NuBpYHX3As65EufcTqCrx7kfA152ztU65+qAl4FVQxC3iIiEKJREnwMc6fa4zNsXipDONbN7zazAzAqqqqpCfGoREQlFKIneetnnQnz+kM51zj3qnFvqnFuakZER4lOLiEgoQkn0ZcCUbo9zgWMhPv9gzhURkSEQSqLfAsw2s+lmFgusAdaF+PwvAdeaWap3E/Zab5+IiIyQARO9c64DuJ9ggi4E1jrn9pjZg2Z2I4CZLTOzMuBW4Kdmtsc7txb4JsEviy3Ag94+EREZIdGhFHLObQA29Nj31W7bWwg2y/R27uPA44OIUWRUqmps5cU9FVQ1tJCVksDMjEScc5j1dutKZPiElOhFJDS1TW28sLuc9TvLefdQDV0OzMB5XRCunjeJlfMz/Q1SxhwlepFBqj/Vzku7K/jvncd4+2ANnV2OGemJ3P/RWXz8/MnMSE+k6mQr//77/Ty7tYy46CiumK3eZTJylOhFBmH/8Ua+9y9FnGhuZ+rEcdx35Qw+vmgy87OTPtREk52SwHc+uYiiikZe2F1BUnwMi6dM8DFyGUuU6EXOgXOOP+yv4pW9x5mblcTP717G4ikT+m1/D0QZty7Npa65jd/vrWBhTgqBKLXXy/DT7JUi5+C9klpe3nucRbkpPP+5y7hgampIN1mjo6L46NxJnGhuZ9fREyMQqYgSvchZO9XWyct7jzM9PZFPLZ1CQmzgrM6fm5XEpKQ4Xt9fjXOhDjIXOXdK9CJnaVPRcU61dXLDwuxz6ioZZcYVszOoaGjhQOXJYYhQ5MPURi9yFqoaW3nnUA1L81KZPCEBgN9sLu217B0XTe3zec6fksLLeyt4bb8m8ZPhpxq9yFl4pfA4MYEorsnPGtTzREdFccmMND6obqK0pnmIohPpnRK9SIgaWtopLG9gybRUxscN/j/Di3KD3SvX7yof9HOJ9EeJXiREL+2uoKPLsTh3aPq/pybGkpuawAYlehlmaqMXCdG6HceY6CXnobIwJ4UXdldwuKaJaWmJHzrWW9t/f+3+In1RjV4kBFWNrbxVXM2inJQhnZRsQU4KoOYbGV5K9CIh2LCrnC4Hi4Z42oLUcbFcMHUC63cq0cvwUaIXCcG6HceYm5lEVnL8kD/3DQuz2XOsgZLqpiF/bhFQohcZUEV9C1sP13Hj4snD8vzXL8wG4MU9FcPy/CJK9CIDeP1AcFDT1fMmDcvzT56QQH52MhsLjw/L84so0YsM4M0D1aSPj2NeVtKwvcbK+ZPYeriOuqa2YXsNGbuU6EX60dXleLO4mitmpw/rEoAr5mfS5eDVfZXD9hoydinRi/Rjb3kDtU1tXDE7fVhfZ2FOChlJcWwsVKKXoadEL9KPNw5UA3D5rOFN9FFRxop5k3htfxVtHV3D+loy9ijRi/TjzeIq5mUlMWkYulX2tGJ+JidbO3jvg9phfy0ZW5ToRfpwqq2TLR/UDXuzzWmXz0onLjqKV9T7RoaYEr1IH94rqaWts4vLZ2eMyOslxAa4bFY6G4uOa+UpGVIhTWpmZquAHwAB4GfOue/0OB4H/BK4EKgBbnPOlZhZDPAzYIn3Wr90zv3zEMYvMmzePFBFbCCK5XkTz+n8vhYk6c+K+ZPYVFSpladkSA1YozezAPAwcB2QD9xuZvk9it0D1DnnZgHfB77r7b8ViHPOLST4JXCfmeUNTegiw+udQzUsmTbhrNeEHYwV8zIB1HwjQyqUppvlQLFz7pBzrg14Gljdo8xq4Alv+zlghQU7HTsg0cyigQSgDWgYkshFhtGJ5jb2HGvg0pkj0z5/WlZKPAtyktXNUoZUKIk+BzjS7XGZt6/XMs65DqAeSCOY9JuAcqAU+Ffn3BldCszsXjMrMLOCqiqtoSn+e/dQLc7BpTPTRvy1V8zLZFtpHSdbO0b8tSUyhdJG39twwJ53ivoqsxzoBCYDqcAbZvaKc+7Qhwo69yjwKMDSpUt1F0r6NFKLcbxzsJpxsYE/Lvc3klbOz+QHGw+wv6KRJdNSR/z1JfKEUqMvA6Z0e5wLHOurjNdMkwLUAncALzrn2p1zlcBbwNLBBi0y3N4+WMOyvInERo98x7QFOclkJsdRWKFWThkaobyLtwCzzWy6mcUCa4B1PcqsA+7ytm8BNrlg/7BS4GoLSgQuBoqGJnSR4VHZ2MKBypNc4kOzDYCZsWJ+JgcqT9LRqVGyMngDJnqvzf1+4CWgEFjrnNtjZg+a2Y1esceANDMrBr4IPODtfxgYD+wm+IXxc+fcziH+G0SG1DsHawB/2udPWzl/Em0dXRzSYiQyBELqR++c2wBs6LHvq922Wwh2pex53sne9ouEs3cO1pAUH815k1N8i+HSmenEBIyiigbmZA7f9MgyNmhkrEgPbx+s4aLpaQSihm9a4oHExwSYlTGeovJGjZKVQVOiF+mmpLqJ0tpmrpwzsv3nezMvO5kTp9qpaGjxOxQZ5ZToRbo5vWzglSM0v01/5norWhVVNPociYx2SvQi3by+v4qpE8eRl57odygkx8eQm5pAUbm6WcrghHQzViRS9Dfgqq2ji7cP1vBnS3oO/PbPvKwkNhZW0tjSTlJ8jN/hyCilGr2Ip+BwLc1tnWHRbHPavKxkHLBPzTcyCEr0Ip7X91cTHWVcOszLBp6N7JR4UhJi1E4vg6JEL+J5fX8VF05LZXxc+LRomhnzspI4UNlIu0bJyjlSohchOO3B3vIGrpwTPs02p83LSqa903GoSqNk5dwo0YsAf9gX7Fb5kTBM9DMyEokNRFGkSc7kHCnRiwAv7q4gZ0IC501O9juUM8QEopg1aTxFFRolK+dGiV7GvMaWdt48UM2qBVkEF0YLP/Oykqg/1c5e9amXcxA+d51EfPLqviraOru4bkGWL68fyiLic7OSMGBjYaWvk63J6KQavYx5L+2uICMpjiVTw3c1pyRvlOxGLRou50CJXsa09s4uXt1XybX5mUT5OFtlKOZlJ7OjrJ5KTXImZ0lNNzKmHTh+kua2Tlb51GxzNuZlJfHy3uNsKqpkzfKB18kdqfV1JfypRi9j2u5j9aQkxHDxDP9WkwpVVnI8ORMSeKWw0u9QZJRRopcxq7Wjk73HGrhuQRYxgfD/KATXkp3Em8VVtLR3+h2OjCLh/+4WGSZ7jjbQ1tnFJy/M9TuUkK2Yn0lLexdvH6z2OxQZRZToZczaVlrHxMRYlk4L3942PV08YyKJsQE138hZ0c1YGZPqmts4VN3EivmTeOq9I72WCccbl3HRAa6YncGmwkrcTS5sB3hJeFGNXsak7UdOALBkyuipzZ+2Yv4kKhpa2H1Uo2QlNEr0MuY459h2uI7p6YmkJsb6Hc5ZWzE/k5iA8Z/byvwORUaJkBK9ma0ys31mVmxmD/RyPM7MnvGObzazvG7HFpnZO2a2x8x2mVn80IUvcvYOVTdR09QW1iNh+zMxMZbrFmTzn1vLaG7r8DscGQUGTPRmFgAeBq4D8oHbzSy/R7F7gDrn3Czg+8B3vXOjgV8Df+mcOw+4CmgfsuhFzsE7B2sYFxtgUe7onTPmf1wyjcbWDtZtP+Z3KDIKhFKjXw4UO+cOOefagKeB1T3KrAae8LafA1ZY8C7RtcBO59wOAOdcjXNOHYDFN3XNbRSWN7Asb+Ko6Dvfl6XTUpmXlcQv3zmsqYtlQKG803OA7t0Syrx9vZZxznUA9UAaMAdwZvaSmW0zsy/19gJmdq+ZFZhZQVVV1dn+DSIh23yoBjO4aPpEv0MZFDPj0xdPY295A+97N5ZF+hJKou+t/1bPKkRfZaKBy4E7vd83m9mKMwo696hzbqlzbmlGRvit8COR4VRbJ1tK6pifncyEcaPvJmxPN12Qw/i4aB574wO/Q5EwF0o/+jJgSrfHuUDPhsHTZcq8dvkUoNbb/5pzrhrAzDYAS4CNg4xb5Kz99v0yTrV3cunM9JDKhzJPvJ/Gx0Vzz+XT+cHGA1y/s5wbFmX7HZKEqVBq9FuA2WY23cxigTXAuh5l1gF3edu3AJtcsOHwJWCRmY3zvgA+AuwdmtBFQtfS3skPNxYzJTWBvLRxfoczZO6/ehbnT5nAl3+7k2MnTvkdjoSpARO91+Z+P8GkXQisdc7tMbMHzexGr9hjQJqZFQNfBB7wzq0D/p3gl8V2YJtzbv3Q/xki/Xvi7RIqGlr4WBgvF3guYgJR/OC2xXR2Ob7w9HYqGzVXvZwppCkQnHMbgA099n2123YLcGsf5/6aYBdLEV/UN7fz8KvFfHRuBjPSx/sdzpDLS0/kWzcv4Itrd3DZdzaxakE2szLGs620jrjoKOZkJpGdEh9RX3BydjTXjUS8H79WTGNrB19aNY/3SyOzh8rNF+SyeEoqv373MGsLjvDfO44RMKPTOX6/9zgpCTGsWTZl4CeSiKREL6OWc47Orv77kL+89zj/8fohPrkkl/nZyaM+0fe3atT09ET+8eP5fOX6+QA8s+UIjS3t7D9+klf3VfLk5lLuviyP7JSEEY1Z/KdEL6NOZWMLW0vq2H2snoZTHWz+oJbbl0/l0plpH1r3devhOv76qW0szEnhwdXn+RjxyAp0uwZJ8TFcOC2V3NQEHnntILf+5B3+4ooZHxosFo6zdMrQUqKXUeVEcxuP/OEgHZ2OWZPGMzcrhrcPVrN+VzlTJ45jzfIpzJ6URMHhWp7ZcoSs5Hgev3sZ42LH9ls9MzmeT12Yy683l/Li7go+cf5kv0OSETS23/0yqnR1OZ7bVoYDvrByNmnj4wD4s8/m8NKeCp56r5TvvbgPgJiAccHUVP7llkV/LDfW5U9OYVneRLaU1PLReZMYH6eP/1ihf2kZNZ54p4RDVU3cfEHOh5J3fEyA1YtzWL04h0NVJ6lpamNhTgrxMQH/gg1Tl81MY0tJLQUltVw1d5Lf4cgIGb2zOsmYcqS2me+8UMTczKR+l/6bkTGeZXkTleT7MCk5nlmTxvPuoZoBb2RL5FCil1HhV+8eprPLcdMFOeoPPkiXzkijoaWDPcfq/Q5FRogSvYS9lvZOni04wjX5maQkxPgdzqg3JyuJiYmxvHOwxu9QZIQo0UvYe3F3BXXN7dx50TS/Q4kIUWZcPCONw7XNHG/QlAljgRK9hL3fbC5lWto4Lp2Z5ncoEeP83BQM2H1UzTdjgRK9hLX9xxt5r6SWO5ZP/dBgKBmcpPgYpqUlskuJfkxQopew9tR7pcQGorjlwly/Q4k4C3OSqWxspbiy0e9QZJgp0UvY6upyrN9ZzlVzMzToaRicNznYfLNhV4Xfocgw04ApCVvvH6mjsrF1wJWT+loJSnO49C85IYapaePYsKucv1kx2+9wZBipRi9ha8OuCmIDUVw9TyM4h8uCySkUVTRyqOqk36HIMFKNXnzXW4389uVTeHF3BVfMTicpXn3nh8uCnBTW7yrnhd0VfP6js/wOR4aJEr2EpZ1l9Rw9cYr/dc2cc36OcF/cOxykJMSwZOoE1u8sV6KPYGq6kbC0YXc50VHGNfMz/Q4l4l2/MJu95Q2UVDf5HYoME9XoJew453hxdwWXzkonZZyabQYy2P+5XLcwm2+tL2TD7nI+d5Vq9ZFINXoJO8cbWjlc08yq87L8DmVMyJmQwOIpE3hB3SwjlhK9hJ295Q0ArMxXb5uRcv3CLHYdrae0ptnvUGQYKNFL2CmqaGDxlAlMSor3O5Qx47oFwbEKL+wu9zkSGQ5K9BJWGk61U1Z3imvydRN2JE2ZOI5FuSls2KVEH4lCSvRmtsrM9plZsZk90MvxODN7xju+2czyehyfamYnzezvhyZsiVRFFcF5V1aqt82Iu35hNjvK6imrU/NNpBkw0ZtZAHgYuA7IB243s/wexe4B6pxzs4DvA9/tcfz7wAuDD1ciXWF5AxMTY5mTOd7vUMac673mmxd366ZspAmlRr8cKHbOHXLOtQFPA6t7lFkNPOFtPwesMG+9NzO7CTgE7BmakCVStXZ0crDqJPOzkrRcoA+mpo1jQU4y69V8E3FCSfQ5wJFuj8u8fb2Wcc51APVAmpklAv8AfKO/FzCze82swMwKqqqqQo1dIkxx5Uk6uhzzspP9DmXMum5BNu+XnuDYiVN+hyJDKJRE31vVqufy8X2V+QbwfedcvzMmOecedc4tdc4tzcjICCEkiUSF5Y3Ex0SRl5bodyhj1vULT/e+UfNNJAkl0ZcBU7o9zgWO9VXGzKKBFKAWuAj4npmVAF8AvmJm9w8yZolAXc5RVNHAvKxkAlpJyjfT0xOZn53MC2q+iSihJPotwGwzm25mscAaYF2PMuuAu7ztW4BNLugK51yecy4PeAj4J+fcj4YodokgpTXNNLd1Mi8rye9QxrwbFmZRcLiOinotHB4pBkz0Xpv7/cBLQCGw1jm3x8weNLMbvWKPEWyTLwa+CJzRBVOkP4UVDQTMmJOpRO+3GxZNBuC5rUcGKCmjRUiTmjnnNgAbeuz7arftFuDWAZ7j6+cQn4wRheUNTM9IJD4m4HcoY9709ESumJ3Or949zH0fmUlMQOMqRzvNXim+q2pspfpkG5fMTPc7lDGpt9kvZ6Qn8saBajbsKmf14p6d7GS00Ve1+K7Qm8Rsvtrnw8bszCRmpCfy87dK/A5FhoASvfiusKKB7JR4JoyL9TsU8USZcfdleWw/coJtpXV+hyODpEQvvqptaqO0ppl5WRokFW4+uSSXpPhoHn3tkN+hyCAp0YuvXi2qxAHzs9VsE24S46L588um8+KeCgpKav0ORwZBiV58tamokqS4aCZPSPA7FOnFfR+ZQWZyHN/83V66unoOiJfRQr1uZER17+HR0dXFK4XHWZiTQpQmMQtL42Kj+dLH5vF3z+5g3Y5j3HSBeuCMRqrRi29Kqptp7ehiviYxC2s3X5DDotwUvvtiESdbO/wOR86BavTim8KKBqKjjJkZmns+nEVFGV/7xHnc+pO3uevx9/jkktwPHb/joqk+RSahUo1efOGco6i8gZkZ44mN1tsw3F04LZW/umomWw/Xsftovd/hyFlSjV58UdnYSl1zO1fO0bTU4arniNms5ARyJiTw/PtHmTJxHCkJMT5FJmdLVSnxxem1YdV/fvQIRBm3LZ1CR1cXawuO0KleOKOGEr34oqi8gckp8aoVjjLpSXHctDiHD6qbeKXwuN/hSIjUdCMjrqm1g9LaZj46b5Lfocg5uGBqKiU1Tby2v4ppaeP8DkdCoBq9jLh9xxtxoEVGRrGPL5pMdko8zxaUUdmgBUrCnRK9jLii8gaS4jUadjSLCURx27IptHd28Q//uRPn1F4fztR0IyOqo6uLA5UnBxwN29sc6RJeJiXFs2pBFr/bWc5T7x1Rf/owpkQvI0qjYSPLxTPSKCxv4Ovr9lDb1MbExD9NNa3EHz7UdCMjSqNhI0uU2R9Hyq7fVe5zNNIXJXoZMRoNG5kmjIvlqrkZFJY3cKCy0e9wpBf6tMmIOVB5krrmduZp7vmIc9msdCYmxvK7neUaSBWGlOhlxGwsrAQ0GjYSxQSiuGFhNlWNrbx7qMbvcKQHJXoZMRsLjzN5gkbDRqp5WUnMyhjPq/sqaW3v9Dsc6SakRG9mq8xsn5kVm9kDvRyPM7NnvOObzSzP23+NmW01s13e76uHNnwZLWqb2thWWqfafAQzM67Jz6S5rZN3VKsPKwMmejMLAA8D1wH5wO1mlt+j2D1AnXNuFvB94Lve/mrgE865hcBdwK+GKnAZXV4tqqTLaTRspJsycRxzM5N440A1jS3tfocjnlBq9MuBYufcIedcG/A0sLpHmdXAE972c8AKMzPn3PvOuWPe/j1AvJnFDUXgMrpsLDrOpKQ4jYYdA1bOz+RUeyc/f6vE71DEE0qizwGOdHtc5u3rtYxzrgOoB9J6lPkk8L5zrrXnC5jZvWZWYGYFVVVVocYuo0RrRyev769mxfxJWht2DMhJTWB+djL/8cYh6ptVqw8HoST63j6ZPftP9VvGzM4j2JxzX28v4Jx71Dm31Dm3NCNDC1FEmjcPVHOytYNr87P8DkVGyMr5k2hs6eCxNw/5HYoQWqIvA6Z0e5wLHOurjJlFAylArfc4F3ge+Ixz7uBgA5bRZ/3OclISYrhsVrrfocgIyU5J4PqFWTz+Vgl1TW1+hzPmhZLotwCzzWy6mcUCa4B1PcqsI3izFeAWYJNzzpnZBGA98GXn3FtDFbSMHi3tnby89zgfOy9To2HHmC+snENTWwf/8YZq9X4b8JPntbnfD7wEFAJrnXN7zOxBM7vRK/YYkGZmxcAXgdNdMO8HZgH/aGbbvR+tNjGGvHGgmsbWDm5YNNnvUGSEzclM4hOLJvOLt0uoOXnGrTkZQSHNXumc2wBs6LHvq922W4BbeznvW8C3BhmjL/qaJlcz8p2d9TuPMWFcDJfO7HlvXsaCv105m9/tPMbDrx7kq5/o2StbRoqmKZZh09LeySuFlXx8UTYxATXbjDWnK0tLpqbyxNslpI6L4a9XzPY5qrFJnz4ZNq/tr+Jkawc3LMr2OxTx0TX5mQSijBf3VPgdypilRC/D5tmCI2QkxXHJDDXbjGVJ8TFcOSedPcca2FJS63c4Y5ISvQyLYydOsamokk8tzSVazTZj3uWzMkiOj+Yb/72H9s4uv8MZc/QJlGGxtuAIXQ7WLNPNa4HY6ChuWDSZ3Ucb+OGmYr/DGXOU6GXIdXY5ntlyhCtmpzNl4ji/w5EwsTAnhT9bksOPNh2gQE04I0qJXobca/srKa9v4Y7lqs3Lh33jxvPISU3gC89s1zw4I0iJXobck++Wkj4+jpX5mX6HImEmKT6Gh267gOMNLXzm8c3Un1KyHwlK9DKk9hyrZ2NRJXdeNFV956VXF05L5ZE7L2RveQOfeUzJfiRowJQMqR+8coCk+GiS42P6HF0ssjI/k0fuvJC/enIrn3zkbX50xwVafWwYqcolQ2b30Xp+v/c4//PyGSTEBvwOR8LcyvxMnvjsck40t7P6R2/x5ObDONdzBnQZCqrRy5B56JX9JMdH89nL8/jdjnK/w5FR4NJZ6bzwt1fwxbXb+T/P7+bt4hr+6c8WkpLQ+/8INdfUuVGilyHx3ge1vFJYyd9dM4fk+Bi/w5Ew1VfyfuKzy/np64f419/vY0fZCX54+wU+RBe51HQjg3aqrZMvPbeD3NQE/vzy6X6HI6NQVJTxV1fNZO19l+Ac3PqTd3h9fxVdasoZEqrRy6B976UiSmqaeeovLiYxTm8pOTs9a/l/ftl0fvt+GS/uqaCkponbl6sH12Dp6smgvHuohp+/VcJdl0zjEs05L0MgITbAHcuncsPCbIoqGnly82HNjzNIqn71oqvLUdfURmJctJa/60dx5Uk+9+Q20hJjmZ4+Xt0pZciYGZfNSic2Oorn3z/Kk5sP8+mLpvkd1qilRN/NH/ZV8uNXD7LnWD1NbZ3EBIz87GSW5k1kZsZ4v8MLK8dOnOIzj20myuCuS/L0hSjDYlneRHDw/PajrC04wp0XTyMQZX6HNeoo0QN1TW18c/1efrvtKHlp47jlwlxOnGqn/EQLu47Ws6OsnotnTOT6BVpAA6Ckuok/f2ILjS0dPHXvxewsq/c7JIlgy6ZPpLWjkw27K/jH/9rNt29agJmS/dkY84m+qKKBe35RwPGGFv7m6ll8/upZxEUH/tgM8fFF2fx+73HeLK6mtLaZq+dPIjd17M7IuLHwOF94ZjuBKOOxu5exICdFiUcC9OwAAAuZSURBVF6G3eWzM2hq6+Q3m0uZkBDD//7YXCX7szCmE/2rRZXc/5ttJMZF89vPXcqi3AlnlIkORHH9wmzy0hJ5btsRbv7x2/zcS3BjSW1TG//2+308ubmU8yYn85NPX6gpiGVEXZufSU5qAj/+w0GizPi7a+co2YdoTCZ65xy/eLuEb/5uL/Ozk/nZXUvJTkno95z8ycncN34mzxYc4bafvsPDdy7hqrmTRihi/zS1dvDUe6X8cFMxJ1s7+OxlefzDqnnEx2iKAxlZZsa3Vi/AOcePXi2myznV7EM05hJ9R2cX3/jvvfzq3cNck5/JQ7ctDrnvd2ZyPM9//jLu/vkWPvuLLXxx5Rw+/9FZREXgzaGHNxWzpaSWzR/Ucqq9k1kZ47n70jwyk+P57bajfocnY1RUlPHtmxYC8OM/HORg1Un+5dbzNRp7AGMq0e8qq+crz+9i19F67rtyBl9aNe+s7+BnJsfz3F9ewlee38W/vbyfraV1fHP1gohoxqhrauPlwuP81/ajvFVcgxH8n8wVszOYGgF/n0SGqCjjn25eyMyM8fzzC0Xc+MM3+aebF3LJzDTV7vsQUqI3s1XAD4AA8DPn3Hd6HI8DfglcCNQAtznnSrxjXwbuATqBv3HOvTRk0Ydo99F6ntxcyjNbSkkbH8fDdyzhhkXn3oMmMS6ah25bzNJpqXxrfSEr/u01Pn3xNP7iyukDNgGFk/bOLvYea+Ctg9W8eaCazR/U0tnlmDIxgZXzJ7FkaioTxsX6HabIGcyM/3nFDM6fMoG/fep97vjZZpblpXLP5TO4dFbaoGv4zjlOtXfS2NJBY0sHbR1ddHR1sWFXBZ1dDocjNhBFbHQUty2bQmJcNImx0WHb9dMGmhbUzALAfuAaoAzYAtzunNvbrczngEXOub80szXAzc6528wsH3gKWA5MBl4B5jjnOvt6vaVLl7qCgoKz/kOcc5xs7aDmZBs1TW18UN3E7qP1vHuohqKKRmKjo1izbAp/d+1cUhIGfhP0Nfin5+x55fWneOjlAzy7NbgY9qLcFD4yJ4NZk8YzPT2RlIQYEmIDJMQEGDfMbwTnHF0Oupyjs8vR1NpB/al2Glo6aDjVTl1zG2V1pzhc08S+ikYKKxpp6wiOOMxKjmduVhLnTU4mZ0KCakYSlnqbvbKlvZO1BUd45A8HKa9vIRBlLJiczNS0RLJT4kmOjyYmEEWUGS3tnZxq76SlvYtT7Z00t3Vw0kvmDS3tnGwNbp9s7aCz6+zm2YkymJgYR0ZSHOnjY8lICm5njP/w7/Hx0X/8koiLDhATsCH5vJnZVufc0l6PhZDoLwG+7pz7mPf4ywDOuX/uVuYlr8w7ZhYNVAAZwAPdy3Yv19frnWuir6hv4eJ/3vihfQkxARbmpPCJ87O58fwcUsaF/i0faqI/7XBNExt2VfDSngp2lJ2gr8va1z9qb//MPYtZL6W6nPtjYg/1fZk+Po5ZkxJZmJPCotwJHDtxiiS1ccoo0N80xW0dXWw9XMfbB6spKKlj3/FGGk6109HjgxGIMhJiAsTHRDEuNpqOzi7iYgLER0cRHxMIbsdEER8dIM77HR0wAmZERRlRZphBe0cXrR1dLJ464Y+VquqTrVQ1tlF1spXqxlaqGltpC2H6BjNwDhZPmcD/+/xl53Rt+kv0oTTd5ABHuj0uAy7qq4xzrsPM6oE0b/+7Pc7N6SXAe4F7vYcnzWxfCHGFpAh49txOTQeqe+68c3DhDKVe4wvFYWAr8MyQhnOGc45vhCi+c+dbbCF+/sL52kE/8R0G7P5zft4+54gIJdH3VtnsWXfsq0wo5+KcexR4NIRYRoyZFfT17RgOFN/gKL5zF86xgeLrTSgTlJQBU7o9zgWO9VXGa7pJAWpDPFdERIZRKIl+CzDbzKabWSywBljXo8w64C5v+xZgkws2/q8D1phZnJlNB2YD7w1N6CIiEooBm268Nvf7gZcIdq983Dm3x8weBAqcc+uAx4BfmVkxwZr8Gu/cPWa2FtgLdACf76/HTZgJq6akXii+wVF85y6cYwPFd4YBe92IiMjopknERUQinBK9iEiEU6LvhZmtMrN9ZlZsZg/4HU9PZlZiZrvMbLuZnf3osqGP53EzqzSz3d32TTSzl83sgPc7Nczi+7qZHfWu4XYzu96n2KaY2atmVmhme8zsb739YXH9+okvXK5fvJm9Z2Y7vPi+4e2fbmabvev3jNeRJFxi+4WZfdDt2i0e9mCcc/rp9kPwhvNBYAYQC+wA8v2Oq0eMJUC633F0i+dKYAmwu9u+7wEPeNsPAN8Ns/i+Dvx9GFy7bGCJt51EcLqR/HC5fv3EFy7Xz4Dx3nYMsBm4GFgLrPH2/wT4qzCK7RfALSMZi2r0Z1oOFDvnDjnn2oCngdU+xxTWnHOvE+xt1d1q4Alv+wngphENqps+4gsLzrly59w2b7sRKCQ4ejwsrl8/8YUFF3TSexjj/TjgauA5b78v16+f2EacEv2ZepvyIWze2B4H/N7MtnrTR4SjTOdcOQSTBRCOq7Tcb2Y7vaYd35qWTjOzPOACgjW/sLt+PeKDMLl+ZhYws+1AJfAywf+Rn3DOdXhFfPsM94zNOXf62n3bu3bf92b/HVZK9GcKadoGn13mnFsCXAd83syu9DugUegRYCawGCgH/s3PYMxsPPCfwBeccw1+xtKbXuILm+vnnOt0zi0mOPJ+OTC/t2IjG5X3oj1iM7MFwJeBecAyYCLwD8MdhxL9mcJ+2gbn3DHvdyXwPME3d7g5bmbZAN7vSp/j+RDn3HHvQ9gF/Ac+XkMziyGYRJ90zv3W2x0216+3+MLp+p3mnDsB/IFgO/gEbzoWCIPPcLfYVnnNYc451wr8nBG4dkr0ZwplygffmFmimSWd3gauBXb3f5Yvuk+LcRfwXz7GcobTSdRzMz5dQwvOWf0YUOic+/duh8Li+vUVXxhdvwwzm+BtJwArCd5HeJXgdCzg0/XrI7aibl/gRvDewbBfO42M7YXXVewh/jTlw7d9DumPzGwGwVo8BKew+I3f8ZnZU8BVBKdfPQ58Dfh/BHs+TAVKgVudc77cEO0jvqsINjs4gr2Y7jvdJj7CsV0OvAHsAk5PXP4Vgu3gvl+/fuK7nfC4fosI3mwNEKy4rnXOPeh9Tp4m2DTyPvBprwYdDrFtIrhehwHbgb/sdtN2eGJRohcRiWxquhERiXBK9CIiEU6JXkQkwinRi4hEOCV6EZEIp0QvEc/MnJn9qtvjaDOrMrPfeY9vPD1LqTcr49/7FavIcBhwKUGRCNAELDCzBOfcKeAa4Ojpgy64HGbYDIoTGWqq0ctY8QJwg7d9O/DU6QNmdreZ/ajnCWY208xe9CaPe8PM5nn7P+HNdf6+mb1iZpne/gxv7vhtZvZTMztsZunesU97c5Nv944Fhv0vFvEo0ctY8TSwxszigUX8aQbG/jwK/LVz7kLg74Efe/vfBC52zl3gPe+XvP1fAzZ5E849T3BUK2Y2H7iN4GR0i4FO4M4h+atEQqCmGxkTnHM7vWl2bwc2DFTem63xUuDZ4JQkAJyeTjYXeMabsyQW+MDbfznBeV9wzr1oZnXe/hXAhcAW77kSCLNJ3iSyKdHLWLIO+FeC89ykDVA2iuCc5r0t8/ZD4N+dc+vM7CqCqy1B71Ncn97/hHPuy2cbsMhQUNONjCWPAw8653YNVNCbc/0DM7sVgjMNmtn53uEU/nQz965up70JfMorfy1wejGOjcAtZjbJOzbRzKYN9o8RCZUSvYwZzrky59wPzuKUO4F7zGwHsIc/LSn5dYJNOm8A1d3KfwO41sy2EVwUphxodM7tBf4vwVXBdhJcBan7NL8iw0qzV4oMEW9JuE7nXIeZXQI80kfTj8iIUhu9yNCZCqw1syigDfgLn+MRAVSjFxGJeGqjFxGJcEr0IiIRToleRCTCKdGLiEQ4JXoRkQj3/wGYekePTmYCAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution plot of \"Mileage\" with KDE plot.\n",
    "sns.distplot(df['Mileage'],kde=True); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Mileage is approximately normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEGCAYAAABhMDI9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3de3xdZZ3v8c8v90vbJE3S0kvatKVcWsEWapGL4FiRy6BVD2jB8eABhxkHZpyXjgpnHEUczhHOHJmjIl6OIAfBguBIVaTDTRGQlrZA6YXa9J4mbZM2SW9JmmT/zh97peyV7qQ77d7Z2dnf9+uVV9Z+9rOe9SzY3d+s9az1LHN3REREeuWkuwMiIjK8KBhERCREwSAiIiEKBhERCVEwiIhISF66O5AMVVVVXltbm+5uiIhklJUrVza7e3Xf8hERDLW1taxYsSLd3RARyShmti1euU4liYhIiIJBRERCFAwiIhKiYBARkRAFg4iIhCgYREQkRMEgIiIhCgYREQlRMIiISMiIuPNZTtwjy7bHLb/uvClD3BMRGS50xCAiIiEKBhERCVEwiIhIiIJBRERCFAwiIhKiYBARkRAFg4iIhCgYREQkRMEgIiIhCgYREQlRMIiISIiCQUREQhQMIiISomAQEZEQBYOIiIQoGEREJCShYDCzy81sg5nVmdmtcd4vNLNHg/eXmVltzHu3BeUbzOyyoKzGzF4ws/VmttbMPh9Tf6yZPWNmG4PfFSe/myIikqjjBoOZ5QL3AlcAs4BrzWxWn2o3Ai3ufipwD3BXsO4sYBEwG7gc+H7QXjfwRXc/E3gvcHNMm7cCz7n7TOC54LWIiAyRRI4Y5gN17r7Z3Y8Ai4GFfeosBB4Mlh8HFpiZBeWL3b3T3bcAdcB8d29091UA7n4AWA9MitPWg8BHT2zXRETkRCQSDJOAHTGv63nnS/yYOu7eDbQBlYmsG5x2mgssC4rGu3tj0FYjMC5ep8zsJjNbYWYrmpqaEtgNERFJRCLBYHHKPME6A65rZqOAJ4B/dPf9CfTlnUbcf+Tu89x9XnV19WBWFRGRASQSDPVATczryUBDf3XMLA8oA/YNtK6Z5RMNhYfd/ZcxdXab2YSgzgRgT6I7IyIiJy+RYHgNmGlm08ysgOhg8pI+dZYA1wfLVwPPu7sH5YuCq5amATOB5cH4w0+A9e7+7QHauh54crA7JSIiJy7veBXcvdvMbgGWArnA/e6+1szuAFa4+xKiX/IPmVkd0SOFRcG6a83sMWAd0SuRbnb3HjO7CPg08JaZvRFs6r+7+1PAt4DHzOxGYDtwTTJ3WEREBmbRP+wz27x583zFihXp7kZGemTZ9rjl1503ZYh7IiJDzcxWuvu8vuW681lEREIUDCIiEqJgEBGREAWDiIiEKBhERCREwSAiIiEKBhERCVEwiIhIiIJBRERCFAwiIhKiYBARkRAFg4iIhCgYREQkRMEgIiIhCgYREQlRMIiISIiCQUREQhQMIiISomAQEZEQBYOIiIQoGEREJETBICIiIQoGEREJUTCIiEiIgkFEREIUDCIiEqJgEBGREAWDiIiEKBhERCREwSAiIiEKBhERCVEwiIhIiIJBRERCFAwiIhKiYBARkRAFg4iIhCgYREQkRMEgIiIhCQWDmV1uZhvMrM7Mbo3zfqGZPRq8v8zMamPeuy0o32Bml8WU329me8xsTZ+2bjeznWb2RvBz5YnvnoiIDNZxg8HMcoF7gSuAWcC1ZjarT7UbgRZ3PxW4B7grWHcWsAiYDVwOfD9oD+CnQVk897j7nODnqcHtkoiInIxEjhjmA3XuvtndjwCLgYV96iwEHgyWHwcWmJkF5YvdvdPdtwB1QXu4+4vAviTsg4iIJFEiwTAJ2BHzuj4oi1vH3buBNqAywXXjucXMVgenmyriVTCzm8xshZmtaGpqSqBJERFJRCLBYHHKPME6iazb133ADGAO0Aj873iV3P1H7j7P3edVV1cfp0kREUlUIsFQD9TEvJ4MNPRXx8zygDKip4kSWTfE3Xe7e4+7R4AfE5x6EhGRoZGXQJ3XgJlmNg3YSXQw+bo+dZYA1wN/Aq4Gnnd3N7MlwCNm9m1gIjATWD7Qxsxsgrs3Bi8/BqwZqL6k3yPLth9Tdt15U9LQExFJhuMGg7t3m9ktwFIgF7jf3dea2R3ACndfAvwEeMjM6ogeKSwK1l1rZo8B64Bu4GZ37wEws58D7weqzKwe+Lq7/wS428zmED3ltBX4m2TusIiIDCyRIwaCS0af6lP2tZjlDuCafta9E7gzTvm1/dT/dCJ9EhGR1NCdzyIiEqJgEBGREAWDiIiEKBhERCREwSAiIiEKBhERCVEwiIhIiIJBRERCFAwiIhKiYBARkRAFg4iIhCgYREQkRMEgIiIhCgYREQlRMIiISIiCQUREQhQMIiISomAQEZEQBYOIiIQoGEREJETBICIiIQoGEREJUTCIiEiIgkEA6Ik4Bzq60t0NERkGFAwCwCubmrn76Q1s3H0g3V0RkTRTMAgAW5oP0ePOz5ZtY/veQ+nujoikkYJBcHfqW9o5bfwoxhTl89M/bWVz08F0d0tE0kTBILS1d3Gws5vTx4/mhguncaQ7wi9X7Ux3t0QkTRQMQn1LOwCTK0qoKC1g/Jgi3tjRmuZeiUi6KBiE+pZ2cs04pawIgJqKEt6sbyUS8TT3TETSQcEg1Lce5pSyIvJzox+HmrHFHOjoZnOzBqFFspGCIctF3NnZ0s6kiuKjZZMrSgB0OkkkSykYslzzwU46uyPUxARD9ehCRhfm8caOljT2TETSRcGQ5XYGA8+TgqMEgBwzzq4p0xGDSJZSMGS5HS3tFOTmMG50Yah8Tk05bzceoKOrJ009E5F0UTBkuZ0th5lYXkyOWah8Tk0F3RFnzc62NPVMRNJFwZDlmg52ckpZ4THl764pAzQALZKNEgoGM7vczDaYWZ2Z3Rrn/UIzezR4f5mZ1ca8d1tQvsHMLospv9/M9pjZmj5tjTWzZ8xsY/C74sR3Twayv6OLjq4I5cUFx7w3bnQRk8qLFQwiWei4wWBmucC9wBXALOBaM5vVp9qNQIu7nwrcA9wVrDsLWATMBi4Hvh+0B/DToKyvW4Hn3H0m8FzwWlKgsbUDgLKS/Ljvz51SzoqtLbjrRjeRbJLIEcN8oM7dN7v7EWAxsLBPnYXAg8Hy48ACM7OgfLG7d7r7FqAuaA93fxHYF2d7sW09CHx0EPsjg9DQFr0iqbw4fjBceGoVu/Z3sKlJN7qJZJNEgmESsCPmdX1QFreOu3cDbUBlguv2Nd7dG4O2GoFx8SqZ2U1mtsLMVjQ1NSWwG9JXQ2sQDCXHnkoCuOjUKgBermsesj6JSPolEgwWp6zvuYX+6iSy7glx9x+5+zx3n1ddXZ2MJrNOQ2s7OQaji/Livl8ztoQpY0t4ScEgklUSCYZ6oCbm9WSgob86ZpYHlBE9TZTIun3tNrMJQVsTgD0J9FFOQENrB2OK8o+5VDXWhadW8eqmvXT3RIawZyKSTokEw2vATDObZmYFRAeTl/SpswS4Pli+GnjeoyOWS4BFwVVL04CZwPLjbC+2reuBJxPoo5yAhtb2fgeee110ahUHOrtZrfsZRLLGcYMhGDO4BVgKrAcec/e1ZnaHmX0kqPYToNLM6oAvEFxJ5O5rgceAdcDTwM3u3gNgZj8H/gScbmb1ZnZj0Na3gEvNbCNwafBaUqChrb3fgede58+oxAxe3qjTSSLZIv7J5T7c/SngqT5lX4tZ7gCu6WfdO4E745Rf20/9vcCCRPolJ64n4uxq62B61agB640tLWD2xDG8VNfM3y+YOUS9E5F00p3PWar5YCddPU7ZcY4YIDrOsGp7Cwc7u4egZyKSbgqGLLXz6KWqxw+GD806ha4e5+r7XqFuzwE2NR3k7x5eyWX3vMi+Q0dS3VURGWIJnUqSkefoXc8JHDGcO7WCBz7zHv7pF2/yl995ie6IU5SXw5GeCF/91VtcOKMKG+DKJhHJLAqGLHX05rY48yTF8xdnjON3n38f3/j1OqpHF3LzX5zK4yvruevptyktyGPuFE1pJTJSKBiy1M7WdkYV5lGUn/jZxHFjirj3U+ccfX3TxdN5/u3dLHmzgWlVpf3eQS0imUVjDFmqobWdieVFJ3UKKDfH+Ldr3k1nd0SzsIqMIAqGLNXY1sHE8uLjVzyOqZWljC0tODqYLSKZT8GQpRpa25lQdvLBADCpvFjBIDKCKBiyUEdXD3sPHWFSeVFS2ptcUUzr4S7d5yAyQigYslDvFUnJOJUU206DjhpERgQFQxZqCO5hSFYwTAraqW9RMIiMBAqGLNT75LZJSQqGovxcqkYV6IhBZIRQMGShhtZ2zGD8mOSMMYAGoEVGEgVDFmpobad6VCEFecn73z+pooS29i4OdHQlrU0RSQ8FQxZqaE3OPQyxek9L6ahBJPMpGLJQQ1t70sYXek0sL8KAnRqAFsl4CoYs4+5Hp8NIpsK8XKpGF+qIQWQEUDBkmZbDXXR0RZJ213OsCWVF7N7fkfR2RWRoKRiyTLJvbotVWVpA6+EuuiORpLctIkNHwZBlek/1JHuMAaByVCEOtB7SlUkimUzPY8gyjUePGAY/xvDIsu0Dvl9ZGn0ew95DnYPvmIgMGzpiyDINbR0U5uUwtjT5D9WpHFUIwF49B1okoykYsszO1nYmlhen5BnNpQW5FOblsPeggkEkkykYskwqLlXtZWZUlhboVJJIhlMwZJnG1g4mpuBS1V5jRxXqiEEkwykYskhXT4TdB5I/HUasytICWg4fobtHl6yKZCoFQxbZ1daBe2ouVe1VWVpAxDVnkkgmUzBkkVTe3Nar98qkrXsPp2wbIpJaCoYs0vuAngkpGnwGqBwVvQx2295DKduGiKSWgiGLHH2kZwoHn0cX5pGfa2xt1hGDSKZSMGSRhtZ2xpYWUFyQm7JtRC9ZLdQRg0gG05QYWWT7vsMpHXjuVTmqgK0pCob+puW47rwpKdmeSDbSEUMW2dx0iOnVpSnfTmVpATv2tdMT8ZRvS0SST8GQJTq6emhoa2d61aiUb6uytJAjPREa23TJqkgmUjBkiS3Nh3BnSI4Yxh69MkkD0CKZSMGQJTY3Rc/5z6geiiOGaDCkapxBRFJLwZAlNjcdBGBaVeqPGMYU51OYl8PWZgWDSCZSMGSJzc2HmFRenNJLVXvlmDG1skR3P4tkqISCwcwuN7MNZlZnZrfGeb/QzB4N3l9mZrUx790WlG8ws8uO16aZ/dTMtpjZG8HPnJPbRQHY1HRwSMYXek2tLNW9DCIZ6rjBYGa5wL3AFcAs4Fozm9Wn2o1Ai7ufCtwD3BWsOwtYBMwGLge+b2a5CbT5JXefE/y8cVJ7KLh79FLVITiN1Ku2soRtew8T0SWrIhknkSOG+UCdu2929yPAYmBhnzoLgQeD5ceBBRZ9RNhCYLG7d7r7FqAuaC+RNiVJmg50crCzm+lDMPDca2plKZ3d0Wm+RSSzJBIMk4AdMa/rg7K4ddy9G2gDKgdY93ht3mlmq83sHjMrjNcpM7vJzFaY2YqmpqYEdiN7bQquSBrKU0m1ldFtac4kkcyTSDDEezhw3/MD/dUZbDnAbcAZwHuAscBX4nXK3X/k7vPcfV51dXW8KhLY3By9ImlojxhKAM2yKpKJEgmGeqAm5vVkoKG/OmaWB5QB+wZYt9823b3RozqBB4iedpKTsLnpEEX5OUwYk7rptvuaWF5MQW6OrkwSyUCJBMNrwEwzm2ZmBUQHk5f0qbMEuD5Yvhp43t09KF8UXLU0DZgJLB+oTTObEPw24KPAmpPZQYnewzCtahQ5OfEO1FIjN8eoGVusIwaRDHTc2VXdvdvMbgGWArnA/e6+1szuAFa4+xLgJ8BDZlZH9EhhUbDuWjN7DFgHdAM3u3sPQLw2g00+bGbVRE83vQH8bfJ2Nzttbj7EWZPKhny7tZWlbMmim9zizfyqWV8lEyU07ba7PwU81afsazHLHcA1/ax7J3BnIm0G5R9IpE+SmM7uHnbsO8zCOX2vF0i9qZWlvLJpL+5O9AAwce7O2ob9/H7DHt6sb+MT82q4dNb4FPVURGLpeQwj3NqG/UQcZk0YPeTbrq0qob2rh6YDnYwb5PjGv/3nBu59YRMQnXvpmXW7+cwFtUyvKiUvVzfsi6SSgmEEij2l8dLG6KW86ZjpdGrvJat7Dw8qGDY3HeSHf9jMVWdP4Osfns2Y4jz+51Nv89NXtjKtqpQbLpxG7hCOl4hkG/3pNcJt23eYsaUFjC7KH/Jt1waXrA52ltX/8dTbFOXn8vUPz6Z6dCGFebnc/pHZ3H312WxpPsTStbtS0V0RCSgYRjB3Z/vew0wZW5KW7U8qLyYvxwY1y+pLG5t5dv1ubv6LU6keHb638RPzanjv9EpeqmvmrZ1tye6uiAR0KmkEazncxYHO7rQFQ15uDtOrS1nfuH/Aer2nvtyd771QR0VJPiUFuXGv8rnyrFNoaG3niVX11FQUU15SkJK+i2QzHTGMYNv3Rf9S770LOR3m1lTw+o5Wore1DKy+pZ3Gtg7ef/o48vsZYM7LyeGT82roiTgvbNBUKCKpoGAYwbbtPUxhXg7jh/CO577mTimn9XBXQndAr9zeQn6uHfeei4rSAt5TW8HKbfvYd+hIsroqIgEFwwi2fd9hasaWkDPIewiSae6UCgBWbWsZsF5XT4TV9a3MnlhGUf7xHyZ0yWnjyDHjhbf3JKWfIvIOBcMI1dnVw662jrSNL/SaOW4UowvzeH3HwMGwvnE/HV0R5k4pT6jdsuJ8zps2ltd3tNB8sDMZXRWRgAafM1y8AVqAHS3tODA1zcGQk2O8u6ac17e3Dlhv1fYWyorzmTGIGWAvPq2a5Vv38ceNTfzDgpkn21URCeiIYYSq23OAHIOaNAcDRMcZ3t51gMNHuuO+v7+ji427DzKnpnxQp71GF+Vz1qQyVte39du2iAyejhhGoIg7b+xo5bTxoxM6X59qc6eU0xNx3qpv47zplce8v2JrCw6cE4xHDMa5U8eyansrT6/ZxcfPmZzQOprsTmRgOmIYgTbtOcj+ju6jA7/pNqcm2o/Xdxx7OqmrJ8LyLXuZOW7UMTe0JaK2soTK0gIeW7Hj+JVFJCEKhhFo5fYWivNzOfOUoZ84L56xpQXUVpbw+vZjB6CfXrOL/R3dnB/nSCIRZsa5Uyt4dfM+PftBJEkUDCNMR1cP6xr2c/bksmE1C+ncKRWs3NZKd08kVP7gK1sZW1rAaScRYnOnVJBj8PjK+pPtpoigYBhx3trZRnfEOXfq8DiN1OvKsybQfLCTB17eerRszc42Vmxr4b3TK0/qXouy4nzeN7Oax1fWHxM8IjJ4GnweQSLuvLZ1H9WjC5lUXnzM+/1d2joUPnjmOBacMY57nv0zV549gcrSAu5euoGSglzOTcJYyLXza/jbn63i9xua+KAe6CNyUnTEMIK8unkv9S3tXDyzatBPTEs1M+P2j8wm4s6tT6zmmh/8iT9ubOIrl59BccHJXzm14MzxjBtdyM+WbUtCb0Wym44YRoi9BztZunYXp40fdUKXffaViqOLmrElfH7Badz19NuMKszjx5+exwdnjU/KtvJzc1g0fwrffX4jO4KpQETkxOiIYQSIuPP4qnpyc4yPzZ087I4WYo0qzOOyWeP57EXT2HOgM6kBdO38Ggx4ZHn6TpmJjAQKhhHg6TW72Lb3MFedPZGy4qF/Uttg5OYYl5w+btDPgE7EhLJiFpw5nsde28GR7qEfhI6487s1jfx2dQOvbdXMr5K5FAwZ7pVNzbxU18x7p1cytyaxCehGsr9671T2HjrCQ68O/VjDH/7cxB83NrNsyz7+4/WdfPf5jTQd0AR/knkUDBns2XW7+e3qRs48ZTRXnT1hWJ9CGioXz6xiwRnj+Nbv1rMqzg11HV09KbmkdeW2fTy3fjdnTy7j9o/M5nOXzKCrJ8L/Wvp20rclkmoKhgzVdKCTLz3+JhPKi/jke6ak9ZkLw4mZ8e1PzOGUsiJufngVe4MpuXe2tvONX69l7h3P8LUla/nmb9bx//60lc6unpPeZlt7F//w8zcoK87no3MmkWNGzdgSLphRxS9W1rO6fuCZZUWGG12VlIHcna/+6i0OHenh+vNrKchTvscqK8nnvk+dy8fve4X33PkseTk5dEUi5JrxkTkT2d/eTVv7EVZua+GhV7dx/QW1J7W97z63kca2dm66eEZo0sIPnDGOt3cd4PYla3nicxfoiE4yhoIhA/16dSNL1+7m1ivOYEzR8B5sTpd3TSrjoRvm8+LGJiIOJfm5fPzcyUwqLz56JdS0qlJ+saKexcu3c915U/p9zvRAdu/v4KFXt/GxuZOPeShSUX4uX778dL78+GqWrt3N5e86JSn7JpJq+lMzw7Qd7uLrT67h3TXlfPaiaenuzrB23vRKvnTZGXzl8jP4+wUzj7kbfE5NBVe9eyLrdx3g+y9sOqFt3PtCHT0R5/P9PCjov5wzmckVxdz/8pYTal8kHRQMGeZ7L2yktb2Lb338rGE1SV6mOn96JWdPLuPeF+qo23NwUOvubG1n8fIdXDNvMlMq499Ql5tjfOaCWpZv2ceanW3J6LJIyumbJYPs2HeYB1/ZxtXnTObMCWPS3Z0R4y/PmkBxQS7//ZdvEYl4wut959mNANzygYEfK3rNvBpKCnJDEwiKDGcaY8ggdy/dQE4OfPFDp6e7Kxkh0buqRxfl889XnsmXn1jN4td2JPQ0t+Vb9vHoih3ceNG0uBMWxiorzufqcyezePkObr3ijBN6IJHIUNIRQ4Z4c0crv36zgb9+33ROKUv+XcPZ7pp5k7lgRiXf/M06Nuw6MGDdjq4evvLEaiZXFPOFS09LqP3rL6jlSE+EhzXJn2QABUMG6Ik4//LkGqpGFfI3l8xId3dGJDPj3z85h9LCPD73s5Uc6Ojqt+49z/6ZLc2H+NbHz6a0MLGD7hnVo/jgmeO4/6UttB3uv22R4UDBkAEe+tNWVte38bUPz2JUgl9EMnjjxhRx73Vz2bbvMF/6xepj5ltydx5eto0fv7iZT86r4aKZVYNq/4sfOp0Dnd384MUTuwJKZKjoW2aY29XWwb/9559538wqPnz2hHR3Z8Q7b3olt11xBv/62/Vc8X9e5JLTxjFlbAmHOrt5dv1uXt/RyiWnVfPVq84cdNtnThjDwndP5IGXt/CZC2oZn4KJBEWSQcEwjEUizld/tYaungj/+tF36c7ZIfLZ901nenUpty9ZF7r/wIAFZ47jx5+eR07Oif2/+MKlp/Pbtxr5znMbufNjZyWpxyLJpWAYptydb/52Hc+u382/XDWLqZWl6e5SVvnAGeO5YEYVX/rFmxzpjlBamMeE8mImlRefcCgATKks4br5U/jZsu1c8a4Jgz4dJTIUNMYwTN33h0088PJWbrhwGjdcWJvu7mSlovxczp9RxSWnj2Ne7djjXpaaqC9edjozx43ibx5aoZveZFhSMAwzbe1dXH3fK9z99AbOnlzG9OpSfr58R7q7JUk0piifB2+YT3lJAZ95YLnCQYadhILBzC43sw1mVmdmt8Z5v9DMHg3eX2ZmtTHv3RaUbzCzy47XpplNC9rYGLRZcHK7mBlaDx/h4WXbuPTbf2DlthbeN7OKq8+drOm0R6jxY4p48Ib5uMOHv/cSn1/8Ousb9w/qzmuRVDnuGIOZ5QL3ApcC9cBrZrbE3dfFVLsRaHH3U81sEXAX8EkzmwUsAmYDE4Fnzaz3jqD+2rwLuMfdF5vZD4K270vGziaLu+MO3rsMwetoOX1e9613qLOb5oOdNLS2s67xAKvrW3m5rpmuHuesSWVcc24NkyqSc9pCki8Zz6nubeNz75/BHzc289RbjTz5RgOjC/OYPWkMY0sLKC3II8eMP+8+QI87PZF3fqpGFWIWfYb2qKK86O/CPEoL8xhdFLMcvB+7XJyfi5nh7kQ8+tnsjjhHeiJ0dkXo7O6hs7vPcneEzq7o8pHuCF09Ecyi93/kmGFATg5H/5DJ6S03yDHIzcmhOD+X4oIcivPzKC7IDV7nUpiXQ08k2ofungg9EacrWO6OeOjfW6R3Ofj3ZdjR7UZ/osu5Ob3bDi/nmJFrhgXr5No77xG03xNxetyJRN5Zxnv3tf/fOTFtGe+87nvRSN/9ibdP7/S3/3ZSKZHB5/lAnbtvBjCzxcBCIDYYFgK3B8uPA9+z6F4sBBa7eyewxczqgvaI16aZrQc+AFwX1HkwaDclwXDHr9fx8+XbQ1/gDPCFngo5BtOrR3H9+bV8dO4kZk8co1NHaZCML/sTUVKQx2WzT+GCGZVUlBTwZn0r6xv3s2HXAQ519uA4nd3RZ0nk5rzz0x2JfoHuPXiEg53dHOzs5lBnN9064hh2er/kT/a7JDYsYsPoB391LhefVp3UPicSDJOA2G+qeuC8/uq4e7eZtQGVQfmrfdadFCzHa7MSaHX37jj1Q8zsJuCm4OVBM9uQwL6crCqgOdmNbgGeA/5lgDqfOvnNpKTv/UlCf3sl1O8kbi+Zqj41hP/Nk2hIPytJlql9P+F+X/LNk9ru1HiFiQRDvOOXvpnXX53+yuONbQxU/9hC9x8BP4r3XqqY2Qp3nzeU20yWTO17pvYbMrfvmdpvyNy+D7d+JzL4XA/UxLyeDDT0V8fM8oAyYN8A6/ZX3gyUB230ty0REUmhRILhNWBmcLVQAdHB5CV96iwBrg+Wrwaed3cPyhcFVy1NA2YCy/trM1jnhaANgjafPPHdExGRwTruqaRgzOAWYCmQC9zv7mvN7A5ghbsvAX4CPBQMLu8j+kVPUO8xogPV3cDN7t4DEK/NYJNfARab2b8CrwdtDxdDeuoqyTK175nab8jcvmdqvyFz+z6s+m2eqsttREQkI+nOZxERCVEwiIhIiIIhQcebFiQN/bnfzPaY2ZqYsrFm9kwwncgzZlYRlJuZfSfo+2ozOydmneuD+hvN7Pp420pyv2vM7AUzW29ma83s8xnU9yIzW25mbwZ9/0ZQHncalxOZKibF/c81s9fN7DcZ1u+tZvaWmb1hZiuCskz4vOn7RvEAAAUfSURBVJSb2eNm9nbweT8/E/oN9N6erZ+BfogOkG8CpgMFwJvArDT36WLgHGBNTNndwK3B8q3AXcHylcDviN4n8l5gWVA+Ftgc/K4IlitS3O8JwDnB8mjgz8CsDOm7AaOC5XxgWdCnx4BFQfkPgM8Fy38H/CBYXgQ8GizPCj5DhcC04LOVOwSfmS8AjwC/CV5nSr+3AlV9yjLh8/Ig8NlguQAoz4R+u7uCIcH/wecDS2Ne3wbcNgz6VUs4GDYAE4LlCcCGYPmHwLV96wHXAj+MKQ/VG6J9eJLonFkZ1XegBFhF9I79ZiCv72eF6FV35wfLeUE96/v5ia2Xwv5OJnqD/QeA3wT9GPb9DrazlWODYVh/XoAxRCc1sEzqd++PTiUlJt60IHGn6kiz8e7eCBD8HheU99f/tO5XcIpiLtG/vDOi78HpmDeAPcAzRP9q7m8al9BUMUDsVDFD3fd/B74M9D7IeqDpZ4ZTvyE6+8F/mtlKi06FA8P/8zIdaAIeCE7f/V8zK82AfgMaY0hUwlN1DFODnbIk5cxsFPAE8I/uvn+gqnHK0tZ3d+9x9zlE/wKfD8R7+HNvP4ZF383sKmCPu6+MLR6gD8Oi3zEudPdzgCuAm83s4gHqDpe+5xE91Xufu88FDhE9ddSf4dJvQMGQqESmBRkOdpvZBIDg956gfLBTk6SUmeUTDYWH3f2XQXFG9L2Xu7cCvyd6Pri/aVwGO1VMqlwIfMTMtgKLiZ5O+vcM6DcA7t4Q/N4D/AfRQB7un5d6oN7dlwWvHycaFMO934CCIVGJTAsyHMROTRI7ncgS4L8GVz68F2gLDmOXAh8ys4rg6ogPBWUpY2ZG9G729e7+7Qzre7WZlQfLxcAHgfX0P43LYKeKSQl3v83dJ7t7LdHP7vPu/qnh3m8AMys1s9G9y0T/P69hmH9e3H0XsMPMTg+KFhCdAWJY9zt2B/ST2GDSlUSvoNkE/PMw6M/PgUagi+hfFTcSPQ/8HLAx+D02qGtEH4y0CXgLmBfTzg1AXfDz34ag3xcRPRReDbwR/FyZIX0/m+g0LauJfjl9LSifTvQLsg74BVAYlBcFr+uC96fHtPXPwT5tAK4Yws/N+3nnqqRh3++gj28GP2t7/+1lyOdlDrAi+Lz8iuhVRcO+3+6uKTFERCRMp5JERCREwSAiIiEKBhERCVEwiIhIiIJBRERCFAwiAzCznmBWz96fE55Z18xeSWbfRFJFl6uKDMDMDrr7qHT3Q2Qo6YhB5AQEzwj4hpmtCp4VcEZQXh3Ms7/KzH5oZtvMrCp472Dw+/1m9vuYufofDu4Ix8zONbM/BBPGLe2dPkFkKCkYRAZW3OdU0idj3mv26ORu9wH/FJR9negUEucQnddnSj/tzgX+kegzDqYDFwZzSH0XuNrdzwXuB+5M/i6JDCzv+FVEslq7R2dTjad3AsCVwMeD5YuAjwG4+9Nm1tLPusvdvR4gmMa7FmgF3gU8ExxA5BKd9kRkSCkYRE5cZ/C7h3f+LcWbJnmgdWPXN2Ctu5+fnO6JnBidShJJrpeATwCY2YeITpyWqA1AtZmdH6yfb2azk99FkYEpGEQG1neM4VvHqf8NotMkryL6YJlG4EAiG3L3I0Snub7LzN4kOvPsBSfRd5ETostVRZLIzAqBHnfvDv7yv2+AMQqRYUljDCLJNQV4zMxygCPAX6e5PyKDpiMGEREJ0RiDiIiEKBhERCREwSAiIiEKBhERCVEwiIhIyP8HOFzbicQt1DIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution plot of \"Engine\" with KDE plot.\n",
    "sns.distplot(df['Engine'],kde=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Engine is right (positively) skewed with couple of medians."
   ]
  },
  {
   "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": [
    "#Distribution plot of \"Price\" with KDE plot.\n",
    "sns.distplot(df['Power'],kde=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Power is right (positively) skewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": [
    "#Distribution plot of \"New_Price\" with KDE plot.\n",
    "sns.distplot(df['New_Price'],kde=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- New_Price is right (positively) skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEGCAYAAABrQF4qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3df5Rb5X3n8fdX0kjj+WHPYA/YGDs2GEJMyQJxHBoa2pO0CWRb3G6hMfScQMNZ2pNwut3+JNtdktLuOU27bdo9ZbehDRtKwpKENl2fjRNCSzZNW0JsCL9sY3CMMcbGHuPfntGMfnz3j3s1FrI0ujOjGY3u/bzOmWPp6tGdR0J89Mz3Pve55u6IiEh8pdrdARERmV0KehGRmFPQi4jEnIJeRCTmFPQiIjGXaXcHai1ZssRXrVrV7m6IiHSUp5566rC7D9V7bN4F/apVq9i6dWu7uyEi0lHM7NVGj6l0IyIScwp6EZGYU9CLiMScgl5EJOYU9CIiMaegFxGJOQW9iEjMKehFRGIusUE/Ml7kmj98nH/ddbjdXRERmVWJDfqDJ8Z4/dgoOw+ebHdXRERmVWKD/lS+CMDIeKnNPRERmV3JDfqxIOjzBQW9iMRb4oNeI3oRibvEBv1pBb2IJERig/6kSjcikhCRgt7MrjOznWa2y8zuqvP4tWb2tJkVzezGOo8vNLPXzewvWtHpVjhzMLbY5p6IiMyupkFvZmngXuB6YC1ws5mtrWm2F7gNeKjBbn4f+M70u9l6ldLNaKHc5p6IiMyuKCP69cAud9/t7uPAw8CG6gbuvsfdnwPOSk0zexdwHvCtFvS3ZSoHY0c1oheRmIsS9MuB16ru7wu3NWVmKeBPgN9q0u4OM9tqZluHh4ej7HrGTuYrI3rV6EUk3qIEvdXZ5hH3/3Fgs7u/Nlkjd7/P3de5+7qhobrXtm05zboRkaSIcnHwfcCKqvsXAPsj7v9HgfeZ2ceBPiBrZqfc/awDunNt4oQpBb2IxFyUoN8CXGxmq4HXgY3ALVF27u6/WLltZrcB6+ZDyMOZ6ZUjKt2ISMw1Ld24exG4E3gU2AF8xd23mdk9ZnYDgJm928z2ATcBnzOzbbPZ6VaYmHWjEb2IxFyUET3uvhnYXLPt7qrbWwhKOpPt4wvAF6bcw1lSmUc/VixTKjvpVL1DESIinS+xZ8ZWRvSgmTciEm+JDPpy2Tk1XuSc3iyg8o2IxFsig36kUMIdhvpygIJeROItkUFfKducuzAMepVuRCTGEhn0lbNiKyN6LWwmInGWyKCvnCw11K8RvYjEXyKD/nRt0KtGLyIxlsignyjdaEQvIgmQyKCfKN1M1OgV9CISX4kM+trSjS4nKCJxlsigrz0YqxG9iMRZYoO+K20s7O4CdDBWROItmUGfL9KXy5BKGd1dKR2MFZFYS2bQjxXp6w4W7uzJZnTClIjEWmKDvjcbBP2CrjSj42dd01xEJDaSGfT5Iv3hiH5BNs1oQSN6EYmvZAb9WFCjB+jJpnUwVkRiLZFBf3qsSG8Y9N1daU2vFJFYi3Qpwbg5OVbk4Ik8Dz25l2Mj44yMl3joyb0A3PKelW3unYhIa0Ua0ZvZdWa208x2mdlddR6/1syeNrOimd1Ytf0KM3vCzLaZ2XNm9pFWdn66TuWL5DJpALrSKcaLOhgrIvHVNOjNLA3cC1wPrAVuNrO1Nc32ArcBD9VsHwE+6u6XAdcBf2ZmAzPt9EyUys5ooUQuE7z0bDpFoaSgF5H4ilK6WQ/scvfdAGb2MLAB2F5p4O57wsfekpju/lLV7f1mdggYAo7NuOfTVFn+INcVjugzGtGLSLxFKd0sB16rur8v3DYlZrYeyAI/rPPYHWa21cy2Dg8PT3XXU1JZ0OytI3qf1d8pItJOUYLe6mybUjKa2TLgQeCX3P2s4bO73+fu69x93dDQ0FR2PWWnaoK+KyzduCvsRSSeogT9PmBF1f0LgP1Rf4GZLQS+Dvxnd//e1LrXepWLjnSHpZtsJoWDRvUiEltRgn4LcLGZrTazLLAR2BRl52H7rwF/4+5fnX43W+fs0k3wB4sOyIpIXDUNencvAncCjwI7gK+4+zYzu8fMbgAws3eb2T7gJuBzZrYtfPovANcCt5nZM+HPFbPySiI6U7o5M70SYFxBLyIxFemEKXffDGyu2XZ31e0tBCWd2ud9EfjiDPvYUqfyNSP68N+CZt6ISEwlbgmEM9MrzxyMBY3oRSS+khv0mTMHY0EHY0UkvhIX9PlCiUzKSKeCg7ATI3qVbkQkphIY9OWJqZUQnDAFmnUjIvGVuKAfK55Z5wagK5xeqRq9iMRV4oL+rBF9RiN6EYm3xAX92SN61ehFJN4SF/T5Qnli5Uo4M6JX6UZE4ipxQV87ok+ZkUkZhaKmV4pIPCUv6Atlurve+rK70imN6EUktpIX9MXSxMlSFdmMrjIlIvGVuKDP1x3Rmw7GikhsJS7o647odd1YEYmxBAa9avQikiyJC/p8oUGNXqUbEYmpxAV9oxG9Vq8UkbhKVNC7e8MRvUo3IhJXiQr6YtkpO3Vn3ah0IyJxlaigzxdKAHVn3WhELyJxFSnozew6M9tpZrvM7K46j19rZk+bWdHMbqx57FYzezn8ubVVHZ+OsXDUXr9GX8ZddXoRiZ+mQW9maeBe4HpgLXCzma2tabYXuA14qOa55wCfAt4DrAc+ZWaDM+/29DQc0WdSlB1KZQW9iMRPlBH9emCXu+9293HgYWBDdQN33+PuzwG19Y8PAY+5+xF3Pwo8BlzXgn5PS2VEn6szogddN1ZE4ilK0C8HXqu6vy/cFkWk55rZHWa21cy2Dg8PR9z11E1WowctVSwi8RQl6K3OtqhD30jPdff73H2du68bGhqKuOupaziir1xlSjNvRCSGogT9PmBF1f0LgP0R9z+T57ZcZUTffdaIXteNFZH4ihL0W4CLzWy1mWWBjcCmiPt/FPigmQ2GB2E/GG5ri6YjegW9iMRQ06B39yJwJ0FA7wC+4u7bzOweM7sBwMzebWb7gJuAz5nZtvC5R4DfJ/iy2ALcE25ri7FCOL2yUY1epRsRiaFMlEbuvhnYXLPt7qrbWwjKMvWeez9w/wz62DJjxfBgbMNZNwp6EYmfRJ0ZOzGi79KsGxFJjkQFfb4yos80mnWjefQiEj+JCnqN6EUkiRIV9GdOmKod0Wt6pYjEV6KCfqxYJp2yiYOvFZlUipTphCkRiadEBX1w0ZH6L1nXjRWRuEpU0AeXEUzXfSybSWl6pYjEUqKCvumIXqUbEYmhRAX9pCN6XSBcRGIqUUE/+YjeVKMXkVhKVNCPFcvkJqvRq3QjIjGUsKDXrBsRSZ5EBX2+UG4Y9Jp1IyJxlaign+xgrGbdiEhcJSvoJzkYq1k3IhJXyQr6JidMqUYvInGUqKBvNr2yVHaKCnsRiZlEBX2zE6YARsMVLkVE4iJRQT/piD7cPjquoBeReElM0BdLZYpl14heRBInUtCb2XVmttPMdpnZXXUez5nZl8PHnzSzVeH2LjN7wMyeN7MdZvbJ1nY/urFw6uRkJ0wBjGhELyIx0zTozSwN3AtcD6wFbjaztTXNbgeOuvsa4LPAZ8LtNwE5d78ceBfwy5UvgblWCfrJZt2ARvQiEj9RRvTrgV3uvtvdx4GHgQ01bTYAD4S3HwE+YGYGONBrZhlgATAOnGhJz6dorMGFwSsqI3rV6EUkbqIE/XLgtar7+8Jtddu4exE4DiwmCP3TwAFgL/Df3P1I7S8wszvMbKuZbR0eHp7yi4gi3+DC4BVZBb2IxFSUoLc622pPIW3UZj1QAs4HVgO/YWYXntXQ/T53X+fu64aGhiJ0aeqaj+iDlzCi0o2IxEyUoN8HrKi6fwGwv1GbsEyzCDgC3AJ8090L7n4I+Bdg3Uw7PR1NR/ThF0BeI3oRiZkoQb8FuNjMVptZFtgIbKppswm4Nbx9I/C4uztBueb9FugFrgZebE3Xp2asEK1GPzJenLM+iYjMhaZBH9bc7wQeBXYAX3H3bWZ2j5ndEDb7PLDYzHYBvw5UpmDeC/QBLxB8Yfwvd3+uxa8hknxlemWTEb1KNyISN5kojdx9M7C5ZtvdVbfzBFMpa593qt72dmg2os+kDEOlGxGJn8ScGZtvMo/ezOhKp3TClIjETmKCvtmIHoL1bnTClIjETWKC/kyNvvFLzqZN8+hFJHYSE/SVEX2j0g0EM280oheRuElO0DdZ1AyCmTeq0YtI3CQn6AslzM4sdVCPRvQiEkfJCfpimVwmRbDWWn3ZdEo1ehGJnUjz6DvZQ0/uBeDZfccxbOJ+PZp1IyJxlJgRfbFUnli4rBHNuhGROEpO0JedzCT1eSA8YUpr3YhIvCQm6AulMplUkxG9SjciEkOJCfpiySdWqGykK50iXyhTLtcuty8i0rkSE/SFcplM0xp9uCZ9UaN6EYmPxAR9pBF9ZaliHZAVkRhJUNBHqNHrurEiEkOJCfpCpBp98EWgA7IiEieJCfpiufmIvrLg2cm8pliKSHwkJugLpebz6HuyQdAfGxmfiy6JiMyJBAV9mWyTWTc92WBFiKMjhbnokojInIgU9GZ2nZntNLNdZnZXncdzZvbl8PEnzWxV1WPvNLMnzGybmT1vZt2t6350hVK5aY1eI3oRiaOmQW9maeBe4HpgLXCzma2taXY7cNTd1wCfBT4TPjcDfBH4FXe/DPgJYM6Hy6WyU3aalm5ymRSZlHHktIJeROIjyoh+PbDL3Xe7+zjwMLChps0G4IHw9iPAByxYD/iDwHPu/iyAu7/p7nM+paVQCi460qx0Y2YM9HSpdCMisRIl6JcDr1Xd3xduq9vG3YvAcWAxcAngZvaomT1tZr9d7xeY2R1mttXMtg4PD0/1NTRVCfquSa4uVTHQk1XpRkRiJUrQ1xsG1y4G06hNBvgx4BfDf3/OzD5wVkP3+9x9nbuvGxoaitClqSmUgu52pZq/3MGeLo4q6EUkRqIE/T5gRdX9C4D9jdqEdflFwJFw+3fc/bC7jwCbgatm2umpmvqIXqUbEYmPKEG/BbjYzFabWRbYCGyqabMJuDW8fSPwuLs78CjwTjPrCb8AfhzY3pquRzcR9E1OmAKN6EUkfppeStDdi2Z2J0Fop4H73X2bmd0DbHX3TcDngQfNbBfBSH5j+NyjZvanBF8WDmx296/P0mtpaKJ0E2FEP9iT5ehIAXef9PqyIiKdItI1Y919M0HZpXrb3VW388BNDZ77RYIplm0zpRF9b5bxYpnRQmniBCoRkU6WiDNjp1KjH+zpAnR2rIjER0KCPvqsm4GeLABHddKUiMREQoJ+KiP6IOg180ZE4iJZQR9x1g3AEc28EZGYSEjQR591MzAxolfQi0g8JCTogxF9swuPAAxUDsaeVulGROIhMUHflbZI8+K70in6cxmdNCUisZGYoM9EmHFTMdDbpdKNiMRGQoLeyUaoz1dUzo4VEYmDhAR98wuDV9NSxSISJwkJ+qmN6M/RxUdEJEYSEvTNrxdbbaAnq4OxIhIbyQj6YplMk8sIVhvsyXIyX6QYTssUEelkyQj6cpnsFEb0g73BXPpjoyrfiEjnS0bQF53MFEs3oIXNRCQekhH05TLZKZVutFSxiMRHMoK+WJ7SiL6ygqUOyIpIHCQj6Ms+pRp9Zb0bzaUXkTiIfdC7+7Rm3YBKNyISD5GC3syuM7OdZrbLzO6q83jOzL4cPv6kma2qeXylmZ0ys99sTbejK7njMKURfU82TTadUulGRGKh6dWvzSwN3Av8FLAP2GJmm9x9e1Wz24Gj7r7GzDYCnwE+UvX4Z4FvtK7b0RWKwVr0UWv0Dz25F4AF2TRbXjkycf+W96ycnQ6KiMyyKOm3Htjl7rvdfRx4GNhQ02YD8EB4+xHgAxauCWxmPwvsBra1pstTM3F1qSmUbgAGFmgZBBGJhyhBvxx4rer+vnBb3TbuXgSOA4vNrBf4HeD3JvsFZnaHmW01s63Dw8NR+x7JmaCf2uGIwV4tbCYi8RAl/eoNhT1im98DPuvupyb7Be5+n7uvc/d1Q0NDEboU3cRlBKcY9AMLujg+WqBUrn2pIiKdpWmNnmAEv6Lq/gXA/gZt9plZBlgEHAHeA9xoZn8EDABlM8u7+1/MuOcRTbd0M9iTpexwMl+YOFNWRKQTRQn6LcDFZrYaeB3YCNxS02YTcCvwBHAj8Li7O/C+SgMz+zRwai5DHqZfuhmoOjtWQS8inaxp0Lt70czuBB4F0sD97r7NzO4Btrr7JuDzwINmtotgJL9xNjs9FdMt3VTm0gd1+t5Wd0tEZM5EGdHj7puBzTXb7q66nQduarKPT0+jfzM23dLNIq13IyIxEfszYytBP5UTpiD4C6Avl9HMGxHpeAkI+qmdMFVtsKeLYxrRi0iHS0DQT29ED7qkoIjEQ2KCfiqLmlUM9nRxbLRA2TWXXkQ6VyKC3oBMaupBP9CTpVR2To0VW98xEZE5koCgdzJpI1x6Z0oqV5o6pksKikgHS0DQl6c8h75i4tqxuki4iHSwBAS9zyDoNaIXkc6XgKCf/og+l0nTk01rRC8iHS0hQT/1+nzFQE+XTpoSkY6WkKCf/ssc7MlqGQQR6WgJCHqf0Yh+sCfL0dPjHFGdXkQ6VAKCfmYj+itXDuDAbz/yHK4Tp0SkAynom1i2aAEfumwp/7DjIF8KLxQuItJJEhD0059eWfHeixZz7SVD/P7/3c7u4UmviigiMu8kIOhnNusGIGXGH/38Oxkrlvnmtjda1DMRkbmRkKCf+ctcuqib5QML2HHgZAt6JSIyd2Id9O7ektJNxTuWLWT7/uMt2ZeIyFyJddCPFad3GcFG1p6/kFcOnyZfKLVkfyIicyFS0JvZdWa208x2mdlddR7PmdmXw8efNLNV4fafMrOnzOz58N/3t7b7k6sEcqtG9GuX9VN22PmGyjci0jmaJqCZpYF7geuBtcDNZra2ptntwFF3XwN8FvhMuP0w8DPufjlwK/BgqzoeRb5QGdG3rnQDsOPAiZbsT0RkLkRJwPXALnff7e7jwMPAhpo2G4AHwtuPAB8wM3P3H7j7/nD7NqDbzHKt6HgUoxMj+taUblYM9tCbTbNdQS8iHSRK0C8HXqu6vy/cVreNuxeB48DimjY/D/zA3cem19WpGx1vbekmlTLesWyhRvQi0lEyEdrUGw7XrgUwaRszu4ygnPPBur/A7A7gDoCVK1dG6FI0+WLrgv6h8KzYdMp45rVjfOl7r2Jm3PKe1vVXRGQ2REnAfcCKqvsXAPsbtTGzDLAIOBLevwD4GvBRd/9hvV/g7ve5+zp3Xzc0NDS1VzCJ/HhrSzcQLIkwVixrRUsR6RhRgn4LcLGZrTazLLAR2FTTZhPBwVaAG4HH3d3NbAD4OvBJd/+XVnU6qtNh0GczrZtFumxRNwAHjo+2bJ8iIrOpaQKGNfc7gUeBHcBX3H2bmd1jZjeEzT4PLDazXcCvA5UpmHcCa4D/YmbPhD/ntvxVNHDoZB6A/u6ulu3zvIXdGHDgeL5l+xQRmU1RavS4+2Zgc822u6tu54Gb6jzvD4A/mGEfp+3g8TwG9OUivcxIspkUi/tyCnoR6RixPjP2wPE8fd0Z0qnW1egBLhzq5eWDJzmRV51eROa/WAf9GyfyLGxh2abifWuWUHbnuy8Nt3zfIiKtFuugP3giz8IFrQ/6xX05/s0FA3x/zxEOn5qz0wJERKYl1kH/xvE8C7tbV5+v9uNvH6JYcv76u6+0ZH+HTub5iT/+Nk+9erQl+xMRqYht0I+OlziRL7JoFkb0AOf2d/Mjyxfx4BN7ODYy8wuHP/rCG+x5c4SvP3dg5p0TEakS26B/40QwK2Y2avQVP/H2IU6Pl3jo+zO/luxjOw4B8MTuN2e8LxGRavEN+nD642zU6CuWLVrAj61ZwgP/uofxcO376TiZL/DEDw/Tn8uw48AJjpye+V8IIiIVsQ36gxMj+tmp0Vfc/r7VHDwxxtefr10VIrrvvDRMoeR84v1rAHhSo3oRaaHYBv1E6WYWR/QAP37xEBcN9fL5f34F92Adt1LZeW7fMR56cu/EF85kHtt+kN5smgVdabLpFA88sWdiETURkZma3eFuG71xPE9vNk13V3pWf8/DW17j8uUD/P0zr/ORz32P46MF9h4ZmVgLf+E3MnzqZy7j3121HLMzJ269cvg05/Rk6cml+faLh7h06UK60inetriH3cOnZ7XPIpIssQ36gyfynBcuQDbbrlw5wD++eJAte44w1J9j7bKFXHRuH4t7s2x+/gC/8dVn+dPHXuLtS/u5/keW8uUtr7H11aMs6EpzzZrFnMgXeceyfgAuHOrj0W1vcFJn3YpIi8Q26N84kWfpwrkJ+q50iv/4k5eQMjtrpcx/f+2FfP+VI/xg71G+/eIhHn/xECvP6eE/ffhSdh06xd//YD+92TRrzg2DfkkvALsPa1QvIq0R26A/eDzP1RfWXuRq9jQqEaXMuPrCxVx94WJGxoscOT3O+QMLSJlxxYpBLhrqY6xYnviCOH9gAblMit3Dp+as7yISb7E8GFsuO4dOjs1Z6SaqnmyGCwZ7SFXV6vu7u1jSd+YyuunwcoVP7z3GC68fb0c3RSRmYhn0h0+PUSz7xEVCOs1PX76MvlyGTzz0tGr1IjJjsQz6g8eDhcbOm6Mafav15DJsfPcK9h0d5a6/fZ5i6czJWOWykw9n9IiIRBHLGn1lDv3Shd28eaozzzJ92+JefutDb+cPv/EiLx86yV3XX8qB43n++ruvsPfICJcvX8SPXrSYS5f2c9FQHxef10cuM7tTSUWkM8Uz6MPruS5d1M22/Sfa3Jvp689luGX9Sr657Q0+9oWtACwfWMB7L1rMq2+O8Lnv/JBycI4WuUyKf/vOZVx32VLWnNvH8sEFHB8psGv4FP25Li6/YFEbX4mItFM8g/5EnnTK3nKQsxOZGT+yfBGXLu3n+dePs2hBF6uX9E6ceFUolXnz9DjDJ8d46eBJHtt+kL97+vW6+7rtvau46/pLZ/0EMhGZf2IZ9AeO5Rnqy7X8EoLtkkmnuHLl4Fnbu9Ipli7sZunCbi5fvohiqcy+o6McGRnn2Mg4C7rSLOnPYRj3/8sr/NPLw1y6tJ9CyTk+WmD45Bhldz52zWpuXr/yrHMAAIZPjrH9wAlymRRXrBjQF4VIB4oU9GZ2HfDnQBr4a3f/w5rHc8DfAO8C3gQ+4u57wsc+CdwOlIBfdfdHW9b7Ov7fzkNsenY/H7ps6Wz+mnkpk06xakkvq+g967Hb3ruKb21/gy17jpI2o7srRX93FyfyBT61aRt/9g8vccl5/fR1ZyiX4cDxUfYfG+VEvjixj660cdn5i3j3qkGuXDnI8dEC2/efYKxY4sqVg1y5coD+7i4yKaMnm6Yvl3nLsg/VCqUyR06Pc25/rmEbEWkNqyzE1bCBWRp4CfgpYB+wBbjZ3bdXtfk48E53/xUz2wj8nLt/xMzWAv8bWA+cD/wDcIm7N5w2sm7dOt+6deu0XswP9h7llr96klVLevnyL1/Nwu4uLQ7WhLvz8qFTfHvnIYZPjjEyXsKAof4cywcWsGxgAecv6ma8WObVIyPsefM0rx8dpRgeHMhlUvTmMnWXVu5KG+f0Zlk+sIDlgz0UikG4HzgxyutHRyk7DPZ08a63nUNfLs3BE2OcGisy2JtlSW+WxX1ZlvTlGOjpojeXoTuT5vR4kWMjBY6PBj/FUpnzBxawfHABaTPGS2UKJWe8WCZfKHHo5BiHTubpz2W4ZGk/qxf30tedYUFXmkLJGS0UKZacTNroSqcY7MlyTm+Wsjsn80VGxs98VBd2ZxjoyZLNpHB3yg7FcplS2SkUnbFSCffgPcll0mQzqbP+qqw8r+xOqexn/i0H+yqWnZQZ/d0ZcpkUZoa74w5O8Lxy5X64n5QZmbSRSZm+NOchD/8bp8P/PpVBTr5Q4pzeLH25DKVy8Fd2sezTni1oZk+5+7p6j0UZ0a8Hdrn77nBnDwMbgO1VbTYAnw5vPwL8hQWfuA3Aw+4+BrxiZrvC/T0xnRcymb1vjvCxL2xhqD/HAx9796xecCROzIxLzuvnkvOCJRiK5TLuQVmo1qXLFgZtSmXeOJGnJ5thsCd4n988Nc7rx0YplMqUPAjakfESp/JFjo6Ms+fNw2RSRm8uw2BPljVDffTmMhw4lueZ145RKpdZ2N1Fris4K/i5fUVO5YsTXyj1ZNMpUinIFxpfCyAdhuZIoTSjawa8ZZ8pozRJv6p1pY2UWVWwR/89lcxuMhZ7i0wqCP2UAj+yyvvr+MT9ibe86rEz7SrPO/MfxsxIGRiGWfDfzgj+u4+Fnzuz4DM7VvM57EobhVKwr6tWDvB3H7+m1S8xUtAvB16rur8PeE+jNu5eNLPjwOJw+/dqnru89heY2R3AHeHdU2a2M1LvGzjvdyZuLgEOz2RfbdSpfVe/51an9hs6t++z1u9XAfvEtJ/+tkYPRAn6ekOD2jFGozZRnou73wfcF6EvU2JmWxv9KTPfdWrf1e+51an9hs7teyf2O8qZsfuAFVX3LwBqL6c00cbMMsAi4EjE54qIyCyKEvRbgIvNbLWZZYGNwKaaNpuAW8PbNwKPe1DA2gRsNLOcma0GLga+35qui4hIFE1LN2HN/U7gUYLplfe7+zYzuwfY6u6bgM8DD4YHW48QfBkQtvsKwYHbIvCJyWbczIKWl4PmUKf2Xf2eW53ab+jcvndcv5tOrxQRkc4Wy9UrRUTkDAW9iEjMxTbozew6M9tpZrvM7K5296cRM1thZt82sx1mts3M/kO4/dNm9rqZPRP+fLjdfa1lZnvM7Pmwf1vDbeeY2WNm9nL479mL9LSZmb296n19xsxOmNmvzcf33MzuN7NDZvZC1ba677EF/nv4mX/OzK6aZ/3+YzN7Mezb18xsINy+ysxGq973v2xXv8P+1Ot7w8+GmX0yfM93mtmH2tPrJoLTq+P1Q3DQ+IfAhUAWeBZY2+5+NejrMuCq8HY/wXITawnONP7NdvevSd/3AEtqtv0RcFd4+y7gM+3uZ4TPyhsEJ5vMu/ccuBa4Cnih2XsMfBj4BsH5K4xrO5wAAARCSURBVFcDT86zfn8QyIS3P1PV71XV7dr906DvdT8b4f+rzwI5YHWYO+l2v4ban7iO6CeWbXD3caCybMO84+4H3P3p8PZJYAd1zh7uIBuAB8LbDwA/28a+RPEB4Ifu/mq7O1KPu/8TwUy2ao3e4w3A33jge8CAmS2bm56+Vb1+u/u33L2ySt73CM6rmXcavOeNTCzz4u6vAJVlXuaVuAZ9vWUb5n14mtkq4ErgyXDTneGfuffPxxIIwVnO3zKzp8JlLADOc/cDEHyJAee2rXfRbCRYeK9ivr/n0Pg97qTP/ccI/vqoWG1mPzCz75jZ+9rVqSbqfTY64j2Pa9BHWnphPjGzPuBvgV9z9xPA/wQuAq4ADgB/0sbuNXKNu18FXA98wsyubXeHpiI8AfAG4Kvhpk54zyfTEZ97M/tdgvNqvhRuOgCsdPcrgV8HHjKzhe3qXwONPhsd8Z7HNeg7aukFM+siCPkvufvfAbj7QXcvuXsZ+Cvm4Z+D7r4//PcQ8DWCPh6slAvCfw+1r4dNXQ887e4HoTPe81Cj93jef+7N7Fbgp4Ff9LDIHZY93gxvP0VQ576kfb082ySfjXn/nkN8gz7Ksg3zgpkZwZnFO9z9T6u2V9dWfw54ofa57WRmvWbWX7lNcKDtBd66HMatwP9pTw8juZmqss18f8+rNHqPNwEfDWffXA0cr5R45gMLLmD0O8AN7j5StX3IguteYGYXEiyVsrs9vaxvks9GZyzz0u6jwbP1QzAD4SWC0cHvtrs/k/Tzxwj+1HsOeCb8+TDwIPB8uH0TsKzdfa3p94UEsw2eBbZV3mOC5an/EXg5/Pecdve1Qf97CK6Gtqhq27x7zwm+iA4ABYLR4+2N3mOCMsK94Wf+eWDdPOv3LoJ6duVz/pdh258PP0PPAk8DPzMP3/OGnw3gd8P3fCdwfbs/M/V+tASCiEjMxbV0IyIiIQW9iEjMKehFRGJOQS8iEnMKehGRmFPQS+KZWSlckfAFM/uqmfU0aLe5suKiSCfR9EpJPDM75e594e0vAU/5W09eM4L/V8rt6qPITGhEL/JW3wXWhGuk7zCz/0FwEs+KcP39JQBm9tFwgatnzezBcNuQmf2tmW0Jf65p4+sQmdD04uAiSWFmGYL1b74Zbno78Evu/vHw8Uq7ywjOhrzG3Q+b2Tlh+z8HPuvu/2xmK4FHgXfM4UsQqUtBLwILzOyZ8PZ3CdYeOh941YN13Wu9H3jE3Q8DuHtl7fKfBNZWvhCAhWbW78F1BkTaRkEvAqPufkX1hjCsTzdob9RfijYF/Ki7j7a2eyIzoxq9yNT9I/ALZrYYgmu4htu/BdxZaWRmV9R5rsicU9CLTJG7bwP+K/AdM3sWqMzQ+VVgXXiQdjvwK+3qo0g1Ta8UEYk5jehFRGJOQS8iEnMKehGRmFPQi4jEnIJeRCTmFPQiIjGnoBcRibn/D3oK30pY26YOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution plot of \"Price\" with KDE plot.\n",
    "sns.distplot(df['Price'],kde=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Price is right (positively) skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": [
    "sns.distplot(df['Kilometers_Driven'],kde=True); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of entries with 0 or negative value=0\n"
     ]
    }
   ],
   "source": [
    "#Plot Kilometers_Driven in Histogram plot.\n",
    "plt.hist(df['Kilometers_Driven'], bins=50)\n",
    "plt.title('Kilometers_Driven')\n",
    "plt.show()\n",
    "print(f'Count of entries with 0 or negative value={np.sum(df.Kilometers_Driven <= 0)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Since \"Kilometers_Driven\" column is highly skewed and does not have any non positive (or zero values) we will do log transformation on that column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log Normal Scaling of Kilometers_Driven column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Lets add new column \"Kilometers_Driven_log\" with log transformed values.\n",
    "cols_to_log = ['Kilometers_Driven']\n",
    "\n",
    "for colname in cols_to_log:\n",
    "    df[colname + '_log'] = np.log(df[colname] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "#Plot Kilometers_Driven_log in Histogram plot.\n",
    "cols_to_log2 = ['Kilometers_Driven_log']\n",
    "for colname in cols_to_log2:\n",
    "    plt.hist(df[colname], bins=50)\n",
    "    plt.title(colname)\n",
    "    plt.show()\n",
    "    print(np.sum(df[colname] <= 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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": [
    "#Distribution plot of \"Kilometers_Driven_log\" with KDE plot.\n",
    "sns.distplot(df['Kilometers_Driven_log'],kde=True); \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "- After log transformation, the Kilometers Driven distribution is closer to normal than the original distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's drop Kilometers_Driven column as we have the log scaled column Kilometers_Driven_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Kilometers_Driven',inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7091 entries, 0 to 7252\n",
      "Data columns (total 14 columns):\n",
      " #   Column                 Non-Null Count  Dtype   \n",
      "---  ------                 --------------  -----   \n",
      " 0   Location               7091 non-null   category\n",
      " 1   Year_By                7091 non-null   category\n",
      " 2   Fuel_Type              7091 non-null   category\n",
      " 3   Transmission           7091 non-null   category\n",
      " 4   Owner_Type             7091 non-null   category\n",
      " 5   Mileage                7091 non-null   float64 \n",
      " 6   Engine                 7091 non-null   float64 \n",
      " 7   Power                  7091 non-null   float64 \n",
      " 8   Seats                  7091 non-null   category\n",
      " 9   New_Price              7091 non-null   float64 \n",
      " 10  Price                  7091 non-null   float64 \n",
      " 11  Company                7091 non-null   category\n",
      " 12  Model                  7091 non-null   category\n",
      " 13  Kilometers_Driven_log  7091 non-null   float64 \n",
      "dtypes: category(8), float64(6)\n",
      "memory usage: 784.0 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlier Analysis and Treatment of Numerical Columns\n",
    "\n",
    "    - Mileage\n",
    "    - Engine\n",
    "    - Power\n",
    "    - New_Price\n",
    "    - Price\n",
    "    - Kilometers_Driven_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the numeric columns for which we will do outlier analysis\n",
      "\n",
      "['Mileage', 'Engine', 'Power', 'New_Price', 'Price', 'Kilometers_Driven_log']\n"
     ]
    }
   ],
   "source": [
    "numeric_columns = df.select_dtypes(include=np.number).columns.tolist()\n",
    "print(f'Here are the numeric columns for which we will do outlier analysis\\n\\n{numeric_columns}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# outlier detection using boxplot\n",
    "plt.figure(figsize=(20,30))\n",
    "\n",
    "for i, variable in enumerate(numeric_columns):\n",
    "                     plt.subplot(5,4,i+1)\n",
    "                     plt.boxplot(df[variable],whis=1.5)\n",
    "                     plt.tight_layout()\n",
    "                     plt.title(variable)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Treat Outliers\n",
    "# Lets treat outliers by flooring and capping\n",
    "def treat_outliers(df,col):\n",
    "    '''\n",
    "    treats outliers in a varaible\n",
    "    col: str, name of the numerical varaible\n",
    "    df: data frame\n",
    "    col: name of the column\n",
    "    '''\n",
    "    Q1=df[col].quantile(0.25) # 25th quantile\n",
    "    Q3=df[col].quantile(0.75)  # 75th quantile\n",
    "    IQR=Q3-Q1\n",
    "    Lower_Whisker = Q1 - 1.5*IQR \n",
    "    Upper_Whisker = Q3 + 1.5*IQR\n",
    "    df[col] = np.clip(df[col], Lower_Whisker, Upper_Whisker) # all the values samller than Lower_Whisker will be assigned value of Lower_whisker \n",
    "                                                            # and all the values above upper_whishker will be assigned value of upper_Whisker \n",
    "    return df\n",
    "\n",
    "def treat_outliers_all(df, col_list):\n",
    "    '''\n",
    "    treat outlier in all numerical varaibles\n",
    "    col_list: list of numerical varaibles\n",
    "    df: data frame\n",
    "    '''\n",
    "    for c in col_list:\n",
    "        df = treat_outliers(df,c)\n",
    "        \n",
    "    return df\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#List the numerical columns in the dataframe and call function 'treat_outliers'\n",
    "numerical_col = df.select_dtypes(include=np.number).columns.tolist()\n",
    "df = treat_outliers_all(df,numerical_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list the treated numerical columns in numeric_columns.\n",
    "numeric_columns = df.select_dtypes(include=np.number).columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets look at box plot to see if outliers has been treated or not\n",
    "plt.figure(figsize=(20,30))\n",
    "\n",
    "for i, variable in enumerate(numeric_columns):\n",
    "                     plt.subplot(5,4,i+1)\n",
    "                     plt.boxplot(df[variable],whis=1.5)\n",
    "                     plt.tight_layout()\n",
    "                     plt.title(variable)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- All the outliers are treated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median Price of New Cars by Location\n",
      "  \n",
      "Location\n",
      "Coimbatore    7.720\n",
      "Bangalore     6.850\n",
      "Kochi         6.535\n",
      "Hyderabad     5.680\n",
      "Mumbai        5.600\n",
      "Ahmedabad     5.500\n",
      "Delhi         5.495\n",
      "Chennai       4.750\n",
      "Pune          4.600\n",
      "Jaipur        4.500\n",
      "Kolkata       4.150\n",
      "Name: Price, dtype: float64\n"
     ]
    },
    {
     "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": [
    "#Bivariate analysis, median Price (in Lakh) of car per Location\n",
    "print('Median Price of New Cars by Location')\n",
    "print('  ')\n",
    "print(df.groupby('Location')['Price'].median().sort_values(ascending=False))\n",
    "df.groupby(['Location'])[[\"Price\"]].median().plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Used car median price in Coimbatore is highest followed by Bangalore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median Price of New Cars by Year_By\n",
      "  \n",
      "Year_By\n",
      "2016-2020    7.500\n",
      "2011-2015    5.345\n",
      "2006-2010    3.130\n",
      "2001-2005    1.450\n",
      "1995-2000    0.850\n",
      "Name: Price, dtype: float64\n"
     ]
    },
    {
     "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": [
    "#Bivariate analysis, median Price (in Lakh) of car per Year_By\n",
    "print('Median Price of New Cars by Year_By')\n",
    "print('  ')\n",
    "print(df.groupby('Year_By')['Price'].median().sort_values(ascending=False))\n",
    "df.groupby(['Year_By'])[[\"Price\"]].median().plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- In last 5 years, the median resale price of used cars has increased.\n",
    "- The median price of used cars is continously increasing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median Price of New Cars by Fuel_Type\n",
      "  \n",
      "Fuel_Type\n",
      "Electric    12.875\n",
      "Diesel       7.570\n",
      "Petrol       4.450\n",
      "CNG          3.280\n",
      "LPG          2.850\n",
      "Name: Price, dtype: float64\n"
     ]
    },
    {
     "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": [
    "#Bivariate analysis, median Price (in Lakh) of car per Fuel_Type\n",
    "print('Median Price of New Cars by Fuel_Type')\n",
    "print('  ')\n",
    "print(df.groupby('Fuel_Type')['Price'].median().sort_values(ascending=False))\n",
    "df.groupby(['Fuel_Type'])[[\"Price\"]].median().plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Median price of Electric used cars is highest, followed by diesel and petrol."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median Price of New Cars by Transmission\n",
      "  \n",
      "Transmission\n",
      "Automatic    16.5\n",
      "Manual        4.6\n",
      "Name: Price, dtype: float64\n"
     ]
    },
    {
     "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": [
    "#Bivariate analysis, median Price (in Lakh) of car per Transmission\n",
    "print('Median Price of New Cars by Transmission')\n",
    "print('  ')\n",
    "print(df.groupby('Transmission')['Price'].median().sort_values(ascending=False))\n",
    "df.groupby(['Transmission'])[[\"Price\"]].median().plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Median used car price of Automatic vehicle is higher than Manual vehicle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median Price of New Cars by Owner_Type\n",
      "  \n",
      "Owner_Type\n",
      "First             5.650\n",
      "Second            4.600\n",
      "Third             3.250\n",
      "Fourth & Above    3.125\n",
      "Name: Price, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAFLCAYAAAD/KMZwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAXX0lEQVR4nO3de5RlZX3m8e9jd2sjFy906WgaKHTUgAgNNowKBoMRELzMBPCOLjXiaBxQM3GYsCYrxpCY8bIcNQYZFUUdFEMco6KoIYAiarqhlUvTBrHFXjihgSDITRp/88c5BUVT0FXV5/R+T53vZ61afc4+u6uePqvrqbfe/e69U1VIktr1kK4DSJIenEUtSY2zqCWpcRa1JDXOopakxlnUktS4xcP4pMuWLavJyclhfGpJWpBWr159fVVNzPTaUIp6cnKSVatWDeNTS9KClORnD/SaUx+S1DiLWpIaZ1FLUuOGMkctSXNx1113sWHDBu64446uowzd0qVLWb58OUuWLJn137GoJXVuw4YN7LjjjkxOTpKk6zhDU1XccMMNbNiwgd13333Wf8+pD0mdu+OOO9h5550XdEkDJGHnnXee828OFrWkJiz0kp4yn3+nRS1JwKJFi1ixYgV77bUXxxxzDLfddtuM+x1xxBHcdNNN2zTbyMxRT5741a4jbNH6dx/ZdQRpQRj09/tsvje322471qxZA8ArX/lKTjnlFN7+9rff83pVUVWcffbZA802G46oJWkzz372s7nqqqtYv349e+yxB29+85vZb7/9+PnPf87k5CTXX389AKeffjp77703++yzD8ceeywAGzdu5KijjmL//fdn//3358ILL9zqPCMzopakbWHTpk187Wtf4/DDDwdg3bp1nHbaaXzkIx+5z36XX345J598MhdeeCHLli3jxhtvBOCEE07gbW97GwcddBDXXHMNhx12GGvXrt2qTBa1JAG33347K1asAHoj6te//vVce+217LbbbjzjGc+43/7nnnsuRx99NMuWLQPg0Y9+NADf+ta3uOKKK+7Z7+abb+aWW25hxx13nHc2i1qSuO8c9XTbb7/9jPtX1YwrOH7zm99w0UUXsd122w0sm3PUkjQPz33ucznzzDO54YYbAO6Z+jj00EP58Ic/fM9+M5X/XFnUkjQPT33qUznppJM4+OCD2Weffe5ZIfLBD36QVatWsffee7PnnntyyimnbPXXSlVt9SfZ3MqVK2vQ16N2eZ60cK1du5Y99tij6xjbzEz/3iSrq2rlTPs7opakxlnUktQ4i1qSGmdRS2rCMI6XtWg+/06LWlLnli5dyg033LDgy3rqetRLly6d09/zhBdJnVu+fDkbNmxg48aNXUcZuqk7vMyFRS2pc0uWLJnTHU/GjVMfktQ4i1qSGmdRS1LjLGpJapxFLUmNm9WqjyTrgVuAu4FND3ThEEnS4M1led7vVtX1Q0siSZqRUx+S1LjZFnUB30iyOslxwwwkSbqv2U59HFhV1yZ5DPDNJFdW1QXTd+gX+HEAu+6664BjStL4mtWIuqqu7f95HfBF4IAZ9jm1qlZW1cqJiYnBppSkMbbFok6yfZIdpx4DhwKXDTuYJKlnNlMfjwW+2L8t+mLg/1TV14eaSpJ0jy0WdVVdDeyzDbJIkmbg8jxJapxFLUmNs6glqXEWtSQ1zqKWpMZZ1JLUOItakhpnUUtS4yxqSWqcRS1JjZvLHV4kzWDyxK92HWFW1r/7yK4jaJ4cUUtS4yxqSWqcRS1JjbOoJalxFrUkNc6ilqTGWdSS1DiLWpIaZ1FLUuMsaklqnEUtSY2zqCWpcRa1JDXOopakxlnUktQ4i1qSGmdRS1LjZl3USRYluSTJV4YZSJJ0X3MZUZ8ArB1WEEnSzGZV1EmWA0cCHxtuHEnS5mY7ov4A8A7gN0PMIkmawRaLOskLgOuqavUW9jsuyaokqzZu3DiwgJI07mYzoj4QeFGS9cDngEOSfGbznarq1KpaWVUrJyYmBhxTksbXFou6qv57VS2vqkngZcC5VfWqoSeTJAGuo5ak5i2ey85VdR5w3lCSSJJm5IhakhpnUUtS4yxqSWqcRS1JjbOoJalxFrUkNc6ilqTGWdSS1DiLWpIaZ1FLUuMsaklqnEUtSY2zqCWpcRa1JDXOopakxlnUktQ4i1qSGmdRS1LjLGpJapxFLUmNs6glqXEWtSQ1bnHXASRpuskTv9p1hC1a/+4jt+nXc0QtSY2zqCWpcRa1JDXOopakxnkwcQyNwsEa2PYHbKRWbXFEnWRpkh8k+WGSy5O8c1sEkyT1zGZEfSdwSFX9KskS4DtJvlZV3xtyNkkSsyjqqirgV/2nS/ofNcxQkqR7zepgYpJFSdYA1wHfrKrvz7DPcUlWJVm1cePGQeeUpLE1q6KuqruragWwHDggyV4z7HNqVa2sqpUTExODzilJY2tOy/Oq6ibgPODwoaSRJN3PbFZ9TCR5ZP/xdsDvAVcOO5gkqWc2qz4eB3wqySJ6xX5mVX1luLEkSVNms+rjR8C+2yCLJGkGnkIuSY2zqCWpcRa1JDXOopakxlnUktQ4i1qSGmdRS1LjLGpJapxFLUmNs6glqXEWtSQ1zqKWpMZZ1JLUOItakhpnUUtS4yxqSWqcRS1JjbOoJalxFrUkNc6ilqTGWdSS1DiLWpIaZ1FLUuMsaklqnEUtSY2zqCWpcRa1JDVui0WdZJck/5RkbZLLk5ywLYJJknoWz2KfTcAfVdXFSXYEVif5ZlVdMeRskiRmMaKuql9U1cX9x7cAa4HfGnYwSVLPnOaok0wC+wLfH0YYSdL9zbqok+wAnAW8tapunuH145KsSrJq48aNg8woSWNtVkWdZAm9kv5sVf39TPtU1alVtbKqVk5MTAwyoySNtdms+gjwcWBtVb1/+JEkSdPNZkR9IHAscEiSNf2PI4acS5LUt8XleVX1HSDbIIskaQaemShJjbOoJalxFrUkNc6ilqTGWdSS1DiLWpIaZ1FLUuMsaklqnEUtSY2zqCWpcRa1JDXOopakxlnUktQ4i1qSGmdRS1LjLGpJapxFLUmNs6glqXEWtSQ1zqKWpMZZ1JLUOItakhpnUUtS4yxqSWqcRS1JjbOoJalxFrUkNc6ilqTGbbGok3wiyXVJLtsWgSRJ9zWbEfUngcOHnEOS9AC2WNRVdQFw4zbIIkmawcDmqJMcl2RVklUbN24c1KeVpLE3sKKuqlOramVVrZyYmBjUp5WkseeqD0lqnEUtSY2bzfK8M4CLgKck2ZDk9cOPJUmasnhLO1TVy7dFEEnSzJz6kKTGWdSS1DiLWpIaZ1FLUuMsaklqnEUtSY2zqCWpcRa1JDXOopakxlnUktQ4i1qSGmdRS1LjLGpJapxFLUmNs6glqXEWtSQ1zqKWpMZZ1JLUOItakhpnUUtS4yxqSWqcRS1JjbOoJalxFrUkNc6ilqTGWdSS1DiLWpIaZ1FLUuNmVdRJDk+yLslVSU4cdihJ0r22WNRJFgF/Azwf2BN4eZI9hx1MktQzmxH1AcBVVXV1Vf0a+Bzw4uHGkiRNSVU9+A7J0cDhVfUH/efHAv+hqt6y2X7HAcf1nz4FWDf4uAO1DLi+6xALiO/nYPl+DtYovJ+7VdXETC8snsVfzgzb7tfuVXUqcOocg3UmyaqqWtl1joXC93OwfD8Ha9Tfz9lMfWwAdpn2fDlw7XDiSJI2N5ui/mfgSUl2T/JQ4GXAPww3liRpyhanPqpqU5K3AOcAi4BPVNXlQ082fCMzTTMifD8Hy/dzsEb6/dziwURJUrc8M1GSGmdRS1LjLGrNW5Ltkjyl6xzSQjebddTS/SR5IfBe4KHA7klWAH9eVS/qNpnGVZJLmeEcjylVtfc2jDNQY1XUSf66qv7blrZpVv6M3uUFzgOoqjVJJruLM5qSfJkHLxd/8M3eC/p//mH/z0/3/3wlcNu2jzM44zb18bwZtj1/m6dYGDZV1S+7DrEAvBd4H/BT4Hbgf/c/fgVc1mGukVNVP6uqnwEHVtU7qurS/seJwGFd59saYzGiTvIm4M3AE5L8aNpLOwIXdpNq5F2W5BXAoiRPAo4HvttxppFTVecDJHlXVf3OtJe+nOSCjmKNuu2THFRV3wFI8ixg+44zbZWxWEed5BHAo4C/AqZfT/uWqrqxm1SjLcnDgZOAQ/ubzgH+oqru6C7V6EqyFjiyqq7uP98dOLuq9ug22ehJsh9wGvAIetNKvwReV1UXdxpsK4xFUU9J8kRgQ1XdmeQ5wN7A6VV1U7fJRk+Sfavqkq5zLBRJDqd39tzV/U2TwBur6pzOQo2gJA8Bjq6qM5PsRK/jRn6KbtyKeg2wkt43wTn0rlnylKo6ostcoyjJPwGPA74AfG6BXFagU0keBvx2/+mVVXVnl3lGVZILNptGGnnjVtQXV9V+Sd4B3F5VH0pySVXt23W2UZTk3wEvAV4K7AR8vqr+ottUo6s/lzrJtGNHVXV6Z4FGVJL/Qe/A7OeBW6e2j/I057gV9feBD9CbW31hVf00yWVVtVfH0UZakqcB7wBeWlUP7TrPKEryaeCJwBrg7v7mqqrju0s1mpL8dIbNVVVP2OZhBmQsVn1M81rgPwMn90t6d+AzHWcaSUn2oDeSPobenTM+B/xRp6FG20pgzxqnkdOQVNXuXWcYtLEZUfdv0vupqnpV11kWgiTfA84AvlBV3khiKyX5AnB8Vf2i6yyjKskhVXVukt+f6fWq+vttnWlQxmZEXVV3J5lI8tD+TXq1FarqGf0bSTw5yaOBdVV1V9e5Rtgy4IokPwDuOYjomYlzcjBwLvDCGV4rYGSLemxG1ABJPgrsR2+1x/SDDO/vLNSISnIwcDqwnt59NXcBXlNVnqQxD/33836mTojReBubEXXftf2Ph9A7K1Hz937g0KpaB5DkyfSmQp7eaaoRVVXnJ3kssH9/0w+q6rouM42q/jLHo7j/Cpo/7yrT1hqroq6qd3adYQFZMlXSAFX14yRLugw0ypK8BHgPvYtcBfhQkj+uqr/rNNho+hK9sxFXM20aaZSNxdRHkg9U1Vsf6EplzgPOXZJP0Hsvp1+hbHFVvba7VKMryQ+B502NopNMAN+qqn26TTZ6FuKS23EZUU+VyXs7TbGwvIne5SSPpzcCvAD4SKeJRttDNpvquIHxu7rloHw3ydOq6tKugwzKuIyod62qa7rOsdD0V33sAfyG3qoPV9PMU5L30Lv2zBn9TS8FLq2qd3SXarQkuYze/8XFwJPoXTflTnoDiRrlGweMS1FfXFX79R+fVVVHdZ1p1CU5EjgF+Am9b4Td6V1E6GudBhth/fW/B9H/DaWqvthxpJGS5N+AFQ/0ev9a1SNpXIr6nut5eG2PwUhyJfCCqrqq//yJwFer6rcf/G9qJv2zZH8xdZnYJNsBj62q9Z0GGyHTB2QLzbjMUdcDPNb8XTdV0n1XAy4nm78vAM+a9vzu/rb9Z95dM3hMkrc/0IujfL7EuBT1Pklupvcr5Xb9x3Dv3NVO3UUbLdNOz708ydnAmfR++B0D/HNnwUbf4ulz/FX16/4xAM3eImAHet/XC8pYFHVVLeo6wwIy/fTcf6V32i7ARnp30dH8bEzyoqr6B4AkL6Z3sSvN3i9G+aSWBzMWc9TaNpLsX1WOquehP8f/WeC36P2GsgF49WbTS3oQC/n4k0WtrZJkT+BlwMuBX1bVyo4jjbQkO9D7vryl6yyjJsmjR/nmAA/GotacJdmNXjG/HNgE7AasdIXC/PWv8/GXwOOr6vn9H4DPrKqPdxxNDfDMJ81Jku8CZwNL6N1E9On07ua+vtNgo++T9O7j+fj+8x8Db+0sjZpiUWuuNtK78uBjgYn+Nn8t23rLqupMemfWUVWbuPeWXBpzFrXmpKpeDDwNuBh4Z//+dI9KckC3yUberUl2pv9DL8kz6F0BTnKOWlsnyWPoXZfi5cAuVbVLx5FGUpL9gA8BewGX0ftt5eiq+lGnwdQEi1oDk2S3Ub6eQheS7A/8vKr+X5LFwBvpXfT+CuBPF+oqBs2NUx8aGEt6Xj4KTJ2R+CzgJOBvgH8DTu0qlNoyFmcmSg1bNG3U/FLg1Ko6CzgryZoOc6khjqilbi3qT3kAPJfeXbSnOJASYFFrKyU5M8nvJ3lYkrO6zjOCzgDOT/Il4Hbg2wBJ/j2u+lCfBxO1VfoHw15Nb9XHR6vqpI4jjZz+UrzHAd+oqlv7254M7FBVF3caTk3wVyvNSZJ3AR+bduDwauAA4OvAwzoLNsKq6nszbPtxF1nUJqc+NFcvnirpJJPAecB7qupV3HvJU0kD5Ihac7Uoya7ArsDHgTdV1blJAjy822jSwmRRa65OpLcy4df0zqA7OMkm4FXARV0GkxYqDyZq3vqj6P8CHAZcApxcVbd3m0paeCxqSWqcBxMlqXEWtSQ1zqKWpMa56kPzkmQCeAMwybT/R1X1uq4ySQuVRa35+hK961J8C28ZJQ2Vqz40L0nWVNWKrnNI48A5as3XV5Ic0XUIaRw4otacJLmF3g1YA2wP3Anc1X9eVbVTh/GkBcmilqTGOfWheUnyj7PZJmnruepDc5JkKb0pj2VJHkVvygNgJ+DxnQWTFjCLWnP1RuCt9Ep5NfcW9c307p4tacCco9acJVkE/ElVvavrLNI4cI5ac1ZVdwMuzZO2EYta8/WNJEf1r0ktaYic+tC89NdTbw9sAu7AddTS0FjUktQ4V31oXpL8zkzbq+qCbZ1FWugcUWteknx52tOlwAHA6qo6pKNI0oLliFrzUlUvnP48yS7A/+wojrSguepDg7IB2KvrENJC5Iha85LkQ/Suoge9H/grgB92l0hauJyj1rwkec20p5uA9VV1YVd5pIXMota8JXko8OT+03VVdVeXeaSFyqLWvCR5DvApYD29k112AV7j8jxp8CxqzUuS1cArqmpd//mTgTOq6undJpMWHld9aL6WTJU0QFX9GFjSYR5pwXLVh+ZrVZKPA5/uP38lvetTSxowpz40L0keBvwhcBC9OeoLgI9U1Z2dBpMWIItac5Jk16q6pusc0jhxjlpz9X+nHiQ5q8sg0riwqDVX028U8ITOUkhjxKLWXNUDPJY0JM5Ra06S3A3cSm9kvR1w29RLeIcXaSgsaklqnFMfktQ4i1qSGmdRS1LjLGpJapxFrc4kWZ7kS0n+JclPkvyv/jWuu8hyUpI1/Y+7pz0+vos80nSu+lAnkgT4PvC3VXVakkXAqcCNVfXHQ/7ai6tq04O8/quq2mGYGaS5cEStrhwC3FFVpwFU1d3A24DXJTk3yd4ASS5J8qf9x+9K8gdJnpPkvCR/l+TKJJ/tFz9Jnp7k/CSrk5yT5HH97ecl+csk5wMnzDZkkkcmuTrJ4mnPf5pkUZLvJPlAkouSXJpkZX+fHZJ8MskP+vlf+OBfRXpwFrW68lQ2uyxqVd0MXAOcBzw7yU707sd4YH+Xg4Bv9x/vC7wV2JPeqewHJlkCfAg4un8Dg08AJ0/7Eo+sqoOr6n2zDVlVNwEXAof3N70COLP/gwXgYVX1THrl/7H+tj8Fvl5VB9D7gfS+JEtn+zWlzXk9anUlzHwKeoDzgTcDPwW+CjwvycOByapa1x8l/6CqNgAkWQNMAjcBewHf7A+wFwG/mPa5Pz/PrB8Djge+ArwWOHbaa2cAVNW5SR6TZAfgUOD5SU7s77MU2BX48Ty/vsacRa2uXA4cNX1DfwS9C3AJsBK4GvgmsAx4A/cdgU+/7vXd9P4vB7i8P8Kdya3zCVpV5yf5cJLfBe6qqiunv7z57v0c/7GqfjKfrydtzqkPdeUfgYcneTVA/2Di+4BP9qdAfg68BPgevemO/8q90x4PZB0wkeSZ/c+5JMlTB5T3M8BngdM22/7S/td6DvCvVXUrcA69ETj91/YdUAaNKYtanajecqP/BByT5F/oTQvcAfxJf5dv0yu+2/qPl7OFoq6qXwNHA3+d5IfAGuBZA4r8WeAR3H/65OYk36U3N/6G/rZ30vshdGmSy4E/G1AGjSmX50mzkORlwGFV9dpp274DvKWq1nSXTOPAOWppC5L8LfB73LvyQ9qmHFFr7CQ5CThms81fqKqTZ9pf6ppFLUmN82CiJDXOopakxlnUktQ4i1qSGmdRS1Lj/j8fw6z+XLOvhQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate analysis, median Price (in Lakh) of car per Owner_Type\n",
    "print('Median Price of New Cars by Owner_Type')\n",
    "print('  ')\n",
    "print(df.groupby('Owner_Type')['Price'].median().sort_values(ascending=False))\n",
    "df.groupby(['Owner_Type'])[[\"Price\"]].median().plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Median used car price of First resale is higher than second resale.\n",
    "- Third and above ownership (resale) reduces the median price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median Price of New Cars by Company\n",
      "  \n",
      "Company\n",
      "Audi             19.225\n",
      "Mercedes-Benz    19.225\n",
      "BMW              19.225\n",
      "Bentley          19.225\n",
      "Isuzu            19.225\n",
      "Jaguar           19.225\n",
      "Porsche          19.225\n",
      "Land             19.225\n",
      "Mini             19.225\n",
      "Jeep             18.500\n",
      "Volvo            18.250\n",
      "Toyota           10.750\n",
      "Mitsubishi        9.950\n",
      "Mahindra          7.570\n",
      "Skoda             6.000\n",
      "Renault           5.490\n",
      "Ford              5.345\n",
      "Honda             4.950\n",
      "Volkswagen        4.890\n",
      "Hyundai           4.600\n",
      "Nissan            4.300\n",
      "Maruti            4.150\n",
      "Datsun            3.100\n",
      "Tata              2.900\n",
      "Fiat              2.600\n",
      "Name: Price, dtype: float64\n"
     ]
    },
    {
     "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": [
    "#Bivariate analysis, median Price (in Lakh) of car per Company\n",
    "print('Median Price of New Cars by Company')\n",
    "print('  ')\n",
    "print(df.groupby('Company')['Price'].median().sort_values(ascending=False))\n",
    "df.groupby(['Company'])[[\"Price\"]].median().plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Median used car price of Luxury brands like Audi, BMW, Mercedes-Benz is higher than the other brands in used car market."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique levels in Location= 11\n",
      "Unique levels in Fuel_Type= 5\n",
      "Unique levels in Transmission= 2\n",
      "Unique levels in Owner_Type= 4\n",
      "Unique levels in Year_By= 5\n",
      "Unique levels in Company= 25\n",
      "Unique levels in Model= 204\n",
      "Unique levels in Seats= 9\n"
     ]
    }
   ],
   "source": [
    "#Lets find the unique levels in each categorical columns\n",
    "print(f'Unique levels in Location= {len(pd.unique(df.Location))}')\n",
    "print(f'Unique levels in Fuel_Type= {len(pd.unique(df.Fuel_Type))}')\n",
    "print(f'Unique levels in Transmission= {len(pd.unique(df.Transmission))}')\n",
    "print(f'Unique levels in Owner_Type= {len(pd.unique(df.Owner_Type))}')\n",
    "print(f'Unique levels in Year_By= {len(pd.unique(df.Year_By))}')\n",
    "print(f'Unique levels in Company= {len(pd.unique(df.Company))}')\n",
    "print(f'Unique levels in Model= {len(pd.unique(df.Model))}')\n",
    "print(f'Unique levels in Seats= {len(pd.unique(df.Seats))}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Since Model has over 200 unique levels, we will not create the dummy vars for this column, for rest, lets create the dummy vars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "sns.heatmap(corr, annot=True); #correlation matrix between different columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Engine and Power are positively correlated to Price of used car.\n",
    "- Price is negatively correlated to Kilometers Driven (log scaled column).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One Hot Encoding- create dummy variables for categorical values\n",
    "\n",
    "    - Location\n",
    "    - Year_By\n",
    "    - Fuel_Type\n",
    "    - Transmission\n",
    "    - Owner_Type\n",
    "    - Company\n",
    "   \n",
    "   ###### We will skip 'Model' variable from One Hot Encoding as it has over 200 unique values, as we saw before\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, columns=['Location'])\n",
    "df = pd.get_dummies(df, columns=['Year_By'])\n",
    "df = pd.get_dummies(df, columns=['Fuel_Type'])\n",
    "df = pd.get_dummies(df, columns=['Transmission'])\n",
    "df = pd.get_dummies(df, columns=['Owner_Type'])\n",
    "df = pd.get_dummies(df, columns=['Company'])\n",
    "df = pd.get_dummies(df, columns=['Seats'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7091 entries, 0 to 7252\n",
      "Data columns (total 68 columns):\n",
      " #   Column                     Non-Null Count  Dtype   \n",
      "---  ------                     --------------  -----   \n",
      " 0   Mileage                    7091 non-null   float64 \n",
      " 1   Engine                     7091 non-null   float64 \n",
      " 2   Power                      7091 non-null   float64 \n",
      " 3   New_Price                  7091 non-null   float64 \n",
      " 4   Price                      7091 non-null   float64 \n",
      " 5   Model                      7091 non-null   category\n",
      " 6   Kilometers_Driven_log      7091 non-null   float64 \n",
      " 7   Location_Ahmedabad         7091 non-null   uint8   \n",
      " 8   Location_Bangalore         7091 non-null   uint8   \n",
      " 9   Location_Chennai           7091 non-null   uint8   \n",
      " 10  Location_Coimbatore        7091 non-null   uint8   \n",
      " 11  Location_Delhi             7091 non-null   uint8   \n",
      " 12  Location_Hyderabad         7091 non-null   uint8   \n",
      " 13  Location_Jaipur            7091 non-null   uint8   \n",
      " 14  Location_Kochi             7091 non-null   uint8   \n",
      " 15  Location_Kolkata           7091 non-null   uint8   \n",
      " 16  Location_Mumbai            7091 non-null   uint8   \n",
      " 17  Location_Pune              7091 non-null   uint8   \n",
      " 18  Year_By_1995-2000          7091 non-null   uint8   \n",
      " 19  Year_By_2001-2005          7091 non-null   uint8   \n",
      " 20  Year_By_2006-2010          7091 non-null   uint8   \n",
      " 21  Year_By_2011-2015          7091 non-null   uint8   \n",
      " 22  Year_By_2016-2020          7091 non-null   uint8   \n",
      " 23  Fuel_Type_CNG              7091 non-null   uint8   \n",
      " 24  Fuel_Type_Diesel           7091 non-null   uint8   \n",
      " 25  Fuel_Type_Electric         7091 non-null   uint8   \n",
      " 26  Fuel_Type_LPG              7091 non-null   uint8   \n",
      " 27  Fuel_Type_Petrol           7091 non-null   uint8   \n",
      " 28  Transmission_Automatic     7091 non-null   uint8   \n",
      " 29  Transmission_Manual        7091 non-null   uint8   \n",
      " 30  Owner_Type_First           7091 non-null   uint8   \n",
      " 31  Owner_Type_Fourth & Above  7091 non-null   uint8   \n",
      " 32  Owner_Type_Second          7091 non-null   uint8   \n",
      " 33  Owner_Type_Third           7091 non-null   uint8   \n",
      " 34  Company_Audi               7091 non-null   uint8   \n",
      " 35  Company_BMW                7091 non-null   uint8   \n",
      " 36  Company_Bentley            7091 non-null   uint8   \n",
      " 37  Company_Datsun             7091 non-null   uint8   \n",
      " 38  Company_Fiat               7091 non-null   uint8   \n",
      " 39  Company_Ford               7091 non-null   uint8   \n",
      " 40  Company_Honda              7091 non-null   uint8   \n",
      " 41  Company_Hyundai            7091 non-null   uint8   \n",
      " 42  Company_Isuzu              7091 non-null   uint8   \n",
      " 43  Company_Jaguar             7091 non-null   uint8   \n",
      " 44  Company_Jeep               7091 non-null   uint8   \n",
      " 45  Company_Land               7091 non-null   uint8   \n",
      " 46  Company_Mahindra           7091 non-null   uint8   \n",
      " 47  Company_Maruti             7091 non-null   uint8   \n",
      " 48  Company_Mercedes-Benz      7091 non-null   uint8   \n",
      " 49  Company_Mini               7091 non-null   uint8   \n",
      " 50  Company_Mitsubishi         7091 non-null   uint8   \n",
      " 51  Company_Nissan             7091 non-null   uint8   \n",
      " 52  Company_Porsche            7091 non-null   uint8   \n",
      " 53  Company_Renault            7091 non-null   uint8   \n",
      " 54  Company_Skoda              7091 non-null   uint8   \n",
      " 55  Company_Tata               7091 non-null   uint8   \n",
      " 56  Company_Toyota             7091 non-null   uint8   \n",
      " 57  Company_Volkswagen         7091 non-null   uint8   \n",
      " 58  Company_Volvo              7091 non-null   uint8   \n",
      " 59  Seats_0.0                  7091 non-null   uint8   \n",
      " 60  Seats_2.0                  7091 non-null   uint8   \n",
      " 61  Seats_4.0                  7091 non-null   uint8   \n",
      " 62  Seats_5.0                  7091 non-null   uint8   \n",
      " 63  Seats_6.0                  7091 non-null   uint8   \n",
      " 64  Seats_7.0                  7091 non-null   uint8   \n",
      " 65  Seats_8.0                  7091 non-null   uint8   \n",
      " 66  Seats_9.0                  7091 non-null   uint8   \n",
      " 67  Seats_10.0                 7091 non-null   uint8   \n",
      "dtypes: category(1), float64(6), uint8(61)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop the dummy vars, that will not add any value (if there are n levels in the categorical variable, create n-1 dummy vars for that column.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Owner_Type_Fourth & Above',inplace=True, axis=1)\n",
    "df.drop('Fuel_Type_CNG',inplace=True, axis=1)\n",
    "df.drop('Transmission_Manual',inplace=True, axis=1)\n",
    "df.drop('Year_By_2016-2020',inplace=True, axis=1)\n",
    "df.drop('Location_Ahmedabad',inplace=True, axis=1)\n",
    "df.drop('Company_Datsun',inplace=True, axis=1)\n",
    "df.drop('Seats_0.0',inplace=True, axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Model Creation and Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split Data for Linear Model\n",
    "# independant variables\n",
    "X = df.drop(['Price','Model'], axis=1)\n",
    "# the dependent variable\n",
    "y = df[['Price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split X and y into training and test set in 70:30 ratio\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression_model = LinearRegression()\n",
    "regression_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The coefficient for Mileage is -0.07937704766475603\n",
      "The coefficient for Engine is 0.00010000282638066883\n",
      "The coefficient for Power is 0.03561155950442796\n",
      "The coefficient for New_Price is 0.05434223641355018\n",
      "The coefficient for Kilometers_Driven_log is -0.7903337688676733\n",
      "The coefficient for Location_Bangalore is 0.8032004877646801\n",
      "The coefficient for Location_Chennai is 0.060032613630473186\n",
      "The coefficient for Location_Coimbatore is 0.6527905187322809\n",
      "The coefficient for Location_Delhi is -0.5073613897045703\n",
      "The coefficient for Location_Hyderabad is 0.4314693125234725\n",
      "The coefficient for Location_Jaipur is 0.10531394825959961\n",
      "The coefficient for Location_Kochi is 0.23684048363001495\n",
      "The coefficient for Location_Kolkata is -1.13451174225929\n",
      "The coefficient for Location_Mumbai is -0.42588309767755933\n",
      "The coefficient for Location_Pune is -0.06481402371227396\n",
      "The coefficient for Year_By_1995-2000 is -4.973767838485363\n",
      "The coefficient for Year_By_2001-2005 is -4.3116675099393476\n",
      "The coefficient for Year_By_2006-2010 is -3.4028446239275576\n",
      "The coefficient for Year_By_2011-2015 is -1.4421862727554617\n",
      "The coefficient for Fuel_Type_Diesel is 1.1890345932903725\n",
      "The coefficient for Fuel_Type_Electric is 8.205261814933866\n",
      "The coefficient for Fuel_Type_LPG is 0.5173159897119717\n",
      "The coefficient for Fuel_Type_Petrol is -0.39952538280611594\n",
      "The coefficient for Transmission_Automatic is 0.8483459796798483\n",
      "The coefficient for Owner_Type_First is 0.18133977019568792\n",
      "The coefficient for Owner_Type_Second is -0.2913693593886376\n",
      "The coefficient for Owner_Type_Third is -0.6606163852850797\n",
      "The coefficient for Company_Audi is 6.979039727883634\n",
      "The coefficient for Company_BMW is 5.99354862007766\n",
      "The coefficient for Company_Bentley is 11.280047797565098\n",
      "The coefficient for Company_Fiat is 0.41707249768323984\n",
      "The coefficient for Company_Ford is 1.6444745231721654\n",
      "The coefficient for Company_Honda is 1.5266179201479555\n",
      "The coefficient for Company_Hyundai is 1.683483366668685\n",
      "The coefficient for Company_Isuzu is 7.395159590060821\n",
      "The coefficient for Company_Jaguar is 6.625988964635284\n",
      "The coefficient for Company_Jeep is 8.356901437660321\n",
      "The coefficient for Company_Land is 9.755088405410767\n",
      "The coefficient for Company_Mahindra is 0.9795146691497766\n",
      "The coefficient for Company_Maruti is 1.824425887710508\n",
      "The coefficient for Company_Mercedes-Benz is 6.176424078038735\n",
      "The coefficient for Company_Mini is 9.421866480603798\n",
      "The coefficient for Company_Mitsubishi is 1.754053743407255\n",
      "The coefficient for Company_Nissan is 1.1101808542466947\n",
      "The coefficient for Company_Porsche is 8.498230179660757\n",
      "The coefficient for Company_Renault is 1.7812015763100564\n",
      "The coefficient for Company_Skoda is 1.2121417296789214\n",
      "The coefficient for Company_Tata is 0.0017878315255325794\n",
      "The coefficient for Company_Toyota is 4.136042147543572\n",
      "The coefficient for Company_Volkswagen is 1.1938177574013322\n",
      "The coefficient for Company_Volvo is 6.342351712378346\n",
      "The coefficient for Seats_2.0 is -2.8051774529150295\n",
      "The coefficient for Seats_4.0 is -4.162126059341239\n",
      "The coefficient for Seats_5.0 is -4.821925541965627\n",
      "The coefficient for Seats_6.0 is -4.300895716516085\n",
      "The coefficient for Seats_7.0 is -3.4995003314226274\n",
      "The coefficient for Seats_8.0 is -4.199843638338372\n",
      "The coefficient for Seats_9.0 is -6.828543394457601\n",
      "The coefficient for Seats_10.0 is -2.5790139767964746\n"
     ]
    }
   ],
   "source": [
    "for idx, col_name in enumerate(X_train.columns):\n",
    "    print(\"The coefficient for {} is {}\".format(col_name, regression_model.coef_[0][idx]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The intercept for our model is 15.333466034855626\n"
     ]
    }
   ],
   "source": [
    "intercept = regression_model.intercept_[0]\n",
    "print(\"The intercept for our model is {}\".format(intercept))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8715231914816532"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression_model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8645064123863024"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#out of sample score (R^2)\n",
    "regression_model.score(X_test, y_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking the Linear Regression Assumptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **No Multicollinearity**\n",
    "2. **Mean of residuals should be 0**\n",
    "3. **Linearity of variables**\n",
    "4. **No Heteroscedacity**\n",
    "5. **Normality of error terms**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Treat Mulicolinearity among independent variables with vif score.\n",
    "####  Removing Multicollinearity\n",
    "To remove multicollinearity\n",
    "  1. We will drop every column one by one, that has VIF score greater than 5.\n",
    "  2. Look at the adjusted R square of all these models\n",
    "  3. Then drop the Variable that makes least change in Adjusted-R square\n",
    "  4. Check the VIF Scores again\n",
    "  5. Continue till we get all VIF scores under 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Multicolinearity\n",
    "# MAPE\n",
    "def mape(targets, predictions):\n",
    "    return np.mean(np.abs((targets - predictions)) / targets) * 100\n",
    "\n",
    "# Adjusted R^2\n",
    "def adj_r2(ind_vars, targets, predictions):\n",
    "    r2 = r2_score(targets, predictions)\n",
    "    n = ind_vars.shape[0]\n",
    "    k = ind_vars.shape[1]\n",
    "    return 1-((1-r2)*(n-1)/(n-k-1))\n",
    "\n",
    "# Model performance check\n",
    "def model_perf(model, inp, out):\n",
    "\n",
    "    y_pred = model.predict(inp)\n",
    "    y_act = out.values\n",
    "\n",
    "    return pd.DataFrame({\n",
    "                \"RMSE\": np.sqrt(mean_squared_error(y_act, y_pred)),\n",
    "                \"MAE\": mean_absolute_error(y_act, y_pred),\n",
    "                \"MAPE\": mape(y_act, y_pred),\n",
    "                \"R^2\": r2_score(y_act, y_pred),\n",
    "                \"Adjusted R^2\": adj_r2(inp, y_act, y_pred)\n",
    "           }, index=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Performance\n",
      "\n",
      "       RMSE       MAE       MAPE       R^2  Adjusted R^2\n",
      "0  2.022981  1.476866  26.925395  0.871523      0.869977\n"
     ]
    }
   ],
   "source": [
    "# Checking model performance on train set\n",
    "print('Training Performance\\n')\n",
    "print(model_perf(regression_model, X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Performance\n",
      "\n",
      "       RMSE       MAE       MAPE       R^2  Adjusted R^2\n",
      "0  2.062959  1.495373  26.615817  0.864506      0.860641\n"
     ]
    }
   ],
   "source": [
    "# Checking model performance on train set\n",
    "print('Testing Performance\\n')\n",
    "print(model_perf(regression_model, X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Engine</th>\n",
       "      <th>Power</th>\n",
       "      <th>New_Price</th>\n",
       "      <th>Kilometers_Driven_log</th>\n",
       "      <th>Location_Bangalore</th>\n",
       "      <th>Location_Chennai</th>\n",
       "      <th>Location_Coimbatore</th>\n",
       "      <th>Location_Delhi</th>\n",
       "      <th>Location_Hyderabad</th>\n",
       "      <th>Location_Jaipur</th>\n",
       "      <th>Location_Kochi</th>\n",
       "      <th>Location_Kolkata</th>\n",
       "      <th>Location_Mumbai</th>\n",
       "      <th>Location_Pune</th>\n",
       "      <th>Year_By_1995-2000</th>\n",
       "      <th>Year_By_2001-2005</th>\n",
       "      <th>Year_By_2006-2010</th>\n",
       "      <th>Year_By_2011-2015</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Electric</th>\n",
       "      <th>Fuel_Type_LPG</th>\n",
       "      <th>Fuel_Type_Petrol</th>\n",
       "      <th>Transmission_Automatic</th>\n",
       "      <th>Owner_Type_First</th>\n",
       "      <th>Owner_Type_Second</th>\n",
       "      <th>Owner_Type_Third</th>\n",
       "      <th>Company_Audi</th>\n",
       "      <th>Company_BMW</th>\n",
       "      <th>Company_Bentley</th>\n",
       "      <th>Company_Fiat</th>\n",
       "      <th>Company_Ford</th>\n",
       "      <th>Company_Honda</th>\n",
       "      <th>Company_Hyundai</th>\n",
       "      <th>Company_Isuzu</th>\n",
       "      <th>Company_Jaguar</th>\n",
       "      <th>Company_Jeep</th>\n",
       "      <th>Company_Land</th>\n",
       "      <th>Company_Mahindra</th>\n",
       "      <th>Company_Maruti</th>\n",
       "      <th>Company_Mercedes-Benz</th>\n",
       "      <th>Company_Mini</th>\n",
       "      <th>Company_Mitsubishi</th>\n",
       "      <th>Company_Nissan</th>\n",
       "      <th>Company_Porsche</th>\n",
       "      <th>Company_Renault</th>\n",
       "      <th>Company_Skoda</th>\n",
       "      <th>Company_Tata</th>\n",
       "      <th>Company_Toyota</th>\n",
       "      <th>Company_Volkswagen</th>\n",
       "      <th>Company_Volvo</th>\n",
       "      <th>Seats_2.0</th>\n",
       "      <th>Seats_4.0</th>\n",
       "      <th>Seats_5.0</th>\n",
       "      <th>Seats_6.0</th>\n",
       "      <th>Seats_7.0</th>\n",
       "      <th>Seats_8.0</th>\n",
       "      <th>Seats_9.0</th>\n",
       "      <th>Seats_10.0</th>\n",
       "      <th>const</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26.60</td>\n",
       "      <td>998.0</td>\n",
       "      <td>58.16</td>\n",
       "      <td>7.87</td>\n",
       "      <td>11.184435</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.67</td>\n",
       "      <td>1582.0</td>\n",
       "      <td>126.20</td>\n",
       "      <td>9.74</td>\n",
       "      <td>10.621352</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.20</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>88.70</td>\n",
       "      <td>8.61</td>\n",
       "      <td>10.736418</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.77</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>88.76</td>\n",
       "      <td>7.87</td>\n",
       "      <td>11.373675</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.20</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>140.80</td>\n",
       "      <td>46.32</td>\n",
       "      <td>10.613271</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Mileage  Engine   Power  New_Price  Kilometers_Driven_log  \\\n",
       "0    26.60   998.0   58.16       7.87              11.184435   \n",
       "1    19.67  1582.0  126.20       9.74              10.621352   \n",
       "2    18.20  1199.0   88.70       8.61              10.736418   \n",
       "3    20.77  1248.0   88.76       7.87              11.373675   \n",
       "4    15.20  1968.0  140.80      46.32              10.613271   \n",
       "\n",
       "   Location_Bangalore  Location_Chennai  Location_Coimbatore  Location_Delhi  \\\n",
       "0                   0                 0                    0               0   \n",
       "1                   0                 0                    0               0   \n",
       "2                   0                 1                    0               0   \n",
       "3                   0                 1                    0               0   \n",
       "4                   0                 0                    1               0   \n",
       "\n",
       "   Location_Hyderabad  Location_Jaipur  Location_Kochi  Location_Kolkata  \\\n",
       "0                   0                0               0                 0   \n",
       "1                   0                0               0                 0   \n",
       "2                   0                0               0                 0   \n",
       "3                   0                0               0                 0   \n",
       "4                   0                0               0                 0   \n",
       "\n",
       "   Location_Mumbai  Location_Pune  Year_By_1995-2000  Year_By_2001-2005  \\\n",
       "0                1              0                  0                  0   \n",
       "1                0              1                  0                  0   \n",
       "2                0              0                  0                  0   \n",
       "3                0              0                  0                  0   \n",
       "4                0              0                  0                  0   \n",
       "\n",
       "   Year_By_2006-2010  Year_By_2011-2015  Fuel_Type_Diesel  Fuel_Type_Electric  \\\n",
       "0                  1                  0                 0                   0   \n",
       "1                  0                  1                 1                   0   \n",
       "2                  0                  1                 0                   0   \n",
       "3                  0                  1                 1                   0   \n",
       "4                  0                  1                 1                   0   \n",
       "\n",
       "   Fuel_Type_LPG  Fuel_Type_Petrol  Transmission_Automatic  Owner_Type_First  \\\n",
       "0              0                 0                       0                 1   \n",
       "1              0                 0                       0                 1   \n",
       "2              0                 1                       0                 1   \n",
       "3              0                 0                       0                 1   \n",
       "4              0                 0                       1                 0   \n",
       "\n",
       "   Owner_Type_Second  Owner_Type_Third  Company_Audi  Company_BMW  \\\n",
       "0                  0                 0             0            0   \n",
       "1                  0                 0             0            0   \n",
       "2                  0                 0             0            0   \n",
       "3                  0                 0             0            0   \n",
       "4                  1                 0             1            0   \n",
       "\n",
       "   Company_Bentley  Company_Fiat  Company_Ford  Company_Honda  \\\n",
       "0                0             0             0              0   \n",
       "1                0             0             0              0   \n",
       "2                0             0             0              1   \n",
       "3                0             0             0              0   \n",
       "4                0             0             0              0   \n",
       "\n",
       "   Company_Hyundai  Company_Isuzu  Company_Jaguar  Company_Jeep  Company_Land  \\\n",
       "0                0              0               0             0             0   \n",
       "1                1              0               0             0             0   \n",
       "2                0              0               0             0             0   \n",
       "3                0              0               0             0             0   \n",
       "4                0              0               0             0             0   \n",
       "\n",
       "   Company_Mahindra  Company_Maruti  Company_Mercedes-Benz  Company_Mini  \\\n",
       "0                 0               1                      0             0   \n",
       "1                 0               0                      0             0   \n",
       "2                 0               0                      0             0   \n",
       "3                 0               1                      0             0   \n",
       "4                 0               0                      0             0   \n",
       "\n",
       "   Company_Mitsubishi  Company_Nissan  Company_Porsche  Company_Renault  \\\n",
       "0                   0               0                0                0   \n",
       "1                   0               0                0                0   \n",
       "2                   0               0                0                0   \n",
       "3                   0               0                0                0   \n",
       "4                   0               0                0                0   \n",
       "\n",
       "   Company_Skoda  Company_Tata  Company_Toyota  Company_Volkswagen  \\\n",
       "0              0             0               0                   0   \n",
       "1              0             0               0                   0   \n",
       "2              0             0               0                   0   \n",
       "3              0             0               0                   0   \n",
       "4              0             0               0                   0   \n",
       "\n",
       "   Company_Volvo  Seats_2.0  Seats_4.0  Seats_5.0  Seats_6.0  Seats_7.0  \\\n",
       "0              0          0          0          1          0          0   \n",
       "1              0          0          0          1          0          0   \n",
       "2              0          0          0          1          0          0   \n",
       "3              0          0          0          0          0          1   \n",
       "4              0          0          0          1          0          0   \n",
       "\n",
       "   Seats_8.0  Seats_9.0  Seats_10.0  const  \n",
       "0          0          0           0      1  \n",
       "1          0          0           0      1  \n",
       "2          0          0           0      1  \n",
       "3          0          0           0      1  \n",
       "4          0          0           0      1  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to compute VIF, we first have to add a constant column having value 1 to our input variables\n",
    "\n",
    "X1 = X.copy()\n",
    "X1['const'] = 1\n",
    "X1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series before feature selection: \n",
      "\n",
      "Mileage                      3.614768\n",
      "Engine                       9.704063\n",
      "Power                        8.526884\n",
      "New_Price                   25.088749\n",
      "Kilometers_Driven_log        1.906629\n",
      "Location_Bangalore           2.471322\n",
      "Location_Chennai             2.917428\n",
      "Location_Coimbatore          3.487009\n",
      "Location_Delhi               3.124973\n",
      "Location_Hyderabad           3.713409\n",
      "Location_Jaipur              2.646811\n",
      "Location_Kochi               3.466835\n",
      "Location_Kolkata             3.095375\n",
      "Location_Mumbai              3.946136\n",
      "Location_Pune                3.382238\n",
      "Year_By_1995-2000            1.040109\n",
      "Year_By_2001-2005            1.374262\n",
      "Year_By_2006-2010            2.346492\n",
      "Year_By_2011-2015            1.991770\n",
      "Fuel_Type_Diesel            30.895838\n",
      "Fuel_Type_Electric           1.047072\n",
      "Fuel_Type_LPG                1.214879\n",
      "Fuel_Type_Petrol            31.355040\n",
      "Transmission_Automatic       2.339646\n",
      "Owner_Type_First            88.333195\n",
      "Owner_Type_Second           80.609740\n",
      "Owner_Type_Third            11.411889\n",
      "Company_Audi                26.578032\n",
      "Company_BMW                 29.261067\n",
      "Company_Bentley              1.145805\n",
      "Company_Fiat                 3.292916\n",
      "Company_Ford                21.305621\n",
      "Company_Honda               41.408355\n",
      "Company_Hyundai             66.404610\n",
      "Company_Isuzu                1.158285\n",
      "Company_Jaguar               5.600130\n",
      "Company_Jeep                 2.307848\n",
      "Company_Land                 5.323746\n",
      "Company_Mahindra            20.625841\n",
      "Company_Maruti              69.486493\n",
      "Company_Mercedes-Benz       34.648808\n",
      "Company_Mini                 3.779715\n",
      "Company_Mitsubishi           3.970279\n",
      "Company_Nissan               7.997232\n",
      "Company_Porsche              2.356783\n",
      "Company_Renault             10.961178\n",
      "Company_Skoda               15.372790\n",
      "Company_Tata                14.262094\n",
      "Company_Toyota              32.789280\n",
      "Company_Volkswagen          22.608615\n",
      "Company_Volvo                3.606554\n",
      "Seats_2.0                   17.288127\n",
      "Seats_4.0                  116.759197\n",
      "Seats_5.0                  966.720046\n",
      "Seats_6.0                   39.719461\n",
      "Seats_7.0                  712.307642\n",
      "Seats_8.0                  163.292989\n",
      "Seats_9.0                    3.042337\n",
      "Seats_10.0                   7.121261\n",
      "const                     9321.045154\n",
      "dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "vif_series1 = pd.Series([variance_inflation_factor(X1.values,i) for i in range(X1.shape[1])],index=X1.columns)\n",
    "print('Series before feature selection: \\n\\n{}\\n'.format(vif_series1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Method to drop all the multicollinear columns and choose which one we should drop\n",
    "def treating_multicollinearity(high_vif_columns, x_train, x_test, y_train, y_test):\n",
    "    \"\"\"\n",
    "    Drop every column that has VIF score greater than 5, one by one.\n",
    "    Look at the adjusted R square of all these models\n",
    "    Look at the RMSE of all these models on test data\n",
    "    \"\"\"\n",
    "    adj_rsq_scores = []\n",
    "    rmse_test_data = []\n",
    "\n",
    "    # build ols models by dropping one of these at a time and observe the Adjusted R-squared\n",
    "    for cols in high_vif_columns:\n",
    "        train = x_train.loc[:, ~x_train.columns.str.startswith(cols)]\n",
    "        test = x_test.loc[:, ~x_test.columns.str.startswith(cols)]\n",
    "        # Create the model\n",
    "        \n",
    "        model = LinearRegression(fit_intercept=False)\n",
    "        model.fit(train, y_train)\n",
    "        # Adj R-Sq\n",
    "        adj_rsq_scores.append(adj_r2(train, y_train, model.predict(train)))\n",
    "        # RMSE (Test data)\n",
    "        y_pred_test_Price = model.predict(test)\n",
    "        y_test_Price = y_test\n",
    "        rmse_test_data.append(np.sqrt(mean_squared_error(y_pred_test_Price, y_test_Price)))\n",
    "\n",
    "    # Add new Adj_Rsq and RMSE after dropping each colmn\n",
    "    temp = pd.DataFrame(\n",
    "        {\n",
    "            \"col\": high_vif_columns,\n",
    "            \"Adj_rsq_after_dropping_col\": adj_rsq_scores,\n",
    "            \"Test RMSE\": rmse_test_data,\n",
    "        }\n",
    "    ).sort_values(by=\"Adj_rsq_after_dropping_col\", ascending=False)\n",
    "\n",
    "    print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      col  Adj_rsq_after_dropping_col  Test RMSE\n",
      "21           Company_Tata                    0.869977   2.062961\n",
      "5        Owner_Type_First                    0.869975   2.063262\n",
      "6       Owner_Type_Second                    0.869972   2.062514\n",
      "0                  Engine                    0.869967   2.063356\n",
      "7        Owner_Type_Third                    0.869955   2.062104\n",
      "30             Seats_10.0                    0.869946   2.065061\n",
      "4        Fuel_Type_Petrol                    0.869935   2.063402\n",
      "24              Seats_2.0                    0.869932   2.063584\n",
      "15       Company_Mahindra                    0.869911   2.064935\n",
      "28              Seats_7.0                    0.869901   2.063185\n",
      "18         Company_Nissan                    0.869897   2.064338\n",
      "20          Company_Skoda                    0.869892   2.062760\n",
      "23     Company_Volkswagen                    0.869876   2.064150\n",
      "25              Seats_4.0                    0.869871   2.063279\n",
      "29              Seats_8.0                    0.869868   2.063470\n",
      "27              Seats_6.0                    0.869867   2.063100\n",
      "26              Seats_5.0                    0.869833   2.063016\n",
      "11          Company_Honda                    0.869808   2.064814\n",
      "10           Company_Ford                    0.869785   2.065587\n",
      "12        Company_Hyundai                    0.869767   2.065504\n",
      "19        Company_Renault                    0.869760   2.065262\n",
      "16         Company_Maruti                    0.869728   2.066085\n",
      "3        Fuel_Type_Diesel                    0.869596   2.063464\n",
      "2               New_Price                    0.869230   2.071856\n",
      "22         Company_Toyota                    0.868862   2.072750\n",
      "9             Company_BMW                    0.868284   2.071840\n",
      "13         Company_Jaguar                    0.868277   2.072159\n",
      "17  Company_Mercedes-Benz                    0.868150   2.074931\n",
      "8            Company_Audi                    0.867682   2.074001\n",
      "14           Company_Land                    0.864535   2.100873\n",
      "1                   Power                    0.860194   2.152052\n"
     ]
    }
   ],
   "source": [
    "high_vif_columns = [\"Engine\",\"Power\",\"New_Price\",\"Fuel_Type_Diesel\",\"Fuel_Type_Petrol\",\"Owner_Type_First\",\"Owner_Type_Second\",\"Owner_Type_Third\",\"Company_Audi\",\"Company_BMW\",\"Company_Ford\",\"Company_Honda\",\"Company_Hyundai\",\"Company_Jaguar\",\"Company_Land\",\"Company_Mahindra\",\"Company_Maruti\",\"Company_Mercedes-Benz\",\"Company_Nissan\",\"Company_Renault\",\"Company_Skoda\",\"Company_Tata\",\"Company_Toyota\",\"Company_Volkswagen\",\"Seats_2.0\",\"Seats_4.0\",\"Seats_5.0\",\"Seats_6.0\",\"Seats_7.0\",\"Seats_8.0\",\"Seats_10.0\"]\n",
    "\n",
    "X_train1 = X_train.copy()\n",
    "X_train1['constant'] = 1\n",
    "\n",
    "X_test1 = X_test.copy()\n",
    "X_test1['constant'] = 1\n",
    "\n",
    "treating_multicollinearity(high_vif_columns, X_train1, X_test1, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series before feature selection: \n",
      "\n",
      "Mileage                      3.623395\n",
      "Engine                      10.012446\n",
      "Power                        8.710976\n",
      "New_Price                   23.443367\n",
      "Kilometers_Driven_log        1.985898\n",
      "Location_Bangalore           2.498680\n",
      "Location_Chennai             2.970170\n",
      "Location_Coimbatore          3.513619\n",
      "Location_Delhi               3.045138\n",
      "Location_Hyderabad           3.701959\n",
      "Location_Jaipur              2.740041\n",
      "Location_Kochi               3.618833\n",
      "Location_Kolkata             3.127199\n",
      "Location_Mumbai              3.934795\n",
      "Location_Pune                3.379362\n",
      "Year_By_1995-2000            1.043981\n",
      "Year_By_2001-2005            1.356418\n",
      "Year_By_2006-2010            2.411400\n",
      "Year_By_2011-2015            2.040902\n",
      "Fuel_Type_Diesel            29.304767\n",
      "Fuel_Type_Electric           1.046237\n",
      "Fuel_Type_LPG                1.197108\n",
      "Fuel_Type_Petrol            29.872976\n",
      "Transmission_Automatic       2.355694\n",
      "Owner_Type_First            82.965042\n",
      "Owner_Type_Second           75.166935\n",
      "Owner_Type_Third            11.481788\n",
      "Company_Audi                 9.524219\n",
      "Company_BMW                 10.929736\n",
      "Company_Bentley              1.043713\n",
      "Company_Fiat                 1.187117\n",
      "Company_Ford                 2.504925\n",
      "Company_Honda                4.166278\n",
      "Company_Hyundai              5.625462\n",
      "Company_Isuzu                1.059209\n",
      "Company_Jaguar               2.594752\n",
      "Company_Jeep                 1.198740\n",
      "Company_Land                 1.568364\n",
      "Company_Mahindra             2.784217\n",
      "Company_Maruti               5.781572\n",
      "Company_Mercedes-Benz       12.058118\n",
      "Company_Mini                 1.945120\n",
      "Company_Mitsubishi           1.780070\n",
      "Company_Nissan               1.481884\n",
      "Company_Porsche              1.276738\n",
      "Company_Renault              1.707723\n",
      "Company_Skoda                3.535195\n",
      "Company_Toyota               5.699564\n",
      "Company_Volkswagen           2.546991\n",
      "Company_Volvo                1.796124\n",
      "Seats_2.0                   14.359829\n",
      "Seats_4.0                   75.409362\n",
      "Seats_5.0                  661.587955\n",
      "Seats_6.0                   26.824704\n",
      "Seats_7.0                  489.024112\n",
      "Seats_8.0                  111.541661\n",
      "Seats_9.0                    2.026420\n",
      "Seats_10.0                   4.082101\n",
      "constant                  6794.569446\n",
      "dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Dropping Company_Tata would have the maximum impact on predictive power of the model (amongst the variables being considered)\n",
    "# We'll drop Company_Tata and check the vif again\n",
    "\n",
    "X_train2 = X_train1.drop('Company_Tata', axis=1)\n",
    "vif_series2 = pd.Series([variance_inflation_factor(X_train2.values,i) for i in range(X_train2.shape[1])],index=X_train2.columns)\n",
    "print('Series before feature selection: \\n\\n{}\\n'.format(vif_series2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      col  Adj_rsq_after_dropping_col  Test RMSE\n",
      "5        Owner_Type_First                    0.869975   2.063262\n",
      "6       Owner_Type_Second                    0.869972   2.062514\n",
      "0                  Engine                    0.869967   2.063356\n",
      "7        Owner_Type_Third                    0.869955   2.062104\n",
      "4        Fuel_Type_Petrol                    0.869935   2.063402\n",
      "14              Seats_2.0                    0.869932   2.063584\n",
      "18              Seats_7.0                    0.869901   2.063185\n",
      "15              Seats_4.0                    0.869871   2.063279\n",
      "19              Seats_8.0                    0.869868   2.063470\n",
      "17              Seats_6.0                    0.869867   2.063100\n",
      "16              Seats_5.0                    0.869833   2.063016\n",
      "10        Company_Hyundai                    0.869767   2.065504\n",
      "11         Company_Maruti                    0.869728   2.066085\n",
      "3        Fuel_Type_Diesel                    0.869596   2.063464\n",
      "2               New_Price                    0.869230   2.071856\n",
      "13         Company_Toyota                    0.868862   2.072750\n",
      "9             Company_BMW                    0.868284   2.071840\n",
      "12  Company_Mercedes-Benz                    0.868150   2.074931\n",
      "8            Company_Audi                    0.867682   2.074001\n",
      "1                   Power                    0.860194   2.152052\n"
     ]
    }
   ],
   "source": [
    "high_vif_columns = [\"Engine\",\"Power\",\"New_Price\",\"Fuel_Type_Diesel\",\"Fuel_Type_Petrol\",\"Owner_Type_First\",\"Owner_Type_Second\",\"Owner_Type_Third\",\"Company_Audi\",\"Company_BMW\",\"Company_Hyundai\",\"Company_Maruti\",\"Company_Mercedes-Benz\",\"Company_Toyota\",\"Seats_2.0\",\"Seats_4.0\",\"Seats_5.0\",\"Seats_6.0\",\"Seats_7.0\",\"Seats_8.0\"]\n",
    "\n",
    "treating_multicollinearity(high_vif_columns, X_train1, X_test1, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series before feature selection: \n",
      "\n",
      "Mileage                      3.622771\n",
      "Engine                      10.008814\n",
      "Power                        8.710976\n",
      "New_Price                   23.442900\n",
      "Kilometers_Driven_log        1.984275\n",
      "Location_Bangalore           2.494732\n",
      "Location_Chennai             2.968777\n",
      "Location_Coimbatore          3.513400\n",
      "Location_Delhi               3.045130\n",
      "Location_Hyderabad           3.701690\n",
      "Location_Jaipur              2.739980\n",
      "Location_Kochi               3.618830\n",
      "Location_Kolkata             3.127199\n",
      "Location_Mumbai              3.934602\n",
      "Location_Pune                3.379255\n",
      "Year_By_1995-2000            1.043981\n",
      "Year_By_2001-2005            1.346737\n",
      "Year_By_2006-2010            2.409039\n",
      "Year_By_2011-2015            2.040637\n",
      "Fuel_Type_Diesel            29.304111\n",
      "Fuel_Type_Electric           1.046235\n",
      "Fuel_Type_LPG                1.197103\n",
      "Fuel_Type_Petrol            29.871751\n",
      "Transmission_Automatic       2.355599\n",
      "Owner_Type_Second            1.175761\n",
      "Owner_Type_Third             1.103793\n",
      "Company_Audi                 9.524211\n",
      "Company_BMW                 10.929729\n",
      "Company_Bentley              1.043676\n",
      "Company_Fiat                 1.187045\n",
      "Company_Ford                 2.504918\n",
      "Company_Honda                4.165458\n",
      "Company_Hyundai              5.624917\n",
      "Company_Isuzu                1.059209\n",
      "Company_Jaguar               2.594701\n",
      "Company_Jeep                 1.198738\n",
      "Company_Land                 1.568352\n",
      "Company_Mahindra             2.782399\n",
      "Company_Maruti               5.781035\n",
      "Company_Mercedes-Benz       12.058107\n",
      "Company_Mini                 1.945063\n",
      "Company_Mitsubishi           1.779944\n",
      "Company_Nissan               1.481852\n",
      "Company_Porsche              1.276717\n",
      "Company_Renault              1.707558\n",
      "Company_Skoda                3.535058\n",
      "Company_Toyota               5.699496\n",
      "Company_Volkswagen           2.546809\n",
      "Company_Volvo                1.796113\n",
      "Seats_2.0                   14.359828\n",
      "Seats_4.0                   75.408429\n",
      "Seats_5.0                  661.586835\n",
      "Seats_6.0                   26.824699\n",
      "Seats_7.0                  489.023953\n",
      "Seats_8.0                  111.538860\n",
      "Seats_9.0                    2.026420\n",
      "Seats_10.0                   4.082053\n",
      "constant                  6211.714259\n",
      "dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Dropping Owner_Type_First would have the maximum impact on predictive power of the model (amongst the variables being considered)\n",
    "# We'll drop Owner_Type_First and check the vif again\n",
    "\n",
    "X_train3 = X_train2.drop('Owner_Type_First', axis=1)\n",
    "vif_series3 = pd.Series([variance_inflation_factor(X_train3.values,i) for i in range(X_train3.shape[1])],index=X_train3.columns)\n",
    "print('Series before feature selection: \\n\\n{}\\n'.format(vif_series3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      col  Adj_rsq_after_dropping_col  Test RMSE\n",
      "0                  Engine                    0.869967   2.063356\n",
      "4        Fuel_Type_Petrol                    0.869935   2.063402\n",
      "11              Seats_2.0                    0.869932   2.063584\n",
      "15              Seats_7.0                    0.869901   2.063185\n",
      "12              Seats_4.0                    0.869871   2.063279\n",
      "16              Seats_8.0                    0.869868   2.063470\n",
      "14              Seats_6.0                    0.869867   2.063100\n",
      "13              Seats_5.0                    0.869833   2.063016\n",
      "7         Company_Hyundai                    0.869767   2.065504\n",
      "8          Company_Maruti                    0.869728   2.066085\n",
      "3        Fuel_Type_Diesel                    0.869596   2.063464\n",
      "2               New_Price                    0.869230   2.071856\n",
      "10         Company_Toyota                    0.868862   2.072750\n",
      "6             Company_BMW                    0.868284   2.071840\n",
      "9   Company_Mercedes-Benz                    0.868150   2.074931\n",
      "5            Company_Audi                    0.867682   2.074001\n",
      "1                   Power                    0.860194   2.152052\n"
     ]
    }
   ],
   "source": [
    "high_vif_columns = [\"Engine\",\"Power\",\"New_Price\",\"Fuel_Type_Diesel\",\"Fuel_Type_Petrol\",\"Company_Audi\",\"Company_BMW\",\"Company_Hyundai\",\"Company_Maruti\",\"Company_Mercedes-Benz\",\"Company_Toyota\",\"Seats_2.0\",\"Seats_4.0\",\"Seats_5.0\",\"Seats_6.0\",\"Seats_7.0\",\"Seats_8.0\"]\n",
    "\n",
    "treating_multicollinearity(high_vif_columns, X_train1, X_test1, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series before feature selection: \n",
      "\n",
      "Mileage                      3.216555\n",
      "Power                        4.976948\n",
      "New_Price                   23.355090\n",
      "Kilometers_Driven_log        1.983332\n",
      "Location_Bangalore           2.494669\n",
      "Location_Chennai             2.968106\n",
      "Location_Coimbatore          3.513368\n",
      "Location_Delhi               3.045025\n",
      "Location_Hyderabad           3.701201\n",
      "Location_Jaipur              2.739683\n",
      "Location_Kochi               3.618754\n",
      "Location_Kolkata             3.126980\n",
      "Location_Mumbai              3.934024\n",
      "Location_Pune                3.379112\n",
      "Year_By_1995-2000            1.043692\n",
      "Year_By_2001-2005            1.346638\n",
      "Year_By_2006-2010            2.401333\n",
      "Year_By_2011-2015            2.040524\n",
      "Fuel_Type_Diesel            29.286972\n",
      "Fuel_Type_Electric           1.038973\n",
      "Fuel_Type_LPG                1.195882\n",
      "Fuel_Type_Petrol            29.576167\n",
      "Transmission_Automatic       2.355555\n",
      "Owner_Type_Second            1.175450\n",
      "Owner_Type_Third             1.103729\n",
      "Company_Audi                 9.522300\n",
      "Company_BMW                 10.915455\n",
      "Company_Bentley              1.040354\n",
      "Company_Fiat                 1.184528\n",
      "Company_Ford                 2.494026\n",
      "Company_Honda                4.160489\n",
      "Company_Hyundai              5.617080\n",
      "Company_Isuzu                1.054602\n",
      "Company_Jaguar               2.583659\n",
      "Company_Jeep                 1.196872\n",
      "Company_Land                 1.567667\n",
      "Company_Mahindra             2.775959\n",
      "Company_Maruti               5.773224\n",
      "Company_Mercedes-Benz       11.997175\n",
      "Company_Mini                 1.943692\n",
      "Company_Mitsubishi           1.767986\n",
      "Company_Nissan               1.475284\n",
      "Company_Porsche              1.258394\n",
      "Company_Renault              1.706336\n",
      "Company_Skoda                3.517323\n",
      "Company_Toyota               5.463846\n",
      "Company_Volkswagen           2.546564\n",
      "Company_Volvo                1.795959\n",
      "Seats_2.0                   14.230435\n",
      "Seats_4.0                   74.133263\n",
      "Seats_5.0                  649.880172\n",
      "Seats_6.0                   26.554826\n",
      "Seats_7.0                  482.723048\n",
      "Seats_8.0                  110.262721\n",
      "Seats_9.0                    2.021093\n",
      "Seats_10.0                   4.053990\n",
      "constant                  5951.472036\n",
      "dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Dropping Engine would have the maximum impact on predictive power of the model (amongst the variables being considered)\n",
    "# We'll drop Fuel_Type_Petrol and check the vif again\n",
    "\n",
    "X_train4 = X_train3.drop('Engine', axis=1)\n",
    "vif_series4 = pd.Series([variance_inflation_factor(X_train4.values,i) for i in range(X_train4.shape[1])],index=X_train4.columns)\n",
    "print('Series before feature selection: \\n\\n{}\\n'.format(vif_series4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      col  Adj_rsq_after_dropping_col  Test RMSE\n",
      "2        Fuel_Type_Petrol                    0.869935   2.063402\n",
      "9               Seats_2.0                    0.869932   2.063584\n",
      "13              Seats_7.0                    0.869901   2.063185\n",
      "10              Seats_4.0                    0.869871   2.063279\n",
      "14              Seats_8.0                    0.869868   2.063470\n",
      "12              Seats_6.0                    0.869867   2.063100\n",
      "11              Seats_5.0                    0.869833   2.063016\n",
      "5         Company_Hyundai                    0.869767   2.065504\n",
      "6          Company_Maruti                    0.869728   2.066085\n",
      "1        Fuel_Type_Diesel                    0.869596   2.063464\n",
      "0               New_Price                    0.869230   2.071856\n",
      "8          Company_Toyota                    0.868862   2.072750\n",
      "4             Company_BMW                    0.868284   2.071840\n",
      "7   Company_Mercedes-Benz                    0.868150   2.074931\n",
      "3            Company_Audi                    0.867682   2.074001\n"
     ]
    }
   ],
   "source": [
    "high_vif_columns = [\"New_Price\",\"Fuel_Type_Diesel\",\"Fuel_Type_Petrol\",\"Company_Audi\",\"Company_BMW\",\"Company_Hyundai\",\"Company_Maruti\",\"Company_Mercedes-Benz\",\"Company_Toyota\",\"Seats_2.0\",\"Seats_4.0\",\"Seats_5.0\",\"Seats_6.0\",\"Seats_7.0\",\"Seats_8.0\"]\n",
    "\n",
    "treating_multicollinearity(high_vif_columns, X_train1, X_test1, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series before feature selection: \n",
      "\n",
      "Mileage                      3.122453\n",
      "Power                        4.970155\n",
      "New_Price                   23.354972\n",
      "Kilometers_Driven_log        1.973429\n",
      "Location_Bangalore           2.494339\n",
      "Location_Chennai             2.967056\n",
      "Location_Coimbatore          3.511267\n",
      "Location_Delhi               3.045025\n",
      "Location_Hyderabad           3.700100\n",
      "Location_Jaipur              2.736432\n",
      "Location_Kochi               3.618549\n",
      "Location_Kolkata             3.125470\n",
      "Location_Mumbai              3.933358\n",
      "Location_Pune                3.378941\n",
      "Year_By_1995-2000            1.043639\n",
      "Year_By_2001-2005            1.346539\n",
      "Year_By_2006-2010            2.401265\n",
      "Year_By_2011-2015            2.040414\n",
      "Fuel_Type_Diesel             2.365810\n",
      "Fuel_Type_Electric           1.015578\n",
      "Fuel_Type_LPG                1.012220\n",
      "Transmission_Automatic       2.354924\n",
      "Owner_Type_Second            1.175287\n",
      "Owner_Type_Third             1.103657\n",
      "Company_Audi                 9.517117\n",
      "Company_BMW                 10.913687\n",
      "Company_Bentley              1.040341\n",
      "Company_Fiat                 1.184350\n",
      "Company_Ford                 2.493426\n",
      "Company_Honda                4.158670\n",
      "Company_Hyundai              5.617057\n",
      "Company_Isuzu                1.054515\n",
      "Company_Jaguar               2.582575\n",
      "Company_Jeep                 1.196471\n",
      "Company_Land                 1.564573\n",
      "Company_Mahindra             2.774854\n",
      "Company_Maruti               5.765109\n",
      "Company_Mercedes-Benz       11.989320\n",
      "Company_Mini                 1.942718\n",
      "Company_Mitsubishi           1.767446\n",
      "Company_Nissan               1.475249\n",
      "Company_Porsche              1.258387\n",
      "Company_Renault              1.706327\n",
      "Company_Skoda                3.516819\n",
      "Company_Toyota               5.461566\n",
      "Company_Volkswagen           2.546295\n",
      "Company_Volvo                1.795498\n",
      "Seats_2.0                   14.230399\n",
      "Seats_4.0                   74.117414\n",
      "Seats_5.0                  649.879808\n",
      "Seats_6.0                   26.554363\n",
      "Seats_7.0                  482.710698\n",
      "Seats_8.0                  110.258839\n",
      "Seats_9.0                    2.021071\n",
      "Seats_10.0                   4.053806\n",
      "constant                  5758.994872\n",
      "dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Dropping Fuel_Type_Petrol would have the maximum impact on predictive power of the model (amongst the variables being considered)\n",
    "# We'll drop Fuel_Type_Petrol and check the vif again\n",
    "\n",
    "X_train5 = X_train4.drop('Fuel_Type_Petrol', axis=1)\n",
    "vif_series5 = pd.Series([variance_inflation_factor(X_train5.values,i) for i in range(X_train5.shape[1])],index=X_train5.columns)\n",
    "print('Series before feature selection: \\n\\n{}\\n'.format(vif_series5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      col  Adj_rsq_after_dropping_col  Test RMSE\n",
      "7               Seats_2.0                    0.869932   2.063584\n",
      "11              Seats_7.0                    0.869901   2.063185\n",
      "8               Seats_4.0                    0.869871   2.063279\n",
      "12              Seats_8.0                    0.869868   2.063470\n",
      "10              Seats_6.0                    0.869867   2.063100\n",
      "9               Seats_5.0                    0.869833   2.063016\n",
      "3         Company_Hyundai                    0.869767   2.065504\n",
      "4          Company_Maruti                    0.869728   2.066085\n",
      "0               New_Price                    0.869230   2.071856\n",
      "6          Company_Toyota                    0.868862   2.072750\n",
      "2             Company_BMW                    0.868284   2.071840\n",
      "5   Company_Mercedes-Benz                    0.868150   2.074931\n",
      "1            Company_Audi                    0.867682   2.074001\n"
     ]
    }
   ],
   "source": [
    "high_vif_columns = [\"New_Price\",\"Company_Audi\",\"Company_BMW\",\"Company_Hyundai\",\"Company_Maruti\",\"Company_Mercedes-Benz\",\"Company_Toyota\",\"Seats_2.0\",\"Seats_4.0\",\"Seats_5.0\",\"Seats_6.0\",\"Seats_7.0\",\"Seats_8.0\"]\n",
    "\n",
    "treating_multicollinearity(high_vif_columns, X_train1, X_test1, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series before feature selection: \n",
      "\n",
      "Mileage                     3.119326\n",
      "Power                       4.946219\n",
      "New_Price                  23.324900\n",
      "Kilometers_Driven_log       1.966525\n",
      "Location_Bangalore          2.494335\n",
      "Location_Chennai            2.967055\n",
      "Location_Coimbatore         3.511067\n",
      "Location_Delhi              3.044999\n",
      "Location_Hyderabad          3.699402\n",
      "Location_Jaipur             2.736416\n",
      "Location_Kochi              3.618487\n",
      "Location_Kolkata            3.125404\n",
      "Location_Mumbai             3.933173\n",
      "Location_Pune               3.378926\n",
      "Year_By_1995-2000           1.043478\n",
      "Year_By_2001-2005           1.345605\n",
      "Year_By_2006-2010           2.397397\n",
      "Year_By_2011-2015           2.038710\n",
      "Fuel_Type_Diesel            2.365804\n",
      "Fuel_Type_Electric          1.015551\n",
      "Fuel_Type_LPG               1.012212\n",
      "Transmission_Automatic      2.354078\n",
      "Owner_Type_Second           1.175232\n",
      "Owner_Type_Third            1.103657\n",
      "Company_Audi                9.516934\n",
      "Company_BMW                10.911752\n",
      "Company_Bentley             1.040277\n",
      "Company_Fiat                1.184340\n",
      "Company_Ford                2.493426\n",
      "Company_Honda               4.158287\n",
      "Company_Hyundai             5.616750\n",
      "Company_Isuzu               1.054501\n",
      "Company_Jaguar              2.582009\n",
      "Company_Jeep                1.196425\n",
      "Company_Land                1.564123\n",
      "Company_Mahindra            2.774818\n",
      "Company_Maruti              5.764948\n",
      "Company_Mercedes-Benz      11.984443\n",
      "Company_Mini                1.942368\n",
      "Company_Mitsubishi          1.767300\n",
      "Company_Nissan              1.475247\n",
      "Company_Porsche             1.256575\n",
      "Company_Renault             1.706134\n",
      "Company_Skoda               3.516001\n",
      "Company_Toyota              5.460499\n",
      "Company_Volkswagen          2.546294\n",
      "Company_Volvo               1.795283\n",
      "Seats_4.0                   6.570498\n",
      "Seats_5.0                  50.317329\n",
      "Seats_6.0                   3.329857\n",
      "Seats_7.0                  39.303509\n",
      "Seats_8.0                   9.948690\n",
      "Seats_9.0                   1.094257\n",
      "Seats_10.0                  1.259162\n",
      "constant                  990.529427\n",
      "dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Dropping Seats_2.0 would have the maximum impact on predictive power of the model (amongst the variables being considered)\n",
    "# We'll drop Seats_2.0 and check the vif again\n",
    "\n",
    "X_train6 = X_train5.drop('Seats_2.0', axis=1)\n",
    "vif_series6 = pd.Series([variance_inflation_factor(X_train6.values,i) for i in range(X_train6.shape[1])],index=X_train6.columns)\n",
    "print('Series before feature selection: \\n\\n{}\\n'.format(vif_series6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      col  Adj_rsq_after_dropping_col  Test RMSE\n",
      "9               Seats_7.0                    0.869901   2.063185\n",
      "7               Seats_4.0                    0.869871   2.063279\n",
      "10              Seats_8.0                    0.869868   2.063470\n",
      "8               Seats_5.0                    0.869833   2.063016\n",
      "3         Company_Hyundai                    0.869767   2.065504\n",
      "4          Company_Maruti                    0.869728   2.066085\n",
      "0               New_Price                    0.869230   2.071856\n",
      "6          Company_Toyota                    0.868862   2.072750\n",
      "2             Company_BMW                    0.868284   2.071840\n",
      "5   Company_Mercedes-Benz                    0.868150   2.074931\n",
      "1            Company_Audi                    0.867682   2.074001\n"
     ]
    }
   ],
   "source": [
    "high_vif_columns = [\"New_Price\",\"Company_Audi\",\"Company_BMW\",\"Company_Hyundai\",\"Company_Maruti\",\"Company_Mercedes-Benz\",\"Company_Toyota\",\"Seats_4.0\",\"Seats_5.0\",\"Seats_7.0\",\"Seats_8.0\"]\n",
    "\n",
    "treating_multicollinearity(high_vif_columns, X_train1, X_test1, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series before feature selection: \n",
      "\n",
      "Mileage                     3.107432\n",
      "Power                       4.946042\n",
      "New_Price                  23.107992\n",
      "Kilometers_Driven_log       1.957678\n",
      "Location_Bangalore          2.494128\n",
      "Location_Chennai            2.967055\n",
      "Location_Coimbatore         3.511057\n",
      "Location_Delhi              3.044888\n",
      "Location_Hyderabad          3.698895\n",
      "Location_Jaipur             2.736361\n",
      "Location_Kochi              3.618471\n",
      "Location_Kolkata            3.125390\n",
      "Location_Mumbai             3.931257\n",
      "Location_Pune               3.378892\n",
      "Year_By_1995-2000           1.043412\n",
      "Year_By_2001-2005           1.344213\n",
      "Year_By_2006-2010           2.391874\n",
      "Year_By_2011-2015           2.035267\n",
      "Fuel_Type_Diesel            2.331244\n",
      "Fuel_Type_Electric          1.015465\n",
      "Fuel_Type_LPG               1.012197\n",
      "Transmission_Automatic      2.353922\n",
      "Owner_Type_Second           1.175134\n",
      "Owner_Type_Third            1.103636\n",
      "Company_Audi                9.390332\n",
      "Company_BMW                10.770774\n",
      "Company_Bentley             1.040265\n",
      "Company_Fiat                1.184241\n",
      "Company_Ford                2.492395\n",
      "Company_Honda               4.158146\n",
      "Company_Hyundai             5.616438\n",
      "Company_Isuzu               1.054472\n",
      "Company_Jaguar              2.548259\n",
      "Company_Jeep                1.194751\n",
      "Company_Land                1.563897\n",
      "Company_Mahindra            2.766585\n",
      "Company_Maruti              5.762929\n",
      "Company_Mercedes-Benz      11.802135\n",
      "Company_Mini                1.931333\n",
      "Company_Mitsubishi          1.766393\n",
      "Company_Nissan              1.474680\n",
      "Company_Porsche             1.204838\n",
      "Company_Renault             1.706127\n",
      "Company_Skoda               3.493500\n",
      "Company_Toyota              5.457983\n",
      "Company_Volkswagen          2.543912\n",
      "Company_Volvo               1.785806\n",
      "Seats_4.0                   1.528511\n",
      "Seats_5.0                   2.757970\n",
      "Seats_6.0                   1.333533\n",
      "Seats_8.0                   1.281659\n",
      "Seats_9.0                   1.010789\n",
      "Seats_10.0                  1.023767\n",
      "constant                  676.150829\n",
      "dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Dropping Seats_7.0 would have the maximum impact on predictive power of the model (amongst the variables being considered)\n",
    "# We'll drop Seats_7.0 and check the vif again\n",
    "\n",
    "X_train7 = X_train6.drop('Seats_7.0', axis=1)\n",
    "vif_series7 = pd.Series([variance_inflation_factor(X_train7.values,i) for i in range(X_train7.shape[1])],index=X_train7.columns)\n",
    "print('Series before feature selection: \\n\\n{}\\n'.format(vif_series7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     col  Adj_rsq_after_dropping_col  Test RMSE\n",
      "3        Company_Hyundai                    0.869767   2.065504\n",
      "4         Company_Maruti                    0.869728   2.066085\n",
      "0              New_Price                    0.869230   2.071856\n",
      "6         Company_Toyota                    0.868862   2.072750\n",
      "2            Company_BMW                    0.868284   2.071840\n",
      "5  Company_Mercedes-Benz                    0.868150   2.074931\n",
      "1           Company_Audi                    0.867682   2.074001\n"
     ]
    }
   ],
   "source": [
    "high_vif_columns = [\"New_Price\",\"Company_Audi\",\"Company_BMW\",\"Company_Hyundai\",\"Company_Maruti\",\"Company_Mercedes-Benz\",\"Company_Toyota\"]\n",
    "\n",
    "treating_multicollinearity(high_vif_columns, X_train1, X_test1, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series before feature selection: \n",
      "\n",
      "Mileage                     3.104192\n",
      "Power                       4.873134\n",
      "New_Price                  22.666626\n",
      "Kilometers_Driven_log       1.956414\n",
      "Location_Bangalore          2.493540\n",
      "Location_Chennai            2.966296\n",
      "Location_Coimbatore         3.509296\n",
      "Location_Delhi              3.043682\n",
      "Location_Hyderabad          3.698780\n",
      "Location_Jaipur             2.736298\n",
      "Location_Kochi              3.617177\n",
      "Location_Kolkata            3.125336\n",
      "Location_Mumbai             3.931223\n",
      "Location_Pune               3.378308\n",
      "Year_By_1995-2000           1.043412\n",
      "Year_By_2001-2005           1.341277\n",
      "Year_By_2006-2010           2.388554\n",
      "Year_By_2011-2015           2.024467\n",
      "Fuel_Type_Diesel            2.303836\n",
      "Fuel_Type_Electric          1.015465\n",
      "Fuel_Type_LPG               1.011990\n",
      "Transmission_Automatic      2.353922\n",
      "Owner_Type_Second           1.174926\n",
      "Owner_Type_Third            1.103625\n",
      "Company_Audi                7.404132\n",
      "Company_BMW                 8.479994\n",
      "Company_Bentley             1.030965\n",
      "Company_Fiat                1.032780\n",
      "Company_Ford                1.239145\n",
      "Company_Honda               1.451570\n",
      "Company_Isuzu               1.040028\n",
      "Company_Jaguar              2.193273\n",
      "Company_Jeep                1.102421\n",
      "Company_Land                1.303788\n",
      "Company_Mahindra            1.808303\n",
      "Company_Maruti              1.742058\n",
      "Company_Mercedes-Benz       9.182365\n",
      "Company_Mini                1.753887\n",
      "Company_Mitsubishi          1.604945\n",
      "Company_Nissan              1.077060\n",
      "Company_Porsche             1.143245\n",
      "Company_Renault             1.157859\n",
      "Company_Skoda               2.473080\n",
      "Company_Toyota              3.292099\n",
      "Company_Volkswagen          1.227853\n",
      "Company_Volvo               1.599651\n",
      "Seats_4.0                   1.499134\n",
      "Seats_5.0                   2.742240\n",
      "Seats_6.0                   1.333234\n",
      "Seats_8.0                   1.279646\n",
      "Seats_9.0                   1.010723\n",
      "Seats_10.0                  1.019375\n",
      "constant                  659.122969\n",
      "dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Dropping Company_Hyundai would have the maximum impact on predictive power of the model (amongst the variables being considered)\n",
    "# We'll drop Company_Hyundai and check the vif again\n",
    "\n",
    "X_train8 = X_train7.drop('Company_Hyundai', axis=1)\n",
    "vif_series8 = pd.Series([variance_inflation_factor(X_train8.values,i) for i in range(X_train8.shape[1])],index=X_train8.columns)\n",
    "print('Series before feature selection: \\n\\n{}\\n'.format(vif_series8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     col  Adj_rsq_after_dropping_col  Test RMSE\n",
      "0              New_Price                    0.869230   2.071856\n",
      "2            Company_BMW                    0.868284   2.071840\n",
      "3  Company_Mercedes-Benz                    0.868150   2.074931\n",
      "1           Company_Audi                    0.867682   2.074001\n"
     ]
    }
   ],
   "source": [
    "high_vif_columns = [\"New_Price\",\"Company_Audi\",\"Company_BMW\",\"Company_Mercedes-Benz\"]\n",
    "\n",
    "treating_multicollinearity(high_vif_columns, X_train1, X_test1, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series before feature selection: \n",
      "\n",
      "Mileage                     3.099354\n",
      "Power                       4.859589\n",
      "Kilometers_Driven_log       1.956107\n",
      "Location_Bangalore          2.493496\n",
      "Location_Chennai            2.965069\n",
      "Location_Coimbatore         3.508581\n",
      "Location_Delhi              3.043266\n",
      "Location_Hyderabad          3.698527\n",
      "Location_Jaipur             2.734947\n",
      "Location_Kochi              3.617011\n",
      "Location_Kolkata            3.124442\n",
      "Location_Mumbai             3.931181\n",
      "Location_Pune               3.377298\n",
      "Year_By_1995-2000           1.043412\n",
      "Year_By_2001-2005           1.341013\n",
      "Year_By_2006-2010           2.388547\n",
      "Year_By_2011-2015           2.024012\n",
      "Fuel_Type_Diesel            2.291246\n",
      "Fuel_Type_Electric          1.015281\n",
      "Fuel_Type_LPG               1.011963\n",
      "Transmission_Automatic      2.345755\n",
      "Owner_Type_Second           1.174172\n",
      "Owner_Type_Third            1.103596\n",
      "Company_Audi                1.862790\n",
      "Company_BMW                 2.184347\n",
      "Company_Bentley             1.028090\n",
      "Company_Fiat                1.032462\n",
      "Company_Ford                1.223194\n",
      "Company_Honda               1.423657\n",
      "Company_Isuzu               1.013309\n",
      "Company_Jaguar              1.219807\n",
      "Company_Jeep                1.058149\n",
      "Company_Land                1.247639\n",
      "Company_Mahindra            1.798902\n",
      "Company_Maruti              1.700671\n",
      "Company_Mercedes-Benz       2.097535\n",
      "Company_Mini                1.182205\n",
      "Company_Mitsubishi          1.342563\n",
      "Company_Nissan              1.076855\n",
      "Company_Porsche             1.110614\n",
      "Company_Renault             1.105734\n",
      "Company_Skoda               1.193901\n",
      "Company_Toyota              1.839412\n",
      "Company_Volkswagen          1.216135\n",
      "Company_Volvo               1.093448\n",
      "Seats_4.0                   1.497906\n",
      "Seats_5.0                   2.735059\n",
      "Seats_6.0                   1.333033\n",
      "Seats_8.0                   1.279642\n",
      "Seats_9.0                   1.010721\n",
      "Seats_10.0                  1.019308\n",
      "constant                  650.894586\n",
      "dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Dropping New_Price would have the maximum impact on predictive power of the model (amongst the variables being considered)\n",
    "# We'll drop New_Price and check the vif again\n",
    "\n",
    "X_train9 = X_train8.drop('New_Price', axis=1)\n",
    "vif_series9 = pd.Series([variance_inflation_factor(X_train9.values,i) for i in range(X_train9.shape[1])],index=X_train9.columns)\n",
    "print('Series before feature selection: \\n\\n{}\\n'.format(vif_series9))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations\n",
    "\n",
    "- VIF scrore for all the variables has come down to quite a good limit (all under 5). We can say that there is no multicollinearity now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Performance\n",
      "\n",
      "       RMSE      MAE       MAPE       R^2  Adjusted R^2\n",
      "0  2.052047  1.49922  27.664243  0.867805      0.866405\n"
     ]
    }
   ],
   "source": [
    "regression_model1 = LinearRegression(fit_intercept=False)\n",
    "regression_model1.fit(X_train9,y_train)\n",
    "\n",
    "print('Training Performance\\n')\n",
    "print(model_perf(regression_model1, X_train9, y_train))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations\n",
    "\n",
    "* Earlier R-squared was 0.871, now it is 0.867.\n",
    "* Now Adjusted R-squared is 0.866, Our model is able to explain 86.4% of variance that shows model is good.\n",
    "* The Adjusted-R squared in initial model (regression_model) it was 86.9% (Where we considered all the variables) this shows that the variables we dropped affected the model by a small number.     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now we'll check rest of the assumptions on new model - regression_model1**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Mean of residuals should be 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0408813981328721e-13"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mean Residual should be zero\n",
    "\n",
    "# predicted values\n",
    "fitted = regression_model1.predict(X_train9)\n",
    "residual = fitted - y_train.values\n",
    "\n",
    "np.mean(residual)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Mean of residual is 1.04, this is close to 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Test of Linearity\n",
    "\n",
    "#### Plot fitted values vs Residuals, if they don’t follow any pattern, then the model is linear otherwise model is showing signs of non-linearity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "sns.residplot(fitted,residual,color=\"green\",lowess=True)\n",
    "plt.xlabel(\"Fitted Values\")\n",
    "plt.ylabel(\"Residual\")\n",
    "plt.title(\"Residual PLOT\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Since there is no pattern in residual plot, model is linear."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Test of Homoscedasticity\n",
    "\n",
    "#### Since the variance is unequal for the residuals across the regression line, the data is heteroscedastic.\n",
    "- As seen above, there is no pattern in residual plot. Hence, this assumption is satisfied."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Test for Normality "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(residual)\n",
    "plt.title('Normality of residuals')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "\n",
    "- Since the residuals are normally distributed, the assumption is satisfied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab\n",
    "import scipy.stats as stats\n",
    "stats.probplot(residual.flatten(),dist=\"norm\",plot=pylab)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations:\n",
    "- As per QQ plot they are approximately normal. So, this assumption is also satisfied."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Mileage', 'Power', 'Kilometers_Driven_log', 'Location_Bangalore',\n",
       "       'Location_Chennai', 'Location_Coimbatore', 'Location_Delhi',\n",
       "       'Location_Hyderabad', 'Location_Jaipur', 'Location_Kochi',\n",
       "       'Location_Kolkata', 'Location_Mumbai', 'Location_Pune',\n",
       "       'Year_By_1995-2000', 'Year_By_2001-2005', 'Year_By_2006-2010',\n",
       "       'Year_By_2011-2015', 'Fuel_Type_Diesel', 'Fuel_Type_Electric',\n",
       "       'Fuel_Type_LPG', 'Transmission_Automatic', 'Owner_Type_Second',\n",
       "       'Owner_Type_Third', 'Company_Audi', 'Company_BMW', 'Company_Bentley',\n",
       "       'Company_Fiat', 'Company_Ford', 'Company_Honda', 'Company_Isuzu',\n",
       "       'Company_Jaguar', 'Company_Jeep', 'Company_Land', 'Company_Mahindra',\n",
       "       'Company_Maruti', 'Company_Mercedes-Benz', 'Company_Mini',\n",
       "       'Company_Mitsubishi', 'Company_Nissan', 'Company_Porsche',\n",
       "       'Company_Renault', 'Company_Skoda', 'Company_Toyota',\n",
       "       'Company_Volkswagen', 'Company_Volvo', 'Seats_4.0', 'Seats_5.0',\n",
       "       'Seats_6.0', 'Seats_8.0', 'Seats_9.0', 'Seats_10.0', 'constant'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train9.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Selecting columns from test data that we used to create our final model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\soumiya\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# first we add the constant to test input variables\n",
    "X_test['constant'] = 1\n",
    "\n",
    "X_test_final = X_test[X_train9.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>Mileage</th>\n",
       "      <th>Power</th>\n",
       "      <th>Kilometers_Driven_log</th>\n",
       "      <th>Location_Bangalore</th>\n",
       "      <th>Location_Chennai</th>\n",
       "      <th>Location_Coimbatore</th>\n",
       "      <th>Location_Delhi</th>\n",
       "      <th>Location_Hyderabad</th>\n",
       "      <th>Location_Jaipur</th>\n",
       "      <th>Location_Kochi</th>\n",
       "      <th>Location_Kolkata</th>\n",
       "      <th>Location_Mumbai</th>\n",
       "      <th>Location_Pune</th>\n",
       "      <th>Year_By_1995-2000</th>\n",
       "      <th>Year_By_2001-2005</th>\n",
       "      <th>Year_By_2006-2010</th>\n",
       "      <th>Year_By_2011-2015</th>\n",
       "      <th>Fuel_Type_Diesel</th>\n",
       "      <th>Fuel_Type_Electric</th>\n",
       "      <th>Fuel_Type_LPG</th>\n",
       "      <th>Transmission_Automatic</th>\n",
       "      <th>Owner_Type_Second</th>\n",
       "      <th>Owner_Type_Third</th>\n",
       "      <th>Company_Audi</th>\n",
       "      <th>Company_BMW</th>\n",
       "      <th>Company_Bentley</th>\n",
       "      <th>Company_Fiat</th>\n",
       "      <th>Company_Ford</th>\n",
       "      <th>Company_Honda</th>\n",
       "      <th>Company_Isuzu</th>\n",
       "      <th>Company_Jaguar</th>\n",
       "      <th>Company_Jeep</th>\n",
       "      <th>Company_Land</th>\n",
       "      <th>Company_Mahindra</th>\n",
       "      <th>Company_Maruti</th>\n",
       "      <th>Company_Mercedes-Benz</th>\n",
       "      <th>Company_Mini</th>\n",
       "      <th>Company_Mitsubishi</th>\n",
       "      <th>Company_Nissan</th>\n",
       "      <th>Company_Porsche</th>\n",
       "      <th>Company_Renault</th>\n",
       "      <th>Company_Skoda</th>\n",
       "      <th>Company_Toyota</th>\n",
       "      <th>Company_Volkswagen</th>\n",
       "      <th>Company_Volvo</th>\n",
       "      <th>Seats_4.0</th>\n",
       "      <th>Seats_5.0</th>\n",
       "      <th>Seats_6.0</th>\n",
       "      <th>Seats_8.0</th>\n",
       "      <th>Seats_9.0</th>\n",
       "      <th>Seats_10.0</th>\n",
       "      <th>constant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5078</th>\n",
       "      <td>24.3</td>\n",
       "      <td>88.50</td>\n",
       "      <td>9.989206</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5948</th>\n",
       "      <td>15.6</td>\n",
       "      <td>106.00</td>\n",
       "      <td>11.418626</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3008</th>\n",
       "      <td>26.6</td>\n",
       "      <td>58.16</td>\n",
       "      <td>10.789340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2538</th>\n",
       "      <td>18.9</td>\n",
       "      <td>67.10</td>\n",
       "      <td>11.884496</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2782</th>\n",
       "      <td>23.0</td>\n",
       "      <td>90.00</td>\n",
       "      <td>11.319959</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Mileage   Power  Kilometers_Driven_log  Location_Bangalore  \\\n",
       "5078     24.3   88.50               9.989206                   0   \n",
       "5948     15.6  106.00              11.418626                   0   \n",
       "3008     26.6   58.16              10.789340                   0   \n",
       "2538     18.9   67.10              11.884496                   0   \n",
       "2782     23.0   90.00              11.319959                   0   \n",
       "\n",
       "      Location_Chennai  Location_Coimbatore  Location_Delhi  \\\n",
       "5078                 0                    1               0   \n",
       "5948                 0                    0               0   \n",
       "3008                 0                    0               1   \n",
       "2538                 0                    0               0   \n",
       "2782                 0                    0               0   \n",
       "\n",
       "      Location_Hyderabad  Location_Jaipur  Location_Kochi  Location_Kolkata  \\\n",
       "5078                   0                0               0                 0   \n",
       "5948                   1                0               0                 0   \n",
       "3008                   0                0               0                 0   \n",
       "2538                   0                1               0                 0   \n",
       "2782                   0                1               0                 0   \n",
       "\n",
       "      Location_Mumbai  Location_Pune  Year_By_1995-2000  Year_By_2001-2005  \\\n",
       "5078                0              0                  0                  0   \n",
       "5948                0              0                  0                  0   \n",
       "3008                0              0                  0                  0   \n",
       "2538                0              0                  0                  0   \n",
       "2782                0              0                  0                  0   \n",
       "\n",
       "      Year_By_2006-2010  Year_By_2011-2015  Fuel_Type_Diesel  \\\n",
       "5078                  0                  0                 1   \n",
       "5948                  1                  0                 1   \n",
       "3008                  0                  1                 0   \n",
       "2538                  1                  0                 0   \n",
       "2782                  0                  1                 1   \n",
       "\n",
       "      Fuel_Type_Electric  Fuel_Type_LPG  Transmission_Automatic  \\\n",
       "5078                   0              0                       0   \n",
       "5948                   0              0                       1   \n",
       "3008                   0              0                       0   \n",
       "2538                   0              0                       0   \n",
       "2782                   0              0                       0   \n",
       "\n",
       "      Owner_Type_Second  Owner_Type_Third  Company_Audi  Company_BMW  \\\n",
       "5078                  0                 0             0            0   \n",
       "5948                  1                 0             0            0   \n",
       "3008                  1                 0             0            0   \n",
       "2538                  0                 0             0            0   \n",
       "2782                  0                 0             0            0   \n",
       "\n",
       "      Company_Bentley  Company_Fiat  Company_Ford  Company_Honda  \\\n",
       "5078                0             0             0              0   \n",
       "5948                0             0             0              0   \n",
       "3008                0             0             0              0   \n",
       "2538                0             0             0              0   \n",
       "2782                0             0             0              0   \n",
       "\n",
       "      Company_Isuzu  Company_Jaguar  Company_Jeep  Company_Land  \\\n",
       "5078              0               0             0             0   \n",
       "5948              0               0             0             0   \n",
       "3008              0               0             0             0   \n",
       "2538              0               0             0             0   \n",
       "2782              0               0             0             0   \n",
       "\n",
       "      Company_Mahindra  Company_Maruti  Company_Mercedes-Benz  Company_Mini  \\\n",
       "5078                 0               1                      0             0   \n",
       "5948                 0               0                      0             0   \n",
       "3008                 0               1                      0             0   \n",
       "2538                 0               1                      0             0   \n",
       "2782                 0               0                      0             0   \n",
       "\n",
       "      Company_Mitsubishi  Company_Nissan  Company_Porsche  Company_Renault  \\\n",
       "5078                   0               0                0                0   \n",
       "5948                   0               0                0                0   \n",
       "3008                   0               0                0                0   \n",
       "2538                   0               0                0                0   \n",
       "2782                   0               0                0                0   \n",
       "\n",
       "      Company_Skoda  Company_Toyota  Company_Volkswagen  Company_Volvo  \\\n",
       "5078              0               0                   0              0   \n",
       "5948              1               0                   0              0   \n",
       "3008              0               0                   0              0   \n",
       "2538              0               0                   0              0   \n",
       "2782              0               0                   0              0   \n",
       "\n",
       "      Seats_4.0  Seats_5.0  Seats_6.0  Seats_8.0  Seats_9.0  Seats_10.0  \\\n",
       "5078          0          1          0          0          0           0   \n",
       "5948          0          1          0          0          0           0   \n",
       "3008          0          1          0          0          0           0   \n",
       "2538          0          1          0          0          0           0   \n",
       "2782          0          1          0          0          0           0   \n",
       "\n",
       "      constant  \n",
       "5078         1  \n",
       "5948         1  \n",
       "3008         1  \n",
       "2538         1  \n",
       "2782         1  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = regression_model1.predict(X_test_final)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checking the performance on train and test**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Performance\n",
      "\n",
      "       RMSE      MAE       MAPE       R^2  Adjusted R^2\n",
      "0  2.052047  1.49922  27.664243  0.867805      0.866405\n"
     ]
    }
   ],
   "source": [
    "print('Training Performance\\n')\n",
    "print(model_perf(regression_model1, X_train9, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Performance\n",
      "\n",
      "       RMSE       MAE       MAPE       R^2  Adjusted R^2\n",
      "0  2.083253  1.513139  26.990932  0.861828      0.858365\n"
     ]
    }
   ],
   "source": [
    "print('Test Performance\\n')\n",
    "print(model_perf(regression_model1, X_test_final, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Now we can finally see that we have low test and train error, also both the errors are comparable, so our model is not suffering from overfitting.**\n",
    "\n",
    "* **Hence we can conclude the model \"regression_model1\" is good for prediction as well as inference purpose.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        Coefficients\n",
      "Mileage                    -0.078061\n",
      "Power                       0.038404\n",
      "Kilometers_Driven_log      -0.795770\n",
      "Location_Bangalore          0.817353\n",
      "Location_Chennai            0.004260\n",
      "Location_Coimbatore         0.666855\n",
      "Location_Delhi             -0.485620\n",
      "Location_Hyderabad          0.417097\n",
      "Location_Jaipur             0.062855\n",
      "Location_Kochi              0.257744\n",
      "Location_Kolkata           -1.151048\n",
      "Location_Mumbai            -0.434762\n",
      "Location_Pune              -0.108928\n",
      "Year_By_1995-2000          -4.975660\n",
      "Year_By_2001-2005          -4.181339\n",
      "Year_By_2006-2010          -3.352069\n",
      "Year_By_2011-2015          -1.389113\n",
      "Fuel_Type_Diesel            1.527054\n",
      "Fuel_Type_Electric          8.703585\n",
      "Fuel_Type_LPG               1.028997\n",
      "Transmission_Automatic      0.885006\n",
      "Owner_Type_Second          -0.496652\n",
      "Owner_Type_Third           -0.823520\n",
      "Company_Audi                7.381129\n",
      "Company_BMW                 6.366283\n",
      "Company_Bentley             9.510791\n",
      "Company_Fiat               -1.014845\n",
      "Company_Ford                0.363309\n",
      "Company_Honda               0.143687\n",
      "Company_Isuzu               7.372290\n",
      "Company_Jaguar              7.038708\n",
      "Company_Jeep                7.535717\n",
      "Company_Land                7.938055\n",
      "Company_Mahindra           -0.286335\n",
      "Company_Maruti              0.361476\n",
      "Company_Mercedes-Benz       6.549175\n",
      "Company_Mini                9.868080\n",
      "Company_Mitsubishi          1.602180\n",
      "Company_Nissan             -0.250393\n",
      "Company_Porsche             6.762522\n",
      "Company_Renault             0.157203\n",
      "Company_Skoda               0.867508\n",
      "Company_Toyota              3.497756\n",
      "Company_Volkswagen         -0.135455\n",
      "Company_Volvo               6.683322\n",
      "Seats_4.0                  -1.068200\n",
      "Seats_5.0                  -1.268296\n",
      "Seats_6.0                  -0.700422\n",
      "Seats_8.0                  -0.622181\n",
      "Seats_9.0                  -3.105555\n",
      "Seats_10.0                  0.115148\n",
      "constant                   13.413441\n",
      "\n",
      "\n",
      "Training Performance\n",
      "\n",
      "       RMSE      MAE       MAPE       R^2  Adjusted R^2\n",
      "0  2.052047  1.49922  27.664243  0.867805      0.866405\n"
     ]
    }
   ],
   "source": [
    "regression_model1 = LinearRegression(fit_intercept=False)\n",
    "regression_model1.fit(X_train9,y_train)\n",
    "\n",
    "# let us check the coefficients and intercept of the model\n",
    "\n",
    "coef_df = pd.DataFrame(regression_model1.coef_.flatten(), \\\n",
    "                       index=X_train9.columns.tolist(), columns=['Coefficients'])\n",
    "print(coef_df)\n",
    "\n",
    "# model performance\n",
    "print('\\n\\nTraining Performance\\n')\n",
    "print(model_perf(regression_model1, X_train9, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observations\n",
    "Now this is our final model which follows all the assumptions and this can be used for interpretations\n",
    "\n",
    "1. Mileage, Kilometers_Driven, car's age and level of ownership (second and above) affects the used car's price negatively.\n",
    "2. Luxury car brands like BMW, Audi, Bentley, Porche etc contributes positively to the used car price.\n",
    "3. Electric Fuel type is also contributing positively to used car price.\n",
    "4. Automatic used cars can sell for higher price than manual transmission.\n",
    "5. Selling used cars in locations like Bangalore and Hyderabad, will increase the value of used car."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forward Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Forward Feature selection\n",
    "X_train_new = X_train9.drop('constant', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  51 out of  51 | elapsed:    1.9s finished\n",
      "\n",
      "[2021-05-06 22:21:35] Features: 1/51 -- score: 0.6527470337087034[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    2.1s finished\n",
      "\n",
      "[2021-05-06 22:21:37] Features: 2/51 -- score: 0.6957113557834214[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    1.8s finished\n",
      "\n",
      "[2021-05-06 22:21:39] Features: 3/51 -- score: 0.7145933079437097[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    1.7s finished\n",
      "\n",
      "[2021-05-06 22:21:41] Features: 4/51 -- score: 0.7362722144530099[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    1.8s finished\n",
      "\n",
      "[2021-05-06 22:21:43] Features: 5/51 -- score: 0.7557624982937133[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    1.8s finished\n",
      "\n",
      "[2021-05-06 22:21:44] Features: 6/51 -- score: 0.7682409140073366[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    1.6s finished\n",
      "\n",
      "[2021-05-06 22:21:46] Features: 7/51 -- score: 0.7764585420001691[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    1.7s finished\n",
      "\n",
      "[2021-05-06 22:21:48] Features: 8/51 -- score: 0.7861134235138805[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    1.7s finished\n",
      "\n",
      "[2021-05-06 22:21:50] Features: 9/51 -- score: 0.7937538870669509[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    1.6s finished\n",
      "\n",
      "[2021-05-06 22:21:52] Features: 10/51 -- score: 0.8047212818580162[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    1.8s finished\n",
      "\n",
      "[2021-05-06 22:21:53] Features: 11/51 -- score: 0.8142690434177663[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    1.9s finished\n",
      "\n",
      "[2021-05-06 22:21:55] Features: 12/51 -- score: 0.8207116921152778[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    1.9s finished\n",
      "\n",
      "[2021-05-06 22:21:57] Features: 13/51 -- score: 0.8291912543065667[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    1.9s finished\n",
      "\n",
      "[2021-05-06 22:21:59] Features: 14/51 -- score: 0.8345207026674238[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    1.9s finished\n",
      "\n",
      "[2021-05-06 22:22:01] Features: 15/51 -- score: 0.8382659287996587[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    1.8s finished\n",
      "\n",
      "[2021-05-06 22:22:03] Features: 16/51 -- score: 0.8416227714623765[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    1.9s finished\n",
      "\n",
      "[2021-05-06 22:22:05] Features: 17/51 -- score: 0.8453154250359287[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    2.2s finished\n",
      "\n",
      "[2021-05-06 22:22:08] Features: 18/51 -- score: 0.8490376638398625[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    2.0s finished\n",
      "\n",
      "[2021-05-06 22:22:10] Features: 19/51 -- score: 0.8512592262057179[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    1.8s finished\n",
      "\n",
      "[2021-05-06 22:22:12] Features: 20/51 -- score: 0.8526252994319288[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    2.2s finished\n",
      "\n",
      "[2021-05-06 22:22:14] Features: 21/51 -- score: 0.8538935344130978[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    2.3s finished\n",
      "\n",
      "[2021-05-06 22:22:16] Features: 22/51 -- score: 0.8553508466427934[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    1.9s finished\n",
      "\n",
      "[2021-05-06 22:22:18] Features: 23/51 -- score: 0.8564743331141067[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    2.3s finished\n",
      "\n",
      "[2021-05-06 22:22:21] Features: 24/51 -- score: 0.8576389934922585[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    2.2s finished\n",
      "\n",
      "[2021-05-06 22:22:23] Features: 25/51 -- score: 0.8585018023491469[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    2.1s finished\n",
      "\n",
      "[2021-05-06 22:22:25] Features: 26/51 -- score: 0.8590902737029855[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    1.9s finished\n",
      "\n",
      "[2021-05-06 22:22:27] Features: 27/51 -- score: 0.8596271425102614[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    2.0s finished\n",
      "\n",
      "[2021-05-06 22:22:29] Features: 28/51 -- score: 0.8601303748061554[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    1.5s finished\n",
      "\n",
      "[2021-05-06 22:22:31] Features: 29/51 -- score: 0.8606423062668483[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    1.6s finished\n",
      "\n",
      "[2021-05-06 22:22:33] Features: 30/51 -- score: 0.861045009028319[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    1.5s finished\n",
      "\n",
      "[2021-05-06 22:22:34] Features: 31/51 -- score: 0.8613591162740069[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    1.4s finished\n",
      "\n",
      "[2021-05-06 22:22:36] Features: 32/51 -- score: 0.8615379712670796[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    1.7s finished\n",
      "\n",
      "[2021-05-06 22:22:38] Features: 33/51 -- score: 0.8616653810377111[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    1.6s finished\n",
      "\n",
      "[2021-05-06 22:22:39] Features: 34/51 -- score: 0.8618246094625747[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    1.5s finished\n",
      "\n",
      "[2021-05-06 22:22:41] Features: 35/51 -- score: 0.8618905788132031[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    1.3s finished\n",
      "\n",
      "[2021-05-06 22:22:42] Features: 36/51 -- score: 0.861988590748042[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    1.5s finished\n",
      "\n",
      "[2021-05-06 22:22:44] Features: 37/51 -- score: 0.8620575339538336[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    1.3s finished\n",
      "\n",
      "[2021-05-06 22:22:45] Features: 38/51 -- score: 0.8621190654468656[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    1.3s finished\n",
      "\n",
      "[2021-05-06 22:22:47] Features: 39/51 -- score: 0.8621312076128171[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    1.3s finished\n",
      "\n",
      "[2021-05-06 22:22:48] Features: 40/51 -- score: 0.8621473650773737[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    1.1s finished\n",
      "\n",
      "[2021-05-06 22:22:49] Features: 41/51 -- score: 0.8621557238315398[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.9s finished\n",
      "\n",
      "[2021-05-06 22:22:50] Features: 42/51 -- score: 0.8621607752789361[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.8s finished\n",
      "\n",
      "[2021-05-06 22:22:51] Features: 43/51 -- score: 0.8621875016975185[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.9s finished\n",
      "\n",
      "[2021-05-06 22:22:52] Features: 44/51 -- score: 0.8621910507452013[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.7s finished\n",
      "\n",
      "[2021-05-06 22:22:53] Features: 45/51 -- score: 0.86218719007198[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.6s finished\n",
      "\n",
      "[2021-05-06 22:22:54] Features: 46/51 -- score: 0.8621464445518668[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.5s finished\n",
      "\n",
      "[2021-05-06 22:22:54] Features: 47/51 -- score: 0.8620829144748449[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.4s finished\n",
      "\n",
      "[2021-05-06 22:22:55] Features: 48/51 -- score: 0.862100237533563[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.3s finished\n",
      "\n",
      "[2021-05-06 22:22:55] Features: 49/51 -- score: 0.8620162753914663[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.2s finished\n",
      "\n",
      "[2021-05-06 22:22:56] Features: 50/51 -- score: 0.8619823486816773[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s finished\n",
      "\n",
      "[2021-05-06 22:22:56] Features: 51/51 -- score: 0.861705754662428"
     ]
    }
   ],
   "source": [
    "from mlxtend.feature_selection import SequentialFeatureSelector as sfs\n",
    "\n",
    "reg = LinearRegression()\n",
    "\n",
    "# Build step forward feature selection\n",
    "sfs1 = sfs(reg,k_features = X_train_new.shape[1], forward=True,   # k_features denotes \"Number of features to select\"\n",
    "           floating=False, scoring= 'r2',\n",
    "           verbose=2, cv=5)\n",
    "\n",
    "# Perform SFFS\n",
    "sfs1 = sfs1.fit(X_train_new, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We can see that Adjusted-R square starts decreasing after addition of 45th feature, so we will proceed only with best 44 features**\n",
    "\n",
    "* Now we'll change k_features to 44."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  51 out of  51 | elapsed:    1.8s finished\n",
      "\n",
      "[2021-05-06 22:22:58] Features: 1/44 -- score: 0.6527470337087034[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    1.8s finished\n",
      "\n",
      "[2021-05-06 22:23:00] Features: 2/44 -- score: 0.6957113557834214[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    2.0s finished\n",
      "\n",
      "[2021-05-06 22:23:02] Features: 3/44 -- score: 0.7145933079437097[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    2.0s finished\n",
      "\n",
      "[2021-05-06 22:23:04] Features: 4/44 -- score: 0.7362722144530099[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    1.9s finished\n",
      "\n",
      "[2021-05-06 22:23:06] Features: 5/44 -- score: 0.7557624982937133[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    1.8s finished\n",
      "\n",
      "[2021-05-06 22:23:08] Features: 6/44 -- score: 0.7682409140073366[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    1.8s finished\n",
      "\n",
      "[2021-05-06 22:23:10] Features: 7/44 -- score: 0.7764585420001691[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    1.7s finished\n",
      "\n",
      "[2021-05-06 22:23:11] Features: 8/44 -- score: 0.7861134235138805[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    1.8s finished\n",
      "\n",
      "[2021-05-06 22:23:13] Features: 9/44 -- score: 0.7937538870669509[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    1.7s finished\n",
      "\n",
      "[2021-05-06 22:23:15] Features: 10/44 -- score: 0.8047212818580162[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    1.8s finished\n",
      "\n",
      "[2021-05-06 22:23:17] Features: 11/44 -- score: 0.8142690434177663[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    1.6s finished\n",
      "\n",
      "[2021-05-06 22:23:19] Features: 12/44 -- score: 0.8207116921152778[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    1.7s finished\n",
      "\n",
      "[2021-05-06 22:23:21] Features: 13/44 -- score: 0.8291912543065667[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    1.8s finished\n",
      "\n",
      "[2021-05-06 22:23:22] Features: 14/44 -- score: 0.8345207026674238[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    1.7s finished\n",
      "\n",
      "[2021-05-06 22:23:24] Features: 15/44 -- score: 0.8382659287996587[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    1.9s finished\n",
      "\n",
      "[2021-05-06 22:23:26] Features: 16/44 -- score: 0.8416227714623765[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    1.8s finished\n",
      "\n",
      "[2021-05-06 22:23:28] Features: 17/44 -- score: 0.8453154250359287[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    1.8s finished\n",
      "\n",
      "[2021-05-06 22:23:30] Features: 18/44 -- score: 0.8490376638398625[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    1.9s finished\n",
      "\n",
      "[2021-05-06 22:23:32] Features: 19/44 -- score: 0.8512592262057179[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    1.9s finished\n",
      "\n",
      "[2021-05-06 22:23:34] Features: 20/44 -- score: 0.8526252994319288[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    2.2s finished\n",
      "\n",
      "[2021-05-06 22:23:36] Features: 21/44 -- score: 0.8538935344130978[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    2.0s finished\n",
      "\n",
      "[2021-05-06 22:23:38] Features: 22/44 -- score: 0.8553508466427934[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    2.0s finished\n",
      "\n",
      "[2021-05-06 22:23:40] Features: 23/44 -- score: 0.8564743331141067[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    1.9s finished\n",
      "\n",
      "[2021-05-06 22:23:43] Features: 24/44 -- score: 0.8576389934922585[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    2.2s finished\n",
      "\n",
      "[2021-05-06 22:23:45] Features: 25/44 -- score: 0.8585018023491469[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    2.3s finished\n",
      "\n",
      "[2021-05-06 22:23:47] Features: 26/44 -- score: 0.8590902737029855[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    1.6s finished\n",
      "\n",
      "[2021-05-06 22:23:49] Features: 27/44 -- score: 0.8596271425102614[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    1.6s finished\n",
      "\n",
      "[2021-05-06 22:23:51] Features: 28/44 -- score: 0.8601303748061554[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    1.6s finished\n",
      "\n",
      "[2021-05-06 22:23:52] Features: 29/44 -- score: 0.8606423062668483[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    1.6s finished\n",
      "\n",
      "[2021-05-06 22:23:54] Features: 30/44 -- score: 0.861045009028319[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    1.7s finished\n",
      "\n",
      "[2021-05-06 22:23:56] Features: 31/44 -- score: 0.8613591162740069[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    1.6s finished\n",
      "\n",
      "[2021-05-06 22:23:57] Features: 32/44 -- score: 0.8615379712670796[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    1.8s finished\n",
      "\n",
      "[2021-05-06 22:23:59] Features: 33/44 -- score: 0.8616653810377111[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    1.8s finished\n",
      "\n",
      "[2021-05-06 22:24:01] Features: 34/44 -- score: 0.8618246094625747[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    1.6s finished\n",
      "\n",
      "[2021-05-06 22:24:03] Features: 35/44 -- score: 0.8618905788132031[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    1.7s finished\n",
      "\n",
      "[2021-05-06 22:24:05] Features: 36/44 -- score: 0.861988590748042[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    1.4s finished\n",
      "\n",
      "[2021-05-06 22:24:06] Features: 37/44 -- score: 0.8620575339538336[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    1.4s finished\n",
      "\n",
      "[2021-05-06 22:24:08] Features: 38/44 -- score: 0.8621190654468656[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    1.3s finished\n",
      "\n",
      "[2021-05-06 22:24:09] Features: 39/44 -- score: 0.8621312076128171[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    1.1s finished\n",
      "\n",
      "[2021-05-06 22:24:11] Features: 40/44 -- score: 0.8621473650773737[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    1.2s finished\n",
      "\n",
      "[2021-05-06 22:24:12] Features: 41/44 -- score: 0.8621557238315398[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    1.1s finished\n",
      "\n",
      "[2021-05-06 22:24:13] Features: 42/44 -- score: 0.8621607752789361[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    1.0s finished\n",
      "\n",
      "[2021-05-06 22:24:14] Features: 43/44 -- score: 0.8621875016975185[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.9s finished\n",
      "\n",
      "[2021-05-06 22:24:15] Features: 44/44 -- score: 0.8621910507452013"
     ]
    }
   ],
   "source": [
    "reg = LinearRegression()\n",
    "\n",
    "# # Build step forward feature selection\n",
    "sfs1 = sfs(reg, k_features = 44, forward=True,\n",
    "           floating=False, scoring='r2',\n",
    "           verbose=2, cv=5)\n",
    "\n",
    "# Perform SFFS\n",
    "sfs1 = sfs1.fit(X_train_new, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 41, 42, 43, 44, 45, 46, 48, 49]\n"
     ]
    }
   ],
   "source": [
    "# Now Which features are important?\n",
    "feat_cols = list(sfs1.k_feature_idx_)\n",
    "print(feat_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Mileage', 'Power', 'Kilometers_Driven_log', 'Location_Bangalore',\n",
       "       'Location_Coimbatore', 'Location_Delhi', 'Location_Hyderabad',\n",
       "       'Location_Kolkata', 'Location_Mumbai', 'Location_Pune',\n",
       "       'Year_By_1995-2000', 'Year_By_2001-2005', 'Year_By_2006-2010',\n",
       "       'Year_By_2011-2015', 'Fuel_Type_Diesel', 'Fuel_Type_Electric',\n",
       "       'Fuel_Type_LPG', 'Transmission_Automatic', 'Owner_Type_Second',\n",
       "       'Owner_Type_Third', 'Company_Audi', 'Company_BMW', 'Company_Bentley',\n",
       "       'Company_Fiat', 'Company_Ford', 'Company_Honda', 'Company_Isuzu',\n",
       "       'Company_Jaguar', 'Company_Jeep', 'Company_Land', 'Company_Mahindra',\n",
       "       'Company_Maruti', 'Company_Mercedes-Benz', 'Company_Mini',\n",
       "       'Company_Nissan', 'Company_Porsche', 'Company_Skoda', 'Company_Toyota',\n",
       "       'Company_Volkswagen', 'Company_Volvo', 'Seats_4.0', 'Seats_5.0',\n",
       "       'Seats_8.0', 'Seats_9.0'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train9.columns[feat_cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we will fit a sklearn model using these features only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_final = X_train9[X_train9.columns[feat_cols]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Mileage', 'Engine', 'Power', 'New_Price', 'Kilometers_Driven_log',\n",
       "       'Location_Bangalore', 'Location_Chennai', 'Location_Coimbatore',\n",
       "       'Location_Delhi', 'Location_Hyderabad', 'Location_Jaipur',\n",
       "       'Location_Kochi', 'Location_Kolkata', 'Location_Mumbai',\n",
       "       'Location_Pune', 'Year_By_1995-2000', 'Year_By_2001-2005',\n",
       "       'Year_By_2006-2010', 'Year_By_2011-2015', 'Fuel_Type_Diesel',\n",
       "       'Fuel_Type_Electric', 'Fuel_Type_LPG', 'Fuel_Type_Petrol',\n",
       "       'Transmission_Automatic', 'Owner_Type_First', 'Owner_Type_Second',\n",
       "       'Owner_Type_Third', 'Company_Audi', 'Company_BMW', 'Company_Bentley',\n",
       "       'Company_Fiat', 'Company_Ford', 'Company_Honda', 'Company_Hyundai',\n",
       "       'Company_Isuzu', 'Company_Jaguar', 'Company_Jeep', 'Company_Land',\n",
       "       'Company_Mahindra', 'Company_Maruti', 'Company_Mercedes-Benz',\n",
       "       'Company_Mini', 'Company_Mitsubishi', 'Company_Nissan',\n",
       "       'Company_Porsche', 'Company_Renault', 'Company_Skoda', 'Company_Tata',\n",
       "       'Company_Toyota', 'Company_Volkswagen', 'Company_Volvo', 'Seats_2.0',\n",
       "       'Seats_4.0', 'Seats_5.0', 'Seats_6.0', 'Seats_7.0', 'Seats_8.0',\n",
       "       'Seats_9.0', 'Seats_10.0', 'constant'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating new x_test with the same variables that we selected for x_train\n",
    "X_test_final = X_test[X_train_final.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        Coefficients\n",
      "Mileage                    -0.080647\n",
      "Power                       0.038689\n",
      "Kilometers_Driven_log      -0.794424\n",
      "Location_Bangalore          0.716648\n",
      "Location_Coimbatore         0.546635\n",
      "Location_Delhi             -0.593567\n",
      "Location_Hyderabad          0.306739\n",
      "Location_Kolkata           -1.259662\n",
      "Location_Mumbai            -0.545215\n",
      "Location_Pune              -0.216201\n",
      "Year_By_1995-2000          -5.007706\n",
      "Year_By_2001-2005          -4.233928\n",
      "Year_By_2006-2010          -3.386604\n",
      "Year_By_2011-2015          -1.407664\n",
      "Fuel_Type_Diesel            1.535426\n",
      "Fuel_Type_Electric          8.696152\n",
      "Fuel_Type_LPG               1.019961\n",
      "Transmission_Automatic      0.896464\n",
      "Owner_Type_Second          -0.514397\n",
      "Owner_Type_Third           -0.845672\n",
      "Company_Audi                7.305450\n",
      "Company_BMW                 6.279078\n",
      "Company_Bentley             9.420107\n",
      "Company_Fiat               -1.049141\n",
      "Company_Ford                0.313461\n",
      "Company_Honda               0.109014\n",
      "Company_Isuzu               7.208099\n",
      "Company_Jaguar              6.972843\n",
      "Company_Jeep                7.473932\n",
      "Company_Land                7.861855\n",
      "Company_Mahindra           -0.432428\n",
      "Company_Maruti              0.334200\n",
      "Company_Mercedes-Benz       6.467534\n",
      "Company_Mini                9.809898\n",
      "Company_Nissan             -0.284264\n",
      "Company_Porsche             6.686578\n",
      "Company_Skoda               0.820657\n",
      "Company_Toyota              3.408032\n",
      "Company_Volkswagen         -0.179317\n",
      "Company_Volvo               6.601558\n",
      "Seats_4.0                  -1.095131\n",
      "Seats_5.0                  -1.308539\n",
      "Seats_8.0                  -0.622837\n",
      "Seats_9.0                  -3.174893\n",
      "Intercept                  13.620678\n",
      "\n",
      "\n",
      "Training Performance\n",
      "\n",
      "       RMSE       MAE       MAPE      R^2  Adjusted R^2\n",
      "0  2.055031  1.503913  27.807482  0.86742      0.866234\n"
     ]
    }
   ],
   "source": [
    "#Fitting linear model\n",
    "regression_model2 = LinearRegression()\n",
    "regression_model2.fit(X_train_final,y_train)\n",
    "\n",
    "# let us check the coefficients and intercept of the model\n",
    "\n",
    "coef_df = pd.DataFrame(np.append(regression_model2.coef_.flatten(), regression_model2.intercept_), \\\n",
    "                       index=X_train_final.columns.tolist()+['Intercept'], columns=['Coefficients'])\n",
    "print(coef_df)\n",
    "\n",
    "# model performance on train set\n",
    "print('\\n\\nTraining Performance\\n')\n",
    "print(model_perf(regression_model2, X_train_final, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Performance\n",
      "\n",
      "       RMSE       MAE       MAPE       R^2  Adjusted R^2\n",
      "0  2.087536  1.518559  27.094889  0.861259      0.858328\n"
     ]
    }
   ],
   "source": [
    "# model performance on test set\n",
    "print('Test Performance\\n')\n",
    "print(model_perf(regression_model2, X_test_final, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Both $R^2$ score and RMSE show that the model fitted is good, has no overfitting and can be used for making predictions.**\n",
    "* **We can observe here that the results from 'regression_model1' and 'regression_model2' models are approximately same.**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Actionable Insights & Recommendations\n",
    "**Conclude with the key takeaways for the business - what would your advice be to grow the business?**\n",
    "\n",
    "### Insights\n",
    "1. Mileage, Kilometers_Driven, car's age and level of ownership (second and above) affects the used car's price negatively.\n",
    "2. Luxury car brands like BMW, Audi, Bentley, Porche etc contributes positively to the used car price.\n",
    "3. Electric Fuel type is also contributing positively to used car price.\n",
    "4. Automatic used cars can sell for higher price than manual transmission.\n",
    "5. Selling used cars in locations like Bangalore and Hyderabad, will increase the value of used car.\n",
    "\n",
    "### Recommendations\n",
    "1. Business can dedicate department to the sale of used Luxury Car Brands like BMW, Audi and Bentley as the price is usually high for these vehicles.\n",
    "2. Electric cars is also another category which is preferred in used cars market, so Business can invest there.\n",
    "3. Other preferred brand in non Luxury segment is Maruti.\n",
    "4. Business can focus on used car markets in cities like Bangalore and Hyderabad where used cars can be valued more.\n",
    "\n",
    "**While making price decisions, business can look at following points to value the used car accurately:**\n",
    "\n",
    "- The higher the Power of the used car, it affects the price positively.\n",
    "- The more the car is driven, affects the price of used car negatively.\n",
    "- Electric Fuel Type increases the value of used car.\n",
    "- The older the car (Manufactured year) the value of the car will decrease.\n",
    "- The price of car when purchased new, does not impact the value of car when reselling.\n",
    "- If the car is Automatic transmission, this will impact positively to used car value.\n",
    "- Sale of used cars after Second ownership (Third and onwards) will negatively affect the value of used car.\n",
    "- In few locations like Bangalore and Coimbatore the used cars can sell for higher price than other cities.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}