{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "048bb88a-9e50-4f77-af52-cc0ca8d43079", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "c798af35-9b68-4648-8c39-b48b02c9e206", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | name | \n", "company | \n", "year | \n", "Price | \n", "kms_driven | \n", "fuel_type | \n", "
---|---|---|---|---|---|---|
0 | \n", "Hyundai Santro Xing XO eRLX Euro III | \n", "Hyundai | \n", "2007 | \n", "80,000 | \n", "45,000 kms | \n", "Petrol | \n", "
1 | \n", "Mahindra Jeep CL550 MDI | \n", "Mahindra | \n", "2006 | \n", "4,25,000 | \n", "40 kms | \n", "Diesel | \n", "
2 | \n", "Maruti Suzuki Alto 800 Vxi | \n", "Maruti | \n", "2018 | \n", "Ask For Price | \n", "22,000 kms | \n", "Petrol | \n", "
3 | \n", "Hyundai Grand i10 Magna 1.2 Kappa VTVT | \n", "Hyundai | \n", "2014 | \n", "3,25,000 | \n", "28,000 kms | \n", "Petrol | \n", "
4 | \n", "Ford EcoSport Titanium 1.5L TDCi | \n", "Ford | \n", "2014 | \n", "5,75,000 | \n", "36,000 kms | \n", "Diesel | \n", "
\n", " | name | \n", "company | \n", "year | \n", "Price | \n", "kms_driven | \n", "fuel_type | \n", "
---|---|---|---|---|---|---|
0 | \n", "Hyundai Santro Xing XO eRLX Euro III | \n", "Hyundai | \n", "2007 | \n", "80,000 | \n", "45,000 kms | \n", "Petrol | \n", "
1 | \n", "Mahindra Jeep CL550 MDI | \n", "Mahindra | \n", "2006 | \n", "4,25,000 | \n", "40 kms | \n", "Diesel | \n", "
2 | \n", "Maruti Suzuki Alto 800 Vxi | \n", "Maruti | \n", "2018 | \n", "Ask For Price | \n", "22,000 kms | \n", "Petrol | \n", "
3 | \n", "Hyundai Grand i10 Magna 1.2 Kappa VTVT | \n", "Hyundai | \n", "2014 | \n", "3,25,000 | \n", "28,000 kms | \n", "Petrol | \n", "
4 | \n", "Ford EcoSport Titanium 1.5L TDCi | \n", "Ford | \n", "2014 | \n", "5,75,000 | \n", "36,000 kms | \n", "Diesel | \n", "
\n", " | name | \n", "company | \n", "year | \n", "Price | \n", "kms_driven | \n", "fuel_type | \n", "
---|---|---|---|---|---|---|
0 | \n", "Hyundai Santro Xing XO eRLX Euro III | \n", "Hyundai | \n", "2007 | \n", "80000 | \n", "45000 | \n", "Petrol | \n", "
1 | \n", "Mahindra Jeep CL550 MDI | \n", "Mahindra | \n", "2006 | \n", "425000 | \n", "40 | \n", "Diesel | \n", "
3 | \n", "Hyundai Grand i10 Magna 1.2 Kappa VTVT | \n", "Hyundai | \n", "2014 | \n", "325000 | \n", "28000 | \n", "Petrol | \n", "
4 | \n", "Ford EcoSport Titanium 1.5L TDCi | \n", "Ford | \n", "2014 | \n", "575000 | \n", "36000 | \n", "Diesel | \n", "
6 | \n", "Ford Figo | \n", "Ford | \n", "2012 | \n", "175000 | \n", "41000 | \n", "Diesel | \n", "
\n", " | name | \n", "company | \n", "year | \n", "Price | \n", "kms_driven | \n", "fuel_type | \n", "
---|---|---|---|---|---|---|
0 | \n", "Hyundai Santro Xing XO eRLX Euro III | \n", "Hyundai | \n", "2007 | \n", "80000 | \n", "45000 | \n", "Petrol | \n", "
1 | \n", "Mahindra Jeep CL550 MDI | \n", "Mahindra | \n", "2006 | \n", "425000 | \n", "40 | \n", "Diesel | \n", "
3 | \n", "Hyundai Grand i10 Magna 1.2 Kappa VTVT | \n", "Hyundai | \n", "2014 | \n", "325000 | \n", "28000 | \n", "Petrol | \n", "
4 | \n", "Ford EcoSport Titanium 1.5L TDCi | \n", "Ford | \n", "2014 | \n", "575000 | \n", "36000 | \n", "Diesel | \n", "
6 | \n", "Ford Figo | \n", "Ford | \n", "2012 | \n", "175000 | \n", "41000 | \n", "Diesel | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
883 | \n", "Maruti Suzuki Ritz VXI ABS | \n", "Maruti | \n", "2011 | \n", "270000 | \n", "50000 | \n", "Petrol | \n", "
885 | \n", "Tata Indica V2 DLE BS III | \n", "Tata | \n", "2009 | \n", "110000 | \n", "30000 | \n", "Diesel | \n", "
886 | \n", "Toyota Corolla Altis | \n", "Toyota | \n", "2009 | \n", "300000 | \n", "132000 | \n", "Petrol | \n", "
888 | \n", "Tata Zest XM Diesel | \n", "Tata | \n", "2018 | \n", "260000 | \n", "27000 | \n", "Diesel | \n", "
889 | \n", "Mahindra Quanto C8 | \n", "Mahindra | \n", "2013 | \n", "390000 | \n", "40000 | \n", "Diesel | \n", "
816 rows × 6 columns
\n", "\n", " | name | \n", "company | \n", "year | \n", "Price | \n", "kms_driven | \n", "fuel_type | \n", "
---|---|---|---|---|---|---|
0 | \n", "Hyundai Santro Xing | \n", "Hyundai | \n", "2007 | \n", "80000 | \n", "45000 | \n", "Petrol | \n", "
1 | \n", "Mahindra Jeep CL550 | \n", "Mahindra | \n", "2006 | \n", "425000 | \n", "40 | \n", "Diesel | \n", "
3 | \n", "Hyundai Grand i10 | \n", "Hyundai | \n", "2014 | \n", "325000 | \n", "28000 | \n", "Petrol | \n", "
4 | \n", "Ford EcoSport Titanium | \n", "Ford | \n", "2014 | \n", "575000 | \n", "36000 | \n", "Diesel | \n", "
6 | \n", "Ford Figo | \n", "Ford | \n", "2012 | \n", "175000 | \n", "41000 | \n", "Diesel | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
883 | \n", "Maruti Suzuki Ritz | \n", "Maruti | \n", "2011 | \n", "270000 | \n", "50000 | \n", "Petrol | \n", "
885 | \n", "Tata Indica V2 | \n", "Tata | \n", "2009 | \n", "110000 | \n", "30000 | \n", "Diesel | \n", "
886 | \n", "Toyota Corolla Altis | \n", "Toyota | \n", "2009 | \n", "300000 | \n", "132000 | \n", "Petrol | \n", "
888 | \n", "Tata Zest XM | \n", "Tata | \n", "2018 | \n", "260000 | \n", "27000 | \n", "Diesel | \n", "
889 | \n", "Mahindra Quanto C8 | \n", "Mahindra | \n", "2013 | \n", "390000 | \n", "40000 | \n", "Diesel | \n", "
816 rows × 6 columns
\n", "\n", " | name | \n", "company | \n", "year | \n", "Price | \n", "kms_driven | \n", "fuel_type | \n", "
---|---|---|---|---|---|---|
0 | \n", "Hyundai Santro Xing | \n", "Hyundai | \n", "2007 | \n", "80000 | \n", "45000 | \n", "Petrol | \n", "
1 | \n", "Mahindra Jeep CL550 | \n", "Mahindra | \n", "2006 | \n", "425000 | \n", "40 | \n", "Diesel | \n", "
2 | \n", "Hyundai Grand i10 | \n", "Hyundai | \n", "2014 | \n", "325000 | \n", "28000 | \n", "Petrol | \n", "
3 | \n", "Ford EcoSport Titanium | \n", "Ford | \n", "2014 | \n", "575000 | \n", "36000 | \n", "Diesel | \n", "
4 | \n", "Ford Figo | \n", "Ford | \n", "2012 | \n", "175000 | \n", "41000 | \n", "Diesel | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
811 | \n", "Maruti Suzuki Ritz | \n", "Maruti | \n", "2011 | \n", "270000 | \n", "50000 | \n", "Petrol | \n", "
812 | \n", "Tata Indica V2 | \n", "Tata | \n", "2009 | \n", "110000 | \n", "30000 | \n", "Diesel | \n", "
813 | \n", "Toyota Corolla Altis | \n", "Toyota | \n", "2009 | \n", "300000 | \n", "132000 | \n", "Petrol | \n", "
814 | \n", "Tata Zest XM | \n", "Tata | \n", "2018 | \n", "260000 | \n", "27000 | \n", "Diesel | \n", "
815 | \n", "Mahindra Quanto C8 | \n", "Mahindra | \n", "2013 | \n", "390000 | \n", "40000 | \n", "Diesel | \n", "
816 rows × 6 columns
\n", "\n", " | year | \n", "Price | \n", "kms_driven | \n", "
---|---|---|---|
count | \n", "816.000000 | \n", "8.160000e+02 | \n", "816.000000 | \n", "
mean | \n", "2012.444853 | \n", "4.117176e+05 | \n", "46275.531863 | \n", "
std | \n", "4.002992 | \n", "4.751844e+05 | \n", "34297.428044 | \n", "
min | \n", "1995.000000 | \n", "3.000000e+04 | \n", "0.000000 | \n", "
25% | \n", "2010.000000 | \n", "1.750000e+05 | \n", "27000.000000 | \n", "
50% | \n", "2013.000000 | \n", "2.999990e+05 | \n", "41000.000000 | \n", "
75% | \n", "2015.000000 | \n", "4.912500e+05 | \n", "56818.500000 | \n", "
max | \n", "2019.000000 | \n", "8.500003e+06 | \n", "400000.000000 | \n", "
\n", " | name | \n", "company | \n", "year | \n", "Price | \n", "kms_driven | \n", "fuel_type | \n", "
---|---|---|---|---|---|---|
0 | \n", "Hyundai Santro Xing | \n", "Hyundai | \n", "2007 | \n", "80000 | \n", "45000 | \n", "Petrol | \n", "
1 | \n", "Mahindra Jeep CL550 | \n", "Mahindra | \n", "2006 | \n", "425000 | \n", "40 | \n", "Diesel | \n", "
2 | \n", "Hyundai Grand i10 | \n", "Hyundai | \n", "2014 | \n", "325000 | \n", "28000 | \n", "Petrol | \n", "
3 | \n", "Ford EcoSport Titanium | \n", "Ford | \n", "2014 | \n", "575000 | \n", "36000 | \n", "Diesel | \n", "
4 | \n", "Ford Figo | \n", "Ford | \n", "2012 | \n", "175000 | \n", "41000 | \n", "Diesel | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
810 | \n", "Maruti Suzuki Ritz | \n", "Maruti | \n", "2011 | \n", "270000 | \n", "50000 | \n", "Petrol | \n", "
811 | \n", "Tata Indica V2 | \n", "Tata | \n", "2009 | \n", "110000 | \n", "30000 | \n", "Diesel | \n", "
812 | \n", "Toyota Corolla Altis | \n", "Toyota | \n", "2009 | \n", "300000 | \n", "132000 | \n", "Petrol | \n", "
813 | \n", "Tata Zest XM | \n", "Tata | \n", "2018 | \n", "260000 | \n", "27000 | \n", "Diesel | \n", "
814 | \n", "Mahindra Quanto C8 | \n", "Mahindra | \n", "2013 | \n", "390000 | \n", "40000 | \n", "Diesel | \n", "
815 rows × 6 columns
\n", "OneHotEncoder()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
OneHotEncoder()
Pipeline(steps=[('columntransformer',\n", " ColumnTransformer(remainder='passthrough',\n", " transformers=[('onehotencoder',\n", " OneHotEncoder(categories=[array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n", " 'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n", " 'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n", " 'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat...\n", " array(['Audi', 'BMW', 'Chevrolet', 'Datsun', 'Fiat', 'Force', 'Ford',\n", " 'Hindustan', 'Honda', 'Hyundai', 'Jaguar', 'Jeep', 'Land',\n", " 'Mahindra', 'Maruti', 'Mercedes', 'Mini', 'Mitsubishi', 'Nissan',\n", " 'Renault', 'Skoda', 'Tata', 'Toyota', 'Volkswagen', 'Volvo'],\n", " dtype=object),\n", " array(['Diesel', 'LPG', 'Petrol'], dtype=object)]),\n", " ['name', 'company',\n", " 'fuel_type'])])),\n", " ('linearregression', LinearRegression())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Pipeline(steps=[('columntransformer',\n", " ColumnTransformer(remainder='passthrough',\n", " transformers=[('onehotencoder',\n", " OneHotEncoder(categories=[array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n", " 'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n", " 'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n", " 'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat...\n", " array(['Audi', 'BMW', 'Chevrolet', 'Datsun', 'Fiat', 'Force', 'Ford',\n", " 'Hindustan', 'Honda', 'Hyundai', 'Jaguar', 'Jeep', 'Land',\n", " 'Mahindra', 'Maruti', 'Mercedes', 'Mini', 'Mitsubishi', 'Nissan',\n", " 'Renault', 'Skoda', 'Tata', 'Toyota', 'Volkswagen', 'Volvo'],\n", " dtype=object),\n", " array(['Diesel', 'LPG', 'Petrol'], dtype=object)]),\n", " ['name', 'company',\n", " 'fuel_type'])])),\n", " ('linearregression', LinearRegression())])
ColumnTransformer(remainder='passthrough',\n", " transformers=[('onehotencoder',\n", " OneHotEncoder(categories=[array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n", " 'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n", " 'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n", " 'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat Diesel',\n", " 'Chevrolet Beat LS', 'Chevrolet B...\n", " 'Volkswagen Vento Konekt', 'Volvo S80 Summum'], dtype=object),\n", " array(['Audi', 'BMW', 'Chevrolet', 'Datsun', 'Fiat', 'Force', 'Ford',\n", " 'Hindustan', 'Honda', 'Hyundai', 'Jaguar', 'Jeep', 'Land',\n", " 'Mahindra', 'Maruti', 'Mercedes', 'Mini', 'Mitsubishi', 'Nissan',\n", " 'Renault', 'Skoda', 'Tata', 'Toyota', 'Volkswagen', 'Volvo'],\n", " dtype=object),\n", " array(['Diesel', 'LPG', 'Petrol'], dtype=object)]),\n", " ['name', 'company', 'fuel_type'])])
['name', 'company', 'fuel_type']
OneHotEncoder(categories=[array(['Audi A3 Cabriolet', 'Audi A4 1.8', 'Audi A4 2.0', 'Audi A6 2.0',\n", " 'Audi A8', 'Audi Q3 2.0', 'Audi Q5 2.0', 'Audi Q7', 'BMW 3 Series',\n", " 'BMW 5 Series', 'BMW 7 Series', 'BMW X1', 'BMW X1 sDrive20d',\n", " 'BMW X1 xDrive20d', 'Chevrolet Beat', 'Chevrolet Beat Diesel',\n", " 'Chevrolet Beat LS', 'Chevrolet Beat LT', 'Chevrolet Beat PS',\n", " 'Chevrolet Cruze LTZ', 'Chevrolet Enjoy', 'Chevrolet E...\n", " 'Volkswagen Vento Comfortline', 'Volkswagen Vento Highline',\n", " 'Volkswagen Vento Konekt', 'Volvo S80 Summum'], dtype=object),\n", " array(['Audi', 'BMW', 'Chevrolet', 'Datsun', 'Fiat', 'Force', 'Ford',\n", " 'Hindustan', 'Honda', 'Hyundai', 'Jaguar', 'Jeep', 'Land',\n", " 'Mahindra', 'Maruti', 'Mercedes', 'Mini', 'Mitsubishi', 'Nissan',\n", " 'Renault', 'Skoda', 'Tata', 'Toyota', 'Volkswagen', 'Volvo'],\n", " dtype=object),\n", " array(['Diesel', 'LPG', 'Petrol'], dtype=object)])
['year', 'kms_driven']
passthrough
LinearRegression()