Car_Price_Prediction.ipynb

import pandas as pd
import numpy as np
Import CSV as Dataset

df = pd.read_csv(r'https://github.com/YBI-Foundation/Dataset/raw/main/Car%20Price.csv')
# df = pd.read_csv(r'C:\user\YBI foundation\Dekstop\car price.csv')
# df = pd.read_csv(r'/content/Car Price.csv')
df.head()
Brand	Model	Year	Selling_Price	KM_Driven	Fuel	Seller_Type	Transmission	Owner
0	Maruti	Maruti 800 AC	2007	60000	70000	Petrol	Individual	Manual	First Owner
1	Maruti	Maruti Wagon R LXI Minor	2007	135000	50000	Petrol	Individual	Manual	First Owner
2	Hyundai	Hyundai Verna 1.6 SX	2012	600000	100000	Diesel	Individual	Manual	First Owner
3	Datsun	Datsun RediGO T Option	2017	250000	46000	Petrol	Individual	Manual	First Owner
4	Honda	Honda Amaze VX i-DTEC	2014	450000	141000	Diesel	Individual	Manual	Second Owner
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4340 entries, 0 to 4339
Data columns (total 9 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   Brand          4340 non-null   object
 1   Model          4340 non-null   object
 2   Year           4340 non-null   int64 
 3   Selling_Price  4340 non-null   int64 
 4   KM_Driven      4340 non-null   int64 
 5   Fuel           4340 non-null   object
 6   Seller_Type    4340 non-null   object
 7   Transmission   4340 non-null   object
 8   Owner          4340 non-null   object
dtypes: int64(3), object(6)
memory usage: 305.3+ KB
df.describe()
Year	Selling_Price	KM_Driven
count	4340.000000	4.340000e+03	4340.000000
mean	2013.090783	5.041273e+05	66215.777419
std	4.215344	5.785487e+05	46644.102194
min	1992.000000	2.000000e+04	1.000000
25%	2011.000000	2.087498e+05	35000.000000
50%	2014.000000	3.500000e+05	60000.000000
75%	2016.000000	6.000000e+05	90000.000000
max	2020.000000	8.900000e+06	806599.000000
Get Categories and counts of categorical data

df[['Brand']].value_counts()
Brand        
Maruti           1280
Hyundai           821
Mahindra          365
Tata              361
Honda             252
Ford              238
Toyota            206
Chevrolet         188
Renault           146
Volkswagen        107
Skoda              68
Nissan             64
Audi               60
BMW                39
Fiat               37
Datsun             37
Mercedes-Benz      35
Mitsubishi          6
Jaguar              6
Land                5
Ambassador          4
Volvo               4
Jeep                3
OpelCorsa           2
MG                  2
Isuzu               1
Force               1
Daewoo              1
Kia                 1
dtype: int64

df[['Model']].value_counts()
Model                               
Maruti Swift Dzire VDI                  69
Maruti Alto 800 LXI                     59
Maruti Alto LXi                         47
Hyundai EON Era Plus                    35
Maruti Alto LX                          35
                                        ..
Mahindra KUV 100 G80 K4 Plus             1
Mahindra KUV 100 mFALCON D75 K8          1
Mahindra KUV 100 mFALCON D75 K8 AW       1
Mahindra KUV 100 mFALCON G80 K2 Plus     1
Volvo XC60 D5 Inscription                1
Length: 1491, dtype: int64
df[['Fuel']].value_counts()
Fuel    
Diesel      2153
Petrol      2123
CNG           40
LPG           23
Electric       1
dtype: int64
df[['Seller_Type']].value_counts()
Seller_Type     
Individual          3244
Dealer               994
Trustmark Dealer     102
dtype: int64
df[['Transmission']].value_counts()
Transmission
Manual          3892
Automatic        448
dtype: int64
df[['Owner']].value_counts()
Owner               
First Owner             2832
Second Owner            1106
Third Owner              304
Fourth & Above Owner      81
Test Drive Car            17
dtype: int64
Get column names

df.columns
Index(['Brand', 'Model', 'Year', 'Selling_Price', 'KM_Driven', 'Fuel',
       'Seller_Type', 'Transmission', 'Owner'],
      dtype='object')
df.shape
(4340, 9)
df.replace({'Fuel': {'Petrol' :0,'Diesel' :1,'CNG' :2,'LPG' :3,'Electric' :4}},inplace=True)
df.replace({'Seller_Type':{'Individual' :0,'Dealer' :1,'Trustmark Dealer' :2}},inplace=True)
df.replace({'Transmission':{'Manual' :0,'Automatic' :1}},inplace=True)
df.replace({'Owner':{'First Owner' :0,'Second Owner' :1,'Third Owner' :2, 'Fourth & Above Owner' : 3 ,'Test Drive Car' :4}},inplace=True)
y = df['Selling_Price']
y.shape
(4340,)
y
0        60000
1       135000
2       600000
3       250000
4       450000
         ...  
4335    409999
4336    409999
4337    110000
4338    865000
4339    225000
Name: Selling_Price, Length: 4340, dtype: int64
X = df[['Year', 'KM_Driven', 'Fuel', 'Seller_Type', 'Transmission', 'Owner']]
X.shape
(4340, 6)
X
Year	KM_Driven	Fuel	Seller_Type	Transmission	Owner
0	2007	70000	0	0	0	0
1	2007	50000	0	0	0	0
2	2012	100000	1	0	0	0
3	2017	46000	0	0	0	0
4	2014	141000	1	0	0	1
...	...	...	...	...	...	...
4335	2014	80000	1	0	0	1
4336	2014	80000	1	0	0	1
4337	2009	83000	0	0	0	1
4338	2016	90000	1	0	0	0
4339	2016	40000	0	0	0	0
4340 rows × 6 columns

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size= 0.3, random_state=252529)
X_train.shape, X_test.shape, y_train.shape, y_test.shape
((3038, 6), (1302, 6), (3038,), (1302,))
from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.fit(X_train, y_train)
LinearRegression()
y_pred = lr.predict(X_test)
y_pred.shape
(1302,)
y_pred
array([-371770.0894898 ,  486507.99307254,  466293.70224552, ...,
        270705.44671921,  489907.00907762,  151389.00724865])
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
mean_squared_error(y_test,y_pred)
166091575974.32596
mean_absolute_error(y_test,y_pred)
226971.74036526895
r2_score(y_test,y_pred)
0.4680119072373259
import matplotlib.pyplot as plt
plt.scatter(y_test,y_pred)
plt.xlabel("Actual Prices")
plt.title("Actual Price vs Preicted Price")
plt.show()

df_new =df.sample(1)
df_new
Brand	Model	Year	Selling_Price	KM_Driven	Fuel	Seller_Type	Transmission	Owner
675	Maruti	Maruti Alto 800 VXI	2015	190000	70000	0	0	0	0
df_new.shape
(1, 9)
X_new = df_new.drop(['Brand','Model','Selling_Price'], axis=1)
y_pred_new = lr.predict(X_new)
y_pred_new
array([353999.12934381])