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Second Hand Car Price Prediction

import pandas as pd
import seaborn as sns
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv("data.csv")
df.head()
print(df['name'].unique())
print(df['fuel'].unique())
print(df['transmission'].unique())
print(df['owner'].unique())
['Maruti Swift Dzire VDI' 'Skoda Rapid 1.5 TDI Ambition' 'Honda City 2017-2020 EXi' ... 'Hyundai i20 2015-2017 Magna' 'Volkswagen Polo IPL II 1.2 Petrol Highline' 'Tata Bolt Revotron XT'] ['Diesel' 'Petrol' 'LPG' 'CNG' 'Electric'] ['Manual' 'Automatic'] ['First Owner' 'Second Owner' 'Third Owner' 'Fourth & Above Owner' 'Test Drive Car']
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 14140 entries, 0 to 14139 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 name 14140 non-null object 1 year 14140 non-null int64 2 km_driven 14140 non-null int64 3 fuel 14140 non-null object 4 transmission 14140 non-null object 5 owner 14140 non-null object 6 mileage 13924 non-null object 7 engine 13890 non-null object 8 max_power 13899 non-null object 9 seats 13887 non-null float64 10 selling_price 14140 non-null int64 dtypes: float64(1), int64(3), object(7) memory usage: 1.2+ MB