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Created 3 years ago
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