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cppm.py
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cppm.py
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import pandas as pd
import numpy as np
import pickle as pk
import streamlit as st
model = pk.load(open('model.pkl', 'rb'))
st.header('Car Price Prediction Model')
cars_Data = pd.read_csv('Cardetails.csv')
def get_brand_name(car_name):
car_name = car_name.split(' ')[0]
return car_name.strip()
cars_Data['name'] = cars_Data['name'].apply(get_brand_name)
index = 0
name = st.selectbox('Select Brand of the Car', cars_Data['name'].unique())
year = st.slider('Select the Mancufacture Year', 1994,2030)
km_driven = st.slider('KM Driven', 1,2000000)
fuel = st.selectbox('Select the type of the fuel', cars_Data['fuel'].unique())
seller_type = st.selectbox('Seller Type', cars_Data['seller_type'].unique())
transmission = st.selectbox('Transmission Type', cars_Data['transmission'].unique())
owner = st.selectbox('Seller Type', cars_Data['owner'].unique())
mileage = st.slider('Car Mileage', 10,45)
engine = st.slider('Engine Capacity', 700,5000)
max_power = st.slider('Maximum Power', 0,200)
seats = st.slider('Seats', 5,10)
if st.button("Predict"):
input_data_model = pd.DataFrame(
[[index, name,year,km_driven,fuel,seller_type,transmission,owner,mileage,engine,max_power,seats]],
columns=['index','name','year','km_driven','fuel','seller_type','transmission','owner','mileage','engine','max_power','seats'])
input_data_model['owner'].replace(['First Owner', 'Second Owner', 'Third Owner',
'Fourth & Above Owner', 'Test Drive Car'],
[1,2,3,4,5], inplace = True)
input_data_model['fuel'].replace(['Diesel', 'Petrol', 'LPG', 'CNG'],
[1,2,3,4], inplace = True)
input_data_model['seller_type'].replace(['Individual', 'Dealer', 'Trustmark Dealer'],
[1,2,3], inplace = True)
input_data_model['transmission'].replace(['Manual', 'Automatic'],
[1,2], inplace= True)
input_data_model['name'].replace(['Maruti', 'Skoda', 'Honda', 'Hyundai', 'Toyota', 'Ford', 'Renault',
'Mahindra', 'Tata', 'Chevrolet', 'Datsun', 'Jeep', 'Mercedes-Benz',
'Mitsubishi', 'Audi', 'Volkswagen', 'BMW', 'Nissan', 'Lexus',
'Jaguar', 'Land', 'MG', 'Volvo', 'Daewoo', 'Kia', 'Fiat', 'Force',
'Ambassador', 'Ashok', 'Isuzu', 'Opel'],
[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31], inplace = True)
# st.write(input_data_model)
car_price = model.predict(input_data_model)
st.markdown('Car Price is going to be '+ str(car_price[0]))