app.py

#kullanıcının girdiği özelliklerdeki arabanın fiyatını tahmin eden web sitesi

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn. linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
import streamlit as st

df=pd.read_excel('cars.xls')
x=df.drop('Price',axis=1)
y=df [ ['Price' ] ]

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size =.20, random_state=42)

preprocessor=ColumnTransformer(
transformers=[
    ('num',StandardScaler(), ['Mileage','Cylinder','Liter','Doors']),
    ('cat',OneHotEncoder(),['Make','Model','Trim','Type' ])
    ]
)

model=LinearRegression()

pipeline=Pipeline(steps=[('preprocessor',preprocessor),('regressor',model)])

pipeline.fit(x_train,y_train)
pred=pipeline.predict(x_test)

rmse=mean_squared_error(pred,y_test) **.5
r2=r2_score(pred,y_test)

def price_pred(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise, sound,leather):
    input_data=pd.DataFrame({
        'Make' : [make],
        'Model': [model],
        'Trim': [trim],
        'Mileage': [mileage],
        'Type' : [car_type],
        'Cylinder':[cylinder],
        'Liter': [liter],
        'Doors' : [doors],
        'Cruise': [cruise],
        'Sound' : [sound],
        'Leather' : [leather]
    })

    prediction=pipeline.predict(input_data)[0]
    return prediction

def main():
    st.title('MLOps Car Price Prediction :red_car:')
    st.write('Enter Car Details to predict the price')
    make=st.selectbox('Make',df['Make' ].unique())
    model=st.selectbox('Model',df[df['Make'] == make] ['Model'].unique())
    trim=st.selectbox('Trim',df[(df['Make'] == make)& (df['Model' ] == model)]['Trim' ].unique())
    mileage=st.number_input('Mileage',200,60000)
    car_type=st.selectbox('Type',df['Type' ].unique())
    cylinder=st.selectbox('Cylinder',df['Cylinder' ].unique())
    liter=st.number_input('Liter',1,6)
    doors=st.selectbox('Doors',df['Doors' ].unique())
    cruise=st.radio('Cruise',[0,1])
    sound=st.radio('Sound',[0,1])
    leather=st.radio('Leather',[0,1])

    if st.button('Predict'):
        price= price_pred(make,model, trim,mileage, car_type, cylinder, liter,doors,cruise, sound,leather)
        price=float(price)
        st.write(f'The predicted price is: ${price:.2f}')

if __name__ == '__main__':
    main()