app.py

#!/usr/bin/env python
# coding: utf-8

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


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#pip install xlrd


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#ls


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df = pd.read_excel('cars.xls')


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# Veri on isleme

X=df.drop('Price', axis=1)
y= df['Price']


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X_train, X_test,y_train,y_test = train_test_split(X,y,test_size=0.2, random_state=42)


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


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my_model = LinearRegression()


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pipe = Pipeline(steps=[('preprocessor', preprocess),('model',my_model)])


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pipe.fit(X_train, y_train)


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y_pred = pipe.predict(X_test)
print('RMSE', mean_squared_error(y_test,y_pred)**.5)
print('R2', r2_score(y_test,y_pred))


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#pip install streamlit
import streamlit as st


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def price(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=pipe.predict(input_data)[0]
    return prediction
st.title("II. El Araba Fiyatı Tahmin:red_car: @drmurataltun")
st.write('Arabanın özelliklerini seçiniz')
make=st.selectbox('Marka',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('Kilometre',100,200000)
car_type=st.selectbox('Araç Tipi',df[(df['Make']==make) &(df['Model']==model)&(df['Trim']==trim)]['Type'].unique())
cylinder=st.selectbox('Cylinder',df['Cylinder'].unique())
liter=st.number_input('Motor hacmi',1,10)
doors=st.selectbox('Kapı sayısı',df['Doors'].unique())
cruise=st.radio('Hız Sbt.',[True,False])
sound=st.radio('Ses Sis.',[True,False])
leather=st.radio('Deri döşeme.',[True,False])
if st.button('Tahmin'):
    pred=price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather)
    st.write('Fiyat:$', round(pred[0],2))


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