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app.py

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+#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()
+
+

requirements.txt

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+streamlit
+scikit-learn
+pandas