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app.py

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+# METEHAN AYHAN
+
+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 OneHotEncoder, StandardScaler
+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=0.2, random_state=42)
+
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('num', StandardScaler(), ['Mileage', 'Cylinder', 'Liter', 'Doors']), #numeric columns
+        ('cat', OneHotEncoder(), ['Make', 'Model', 'Trim', 'Type']) #categorical columns
+    ]
+)
+
+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_df = 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_df)[0]
+    return prediction
+
+
+st.title('MLOps - Car Price Prediction :red_car:')
+st.write('Enter Car Details to get the Price Prediction')
+
+def main():
+    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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+pandas
+scikit-learn
+streamlit
+openpyxl