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| import streamlit as st | |
| import pandas as pd | |
| import joblib | |
| import pandas as pd | |
| from sklearn.preprocessing import LabelEncoder, PolynomialFeatures | |
| def preprocess_input(input_data): | |
| #konvert input ke df | |
| if not isinstance(input_data, pd.DataFrame): | |
| input_data = pd.DataFrame([input_data]) | |
| #Pake label encoder | |
| label_encoder = LabelEncoder() | |
| input_data['cbwd'] = label_encoder.fit_transform(input_data['cbwd']) | |
| #fitur engineering | |
| input_data['season'] = input_data['month'].apply(lambda x: (x % 12 + 3) // 3) | |
| input_data['day_of_week'] = pd.to_datetime(input_data[['year', 'month', 'day']]).dt.dayofweek | |
| input_data['is_weekend'] = input_data['day_of_week'].apply(lambda x: 1 if x >= 5 else 0) | |
| input_data['TEMP_Iws'] = input_data['TEMP'] * input_data['Iws'] | |
| input_data['DEWP_PRES'] = input_data['DEWP'] * input_data['PRES'] | |
| poly = PolynomialFeatures(degree=2, include_bias=False) | |
| poly_features = poly.fit_transform(input_data[['DEWP', 'TEMP', 'PRES', 'Iws']]) | |
| poly_feature_names = poly.get_feature_names_out(['DEWP', 'TEMP', 'PRES', 'Iws']) | |
| poly_df = pd.DataFrame(poly_features, columns=poly_feature_names, index=input_data.index) | |
| input_data = pd.concat([input_data, poly_df], axis=1) | |
| #pilih fitur buat prediksi | |
| features = ['year', 'month', 'day', 'hour', 'DEWP', 'TEMP', 'PRES', 'cbwd', 'Iws', 'Is', 'Ir', | |
| 'season', 'day_of_week', 'is_weekend', 'TEMP_Iws', 'DEWP_PRES'] + list(poly_feature_names) | |
| return input_data[features] | |
| #loading model | |
| model = joblib.load('random_forest_predictor_pipeline_model.pkl') | |
| st.title('Beijing PM2.5 Prediction') | |
| year = st.number_input('Year', min_value=2000, max_value=2050, value=2024) | |
| month = st.number_input('Month', min_value=1, max_value=12, value=1) | |
| day = st.number_input('Day', min_value=1, max_value=31, value=1) | |
| hour = st.number_input('Hour', min_value=0, max_value=23, value=0) | |
| DEWP = st.number_input('DEWP', value=0.0) | |
| TEMP = st.number_input('TEMP', value=0.0) | |
| PRES = st.number_input('PRES', value=1000.0) | |
| cbwd = st.selectbox('cbwd', ['NE', 'SE', 'NW', 'cv']) | |
| Iws = st.number_input('Iws', value=0.0) | |
| Is = st.number_input('Is', value=0.0) | |
| Ir = st.number_input('Ir', value=0.0) | |
| if st.button('Predict PM2.5'): | |
| #tombol buat inp | |
| input_data = { | |
| 'year': year, 'month': month, 'day': day, 'hour': hour, | |
| 'DEWP': DEWP, 'TEMP': TEMP, 'PRES': PRES, 'cbwd': cbwd, | |
| 'Iws': Iws, 'Is': Is, 'Ir': Ir | |
| } | |
| #prediksi inputnya | |
| processed_input = preprocess_input(input_data) | |
| prediction = model.predict(processed_input) | |
| st.success(f'The predicted PM2.5 value is: {prediction[0]:.2f}') | |