Spaces:
Running
Running
| # Mengimpor library | |
| import pandas as pd | |
| import streamlit as st | |
| import pickle | |
| # Menghilangkan warning | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| # Menulis judul | |
| st.markdown("<h1 style='text-align: center; '> Real Estate Price Prediction </h1>", unsafe_allow_html=True) | |
| st.markdown('---'*10) | |
| # Fungsi untuk prediksi | |
| def final_prediction(values, model): | |
| global prediction | |
| prediction = model.predict(values) | |
| return prediction | |
| # Ini merupakan fungsi utama | |
| def main(): | |
| # Nilai awal | |
| Age = 32.0 | |
| Distance_MRT = 84.87882 | |
| Total_Sotres = 10 | |
| Latitude = 24.98298 | |
| longitude = 121.54024 | |
| with st.container(): | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| Age = st.number_input('Age', value=Age) | |
| with col2: | |
| Distance_MRT = st.number_input('Distance_MRT', value=Distance_MRT) | |
| with col3: | |
| Total_Sotres = st.number_input('Total_Sotres', value=Total_Sotres) | |
| st.markdown('---'*10) | |
| with st.container(): | |
| col4, col5 = st.columns(2) | |
| with col4: | |
| Latitude = st.number_input('Latitude', value=Latitude) | |
| with col5: | |
| longitude = st.number_input('longitude', value=longitude) | |
| data = { | |
| 'Age': Age, | |
| 'Distance_MRT': Distance_MRT, | |
| 'Total_Sotres': Total_Sotres, | |
| 'Latitude': Latitude, | |
| 'longitude': longitude, | |
| } | |
| kolom = list(data.keys()) | |
| df_final = pd.DataFrame([data.values()],columns=kolom) | |
| # load model | |
| my_model = pickle.load(open('model_regresi_realestate.pkl', 'rb')) | |
| # Predict | |
| result = round(float(final_prediction(df_final, my_model)),2) | |
| st.markdown('---'*10) | |
| st.write('<center><b><h3>Predicted Price= ', result,'</b></h3>', unsafe_allow_html=True) | |
| if __name__ == '__main__': | |
| main() | |