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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import pickle | |
| from tensorflow.keras.models import load_model | |
| def run() : | |
| # Load Model | |
| with open('preprocessor_churn.pkl', 'rb') as file_1: | |
| preprocessor = pickle.load(file_1) | |
| model_churn = load_model('churn_best_model.h5', compile=False) | |
| # Membuat Title | |
| st.markdown("<h1 style='text-align: center;'>Churn Customer Prediction</h1>", unsafe_allow_html=True) | |
| # Menambahkan Deskripsi form | |
| st.write('Page ini berisi pemodelan untuk memprediksi churn customer. Silakan masukkan data Anda pada form dibawah ini.') | |
| #Membuat Form | |
| with st.form(key= 'form_customer'): | |
| st.markdown('### **Data Customer**') | |
| user_id = st.text_input('User ID',value= '') | |
| gender = st.selectbox('Gender',('M','F'),index=1) | |
| age = st.slider('Age',10,90,30) | |
| region_category = st.radio('Region', options=['City','Village','Town'], horizontal=True) | |
| internet_option = st.selectbox('Internet Option',('Wi-Fi','Fiber_Optic', 'Mobile_Data'),index=1) | |
| medium_of_operation = st.radio('Medium', options=['Desktop','Smartphone','Both'], horizontal=True) | |
| st.markdown('---') | |
| st.markdown('### **Login Data**') | |
| days_since_last_login = st.slider('Days Since Last Login',0,30,3) | |
| avg_frequency_login_days = st.slider('Avg Frequency Login Days',0,90,14) | |
| st.markdown('---') | |
| st.markdown('### **Membership Data**') | |
| joined_through_referral = st.selectbox('Referral',('Yes','No'),index=1) | |
| membership_category = st.selectbox('Membership Category',('No Membership','Basic Membership','Silver Membership', 'Premium Membership', 'Gold Membership', 'Platinum Membership'),index=1) | |
| st.markdown('---') | |
| st.markdown('### **Transaction Data**') | |
| points_in_wallet = st.number_input('Points in Wallet', min_value=0, max_value=2070, value=600 ,step=1) | |
| avg_transaction_value = st.number_input('Avg Transaction Value', min_value=800, max_value=90000, value=30000 ,step=1) | |
| preferred_offer_types = st.radio('Offer Types', options=['Without Offers','Credit/Debit Card Offers','Gift Vouchers/Coupons'], horizontal=True) | |
| used_special_discount = st.selectbox('Used Special Discount',('Yes','No'),index=1) | |
| past_complaint = st.selectbox('Past Complaint',('Yes','No'),index=1) | |
| feedback = st.selectbox('Feedback',('Poor Website','Poor Customer Service', 'Too many ads', 'Poor Product Quality', 'No reason specified', 'Products always in Stock', 'Reasonable Price', 'Quality Customer Care', 'User Friendly Website'),index=1) | |
| submitted = st.form_submit_button('Predict') | |
| # Create New Data | |
| data_inf = { | |
| 'user_id' : user_id, | |
| 'age' : age, | |
| 'gender' : gender, | |
| 'region_category' : region_category, | |
| 'internet_option' : internet_option, | |
| 'medium_of_operation' : medium_of_operation, | |
| 'days_since_last_login' : days_since_last_login, | |
| 'avg_frequency_login_days' : avg_frequency_login_days, | |
| 'joined_through_referral' : joined_through_referral, | |
| 'membership_category' : membership_category, | |
| 'points_in_wallet' : points_in_wallet, | |
| 'avg_transaction_value' : avg_transaction_value, | |
| 'used_special_discount' : used_special_discount, | |
| 'past_complaint' : past_complaint, | |
| 'preferred_offer_types' : preferred_offer_types, | |
| 'feedback' : feedback | |
| } | |
| data_inf = pd.DataFrame([data_inf]) | |
| data_inf | |
| if submitted : | |
| # Feature Scaling and Feature Encoding | |
| data_final = preprocessor.transform(data_inf) | |
| # Predict using Linear Regression | |
| y_inf_pred = np.where(model_churn.predict(data_final) >= 0.5, 1, 0) | |
| if y_inf_pred == 1: | |
| prediction = 'Churn' | |
| else: | |
| prediction = 'Not Churn' | |
| st.write('##### This customer is predicted:', prediction) | |
| if __name__ == '__main__': | |
| run() |