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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import pickle | |
| import json | |
| from datetime import date, datetime, time | |
| from tensorflow.keras.models import load_model | |
| # load model | |
| with open('final_pipeline.pkl', 'rb') as file_1: | |
| model_pipeline = pickle.load(file_1) | |
| model_ann = load_model('churn_model.h5') | |
| def run(): | |
| # membuat judul | |
| st.title('Churn Prediction') | |
| # membuat kolom input | |
| with st.form(key='Churn Status'): | |
| user_id = st.text_input('User ID', value='') | |
| age = st.number_input('Age', min_value=10, max_value=70, value=26, step=1) | |
| gender = st.radio('Gender', ('M','F')) | |
| region_category = st.selectbox('Region Category', ('Town','City','Village'),index=1) | |
| membership_category = st.selectbox('Membership Category', ('No Membership', 'Basic Membership', 'Silver Membership', 'Gold Membership', 'Premium Membership', 'Platinum Membership' ),index=1) | |
| joining_date = st.date_input('Joining Date', date.today()) | |
| joined_through_referral = st.radio('Join By Referral', ('Yes','No')) | |
| preferred_offer_types = st.selectbox('Preferred Offer Types', ('Gift Vouchers/Coupons','Credit/Debit Card Offers', 'Without Offers'),index=1) | |
| medium_of_operation = st.selectbox('Medium of Operation', ('Desktop', 'Smartphone', 'Both'),index=1) | |
| internet_option = st.selectbox('Internet Option', ('Wi-Fi', 'Mobile_Data', 'Fiber_Optic'),index=1) | |
| last_visit_time = st.time_input('Last Visit Time ', time(hour=9, minute=0)) | |
| days_since_last_login = st.number_input('Days Since Last Login', min_value=0, max_value=30, value=26, step=1) | |
| avg_time_spent = st.number_input('Average Time Spent', min_value=0, max_value=400, value=26, step=1) | |
| avg_transaction_value = st.number_input('Average Transaction Value ', min_value=750, max_value=100000, value=800, step=50) | |
| avg_frequency_login_days = st.number_input('Average Frequency Login Days', min_value=0, max_value=70, value=26, step=1) | |
| points_in_wallet = st.number_input('Points In Wallet', min_value=0, max_value=2100, value=260, step=10) | |
| used_special_discount = st.radio('Use Special Discount', ('Yes','No')) | |
| offer_application_preference = st.radio('Offer Application Preference', ('Yes','No')) | |
| past_complaint = st.radio('Past Complain', ('Yes','No')) | |
| complaint_status = st.selectbox('Complaint Status', ('Not Applicable','Unsolved', 'Solved', 'Solved in Follow-up', 'No Information Available'),index=1) | |
| feedback = st.selectbox('Feedback', ('Poor Product Quality','No reason specified', 'Too many ads', 'Poor Website', 'Poor Customer Service', 'Reasonable Price', 'User Friendly Website', 'Products always in Stock', 'Quality Customer Care'),index=1) | |
| submitted = st.form_submit_button('Predict') | |
| # membuat data-set baru | |
| data_inf = { | |
| 'user_id':user_id, | |
| 'age':age, | |
| 'gender':gender, | |
| 'region_category':region_category, | |
| 'membership_category':membership_category, | |
| 'joining_date':joining_date, | |
| 'joined_through_referral':joined_through_referral, | |
| 'preferred_offer_types': preferred_offer_types, | |
| 'medium_of_operation': medium_of_operation, | |
| 'internet_option': internet_option, | |
| 'last_visit_time':last_visit_time, | |
| 'days_since_last_login': days_since_last_login, | |
| 'avg_time_spent': avg_time_spent, | |
| 'avg_transaction_value':avg_transaction_value, | |
| 'avg_frequency_login_days': avg_frequency_login_days, | |
| 'points_in_wallet': points_in_wallet, | |
| 'used_special_discount': used_special_discount, | |
| 'offer_application_preference': offer_application_preference, | |
| 'past_complaint': past_complaint, | |
| 'complaint_status': complaint_status, | |
| 'feedback': feedback | |
| } | |
| data_inf = pd.DataFrame([data_inf]) | |
| st.dataframe(data_inf) | |
| if submitted: | |
| # transform inference-Set | |
| data_inf_transform = model_pipeline.transform(data_inf) | |
| # predict using neural network | |
| y_pred_inf = model_ann.predict(data_inf_transform) | |
| y_pred_inf = np.where(y_pred_inf >= 0.5, 1, 0) | |
| churn_prediction = "Yes" if y_pred_inf == 1 else "No" | |
| st.write('# Churn : ', churn_prediction) | |
| if __name__ == '__main__': run() |