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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import pickle | |
| import datetime | |
| from tensorflow.keras.models import load_model | |
| # load files | |
| with open('full_pipeline.pkl', 'rb') as file_1: | |
| full_pipeline = pickle.load(file_1) | |
| model_ann = load_model('best_model.h5') | |
| def run(): | |
| with st.form(key='from_churn'): | |
| st.title('Prediction Page') | |
| st.subheader('We Calculate your metric to check Customer Churn Score') | |
| st.write('*`Please fill columns below to predict`*') | |
| # buat variabel | |
| age = st.number_input('Age', min_value=0, | |
| max_value=99, value=40, step=1) | |
| st.write('F = Female | M = Male') | |
| gender_pick = st.radio('Gender', ('M', 'F'), index=1) | |
| region = st.selectbox('Region', ('City', 'Village', 'Town')) | |
| member = st.selectbox('Membership', ('No Membership', 'Basic Membership', 'Silver Membership', | |
| 'Premium Membership', 'Gold Membership', 'Platinum Membership')) | |
| joined = st.date_input('Date when a customer became member', | |
| datetime.date(2023, 12, 31)) | |
| join_ref = st.radio( | |
| 'Are Customer Joined Using Referral?', ('Yes', 'No'), index=1) | |
| pref_offer = st.selectbox('Type of offer to customer', ('Without Offers', 'Credit/Debit Card Offers', | |
| 'Gift Vouchers/Coupons')) | |
| medium = st.selectbox( | |
| 'Type of Device', ('Smartphone', 'Desktop', 'Both')) | |
| internet_opt = st.selectbox( | |
| 'Type of internet service', ('Wi-Fi', 'Fiber_Optic', 'Mobile_Data')) | |
| last_visit = st.time_input('The last time a customer visited the website', | |
| datetime.time(15, 00), step=300) | |
| last_login = st.number_input('Number of days since a customer last logged into the website', | |
| min_value=0, max_value=366, value=4, step=1) | |
| time_spent = st.number_input('Average time (minutes) customer spent in the platform', | |
| min_value=0, max_value=5000, value=30, step=1) | |
| trx_value = st.number_input('Average transaction value of a customer', min_value=0, | |
| max_value=10_000, value=500, step=5) | |
| login_days = st.number_input('Number of times a customer has logged in to the website', min_value=0, | |
| max_value=10_000, value=25, step=1) | |
| point_wallet = st.number_input('Points awarded to a customer on each transaction', min_value=0, | |
| max_value=50_000, value=1000, step=10) | |
| used_discount = st.radio( | |
| 'Whether a customer uses special discounts offered', ('Yes', 'No'), index=1) | |
| pref = st.radio('Whether a customer prefers offers', | |
| ('Yes', 'No'), index=1) | |
| past_comp = st.radio( | |
| 'Whether a customer has raised any complaints', ('Yes', 'No'), index=1) | |
| comp_status = st.selectbox('Whether the complaints raised by a customer was resolved', ('No Information Available', 'Not Applicable', 'Unsolved', | |
| 'Solved', 'Solved in Follow-up')) | |
| feedback = st.selectbox('Feedback provided by a customer', ('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')) | |
| st.markdown('---') | |
| submitted = st.form_submit_button('Predict') | |
| data_inf = { | |
| 'age': age, | |
| 'gender': gender_pick, | |
| 'region_category': region, | |
| 'membership_category': member, | |
| 'joined_through_referral': join_ref, | |
| 'preferred_offer_types': pref_offer, | |
| 'medium_of_operation': medium, | |
| 'internet_option': internet_opt, | |
| 'days_since_last_login': last_login, | |
| 'avg_time_spent': time_spent, | |
| 'avg_transaction_value': trx_value, | |
| 'avg_frequency_login_days': login_days, | |
| 'points_in_wallet': point_wallet, | |
| 'used_special_discount': used_discount, | |
| 'offer_application_preference': pref, | |
| 'past_complaint': past_comp, | |
| 'complaint_status': comp_status, | |
| 'feedback': feedback | |
| } | |
| data_inf = pd.DataFrame([data_inf]) | |
| # st.dataframe(data_inf.T, width=800, height=495) | |
| if submitted: | |
| # predict using full_pipeline | |
| data_pipeline = full_pipeline.transform(data_inf) | |
| y_pred_inf = model_ann.predict(data_pipeline) | |
| y_pred_inf = np.where(y_pred_inf >= 0.5, 1, 0) | |
| y_pred_inf | |
| if y_pred_inf == 0: | |
| st.write('Not Churn') | |
| else: | |
| st.write('Churn') | |
| st.markdown('---') | |
| if __name__ == '__main__': | |
| run() | |