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| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import tensorflow as tf | |
| from tensorflow.keras.models import load_model | |
| import pickle | |
| st.title("Churn Prediction for IT cloud Client") | |
| # import model and preprocess | |
| model = load_model('model_tuned.hdf5') | |
| preprocess = pickle.load(open("preprocessing.pkl", "rb")) | |
| st.write('fill your profile:') | |
| # user input | |
| age = st.slider(label='age:', min_value=0, max_value=150, value=42, step=1) | |
| sex = st.radio(label='gender:', options=['Female', 'Male']) | |
| reg = st.radio(label='Region?', options=['Town', 'City', 'Village']) | |
| mbr = st.selectbox(label='Membership?', options=['Platinum Membership', 'Premium Membership', | |
| 'Gold Membership', 'Silver Membership', | |
| 'Basic Membership', 'No Membership']) | |
| ten = st.number_input(label='How long you have been using our services (in months)?:', min_value=0, max_value=999, value=42, step=1) | |
| ref = st.radio(label='you join us via referral?', options=['Yes', 'No']) | |
| ofr = st.radio(label='Which offer that you are using rn?', options=['Credit/Debit Card Offers', | |
| 'Gift Vouchers/Coupons', | |
| 'Without Offers']) | |
| med = st.radio(label='What gadget for transaction do you usually use?', options=['Smartphone', | |
| 'Desktop', | |
| 'Both']) | |
| itt = st.radio(label='internet type?', options=['Wi-Fi', 'Mobile_Data', 'Fiber_Optic']) | |
| day = st.number_input(label='last login?:', min_value=0, max_value=999, value=42, step=1) | |
| avt = st.number_input(label='spend on the website (in minutes)?:', min_value=0.00, max_value=9999.99, value=0.00, step=0.01) | |
| mch = st.number_input(label='monthly charge:', min_value=0.00, max_value=9999999.99, value=0.00, step=0.01) | |
| alp = st.number_input(label='login period in days:', min_value=0.000000, max_value=999.999999, value=0.000000, step=0.000001) | |
| pts = st.number_input(label='points in wallet:', min_value=0.000000, max_value=99999.999999, value=0.000000, step=0.000001) | |
| spc = st.radio(label='special discount?', options=['Yes', 'No']) | |
| ofa = st.radio(label='prefer offers?', options=['Yes', 'No']) | |
| com = st.radio(label='past complaint?', options=['Yes', 'No']) | |
| cst = st.selectbox(label='your past complaint status?', options=['Solved', 'Solved in Follow-up', 'Unsolved', | |
| 'Not Applicable', 'No Information Available']) | |
| fdb = st.selectbox(label='your past feedback?', options=['Too many ads', 'Poor Product Quality', 'Poor Website', | |
| 'Poor Customer Service', 'Products always in Stock', | |
| 'Reasonable Price', 'Quality Customer Care', | |
| 'User Friendly Website', 'No reason specified']) | |
| # convert into dataframe | |
| data = pd.DataFrame({'age': [age], | |
| 'gender': [sex], | |
| 'region_category': [reg], | |
| 'membership_category':[mbr], | |
| 'tenure': [ten], | |
| 'joined_through_referral': [ref], | |
| 'preferred_offer_types': [ofr], | |
| 'medium_of_operation': [med], | |
| 'internet_option': [itt], | |
| 'days_since_last_login': [day], | |
| 'avg_time_spent': [avt], | |
| 'avg_transaction_value': [mch], | |
| 'avg_frequency_login_days': [alp], | |
| 'points_in_wallet':[pts], | |
| 'used_special_discount': [spc], | |
| 'offer_application_preference': [ofa], | |
| 'past_complaint': [com], | |
| 'complaint_status': [cst], | |
| 'feedback': [fdb] | |
| }) | |
| # convert gender to the real values in table | |
| if sex == 'Female': | |
| sex = 'F' | |
| else: | |
| sex = 'M' | |
| # preprocess the input from user | |
| data_final = preprocess.transform(data) | |
| # prediction | |
| if st.button('Predict'): | |
| prediction = model.predict(data_final) | |
| prediction = np.where(prediction >= 0.5, 1, 0) | |
| if prediction == 0: | |
| prediction = 'Congratulations, this person will absolutely stay!' | |
| else: | |
| prediction = 'dangers gawat, we need to save this person!' | |
| st.write('Prediction result: ') | |
| st.write(prediction) |