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app.py

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+import streamlit as st
+import pandas as pd
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+import pickle
+
+st.title("Churn Prediction for Telecom Client")
+
+# import model and preprocess
+
+model = load_model('model.h5')
+preprocess = pickle.load(open("preprocess.pkl", "rb"))
+
+st.write('Please fill your information:')
+
+# user input
+
+age = st.slider(label='Enter your age:', min_value=0, max_value=150, value=42, step=1)
+sex = st.radio(label='Enter your gender:', options=['Female', 'Male'])
+reg = st.radio(label='In which region category do you stay?', options=['Town', 'City', 'Village'])
+mbr = st.selectbox(label='Which membership that you are on now?', 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='Did you join us through referral?', options=['Yes', 'No'])
+ofr = st.radio(label='Which offer that you are currently using?', options=['Credit/Debit Card Offers',
+                                                                               'Gift Vouchers/Coupons',
+                                                                                'Without Offers'])
+med = st.radio(label='What medium of transaction do you usually use?', options=['Smartphone',
+                                                                                'Desktop',
+                                                                                'Both'])
+itt = st.radio(label='Which internet type that you are currently using?', options=['Wi-Fi', 'Mobile_Data', 'Fiber_Optic'])
+day = st.number_input(label='How long has it been since your last login?:', min_value=0, max_value=999, value=42, step=1)
+avt = st.number_input(label='How long do you usually 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='Enter your average monthly charge:', min_value=0.00, max_value=9999999.99, value=0.00, step=0.01)
+alp = st.number_input(label='Enter your average login period in days:', min_value=0.000000, max_value=999.999999, value=0.000000, step=0.000001)
+pts = st.number_input(label='Enter your points in wallet:', min_value=0.000000, max_value=99999.999999, value=0.000000, step=0.000001)
+spc = st.radio(label='Did you use special discount?', options=['Yes', 'No'])
+ofa = st.radio(label='Do you prefer offers?', options=['Yes', 'No'])
+com = st.radio(label='Do you have past complaint?', options=['Yes', 'No'])
+cst = st.selectbox(label='What was your past complaint status?', options=['Solved', 'Solved in Follow-up', 'Unsolved',
+                                                                       'Not Applicable', 'No Information Available'])
+fdb = st.selectbox(label='What was 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).tolist()[0]
+
+    if prediction == 0:
+        prediction = 'Congratulations, this person most likely will stay!'
+    else:
+        prediction = 'Churn alert, we need to save this person!'
+
+    st.write('Prediction result: ')
+    st.write(prediction)

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5badfe90705cb7f96b35258defa610ea1216d27036bb06739b6def8a261e2d2d
+size 38848

preprocess.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a931bb04b08fb688a4523af9e170994801ae99aba0b9c0e30c7623707b56b7d6
+size 7306

requirements.txt

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+streamlit == 1.22.0
+pandas == 2.0.2
+scikit-learn == 1.2.2
+imbalanced-learn == 0.10.1
+daal4py == 2023.1.1
+feature_engine == 1.6.0
+tensorflow == 2.12.0