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app.py

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+import streamlit as st
+import pandas as pd
+import pickle
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+
+# import preproses
+preproses = pickle.load(open("preproses.pkl", "rb"))
+
+# import model
+model = load_model('model.h5')
+
+#title
+st.title("Customer Churn Predictions")
+st.write("Created by Sihar Pangaribuan")
+
+# User imput
+user_id = st.text_input('Input ID of a customer', value='')
+age = st.number_input(label='Age of a customer', min_value=10, max_value=64, value=10, step=1)
+gender = st.selectbox(label='Gender of a customer', options=['F','M'])
+region_category = st.selectbox(label='Select Region that a customer belongs to', options=['City', 'Village', 'Town'])
+membership_category = st.selectbox(label='Select Category of the membership that a customer is using', options=['No Membership', 'Basic Membership', 'Silver Membership', 'Premium Membership', 'Gold Membership', 'Platinum Membership'])
+joining_date = st.text_input('Date when a customer became a member', value='')
+joined_through_referral = st.selectbox(label='Whether a customer joined using any referral code or ID ?', options=['Yes','No'])
+preferred_offer_types = st.selectbox(label='Select Type of offer that a customer prefers', options=['Without Offers', 'Credit/Debit Card Offers', 'Gift Vouchers/Coupons'])
+medium_of_operation = st.selectbox(label='Select Medium of operation that a customer uses for transactions', options=['Desktop', 'Smartphone', 'Both'])
+internet_option = st.selectbox(label='Select Type of internet service a customer uses', options=['Wi-Fi', 'Fiber_Optic', 'Mobile_Data'])
+last_visit_time = st.text_input('Input The last time a customer visited the website', value='')
+days_since_last_login = st.number_input(label='Imput Number of days since a customer last logged into the website', min_value=-999, max_value=26, value=-999, step=1)
+avg_time_spent = st.number_input(label='Imput Average time spent by a customer on the website', min_value=0.0, max_value=3235.6, value=0.0, step=0.1)
+avg_transaction_value = st.number_input(label='Imput Average transaction value of a customer', min_value=800.46, max_value=99914.05, value=800.46, step=0.1)
+avg_frequency_login_days = st.number_input(label='Imput Number of times a customer has logged in to the website', min_value=0.0, max_value=73.07, value=0.0, step=0.1)
+points_in_wallet = st.number_input(label='Imput Points awarded to a customer on each transaction', min_value=0.0, max_value=2069.06, value=0.0, step=0.1)
+used_special_discount = st.selectbox(label='Whether a customer uses special discounts offered?', options=['Yes','No'])
+offer_application_preference = st.selectbox(label='Whether a customer prefers offers?', options=['Yes','No'])
+past_complaint = st.selectbox(label='Whether a customer has raised any complaints?', options=['Yes','No'])
+complaint_status = st.selectbox(label='Select the complaint status', options=['No Information Available', 'Not Applicable', 'Unsolved', 'Solved', 'Solved in Follow-up'])
+feedback = st.selectbox(label='Select the feedback', options=['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'])
+
+# Convert ke data frame
+data = pd.DataFrame({
+    '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]
+            })
+
+# Transfom data
+data = preproses.transform(data)
+
+# model predict
+if st.button('Predict'):
+    prediction = model.predict(data).tolist()[0]
+
+    if prediction == 1:
+        prediction = 'Froud'
+    else:
+        prediction = 'Not Froud'
+
+    st.write('The Prediction is: ')
+    st.write(prediction)

requirements.txt

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+streamlit
+pandas
+numpy
+scikit-learn
+imbalanced-learn
+daal4py
+feature_engine
+tensorflow-cpu