first push

app.py

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+# Import library
+import streamlit as st
+import pandas as pd
+import numpy as np
+import pickle
+from tensorflow.keras.models import load_model
+
+# Load All Files
+# Model KNN
+# Load All Files
+# Preprocessor
+with open('preprocessor.pkl', 'rb') as file_1:
+  preprocessor = pickle.load(file_1)
+
+model = load_model('best_model.h5')
+
+st.subheader('Customer Churn Prediction')
+st.write('Please Fill The Information Below')
+
+# Variabel for input data
+membership = ['Silver Membership', 'Gold Membership', 'No Membership',
+       'Platinum Membership', 'Premium Membership', 'Basic Membership']
+membership_category = st.radio('Membership',(membership))
+
+feedback_cat = ['Poor Product Quality', 'Poor Website', 'Products always in Stock',
+       'Poor Customer Service', 'Reasonable Price', 'No reason specified',
+       'User Friendly Website', 'Too many ads', 'Quality Customer Care']
+feedback = st.radio('Feedback',(feedback_cat))
+
+offer_application_preference = st.radio('Prefer Offer',('Yes', 'No'))
+
+preferred_offer_types = st.radio('Offer Type',('Gift Vouchers/Coupons', 'Credit/Debit Card Offers','Without Offers'))
+
+joined_through_referral = st.radio('Using Refferall',('Yes', 'No'))
+
+points_in_wallet = st.number_input('Points In Wallet',0.00, 1500.00)
+
+avg_time_spent = st.number_input('Time Spent On Website (Hours)',0.00, 3050.00)
+
+avg_transaction_value = st.number_input('Total Transcation Amount (USD)',0.00, 99900.00)
+
+avg_frequency_login_days = st.slider('Login Website In A Day',0, 70)
+
+days_since_last_login = st.slider('Days Since Last Login',0, 30)
+
+days_since_join = st.slider('Days Since Join',0, 30)
+
+# make buttom for prediction
+
+if st.button('Predict'):
+    data_inf =  pd.DataFrame({'membership_category': membership_category, 'feedback': feedback,
+                        'points_in_wallet': points_in_wallet,'avg_transaction_value': avg_transaction_value,
+                         'avg_frequency_login_days' : avg_frequency_login_days,'joined_through_referral' : joined_through_referral,
+                         'offer_application_preference' : offer_application_preference, 'preferred_offer_types' : preferred_offer_types,
+                         'avg_time_spent' : avg_time_spent, 'days_since_join' : days_since_join,
+                         'days_since_last_login' : days_since_last_login}, index=[0])
+
+# Preprocess data inf    
+    data_inf_trans = preprocessor.transform(data_inf)
+    
+# prediction using model
+    y_pred = model.predict(data_inf_trans, verbose=0)
+
+# Round the prediction
+    y_pred = np.round(y_pred)
+    if y_pred == 1:
+      y_pred = 'Chrun'
+    else:
+      y_pred = 'Not Churn'
+
+# make prediction into dataframe
+    st.subheader('The Customer Will be')
+    st.subheader(y_pred)

best_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b04b7772868a185588d030366eb58edea8311ff2dd9146885debeb6b863c8a70
+size 60488

preprocessor.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c7efe5da26eea2946adbb9e6ccb2d89096a24cf687ea6d995e061e7f48a087fd
+size 3640