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Browse files- app.py +73 -0
- best_model.h5 +3 -0
- preprocessor.pkl +3 -0
app.py
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# Import library
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import streamlit as st
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import pandas as pd
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import numpy as np
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import pickle
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from tensorflow.keras.models import load_model
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# Load All Files
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# Model KNN
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# Load All Files
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# Preprocessor
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with open('preprocessor.pkl', 'rb') as file_1:
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preprocessor = pickle.load(file_1)
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model = load_model('best_model.h5')
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st.subheader('Customer Churn Prediction')
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st.write('Please Fill The Information Below')
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# Variabel for input data
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membership = ['Silver Membership', 'Gold Membership', 'No Membership',
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'Platinum Membership', 'Premium Membership', 'Basic Membership']
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membership_category = st.radio('Membership',(membership))
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feedback_cat = ['Poor Product Quality', 'Poor Website', 'Products always in Stock',
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'Poor Customer Service', 'Reasonable Price', 'No reason specified',
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'User Friendly Website', 'Too many ads', 'Quality Customer Care']
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feedback = st.radio('Feedback',(feedback_cat))
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offer_application_preference = st.radio('Prefer Offer',('Yes', 'No'))
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preferred_offer_types = st.radio('Offer Type',('Gift Vouchers/Coupons', 'Credit/Debit Card Offers','Without Offers'))
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joined_through_referral = st.radio('Using Refferall',('Yes', 'No'))
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points_in_wallet = st.number_input('Points In Wallet',0.00, 1500.00)
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avg_time_spent = st.number_input('Time Spent On Website (Hours)',0.00, 3050.00)
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avg_transaction_value = st.number_input('Total Transcation Amount (USD)',0.00, 99900.00)
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avg_frequency_login_days = st.slider('Login Website In A Day',0, 70)
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days_since_last_login = st.slider('Days Since Last Login',0, 30)
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days_since_join = st.slider('Days Since Join',0, 30)
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# make buttom for prediction
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if st.button('Predict'):
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data_inf = pd.DataFrame({'membership_category': membership_category, 'feedback': feedback,
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'points_in_wallet': points_in_wallet,'avg_transaction_value': avg_transaction_value,
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'avg_frequency_login_days' : avg_frequency_login_days,'joined_through_referral' : joined_through_referral,
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'offer_application_preference' : offer_application_preference, 'preferred_offer_types' : preferred_offer_types,
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'avg_time_spent' : avg_time_spent, 'days_since_join' : days_since_join,
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'days_since_last_login' : days_since_last_login}, index=[0])
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# Preprocess data inf
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data_inf_trans = preprocessor.transform(data_inf)
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# prediction using model
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y_pred = model.predict(data_inf_trans, verbose=0)
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# Round the prediction
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y_pred = np.round(y_pred)
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if y_pred == 1:
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y_pred = 'Chrun'
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else:
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y_pred = 'Not Churn'
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# make prediction into dataframe
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st.subheader('The Customer Will be')
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st.subheader(y_pred)
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best_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:b04b7772868a185588d030366eb58edea8311ff2dd9146885debeb6b863c8a70
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size 60488
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preprocessor.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:c7efe5da26eea2946adbb9e6ccb2d89096a24cf687ea6d995e061e7f48a087fd
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size 3640
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