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| import streamlit as st | |
| import pandas as pd | |
| import joblib | |
| # Tampilan judul halaman | |
| st.markdown("========================================================================================") | |
| st.markdown("<h1 style='text-align: center;'>Welcome to the Churn Prediction Model</h1>", unsafe_allow_html=True) | |
| st.markdown("<br>", unsafe_allow_html=True) | |
| st.markdown("========================================================================================") | |
| st.caption(' ') | |
| st.caption('Please enter the customer feature data input on the left side of your screen to get started!') | |
| with st.expander('Input Feature Explanation '): | |
| st.caption(''' | |
| |Feature|Explanation| | |
| |---|---| | |
| |Tenure | Tenure of customer in organization| | |
| |Complain | Any complaint has been raised in last month| | |
| |Day Since Last Order | Day Since last order by customer| | |
| |Cashback Amount | Average cashback in last month| | |
| |Number Of Device Registered | Total number of deceives is registered on particular customer| | |
| |Satisfaction Score | Satisfactory score of customer on service| | |
| |Prefered Order Cat | Preferred order category of customer in last month| | |
| |Marital Status | Marital status of customer| | |
| ''') | |
| st.sidebar.markdown("===================================") | |
| st.sidebar.markdown("<h1 style='text-align: center;'>Input Data Customer</h1>", unsafe_allow_html=True) | |
| st.sidebar.markdown("<br>", unsafe_allow_html=True) | |
| st.sidebar.markdown("===================================") | |
| def user_input(): | |
| tenure = st.sidebar.slider('Tenure', min_value=0, max_value=60, value=30) | |
| complain = st.sidebar.selectbox('Complain', ['none', 'any']) | |
| day_since_last_order = st.sidebar.slider('Days Since Last Order', min_value=0, max_value=365, value=30) | |
| cashback_amount = st.sidebar.slider('Cashback Amount', min_value=0, max_value=1000, value=500) | |
| number_of_device_registered = st.sidebar.slider('Number of Devices Registered', min_value=0, max_value=6, value=3) | |
| satisfaction_score = st.sidebar.slider('Satisfaction Score', min_value=1, max_value=5, value=3) | |
| prefered_order_cat = st.sidebar.selectbox('Preferred Order Category', ['Laptop & Accessory', 'Mobile', 'Mobile Phone', | |
| 'Others','Fashion', 'Grocery']) | |
| marital_status = st.sidebar.selectbox('Marital Status', ['Single', 'Married', 'Divorced']) | |
| if complain == 'none' : | |
| complain = 0 | |
| else : | |
| complain = 1 | |
| data = { | |
| 'tenure': tenure, | |
| 'complain' : complain, | |
| 'day_since_last_order': day_since_last_order, | |
| 'cashback_amount': cashback_amount, | |
| 'number_of_device_registered': number_of_device_registered, | |
| 'satisfaction_score': satisfaction_score, | |
| 'prefered_order_cat': prefered_order_cat, | |
| 'marital_status': marital_status, | |
| } | |
| features = pd.DataFrame(data, index=[0]) | |
| return features | |
| # Menjalankan fungsi input pengguna | |
| input = user_input() | |
| # Menampilkan hasil input pengguna dalam bentuk tabel | |
| st.markdown("<h2 style='text-align: left;'>User Input Result</h2>", unsafe_allow_html=True) | |
| st.table(input) | |
| # Memuat model yang telah disimpan sebelumnya | |
| load_model = joblib.load("model_xgb.pkl") | |
| # Tombol untuk memprediksi | |
| if st.button("Predict", help='Click me!'): | |
| # Melakukan prediksi menggunakan model | |
| prediction = load_model.predict(input) | |
| # Menampilkan hasil prediksi | |
| if prediction == 1: | |
| prediction = 'The customer is likely to churn' | |
| else: | |
| prediction = 'The customer is likely to stay' | |
| st.markdown("<h4 style='text-align: center;'>Based on the information provided by the user, the model predicts:</h4>", unsafe_allow_html=True) | |
| st.markdown(f"<h1 style='text-align: center;'>{prediction}</h1>", unsafe_allow_html=True) | |
| # Menampilkan hasil tambahan jika input termasuk dalam salah satu jenis fraud | |
| if prediction != "The customer is likely to stay": | |
| st.warning('This customer falls into the churn category. Please take appropriate action to retain the customer') |