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app.py

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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('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=100, value=50)
+    complain = st.sidebar.selectbox('Complain', [0, 1])
+    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=10, value=5)
+    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'])
+    
+    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')

requirements.txt

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+pandas
+joblib
+scikit-learn