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app.py

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+
+import streamlit as st
+import pandas as pd
+import joblib
+
+# Load the trained model
+def load_model():
+    return joblib.load("churn_prediction_model_v1_0.joblib")
+
+model = load_model()
+
+# Streamlit UI for Customer Churn Prediction
+st.title("Telecom Customer Churn Prediction App")
+st.write("This tool predicts customer churn risk based on their details. Enter the required information below.")
+
+# Collect user input based on dataset columns
+SeniorCitizen = st.selectbox("Senior citizen", ["Yes", "No"])
+Partner = st.selectbox("Does the customer have a partner?", ["Yes", "No"])
+Dependents = st.selectbox("Does the customer have dependents?", ["Yes", "No"])
+PhoneService = st.selectbox("Does the customer have phone service?", ["Yes", "No"])
+InternetService = st.selectbox("Type of Internet Service", ["DSL", "Fiber optic", "No"])
+Contract = st.selectbox("Type of Contract", ["Month-to-month", "One year", "Two year"])
+PaymentMethod = st.selectbox("Payment Method", ["Electronic check", "Mailed check", "Bank transfer", "Credit card"])
+tenure = st.number_input("Tenure (Months with the company)", min_value=0, value=12)
+MonthlyCharges = st.number_input("Monthly Charges", min_value=0.0, value=50.0)
+TotalCharges = st.number_input("Total Charges", min_value=0.0, value=600.0)
+
+# Convert categorical inputs to match model training
+input_data = pd.DataFrame([{
+    'SeniorCitizen': 1 if SeniorCitizen == "Yes" else 0,
+    'Partner':Partner,
+    'Dependents': Dependents,
+    'tenure': tenure,
+    'PhoneService': PhoneService,
+    'InternetService': InternetService,
+    'Contract': Contract,
+    'PaymentMethod': PaymentMethod,
+    'MonthlyCharges': MonthlyCharges,
+    'TotalCharges': TotalCharges
+}])
+
+
+# Set classification threshold
+classification_threshold = 0.5
+
+# Predict button
+# Predict button
+if st.button("Predict"):
+    prediction_proba = model.predict_proba(input_data)[0, 1]
+    prediction = (prediction_proba >= classification_threshold).astype(int)
+    result = "churn" if prediction == 1 else "not churn"
+    st.write(f"Based on the information provided, the customer is likely to **{result}**.")

churn_prediction_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1890379bd8a492620e028476e5e1c8f63cffabc25741b518f80a8c2ef11c919b
+size 340069

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+joblib==1.4.2
+streamlit==1.43.2