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app.py

@@ -14,6 +14,7 @@ st.title("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("Is the customer a SeniorCitizen?", ["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"])
@@ -26,13 +27,14 @@ 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': 1 if Partner == "Yes" else 0,
     'Dependents': 1 if Dependents == "Yes" else 0,
     'PhoneService': 1 if PhoneService == "Yes" else 0,
     'InternetService': InternetService,
     'Contract': Contract,
     'PaymentMethod': PaymentMethod,
-    'Tenure': Tenure,
+    'tenure': Tenure,
     'MonthlyCharges': MonthlyCharges,
     'TotalCharges': TotalCharges
 }])