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app.py

@@ -11,39 +11,39 @@ model = load_model()
 
 # Streamlit UI for Customer Churn Prediction
 st.title("Customer Churn Prediction App")
-st.write("The Customer Churn Prediction App is an internal tool for bank staff that predicts whether customers are at risk of churning based on their details.")
-st.write("Kindly enter the customer details to check whether they are likely to churn.")
-
-# Collect user input
-CreditScore = st.number_input("Credit Score (customer's credit score)", min_value=300, max_value=900, value=650)
-Geography = st.selectbox("Geography (country where the customer resides)", ["France", "Germany", "Spain"])
-Age = st.number_input("Age (customer's age in years)", min_value=18, max_value=100, value=30)
-Tenure = st.number_input("Tenure (number of years the customer has been with the bank)", value=12)
-Balance = st.number_input("Account Balance (customer’s account balance)", min_value=0.0, value=10000.0)
-NumOfProducts = st.number_input("Number of Products (number of products the customer has with the bank)", min_value=1, value=1)
-HasCrCard = st.selectbox("Has Credit Card?", ["Yes", "No"])
-IsActiveMember = st.selectbox("Is Active Member?", ["Yes", "No"])
-EstimatedSalary = st.number_input("Estimated Salary (customer’s estimated salary)", min_value=0.0, value=50000.0)
+st.write("This tool predicts customer churn risk based on their details. Enter the required information below.")
+
+# Collect user input based on dataspathet columns
+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([{
-    'CreditScore': CreditScore,
-    'Geography': Geography,
-    'Age': Age,
+    '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,
-    'Balance': Balance,
-    'NumOfProducts': NumOfProducts,
-    'HasCrCard': 1 if HasCrCard == "Yes" else 0,
-    'IsActiveMember': 1 if IsActiveMember == "Yes" else 0,
-    'EstimatedSalary': EstimatedSalary
+    'MonthlyCharges': MonthlyCharges,
+    'TotalCharges': TotalCharges
 }])
 
-# Set the classification threshold
-classification_threshold = 0.45
+# Set classification threshold
+classification_threshold = 0.5
 
 # 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}.")
+    st.write(f"Prediction: The customer is likely to **{result}**.")
+    st.write(f"Churn Probability: {prediction_proba:.2f}")

churn_prediction_model_v1_0.joblib

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:fbfe0e0f47d3294eab34fe663bdf36d11f18930601e6258872ce4edf0b881ac4
-size 302538
+oid sha256:1890379bd8a492620e028476e5e1c8f63cffabc25741b518f80a8c2ef11c919b
+size 340069