ghinaAI/Telecom_Customer_Churn_Prediction

1import joblib2import pandas as pd3import gradio as gr4 5# Load model6model = joblib.load("best_xgb_model.pkl")7 8# Expected features9expected_columns = [10    'SeniorCitizen', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines',11    'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV',12    'StreamingMovies', 'PaperlessBilling', 'MonthlyCharges', 'TotalCharges',13    'PaymentMethod_Credit card (automatic)', 'PaymentMethod_Electronic check',14    'PaymentMethod_Mailed check', 'InternetService_Fiber optic', 'InternetService_No',15    'Contract_One year', 'Contract_Two year',16    'TenureGroup_Experienced', 'TenureGroup_Loyal'17]18 19# Suggestion generator20def generate_suggestion(customer):21    suggestions = []22 23    if customer["Contract_One year"] == 0 and customer["Contract_Two year"] == 0:24        suggestions.append("Offer a yearly plan with 2 months free.")25    if customer["MonthlyCharges"] > 80:26        suggestions.append("Propose a discounted or lower-cost plan.")27    if customer["TechSupport"] == 0:28        suggestions.append("Provide 3 months of free tech support.")29    if customer["OnlineSecurity"] == 0:30        suggestions.append("Include online security in their plan as a free trial.")31    if customer["Partner"] == 0 and customer["Dependents"] == 0:32        suggestions.append("Offer individual loyalty rewards or referral bonuses.")33    if customer.get("tenure", 0) < 3:34        suggestions.append("Assign a dedicated onboarding assistant to help during the first months.")35    if customer["StreamingTV"] == 0 and customer.get("InternetService_Fiber optic", 0) == 1:36        suggestions.append("Bundle free streaming service for 3 months with high-speed fiber.")37    if customer["PaperlessBilling"] == 1:38        suggestions.append("Offer cashback for using paperless billing.")39 40    addon_services = ["OnlineSecurity", "OnlineBackup", "DeviceProtection", "TechSupport", "StreamingTV"]41    if all(customer[feature] == 0 for feature in addon_services):42        suggestions.append("Offer a bundled package with multiple services at a discounted rate.")43 44    if not suggestions:45        suggestions.append("Send a satisfaction survey with a loyalty reward.")46 47    return "\n - " + "\n - ".join(suggestions)48 49# Main prediction50def predict_churn(51    SeniorCitizen, Partner, Dependents, tenure, PhoneService, MultipleLines,52    InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport,53    StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod,54    MonthlyCharges, TotalCharges55):56    encoded = {57        "SeniorCitizen": int(SeniorCitizen),58        "Partner": 1 if Partner == "Yes" else 0,59        "Dependents": 1 if Dependents == "Yes" else 0,60        "PhoneService": 1 if PhoneService == "Yes" else 0,61        "MultipleLines": 1 if MultipleLines == "Yes" else 0,62        "OnlineSecurity": 1 if OnlineSecurity == "Yes" else 0,63        "OnlineBackup": 1 if OnlineBackup == "Yes" else 0,64        "DeviceProtection": 1 if DeviceProtection == "Yes" else 0,65        "TechSupport": 1 if TechSupport == "Yes" else 0,66        "StreamingTV": 1 if StreamingTV == "Yes" else 0,67        "StreamingMovies": 1 if StreamingMovies == "Yes" else 0,68        "PaperlessBilling": 1 if PaperlessBilling == "Yes" else 0,69        "MonthlyCharges": float(MonthlyCharges),70        "TotalCharges": float(TotalCharges),71        "PaymentMethod_Credit card (automatic)": 1 if PaymentMethod == "Credit card (automatic)" else 0,72        "PaymentMethod_Electronic check": 1 if PaymentMethod == "Electronic check" else 0,73        "PaymentMethod_Mailed check": 1 if PaymentMethod == "Mailed check" else 0,74        "InternetService_Fiber optic": 1 if InternetService == "Fiber optic" else 0,75        "InternetService_No": 1 if InternetService == "No" else 0,76        "Contract_One year": 1 if Contract == "One year" else 0,77        "Contract_Two year": 1 if Contract == "Two year" else 0,78        "TenureGroup_Experienced": 1 if 12 < tenure <= 36 else 0,79        "TenureGroup_Loyal": 1 if tenure > 36 else 0,80        "tenure": tenure81    }82 83    input_df = pd.DataFrame([encoded])84    for col in expected_columns:85        if col not in input_df.columns:86            input_df[col] = 087    input_df = input_df[expected_columns]88 89    prediction = model.predict(input_df)[0]90    churn_prob = model.predict_proba(input_df)[:, 1][0]91    result = "Churn" if prediction == 1 else "No Churn"92 93    if prediction == 1:94        suggestion = generate_suggestion(encoded)95        return f"⚠️ **Prediction**: {result} (Probability: {churn_prob:.2%})\n\n🤖 **Suggestions**:{suggestion}"96    else:97        return f"✅ **Prediction**: {result} (Probability: {churn_prob:.2%})"98 99# Sample customers100def get_customers_from_db():101    return [102        (0, 'Yes', 'No', 6, 'Yes', 'No', 'DSL', 'Yes', 'No', 'Yes', 'No', 'Yes', 'No', 'Month-to-month', 'Yes', 'Credit card (automatic)', 500.00, 200.00),103        (1, 'No', 'Yes', 12, 'No', 'No', 'DSL', 'No', 'Yes', 'No', 'Yes', 'No', 'Yes', 'Month-to-month', 'No', 'Credit card (automatic)', 300.00, 3600.00),104        (0, 'Yes', 'Yes', 24, 'Yes', 'Yes', 'DSL', 'Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'Yes', 'Month-to-month', 'Yes', 'Credit card (automatic)', 150.00, 3600.00)105    ]106 107def predict_all_customers():108    customers = get_customers_from_db()109    results = []110 111    for i, row in enumerate(customers, start=1):112        result = predict_churn(*row)113        results.append(f"### 🧑‍💼 Customer {i}:\n{result}")114 115    return "\n\n---\n\n".join(results)116 117# Individual interface118individual_app = gr.Interface(119    fn=predict_churn,120    inputs=[121        gr.Radio(["0", "1"], label="Senior Citizen"),122        gr.Radio(["Yes", "No"], label="Partner"),123        gr.Radio(["Yes", "No"], label="Dependents"),124        gr.Slider(0, 72, step=1, label="Tenure (months)"),125        gr.Radio(["Yes", "No"], label="Phone Service"),126        gr.Radio(["Yes", "No"], label="Multiple Lines"),127        gr.Dropdown(["DSL", "Fiber optic", "No"], label="Internet Service"),128        gr.Radio(["Yes", "No"], label="Online Security"),129        gr.Radio(["Yes", "No"], label="Online Backup"),130        gr.Radio(["Yes", "No"], label="Device Protection"),131        gr.Radio(["Yes", "No"], label="Tech Support"),132        gr.Radio(["Yes", "No"], label="Streaming TV"),133        gr.Radio(["Yes", "No"], label="Streaming Movies"),134        gr.Dropdown(["Month-to-month", "One year", "Two year"], label="Contract"),135        gr.Radio(["Yes", "No"], label="Paperless Billing"),136        gr.Dropdown(137            ["Credit card (automatic)", "Electronic check", "Mailed check", "Bank transfer (automatic)"],138            label="Payment Method"139        ),140        gr.Number(label="Monthly Charges"),141        gr.Number(label="Total Charges"),142    ],143    outputs=gr.Textbox(),144    title="📉 Individual Customer Churn Prediction",145    description="Fill in customer data to predict churn and receive suggestions."146)147 148# Bulk prediction interface149bulk_app = gr.Interface(150    fn=predict_all_customers,151    inputs=[],152    outputs=gr.Markdown(label="Results"),153    title="📂 Bulk Prediction for Customers",154    description="Displays churn prediction and suggestions for multiple customers."155)156 157# Tabs for both interfaces158demo = gr.TabbedInterface(159    interface_list=[individual_app, bulk_app],160    tab_names=["🧍 Individual Prediction", "📊 Bulk Prediction"]161)162 163if __name__ == "__main__":164    demo.launch()165