Interactive demo of a machine learning model that predicts customer churn probability using ensemble methods (Random Forest + XGBoost) with 89% accuracy.
Random ForestXGBoostSMOTEFeature Engineering
Customer Information
Enter customer details to predict churn probability
Enter customer information to see prediction results
Model Performance Metrics
Evaluation on test dataset (1,409 samples)
Technical Implementation
Model Architecture
• Ensemble of Random Forest and XGBoost classifiers
• SMOTE for handling class imbalance (73% non-churn, 27% churn)
• 5-fold cross-validation for robust performance estimation
• Hyperparameter tuning using GridSearchCV
Feature Engineering
• RFM (Recency, Frequency, Monetary) analysis
• Customer lifetime value calculations
• Behavioral pattern extraction from usage data
• Interaction features between contract type and tenure