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This canvas implements an end-to-end customer churn prediction and retention optimization system that cleans IBM telecom data, builds baseline and tuned gradient boosting models, segments customers by churn risk, recommends targeted retention actions, and optimizes spending under budget constraints while logging outcomes for continuous retraining. The workflow flows from data ingestion → feature engineering → model training & tuning → explainability analysis → retention recommendations → ROI-optimized budget allocation → feedback logging → API deployment, enabling data-driven, revenue-maximizing retention strategies.