Capstone B: Churn-ML (XGB/LGBM + SHAP + Thresholds)

# Capstone B: Churn-ML (XGB/LGBM + SHAP + Thresholds) ## Brief Ship a churn model on SaaS churn/LTV data: boosting model, SHAP story, threshold policy tied to retention economics. WE W3-W4 bar. ## Dataset SaaS churn/LTV train/holdout; hidden retention-cost matrix for grading. ## Starter guardrails (88) - Pipeline skeleton + leakage checklist (date/id/group handling). - SHAP story slots: global plot + top-5 business sentences. ## Deterministic checks Holdout PR-AUC >= 0.72; threshold policy cites cost matrix; SHAP artifacts present. ## 100-pt rubric - Pipeline + leakage checklist: 20 - Model quality vs bar: 20 - SHAP story (top-5 + honesty): 20 - Threshold policy economics: 20 - Holdout bar met: 10 - Reproducibility: 10