Build and evaluate a full ML pipeline

You must predict both (1) probability that a user will spend >$0 in the next 7 days (classification) and (2) expected spend in the next 7 days (regression). Training data are events and orders up to 2025-08-31; predictions start on 2025-09-01. Design an end-to-end pipeline: feature generation (including time-windowed aggregates), leakage controls (e.g., excluding post-cutoff signals like refund_time), time-based cross-validation, handling class imbalance, and model choices for each task. Specify metrics (e.g., PR-AUC, calibrated Brier, pinball loss for quantiles), a calibration plan, and how you’d pick a threshold given an asymmetric cost matrix. Describe how you’d detect and mitigate segment-specific regressions, choose and justify an offline/online evaluation plan (with rollout and holdbacks), and set up post-deployment monitoring for drift, label delay, and model decay. Finally, provide two concrete examples of features that are predictive but risky for leakage and how you’d re-specify them safely.