Build and evaluate airline delay prediction model

You are given several CSVs for the classic airline delay challenge with columns like flight_date, carrier, flight_num, origin, dest, sched_dep, sched_arr, dep_delay_min, arr_delay_min, distance, aircraft_type, weather_features_*, and holiday_flag. a) Define a binary target and justify it: e.g., late_arrival = arr_delay_min > 15. b) Detail a leakage-aware feature set: include weather forecasts at origin/dest, route history aggregates up to t−7 days, time-of-day, day-of-week, month, distance, carrier- and airport-level rolling stats; exclude or properly lag any features that encode future information (e.g., actual arrival times). c) Specify a time-based split (e.g., train up to 2024-06, validate 2024-07–2024-09, test 2024-10–2025-03), class imbalance handling, and primary metrics (PR-AUC, calibrated Brier). d) Compare a strong baseline (regularized logistic regression with target encoding) versus gradient boosting (e.g., XGBoost/LightGBM): hyperparameters to search, early stopping, monotonic constraints if used. e) Explain how you would do rolling-origin cross-validation and backtesting of threshold policies (e.g., proactive swaps or buffers) with cost-sensitive evaluation that prices false negatives at 5× false positives. f) Productionization: 20 ms/flight latency budget, 50 MB model size, feature store vs on-the-fly aggregation, drift detection, and periodic retraining cadence. g) Deliverables: reproducible notebook, clean data pipeline, model cards with fairness slices across carriers/airports, and an exec summary with recommended operational policy and estimated ROI.