Present and critique an airline delay analysis

In this role-play, you get 15 minutes to study a slide deck about forecasting airline departure delays, followed by 20 minutes to present your findings to the Director of Operations. The existing work fits a linear regression where the date is treated as a plain integer, and its charts combine weekdays and months with no adjustments for seasonal patterns.

Tasks:

1. Evaluate the feature engineering choices — for instance, whether date should be categorical or cyclical, how weather data is joined, whether route-level effects are captured, and leakage hazards such as including wheels-off time.
2. Recommend a modeling strategy — regression versus classification for delays over 15 minutes — along with suitable metrics (RMSE/MAE or AUC-PR/cost) and a cross-validation scheme that honors chronological order.
3. Identify two charts you would swap out, describe the replacements and the reasoning, and craft a plain-language story that connects the insights to concrete actions, such as adding crew buffers by route and time of day.
4. Give a specific recommendation that cuts compensation payouts by 5% while keeping cancellations flat, and lay out an experiment or KPI plan to confirm the effect within four weeks.

Overview: This case tests a data scientist on time-series feature engineering and seasonality, joining data and spotting leakage, hierarchical route and airport effects, choosing between regression and classification, selecting metrics, time-aware cross-validation, critiquing visualizations, and designing experiments or KPIs.