You may be asked one or both of the following machine-learning case questions: 1. Flight-delay prediction case An airline wants a model that predicts departure delay in minutes for each flight 2 hours before scheduled departure. You have historical flight operations data, airport congestion, aircraft and route information, weather forecasts, and crew or maintenance signals. Propose a regression-based approach and explain: - how you define the target and avoid label leakage; - which features you would engineer; - how you would split training and validation data over time; - which evaluation metrics you would use, such as MAE, RMSE, or quantile loss, and why; - how you would handle missing data, outliers, and highly correlated variables; - whether multicollinearity is harmful for prediction, interpretability, or both; - what threshold would make you call a correlation high, and why; - alternatives to dropping correlated features, such as regularization, feature clustering, PCA, or tree-based models; - if you remove a feature, how you would estimate that feature's business impact; - how you would turn model outputs into concrete operational recommendations for the airline. Assume delays are right-skewed, severe delays are rare but costly, and airport-specific operational policies differ across hubs. 2. Watchlist face-recognition case A bank wants to use branch camera feeds to flag whether an entering customer matches a watchlist of known robbers. Describe how you would design the model and decision system. Address: - closed-set versus open-set recognition; - data collection and labeling; - low base rates and class imbalance; - false-positive versus false-negative costs; - threshold selection, calibration, and human review; - fairness, privacy, consent, and legal risk; - latency and on-device versus server inference; - monitoring for drift, spoofing, and adversarial attacks. For both cases, explain not only the modeling approach but also the business and ethical tradeoffs.