Applying Predictive Analytics to Flight Delays

How have you accounted for - Flights getting delayed and Airlines fixing the issue - during your model training. If you haven't accounted for this, I assume the precision wouldn't be good for the future data.

Predicting flight delays has been a favorite topic of research in the Air Traffic Management (ATM) community. There has been ongoing research on this topic even before Machine Learning gained traction. Accurate predictions of far out delays is of course impressive but refining these predictions to a point of making a product that someone will actually pay for is going to be steep challenge. The primary challenge is to account for airline operations through publicly available data e.g., airlines usually pad schedules to account for delays, flight arriving at the end of the day are more likely to get delayed, flights between hubs are less likely to be delayed - data features that can model these do not exist in public data. Instances that cause the most disruption (mechanical failure, staff shortage etc.) are impossible to predict with just schedule data and this is where people would be willing to pay of such an app.

Yes, you make very good points here about seasonality and weather. We have looked at training the model on a small window of flight dates vs randomly sampling across all dates and the former performed better in testing. We wanted to limit our modeling complexity to random forest and gradient boosting as an initial proof of concept here. Our plan is to retrain the current weather model weekly while monitoring performance, store the data, and then maybe look to using a more complex model like a neural net to train a model using data across all dates.