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.