# Context

Every day, in US, there are thousands of flights departures and arrivals (prior to COVID): unfortunately, as you may have noticed yourself, flight delays are not a rare event!!

In this notebook, we play the role of a data scientist working in the travel industry, specifically on air transportation of passengers. We want to explore the data collected by the Department of Transportation (DoT) to understand passengers' behavior, as well as the properties of all flights, across several airline companies.

Now, given historical data about flights in the country, including the delay information that was computed a-posteriori (so the ground truth is available), we want to build a model that can be used to predict how many minutes of delay a flight might experience in the future. This model should provide useful information for the airport to manage better its resources, to minimize the delays and their impact on the journey of its passengers. Alternatively, astute passengers could even use the model to choose the best time for flying, such as to avoid delays.