  Airlines are critical today for carrying people and commodities on time. Any
delay in the schedule of these planes can potentially disrupt the business and
trade of thousands of employees at any given time. Therefore, precise flight
delay prediction is beneficial for the aviation industry and passenger travel.
Recent research has focused on using artificial intelligence algorithms to
predict the possibility of flight delays. Earlier prediction algorithms were
designed for a specific air route or airfield. Many present flight delay
prediction algorithms rely on tiny samples and are challenging to understand,
allowing almost no room for machine learning implementation. This research
study develops a flight delay prediction system by analyzing data from domestic
flights inside the United States of America. The proposed models learn about
the factors that cause flight delays and cancellations and the link between
departure and arrival delays.

Machine Learning