Predicting Flight Delays With Error Calculation Using Machine Learned Classifiers

Abstract

One of the biggest issues in the airline industry is flight delays. Air traffic congestion, brought on by the expansion of the aviation industry over the last two decades, has been a major source of flight delays. There is a detrimental effect on the environment and on good fortune when flights are delayed. Commercial airlines can suffer huge financial losses due to flight delays. Consequently, they take all necessary steps to ensure that aircraft delays and cancellations are minimized. In this study, we forecast the likelihood of a certain flight's arrival delay using a variety of machine learning models, including Logistic Regression, Decision Tree Regression, Bayesian Ridge, Random Forest Regression, and Gradient Boosting Regression.