Predicting Delay in Flights using Machine Learning

Abstract

Flight delays are being caused by an increase in air traffic as a result of the aviation industry's expansion. There are both economic and environmental consequences to flight delays. The task of supervising air traffic is growing more and more difficult. Many factors contribute to flight delays, such as security concerns, mechanical faults, weather conditions, airport congestion, and so on. This paper proposes machine learning algorithms such as Random Forest, Decision Tree, MLP Classifier, Naive Bayes, and KNN classifier to alleviate these problems. Predicting Aircraft Delays, which is a major source of economic output for many countries, is the primary goal of this study, which uses machine learning algorithms to identify and eliminate flight delays. This will help save a significant amount of money in the long run.