Analyzing Flight Delays with Predictive Machine Learning Tools

Predicting aircraft delays is essential for commercial aviation decision-making because of the detrimental effects they have on airlines, airports, and passengers. Commercial airlines constantly strive to reduce disruptions since delays lead to large financial losses. Traffic congestion has increased as a result of the aviation industry’s explosive growth, and delays are frequently brought on by unanticipated circumstances like bad weather. This work intends to evaluate airplane delay data in order to precisely estimate delays using a variety of machine learning approaches. We seek to identify the best method for delay prediction by comparing algorithms like Bayesian Regression, Random Forest, Decision Tree, Logistic Regression, and Gradient Boosting. When these delays are consistently predicted, airlines can take preventative action and passengers can better plan for interruptions.