Analyzing Flight Delays with Predictive Machine Learning Tools

Analyzing Flight Delays with Predictive Machine Learning Tools

Dr. Rishabh Garg
Assistant Professor Department of Computer Applications

Lovely Professional University Phagwara, India

Email: dr.rishabhgarg@example.com

Kammari Chethan Chary
PG Student

Department of Computer Applications Lovely Professional University Phagwara, India

Email: chethan.k@example.com

B. Sankar Narayana
PG Student

Department of Computer Applications Lovely Professional University Phagwara, India

Email: sankar.n@example.com

Abstract—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.

Keywords:Flight Prediction, Machine Learning, Logistic Regression, Decision Tree, Bayesian Ridge, Random Forest, Gradient Boosting, U.S. Flight Data.