Flight Delay Prediction Using Machine Learning: A Comparative Study of Ensemble Techniques

Abstract

Machine learning is a promising tool for predicting flight delays. Accurately predicting flight delays in aviation enhances operational efficiency and passenger contentment. Accurate predictions are critical to improving operational efficiency and passenger satisfaction. The study aims to develop a robust predictive model for domestic flights and identify key variables affecting delays. This investigation transcends the confines of traditional prediction methodologies by embracing the potency of ensemble techniques, thereby imbuing the model with the capacity to capture intricate patterns and dependencies within the dataset holistically. By adopting a comparative approach, this study systematically evaluates a spectrum of ensemble methods, unravelling their strengths and weaknesses in the context of flight delay prediction. The study’s results highlight the strong predictive performance of stacking methods (92.4%) and random forest (91.2%), which effectively capture patterns while cautioning about the sensitivity of AdaBoostClassifier (51.6%) to noisy data. This research has the potential to augment the precision and applicability of flight delay prediction, fostering operational enhancements within the aviation industry while increasing passenger satisfaction.