Airline-Specific Flight Delay Prediction with Tree-Based Models and Network Metrics

Flight delays present a significant challenge in modern air traffic management and affect airlines, passengers, and the economy. This study proposes a comprehensive approach to predicting flight delays using tree-based machine learning models, integrating flight and weather data with advanced feature engineering techniques. New features, including historical delay metrics and network centrality measures, are derived to enhance predictive accuracy. The dataset is grouped by airlines to account for variations in flight delay patterns across different airlines. Tree-based ensemble models, including random forest, XGBoost, CatBoost, lightGBM, and extra trees, are employed. Results show that prediction metrics improve when models are trained on airline-specific data compared to using the entire dataset with airlines as a feature. For airline-specific analysis, the random forest model achieves the highest average accuracy (92.6%) and precision (97.0%), while the extra trees model achieves the highest average recall (88.5%) and AUC-ROC (97.5%), and both models achieve the highest F1-score (92.2%). These findings emphasize the importance of analyzing airline-specific dynamics and provide actionable insights for mitigating delays. This study advances flight delay prediction by integrating domain-specific features with robust machine learning models.