Flight Delay Prediction Using Random Forest with Enhanced Feature Engineering

Delays in air travel continue to disrupt aviation operations, causing passenger inconveniences and economic losses. This research proposes a flight delay prediction framework using a random forest model enhanced with carefully engineered features from flight and weather data. Key features introduced include historical delay metrics and network centrality attributes designed to capture flight network dependencies. The proposed model achieves remarkable predictive performance, with 92.36% accuracy, 98.29% precision, 86.22% recall, 91.86% F1 score, and 96.78% AVC-ROC. These results demonstrate the potential of combining Random Forest with domain-specific features to predict flight delays effectively.