FLIGHT DELAY PREDICTION USING MACHINE LEARNING

Flight delays are a persistent issue in the aviation industry, affecting passenger satisfaction, airline operations,
and airport efficiency. These delays can be caused by various factors such as weather conditions, technical
issues, air traffic congestion, or crew unavailability. Unpredictable delays not only inconvenience travelers but
also lead to significant financial losses for airlines and logistical disruptions across the network. As the volume
of air traffic continues to grow, there is an urgent need for systems that can forecast potential delays accurately
and in advance. This project proposes a machine learning-based flight delay prediction system that leverages
historical flight data along with additional features such as weather reports, flight schedules, and airport traffic
information. Multiple machine learning algorithms—including Random Forest, Decision Tree, and XGBoost—
were trained and evaluated to determine the most effective model for predicting delays. Data preprocessing
techniques such as feature selection, normalization, and label encoding were applied to ensure data quality and
model performance. The model predicts whether a given flight is likely to be delayed, helping airlines and
passengers plan accordingly. The results demonstrate that machine learning can significantly enhance the
accuracy of delay predictions compared to traditional rule-based systems. By integrating predictive analytics
into airline operations, the system can aid in resource allocation, improve passenger communication, and reduce
cascading delays across routes. This approach not only offers a practical solution to a real-world problem but
also highlights the potential of artificial intelligence in optimizing air travel operations.