Enhancing Travel Experience: Predicting Flight Delays for Informed Journey Planning

Flight delays pose significant inconveniences for travelers, potentially causing missed connections, schedule adjustments, and time wastage. This study presents a machine-learning driven approach to mitigate these challenges by developing an application that predicts flight delays, empowering passengers with insights to minimize travel disruptions. Leveraging diverse machine learning algorithms and datasets from the United States Department of Transportation and the National Oceanic and Atmospheric Administration Service, our model aids travelers in making informed decisions by suggesting optimal flight times and carriers based on historical flight data and weather conditions. Addressing the issue of imbalanced data, we explore techniques such as Synthetic Minority Over-sampling Technique (SMOTE) and under-sampling. Our comparative analysis highlights the superior performance of Light Gradient Boosting Machine (L-GBM) in predicting flight delays. With an F1-score of and an AUC value of , our study offers a promising solution to enhance passenger experiences through improved flight recommendations.