DelayDetect: Pre-takeoff Delay Anticipation

Abstract

Abstract: Flight delays pose significant challenges to both passengers and airlines, leading to inconvenience, financial losses, and operational disruptions. A comprehensive system is introduced to address the issue of airline flight delays through the application of machine learning models. Leveraging comprehensive datasets encompassing historical flight records, meteorological data, airport congestion patterns and other variables, this model aims to construct predictive models capable of accurately forecasting the probability of flight delays. Various machine learning methodologies including random forests and regression models, are systematically examined in this predictive task. Performance evaluation is conducted employing established metrics such as accuracy and precision. In conclusion, flight delay anticipation is a valuable tool that can revolutionize the aviation industry by minimizing disruptions, reducing costs, and increasing passenger satisfaction.