FlyDelayNo: Machine learning approach for prediction of on-time performance of flights

Abstract

"The aviation industry grapples with the persistent challenge of flight delays, impacting millions of passengers and inflicting significant economic and operational strains on airlines and airports. These delays arise from a confluence of factors including adverse weather conditions, technical failures, and air traffic control constraints, leading to a ripple effect of disruptions. This project addresses the problem by developing a system that not only predicts flight delays with higher accuracy but also provides actionable insights for stakeholders to mitigate the associated inconvenience and costs. To tackle this pervasive issue, the approach integrates a blend of historical data analysis, real-time information processing, and advanced machine learning algorithms. The author constructs a predictive model harnessing a rich dataset comprising historical flight performance, weather patterns, and air traffic control data. The model is designed to evolve through machine learning, constantly refining its accuracy with each new data input. The methodology also encompasses the development of a user-friendly interface that provides real-time delay predictions, enabling passengers and airlines to make informed decisions promptly. As the implementation phase is still underway, initial results are pending to establish the efficacy of the proposed system. However, preliminary data analysis and model training suggest a promising direction towards achieving the goal of delivering timely and accurate flight delay predictions. The ultimate expectation is that the system will significantly enhance operational efficiency for airlines and airports while improving the passenger experience by providing reliable delay forecast."