SMART AVIATION: SECURE, COLLABORATIVE FLIGHT DELAY PREDICTION WITH MACHINE LEARNING

Abstract

Airlines play a crucial role in global mobility, with passenger satisfaction closely tied to flight punctuality. Leading carriers such as Qatar Airways and Etihad Airways have set high benchmarks for service and innovation. However, delays and cancellations—stemming from weather, congestion, technical issues, or logistical challenges—disrupt schedules, damage reputations, and impact profitability. Traditional manual analysis often struggles to deliver timely insights given the sheer volume of aviation data. This paper introduces a secure, collaborative machine learning platform for predicting flight delays, leveraging supervised learning on publicly available Kaggle data. The model identifies delay patterns, root causes, and potential conflicts before they arise, improving situational awareness and enabling proactive interventions. By facilitating data sharing and collaboration among airlines, airports, and regulators, the platform promotes smarter, more passenger-centric air travel management, transforming flight delay handling into a more efficient and anticipatory process