Review of Prediction of Delay in Flights using Machine Learning Techniques

Abstract

Predicting flight delays is crucial for the aviation industry to improve operational efficiency and enhance passenger experience. Machine learning techniques have emerged as powerful tools for forecasting flight delays by leveraging historical data and various features. This review provides an overview of the prediction of delay in flights using machine learning techniques. The review highlights the importance of data quality in achieving accurate predictions. Comprehensive and reliable datasets, encompassing factors such as historical flight data, weather conditions, airport congestion, and aircraft information, are essential for robust models. Effective feature engineering is another crucial aspect, as it enables capturing relevant indicators such as departure/arrival time, airline, airport, weather conditions, previous delays, and holidays