State-of-art machine learning techniques to predict airlines delay

Abstract

Nowadays everyone is becoming extremely busy that makes them follow the time very precisely. In the commercial aviation sector, flight delays are a significant cause of dissatisfaction with customers. So, the prediction of flight delays plays a pivotal role in travelers' comfort and alleviates the airline's economic losses. This paper analyzes the performance of the machine learning algorithms such as Random Forest, AdaBoost, and XGBoost classifier to handle the delay time prediction of flight by considering multiple parameters such as weather conditions, flight schedule, etc., that are responsible for flight delay. The paper does a detailed comparative analysis of the algorithms used. Our study can also be applied to various other applications, such as predicting demand-based airline fares.