Comparative study of ensemble machine learning techniques for airline delay prediction

Abstract

Airlines generate highest economy domain for most of the countries in the world. Air-traffic jamming causing flight delays, this problem face by aviation industry. Due to flight delay more impact on economic as well as environmental properties. Air traffic management is challenging to aviation business. Due to this challenge make huge loss as well as budget loss also. So many reasons impact in flight delay to weather conditions, security problems, airport traffic and mechanical problems etc. We propose hybrid machine learning models for prediction of airline delay and saves huge turnovers using ensemble machine learning models. Our research talk about few machine learning algorithms predicts the more than 90% accuracy rate. XGBoost algorithm provide more than 90% accuracy. But one thing talks about this airline delay prediction, it purely based on dataset with corresponding suitable algorithm.