Flight delay prediction based on machine learning method

The increasing growth of the aviation industry has been accompanied by increasing problems of flight delays. This research aims to predict flight delays by employing machine learning models to enhance the effectiveness of the aviation transportation system. Domestic flight takeoff and landing data from the United States in January 2019 and 2020 were used for the research, and data preprocessing was carried out. Four machine learning models (XGBoost, Random Forest, LightGBM, and Gradient Boosting Tree) were used for model training, followed by an assessment of their respective performances on a designated test set. The outcomes indicate that the XGBoost model outperforms others in accuracy and the AUC of the test set, followed by the LightGBM model, while the Random Forest model shows overfitting on the training set. This research offers a comprehensive analysis and resolution to the issue of flight delay prediction, which helps airlines and passengers to make rational decisions.