Machine Learning Application: Flight Delay Prediction

Yuan Chai

2024

Abstract

As China's civil aviation industry continues to grow, air travel is becoming a popular means of transportation. However, flight delays have become a significant issue for passengers, leading to various impacts such as wasted time, financial losses, and emotional stress. For airlines, delays increase operational costs and damage brand reputation. This paper aims to predict flight delays at Chinese airports using advanced machine learning techniques, with the goal of improving operational efficiency and providing better service to passengers. The predictive model presented in this work is designed to foresee flight arrival delays by employing supervised machine learning algorithmThe paper provides a predictive model that uses supervised machine learning methods to anticipate flight arrival delays. Flight data from numerous Chinese airports, along with weather data, were collected and used during the training of the predictive model. A Multi-Layer Perceptron was applied to build the flight delay prediction model, and extensive data preprocessing was conducted. Hyperparameter tuning was carried out to optimize performance. The model was evaluated using cross-validation to ensure its accuracy and generalization ability. Finally, optimization techniques were applied to address any shortcomings and further enhance the model’s performance. The Validation score, loss and Accuracy rate of this paper on the data set were 0.958, 0.132 and 92.6%, respectively.

Paper Citation

in Harvard Style

Chai Y. (2024). Machine Learning Application: Flight Delay Prediction. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 5-9. DOI: 10.5220/0013486400004619

in Bibtex Style

@conference{daml24,
author={Yuan Chai},
title={Machine Learning Application: Flight Delay Prediction},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={5-9},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013486400004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Machine Learning Application: Flight Delay Prediction
SN - 978-989-758-754-2
AU - Chai Y.
PY - 2024
SP - 5
EP - 9
DO - 10.5220/0013486400004619
PB - SciTePress