Machine Learning Approach of Predicting Airline Flight Delay using Naïve Bayes Algorithm

Ahmad Adib Baihaqi Shukri; Syarifah Adilah Mohamed Yusoff; Saiful Nizam Warris; Mohd Saifulnizam Abu Bakar; Rozita Kadar

doi:10.24191/jcrinn.v9i2.460

Machine Learning Approach of Predicting Airline Flight Delay using Naïve Bayes Algorithm

Authors

Ahmad Adib Baihaqi Shukri College of Computing, Informatics, and Mathematics, UiTM Cawangan Terengganu Branch, Kuala Terengganu Campus, Kuala Terengganu, Malaysia
Syarifah Adilah Mohamed Yusoff Jabatan Sains Komputer dan Matematik, UiTM Cawangan Pulau Pinang, Malaysia
Saiful Nizam Warris Department of Computer Sciences and Mathematics, UiTM Pulau Pinang Branch, Permatang Pauh Campus, Penang, Malaysia
Mohd Saifulnizam Abu Bakar Department of Computer Sciences and Mathematics, UiTM Pulau Pinang Branch, Permatang Pauh Campus, Penang, Malaysia
Rozita Kadar Department of Computer Sciences and Mathematics, UiTM Pulau Pinang Branch, Permatang Pauh Campus, Penang, Malaysia

DOI:

https://doi.org/10.24191/jcrinn.v9i2.460

Keywords:

machine learning, Flight Delay, Naive bayes, Prediction

Abstract

The aviation industry plays a critical role in global transportation, facilitating economic growth and revolutionizing travel. However, flight delays have become a growing concern, impacting both airlines and passengers. This study aims to study the Naïve Bayes algorithm for flight delay prediction. The objective is to develop a reliable flight delay prediction model using the Naïve Bayes algorithm and evaluate its performance. The data set that records flight delay and cancellation data from U.S Department of Transportation’s (DOT) was used for the prediction. This study has modified the parameter tuning for Gaussian Naïve Bayes to identify optimum values specifically to construct model for this flight delay dataset. The performance of parameters tuning Gaussian Naïve Bayes model was compared with another two well-known algorithms which are K-Nearest Neighbors (KNN) and Support Vector Machine (SVM)). The KNN and SVM algorithms were also trained and tested to complete the binary classification of flight delays for benchmarking purposes. The evaluation of algorithms was fulfilled by comparing the values of accuracy, specificity and ROC AUC score. The comparative analysis showed that the Gaussian Naïve Bayes has the best performance with an accuracy of 93% and KNN has the worst performance with ROC AUC score 63%.

Downloads

Download data is not yet available.

References

Biswas, M., Dhoom, T., & Barua, S. (2018). Weather forecast prediction: An integrated approach for analyzing and measuring weather data. International Journal of Computer Applications, 182(34), 20-24.

Buhalis, D. (2004). eAirlines: Strategic and tactical use of ICTs in the airline industry. Information & Management, 41(7), 805-825. https://doi.org/10.1016/j.im.2003.08.015

Esmaeilzadeh, E., & Mokhtarimousavi, S. (2020). Machine learning approach for flight departure delay prediction and analysis. Transportation Research Record, 2674(8), 145-159. https://doi.org/10.1177/0361198120930014

Freestone, R. (2009). Planning, sustainability and airport-led urban development. International Planning Studies, 14(2), 161-176. https://doi.org/10.1080/13563470903021217

Gnaneswar, B., & Jebarani, M. E. (2017). A review on prediction and diagnosis of heart failure. In 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (pp. 1-3). IEEE Xplore. https://doi.org/10.1109/ICIIECS.2017.8276033

Gui, G., Liu, F., Sun, J., Yang, J., Zhou, Z., & Zhao, D. (2019). Flight delay prediction based on aviation big data and machine learning. IEEE Transactions on Vehicular Technology, 69(1), 140-150. https://doi.org/10.1109/TVT.2019.2954094

Gultepe, I., Sharman, R., Williams, P. D., Zhou, B., Ellrod, G., Minnis, P., Trier, S., Griffin, S., Yum, S. S., & Gharabaghi, B. (2019). A review of high impact weather for aviation meteorology. Pure and Applied Geophysics, 176, 1869-1921. https://doi.org/10.1007/s00024-019-02168-6

Hayes, A. (2023). Bayes' Theorem: What It Is, the Formula, and Examples. https://www.investopedia.com/terms/b/bayes-theorem.asp

Khaksar, H., & Sheikholeslami, A. (2019). Airline delay prediction by machine learning algorithms. Scientia Iranica, 26(5), 2689-2702. https://doi.org/10.24200/SCI.2017.20020

Kulesa, G. (2003). Weather and aviation: How does weather affect the safety and operations of airports and aviation, and how does FAA work to manage weather-related effects? In The Potential Impacts of Climate Change on Transportation US Department of Transportation Center for Climate Change and Environmental Forecasting. US Environmental Protection Agency; US Department of Energy; and US Global Change Research Program. http://climate.dot.gov/documents/workshop1002/kulesa.pdf

Nigam, R., & Govinda, K. (2017). Cloud based flight delay prediction using logistic regression. In 2017 International Conference on Intelligent Sustainable Systems (ICISS) (pp. 662-667). https://doi.org/10.1109/ISS1.2017.8389254

Ray, S. (2023). Naive bayes classifier explained: Applications and practice problems of naive bayes classifier. Analytics Vidhya, 11(9).

Ramalingam, K. (2007). Challenges in Indian civil aviation and opportunities to designers and manufacturers-2007. Journal on Design and Manufacturing Technologies, 1(1), 5-10.

Tao, J., Man, H., & Yanling, L. (2021). Flight delay prediction based on LightGBM. In 2021 IEEE 3rd International Conference on Civil Aviation Safety and Information Technology (ICCASIT) (pp. 1248-1251). IEEE Xplore. https://doi.org/10.1109/ICCASIT53235.2021.9633431

Tang, Y. (2021). Airline flight delay prediction using machine learning models. In the Proceedings of 2021 5th International Conference on E-Business and Internet (pp. 151-154). ACM Digital Library. https://doi.org/10.1145/3497701.3497725

Venkatesh, Ranjitha, K.V., Venkatesh Prasad, B.S. (2020). Optimization scheme for text classification using machine learning naïve bayes classifier. In A. Kumar, M. Paprzycki, & V. Gunjan, (eds) ICDSMLA 2019. Lecture Notes in Electrical Engineering, vol 601. Springer. https://doi.org/10.1007/978-981-15-1420-3_61

Wang, J., & Pan, W. (2022). Flight delay prediction based on ARIMA. In 2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI) (pp. 186-190). IEEE Xplore. https://doi.org/10.1109/ICCEAI55464.2022.00047

Wang, Z., Liu, H., & Chu, F. (2022). Flight arrival delay time prediction based on machine learning. In 2022 3rd Asia Conference on Computers and Communications (ACCC) (pp. 35-39). IEEE Xplore. https://doi.org/10.1109/ACCC58361.2022.00013

Wickramasinghe, I., & Kalutarage, H. (2021). Naive Bayes: Applications, variations and vulnerabilities: a review of literature with code snippets for implementation. Soft Computing, 25(3), 2277-2293. https://doi.org/10.1007/s00500-020-05297-6