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@article{174975,
        author = {Bondali Venkatesh and Gande Dinesh and Bysa Praneeth kumar and Kotala Manikanta and Mr.S.Ramadoss},
        title = {Flight delay prediction using machine learning algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1202-1208},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174975},
        abstract = {Accurate flight delay prediction is fundamental to establish the more efficient airline business. Recent studies have been focused on applying machine learning methods to predict the flight delay. Most of the previous prediction methods are conducted in a single route or airport. This paper explores a broader scope of factors which may potentially influence the flight delay, and compares several machine learning-based models in designed generalized flight delay prediction tasks. To build a dataset for the proposed scheme, automatic dependent surveillance broadcast (ADS-B) messages are received, pre-processed, and integrated with other information such as flight schedule, and airport information. The designed prediction tasks contain different classification tasks and a regression task. Experimental results show that long short-term memory (LSTM) is capable of handling the obtained aviation sequence data, but overfitting problem occurs in our limited dataset. Compared with the previous schemes, the proposed random forest Logistic Regression, Decision Tree-based models can obtain higher prediction accuracy (90.2% for the binary classification) and can overcome the overfitting problem.},
        keywords = {},
        month = {April},
        }