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@article{199739,
        author = {Ved Waje and Abhirat More and Pranita Bannore and Harshita Lohana},
        title = {JetLagged - Prediction of Airline Flight Delay},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {14857-14863},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199739},
        abstract = {Flight delays and cancellations are prevalent challenges in the airline industry, arising from factors such as weather conditions, technical malfunctions, air traffic congestion, and crew availability. This project focuses on developing a deep learning-based application to identify and analyze the primary causes of flight disruptions. The application integrates diverse data sources, including weather reports, flight schedules, and historical delay records, to detect patterns and correlations contributing to delays. An advanced predictive approach is employed using the LightGBM regressor combined with Op- tuna for Bayesian optimization, ensuring high accuracy and efficient model performance. By analyzing complex datasets, the application provides real-time alerts and recommendations for passengers, aiding airlines in enhancing decision-making and operational efficiency. Ultimately, this project aims to improve air travel reliability, reduce operational costs, and contribute to the advancement of smart transportation systems},
        keywords = {Flight delay prediction, Machine learning, Classification, Light GBM, Linear Regression.},
        month = {April},
        }