Abstract
Online purchases have become the major method of purchasing airline tickets due to the rapid expansion and widespread application of web technologies. Customers find it difficult to purchase tickets at a reduced cost due to the lack of information regarding pricing issues. Existing prediction methods that rely on tomorrow's price projection may be inaccurate, resulting in missed opportunities to purchase tickets in the future. Hence, our proposed system deals with the problem of calculating flight prices that keep changing dynamically due to a lot of factors such as time, season, duration, special events, climate change, etc. To reduce middle-agent cost and difficulties on the customer part, an easy-to-use prediction calculator that provides flight search and booking facilities all in just one-click is developed. The data analysis part of the proposed system is based on datasets collected from open-source platforms, and predicted values use Random Forest Regression as the model with the prediction accuracy being 79% for test data. The prediction model undergoes hyperparameter tuning and adaptive boosting to increase efficiency gaining an increase to 81% from the usage of the former and 85% for the latter.
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Sherly Puspha Annabel, L., Ramanan, G., Prakash, R., Sreenidhi, S. (2023). Machine Learning-Based Approach for Airfare Forecasting. In: Saraswat, M., Chowdhury, C., Kumar Mandal, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 552. Springer, Singapore. https://doi.org/10.1007/978-981-19-6634-7_65
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DOI: https://doi.org/10.1007/978-981-19-6634-7_65
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