Authors : Adwait Chavan; Ishika Rathod; Sarika Bobde

Volume/Issue : Volume 9 - 2024, Issue 9 - September

---

Google Scholar : https://tinyurl.com/zczm54xf

Scribd : https://tinyurl.com/yc3z5axc

DOI : https://doi.org/10.38124/ijisrt/IJISRT24SEP1688

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

---

Abstract : Flight fare prediction is a vital component in helping consumers make informed decisions regarding travel expenses. Airline ticket prices fluctuate due to a variety of factors such as demand, time of purchase, and flight routes. In this research, we propose a machine learning-based solution for predicting flight fares using historical data. Models like Random Forest, Gradient Boosting, and Support Vector Machines (SVM) are employed to analyze flight data and produce reliable predictions. This study demonstrates how predictive models can benefit customers by offering insights into pricing trends, thus optimizing their flight booking process.

Keywords : Flight Fare Prediction, Machine Learning, Random Forest, Dynamic Pricing, Predictive Modeling, SVM, Gradient Boosting.

References :

1. Kakaraparthi, A., & Karthick, V. (2022). A Secure and Cost-Effective Platform for Employee Management System Using Lightweight Standalone Framework over Diffie Hellman’s Key Exchange Algorithm. ECS Transactions, 107(1), 13663–13674. doi:10.1142/S0217590821500521.
2. Tziridis, K., Kalampokas, Th., & Papakostas, G. A. (2017). Airfare Prices Prediction Using Machine Learning Techniques. 25th European Signal Processing Conference (EUSIPCO). doi:10.23919/EUSIPCO.2017.8081387.
3. Groves, W., & Gini, M. (2013). An Agent for Optimizing Airline Ticket Purchasing. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (pp. 593–600). doi:10.5555/2484920.2485049.
4. Brown, N., & Taylor, J. (2004). Air Fare: Stories, Poems & Essays on Flight. Sarabande Books.
5. Lok, J. C. (2018). Prediction Factors Influence Airline Fuel Price Changing Reasons. International Journal of Forecasting, 34(3), 453–462. doi:10.1016/j.ijforecast.2018.01.006.
6. Panwar, B., Dhuriya, G., Johri, P., Yadav, S. S., & Gaur, N. (2021). Stock Market Prediction Using Linear Regression and SVM. 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). doi:10.1109/ICACITE51222.2021.9404733.
7. Purey, P., & Patidar, A. (2018). Stock Market Close Price Prediction Using Neural Network and Regression Analysis. International Journal of Computer Sciences and Engineering, 6(8), 266–271. doi:10.26438/ijcse/v6i8.266271.
8. Ataman, G., & Kahraman, S. (2021). Stock Market Prediction in BRICS Countries Using Linear Regression and Artificial Neural Network Hybrid Models. The Singapore Economic Review, 66(5), 1-19. doi:10.1142/S0217590821500521.
9. Chawla, P., Sharma, A., & Kumar, M. (2020). Flight Fare Prediction: A Regression Approach Using Machine Learning Algorithms. International Journal of Advanced Research in Computer Science, 11(1), 112–118. doi:10.26483/ijarcs.v11i1.6478.
10. Wilson, P., & Böhme, T. (2020). Revenue Management with Machine Learning: Dynamic Airline Pricing Prediction. Journal of Revenue and Pricing Management, 19(5), 344–362. doi:10.1057/s41272-020-00255-2.