Flight Price Prediction using Machine Learning

Abstract

This article will examine the issue of foreseeing air passages. To do this, a great deal of things has been distinguished, and you believe that the qualities of a typical airplane will influence the cost of aircraft tickets. Highlights are utilized in eight current AI strategies, used to foresee airplane costs, and model execution is thought about. As well as cautiously anticipating each model, this paper cautiously inspects the data used to distinguish carrier tickets.

Introduction

The travel and airline industry is rapidly evolving, with airfare pricing being highly dynamic and influenced by various economic, marketing, social, and operational factors. Predicting flight ticket prices is challenging due to this volatility. Recent advances in Machine Learning (ML) enable better forecasting of airfare costs by analyzing large datasets and complex patterns.

This study reviews existing research and ML approaches—such as regression trees, support vector machines, and neural networks—that have been applied to predict airfares with high accuracy. It focuses on Indian domestic routes, analyzing millions of ticket records collected over months to identify patterns and optimal times to buy cheaper tickets.

The proposed system integrates data cleaning, feature extraction, and training ML models (like K-Nearest Neighbors and Random Forest) on publicly available datasets to forecast airfare prices. The workflow includes data collection, preprocessing, model training, hyperparameter tuning, testing, and evaluation. Performance metrics such as F1 score and accuracy validate model effectiveness, with Random Forest and Decision Tree regressors showing high predictive power.

The study aims to provide consumers with reliable airfare price predictions to help them make informed purchasing decisions and save money, leveraging advanced ML algorithms and real-world data.

Conclusion

The venture depends on research in the \"Airplane Prices\" area. We have gathered flight cost data on the kaggle site and have shown that it is feasible to anticipate aircraft costs in view of duty data. The aftereffects of the review show that the ML model is a satisfactory instrument at foreseeing airplane costs. Other significant elements in anticipating carrier tickets are information assortment and determination, where we reach significant inferences. Because of our examination, we take care of various elements that altogether affect aircraft ticket booking. Notwithstanding the chose things, there are alternate ways of working on the legitimacy of the speculation. It is feasible to grow this action in the future by anticipating the expense of aircraft tickets for the whole carrier program.

References

[1] Garrow, L. A., Jones, S. P., & Parker, R. A. (2007). Airline revenue management: Seat allocation considering passenger seat preferences. Transportation Research Part B: Methodological, 41(7), 819-837. [2] Etzioni, O., Tuchinda, R., Knoblock, C. A., & Yates, A. (2003). To buy or not to buy: mining airfare data to minimize ticket purchase price. Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. [3] Chakraborty, T., Roy, S., & Basu, A. (2019). Flight Fare Prediction using Machine Learning Techniques. International Journal of Computer Applications, 178(42), 10-14. [4] https://github.com/skillcate/flight-price-prediction

Copyright

Copyright © 2025 V Satish. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.