ARGE-SCALE AIRLINE TICKET PRICE PREDICTION USING ENSEMBLE MACHINE LEARNING ALGORITHMS

Authors : Muzaffer Ertürk, Murat Emeç, Ayşe Atılgan Sarıdoğan, Nabi Küçükgergerli

Pages : 436-446

Doi:10.52122/nisantasisbd.1719245

View : 81 | Download : 70

Publication Date : 2025-06-30

Article Type : Research Paper

Abstract :Airline ticket price prediction represents a highly complex and dynamic challenge, primarily due to the multifactorial and time-sensitive nature of airline pricing strategies. Accurate forecasting of ticket prices holds substantial value for both consumers, by enabling optimal purchase decisions, and airline companies, by supporting data-driven revenue management and dynamic pricing. In this study, we conduct a comprehensive analysis of a large-scale flight booking dataset obtained from the “Ease My Trip” platform, encompassing over 300,000 records of flight options between major Indian metropolitan cities. A suite of advanced machine learning algorithms, including Linear Regression, CatBoost, LightGBM, Random Forest, and XGBoost, was implemented to model and predict ticket prices. A comparative evaluation of these models reveals that ensemble and boosting algorithms, particularly XGBoost and Random Forest, deliver superior predictive performance, with XGBoost achieving an R² of 0.98 and a mean absolute error (MAE) of $2,035.51. These findings underscore the critical importance of employing robust machine learning techniques and incorporating a diverse set of features for reliable airline ticket price prediction. The results offer valuable insights for both passengers seeking cost-effective travel and airline stakeholders aiming to optimise revenue management strategies.
Keywords : Uçak bileti fiyat tahmini, Makine öğrenimi, Topluluk yöntemleri, Uçak ücreti tahmini, Büyük veri analitiği

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