 Airline dynamic price prediction using machine learningKushal Kumar Ruia, Utkarsh Daga, Aditya Tripathi, Maruf Nissar Rahman and Saurabh Bilgaiyan International Journal of Productivity and Quality Management, 2022, vol. 36, issue 2, 187-207 Abstract: Nowadays, to optimise revenue, airlines use dynamic pricing techniques. Earlier demand was predicted by retrospective analysis of sales data across various sales channels. This technique becomes less reliable when the volatility of the market and competition increases. In this paper, different models have been built using machine learning techniques like regression which could predict the ticket prices of airlines based on features such as journey route, historical ticket price, etc. Along with this, the dependency of important features on accuracy has also been studied. An experimental study of this paper reveals that regression algorithms can be used to predict airline price as the highest R2 score achieved on testing data is 0.85 using gradient boosting regressor. The objective of this paper is to understand the key features that affect the airline price and compare the accuracy of the different machine learning algorithms with the selection of different features. Keywords: machine learning; prediction model; air tickets prices; regression; R 2 score; cross-validation. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijpqma:v:36:y:2022:i:2:p:187-207 Access Statistics for this article More articles in International Journal of Productivity and Quality Management from Inderscience Enterprises Ltd |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.