Airfare prices prediction using machine learning techniques
Autor: | Th. Kalampokas, George A. Papakostas, K. Tziridis, Konstantinos I. Diamantaras |
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Rok vydání: | 2017 |
Předmět: |
Dependency (UML)
Computer science business.industry 02 engineering and technology Atmospheric model computer.software_genre Machine learning Set (abstract data type) Support vector machine 020204 information systems Ticket 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Data mining State (computer science) business computer |
Zdroj: | EUSIPCO |
DOI: | 10.23919/eusipco.2017.8081365 |
Popis: | This paper deals with the problem of airfare prices prediction. For this purpose a set of features characterizing a typical flight is decided, supposing that these features affect the price of an air ticket. The features are applied to eight state of the art machine learning (ML) models, used to predict the air tickets prices, and the performance of the models is compared to each other. Along with the prediction accuracy of each model, this paper studies the dependency of the accuracy on the feature set used to represent an airfare. For the experiments a novel dataset consisting of 1814 data flights of the Aegean Airlines for a specific international destination (from Thessaloniki to Stuttgart) is constructed and used to train each ML model. The derived experimental results reveal that the ML models are able to handle this regression problem with almost 88% accuracy, for a certain type of flight features. |
Databáze: | OpenAIRE |
Externí odkaz: |
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