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.
SVM-random forest
data mining
₹12000 (INR)
IEEE 2017