Research Information System
Veri Bilimi Dergisi, vol.4, no.2, pp.57-61, 2021 (Peer-Reviewed Journal)
At present, the number of passengers preferring to use the airline is increasing with each passing day. Thus, correctly analysing the airfare prices is essential to raise awareness of passengers. Some researchers have applied different kinds of Machine Learning (ML) algorithms to predict the airfare prices. However, to the best of our knowledge, penalized regression methods have not been used to analyse the airfare prices. Ridge, Lasso and Elastic Net regressions are penalized regression methods. The dataset used in this study consists of 1814 one-way flights from Greece to Germany. The developed Ridge, Lasso and Elastic Net methods were achieved to provide convincing results (MSE) for airfare prices analysis (Ridge:160103, Lasso:159280, Elastic Net:174203). The results and findings reveal that the proposed Lasso method is potentially better than the others in the analysis of datasets consisting one-way of flights.