Airfare Forecasting using Machine Learning to Predict Prices

Abstract

Abstract: The issue of predicting ticket 111 costs is the focus of this essay. With the assumption that these characteristics have an impact on the cost of an airline ticket, a set of features typical of a normal flight is determined for this purpose. Eight cutting edge machine learning (ML) models using the characteristics are trained to forecast the cost of airline tickets, and the models' output is contrasted with one another. This work examines how the feature set used to represent an airline affects accuracy as well as the prediction accuracy of each model. To train each machinelearning model for the trials, a unique dataset including 1814 Aegean Airlines data flights for a particular foreign destination (from Thessaloniki to Stuttgart) was created. Key Words: Airfare price prediction, Machine learning models, Feature dependency, Regression accuracy.