A MACHINE LEARNING-BASED HOLISTIC FRAMEWORK FOR AIRFARE PRICE PREDICTION

Professionals have developed new pricing plans and methods as a result of market globalisation, which boosts worldwide competitiveness. In order to determine the best pricing strategy, airline firms often adjust the cost of tickets by taking into account a number of variables based on their own proprietary rules and algorithms. Artificial Intelligence (AI) models have been used recently for the latter job because of its various potentials in data generalisation, compactness, and quick adaptation. This study uses artificial intelligence (AI) techniques to analyse ticket price prediction in order to identify commonalities in the pricing strategies of various airline firms. More precisely, 136.917 data flights of Aegean, Turkish, Austrian, and Lufthansa Airlines for six well-known worldwide locations are used to extract a set of useful attributes. A