Predicting Airfare Price Using Machine Learning Techniques: A Case Study for Turkish Touristic Cities

Airline ticket price is influenced by several elements, such as flight distance, purchasing time, number of transfers, etc. Furthermore, every carrier has its own proprietary rules and techniques to determine the ticket price accordingly. With recent improvements in Machine Learning (ML), these rules could be inferred and the price variation could be modeled. In this study, we first created the first dataset containing flight prices for Turkey. The flight price dataset consists of over 1000 domestic flights towards the touristic cities in Turkey. We then use machine learning algorithms to model the ticket price based on different origin and destination pairs. We achieved promising results for predicting the flight ticket price on our dataset.