Survey on Air Price Prediction using Machine Learning Algorithms

Abstract: The existing airfare prediction method uses very complicated methods and algorithms for the prediction. They consider several financial and commercial factors and the prices changes dynamically which makes it difficult for customers to purchase the air ticket. Airlines implement dynamic pricing for their tickets, and base their pricing decisions on demand estimation models. The reason for such a complicated system is that each flight only has a set number of seats to sell, so airlines have to regulate demand. In the case where demand is expected to exceed capacity, the airline may increase prices, to decrease the rate at which seats fill. On the other hand, a seat that goes unsold represents a loss of revenue, and selling that seat for any price above the service cost for a single passenger would have been a more preferable scenario.

Keywords: Air fare, Random forest, Linear regression