Quantum of Airfare: Machine Learning’s Role in Flight Fare Predictions

Abstract

It is currently challenging for consumers to book a trip at the lowest prices at any time of year due to the abundance of flights offered by different airlines to a chosen destination at varying prices for different days and different seats. This frequently results in customers paying more than is necessary or tak-ing too long to locate the best deal on the flight they are looking for. This paper forecasts the cost of the flight, taking into account every variable that could im-pact the cost, such as the seat, the day of the journey, the kind of seat, etc. Three machine learning techniques were employed for the prediction of the flight price. The best model among the three was used for hype parameter ad- justment and model optimization.