A Prediction of Flight Fare Using K-Nearest Neighbors

Abstract

Someone who buys plane tickets on a regular basis can be able to predict the optimum moment to buy an airline ticket in order to afford the greatest price. For revenue management, many airlines modify ticket pricing. when the demand for the ticket is high, the airline may hike the pricing. To calculate the minimal airfare, data for a given air route was gathered over a period of time, including parameters such as departure time, arrival time, and airways. To use Machine Learning (ML) models, features are retrieved from the obtained data. The cost of a plane ticket is determined by several distinct strands. The main purpose of the research is to find out the factors that impacts drive. airfare price fluctuations and how they connect to price changes. Then, based on this data, create a mechanism to assist purchasers in deciding whether or not to purchase a ticket. The KNN technique is used in this paper to estimate prices at a given time using machine learning regression approaches.

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