Flight Price Prediction Using Machine Learning

Abstract

Currently, everyone loves to travel by flights. Going along with the study, the charge of travelling through a plane change now and then which also includes the day and night time. Additionally, it changes with special times of the year or celebration seasons. There are a few unique elements upon which the cost of air transport depends. The salesperson has data regarding each of the variables, however, buyers can get confined information which is not sufficient to foresee the airfare costs. Considering the provisions, for example, time of the day, the number of days remaining and the time of take-off this will provide the perfect time to purchase the plane ticket. The motivation behind this paper is to concentrate on every component that impacts the variations in the costs of this means of transport and how these are connected with the diversity in the airfare. Subsequently, at that point, utilizing this data, construct a framework that can help purchasers when to purchase a ticket. Machine Learning algorithms prove to be the best solution for the above-discussed problems. In this project, there is an implementation of Artificial Neural Network (ANN), LR (Linear Regression), DT (Decision Tree), and RF (Random Forest).