Flight Price Prediction for Users by Machine Learning Techniques

Abstract: People who frequently travel through flight will have better knowledge on best discount and right time to buy the ticket. For the business purpose many airline companies change prices according to the seasons or time duration. They will increase the price when people travel more. Estimating the highest prices of the airlines data for the route is collected with features such as Duration, Source, Destination, Arrival, Departure. Features are taken from chosen dataset and in this paper, we have used machine learning techniques and regression strategies for prediction of the price wherein the airline price ticket costs vary overtime. We have implemented flight price prediction for users by using decision tree and random forest algorithms. Decision tree shows the best accuracy of 80% for predicting the flight price. Also, we have done correlation tests and ANOVA test for the statistical analysis.

Keywords: Feature selection, Airfare price, Machine learning, Pricing Models, Prediction Model, Random Forest.