Airlines Ticket Price Prediction Using Machine Learning Approach

Abstract

The price of airline tickets is the most unstable thing nowadays. It changes abruptly during the morning and evening time. The passengers are always looking to get the tickets at the lowest price, on the other hand the sellers (Airlines) are trying to earn a huge revenue. We can see that the prices change within a short time because of some factors for which the prices are affected. There are some factors like purchasing time, fuel price, flight distance etc. The prices of the airfare depends on these factors. The passengers are not allowed to access the previous data of the flight prices to predict the best price for them but the airlines have all the information about that. In this research, we tried to find out a best model for predicting the airfare by which the passenger can get the best predicted price to travel. We have used the Random Forest regression algorithm, Decision Tree algorithm and Linear Regression Algorithm to predict the price of airline tickets. For applying the ML algorithms, we have extracted the best features from the collected data and after finishing all of the tasks we got the prediction accuracy 90.47% in Random Forest Regression, 79.20% in Decision Tree and 72.77% in Linear Regression. After all, we got the best model which is Random forest Regression Algorithm to predict the airfare price. By using this system, the customers will get a better prediction that can help them buy tickets at a lower price.