Airlines Ticket Price Prediction Using Machine learning approach - Bhargavi, Kusam; Sai, A. Lahari; Rao, K. Thirupathi (International Journal of Computer Sciences and Engineering, 2022)
The main theme of this paper deals with the prediction of airlines prices as they may vary many times based on different constraints or attributes of the dataset which affect the fare of airlines. There are many ML(machine learning) models which help us to predict mainly two types of customer side models. The main focus of them is to provide the optimal time to buy a ticket and the fare of tickets should be as cheap as possible. We have considered a dataset which is consisting of ten thousand six hundred and eighty-three entries and eleven columns or attributes. For this paper, we have plotted dist plots and used some of the algorithms/methods to get the accuracy. This project helps the customers/buyers to tell the best time to purchase the ticket and that too with the lowest price. Out of all the methods used, We found random forest gives the most accuracy than the other models.