Next Flight Prediction for PKX's Frequent Flyers

Abstract

Frequent flyers have been shown to have a significant impact on airline long-term profitability, so it is becoming increasingly crucial to understand their needs. Objective of this paper is to forecast the next flight of frequent flyers, which will not only improve the customer experience but also assist airlines optimize their ticketing service platforms. In the data preparation phase, we use methods including data transformation and genetic algorithms (GA) to directly extract statistical features or excavate new predictors, which is inspired by the characteristics of the frequent flyers of Beijing Daxing International Airport (PKX). We synthesize ensemble models, linear regression models, evolutionary computation models, and fusion models for prediction. The hyperparameters can be optimized by the Tree-structured Parzen Estimator (TPE). After extensive comparison, the model developed with the average fusion strategy obtains the best prediction on the test set with a minimal MSLE of 0.7893.