LightGBM: an Effective Decision Tree Gradient Boosting Method to Predict Customer Loyalty in the Finance Industry

This study presents an implementation of a Machine Learning model to predict customer loyalty for a financial company. We compare the accuracy of two Gradient Boosting Decision Tree Models: XGBoosting and the LightGBM algorithm, which has not yet been used for customer loyalty prediction. We apply these methods to predict credit card customers' loyalty scores for a financial company. The dataset has been made available through a Kaggle's competition. We assess customer loyalty prediction accuracy through RMSE and find that LightGBM performs better than XGBoosting.