Predicting financial well-being using observable features and gradient boosting

This contribution delves into the utilization of advanced gradient boosting techniques for forecasting subjective assessments of financial well-being, employing the comprehensive Consumer Financial Protection Bureau (CFPB) National Financial Well-being dataset. 

With a focus on ensuring interpretability, this study endeavors to discern the pivotal observable factors crucial for precise predictions. 

Subsequently, these pivotal features undergo examination via factor analysis, aiming to unveil latent themes embedded within the dataset, thus contributing to a deeper understanding of the intricacies surrounding financial well-being.