Analyzing and predicting global happiness index via integrated multilayer clustering and machine learning models.

ABSTRACT: This study addresses the research objective of predicting global happiness and identifying its key drivers. We propose a novel predictive framework that integrates unsupervised and supervised machine learning techniques to uncover the complex patterns underlying happiness scores across nations. Initially, we apply K-Means clustering to group countries based on similarities in their happiness patterns. For the first time, these cluster assignments are subsequently incorporated as additional features into ensemble learning models-specifically, Random Forests and XGBoost-to enhance the prediction of happiness scores. This hierarchical analysis approach yields a significant improvement in predictive performance, with an approximate 12% increase in R² compared to models that do not include clus

SUBMITTER: Yang B 

PROVIDER: S-EPMC12043169 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature