Application of Support Vector Regression (SVR) with a Radial Basis Function (RBF) Kernel for Predicting the Global Happiness Index

Abstract

The Global Happiness Index is widely used to measure countries' well-being across social, economic, and health-related dimensions. The complex and non-linear relationships among these dimensions often limit the predictive performance of conventional linear regression models. This study aims to evaluate the effectiveness of Support Vector Regression (SVR) with a Radial Basis Function (RBF) kernel in predicting Global Happiness Index scores. The study used data from 158 countries obtained from Kaggle, including GDP per capita, social support, healthy life expectancy, freedom, trust in government, and generosity as predictor variables. Data preprocessing was performed before splitting the dataset into training and testing sets, and the optimal SVR parameters were determined using Grid Search with K-fold cross-validation. The optimal SVR-RBF model produced an RMSE of 0.4462 and an MAE of 0.3829 on the testing data. In addition, the model achieved an R² value of 0.8328, indicating that it explained 83.28% of the variation in Global Happiness Index scores. These results suggest that SVR with an RBF kernel is an effective approach for modeling complex nonlinear relationships and can be used as a reliable tool for predicting national happiness levels.