Accuracy Comparison of Machine Learning Algorithms on World Happiness Index Data

Sadullah Çelik, Bilge Doğanlı, Mahmut Ünsal Şaşmaz () and Ulas Akkucuk
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Sadullah Çelik: Department of International Trade and Finance, Nazilli Faculty of Economics and Administrative Sciences, Aydın Adnan Menderes University, Nazilli 09010, Türkiye
Bilge Doğanlı: Department of International Trade and Finance, Nazilli Faculty of Economics and Administrative Sciences, Aydın Adnan Menderes University, Nazilli 09010, Türkiye
Mahmut Ünsal Şaşmaz: Department of Public Finance, Faculty of Economics and Administrative Sciences, Usak University, Usak 64000, Türkiye
Ulas Akkucuk: Department of Management, Faculty of Economics and Administrative Sciences, Bogaziçi University, Istanbul 34342, Türkiye

Mathematics, 2025, vol. 13, issue 7, 1-27

Abstract: This study aims to compare the accuracy performances of different machine learning algorithms (Logistic Regression, Decision Tree, Support Vector Machines (SVMs), Random Forest, Artificial Neural Network, and XGBoost) using World Happiness Index data. The study is based on the 2024 World Happiness Report data and employs indicators such as Ladder Score, GDP Per Capita, Social Support, Healthy Life Expectancy, Freedom to Determine Life Choices, Generosity, and Perception of Corruption. Initially, the K-Means clustering algorithm is applied to group countries into four main clusters representing distinct happiness levels based on their socioeconomic profiles. Subsequently, classification algorithms are used to predict the cluster membership and the accuracy scores obtained serve as an indirect measure of the clustering quality. As a result of the analysis, Logistic Regression, Decision Tree, SVM, and Neural Network achieve high accuracy rates of 86.2%, whereas XGBoost exhibits the lowest performance at 79.3%. Furthermore, the practical implications of these findings are significant, as they provide policymakers with actionable insights to develop targeted strategies for enhancing national happiness and improving socioeconomic well-being. In conclusion, this study offers valuable information for more effective classification and analysis of World Happiness Index data by comparing the performance of various machine learning algorithms.

Keywords: machine learning algorithms; world happiness index; socioeconomic factors; k-means clustering; classification accuracy; logistic regression; artificial neural networks; XGBoost (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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