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Accuracy Comparison of Machine Learning Algorithms on World Happiness Index Data

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sadullah Çelik & Bilge Doğanlı & Mahmut Ünsal Şaşmaz & Ulas Akkucuk, 2025. "Accuracy Comparison of Machine Learning Algorithms on World Happiness Index Data," Mathematics, MDPI, vol. 13(7), pages 1-27, April.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:7:p:1176-:d:1626885
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    References listed on IDEAS

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    1. Rachana Jaiswal & Shashank Gupta, 2024. "Money talks, happiness walks: dissecting the secrets of global bliss with machine learning," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 22(1), pages 111-158, January.
    2. Guoping Zeng, 2025. "On impurity functions in decision trees," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 54(3), pages 701-719, February.
    3. Abdelhamid Zaidi & Asamh Saleh M. Al Luhayb & Huaiyu Wang, 2023. "Two Statistical Approaches to Justify the Use of the Logistic Function in Binary Logistic Regression," Mathematical Problems in Engineering, Hindawi, vol. 2023, pages 1-11, April.
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