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Accuracy Comparison of Machine Learning Algorithms on World Happiness Index Data

Đã lưu trong:
Xuất bản năm:Mathematics vol. 13, no. 7 (2025), p. 1176
Tác giả chính: Çelik, Sadullah
Tác giả khác: Doğanlı, Bilge, Mahmut Ünsal Şaşmaz, Akkucuk, Ulas
Được phát hành:
MDPI AG
Những chủ đề:
Machine learning
Accuracy
Socioeconomic factors
Happiness
Datasets
Cluster analysis
Classification
Regression analysis
Social support
Support vector machines
Clustering
Artificial neural networks
Life expectancy
Well being
Algorithms
Probability
Decision trees
Vector quantization
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Bài tóm tắt: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.
số ISSN:2227-7390
DOI:10.3390/math13071176
Nguồn:Engineering Database