Accuracy Comparison of Machine Learning Algorithms on World Happiness Index Data

Mathematics, Journal Year: 2025, Volume and Issue: 13(7), P. 1176 - 1176

Published: April 2, 2025

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 is based on 2024 Report data employs indicators such as Ladder Score, GDP Per Capita, Social Support, Healthy Life Expectancy, Freedom Determine Choices, Generosity, Perception Corruption. Initially, K-Means clustering algorithm applied group countries into four main clusters representing distinct happiness levels their socioeconomic profiles. Subsequently, classification are used predict cluster membership scores obtained serve an indirect measure quality. As a result analysis, Logistic SVM, Network achieve high rates 86.2%, whereas XGBoost exhibits lowest performance at 79.3%. Furthermore, practical implications these findings significant, they provide policymakers with actionable insights develop targeted strategies for enhancing national improving well-being. In conclusion, this offers valuable information more effective analysis by comparing various algorithms.

Language: Английский

A new approach for predicting academic performance

Procedia Computer Science, Journal Year: 2025, Volume and Issue: 257, P. 1257 - 1262

Published: Jan. 1, 2025

Language: Английский