Accuracy Comparison of Machine Learning Algorithms on World Happiness Index Data

Accuracy Comparison of Machine Learning Algorithms on World Happiness Index Data

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 R...

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Bibliographic Details
Main Authors: Sadullah Çelik, Bilge Doğanlı, Mahmut Ünsal Şaşmaz, Ulas Akkucuk
Format: Article
Language:English
Published: MDPI AG 2025-04-01
Series:Mathematics
Subjects:
machine learning algorithms
world happiness index
socioeconomic factors
k-means clustering
classification accuracy
logistic regression
Online Access:https://www.mdpi.com/2227-7390/13/7/1176
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