University Seminar (CCAS): Computability, Complexity, and Algebraic Structure- Accuracy Comparison of Machine Learning Algorithms on World Happiness Index Data

Time: Friday, April 25, 2:30 – 3:30pm

Place: Phillips Hall, Room 209

Speaker: Dr. Sadullah Celik, GWU and Aydın Adnan Menderes University (Turkey)

Title: Accuracy Comparison of Machine Learning Algorithms on World Happiness Index Data

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