Evaluating Factors Influencing University Ranking Based on QS Ranking 2023-2024 Using Machine Learning Algorithms

This paper examines the use of machine learning algorithms to assess the factors influencing university rankings, with a focus on the QS World University Rankings for 2023-2024. The study employs multiple machine learning techniques, including Linear Regression, Multi-Layer Perceptron (MLP), Decision Trees, Random Forest, Gradient Boosted Trees, and H2O Deep Learning, to evaluate the relative importance of various ranking attributes. Key findings reveal that Gradient Boosted Trees and MLP models outperform other algorithms, achieving an R² of 0.978 and a Mean Absolute Percentage Error (MAPE) of 7.2% on test data. Academic Reputation, Citations per Faculty, and Sustainability emerged as the most significant factors, while attributes like Age Band and Size had minimal impact. These results provide actionable insights for universities aiming to enhance their global rankings. The study concludes that machine learning offers a robust framework for understanding and predicting university performance, enabling institutions to refine their strategies and improve their global competitiveness