Classification of University Excellence: A Multi-dimensional Exploration of Ranking Criteria Using Data Science and Visualization Technology

This study analyzed data from the QS World University Ranking website, compiling a comprehensive dataset of 4220 records and 25 attributes. The data was used to examine the spread of universities across continents, their sizes, research intensity, and status. European universities had the largest representation with 1449 ranked institutions. The study also projected the future number of universities in each continent for the next five years using linear regression and exponential smoothing models. Significant correlations were discovered between academic reputation, employer reputation, citations per faculty, and overall scores. Machine learning regression algorithms were used to predict university scores based on input parameters. Random forest and gradient boosting were found superior in terms of precision and explanatory power. The study presents insights into trends in global higher education and offers tools to anticipate future changes in academia.