An Improved K-means Clustering Algorithm Towards an Efficient Educational and Economical Data Modeling

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Education is one of the most crucial pillars for the sustainable development of societies. It is essential for each country to assess its level of access to education. However, the conventional methods of ranking access to education have their limitations. Therefore, there is a need for strategic planning to develop a new classification methods. This study aims to address this need by developing an innovative and efficient unsupervised K-Means model capable of predicting global access to education. The novel approach adopted in this research fills a gap in traditional ranking methods for assessing access to education. Utilizing statistical analysis of data sourced from the World Bank, we evaluated education access across 217 countries spanning various continents and levels of development. By employing economic and educational factors as input for the K-Means algorithm, we successfully identified three distinct clusters, each comprising countries with similar levels of education access. The reliability of our approach was reinforced through rigorous statistical testing to validate the results. Furthermore, we compared the economies of countries within each cluster using primary data, enabling specific recommendations at the economic level to assist countries with limited education access in enhancing their circumstances. Finally, this study makes a significant contribution by introducing a new approach to globally assess education access. The findings provide practical recommendations to aid countries in improving their educational opportunities.