Abstract
This research describes a framework for utilizing machine learning and data visualization with publicly available education data to create educational reforms. This study aims to address the systemic issues hindering equal learning opportunities between developed and developing countries by analyzing key global education indicators. With the help of clustering and predictive analysis, the work reveals significant factors affecting education, including but not limited to the completion rates, tertiary education enrollment, and literacy rates. The framework also leverages social media and machine learning to drive educational reforms and reduce policy implementation lag by informing policymakers.
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