Date of Publication :25th July 2024

Abstract:A comprehensive understanding of student achievement in education extends beyond traditional measures and encompasses many dimensions. This research uses advanced clustering and classification techniques to reveal micropatterns in student data, focusing on attendance, course markers, and achievement Primary goals include identifying specific groups of students, develop predictive segmentation models, and provide actionable insights for instructional interventions tailored to individual Specific needs groups of diverse learners Provides a means for tailoring strategies to address them. The importance of this research lies in its potential to reshape educational practices, creating an environment where every student can thrive. Through an extensive analysis of methods, data, and findings, this paper makes a valuable contribution to educational data analysis and improves data-driven decision-making to improve student outcomes.

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