Abstract

Data mining techniques are widely employed across organizations to analyze extensive datasets and extract valuable insights for decision-making. Similarly, in the education sector, data mining plays a pivotal role in various applications, including student performance based on marks, attendance, faculty feedback, extracurricular activities, incidents like ragging, and stress levels. Specifically, the K-Nearest Neighbors (KNN) algorithm is utilized to assess and analyze student performance. This algorithm effectively identifies patterns and trends within student data, providing educators and administrators with actionable information to enhance educational outcomes and support student development.