An Evaluation of Prediction Method for Educational Data Mining Based on Dimensionality Reduction

In the area of educational data mining (EDM), it is important to develop technologically sophisticated solutions. An exponential growth in educational data raises the possibility that conventional methods could be constrained as well as misinterpreted. Thus, the field of education is becoming increasingly concerned in resurrecting data mining methods. This work thoroughly analyzes and predicts students’ academic success using logistic regression, linear discriminant analysis (LDA), and principal component analysis (PCA) to keep track of the students’ future performance in ahead. Logistic regression is enhanced by comparing LDA and PCA in a bid to improve precision. The findings demonstrate that LDA improved the accuracy of the logistic regression classifier by 8.86% as compared to PCA’s output, which produced 35 more correctly classified data. As a result, it is demonstrated that this model is effective for forecasting students’ performance using students’ historical data.