Data Mining : A prediction of performer or underperformer using classification

TL;DR

This paper applies the Naive Bayes classification technique to student data to predict performers and underperformers, aiming to help institutions improve student retention and performance.

Contribution

It introduces the use of Naive Bayes classification for predicting student performance, providing a new approach for educational data analysis.

Findings

Naive Bayes effectively predicts student performance.

The method can help reduce dropout rates.

Institutions can identify at-risk students early.

Abstract

Now a day's students have a large set of data having precious information hidden. Data mining technique can help to find this hidden information. In this paper, data mining techniques name Byes classification method is used on these data to help an institution. Institutions can find those students who are consistently perform well. This study will help to institution reduce the drop put ratio to a significant level and improve the performance level of the institution.