Building a Classification Model for Enrollment In Higher Educational Courses using Data Mining Techniques

TL;DR

This paper develops a classification model using data mining techniques to analyze student enrollment data in higher education, aiming to assist management in improving educational standards.

Contribution

It introduces a novel classification model specifically designed for student enrollment data in higher education using data mining methods.

Findings

Identification of meaningful enrollment patterns

Enhanced understanding of student enrollment factors

Potential for improved educational management

Abstract

Data Mining is the process of extracting useful patterns from the huge amount of database and many data mining techniques are used for mining these patterns. Recently, one of the remarkable facts in higher educational institute is the rapid growth data and this educational data is expanding quickly without any advantage to the educational management. The main aim of the management is to refine the education standard; therefore by applying the various data mining techniques on this data one can get valuable information. This research study proposed the "classification model for the student's enrollment process in higher educational courses using data mining techniques". Additionally, this study contributes to finding some patterns that are meaningful to management.