Cluster Model for parsimonious selection of variables and enhancing Students Employability Prediction

TL;DR

This paper introduces a cluster-based model that improves students employability prediction by selecting relevant variables from large, unbalanced educational datasets, enhancing predictive accuracy.

Contribution

The paper presents a novel clustering approach for variable selection in educational data mining, specifically targeting employability prediction.

Findings

Improved accuracy in employability prediction models.

Effective variable reduction from large, unbalanced datasets.

Enhanced preprocessing method for educational data analysis.

Abstract

Educational Data Mining (EDM) is a promising field, where data mining is widely used for predicting students performance. One of the most prevalent and recent challenge that higher education faces today is making students skillfully employable. Institutions possess large volume of data; still they are unable to reveal knowledge and guide their students. Data in education is generally very large, multidimensional and unbalanced in nature. Process of extracting knowledge from such data has its own set of problems and is a very complicated task. In this paper, Engineering and MCA (Masters in Computer Applications) students data is collected from various universities and institutes pan India. The dataset is large, unbalanced and multidimensional in nature. A cluster based model is presented in this paper, which, when applied at preprocessing stage helps in parsimonious selection of variables and improves the performance of predictive algorithms. Hence, facilitate in better prediction of Students Employability.