Modelling Student Employability on an Academic Basis

Author:

Srivastava Vishal1ORCID,Singh Ashish Kumar2,David Arokiaraj1ORCID,Rai Neel3

Affiliation:

1. Jain University (Deemed), India

2. Raj Kumar Goel Institute of Technology, Ghaziabad, India

3. NTPC Business School, India

Abstract

With the population growth and the employability scarcity, the placement of students has become a significant concern. Problems of global ageing and miss-match of student skill and knowledge can be witnessed easily. Fewer works of literature are available to predict the placement of students. This study aims to create a supervised machine learning (SML) model to predict the employability of graduates based on their academic scores and streams. The study used the decision-tree technique to create the SML model. The model can predict the placement chance based on students' academic scores and streams with 65% accuracy. Some new theoretical and practical contributions have been discussed.

Publisher

IGI Global

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