Performance Comparison of Machine Learning Algorithms that Predicts Students’ Employability

14 Pages Posted: 9 Mar 2018

See all articles by J. Akilandeswari

J. Akilandeswari

Sona College of Technology

G Jothi

Sona College of Technology

Date Written: November 15, 2017

Abstract

Educational data mining is a field related to developing processes or methods by exploring large scale data to understand students’ performance and devise methods to improve. As in other fields, the availability of data in electronic form helps us to leverage it to get insights. Those insights can be used further to improve the academic system. In this paper, we explore the probability of students’ placement during campus drives through six different Machine Learning (ML) algorithms. Our objective is to narrow down on the better performing ML algorithms and further identify the causal parameters. The students’ demographical and performance data are collected and analyzed. The empirical results show that logistic regression algorithm and support vector machine gives better results.

Keywords: Educational Data Mining, Machine Learning, Predictive Analysis, Classification

Suggested Citation

Akilandeswari, J. and Jothi, G, Performance Comparison of Machine Learning Algorithms that Predicts Students’ Employability (November 15, 2017). Proceedings of the International Conference on Intelligent Computing Systems (ICICS 2017 – Dec 15th - 16th 2017) organized by Sona College of Technology, Salem, Tamilnadu, India, Available at SSRN: https://ssrn.com/abstract=3134357 or http://dx.doi.org/10.2139/ssrn.3134357

J. Akilandeswari (Contact Author)

Sona College of Technology ( email )

Junction Main Road
Suramangalam
Salem, Tamil Nadu 636005
India

G Jothi

Sona College of Technology ( email )

Junction Main Road
Suramangalam
Salem, Tamil Nadu 636005
India

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