Title: Machine learning classification models for student placement prediction based on skills

Authors: Laxmi Shanker Maurya; Md Shadab Hussain; Sarita Singh

Addresses: Department of Computer Science and Engineering, Shri Ram Murti Smarak College of Engineering and Technology, Bareilly 243001, Uttar Pradesh, India ' Department of Computer Science and Engineering, Shri Ram Murti Smarak College of Engineering and Technology, Bareilly 243001, Uttar Pradesh, India ' Department of Computer Science and Engineering, Shri Ram Murti Smarak College of Engineering and Technology, Bareilly 243001, Uttar Pradesh, India

Abstract: Placement plays a vital role for engineering students in their career planning. Placement is also important for engineering institutions to maintain the ranking in university. In this paper, we have proposed a few supervised machine learning classification models, which may be used to predict the placement of a student based on skills like aptitude, coding, communication and technical. We also compare the results of different proposed classification models. The classification algorithms support vector machine, Gaussian naive Bayes, K-nearest neighbour, random forest, decision tree, stochastic gradient descent and logistic regression were used.

Keywords: supervised learning; classification model; skill level; placement decision.

DOI: 10.1504/IJAISC.2022.126337

International Journal of Artificial Intelligence and Soft Computing, 2022 Vol.7 No.3, pp.194 - 207

Received: 21 Nov 2020
Accepted: 16 Jan 2022
Published online: 21 Oct 2022 *