StEPS: A Development of Students' Employability Prediction System using Logistic Regression Model Based on Principal Component Analysis

Abstract

Predicting students' employability prior to graduation can be a great tool for every HEI's career center to intervene timely and to take steps on how to improve the weaknesses of the students to become more employable. At present, there is no tool that can be used to determine undergraduate students who are at risk of unemployment or becoming disadvantaged because vulnerabilities are not detected early. In this study the principal component analysis (PCA) and logistic regression were used to determine the most predictive features in the students' employability prediction system (STEPS). The Dataset used consist of 1000 information of engineering students who took their on-the Job training from School year 2017ߝ2018 to School year 2019. The features used were professionalism and branding, confidence, comprehension, communication skills, growth potential, student performance rating. Upon using PCA, the experiments resulted to communication skills growth potential and student performance rating obtained the most predictive attributes that affects the employability prediction.