Predicting Students' Employability using Machine Learning Approach

Abstract

This study aims to apply an approach using machine learning for predicting students' employability. The researchers conducted a case study that involved 27,000 information (3000 observations and 9 features) of students' Mock Job Interview Evaluation Results, On-the Job Training (OJT) Student Performance Rating and General Point Average (GPA) of students enrolled in OJT course of School Year 2015 to School Year 2018. Three learning algorithms were used such as Decision Trees (DT), Random Forest (RF), and Support vector machine (SVM) in order to understand how students get employed. The three algorithms were evaluated through the performance matrix as accuracy measures, precision and recall measures, f1-score and support measures. During the experiments Support Vector machine (SVM) obtained 91.22% in accuracy measures which was significantly better than all of the learning algorithms, DT 85%, RF 84%. The learning curve produced during the experiment displays the training error results which were above the one for validation error while the validation curve displays the testing output where gamma was best at 10 to 100 in gamma 5. This concludes that the model produced with SVM was not underfit and over-fit. This study is very promising that lead to the researchers to be motivated to enhanced the process and to validate the produced predictive model for further study.