Malaysian Community College Graduates Employability Prediction Model Using Machine Learning Approach

Abstract

Community College, as TVET institution under the Ministry of Higher Education, offers industry-relevant skills training to ensure graduates' employability in the global labor market. However, producing graduates who meet industry demands remains a challenge, and industries continue to face difficulties in obtaining skilled graduates. In Malaysia, there is limited research on predictive models for employability rates among graduates of TVET Malaysian institutions. This research utilizes Python to investigate the significant factors influencing employability among Malaysian Community College graduates, determined by a specific indicator of whether they will be employed. Our contribution lies in developing an accurate employability prediction model using machine learning algorithms such as Logistic Regression, Neural Networks, and Random Forest. The dataset used consisted of 10,427 instances and 14 attributes, from which six significant factors were identified. Among the models, Random Forest outperformed the other machine learning models, and hyperparameter tuning using RandomizedSearch further improved the accuracy of the model to 84.8%. This study aims to identify the most accurate and interpretable model, providing valuable insights for educational institutions to enhance their employability strategies.