Using Machine Learning to Predict Internship Effectiveness in Employability

The main purpose of this research shall be to assess the suitability of applying internships when estimating students' employability through machine learning techniques. One analysis was done using a new database containing 1000 records with the variables of General Point Average (GPA), number of internships, supervisor ratings, organization type, employment offer status and demographic data. The data collection took place among structured undergraduate students who participated in business internship programs as their course requirements. In this study, eight machine learning methods were evaluated, including Random Forest (RF), Gradient Boosting, AdaBoost, Multi-Layer Perceptron (MLP), Gaussian Naive Bayes (GNB), XGBoost, k-Nearest Neighbors (KNN), and LightGBM. The alumni employability outcomes were predicted using these models and assessed based on four measures: accuracy, precision, recall, and F1-score. Among these models, Gaussian Naive Bayes demonstrated the highest performance, with an accuracy of 65% and an F1-score of 65.02%. AdaBoost also performed relatively well, achieving an accuracy of 59% and an F1-score of 58.89%. In contrast, the other models exhibited varying levels of performance. Random Forest achieved an accuracy of 52%with an F1-score of 52.04%, while XGBoost performed the worst, with an accuracy and F1-score of 49%. These results underscore the need to consider some internship characteristics, including task content and relevance, ratings for intern performance, and subsequent employment opportunities as critical dimensions in employability. While the obtained accuracy values suggest directions for enhancing the use of machine learning models to estimate internship effectiveness and determine essential factors influencing the graduate employability, this research confirms the continuing relevance of the field. The findings suggest the need for further research trying to improve the accuracy of the existing predictive models for practical use.