Performance Evaluation of Machine Learning Predictive Analytical Model for Determining the Job Applicants Employment Status

Abstract

Several higher institution of learning faces issue or difficulty of turning out more than 90% of their graduates who can competently satisfy and meet the requirements of the industry. However, the industry is also confronted with the difficulty of sourcing skilled tertiary institution graduates that match their needs. Failure or success of any organization depends mostly on how its workforce is recruited and retained. Therefore, the selection of an acceptable or satisfactory candidate for the job position is one of the major and vital problems of management decision-making. This work, therefore, proposes a modern, accurate and worthy machine learning classification model that can be deployed, implemented, and put to use when making predictions and assessments on job applicant's attributes from their academic performance datasets in other to meet the selection criteria for the industry. Both supervised and unsupervised machine learning classifiers were considered in this work. Naïve Bayes, Logistic Regression, support vector machine (SVM). Random Forest and Decision tree performed well, but Logistic Regression outperformed others with 93% accuracy.