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Title |
Utilizing Machine Learning Algorithms for Recruitment Predictions of IT Graduates in the Saudi Labor Market |
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Author |
Munirah Alghamlas and Reham Alabduljabbar |
| Citation |
Vol. 24 No. 3 pp. 113-124 |
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Abstract |
One of the goals of the Saudi Arabia 2030 vision is to ensure full employment of its citizens. Recruitment of graduates depends on the quality of skills that they may have gained during their study. Hence, the quality of education and ensuring that graduates have sufficient knowledge about the in-demand skills of the market are necessary. However, IT graduates are usually not aware of whether they are suitable for recruitment or not. This study builds a prediction model that can be deployed on the web, where users can input variables to generate predictions. Furthermore, it provides data-driven recommendations of the in-demand skills in the Saudi IT labor market to overcome the unemployment problem. Data were collected from two online job portals: LinkedIn and Bayt.com. Three machine learning algorithms, namely, Support Vector Machine, k-Nearest Neighbor, and Na?ve Bayes were used to build the model. Furthermore, descriptive and data analysis methods were employed herein to evaluate the existing gap. Results showed that there existed a gap between labor market employers’ expectations of Saudi workers and the skills that the workers were equipped with from their educational institutions. Planned collaboration between industry and education providers is required to narrow down this gap. |
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Keywords |
Classification, data mining, machine learning, labor market, prediction, Saudi Arabia, skills gap, recruitment |
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