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Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Journal of Information and Optimization Sciences
Open Access Research Article

Predicting IT graduate student’s employability using machine learning and deep learning hybrid approaches

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pp. 1989–2001Vol. 47Issue 5-BMay 2026DOI: 10.47974/JIOS-2290XML
Received:
01 Apr 2025
Published Online:
23 Apr 2026
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2290
Pages:
1989–2001

Abstract

The growing demand for skilled professionals highlights the need for accurate prediction of employability and placement success among engineering students. Traditional methods of assessing placement success often rely on static parameters like academic percentage, written test score, interviews, and resume information. These methods fail to capture the dynamic interdependencies between academic performance, technical abilities, interpersonal skills, analytical problem-solving, critical analysis, and decision-making capabilities. This study proposes machine learning and deep learning-based approach to address these limitations, leveraging data-driven techniques to enhance prediction accuracy and also helps in identifying the important graduate feature for placement success. The proposed methodology integrates diverse datasets, including academic records, aptitude marks, soft skills assessment marks, and behavioural attributes. After data pre-processing 32 feature are taken to train and validate the proposed model. Findings reveal that the proposed model attains an 81.7% prediction accuracy, surpassing conventional approaches such as LR, RF, and GBM. Moreover, Feature importance analysis emphasizes the significance of the dominant role of specific parameters, such as technical skills and aptitude competency, in influencing employability outcomes. This study underscores the potential of machine learning and deep learning techniques in forecasting placement success based on their skills. Education institutes can identify skill gaps in students who are less likely to be placed and they can run specific training programs, workshops, or certifications to boost employability.

Keywords

Employability predictionPlacement successAcademic scoresTechnical skillsInterpersonal skillsAnalytical problem-solvingCritical analysisDecision-making capabilities

Subject Classifications

68T0768T0562H30

References

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