Synergizing Hybrid Ensemble Learning and Generative AI For Robust and Explainable Student Placement Prediction

  • Unique Paper ID: 206687
  • PageNo: 202-214
  • Abstract:
  • The worldwide digital transformation has brought about essential changes which require software development to become the primary economic driver which needs to establish an exact talent acquisition process. The existing campus recruitment system which higher education institutions use results in operational delays because of its manual processes and informational gaps and its evaluation system which neither analyzes data nor predicts outcomes. The multi-tenant SaaS platform PlacementPro presents a solution which substitutes traditional administrative processes with its Signal-Aware decision support system. The core architecture employs a Three-Pillar Hybrid Ensemble system which combines three machine learning models Support Vector Machines (SVM), Random Forest and XGBoost, to measure employability through 26 distinct assessment criteria. The Synthetic-Robustness Framework we developed uses SMOTE augmentation together with Gaussian Noise Injection to create institutional datasets that achieved 90.89% peak accuracy through validation testing. The system uses Google Gemini API to perform unstructured data processing which includes automatic Job Description (JD) extraction and Generative AI-based career guidance. The system implementation uses SHAP (SHapley Additive exPlanations) to generate transparency for placement success through its evaluation of Academic Consistency and Technical Maturity as the key success factors. The research findings show that the combination of machine learning algorithms with generative intelligence successfully connects academic readiness with professional excellence.

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{206687,
        author = {P Prajwal and Athmaranjan K and Ishwarya and Vinay Rao},
        title = {Synergizing Hybrid Ensemble Learning and Generative AI For Robust and Explainable Student Placement Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {202-214},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206687},
        abstract = {The worldwide digital transformation has brought about essential changes which require software development to become the primary economic driver which needs to establish an exact talent acquisition process. The existing campus recruitment system which higher education institutions use results in operational delays because of its manual processes and informational gaps and its evaluation system which neither analyzes data nor predicts outcomes. The multi-tenant SaaS platform PlacementPro presents a solution which substitutes traditional administrative processes with its Signal-Aware decision support system. The core architecture employs a Three-Pillar Hybrid Ensemble system which combines three machine learning models Support Vector Machines (SVM), Random Forest and XGBoost, to measure employability through 26 distinct assessment criteria. The Synthetic-Robustness Framework we developed uses SMOTE augmentation together with Gaussian Noise Injection to create institutional datasets that achieved 90.89% peak accuracy through validation testing. The system uses Google Gemini API to perform unstructured data processing which includes automatic Job Description (JD) extraction and Generative AI-based career guidance. The system implementation uses SHAP (SHapley Additive exPlanations) to generate transparency for placement success through its evaluation of Academic Consistency and Technical Maturity as the key success factors. The research findings show that the combination of machine learning algorithms with generative intelligence successfully connects academic readiness with professional excellence.},
        keywords = {Placement prediction, XGBoost, support vector machine, SMOTE, generative AI, Google Gemini, institutional decision support system, MLOps, full-stack deployment.},
        month = {July},
        }

Cite This Article

Prajwal, P., & K, A., & Ishwarya, , & Rao, V. (2026). Synergizing Hybrid Ensemble Learning and Generative AI For Robust and Explainable Student Placement Prediction. International Journal of Innovative Research in Technology (IJIRT), 202–214.

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