August 1, 2025IAES International Journal of Artificial IntelligenceOpen Access

Optimized ensemble modeling approach for student cumulative grade point average prediction using regression models

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Authors

HGHemalatha GunasekaranUniversity of Applied Science and Technology RAR.V. AmalrajUniversity of Applied Science and Technology AJA. JesudossUniversity of Applied Science and Technology

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Implication

This research demonstrates improved prediction of cumulative grade point average in students using regression models, highlighting Bayesian optimization and ensemble techniques.

Key Points

The ensemble modeling approach achieved an impressive R² score of 94.45%, indicating high prediction accuracy for student GPA.
Support vector regression, when fine-tuned through Bayesian optimization, reached a notable R² score of 94.11%.
This analytical method utilized both Bayesian optimization and GridSearchCV for hyper-parameter tuning to enhance model performance.
These findings underline the potential of advanced modeling techniques in reducing student dropout rates and improving academic support.

Cite This Study

Gunasekaran et al. (2025) studied this question.

synapsesocial.com/papers/68af5bafad7bf08b1eadf0c8 https://doi.org/10.11591/ijai.v14.i4.pp3074-3088

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Implication

This research demonstrates improved prediction of cumulative grade point average in students using regression models, highlighting Bayesian optimization and ensemble techniques.

Optimized ensemble modeling approach for student cumulative grade point average prediction using regression models

Discussion

Member takes

Implication

Key Points

Cite This Study

Optimized ensemble modeling approach for student cumulative grade point average prediction using regression models

Discussion

Member takes

Implication

Key Points

Cite This Study

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