June 30, 2023Advances in Multidisciplinary & Scientific Research Journal Publication

Modelled Machine Learning Algorithms to Predict Students Academic Performance in Tertiary Institutions

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The model achieves a graduation prediction accuracy of 92%, highlighting the effectiveness of ensemble methods like Random Forest.
Random Forest surpassed Support Vector Machines and Logistic Regression, with accuracy metrics of 91% and 90%, respectively.
This observational analysis utilizes a synthetic dataset containing 4,424 instances and 35 features, including socio-economic and academic data.
The findings suggest implementing predictive analytics could guide interventions for at-risk students, enhancing overall graduation outcomes.

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Implication

Analytical methods demonstrate a 92% accuracy in predicting graduation rates for at-risk students, indicating the power of predictive analytics in education.

Cite This Study

Adamu-Fika et al. (2023) studied this question.

synapsesocial.com/papers/68c23837b210217d64776908 https://doi.org/10.22624/aims/accrabespoke2023p39x

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Implication

Analytical methods demonstrate a 92% accuracy in predicting graduation rates for at-risk students, indicating the power of predictive analytics in education.

Modelled Machine Learning Algorithms to Predict Students Academic Performance in Tertiary Institutions

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Discussion

Implication

Cite This Study

Modelled Machine Learning Algorithms to Predict Students Academic Performance in Tertiary Institutions

Key Points

Discussion

Implication

Cite This Study

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