Data driven decisions in education using a comprehensive machine learning framework for student performance prediction

Data driven decisions in education using a comprehensive machine learning framework for student performance prediction

Abstract Accurately predicting student performance is essential for improving educational outcomes and guiding targeted interventions. This study applies eight advanced machine learning models-Decision Trees, Random Forest, Lasso, K-Nearest Neighbors, XGBoost, CatBoost, AdaBoost, and Gradient Boosti...

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Bibliographic Details
Main Authors: Muhammad Nadeem Gul, Waseem Abbasi, Muhammad Zeeshan Babar, Abeer Aljohani, Muhammad Arif
Format: Article
Language:English
Published: Springer 2025-07-01
Series:Discover Computing
Subjects:
Machine learning
Performance prediction
Common regression metrics (MAE, MSE and RMSE)
Lasso
K-Neighbours
Decision Trees
Online Access:https://doi.org/10.1007/s10791-025-09585-3
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https://doi.org/10.1007/s10791-025-09585-3

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