# Analysis and Prediction of At-Risk Students Using Machine Learning Algorithms

arXiv: https://arxiv.org/abs/2606.20617 · HTML: https://mlsanity.com/paper/2606.20617/

## Key facts

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- First posted on arXiv 24 May 2026.

## Abstract (arXiv)

Student attrition represents a significant challenge for higher education institutions because it impacts both academic results and financial viability. Machine learning provides an effective solution to identify students who require assistance before they leave their academic programs. The research investigates how machine learning approaches enable institutions to predict student withdrawal and enrollment cancellation through data-driven insights for strategic decisionmaking. The evaluation of models includes Logistic Regression, Random Forest, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) based on academic performance and demographic data and enrollment records. The results show that logistic regression and linear SVM models produced the highest accuracy which demonstrates ML's capability to detect students at risk.