Classification of Student Graduation Using Logistic Regression

Abstract

Evaluating student graduation status is a critical component of educational assessment that necessitates accurate data analysis. This study proposes a classification approach for predicting student graduation outcomes using the Logistic Regression algorithm, with Average Grade and Attendance as predictive variables. The dataset was divided into 70% training and 30% testing subsets, and model performance was assessed using Accuracy, Precision, Recall, and F1-Score metrics. Additionally, 5-Fold Cross Validation was conducted to ensure the robustness and generalizability of the model. The results demonstrate that Logistic Regression achieves a satisfactory average accuracy and maintains a balanced trade-off between precision and recall. Visual analyses, including the confusion matrix and performance bar charts, provide a comprehensive depiction of the model's predictive capabilities. This research underscores the potential of Logistic Regression as an effective method for classifying student graduation status based on academic and attendance data.