<![CDATA[Student graduation prediction is an important issue in higher education as it is closely related to the evaluation of academic success. Various machine learning algorithms have been applied to predict student graduation based on academic data. This study conducts a comparative analysis of three classification algorithms, namely Logistic Regression, Random Forest, and K-Nearest Neighbor, using a simulated dataset consisting of 200 student records with attributes including age, department, GPA, and graduation year. The research stages include data preprocessing, data splitting, model training, and performance evaluation using classification metrics. Experimental results indicate that Logistic Regression and Random Forest achieve the best performance with an accuracy of 100%, while the K-Nearest Neighbor algorithm attains an accuracy of 80%. These findings highlight that data characteristics and algorithm selection significantly affect the accuracy of student graduation prediction.]]>
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