<![CDATA[This research focuses on predicting students' grades and learning interests using the Support Vector Machine (SVM) method in education. It is crucial for students to determine the subjects they are most interested in to achieve optimal results. Data mining, particularly through the SVM method, can help identify students' learning interests based on historical data. SVM analyzes various variables related to students' academic performance, such as test scores, assignments, attendance, and class participation. By analyzing this data, SVM builds a model that predicts students' interest in specific subjects. This model provides insights into students' potential academic performance. The predictions generated by this SVM model can be used by educators to design more personalized learning strategies. By understanding students' interests and potential, educators can offer lesson recommendations that align with individual needs, enhancing students' motivation and academic performance. Moreover, these predictions assist students in making more informed decisions regarding subject selection, optimizing their potential and achieving better educational outcomes. This research aims to provide a tool that helps direct students' learning interests effectively, thereby improving the overall quality of education and academic results.]]>
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