Predicting a student's performance could be helpful in a variety of contexts related to the university-level learning process. Numerous papers have been produced that analyse distinct characteristics or aspects crucial to comprehending and enhancing pupils' academic achievement. This study has developed a model that, with the aid of historical student records, can assist students in improving their exam performance by foretelling student achievement. Therefore, it is clear that the issue is one of classification, and the suggested model assigns a student to a category depending on the information provided. The methodology used affects data mining success. To lessen sample-related bias in our investigation, we used repeated k-fold cross validation. One of the causes of accurate prediction outcomes is this. The accuracy of the prediction models was then further increased by parameter tuning or hyperparameter optimization. The results showed an increase in accuracy after parameter adjustment. Additionally, researchers have looked into the functions of Naive Bayes, Decision trees, and Support Vector Machine classifiers. Using the dataset, we create models, after which we assess the students' performance. The findings indicate that decision tree is the second-best predictor, with 93.4% accuracy, and support vector machine is the best, with 96.0% accuracy. The accuracy of Nave Bayes is the lowest at 83.3%. Although the constructed model can offer accurate predictions, there is still much work to be done to incorporate these proposed methods into other predictive algorithms to generate a better performance and user experience.