Every educational institute maintains a proper database on their student performance and activities. This information is incredibly useful in the realm of education., particularly for evaluating the performance of students. It is true that evaluating student performance has grown difficult due to the lack of comparison between different resampling methods due to the imbalanced data sets in this discipline. Some of these resampling models such as Random Forest, Artificial Neural Network and Logistic Regression. Furthermore are compared in this paper and the model validation we used here is 5-fold cross validation. These resampling methods provide an accurate output on the current performance of students and state the variance in their performance. This provides a reliable source to view and check the performance of students.