Academic performance prediction using machine learning algorithms

The objective of the study is to use a method to predict student performance during the semesters and to compare accuracy perceptron for a dataset of student performance. In this regard, Machine Learning techniques were applied to the student performance dataset provided by the Kaggle.com website. Multilayer Perceptron, Random Forest, SVM, Naïve Bayes, Decision tree and K-NN algorithms were used to predict the Grade result of students as a factor of performance. The Student Performance dataset is used to forecast how well students will perform in their tests. As a result, with 94.9% accuracy, the results were predicted