Analysis and prediction of factors affecting student grades based on machine learning algorithms

The importance of analyzing and predicting the impact of student achievement is a key topic in education. Based on the open data set provided by the University of California, Irvine as a data sample, we propose a machine learning algorithm to cluster and predict the performance of students and students’ grades, so as to obtain the correlation between relevant factors and students’ grades. The experimental results indicate that this machine learning algorithm can better analyze the relationship between relevant factors and academic performance and predict students’ daily performance. Thus educators can teach students in accordance with their aptitude, thereby improving their grades.