Student Performance Analysis Using Machine Learning Algorithm

Student Performance Analysis System is an emerging field and is very crucial to schools and universities in helping their students and professors. Most of the pre-existing methods are based only on past academic performance of students. This paper aims to develop models which can predict the student's performance and grades while keeping in mind other equally essential personality factors like interests, attributes and opinions (IAO variables) which affect their lifestyle. It uses various machine learning and deep learning techniques to predict the performance of students, and basic exploratory data analysis to derive various correlations of student's performance with psychographic attributes. Education is a prerequisite for a prosperous and good life, and it also helps in enhancing people's lives with meaning and excellence. Furthermore, education is viewed as a fundamental prerequisite for building self-confidence and providing the resources required to participate in today's speedily changing world. The progress of the educational institute's students can be used to quantify the institute's growth. Furthermore, education is viewed as a fundamental prerequisite for building self-confidence and providing the resources required to participate in today's rapidly changing world.

Keywords: Decision Tree, Random forest, XGBoost , performance Anlysis.