Students are the main stakeholders of institutions and their performance plays a significant role in country development. The aim of institutions is to give excellence educations to their students. It has been observed in the previous works, students of slow performance and dropouts are the most vital issues. Due to early detection of slow performers and dropouts of students, help the teachers, administrator, and management to take appropriate actions at the right time for improving the overall performance of the students. The purpose of this study is to analyze different data mining and machine learning techniques on student data and find which technique gives better accuracy. And, we also find different factors like socio-demographic, psychological factors, attendance of students, understanding level of students, previous grades, study time, parent’s status, internet usage, travel time, extracurricular activities, and also health factors affect the performance of students. Data mining is a process of analyzing data and turns it into useful information.

Students Academic Performance, Data Mining, Machine Learning

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