Student Attribute Prediction Using ID3

These days, every university has its own management system for keeping track of the students' data. Recent trends in higher education have seen an increase in student dropout rates, which affects both the institution's reputation and the student's career. Data mining techniques are used to examine and forecast a student's success in a certain course. In order to solve problems through data analysis, it is necessary to filter through massive data sets to find patterns and build links. The use of data mining techniques enables businesses to forecast future trends. As part of our suggested approach, we notify students of their results on a regular basis. This will allow us to predict student performance using data mining and ensure that the student is fully aware of his or her performance. In order to maintain track of students, this project suggests using ID3 to forecast student attributes. The system keeps track of all student entrance data, course data, topic data, student mark data, attendance data, etc. with the goal of boosting students' success graphs. It inputs the academic background of the students and outputs their upcoming performances according to semester.