  An educational institution needs to have an approximate prior knowledge of
enrolled students to predict their performance in future academics. This helps
them to identify promising students and also provides them an opportunity to
pay attention to and improve those who would probably get lower grades. As a
solution, we have developed a system which can predict the performance of
students from their previous performances using concepts of data mining
techniques under Classification. We have analyzed the data set containing
information about students, such as gender, marks scored in the board
examinations of classes X and XII, marks and rank in entrance examinations and
results in first year of the previous batch of students. By applying the ID3
(Iterative Dichotomiser 3) and C4.5 classification algorithms on this data, we
have predicted the general and individual performance of freshly admitted
students in future examinations.
