  Educational data mining (EDM) is defined as the area of scientific inquiry
centered around the development of methods for making discoveries within the
unique kinds of data that come from educational settings, and using those
methods to better understand students and the settings which they learn in.
Data mining enables organizations to use their current reporting capabilities
to uncover and understand hidden patterns in vast databases. As a result of
this insight, institutions are able to allocate resources and staff more
effectively. In this paper, we present a real-world experiment conducted in
Shree Rayeshwar Institute of Engineering and Information Technology (SRIEIT) in
Goa, India. Here we found the relevant subjects in an undergraduate syllabus
and the strength of their relationship. We have also focused on classification
of students into different categories such as good, average, poor depending on
their marks scored by them by obtaining a decision tree which will predict the
performance of the students and accordingly help the weaker section of students
to improve in their academics. We have also found clusters of students for
helping in analyzing student's performance and also improvising the subject
teaching in that particular subject.
