A country’s growth is strongly measured by
quality of its education system. Education sector, across
the globe has witnessed sea change in its functioning.
Today it is recognized as an industry and like any other
industry it is facing challenges, the major challenges of
higher education being decrease in students’ success
rate and their leaving a course without completion. An
early prediction of students’ failure may help the
management provide timely counseling as well coaching
to increase success rate and student retention. We use
different classification techniques to build performance
prediction model based on students’ social integration,
academic integration, and various emotional skills
which have not been considered so far. Two algorithms
J48 (Implementation of C4.5) and Random Tree have
been applied to the records of MCA students of colleges
affiliated to Guru Gobind Singh Indraprastha
University to predict third semester performance.
Random Tree is found to be more accurate in
predicting performance than J48 algorithm.