  Data Mining is best-known for its analytical and prediction capabilities. It
is used in several areas such as fraud detection, predicting client behavior,
money market behavior, bankruptcy prediction. It can also help in establishing
an educational ecosystem, which discovers useful knowledge, and assist
educators to take proactive decisions to boost student performance and
employability. This paper presents an empirical study that compares varied
classification algorithms on two datasets of MCA (Masters in Computer
Applications) students collected from various affiliated colleges of a reputed
state university in India. One dataset includes only primary attributes,
whereas other dataset is feeded with secondary psychometric attributes in it.
The results showcase that solely primary academic attributes do not lead to
smart prediction accuracy of students employability, once they square measure
within the initial year of their education. The study analyzes and stresses the
role of secondary psychometric attributes for better prediction accuracy and
analysis of students performance. Timely prediction and analysis of students
performance can help Management, Teachers and Students to work on their gray
areas for better results and employment opportunities.
