  Now-a-days the amount of data stored in educational database increasing
rapidly. These databases contain hidden information for improvement of
students' performance. Educational data mining is used to study the data
available in the educational field and bring out the hidden knowledge from it.
Classification methods like decision trees, Bayesian network etc can be applied
on the educational data for predicting the student's performance in
examination. This prediction will help to identify the weak students and help
them to score better marks. The C4.5, ID3 and CART decision tree algorithms are
applied on engineering student's data to predict their performance in the final
exam. The outcome of the decision tree predicted the number of students who are
likely to pass, fail or promoted to next year. The results provide steps to
improve the performance of the students who were predicted to fail or promoted.
After the declaration of the results in the final examination the marks
obtained by the students are fed into the system and the results were analyzed
for the next session. The comparative analysis of the results states that the
prediction has helped the weaker students to improve and brought out betterment
in the result.
