Predicting Student Performance Using Decision Tree Classifiers and Information Gain

As competitive environment is prevailing among the academic institutions, challenge is to increase the quality of education through data mining. Student's performance is of great concern to the higher education. In this paper, we have applied data mining techniques by evaluating student's data using decision trees which is helpful in predicting the student's results. In this paper, we have calculated the Entropy of the attributes taken in Educational Data Set and the attribute having highest Information Gain is taken as the root node to split further. The results generated using Data Mining Techniques help faculty members to focus on students who are getting poor class results.