Comparison of classification techniques for predicting the performance of students academic environment

The aim of this study is to compares some classification techniques used to predict the performance of student. It is helps to analyse the slow leaner in the semester exams that are likely study in poor which are used to improve their skill as early to achieve the goal in end semester. The task can be processed based on the several attributes to predict the performance of the student activity respectively. In this research, the paper have been focused the improvement of Prediction/ classification techniques which are used to analyse the skill expertise based on their academic performance by the scope of knowledge. Also the paper shows the comparative performance of C4.5 algorithm, AODE, Naïve Bayesian classifier algorithm, Multi Label K-Nearest Neighbor algorithm to find the well suited accuracy of classification algorithm and decision tree algorithm to analysis the performance of the students which can be experimented in Weka tool.