Indian Journal of Science and Technology

Abstract

Background/Objectives: Excellence of Universities is based on students’ success in their academic and it is possible if the students are instructed or counseled before getting admitted in their post graduation. So, we have developed a model for the post graduating students to utilize their intelligence in right direction. Methods/Statistical Analysis: If students are given admission in right course then their academic success is guaranteed by the university. To formulate the prediction, decision tree classifiers are best suitable as it has potential to generate comprehensible output. It is generating the tree and rules which will be used to formulate the predictions. Hence, this approach is of two steps approach known as training phase and testing phase. Findings: The model trains on the basis of the defined instances and from the defined instances the classified builds the rules. These rules are used to formulate prediction for unknown valued instances. This article depicts the customized classification model to predict the Post-Graduation degree of the students. The model is based on J48 decision tree algorithm for classification. The model is trained by the data collected through survey of different institutions with the purpose of differentiating and predicting students’ choice and to generate unbiased result. We obtained certain patterns of the students preferences to select their post graduation course. On the basis of such rules which are derived from historical data, are used to predict post graduation course for unknown instance. We have used J48 classification algorithm for decision tree to predict the post graduation course based on their academic history and other identified parameters. We have identified total 14 parameters to predict the class label of 15thattribute. Applications/ Improvements: We have customized a model using Weka which uses the J48 algorithm to predict students’ post graduation degree. We have obtained 94.03% accuracy of prediction against 4 classes as final attribute. 

Keywords: Classification, Customization in Weka, Post Graduation Course Selection, Prediction Model, Weka