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Modified Classification and Prediction Model for Improving Accuracy of Student Placement Prediction

The need for a better classification and prediction system in educational data mining field has highlighted the problems related to the student performance. The aim of this paper is to provide an improved model in comparison to the performance of the existing model by modifying the method used in the previous model. The objectives for achieving the aim includes applying the clustering techniques on the student placement dataset and comparing performance of the clustering techniques such as canopy, EM, FarthestFirst, HierarchicalClusterer and SimpleKMeans. The EM clustering technique is identified as better techniques with 92.5% accuracy. The output information of the clustering in the form of clusters is appended to the student dataset which is used as input to the Naïve Bayes technique for predicting the class of student instances. This combined approach is compared to previous approach of using only Naïve Bayes for prediction purpose. It resulted in increased performance of Naïve Bayes from 95.99% to 98.75%. Hence, the earlier model is modified by adding stage of EM Clustering technique as well as two more required stages of “Institute conducts Mock test and Mock Interviews” and “Campus Placement”. This modified classification and prediction model has improved efficiency and may help the institute management in a better way of handling the problem of student’s placement.