Placement Prediction Decision Support System using Data Mining

With the growth of voluminous amount of data in educational institutes', the need is to mine the large dataset to produce some useful information out of it. In this research we focused on to form a decision support system for the educational institutes' which can help them to know about the placement possibility of students. Our research is not limited to find out placement possibility but we did multi-level analysis on student performance dataset which will predict that what level of interview process a student is likely to pass. For this we have applied Naïve Bayes and Improved Naïve Bayes which is integrated with relief feature selection technique to obtain the prediction. Data analysis was done using NetBeans and WEKA. For this our proposed technique gave better accuracy than existing naïve Bayes which was 84.7% and naïve Bayes gave 80.96% accuracy.