Abstract: Students academic achievements and their placement in campus selection becomes as challenging issue in the educational system. Monitoring the students progress for their campus placement helps in monitoring the students progression in the academic environment. Recently, educational data mining provides a deep motivation to students for taking an effective decision as academic planners. This also helps the educational institutions to have good intake of students based on students academic achievements and appointments through the campus interview. In academic units, implementing this method will help in evaluating and analyzing students and help the educators and institutions to make important decision that will assist the students. This study demonstrates a novel method named Improved Decision Tree (IDT) for segregating the eligible students for the campus selection based on the academic performance measures. This model, based on the evaluated result obtained, it provides the suggestions in students placement predicting. By using the proposed method, the relationship among students academic performance and their campus placement is analyzed. Under this study information related to students performance measures is analyzed in different perspectives to learn the achievements of the students through their activities.
Subitha Sivakumar and Rajalakshmi Selvaraj, 2017. Adaptive Model for Campus Placement Prediction using Improved Decision Tree. Journal of Engineering and Applied Sciences, 12: 6069-6075.