Implement of salary prediction system to improve student motivation using data mining technique

Abstract

This paper presents a salary prediction system using a profile of graduated students as a model. A data mining technique is applied to generate a model to predict a salary for individual students who have similar attributes to the training data. In this work, we also made an experiment to compare five data mining techniques including Decision trees, Naive Bayes, K-Nearest neighbor, Support vector machines, and Neural networks to find the suitable technique to the salary prediction. In the experiment, 13,541 records of graduated student data were used with 10-fold cross validation method. Results showed that K-Nearest neighbor provided the best efficiency to be used as a model for salary prediction. For usage evaluation, a questionnaire survey was conducted with 50 user samplings and a result showed that the system was effective in boosting students' motivation for studying and also gave them a positive future viewpoint. The result also informed that they found they satisfied with the implemented system since the system was easy to use, and the prediction results were simple to understand without requiring any background knowledge.