Random Forest for Salary Prediction System to Improve Students' Motivation

Abstract

A salary prediction model was generated for graduate students using a data mining technique to generate for individuals with similar training attributes. An experiment was also conducted to compare the two data mining techniques Decision Trees ID3, C4.5 and Random Forest to determine the most suitable technique for salary prediction, tuned with key important parameters to improve the accuracy of the results. Random Forest gave the best accuracy at 90.50%, while Decision Trees ID3 and C4.5 returned lower accuracies at 61.37% and 73.96%, respectively for 13,541 records of graduate students using a 10-fold cross-validation method. Random Forest generated the best efficiency model for salary prediction. A questionnaire survey was conducted to determine usage evaluation with 50 samples. Results indicated that the system was effective in boosting students' motivation for studying, and also gave them a positive future viewpoint. The results also suggested that the students were satisfied with the implemented system since it was easy to use, and the prediction results were simple to understand without any previous background statistical knowledge.