The Application of Data Mining to Build Classification Model for Predicting Graduate Employment

Abstract

Data mining has been applied in various areas because of its ability to rapidly analyze vast amounts of data. This study is to build the Graduates Employment Model using classification task in data mining, and to compare several of data-mining approaches such as Bayesian method and the Tree method. The Bayesian method includes 5 algorithms, including AODE, BayesNet, HNB, NaviveBayes, WAODE. The Tree method includes 5 algorithms, including BFTree, NBTree, REPTree, ID3, C4.5. The experiment uses a classification task in WEKA, and we compare the results of each algorithm, where several classification models were generated. To validate the generated model, the experiments were conducted using real data collected from graduate profile at the Maejo University in Thailand. The model is intended to be used for predicting whether a graduate was employed, unemployed, or in an undetermined situation.

References in corpus (1)

• Data Mining Applications: A comparative Study for Predicting Student's performance

Cited by in corpus (4)

• Performance Analysis and Prediction in Educational Data Mining: A Research Travelogue
• A Predictive Model using Machine Learning Algorithm in Identifying Students Probability on Passing Semestral Course
• Role of Secondary Attributes to Boost the Prediction Accuracy of Students Employability Via Data Mining
• Multidimensional Analysis of Psychological Factors affecting Students Academic Performance