Dealing with Imbalanced Data for Forecasting Model of Higher Education Selection of Grade 9 Students in Muang District Nakhonsawan Province
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Abstract
Nowadays big data has become more and more active because most organisations need to review their organisational performance in the past years and to seek impact factors both strengths and weaknesses for problem solving, operation plan and organisational development including the future prediction. A problem of big data is imbalanced data resulting in less predictive accuracy. Therefore, the study aims to create a proper model of multinomial logistic regression and to compare the predictive accuracy of the models between imbalanced data and balanced data using random over-sampling method and random under-sampling for correcting the data balancing. A real dataset of higher education selection of grade 9 students in Nakhonsawan, 2,692 students, was applied. It was found that main impact factor on students’ decision was demographic. Moreover, the results revealed that the accuracy of forecasting models were more than 60% whereas the predictive accuracy of balanced data was better than another.
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