The Effect of Handling Imbalanced Datasets Methods on Prediction of Entrepreneurial Competency in University Students

Abstract

As of today entrepreneurs and entrepreneurship are considered to be the integral parts of the economic and technological advancements. Entrepreneurs are promoted in many countries because of their high return on investment opportunities both in terms of income and new inventions. Numerous studies prove that entrepreneurs have many traits in common and these common traits can correlate with each other. Based on these common traits, potential entrepreneurs can be predicted, current entrepreneurs can be improved by realising their weak sides and the ones who wish to be entrepreneurs can be provided with insights. A machine learning approach can light the way for a better rewarding future for entrepreneurship, helping these goals significantly. There exist several studies for the prediction of entrepreneurial competency with the use of machine learning algorithms. Most machine learning methods perform better accuracy and F1-score imbalanced data instead in imbalanced data. This study focuses on utilizing imbalanced class handling methods to increase prediction performance. Random Oversampling, Random Undersampling, SMOTE, and NearMiss methods are used to handling imbalanced data for this purpose in this study. The performance of the machine learning algorithms with Imbalanced Data Handling methods is compared with the machine learning algorithms. Comparisons were made using accuracy, precision, recall, F1-Score as performance parameters. The comparison shows a noticeable performance increase using machine learning algorithms with handling imbalanced dataset methods.