ISSN: 2222-6990
Open access
The research aims to identify a suitable machine learning model between various machine learning (ML) systems analysed. The input data are factors identified through principal component analysis (PCA) of potential entrepreneurs in the B40 category by analysing 1000 responses from this group through a survey instrument. The following machine learning systems are tested; Random Forest, Extra Trees, K-Neighbots, SVC, Ridge Classifier, Logistic Regression and Decision Tree to select and identify a case-based reasoning artificial intelligence (AI) system best suited in this scenario. Given the data set size, results based on accuracy indicate the best algorithm is Logistic Regression.
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In-Text Citation: (Gopal & Nair, 2021)
To Cite this Article: Gopal, S., & Nair, S. (2021). Machine Learning Model Development for Screening Potential Entrepreneurs in the B40 (Bottom 40%) for Targeting Assistance. International Journal of Academic Research in Business and Social Sciences, 11(12), 1557–1567.
Copyright: © 2021 The Author(s)
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