Classification and Estimation of High-Risk Factors to Low-Risk Factors in Approving Loan through Creditworthiness of Bank Customers using SVM Algorithm and Analyze its Performance over Logistic Regression in terms of Accuracy

Authors

  • Ch.Venkata Sandeep
  • Dr.T. Devi

DOI:

https://doi.org/10.47750/pnr.2022.13.S04.212

Keywords:

Logistic Regression, Machine Learning, Novel Support Vector Machine,Creditworthiness, Mean Accuracy, Prediction.

Abstract

Aim: To analyze the accuracy of Support Vector Machine (SVM) algorithms over Logistic Regression (LR) used to approve bank loans. Materials and Methods: The existing model uses a Logistic Regression algorithm and the proposed model employs a Novel Support Vector Machine. The 20 sample values are used to find out the mean, std. deviation, std. error means. The sample size was measured as 40 for both the groups using G power (80%). Results: The resultant graph explains the comparison of the mean accuracy values of algorithms Novel Support Vector Machine and Logistic Regression where the mean accuracy of the Support Vector Machine is about 69.5% and the mean accuracy value of the Logistic Regression is about 66.5%. The independent sample T-test shows that p=1.0, since p>0.05 there exists insigniface between the SVM and LR. Conclusion: The mean accuracy rate of the Novel Support Vector Machine algorithm has been improved to 69.5% compared to Logistic Regression which is having around 66.5% mean accuracy.

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Published

2022-10-07

Issue

Section

Articles

How to Cite

Classification and Estimation of High-Risk Factors to Low-Risk Factors in Approving Loan through Creditworthiness of Bank Customers using SVM Algorithm and Analyze its Performance over Logistic Regression in terms of Accuracy. (2022). Journal of Pharmaceutical Negative Results, 1756-1763. https://doi.org/10.47750/pnr.2022.13.S04.212