Abstract
Exploring machine learning approaches to enhance the effectiveness and precision of procedures related to bank loan approval. This investigation encompasses various methods such as logistic regression, decision trees, linear regression, as well as GaussianNB, Random Forest, and SVM. Utilizing a substantial dataset containing past loan applications and diverse applicant attributes like demographics, credit scores, income levels, and employment histories. The research endeavors to evaluate the recall, accuracy, precision, and F1-score metrics of various algorithms. Additionally, it investigates the interpretability and transparency of machine learning models to offer further insight into the variables affecting decisions on loan acceptance. The study emphasizes the efficacy of logistic regression, which outperformed SVM (77%), GaussianNB (78%), random forests (78%), and decision trees (69%), achieving the highest accuracy of 80% in loan approval. By implementing this model, we can enhance ML-driven loan approval processes within the banking industry, thereby elevating decision-making standards and enhancing consumer satisfaction. KEYWORDS— Machine Learning Algorithms, Loan Approval, LogisticRegression, DecisionTree, Linear Regression, GaussianNB, RandomForest, SupportVectorMachine (SVM), Decision-Making,
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: EPRA International Journal of Multidisciplinary Research (IJMR)
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.