Atharva K. Ingale, Laksh R. Bhamare, Rakhamaji G. Nagapure, Rutik K. Pimpale , Govind Pole
Machine Learning, Supervised learning, Support vector machine, Decision Tree, Random Forest, Dataset, ML algorithms and ML models
— Technology has brought significant advancements to the banking industry. With loan applications flooding in every day, it has become more challenging to approve loans. Banks must adhere to strict policies when selecting a candidate for loan approval, considering several criteria to find the most suitable candidate. Manually checking each application for loan approval is arduous and risky. To overcome this, we employ machine learning to predict the loan candidate's creditworthiness based on their prior performance. Loans generate a substantial proportion of bank profits, but identifying legitimate applicants who will repay the loan is difficult. Therefore, we are developing a machine learning-based loan prediction system that will autonomously select qualified applicants, reducing loan processing time significantly. We are utilizing three machine learning techniques, Random Forest Classifier, Decision Tree and Support Vector Machine (SVM), to predict the loan data. Logistic Regression estimates the parameters of a logistic model, while SVM is a popular Supervised Learning algorithm primarily used for Classification problems in Machine Learning
Article Details
Unique Paper ID: 160347

Publication Volume & Issue: Volume 10, Issue 1

Page(s): 396 - 402
Article Preview & Download

Share This Article

Join our RMS

Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Call For Paper

Volume 11 Issue 1

Last Date for paper submitting for Latest Issue is 25 June 2024

About Us enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on

Social Media

Google Verified Reviews