LOAN PREDICTION SYSTEM USING SVM, DT and RF
Author(s):
Atharva K. Ingale, Laksh R. Bhamare, Rakhamaji G. Nagapure, Rutik K. Pimpale , Govind Pole
Keywords:
Machine Learning, Supervised learning, Support vector machine, Decision Tree, Random Forest, Dataset, ML algorithms and ML models
Abstract
— 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
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