LOAN APPROVAL PREDICTION USING MACHINE LEARNING

LOAN APPROVAL PREDICTION USING MACHINE LEARNING

Mrs. R.L. Indu lekha, M.E.,

Assistant Professor, Department of CSE, Adhiyamaan College of Engineering, TamilNadu, India

Prasath G, Pugazhendhi S, Surendar U

UG Scholar, Department of CSE, Adhiyamaan College of Engineering, TamilNadu, India

Abstract: Both human existence and quality of life have benefited from technological advances. Every day we strive to create something fresh and unique. We have machines that support our lives and make them relatively complete in the banking sector. Candidates will obtain evidence / guarantee before the loan amount is approved. The system's decision on whether to approve the application is based on the candidate's historical data. Every day, many people in the banking sector seek credit, but bank reserves are limited. In this scenario, it is very useful to make correct predictions using the class function algorithm. Examples are logistic regression, random forest classifiers, support vector machine classifiers, and other algorithms. A bank's profit or loss depends on the size of the loan and whether the customer or the customer repays the loan. The most important aspect of banking is the collection of loans. The improvement process is very important in the banking industry. Using the candidate's historical data, we built a machine learning model that uses multiple classification techniques. The main goal of this task is to use a machine learning model trained with historical data to predict whether a new application will be loaned.

Keywords:- Machine learning, Data, Loan, Training, Testing, Prediction