• N.Harika
• S.Sunanda

With the enhancement in the banking sector lots of people are applying for bank loans but the bank has its limited assets which it has to grant to limited people only, so finding out to whom the loan can be granted which will be a safer option for the bank is a typical process. So in this paper we try to reduce this risk factor behind selecting the safe person so as to save lots of bank efforts and assets. This is done by mining the Big Data of the previous records of the people to whom the loan was granted before and on the basis of these records/experiences the machine was trained using the machine learning model which give the most accurate result. The main objective of this paper is to predict whether assigning the loan to particular person will be safe or not. This paper is divided into four sections (i) Data Collection (ii) Comparison of machine learning models on collected data (iii) Training of system on most promising model (iv) Testing. Loans are the major requirement of the modern world.By this only, Banks get a major part of the total profit.It is beneficial for students to manage their education and living.

Bank Loan Prediction, Big Data, Decision Tree, Random Forest, K-nearest neighbor, AdaBoost, Customer Eligibility, Data Collection, Financial Sector

"Customer Loan Prediction Analysis using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.457-463, June-2024, Available :http://www.jetir.org/papers/JETIRGL06076.pdf

"Customer Loan Prediction Analysis using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp457-463, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06076.pdf