• Mahesh Shesharao Rathod
• Dr. V H Deshmukh

Recently, with the advance of electronic commerce and big data technology, P2P online lending platforms have brought opportunities to businessmen, but at the same time, they are also faced with the risk of user loan default, which is related to the sustainable and healthy development of platforms. Therefore, based on ensemble classifier using classification algorithms such as Random Forest algorithm, SVM, Naive Bayes, multilayer perceptron (MLP) and K-nearest neighbors (KNN) algorithm, a credit risk prediction model can be built in view of the real-world user loan data. With the improvement within the banking sector, several individuals area unit applying for bank loans. However the bank has its restricted assets so they grant loans to restricted individuals solely, thus sorting out to whom the loan is be lent so that the borrower is a safer choice for the bank. Thus we try to cut back this risk issue behind choosing the safe person thus on save efforts and assets of lenders. This can be done by mining the large knowledge of the previous records of the individuals to whom the loan was granted before and based on those records and experiences the machine will be trained with the machine learning model that offer the foremost correct result. The objective of the proposed system is to predict whether or not assignment of loan to a person is going to be safe or not. So it will be split into four sections(i)Data assortment (ii) Comparison of machine learning models on collected knowledge (iii) coaching of system on most promising model(iv) Testing.

Loan Prediction, Ensemble Classifier, SVM, MLP, KNN, Naïve Bayes

"A Review on Credit Prediction and Risk analysis on Loans Data using Machine Learning Classification Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.l41-l44, May-2022, Available :http://www.jetir.org/papers/JETIR2205C06.pdf

"A Review on Credit Prediction and Risk analysis on Loans Data using Machine Learning Classification Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppl41-l44, May-2022, Available at : http://www.jetir.org/papers/JETIR2205C06.pdf