• Akshay Anil Nalkol
• Sainath Govind Ibitwar
• Durgesh Arvind Kirpal
• Prof. S.P. Gade

A key activity within the banking industry is to extend credit to customers. Hence,credit risk analysis is critical for financial risk management. There are various methods used to perform credit risk analysis.s. Credit risk analysis is becoming an important field in financial risk management. For the evaluation of the credit risk of the customer dataset, numerous credit risk analysis methodologies are employed. The challenging task that requires a thorough examination of the customer credit dataset or the data provided by the customer is the evaluation of the credit risk datasets that results in the decision to grant the loan of the customer or refuse the application of the customer. In this study, we review various credit risk analysis methods that are applied to the evaluation of credit risk datasets. The discussion and comparison of the best method for classifying these datasets serves as the work's conclusion. The machine learning models utilised for this paper include K-Means Clustering, Naive Bayes Classifier, Decision Trees, and Extreme Learning Machine (ELM).

Machine Learning, Decision Tree, Naïve Bayes Classifier, K-Means Clustering, Extreme Learning Machine

"Credit Risk Analysis Using Naive Bayes In Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 11, page no.d557-d560, November-2022, Available :http://www.jetir.org/papers/JETIR2211375.pdf

"Credit Risk Analysis Using Naive Bayes In Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 11, page no. ppd557-d560, November-2022, Available at : http://www.jetir.org/papers/JETIR2211375.pdf