• Sarang R. Pawar,
• Prof.S. R. Kale
• Tushar M. Behare
• Sakshi H. Hingwe
• Shivraj A. Khuje

This review paper explores a machine learning-based loan prediction system, emphasizing the challenges in manually assessing loan eligibility and the benefits of automating the process. Comparing various algorithms, including Decision Tree, Random Forest, and Regression, the study highlights Random Forest's superior accuracy in handling extensive datasets. The architecture involves data input, pre-processing, classifier finalization, model training, and application to test data. Additionally, the paper introduces Naive Bayes as an efficient classification algorithm. The literature review examines related works, and the conclusion underscores the significance of Naive Bayes in loan classification, suggesting avenues for future enhancements. Overall, the paper offers valuable insights into advancing loan prediction systems.

oan, Machine Learning, Random Forest and Classification, Naïve Bayes.

"REVIEW PAPER ON LOAN PREDICTION SYSTEM USING MACHINE LEARNING ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.a584-a586, February-2024, Available :http://www.jetir.org/papers/JETIR2402074.pdf

"REVIEW PAPER ON LOAN PREDICTION SYSTEM USING MACHINE LEARNING ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppa584-a586, February-2024, Available at : http://www.jetir.org/papers/JETIR2402074.pdf