IJSRP, Volume 11, Issue 6, June 2021 Edition [ISSN 2250-3153]
L. Udaya Bhanu, Dr. S. Narayana
Customer loan prediction is usually life time issue so; each and every retail bank faces the issue at the minimum lifetime. If done exactly, it can spare a lot’s of man hours at the conclusion of a retail bank. If Company wants to semi automate the loan acceptability process (real time) based on customer detail provided while filling online application form. These subtle elements are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this method, they have given an issue to recognize the customers segments; those are allowed for loan amount total so they can clearly target these customers. We need to predict whether or not a loan would be approved. In a classification problem, we need to predict separate values based on a given set of self-sufficient variable(s). What’s our objective is to implement machine learning model so as to classify, to the best doable degree of accuracy, and dataset gathered from Kaggle. Random forest classification method shows best accuracy in classifying given on loan candidates using python help on Jupyter notebook.