Bank Loan Defaulter Prediction is a project aimed at predicting whether a borrower is likely to default on their loan payments or not. The project is typically undertaken by financial institutions such as banks, credit unions, and other lending institutions who want to minimize their risk of financial loss due to loan defaults.

The project involves building a predictive model based on historical data of borrowers, including their credit history, income, employment status, loan amount, and other relevant information. The model is trained using machine learning algorithms to identify patterns and correlations in the data that can help predict the likelihood of loan default.

Once the model is trained, it can be used to predict the probability of loan default for new loan applications. This can help the lending institution make informed decisions about whether to approve or reject a loan application, and if approved, what terms and conditions to offer to minimize the risk of default.

Overall, the Bank Loan Defaulter Prediction Project is an essential tool for financial institutions to manage their risk and ensure the sustainability of their lending operations.

PM-Project_Report:- Report of the project.

PM-Project_Python:- Model in python programming code.

PM-Project_Rstudio:- Model in R programming code.

PM-Project_STR:- "str" file of the model of IBM SPSS Modeler.

Link for the presentation of the project:- https://tome.app/ganpat-university-f5b/predicting-loan-defaulters-with-bayesian-networks-clgq2twpb01eb5m41m4fwfe9p