Loan Default Prediction Using Machine Learning 

Welcome to my Loan Default Prediction tool! This project is all about leveraging data and machine learning to predict whether a borrower is likely to default on a loan. It's designed to assist financial institutions in making informed lending decisions. 

1. Data-Driven Insights: I worked with a dataset that includes borrower details like credit scores, income, loan amounts, and repayment history.

2. Exploratory Data Analysis: I analyzed patterns and relationships, such as how FICO scores and debt-to-income ratios impact repayment behavior.

3. Machine Learning Modeling: I built and tested various models, including Logistic Regression and Decision Trees, to predict loan default risk. After tuning the models, I identified the one that performed the best.

4. Results Visualization: To make the insights clear, I created visuals like risk distributions and ROC curves to show how well the model works.

• Accuracy: The model can classify loans as "default" or "fully paid" with high precision.

• Insights for Lenders: It highlights which features (like income or credit history) are most important for predicting repayment.

• Interactive Capability: I’m working on a feature where users can input their own loan data and get a risk assessment instantly.

                      Click here to visit the GitHub repository for more details: GitHub Repository