Credit Risk Prediction using Machine Learning

This project builds a machine learning model to predict whether a customer is high-risk or low-risk for credit lending. The model uses factors such as income, credit score, loan amount, employment status, past defaults, and repayment history.

The workflow includes data preprocessing, handling missing values, converting categorical variables, feature scaling, and training classification models like Logistic Regression, Random Forest, or XGBoost.

Model performance is evaluated using accuracy, F1-score, confusion matrix, and ROC-AUC.

This helps financial institutions decide whether to approve, reject, or further review a loan application based on the predicted credit risk.