Credit Default Risk Prediction – Machine Learning Pipeline

• Designed and implemented an end-to-end ML pipeline on large-scale financial data (300K+ records).

• Handled missing values, feature encoding, scaling, and class imbalance.

• Compared Logistic Regression and Decision Tree using ROC-AUC and performance metrics.

• Applied PCA for dimensionality reduction and K-Means for customer risk segmentation.