Predictive analytics project focused on improving credit risk classification using imbalanced data techniques. Applies oversampling, undersampling, and ensemble learning to enhance model performance. Evaluates models based on accuracy, precision, recall, and financial impact of high-risk loan detection.

Use scikit-learn and imbalanced-learn machine learning libraries to assess credit card risk.