Article
Optimizing Relational Databases for High-Performance Binary Classification
Machine learning models in financial systems are vulnerable to adversarial attacks and corrupted training data. This paper implements a data enhancement framework for binary classification of relational data to improve model robustness and accuracy. Using the German Credit Dataset, the system trains multiple models (Logistic Regression, Random Forest, Gradient Boosting, SVM, KNN) with preprocessing including feature scaling and categorical encoding. Corrupted data attributes are detected and corrected, and adversarial examples are introduced during training for robustness. The best model is automatically selected and integrated into a Django web application for real-time credit risk prediction. Experimental results show that data enhancement improves average classification accuracy from 71.3% to 79.8% and reduces adversarial vulnerability by 42%, demonstrating effective combination of data enhancement with web-based deployment for robust credit risk assessment.
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