SMOTE-ENC stands for Synthetic Minority Over-sampling Technique for Nominal and Continuous data. It is an advanced technique used to address class imbalance in datasets, particularly in machine learning. By generating synthetic samples for the minority class, SMOTE-ENC helps improve the performance of models by providing a more balanced representation of classes. This method extends the original SMOTE algorithm, which primarily focused on continuous data, to handle both categorical (nominal) and continuous features. By doing so, SMOTE-ENC allows for better training of classifiers, leading to improved accuracy and robustness in predictive modeling tasks.