Imbalanced data describes a dataset where some categories are far more frequent than others, which biases a model toward the majority class. Imagine a Casablanca bank training a fraud detector: if only one transaction in a thousand is fraudulent, a lazy model can reach 99.9 percent accuracy by predicting «never fraud» and still be useless. Remedies include collecting more minority examples, oversampling, generating synthetic data, or using metrics like precision and recall instead of raw accuracy.