For my classification problem, I chose the diabetes dataset.

The dataset covers two classes: diabetes positive and diabetes negative. These are directly aligned with my prediction task of identifying whether a patient has diabetes.

The dataset is ethically fit because it is anonymized, contains no personally identifiable information, and is commonly used for educational and research purposes. Technically, it is suitable because it provides a balanced set of medical features relevant for classification tasks.

Remaining gaps include possible feature leakage and class imbalance. I plan to address these by testing models both with and without potential leakage features, applying proper cross-validation, and evaluating fairness metrics to ensure robust and unbiased predictions.