Stroke-risk category classification — educational ML workflow

Research applicability

An educational machine-learning workflow for classifying a supplied dataset’s Low, Moderate and High stroke-risk labels. Repository documentation describes exploratory analysis, preprocessing, feature engineering and comparison of a linear-regression baseline with a random-forest classifier.

Getting started

Editorial suggestion, not executed: read the data notice, reconcile notebook and data filenames, and audit label provenance and train/test preprocessing before attempting the example.

Maintenance

Current README/abstract access is not a maintenance or deployment audit.

License

Check original code/document and data terms before reuse; license file not audited.

Workflow & human oversight

Resource-level automation is unspecified; researchers select tasks, review sources, validate outputs and authorize consequential actions.

Workflow level is not an evidence score; higher is not better evidence.

Approach

An educational machine-learning workflow for classifying a supplied dataset’s Low, Moderate and High stroke-risk labels. Repository documentation describes exploratory analysis, preprocessing, feature engineering and comparison of a linear-regression baseline with a random-forest classifier.

Limitations

README-only review; no notebook execution, dataset inspection or clinical validation. Dataset provenance and label generation are not independently established. The README cautions that linear regression is not designed for categorical classification; nominal encoding and train-only preprocessing require audit, not an inference of executed defects. README notebook/data filenames are inconsistent across sections.

Reproducibility

Primary text inspected only; no code, data or outcomes reproduced.

Adoption requirements

Python/Colab and pandas, NumPy, plotting packages and scikit-learn; inspect the experiment branch and actual filenames before running. Kaggle data retain separate terms. Use only for education; no diagnosis or treatment decisions and no private patient uploads.