Complete and Reproducible baseline for tabular breast-cancer diagnosis on the WDBC dataset. The project includes tidy EDA, feature prep, stratified train/test splits, and side-by-side benchmarking of Logistic Regression, SVC, Decision Tree, and Random Forest—delivering ~98% test accuracy with clear visuals and comparisons.(Research/educational use)
python data-science machine-learning random-forest scikit-learn classification logistic-regression support-vector-machine reproducibility decision-tree model-evaluation breast-cancer wdbc random-forest-regression google-colab healthcare-ai eature-engineering
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Updated
Jul 25, 2025 - Jupyter Notebook