Side-by-Side Classifier Comparison Tool for Clinical Datasets

Added Jun 2025 3 design docs

Choosing a classifier for clinical data is rarely obvious: K-Nearest Neighbors, Support Vector Machines, and Decision Trees each behave differently on the same patient dataset, and the trade-offs only become clear when the models are examined side by side. Practitioners need a fast way to compare candidates before committing to one. The intern builds an interactive Streamlit application where users upload a healthcare dataset, configure KNN, SVM, and Decision Tree models from scikit-learn, and tune each model's hyperparameters directly from the sidebar. The app renders decision boundary plots so users can see how each algorithm carves up the feature space, and evaluates every model with confusion matrices, ROC curves, and F1 scores computed over pandas and NumPy pipelines. Placing all the metrics in one view turns abstract algorithm differences into concrete, comparable evidence. Completing the project gives the intern practical judgment about classifier selection and evaluation on health data — including why raw accuracy misleads on medical problems where classes are imbalanced — plus experience building the kind of comparison tooling data science teams actually use during model selection.

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