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Cancer Detection API With SVM Kernel Selection and Tuning

Added Jun 2025 3 design docs

Early detection changes cancer outcomes, and classical machine learning still earns its place in clinical decision support: Support Vector Machines remain strong performers on structured diagnostic measurements. This project builds a diagnostic prediction service with the engineering rigor a healthcare setting implies.

The intern trains an SVM classifier with scikit-learn on diagnostic feature data, systematically comparing linear, polynomial, and RBF kernels and tuning hyperparameters such as C and gamma to balance sensitivity against overfitting, with the experiments documented in Jupyter. The tuned model is serialized and served through a FastAPI backend whose prediction endpoints validate input features and return class probabilities, and whose auto-generated Swagger UI allows interactive testing without writing a client. Throughout, the intern focuses on evaluation metrics appropriate for medical screening, where a false negative costs far more than a false positive.

The project demonstrates disciplined model selection, kernel method fluency, and clean inference API design — showing the intern can carry a healthcare prediction task from experimentation all the way to a service another application could integrate.

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