Breast Cancer Risk Prediction: Multi-Modal ML System with Flask Web App

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Release Title

v1.0 — Breast Cancer Risk Prediction: Multi-Modal ML System with Flask Web App

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Release Description

This release delivers a complete, end-to-end breast cancer risk prediction system, built as a production-style machine learning application rather than a standalone model.

The project integrates multi-modal clinical features into a unified ML pipeline covering data preprocessing, model training, evaluation, and inference, with a clear emphasis on reproducibility, interpretability, and deployment readiness.

A Flask web application provides an interactive interface for real-time risk scoring, enabling users to submit inputs and receive probability-based risk estimates in a clear, user-friendly format. The system is designed to reflect how ML models are embedded into real decision-support tools, not just trained offline.

Key highlights:

• Multi-modal machine learning pipeline for breast cancer risk prediction
• Clear separation between data processing, training, and inference
• Probability-based outputs framed as risk assessment, not diagnosis
• Production-style Flask web app for interactive prediction
• Reproducible project structure suitable for further extension or deployment

This release demonstrates a balance of data science rigour and software engineering discipline, positioning the project as a portfolio-grade example of applied clinical machine learning and end-to-end ML system design.

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