ENGINEERING CASE STUDY / INTERACTIVE ML PREDICTION APP
Heart Disease Prediction & Analysis
This project demonstrates the full path from a trained machine-learning artifact to an interactive user-facing prediction application.
01 / CONTEXT
From model to application.
A Streamlit-based machine-learning application that loads a trained heart-disease model, accepts patient features, predicts class and probability, and presents risk information through an interactive interface.
02 / UNDER THE SURFACE
A connected system.
01StreamlitPatient feature inputs
02pandas / NumPyFeature data
03scikit-learnTrained model
04PredictionClass & probability
A conceptual overview of the components documented in the project.
03 / THE DETAILS MATTER
Engineering in practice.
- Repository includes a serialized optimized model, feature metadata, and the interactive Streamlit application.
- Application uses model.predict and model.predict_proba to generate both a class prediction and percentage risk probability.
- UI exposes model metadata including model type, accuracy, and feature count when loaded.
This is a machine-learning demonstration. Dataset details, model type, and verified evaluation metrics have not yet been supplied. No clinical claims or unverified accuracy figures are presented.
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