🧠 Healthcare Stroke Prediction System

This is a Machine Learning-powered web application that predicts the likelihood of a patient experiencing a stroke based on various clinical and demographic factors (such as age, hypertension, heart disease, smoking status, and BMI).

The application is built using Streamlit for the frontend interface and Scikit-Learn for the underlying Machine Learning pipeline.

🚀 Live Demo

You can interact with the live application directly on Hugging Face Spaces.

🛠️ Features

Real-time Prediction: Input patient details to get instant stroke risk probability.
User-Friendly Dashboard: Clean and intuitive UI built with Streamlit.
Robust ML Pipeline: Features automated scaling and categorical encoding using saved preprocessing artifacts (scaler.pkl, columns.pkl).

🗂️ Project Structure

The repository structure for this Hugging Face Space is as follows:

├── app.pytext              # Main Streamlit application script
├── requirements.txt       # Python dependencies
├── model.pkl              # Trained Machine Learning Model (Trained using Jupyter Notebook)
├── scaler.pkl             # Fitted Scaler for numerical features
├── columns.pkl            # Saved feature columns list for consistency
└── README.md              # Project documentation and HF configuration

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