🫀 Heart Disease Prediction using Machine Learning

📌 Overview

This project develops a machine learning model to predict the likelihood of heart disease based on clinical features, including age, blood pressure, cholesterol levels, and other relevant factors. The goal is to assist in early detection and support medical decision-making through data-driven insights

🚀 Features

• Exploratory Data Analysis
• Data Preprocessing: Feature scaling
• Model Selection: Evaluation of multiple ML classifiers (e.g., Random Forest, XGBoost)
• Performance Metrics: Accuracy, precision, recall, F1-score, ROC-AUC curve
• Deployment: Interactive web interface using Streamlit

🛠️ Installation

Clone the repository

git clone https://github.com/auspicie/Heart-Disease_Prediction-ML.git
cd heart-disease-prediction

**Install dependencies**
pip install -r requirements.txt

💻 Usage

Run the Streamlit app: streamlit run heart_disease_app.py

Interact with the app: Input values and view predictions in your browser.

✨ Example Prediction

Input: Male, Age: 58, Cholesterol: 230, ...
Output: ✅ Unlikely to have heart disease

📷 Streamlit App Preview

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📊 Dataset

Source: Kaggle

Clinical Features: Age, cholesterol, blood pressure, chest pain type, smoking history, etc.

🤝 Contributing

Contributions are welcome! Feel free to: Open an issue Submit a pull request

📄 License

This project is licensed under the MIT License.

📌 Notes

• Ensure that the heart_disease_model.pkl and scaler.pkl are in the same directory as the app.

Author: Samsudeen Bankole

Built with ❤️ using Streamlit and Scikit-learn.