🫀 Heart Disease Prediction Model

This project is a Machine Learning-based web app that predicts the likelihood of heart disease in a patient using medical input data.

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📌 Features

• ✅ Predicts risk of heart disease using patient health metrics
• ✅ Built using Python and Scikit-learn
• ✅ Simple and clean web interface using Streamlit / Flask / FastAPI
• ✅ Model trained on real medical dataset (e.g., UCI Heart Disease Dataset)

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🧠 Technologies Used

• Python 3.x
• Scikit-learn
• Pandas / NumPy
• Matplotlib / Seaborn (for EDA)
• Flask / Streamlit / FastAPI (for deployment)
• HTML/CSS (if applicable for frontend)

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heart-disease-prediction/ ├── 📁 app/ # Application files (Frontend + Backend) │ ├── app.py # Main application (Flask/Streamlit/FastAPI) │ ├── 📁 templates/ # HTML templates (only for Flask) │ ├── 📁 static/ # CSS, JS, images (for styling) │ └── 📁 components/ # Reusable components (optional) │ ├── 📁 model/ # Machine Learning model files │ ├── heart_disease_model.pkl # Trained ML model │ └── train_model.py # Script to train the model │ ├── 📁 data/ # Dataset and data processing │ └── heart.csv # Original dataset │ ├── 📁 notebooks/ # Jupyter notebooks (for EDA, model testing) │ └── heart_analysis.ipynb # Exploratory data analysis notebook │ ├── requirements.txt # Python dependencies ├── README.md # Project documentation └── .gitignore # Git ignore rules

The model takes the following input features:

• Age
• Sex
• Chest pain type
• Resting blood pressure
• Cholesterol level
• Fasting blood sugar
• Resting ECG results
• Max heart rate achieved
• Exercise-induced angina
• ST depression (oldpeak)
• Slope of the ST segment
• Number of major vessels
• Thalassemia

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