📦 Live API: Swagger Docs
📦 Live APPLICATION: Heart Disease Prediction
🫀 Heart Disease Prediction API
A FastAPI-powered REST API that serves predictions from a machine learning model trained to detect heart disease. This project focuses on containerization with Docker and deployment to the cloud using Render. Built as part of a hands-on assignment to demonstrate practical DevOps, ML, and API development skills.
🔍 Features
✅ /health — Health check endpoint
📄 /info — Returns model metadata
🔮 /predict — Accepts patient data and returns heart disease prediction (True/False)
🐳 Dockerized for local and cloud deployment
☁️ Live deployment on Render
🧠 Model
Trained on the Heart Disease UCI dataset
Model type: Random Forest Classifier
Saved using joblib and served as a REST API
🚀 Tech Stack
FastAPI
scikit-learn
Docker & Docker Compose
Render (for cloud deployment)
Pydantic (for input validation)
📦 Installation (Local) bash Copy Edit git clone https://github.com/yourusername/heart-disease-api.git cd heart-disease-api docker-compose up --build Then visit: http://localhost:8000/docs
📦 Train model locally
python -m pip install -r requirements.txt
python train_model.py
# This will create model/heart_model.joblib
📁 Project Structure
FastAPI_Docker_HeartDiseasePrediction/
├─ app/
│ ├─ __init__.py
│ ├─ main.py # Your FastAPI app code
│ ├─ schemas.py # Pydantic schemas
│ ├─ utils.py # Helper functions, model loading, predictions
│ └─ templates/
│ └─ index.html # Your HTML template file
├─ model/
│ └─ heart_model.joblib # Trained model file
├─ data/
│ └─ heart.csv # Dataset file
├─ train_model.py # Script to train model and save joblib file
├─ Dockerfile # Docker build instructions
├─ docker-compose.yml # (optional) docker-compose config
├─ requirements.txt # Python dependencies
├─ README.md # Project documentation
└─ .gitignore # Git ignore rules
📸 Screenshots
✍️ Author
Debbrath Debnath