Patient Readmission Prediction

Project Overview

This project aims to predict patient readmissions using machine learning models. Hospitals and insurance companies can leverage this to improve patient care and reduce costs by identifying high-risk patients.

📂 Repository Structure

📦 patient-readmission-prediction
├── 📁 data                    # Sample de-identified datasets
│   ├── raw/                   # Original dataset
│   ├── processed/             # Preprocessed dataset
│   └── README.md              # Data description
├── 📁 notebooks               # Jupyter notebooks for EDA & modeling
│   ├── 01_data_exploration.ipynb
│   ├── 02_feature_engineering.ipynb
│   ├── 03_model_training.ipynb
│   ├── 04_model_evaluation.ipynb
│   └── 05_deployment_demo.ipynb
├── 📁 src                     # Source code for ML pipeline
│   ├── data_preprocessing.py
│   ├── model_training.py
│   ├── model_evaluation.py
│   ├── predict.py             # Script to test predictions locally
│   └── config.py              # Configuration settings
├── 📁 api                     # FastAPI/Flask-based REST API
│   ├── app.py                 # API endpoints for prediction
│   ├── requirements.txt       # API dependencies
│   ├── Dockerfile             # Containerization
│   └── README.md              # API usage documentation
├── 📁 deployment              # AWS deployment scripts
│   ├── terraform/             # Terraform scripts for AWS infra
│   ├── cloudformation/        # AWS CloudFormation templates
│   ├── lambda/                # AWS Lambda function
│   ├── sagemaker/             # SageMaker training & deployment scripts
│   ├── api_gateway/           # API Gateway configurations
│   ├── inference_pipeline.py  # AWS inference pipeline
│   ├── README.md              # Deployment guide
│   └── serverless.yml         # Serverless framework script
├── 📁 tests                   # Unit and integration tests
│   ├── test_data_preprocessing.py
│   ├── test_model_training.py
│   ├── test_api.py
│   └── README.md
├── 📁 models                  # Saved ML models & artifacts
│   ├── model.pkl              # Trained ML model
│   ├── model_metadata.json    # Metadata for model versioning
│   ├── feature_importance.png # Feature importance plot
│   └── README.md
├── 📁 docs                    # Documentation & reports
│   ├── project_report.pdf
│   ├── architecture_diagram.png
│   ├── business_case.pdf
│   ├── setup_guide.md
│   ├── deployment_guide.md
│   └── README.md
├── .github/workflows          # CI/CD pipeline for automation
│   ├── deploy.yml             # GitHub Actions for deployment
│   ├── test.yml               # Automated tests on push
│   ├── docker_build.yml       # Docker build pipeline
│   └── README.md
├── .gitignore                 # Ignore unnecessary files
├── LICENSE                    # Open-source license (MIT, Apache, etc.)
├── README.md                  # Project overview & setup instructions
└── requirements.txt           # Python dependencies

🚀 Getting Started

1️⃣ Prerequisites

• Python 3.8+
• AWS CLI installed & configured (aws configure)
• Docker (for containerized API deployment)
• Terraform (for AWS infrastructure automation)

2️⃣ Setup

Clone the repository:

git clone https://github.com/nagakirankasi/patient-readmission-prediction.git
cd patient-readmission-prediction

Install dependencies:

pip install -r requirements.txt

3️⃣ Running Jupyter Notebooks (for EDA & Model Training)

jupyter notebook notebooks/

4️⃣ Running the API Locally

cd api
uvicorn app:app --host 0.0.0.0 --port 8000

5️⃣ Deployment Guide

Refer to deployment/README.md for AWS deployment steps.

📜 License

This project is licensed under the MIT License.

🤝 Contribution

Feel free to submit issues or pull requests to improve this project!