Welcome to Insurance Predictor's documentation!
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.. figure:: ../img/app_screenshot.png
   :alt: app_screenshot
   :align: center

   `VIEW DEPLOYED APP HERE <https://insurance-predictions.azurewebsites.net/>`__

This project demonstrates a MLOps pipeline for deploying a machine learning model into a production-ready web application. The goal is to help an insurance company forecast patient charges using input like age, gender, BMI, number of children, and smoking status.

Technical Architecture
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.. figure:: ../img/app_diagram.svg
   :alt: Application Architecture
   :align: center
   :width: 100%

   System architecture overview

**Machine Learning Model**

- **Algorithm**: Ensemble model using PyCaret AutoML framework
- **Training Data**: US Insurance dataset with demographic and health features
- **Target Variable**: Insurance charge amount (USD)
- **Model Format**: Serialized pickle file for production deployment

**Web Application**

- **Backend**: Python Flask application serving predictions via REST API
- **Frontend**: Responsive HTML/CSS interface with JavaScript validation
- **API**: JSON-based endpoints for programmatic access
- **Server**: Production-ready Waitress WSGI server for concurrent requests

**Containerization & Deployment**

- **Container**: Docker image based on Python 3.11 runtime
- **Registry**: Azure Container Registry for secure image storage
- **Orchestration**: Azure Web App for container hosting

**CI/CD Pipeline**

- **Version Control**: Git with GitHub for repository management
- **Automation**: GitHub Actions workflows for build/test/deploy
- **Testing**: Pytest-based unit test suite covering API endpoints
- **Security**: Encrypted secrets for cloud resource credentials

Contents
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.. toctree::

   install
   deployment
   api
   unit_test
   documentation