Welcome to Insurance Predictor’s documentation!

app_screenshot

VIEW DEPLOYED APP HERE

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

Application Architecture

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