House Price Prediction End 2 End project

This is a simple Python MLOps end-to-end project that solves the classical Ames Housing regression task and integrates it into a fully runnable application with:

• A working FastAPI backend

• A Streamlit UI frontend

• MLflow experiment tracking

• Docker & Docker Compose support

The goal of this project is to demonstrate a complete ML lifecycle: data → training → experiment tracking → API → UI → containerization.

Instructions

There are 2 main components you can run and demonstrate:

Run the API and Streamlit App

Step 1 — Run the FastAPI Backend

Navigate to the project root directory and run:

uvicorn src.api.main:app --reload

The API will start at:

http://127.0.0.1:8000

You can test it using:

http://127.0.0.1:8000/docs

Step 2 — Run the Streamlit Frontend

In a new terminal, run:

streamlit run src/app.py

The Streamlit app will open automatically in your browser

Run with Docker & Docker Compose

This project includes:

Dockerfile

docker-compose.yaml

Step 1 — Build Containers

docker-compose build

Step 2 — Run Containers

docker-compose up

Or detached mode:

docker-compose up -d

This will:

Run three services:-

• Run the API
• Run the Streamlit UI
• Run the house monitoring service (checks for new data presence by checking for instances of dataset/new_data.csv and then checks for data drift)

To stop:

docker-compose down