🏠 House Price Predictor – An MLOps Learning Project

Welcome to the House Price Predictor project! This is a real-world, end-to-end MLOps use case designed to help you master the art of building and operationalizing machine learning pipelines.

You'll start from raw data and move through data preprocessing, feature engineering, experimentation, model tracking with MLflow, and optionally using Jupyter for exploration – all while applying industry-grade tooling.

🚀 Want to master MLOps from scratch?
Check out the MLOps Bootcamp at School of DevOps to level up your skills.

📦 Project Structure

house-price-predictor/ ├── configs/ # YAML-based configuration for models ├── data/ # Raw and processed datasets ├── deployment/ │ └── mlflow/ # Docker Compose setup for MLflow ├── models/ # Trained models and preprocessors ├── notebooks/ # Optional Jupyter notebooks for experimentation ├── src/ │ ├── data/ # Data cleaning and preprocessing scripts │ ├── features/ # Feature engineering pipeline │ ├── models/ # Model training and evaluation ├── requirements.txt # Python dependencies └── README.md # You’re here!

🛠️ Setting up Learning/Development Environment

To begin, ensure the following tools are installed on your system:

• Python 3.11
• Git
• Visual Studio Code or your preferred editor
• UV – Python package and environment manager
• Docker Desktop or Podman Desktop

🚀 Preparing Your Environment

1. Fork this repo on GitHub.

2. Clone your forked copy:

bash # Replace xxxxxx with your GitHub username or org git clone https://github.com/xxxxxx/house-price-predictor.git cd house-price-predictor

1. Setup Python Virtual Environment using UV:

bash uv venv --python python3.11 source .venv/bin/activate

1. Install dependencies:

bash uv pip install -r requirements.txt

📊 Setup MLflow for Experiment Tracking

To track experiments and model runs:

bash cd deployment/mlflow docker compose -f mlflow-docker-compose.yml up -d docker compose ps

🐧 Using Podman? Use this instead:

bash podman compose -f mlflow-docker-compose.yml up -d podman compose ps

Access the MLflow UI at http://localhost:5555

📒 Using JupyterLab (Optional)

If you prefer an interactive experience, launch JupyterLab with:

```bash uv python -m jupyterlab

or

python -m jupyterlab ```

🔁 Model Workflow

🧹 Step 1: Data Processing

Clean and preprocess the raw housing dataset:

bash uv run src/data/run_processing.py --input data/raw/house_data.csv --output data/processed/cleaned_house_data.csv

🧠 Step 2: Feature Engineering

Apply transformations and generate features:

bash uv run src/features/engineer.py --input data/processed/cleaned_house_data.csv --output data/processed/featured_house_data.csv --preprocessor models/trained/preprocessor.pkl

📈 Step 3: Modeling & Experimentation

Train your model and log everything to MLflow:

bash uv run src/models/train_model.py --config configs/model_config.yaml --data data/processed/featured_house_data.csv --models-dir models --mlflow-tracking-uri http://localhost:5555

Building FastAPI and Streamlit

The code for both the apps are available in src/api and streamlit_app already. To build and launch these apps

• Add a Dockerfile in the root of the source code for building FastAPI
  
• Add streamlit_app/Dockerfile to package and build the Streamlit app
  
• Add docker-compose.yaml in the root path to launch both these apps. be sure to provide API_URL=http://fastapi:8000 in the streamlit app's environment.

Once you have launched both the apps, you should be able to access streamlit web ui and make predictions.

You could also test predictions with FastAPI directly using

``` curl -X POST "http://localhost:8000/predict" \ -H "Content-Type: application/json" \ -d '{ "sqft": 1500, "bedrooms": 3, "bathrooms": 2, "location": "suburban", "year_built": 2000, "condition": fair }'

```

Be sure to replace http://localhost:8000/predict with actual endpoint based on where its running.

🧠 Learn More About MLOps

This project is part of the MLOps Bootcamp at School of DevOps, where you'll learn how to:

• Build and track ML pipelines
• Containerize and deploy models
• Automate training workflows using GitHub Actions or Argo Workflows
• Apply DevOps principles to Machine Learning systems

🔗 Get Started with MLOps →

🤝 Contributing

We welcome contributions, issues, and suggestions to make this project even better. Feel free to fork, explore, and raise PRs!

Happy Learning!
— Team School of DevOps