ML Project

A Python machine learning project using Scikit-learn, Setuptools, Virtual Environment, Git, and Docker.

Project Structure

ml_project/
│
├── src/
│   └── ml_project/
│       ├── __init__.py
│       └── train.py
│
├── tests/
│
├── data/
│
├── models/
│
├── .gitignore
├── .dockerignore
├── Dockerfile
├── pyproject.toml
└── README.md

Requirements

• Python 3.10+
• Git
• Docker

Local Development

1. Clone the repository

git clone <your-repository-url>
cd ml_project

2. Create a virtual environment

python3 -m venv .venv

3. Activate the virtual environment

macOS/Linux:

source .venv/bin/activate

Windows:

.venv\Scripts\activate

4. Install the project

Install the project and its dependencies using pyproject.toml:

python -m pip install -e .

Run the Project

python src/ml_project/train.py

Dependencies

The project dependencies are defined in pyproject.toml.

Current dependencies include:

• NumPy
• Pandas
• Scikit-learn
• Joblib

Docker

Build the Docker image

docker build -t ml-project .

Run the Docker container

docker run --rm ml-project

Git Workflow

Create a new branch for development:

git checkout -b dev

Check the current status:

git status

Add changes:

git add .

Commit changes:

git commit -m "Add ML project"

Push the branch to GitHub:

git push -u origin dev

Configuration

Project configuration and dependencies are managed through:

pyproject.toml

The local virtual environment is stored in:

.venv/

The virtual environment is intentionally excluded from Git using .gitignore.

License

This project is for learning and development purposes.