predict-sales-ml 0.2.0

ML toolkit for time-series sales prediction with feature engineering, validation, and hyperparameter optimization

ML Internship Project Template

Welcome! This repository serves as a starting point for your project. The structure here is minimal, designed to give you a foundation to build upon. Feel free to modify, expand, and customize it as needed.

Repository Structure

• .gitignore and .dockerignore: Prevents unnecessary files from being pushed to version control.
• .pre-commit-config.yaml: Configures pre-commit hooks to enforce code style and linting checks automatically before each commit.
• .ruff.toml: Enforces Python linting & code style.
• uv.lock: Lists essential Python libraries. Add to it as your project grows.
• pyproject.toml: Basic setup script if you turn your project into a package.
• README.md: This file. Update it as your project evolves!
• Dockerfile, compose.yaml: Docker configuration for MLflow tracking server.
• notebooks/: Store your Jupyter notebooks here.
• scripts/: Place your project scripts here (e.g., for data processing or model training).
• data/: A global folder where your data can be stored in different formats (e.g. raw and processed)

Further Development

This template provides a basic structure. You should develop it further based on the specific requirements of your project. Here are some ideas:

• Add configurations or hyperparameters in a config.yaml.
• Write tests for your code in a tests/ folder.
• Document your work here in the README.md as your project progresses.
• Convert your .ipynb notebooks into .py files and add to your commits, this will simplify collaboration and PR review.

Good luck with your internship!