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This template is the result of my years refining the best way to structure a data science project so that it is reproducible and maintainable.
This template allows you to:
:white_check_mark: Create a readable structure for your project
:white_check_mark: Automatically run tests when committing your code
:white_check_mark: Enforce type hints at runtime
:white_check_mark: Check issues in your code before committing
:white_check_mark: Efficiently manage the dependencies in your project
:white_check_mark: Create short and readable commands for repeatable tasks
:white_check_mark: Rerun only modified components of a pipeline
:white_check_mark: Automatically document your code
:white_check_mark: Observe and automate your code
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├── data
│ ├── final # data after training the model
│ ├── processed # data after processing
│ ├── raw # raw data
├── docs # documentation for your project
├── .flake8 # configuration for flake8 - a Python formatter tool
├── .gitignore # ignore files that cannot commit to Git
├── Makefile # store useful commands to set up the environment
├── models # store models
├── notebooks # store notebooks
├── .pre-commit-config.yaml # configurations for pre-commit
├── pyproject.toml # dependencies for poetry
├── README.md # describe your project
├── src # store source code
│ ├── __init__.py # make src a Python module
│ ├── config.py # store configs
│ ├── process.py # process data before training model
│ ├── run_notebook.py # run notebook
│ └── train_model.py # train model
└── tests # store tests
├── __init__.py # make tests a Python module
├── test_process.py # test functions for process.py
└── test_train_model.py # test functions for train_model.py
Install Cookiecutter:
pip install cookiecutter
Create a project based on the template:
cookiecutter https://github.com/khuyentran1401/data-science-template --checkout prefect-poetry
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