Cookiecutter inspired template for CorrelAid Python analysis projects.
Project based on the cookiecutter data science project template. #cookiecutterdatascience
summarize in three sentences what this project is about and what central features it has.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data <- see README in data folder
│ ├── processed_gdpr
│ ├── processed
│ └── raw
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ and a short `-` delimited description, e.g.
│ `01-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
How can a environment for your project be created/updated?
Please make sure that the setup steps are:
- platform-independent (e.g. be aware of issues like this), at least MacOS and Windows (this is important in case CorrelAid employees have to provide support after the project has ended.
- computer-independent: must work for all team members!
You need the following data files in order to run this project:
include output from tree command (or similar on windows)
[the following can also be moved to the wiki if you decide to have one]
Default Definition of Done can be found here. Adapt if needed.
[the following can also be moved to the wiki if you decide to have one]
explain how the output(s) of this project can be handled/operated, for example:
- how to create reports
- where to create/find the data visualizations
- how to update data
- what would need to be updated if someone wanted to re-run your analysis with different data
be honest about the limitations of your project, e.g.:
- methodological: maybe another model would be more suitable?
- reproducibility: what are limits of reproducibility? is there something hard-coded/specific to the data that you used?
- best practices: maybe some code is particularly messy and people working on it in the future should know about it in advance?