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Update directory tree in README template (#397)
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‎{{ cookiecutter.repo_name }}/README.md‎

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│ ├── processed <- The final, canonical data sets for modeling.
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│ └── raw <- The original, immutable data dump.
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│
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├── docs <- A default mkdocs project; see mkdocs.org for details
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├── docs <- A default mkdocs project; see www.mkdocs.org for details
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│
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├── models <- Trained and serialized models, model predictions, or model summaries
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│
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├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
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│ the creator's initials, and a short `-` delimited description, e.g.
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│ `1.0-jqp-initial-data-exploration`.
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│
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├── pyproject.toml <- Project configuration file with package metadata for {{ cookiecutter.module_name }}
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│ and configuration for tools like black
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├── pyproject.toml <- Project configuration file with package metadata for
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│ {{ cookiecutter.module_name }} and configuration for tools like black
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│
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├── references <- Data dictionaries, manuals, and all other explanatory materials.
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│
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│
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├── setup.cfg <- Configuration file for flake8
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│
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└── {{ cookiecutter.module_name }} <- Source code for use in this project.
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└── {{ cookiecutter.module_name }} <- Source code for use in this project.
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│
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├── __init__.py <- Makes {{ cookiecutter.module_name }} a Python module
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├── __init__.py <- Makes {{ cookiecutter.module_name }} a Python module
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│
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├── data <- Scripts to download or generate data
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│ └── make_dataset.py
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├── config.py <- Store useful variables and configuration
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│
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├── features <- Scripts to turn raw data into features for modeling
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│ └── build_features.py
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├── dataset.py <- Scripts to download or generate data
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│
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├── models <- Scripts to train models and then use trained models to make
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│ │ predictions
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│ ├── predict_model.py
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│ └── train_model.py
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├── features.py <- Code to create features for modeling
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│
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└── visualization <- Scripts to create exploratory and results oriented visualizations
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└── visualize.py
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├── modeling
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│ ├── __init__.py
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│ ├── predict.py <- Code to run model inference with trained models
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│ └── train.py <- Code to train models
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│
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└── plots.py <- Code to create visualizations
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```
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