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license: mit |
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--- |
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Project Organization |
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------------ |
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├── LICENSE |
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├── Makefile <- Makefile with commands like `make dirs` or `make clean` |
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├── README.md <- The top-level README for developers using this project. |
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├── data |
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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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├── 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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├── references <- Data dictionaries, manuals, and all other explanatory materials. |
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├── reports <- Generated analysis as HTML, PDF, LaTeX, etc. |
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│ └── figures <- Generated graphics and figures to be used in reporting |
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│ └── metrics.txt <- Relevant metrics after evaluating the model. |
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│ └── training_metrics.txt <- Relevant metrics from training the model. |
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│ |
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├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g. |
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│ generated with `pip freeze > requirements.txt` |
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│ |
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├── setup.py <- makes project pip installable (pip install -e .) so src can be imported |
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├── src <- Source code for use in this project. |
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│ ├── __init__.py <- Makes src a Python module |
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│ │ |
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│ ├── data <- Scripts to download or generate data |
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│ │ ├── great_expectations <- Folder containing data integrity check files |
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│ │ ├── make_dataset.py |
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│ │ └── data_validation.py <- Script to run data integrity checks |
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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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│ │ |
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│ └── visualization <- Scripts to create exploratory and results oriented visualizations |
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│ └── visualize.py |
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│ |
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├── .pre-commit-config.yaml <- pre-commit hooks file with selected hooks for the projects. |
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├── dvc.lock <- constructs the ML pipeline with defined stages. |
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└── dvc.yaml <- Traing a model on the processed data. |
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-------- |
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<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p> |
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--- |
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To create a project like this, just go to https://dagshub.com/repo/create and select the **Cookiecutter DVC** project template. |
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Made with 🐶 by [DAGsHub](https://dagshub.com/). |
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