mirror of
https://github.com/RYDE-WORK/lnp_ml.git
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62 lines
2.6 KiB
Markdown
62 lines
2.6 KiB
Markdown
# lnp-ml
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<a target="_blank" href="https://cookiecutter-data-science.drivendata.org/">
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<img src="https://img.shields.io/badge/CCDS-Project%20template-328F97?logo=cookiecutter" />
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</a>
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A short description of the project.
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## Project Organization
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```
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├── LICENSE <- Open-source license if one is chosen
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├── Makefile <- Makefile with convenience commands like `make data` or `make train`
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├── README.md <- The top-level README for developers using this project.
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├── data
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│ ├── external <- Data from third party sources.
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│ ├── interim <- Intermediate data that has been transformed.
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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 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
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│ lnp_ml 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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├── 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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│
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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.cfg <- Configuration file for flake8
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│
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└── lnp_ml <- Source code for use in this project.
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│
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├── __init__.py <- Makes lnp_ml a Python module
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│
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├── config.py <- Store useful variables and configuration
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│
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├── dataset.py <- Scripts to download or generate data
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│
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├── features.py <- Code to create features for modeling
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│
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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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--------
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