Project Structure

The organization of the project is the following:

├── LICENSE
├── Makefile            <- Makefile with commands like `make data` or `make train`
├── README.md           <- The top-level README for developers using this project.
├── data
│   ├── external        <- Data from third party sources.
│   ├── interim         <- Intermediate data that has been transformed.
│   ├── processed       <- The final, canonical data sets for modeling.
│   └── raw             <- The original, immutable data dump.
│
├── 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),
│                          the creator's initials, and a short `-` delimited description, e.g.
│                          `1.0-jqp-initial-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`
│
├── environment.yml     <- The Anaconda environment requirements file for reproducing the analysis environment.
│                          This file is used by Anaconda to create the project environment.
│
├── src                 <- Source code for use in this project.
│   └── aves        <- Main package
│       ├── __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.testrun.org

Project based on the cookiecutter data science project template