moshi-aware ============================== Data cleaning, feature engineering and analysis for Aware sensors Project Organization ------------ ├── 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` │ ├── 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.testrun.org --------

Project based on the cookiecutter data science project template. #cookiecutterdatascience