Work related to the data case for my job interview at Ginkgo Analytics. Specifically, a short exploration and modelling of sensor data in order to predict and warn about upcoming device failures.
To create the data:
Run make_dataset.py and make_features.py.
To build and evaluate the model:
Run train_model.py.
├── LICENSE
├── 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
│ └── pull_sensor_data.py
│
├── features <- Scripts to turn raw data into features for modeling
│ ├── make_features.py
│ ├── helper.py <- Miscellanous functions
│ └── relabel_and_clean_nan.py
│
├── models <- Scripts to train models and then use trained models to make
│ │ predictions
│ └── train_model.py
│
└── visualization <- Scripts to create exploratory and results oriented visualizations
└── visualize.py
Project based on the cookiecutter data science project template. #cookiecutterdatascience