This Machine Learning project aims at building a Machine Learning model that is able to classify SCP articles by their Object Class.
The setup make target will create a virtual environment as well as copy the
logging configuration file from the template.
make setupThen, to fetch the SCP articles via the web crawler and preprocess them, use the data target.
make dataExplore the data in data/processed. If you want to be inspired, take a look at
my Jupyter Notebooks.
This project is documented through various blog posts. At the moment, the following have been written:
Furthermore, the git tags point at exercises and their solutions that are referenced in the blog posts.
The web site http://www.scp-wiki.net/ is a site for collaborative writing. The fictional SCP (short for "Secure, Contain, Protect") foundation aims to contain so-called SCPs that are anomalous fictional objects.
The articles about SCPs follow a fixed format that potentially makes it possible to predict the Object Class from the article text.
The following new or modified make targets are available.
make data/raw- Executes the web crawler run to fetch data.make data- Check requirements, execute the web crawler (if necessary) and preprocess the raw data.make clean- Deletes compiled Python files and__pycache__directories (standard behavior) as well as log files (new behavior)
├── 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