Classificador BNCC

==============================

Nesse projeto iremos criar um classificador de questão da BNCC

Project Organization - TODO!

---

├── 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
│   │
│   ├── 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.readthedocs.io

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Best Practices

• Environment: Here we'll be using Pipenv to manage all the packages utilized on the project. So, to make the environment active, you'll need to run pipenv shell.
• Installing all packages: To install all the packages already versioned on the repository you'll need to run pipenv install.
• Package installations: It is a best practice to install a package with the version, for example: pipenv install pandas==1.0.3. After this command the package will be listed on the Pipfile and Pipfile.lock, making possible the reproducibility.
• Naming notebooks: To name your notebooks is a good practice to put your name within the date and a descriptive name, for example: luciano-20-11-numeric-eda.ipynb
• .gitignore: Every sensible information, or files that we don't can expose to the public will be put here on .gitignore.
• Data: Don't put the dataset on the repository, is a good practice to put a indication of the file pointing to the place where the data is stored.
• Running notebooks: To run your notebooks with the Pipenv you'll need to run pipenv run jupyter lab. Or you can use vscode to run those notebooks.