Wrangle and analyze NBA statistics and betting information.
Prerequisites
The environment configuration relies on an Anaconda Distribution. For example, I use micromamba on my Apple Silicon device. You should be familiar with the following in order to use this package:
# Clone the repository, ideally via ssh like:
git clone [email protected]:jsehnert101/nba-analytics.git
# Create conda env with required packages
conda env create -f environment.yml
# Activate conda environment
conda activate nba
# Install local packages
pip install -e ./src
# Test environment configuration
python test_environment.py
# Get coding!
# If you're looking for ways to get started, check our example notebooks under docs/examples.Refer to the notebooks in docs/examples for more information on retrieving and storing data locally, computing advanced statistics, applying machine learning models, and more!
This project organization allows us to effectively build out this package over time. All exploratory work should go inside a relevant subfolder within the eda (exploratory data analysis) folder. If no appropriate category for you work exists, create it. Please do not push to the src folder without a code review. If you plan on making substantial changes, create a branch and request approval from Jake Sehnert prior to merge.
├── 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.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience