Kaggle NBA - Group 6 | All-Star

Team repository for the NBA Career Prediction Kaggle Competition from UTS Advanced Data Science for Innovation.

• Kai-Ping Wang
• Sampath Pitchandi
• Ron Au

Option A) Run with pipenv

Install dependencies

pipenv install

Run Jupyter Lab

pipenv run jupyter lab

Option B) Run with Docker

Build image:

docker build -t kaggle-nba .

Run image container

Windows (cmd):

docker run -itp 8888:8888 -v %cd%:/home/kaggle --name all-star kaggle-nba

Windows (Powershell):

docker run -itp 8888:8888 -v ${PWD}:/home/kaggle --name all-star kaggle-nba

Mac/Linux:

docker run -itp 8888:8888 -v "$PWD":/home/kaggle --name all-star kaggle-nba

Project Organization

├── Dockerfile         <- Document containing build instructions for Docker image
├── LICENSE            <- MIT License
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── Pipfile            <- The requirements file for managing dependency installations
├── Pipfile.lock       <- Locks package versions for dependency installations
├── 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:
│                         <lastname>_<firstname>-week<week_number>_<description> e.g.
│                         wang_kai-ping-week1_1.0-train-data-exploration.ipynb
│
├── 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
│
├── 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