{"id":17793320,"url":"https://github.com/ronvoluted/kaggle-nba","last_synced_at":"2026-04-15T19:38:32.782Z","repository":{"id":54218872,"uuid":"335358806","full_name":"ronvoluted/kaggle-nba","owner":"ronvoluted","description":"Team repository for the NBA Career Prediction Kaggle Competition from UTS Advanced Data Science for Innovation","archived":false,"fork":false,"pushed_at":"2021-03-03T01:57:03.000Z","size":10556,"stargazers_count":0,"open_issues_count":1,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-02T02:17:53.723Z","etag":null,"topics":["kaggle","machine-learning","python"],"latest_commit_sha":null,"homepage":"https://kaggle.com/c/uts-advdsi-nba-career-prediction","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ronvoluted.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-02-02T16:49:03.000Z","updated_at":"2021-03-03T01:57:05.000Z","dependencies_parsed_at":"2022-08-13T09:21:26.503Z","dependency_job_id":null,"html_url":"https://github.com/ronvoluted/kaggle-nba","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ronvoluted/kaggle-nba","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ronvoluted%2Fkaggle-nba","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ronvoluted%2Fkaggle-nba/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ronvoluted%2Fkaggle-nba/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ronvoluted%2Fkaggle-nba/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ronvoluted","download_url":"https://codeload.github.com/ronvoluted/kaggle-nba/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ronvoluted%2Fkaggle-nba/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":267400739,"owners_count":24081191,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-07-27T02:00:11.917Z","response_time":82,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["kaggle","machine-learning","python"],"created_at":"2024-10-27T11:05:59.555Z","updated_at":"2026-04-15T19:38:27.749Z","avatar_url":"https://github.com/ronvoluted.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Kaggle NBA - Group 6 | All-Star\nTeam repository for the [NBA Career Prediction Kaggle Competition](https://www.kaggle.com/c/uts-advdsi-nba-career-prediction/overview) from UTS Advanced Data Science for Innovation.\n\n- Kai-Ping Wang\n- Sampath Pitchandi\n- Ron Au\n\n## Option A) Run with pipenv\n### Install dependencies\n```bash\npipenv install\n```\n### Run Jupyter Lab\n```bash\npipenv run jupyter lab\n```\n\n\n## Option B) Run with Docker\n### Build image:\n\n```bash\ndocker build -t kaggle-nba .\n```\n\n### Run image container\n\n#### Windows (cmd):\n```cmd\ndocker run -itp 8888:8888 -v %cd%:/home/kaggle --name all-star kaggle-nba\n```\n\n#### Windows (Powershell):\n```cmd\ndocker run -itp 8888:8888 -v ${PWD}:/home/kaggle --name all-star kaggle-nba\n```\n\n#### Mac/Linux:\n```bash\ndocker run -itp 8888:8888 -v \"$PWD\":/home/kaggle --name all-star kaggle-nba\n```\n\n## Project Organization\n\n    ├── Dockerfile         \u003c- Document containing build instructions for Docker image\n    ├── LICENSE            \u003c- MIT License\n    ├── Makefile           \u003c- Makefile with commands like `make data` or `make train`\n    ├── Pipfile            \u003c- The requirements file for managing dependency installations\n    ├── Pipfile.lock       \u003c- Locks package versions for dependency installations\n    ├── README.md          \u003c- The top-level README for developers using this project\n    ├── data\n    │   ├── external       \u003c- Data from third party sources.\n    │   ├── interim        \u003c- Intermediate data that has been transformed.\n    │   ├── processed      \u003c- The final, canonical data sets for modeling.\n    │   └── raw            \u003c- The original, immutable data dump.\n    │\n    ├── docs               \u003c- A default Sphinx project; see sphinx-doc.org for details\n    │\n    ├── models             \u003c- Trained and serialized models, model predictions, or model summaries\n    │\n    ├── notebooks          \u003c- Jupyter notebooks. Naming convention is:\n    │                         \u003clastname\u003e_\u003cfirstname\u003e-week\u003cweek_number\u003e_\u003cdescription\u003e e.g.\n    │                         wang_kai-ping-week1_1.0-train-data-exploration.ipynb\n    │\n    ├── references         \u003c- Data dictionaries, manuals, and all other explanatory materials\n    │\n    ├── reports            \u003c- Generated analysis as HTML, PDF, LaTeX, etc.\n    │   └── figures        \u003c- Generated graphics and figures to be used in reporting\n    │\n    ├── src                \u003c- Source code for use in this project.\n    │   ├── __init__.py    \u003c- Makes src a Python module\n    │   │\n    │   ├── data           \u003c- Scripts to download or generate data\n    │   │   └── make_dataset.py\n    │   │\n    │   ├── features       \u003c- Scripts to turn raw data into features for modeling\n    │   │   └── build_features.py\n    │   │\n    │   ├── models         \u003c- Scripts to train models and then use trained models to make\n    │   │   │                 predictions\n    │   │   ├── predict_model.py\n    │   │   └── train_model.py\n    │   │\n    │   └── visualization  \u003c- Scripts to create exploratory and results oriented visualizations\n    │       └── visualize.py\n    │\n    └── tox.ini            \u003c- tox file with settings for running tox; see tox.readthedocs.io\n\n---\n\nProject based on the [cookiecutter data science project template](https://drivendata.github.io/cookiecutter-data-science)\n#cookiecutterdatascience\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fronvoluted%2Fkaggle-nba","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fronvoluted%2Fkaggle-nba","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fronvoluted%2Fkaggle-nba/lists"}