{"id":19352069,"url":"https://github.com/twsl/heavydrinkingdetection","last_synced_at":"2026-05-15T01:08:03.963Z","repository":{"id":103300542,"uuid":"271290414","full_name":"twsl/HeavyDrinkingDetection","owner":"twsl","description":"Heavy Drinking Detection using Smartphone Data","archived":false,"fork":false,"pushed_at":"2020-10-30T16:56:41.000Z","size":14112,"stargazers_count":1,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-09-01T18:42:40.151Z","etag":null,"topics":["catboost","data-science","jupyter-notebook","lightgbm","optuna","scikit-learn","sklearn","xgboost"],"latest_commit_sha":null,"homepage":"","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/twsl.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-06-10T13:53:13.000Z","updated_at":"2023-04-26T12:14:13.000Z","dependencies_parsed_at":"2023-03-13T15:08:49.605Z","dependency_job_id":null,"html_url":"https://github.com/twsl/HeavyDrinkingDetection","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/twsl/HeavyDrinkingDetection","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/twsl%2FHeavyDrinkingDetection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/twsl%2FHeavyDrinkingDetection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/twsl%2FHeavyDrinkingDetection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/twsl%2FHeavyDrinkingDetection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/twsl","download_url":"https://codeload.github.com/twsl/HeavyDrinkingDetection/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/twsl%2FHeavyDrinkingDetection/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279000701,"owners_count":26082805,"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":"2025-10-08T02:00:06.501Z","response_time":56,"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":["catboost","data-science","jupyter-notebook","lightgbm","optuna","scikit-learn","sklearn","xgboost"],"created_at":"2024-11-10T04:38:02.807Z","updated_at":"2025-10-08T19:09:18.157Z","avatar_url":"https://github.com/twsl.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# HeavyDrinkingDetection\n\n[![xgboost](https://img.shields.io/conda/vn/conda-forge/xgboost?label=xgboost)](https://xgboost.ai/)\n[![lightgbm](https://img.shields.io/conda/vn/conda-forge/lightgbm?label=lightgbm)](https://github.com/microsoft/LightGBM)\n[![catboost](https://img.shields.io/conda/vn/conda-forge/catboost?label=catboost)](https://catboost.ai/)\n[![scikit-learn](https://img.shields.io/conda/vn/conda-forge/scikit-learn?label=scikit-learn)](https://scikit-learn.org/stable/)\n[![optuna](https://img.shields.io/conda/vn/conda-forge/optuna?label=optuna)](https://optuna.org)\n\nHeavy Drinking Detection using Smartphone Data.\n\nA short university project playing around with various machine learning frameworks trying to solve a regression problem.\n\n## Setup\n\nPackage install commands can be found within the notebooks.\n\n## Project Organization\n\n    ├── LICENSE\n    ├── Makefile           \u003c- Makefile with commands like `make data` or `make train`\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 a number (for ordering),\n    │                         the creator's initials, and a short `-` delimited description, e.g.\n    │                         `1.0-jqp-initial-data-exploration`.\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    ├── requirements.txt   \u003c- The requirements file for reproducing the analysis environment, e.g.\n    │                         generated with `pip freeze \u003e requirements.txt`\n    │\n    ├── setup.py           \u003c- makes project pip installable (pip install -e .) so src can be imported\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\n\u003cp\u003e\u003csmall\u003eProject based on the \u003ca target=\"_blank\" href=\"https://drivendata.github.io/cookiecutter-data-science/\"\u003ecookiecutter data science project template\u003c/a\u003e. #cookiecutterdatascience\u003c/small\u003e\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftwsl%2Fheavydrinkingdetection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftwsl%2Fheavydrinkingdetection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftwsl%2Fheavydrinkingdetection/lists"}