{"id":20789184,"url":"https://github.com/umstek/dengai","last_synced_at":"2025-10-11T13:32:57.964Z","repository":{"id":81833144,"uuid":"135007178","full_name":"umstek/DengAI","owner":"umstek","description":"Solution for DengAI Competition by DrivenData (CS4642 Data Mining and Information Retrieval, CS4622 Machine Learning - assignments)","archived":false,"fork":false,"pushed_at":"2024-04-03T00:00:42.000Z","size":23433,"stargazers_count":2,"open_issues_count":1,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-09-01T21:48:19.329Z","etag":null,"topics":["data-science","dengai","drivendata"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/umstek.png","metadata":{"files":{"readme":"Readme.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2018-05-27T01:40:07.000Z","updated_at":"2019-06-06T10:41:13.000Z","dependencies_parsed_at":null,"dependency_job_id":"dc8d216e-2bc2-4972-87e8-776bcc547b76","html_url":"https://github.com/umstek/DengAI","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/umstek/DengAI","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/umstek%2FDengAI","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/umstek%2FDengAI/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/umstek%2FDengAI/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/umstek%2FDengAI/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/umstek","download_url":"https://codeload.github.com/umstek/DengAI/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/umstek%2FDengAI/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279007339,"owners_count":26084282,"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":"2025-10-11T02:00:06.511Z","response_time":55,"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":["data-science","dengai","drivendata"],"created_at":"2024-11-17T15:20:12.183Z","updated_at":"2025-10-11T13:32:57.944Z","avatar_url":"https://github.com/umstek.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# DengAI\n\n## Reports and Presentations  \n### [Presentation](https://github.com/umstek/DengAI/blob/master/DengAI.pdf) for CS4622 (Machine Learning)  \n\n### [Report](https://github.com/umstek/DengAI/blob/master/Machine%20Learning%20Report%20-%20Group%2030.pdf) for CS4622 (Machine Learning)  \n\n### [Report](https://github.com/umstek/DengAI/blob/master/Data%20Mining%20Report%20-%20Group%2030.pdf) for CS4642 (Data Mining and Information Retrieval)  \n\n\n## Results  \nCurrent best result: 19.3798 (MAE), Rank 89 as of July 27 - 2018.  \nSee [Generated files](https://github.com/umstek/DengAI/releases/tag/v1) for a complete list of intermediate generated files and submissions.    \n\n\n## Directory contents  \n+ The `.` root directory contains the data files downloaded from _drivendata_ and some milestone submissions.  \n+ `deprecated` folder contains the first approaches to the problem with _Matlab regression learner_ and _Orange3_ (with minimal preprocessing) and the resulting `.csv` files.  \n+ `Neural Networks` folder contains the first approaches to the problem with deep neural networks with _Keras_ and _Tensorflow_.  \n+ `Negative Binominal Regression` contains the DengAI benchmark model built with _Jupyter Notebook_ and _sklearn_, _statsmodels_ etc.  \n+ `Interactive Python 1` contains the approaches that do general preprocessing with _Jupyter Notebook_, _pandas_, _sklearn_, _statsmodels_, _seaborn_ and uses various models for prediction.  \n+ `Interactive Python 2` contains a pipeline that processes the files in various stages using _Jupyter Notebook_, _pandas_, _sklearn_, _statsmodels_, _seaborn_, and _R_'s STL (time series decomposition) borrowed with the _r2py_ bridge. This pipeline does preprocessing, visualization, analysing, automatic selection of features, best model selection etc. The best working model is a time series decomposing predicter with a linear regression model.  \n+ `Orange` folder contains an Orange3 pipeline that tests cross-validated errors of various learners with preprocessing, feature engineering etc.  \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fumstek%2Fdengai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fumstek%2Fdengai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fumstek%2Fdengai/lists"}