{"id":18554204,"url":"https://github.com/nancyhamdan/wids-2022","last_synced_at":"2025-07-27T07:08:41.736Z","repository":{"id":217783183,"uuid":"475203524","full_name":"nancyhamdan/WiDS-2022","owner":"nancyhamdan","description":"Predicting building energy consumption as part of the WiDS 2022 Datathon ","archived":false,"fork":false,"pushed_at":"2022-03-28T23:29:13.000Z","size":12,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-01T05:17:38.716Z","etag":null,"topics":["ensemble","gradientboostingregressor","lgbm","lgbmregressor","random-forest","randomforest-regressor","regression"],"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/nancyhamdan.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}},"created_at":"2022-03-28T22:50:12.000Z","updated_at":"2023-02-09T14:00:37.000Z","dependencies_parsed_at":"2024-01-18T08:22:44.690Z","dependency_job_id":"f3c52bfd-1c4a-43e5-a33b-bd7c74bbb8d6","html_url":"https://github.com/nancyhamdan/WiDS-2022","commit_stats":null,"previous_names":["nancyhamdan/wids-2022"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/nancyhamdan/WiDS-2022","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nancyhamdan%2FWiDS-2022","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nancyhamdan%2FWiDS-2022/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nancyhamdan%2FWiDS-2022/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nancyhamdan%2FWiDS-2022/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nancyhamdan","download_url":"https://codeload.github.com/nancyhamdan/WiDS-2022/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nancyhamdan%2FWiDS-2022/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":267320257,"owners_count":24068527,"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":["ensemble","gradientboostingregressor","lgbm","lgbmregressor","random-forest","randomforest-regressor","regression"],"created_at":"2024-11-06T21:20:13.158Z","updated_at":"2025-07-27T07:08:41.719Z","avatar_url":"https://github.com/nancyhamdan.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Predicting Building Energy Consumption - WiDS 2022 Datathon\n\nThis is my solution for the [WiDS 2022 Datathon](https://www.kaggle.com/competitions/widsdatathon2022)\n\nTo help combat climate change, this year's WiDS datathon was to predict building energy consumption using a dataset that details buildings characteristics and weather conditions of the area the buildings are at. My solution included minimal feature enginnering and used a blend of a LightGBM and a Random Forest model to get the final predictions. My solution's RMSE score of 22.573 on private test data ranked 81/829 in the datathon, you can view the leaderboard [here](https://www.kaggle.com/competitions/widsdatathon2022/leaderboard).","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnancyhamdan%2Fwids-2022","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnancyhamdan%2Fwids-2022","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnancyhamdan%2Fwids-2022/lists"}