{"id":19494296,"url":"https://github.com/prneidhardt/model-tuning","last_synced_at":"2026-02-16T19:35:19.329Z","repository":{"id":162737812,"uuid":"497652765","full_name":"prneidhardt/Model-Tuning","owner":"prneidhardt","description":"ReneWind Project","archived":false,"fork":false,"pushed_at":"2024-11-11T21:18:50.000Z","size":13943,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-07T02:05:23.962Z","etag":null,"topics":["hyperparameter-tuning","regularization"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/prneidhardt.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":"2022-05-29T16:55:35.000Z","updated_at":"2024-11-11T21:18:54.000Z","dependencies_parsed_at":"2024-11-10T21:30:26.974Z","dependency_job_id":"b15a85a7-5cc9-46ff-940b-849bdf2c9dd3","html_url":"https://github.com/prneidhardt/Model-Tuning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/prneidhardt/Model-Tuning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/prneidhardt%2FModel-Tuning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/prneidhardt%2FModel-Tuning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/prneidhardt%2FModel-Tuning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/prneidhardt%2FModel-Tuning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/prneidhardt","download_url":"https://codeload.github.com/prneidhardt/Model-Tuning/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/prneidhardt%2FModel-Tuning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29516170,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-16T18:37:19.720Z","status":"ssl_error","status_checked_at":"2026-02-16T18:36:46.920Z","response_time":115,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["hyperparameter-tuning","regularization"],"created_at":"2024-11-10T21:29:09.746Z","updated_at":"2026-02-16T19:35:19.308Z","avatar_url":"https://github.com/prneidhardt.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Model-Tuning\n- Project delivered in January 2022\n- Repository includes two files:\n  - Jupyter notebook with Python code written for data analysis and model building\n  - CSV file includes data imported into notebook\n## Problem Statement\n- \"ReneWind\" is a company working on improving the machinery/processes involved in the production of wind energy using machine learning and has collected data of generator failure of wind turbines using sensors. The objective is to build various classification models, tune them and find the best one that will help identify failures so that the generator could be repaired before failing/breaking and the overall maintenance cost of the generators can be brought down.\n## Skills and Tools\n- Exploratory Data Analysis (Variable identification, Univariate analysis, Bivariate analysis)\n- Up and downsampling\n- Regularization\n- Hyperparameter tuning\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprneidhardt%2Fmodel-tuning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprneidhardt%2Fmodel-tuning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprneidhardt%2Fmodel-tuning/lists"}