{"id":23024524,"url":"https://github.com/derrickbaruga7/r-logistic-regression","last_synced_at":"2025-04-02T19:41:09.169Z","repository":{"id":250324888,"uuid":"834138953","full_name":"derrickbaruga7/R-Logistic-Regression","owner":"derrickbaruga7","description":"The analysis in R involved logistic regression on the 'Telco-Customer-Churn' dataset to predict customer churn. Initial models were refined to address non-significant variables and multicollinearity. The final model achieved 81.17% accuracy and 67.53% sensitivity.","archived":false,"fork":false,"pushed_at":"2024-07-26T13:59:59.000Z","size":4,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-08T10:20:35.500Z","etag":null,"topics":["data-science","logistic-regression","r"],"latest_commit_sha":null,"homepage":"","language":"R","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/derrickbaruga7.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":"2024-07-26T13:57:02.000Z","updated_at":"2024-07-26T14:11:05.000Z","dependencies_parsed_at":"2024-07-26T15:34:36.315Z","dependency_job_id":null,"html_url":"https://github.com/derrickbaruga7/R-Logistic-Regression","commit_stats":null,"previous_names":["derrickbaruga7/r-logistic-regression"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/derrickbaruga7%2FR-Logistic-Regression","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/derrickbaruga7%2FR-Logistic-Regression/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/derrickbaruga7%2FR-Logistic-Regression/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/derrickbaruga7%2FR-Logistic-Regression/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/derrickbaruga7","download_url":"https://codeload.github.com/derrickbaruga7/R-Logistic-Regression/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246882916,"owners_count":20849334,"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","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","logistic-regression","r"],"created_at":"2024-12-15T13:19:21.733Z","updated_at":"2025-04-02T19:41:09.144Z","avatar_url":"https://github.com/derrickbaruga7.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# R-Logistic-Regression\n\n\nThe analysis in R involved logistic regression on the 'Telco-Customer-Churn' dataset to predict customer churn. Initial models were refined to address non-significant variables and multicollinearity. The final model achieved 81.17% accuracy and 67.53% sensitivity but had a low AUC of 0.145, indicating limited effectiveness in distinguishing churn cases. Significant predictors included tenure, contract type, and billing options.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fderrickbaruga7%2Fr-logistic-regression","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fderrickbaruga7%2Fr-logistic-regression","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fderrickbaruga7%2Fr-logistic-regression/lists"}