{"id":19608719,"url":"https://github.com/sequint/heart_disease_ml_model","last_synced_at":"2025-09-15T23:30:47.339Z","repository":{"id":255584292,"uuid":"852457452","full_name":"sequint/heart_disease_ml_model","owner":"sequint","description":"A comprehensive data pipeline to predict heart disease using machine learning techniques","archived":true,"fork":false,"pushed_at":"2024-09-04T21:02:30.000Z","size":7,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-26T17:16:13.331Z","etag":null,"topics":[],"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/sequint.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":"2024-09-04T20:53:37.000Z","updated_at":"2024-12-17T16:45:47.000Z","dependencies_parsed_at":"2024-09-06T05:55:55.436Z","dependency_job_id":"4c913ce5-7088-4c2c-9136-f773aebe4510","html_url":"https://github.com/sequint/heart_disease_ml_model","commit_stats":null,"previous_names":["sequint/heart_disease_ml_model"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/sequint/heart_disease_ml_model","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sequint%2Fheart_disease_ml_model","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sequint%2Fheart_disease_ml_model/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sequint%2Fheart_disease_ml_model/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sequint%2Fheart_disease_ml_model/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sequint","download_url":"https://codeload.github.com/sequint/heart_disease_ml_model/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sequint%2Fheart_disease_ml_model/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":275336604,"owners_count":25446821,"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":"2025-09-15T02:00:09.272Z","response_time":75,"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":[],"created_at":"2024-11-11T10:16:41.650Z","updated_at":"2025-09-15T23:30:47.056Z","avatar_url":"https://github.com/sequint.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Heart Disease Predictor Project\n\nThis project aims to develop a comprehensive data pipeline to predict heart disease using machine learning techniques.\nThe goal is to collect, process, and analyze heart disease data to build a predictive model that can provide actionable insights for healthcare providers.\nThis project is driven by the need to support early identification of individuals at risk, enabling timely interventions and improving patient outcomes.\nEarly detection is crucial as it can significantly reduce the morbidity and mortality associated with heart disease, which remains a leading cause of death worldwide.\nThe predictive model developed in this project will analyze medical history and clinical data to identify patterns and correlations that are indicative of heart disease.\nThis will assist healthcare professionals in making informed decisions about patient care, ultimately improving the preventive approach to heart disease and reducing healthcare costs.\nThe project involves multiple stages including data collection from the UCI Heart Disease dataset, data preprocessing, feature engineering, model selection and training, hyperparameter tuning, and model evaluation.\nThe successful implementation of this model could revolutionize the preventive approach to heart disease by providing a reliable method for early detection.\n\n## Contributors\n\n- Michael Kalajian\n- Steven Quintana\n\n## Technologies\n\n- Python\n- Pandas\n- Pandas DataFrames\n- NumPy\n- Scikit-learn\n- Joblib\n- Matplotlib\n- Seaborn\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsequint%2Fheart_disease_ml_model","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsequint%2Fheart_disease_ml_model","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsequint%2Fheart_disease_ml_model/lists"}