{"id":31649317,"url":"https://github.com/tdeepa20/heart-disease-prediction-using-ml","last_synced_at":"2026-04-28T12:02:35.862Z","repository":{"id":312043155,"uuid":"1046066107","full_name":"tdeepa20/heart-disease-prediction-using-ml","owner":"tdeepa20","description":"Machine Learning models to predict heart disease using patient medical data","archived":false,"fork":false,"pushed_at":"2025-08-28T06:29:04.000Z","size":208,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-10-07T07:44:15.583Z","etag":null,"topics":["datascience","jupyer-notebook","machinelearning","numpy","python","sklearn"],"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/tdeepa20.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,"zenodo":null}},"created_at":"2025-08-28T06:19:05.000Z","updated_at":"2025-08-28T06:30:24.000Z","dependencies_parsed_at":"2025-08-28T13:21:54.835Z","dependency_job_id":null,"html_url":"https://github.com/tdeepa20/heart-disease-prediction-using-ml","commit_stats":null,"previous_names":["tdeepa20/heart-disease-prediction-using-ml"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/tdeepa20/heart-disease-prediction-using-ml","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tdeepa20%2Fheart-disease-prediction-using-ml","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tdeepa20%2Fheart-disease-prediction-using-ml/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tdeepa20%2Fheart-disease-prediction-using-ml/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tdeepa20%2Fheart-disease-prediction-using-ml/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tdeepa20","download_url":"https://codeload.github.com/tdeepa20/heart-disease-prediction-using-ml/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tdeepa20%2Fheart-disease-prediction-using-ml/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32379629,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-28T11:25:28.583Z","status":"ssl_error","status_checked_at":"2026-04-28T11:25:05.435Z","response_time":56,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 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":["datascience","jupyer-notebook","machinelearning","numpy","python","sklearn"],"created_at":"2025-10-07T07:42:08.802Z","updated_at":"2026-04-28T12:02:35.837Z","avatar_url":"https://github.com/tdeepa20.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"                        Heart Disease Prediction using Machine Learning\n\nHeart disease is one of the leading health concerns worldwide, and early detection can help reduce risks and save lives.  \nThis project uses **Machine Learning algorithms** to predict the likelihood of heart disease based on patient medical features.  \n\n## Project Workflow\n1. Import dataset  \n2. Data preprocessing (handling missing values, scaling, encoding, outlier detection, etc.)  \n3. Model building (Logistic Regression, Random Forest, etc.)  \n4. Model evaluation\n\n\n## Models Used\n- Logistic Regression  \n- Naive Bayes  \n- Support Vector Machine (SVM)  \n- K-Nearest Neighbors (KNN)  \n- Decision Tree  \n- Random Forest  \n- XGBoost  \n- Neural Network  \n\n## Requirements\n- Python 3.x  \n- Jupyter Notebook  \n- pandas, numpy, matplotlib, seaborn, scikit-learn  \n\n---\n\n## Insights\n- Logistic Regression gave the best accuracy in this dataset(88.52%).\n\n\n---\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftdeepa20%2Fheart-disease-prediction-using-ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftdeepa20%2Fheart-disease-prediction-using-ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftdeepa20%2Fheart-disease-prediction-using-ml/lists"}