{"id":15134302,"url":"https://github.com/sayaliyewale/heart-attack-prediction","last_synced_at":"2025-10-23T09:31:28.724Z","repository":{"id":242826326,"uuid":"810673100","full_name":"SayaliYewale/Heart-Attack-Prediction","owner":"SayaliYewale","description":"Topic Name: Heart Attack Prediction","archived":false,"fork":false,"pushed_at":"2024-06-05T09:36:57.000Z","size":869,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-30T17:38:32.262Z","etag":null,"topics":["datetime","intellij-idea","jupyter-notebook","logistic-regression","machine-learning-algorithms","matplotlib-pyplot","mysql-database","numpy","panda","pycharm-ide","sklearn","tkinter-gui"],"latest_commit_sha":null,"homepage":"","language":"Python","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/SayaliYewale.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":"2024-06-05T06:39:15.000Z","updated_at":"2024-06-30T11:49:43.000Z","dependencies_parsed_at":"2024-06-05T08:05:58.771Z","dependency_job_id":"66475e9f-88f5-4ef1-ac69-b037a919312f","html_url":"https://github.com/SayaliYewale/Heart-Attack-Prediction","commit_stats":null,"previous_names":["sayaliyewale/heart-attack-prediction"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SayaliYewale%2FHeart-Attack-Prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SayaliYewale%2FHeart-Attack-Prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SayaliYewale%2FHeart-Attack-Prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SayaliYewale%2FHeart-Attack-Prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SayaliYewale","download_url":"https://codeload.github.com/SayaliYewale/Heart-Attack-Prediction/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":237807489,"owners_count":19369597,"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":["datetime","intellij-idea","jupyter-notebook","logistic-regression","machine-learning-algorithms","matplotlib-pyplot","mysql-database","numpy","panda","pycharm-ide","sklearn","tkinter-gui"],"created_at":"2024-09-26T05:04:19.298Z","updated_at":"2025-10-23T09:31:28.334Z","avatar_url":"https://github.com/SayaliYewale.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Heart-Attack-Prediction using machine learning algorithm\n-It is graphical user interface system which based on Tkinter library.\n\n-Machine learning algorithm = Logistic Regression \n\n-Logistic regression is a supervised machine learning algorithm that accomplishes binary classification tasks by predicting the probability of an outcome, event, or observation. The model delivers a binary or dichotomous outcome limited to two possible outcomes: yes/no, 0/1, or true/fals\n\n###Column Information:\n - age\n - sex\n - Chest pain type (4 values)\n - Resting blood pressure\n - Serum cholestoral in mg/dl\n - Fasting blood sugar \u003e 120 mg/dl\n - Resting electrocardiographic results (values 0,1,2)\n - Maximum heart rate achieved\n - Exercise induced angina\n - Oldpeak = ST depression induced by exercise relative to rest\n - The slope of the peak exercise ST segment\n - Number of major vessels (0-3) colored by flourosopy\n - Thal: 0 = normal; 1 = fixed defect; 2 = reversable defect\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsayaliyewale%2Fheart-attack-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsayaliyewale%2Fheart-attack-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsayaliyewale%2Fheart-attack-prediction/lists"}