{"id":15136038,"url":"https://github.com/tanmayee2010/heart-attack-prediction","last_synced_at":"2026-01-21T06:17:44.465Z","repository":{"id":242825905,"uuid":"810673864","full_name":"Tanmayee2010/Heart-Attack-Prediction","owner":"Tanmayee2010","description":"Heart Attack Prediction Using Machine Learning Algorithm","archived":false,"fork":false,"pushed_at":"2024-06-05T09:36:38.000Z","size":882,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-15T20:41:58.847Z","etag":null,"topics":["datetime","datetime-library","jupyter-notebook","logistic-regression-algorithm","machine-learning","machine-learning-algorithms","matplotlib-python","mysql","numpy","pandas-python","pycharm-ide","tkinter"],"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/Tanmayee2010.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":"2024-06-05T06:41:03.000Z","updated_at":"2024-09-25T16:05:58.000Z","dependencies_parsed_at":"2024-06-05T08:01:28.936Z","dependency_job_id":"bff4c4be-8975-4a87-a0dd-7995572b51e3","html_url":"https://github.com/Tanmayee2010/Heart-Attack-Prediction","commit_stats":null,"previous_names":["tanmayee2010/heart-attack-prediction"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Tanmayee2010%2FHeart-Attack-Prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Tanmayee2010%2FHeart-Attack-Prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Tanmayee2010%2FHeart-Attack-Prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Tanmayee2010%2FHeart-Attack-Prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Tanmayee2010","download_url":"https://codeload.github.com/Tanmayee2010/Heart-Attack-Prediction/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248258070,"owners_count":21073848,"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","datetime-library","jupyter-notebook","logistic-regression-algorithm","machine-learning","machine-learning-algorithms","matplotlib-python","mysql","numpy","pandas-python","pycharm-ide","tkinter"],"created_at":"2024-09-26T06:03:47.600Z","updated_at":"2026-01-21T06:17:44.427Z","avatar_url":"https://github.com/Tanmayee2010.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Heart-Attack-Prediction\nIt is a Graphical User Interface System which is based on tkinter library\n\n**What is Logistic Regression ?**\n\nLogistic Regression is a statistical and machine-learning techniques classifying records of a dataset based on the values of the input fields . It predicts a dependent variable based on one or more set of independent variables to predict outcomes . It can be used both for binary classification and multi-class classification.\n\n**##Objective**\nThe primary objective of this study was to classify heart disease using logistic regression model and a real-world dataset. \nThe logistic regression algorithm was applied to a dataset of patients with heart disease to predict the presence of the disease.\n\n\n\n## Dataset \n\n[Dataset: (https://github.com/Tanmayee2010/Heart-Attack-Prediction/blob/main/heart.csv)]\n\n\n    \n#### Columns 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## Libraries Used - \n  1. Pandas *(for data manipulation)*\n  2. Matplotlib *(for data visualization)*\n  3. Numpy *(for numerical calculation)*\n  4. Scikit-Learn *(for data modeling)*\n  5. tkinter *(for GUI)*\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftanmayee2010%2Fheart-attack-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftanmayee2010%2Fheart-attack-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftanmayee2010%2Fheart-attack-prediction/lists"}