{"id":22842722,"url":"https://github.com/ziraddingulumjanly/unsupervised-learning-implementation-on-heartattackdataset","last_synced_at":"2025-03-31T05:09:48.845Z","repository":{"id":265433226,"uuid":"895947034","full_name":"ziraddingulumjanly/Unsupervised-learning-implementation-on-HeartAttackDataset","owner":"ziraddingulumjanly","description":"This study aims to identify distinct subgroups within a dataset of patients with heart attack-related features using unsupervised learning techniques: k-means and Hierarchical","archived":false,"fork":false,"pushed_at":"2024-11-29T11:05:18.000Z","size":1477,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-06T09:23:59.610Z","etag":null,"topics":["dataset","heartattack","kaggle","kmeans-algorithm","kmeans-clustering","pca-analysis","tsne-algorithm","unsupervised-machine-learning"],"latest_commit_sha":null,"homepage":"https://www.kaggle.com/code/nourhankarm/heart-attack-detection/input?select=Heart+Attack.csv ","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ziraddingulumjanly.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2024-11-29T08:38:03.000Z","updated_at":"2024-11-29T11:05:22.000Z","dependencies_parsed_at":"2024-11-29T11:26:22.471Z","dependency_job_id":"ec5b1159-7891-40d5-813c-4fb59dcad981","html_url":"https://github.com/ziraddingulumjanly/Unsupervised-learning-implementation-on-HeartAttackDataset","commit_stats":null,"previous_names":["ziraddingulumjanly/kmeansandhierarchicalclusteringinhearattackdataset"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ziraddingulumjanly%2FUnsupervised-learning-implementation-on-HeartAttackDataset","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ziraddingulumjanly%2FUnsupervised-learning-implementation-on-HeartAttackDataset/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ziraddingulumjanly%2FUnsupervised-learning-implementation-on-HeartAttackDataset/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ziraddingulumjanly%2FUnsupervised-learning-implementation-on-HeartAttackDataset/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ziraddingulumjanly","download_url":"https://codeload.github.com/ziraddingulumjanly/Unsupervised-learning-implementation-on-HeartAttackDataset/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246418642,"owners_count":20773938,"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":["dataset","heartattack","kaggle","kmeans-algorithm","kmeans-clustering","pca-analysis","tsne-algorithm","unsupervised-machine-learning"],"created_at":"2024-12-13T02:09:38.144Z","updated_at":"2025-03-31T05:09:48.829Z","avatar_url":"https://github.com/ziraddingulumjanly.png","language":"Python","readme":"# Implementation of k-means and Hierarchical clustering methods to the Heart Attack Dataset\n\nThe identification and grouping of individuals based on these health indicators are crucial for understanding patterns that may correlate with heart attack risk. By partitioning the data into meaningful groups without relying on explicit outcome labels, the analysis highlights trends in health indicators and provides insights into natural groupings of health profiles. 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