{"id":19625321,"url":"https://github.com/chhavi-trivedi/prediction-using-unsupervised-ml","last_synced_at":"2026-05-09T02:18:08.870Z","repository":{"id":112307898,"uuid":"310780738","full_name":"Chhavi-Trivedi/Prediction-using-Unsupervised-ML","owner":"Chhavi-Trivedi","description":"K-means clustering of iris dataset with visualization.","archived":false,"fork":false,"pushed_at":"2020-11-07T06:22:13.000Z","size":780,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-10T07:32:27.556Z","etag":null,"topics":["datascience","k-means-clustering","machine-learning","python","unsupervised-machine-learning"],"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/Chhavi-Trivedi.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":"2020-11-07T06:14:54.000Z","updated_at":"2023-07-21T05:20:58.000Z","dependencies_parsed_at":"2023-05-12T19:15:25.148Z","dependency_job_id":null,"html_url":"https://github.com/Chhavi-Trivedi/Prediction-using-Unsupervised-ML","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Chhavi-Trivedi/Prediction-using-Unsupervised-ML","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Chhavi-Trivedi%2FPrediction-using-Unsupervised-ML","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Chhavi-Trivedi%2FPrediction-using-Unsupervised-ML/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Chhavi-Trivedi%2FPrediction-using-Unsupervised-ML/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Chhavi-Trivedi%2FPrediction-using-Unsupervised-ML/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Chhavi-Trivedi","download_url":"https://codeload.github.com/Chhavi-Trivedi/Prediction-using-Unsupervised-ML/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Chhavi-Trivedi%2FPrediction-using-Unsupervised-ML/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279003189,"owners_count":26083533,"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":"2025-10-10T02:00:06.843Z","response_time":62,"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":["datascience","k-means-clustering","machine-learning","python","unsupervised-machine-learning"],"created_at":"2024-11-11T11:41:06.665Z","updated_at":"2025-10-10T07:33:00.609Z","avatar_url":"https://github.com/Chhavi-Trivedi.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Prediction-using-Unsupervised-ML\n**OBJECTIVE-Clustering of Iris Dataset**\n\nFrom the given ‘Iris’ dataset, predict the optimum number of clusters and\nrepresent it visually.\n\n**ALGORITHM USED - K-Means**\n\nK-means is a centroid-based algorithm, or a distance-based algorithm, where we calculate the distances to assign a point to a cluster. In K-Means, each cluster is associated with a centroid.\n\n**Workflow behind Implemention of K-Means Clustering**\n1. Choose the number of clusters k\n\n2. Select k random points from the data as centroids\n\n3. Assign all the points to the closest cluster centroid\n\n4. Recompute the centroids of newly formed clusters\n\n5. Repeat steps 3 and 4\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchhavi-trivedi%2Fprediction-using-unsupervised-ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchhavi-trivedi%2Fprediction-using-unsupervised-ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchhavi-trivedi%2Fprediction-using-unsupervised-ml/lists"}