{"id":22445459,"url":"https://github.com/anas436/customer-segmentation-and-generating-random-dataset-using-k-means-clustering-with-python","last_synced_at":"2026-04-16T10:02:35.055Z","repository":{"id":110394418,"uuid":"533444916","full_name":"Anas436/Customer-Segmentation-and-Generating-Random-Dataset-using-K-Means-Clustering-with-Python","owner":"Anas436","description":null,"archived":false,"fork":false,"pushed_at":"2022-09-06T18:05:23.000Z","size":258,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-26T16:03:19.317Z","etag":null,"topics":["axes3d","cluster","jupyterlab","kmeans","matplotlib","numpy","pandas","python3","random","scikit-learn","sklearn","standardscaler"],"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/Anas436.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":"2022-09-06T18:03:04.000Z","updated_at":"2022-09-07T06:13:28.000Z","dependencies_parsed_at":"2023-04-23T13:35:28.430Z","dependency_job_id":null,"html_url":"https://github.com/Anas436/Customer-Segmentation-and-Generating-Random-Dataset-using-K-Means-Clustering-with-Python","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Anas436/Customer-Segmentation-and-Generating-Random-Dataset-using-K-Means-Clustering-with-Python","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anas436%2FCustomer-Segmentation-and-Generating-Random-Dataset-using-K-Means-Clustering-with-Python","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anas436%2FCustomer-Segmentation-and-Generating-Random-Dataset-using-K-Means-Clustering-with-Python/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anas436%2FCustomer-Segmentation-and-Generating-Random-Dataset-using-K-Means-Clustering-with-Python/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anas436%2FCustomer-Segmentation-and-Generating-Random-Dataset-using-K-Means-Clustering-with-Python/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Anas436","download_url":"https://codeload.github.com/Anas436/Customer-Segmentation-and-Generating-Random-Dataset-using-K-Means-Clustering-with-Python/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anas436%2FCustomer-Segmentation-and-Generating-Random-Dataset-using-K-Means-Clustering-with-Python/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31880884,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-16T09:23:21.276Z","status":"ssl_error","status_checked_at":"2026-04-16T09:23:15.028Z","response_time":69,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 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":["axes3d","cluster","jupyterlab","kmeans","matplotlib","numpy","pandas","python3","random","scikit-learn","sklearn","standardscaler"],"created_at":"2024-12-06T03:14:30.991Z","updated_at":"2026-04-16T10:02:35.037Z","avatar_url":"https://github.com/Anas436.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Customer-Segmentation-and-Generating-Random-Dataset-using-K-Means-Clustering-with-Python\n\n\n\n## Objectives\n\nAfter completing this lab you will be able to:\n\n*   Use scikit-learn's K-Means Clustering to cluster data\n\n\n## Introduction\n\nThere are many models for **clustering** out there. In this notebook, we will be presenting the model that is considered one of the simplest models amongst them. Despite its simplicity, the **K-means** is vastly used for clustering in many data science applications, it is especially useful if you need to quickly discover insights from **unlabeled data**. In this notebook, you will learn how to use k-Means for customer segmentation.\n\nSome real-world applications of k-means:\n\n*   Customer segmentation\n*   Understand what the visitors of a website are trying to accomplish\n*   Pattern recognition\n*   Machine learning\n*   Data compression\n\nIn this notebook we practice k-means clustering with 2 examples:\n\n*   k-means on a random generated dataset\n*   Using k-means for customer segmentation\n\n\n\u003ch1\u003eTable of contents\u003c/h1\u003e\n\n\u003cdiv class=\"alert alert-block alert-info\" style=\"margin-top: 20px\"\u003e\n    \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"https://#random_generated_dataset\"\u003ek-Means on a randomly generated dataset\u003c/a\u003e\u003c/li\u003e\n            \u003col\u003e\n                \u003cli\u003e\u003ca href=\"https://#setting_up_K_means\"\u003eSetting up K-Means\u003c/a\u003e\u003c/li\u003e\n                \u003cli\u003e\u003ca href=\"https://#creating_visual_plot\"\u003eCreating the Visual Plot\u003c/a\u003e\u003c/li\u003e\n            \u003c/ol\u003e\n        \u003cp\u003e\u003c/p\u003e\n        \u003cli\u003e\u003ca href=\"https://#customer_segmentation_K_means\"\u003eCustomer Segmentation with K-Means\u003c/a\u003e\u003c/li\u003e\n            \u003col\u003e\n                \u003cli\u003e\u003ca href=\"https://#pre_processing\"\u003ePre-processing\u003c/a\u003e\u003c/li\u003e\n                \u003cli\u003e\u003ca href=\"https://#modeling\"\u003eModeling\u003c/a\u003e\u003c/li\u003e\n                \u003cli\u003e\u003ca href=\"https://#insights\"\u003eInsights\u003c/a\u003e\u003c/li\u003e\n            \u003c/ol\u003e\n    \u003c/ul\u003e\n\u003c/div\u003e\n\u003cbr\u003e\n\u003chr\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanas436%2Fcustomer-segmentation-and-generating-random-dataset-using-k-means-clustering-with-python","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanas436%2Fcustomer-segmentation-and-generating-random-dataset-using-k-means-clustering-with-python","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanas436%2Fcustomer-segmentation-and-generating-random-dataset-using-k-means-clustering-with-python/lists"}