{"id":18859970,"url":"https://github.com/athari22/customer-segmentation","last_synced_at":"2026-02-08T17:30:13.824Z","repository":{"id":211697268,"uuid":"573091337","full_name":"Athari22/Customer-Segmentation","owner":"Athari22","description":"Customer Segmentation using Machine Learning","archived":false,"fork":false,"pushed_at":"2022-12-01T17:51:39.000Z","size":294,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-30T20:16:01.893Z","etag":null,"topics":["data-visualization","kmeans","kmeans-clustering","machine-learning","ml","pca","seaborn"],"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/Athari22.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}},"created_at":"2022-12-01T17:18:57.000Z","updated_at":"2022-12-01T17:54:25.000Z","dependencies_parsed_at":"2023-12-10T10:25:22.909Z","dependency_job_id":"80a70402-33a4-4f41-92f6-4460efb22663","html_url":"https://github.com/Athari22/Customer-Segmentation","commit_stats":null,"previous_names":["athari22/customer-segmentation"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Athari22%2FCustomer-Segmentation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Athari22%2FCustomer-Segmentation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Athari22%2FCustomer-Segmentation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Athari22%2FCustomer-Segmentation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Athari22","download_url":"https://codeload.github.com/Athari22/Customer-Segmentation/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239800488,"owners_count":19699127,"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":["data-visualization","kmeans","kmeans-clustering","machine-learning","ml","pca","seaborn"],"created_at":"2024-11-08T04:20:06.592Z","updated_at":"2026-02-08T17:30:13.783Z","avatar_url":"https://github.com/Athari22.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Customer Segmentation using Machine Learning\n## Description\n![img](https://github.com/Athari22/Customer-Segmentation/blob/main/Customer%20Segmentation%20using%20Machine%20Learning/segmentation.png)\n\n\nLet’s say, you decided to buy a t-shirt from a brand online. Have you ever thought that who else bought the same t-shirt? People, who have similar to you, right? Same age, same hobbies, same gender, etc. \n\n\nToday, many businesses are going online and therefore online marketing is essential to retain customers. However, considering all customers as equal and targeting them all with similar marketing strategies is not an efficient way, since it also annoys the customers by neglecting their individuality, so customer segmentation has become very popular and has also become a viable solution. So, we actually try to find and group customers based on common characteristics such as age, gender, living area, spending behavior, etc. So that we can market the customers effectively.\n\n## Problems you want to find answers \nThe goal of customer segmentation is to divide the company’s customers based on their demographic characteristics (age, gender, marital status) and their behavior characteristics (types of products ordered, annual income). It’s a better approach for customer segmentation to focus on behavioral aspects rather than demographic characteristics since they do not emphasize individuality of customers.\n\n## Dataset\n### Context\nThis [dataset](https://www.kaggle.com/datasets/vjchoudhary7/customer-segmentation-tutorial-in-python) is created only for the learning purpose of the customer segmentation concepts , also known as market basket analysis .\n### Content\nYou are owing a supermarket mall and through membership cards , you have some basic data about your customers like Customer ID, age, gender, annual income and spending score.\nSpending Score is something you assign to the customer based on your defined parameters like customer behavior and purchasing data.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fathari22%2Fcustomer-segmentation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fathari22%2Fcustomer-segmentation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fathari22%2Fcustomer-segmentation/lists"}