{"id":18646928,"url":"https://github.com/jonad/customer_segment","last_synced_at":"2026-04-10T01:04:00.901Z","repository":{"id":129494111,"uuid":"80135946","full_name":"jonad/customer_segment","owner":"jonad","description":"Creating customer segments using unsupervised machine learning algorithm.","archived":false,"fork":false,"pushed_at":"2017-11-28T21:00:02.000Z","size":1013,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2024-12-27T12:09:42.024Z","etag":null,"topics":["clustering","clustering-algorithm","gaussian-mixture-models","jupyter-notebook","kmeans-clustering","matplotlib","numpy","pandas","python","scikit-learn"],"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/jonad.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":"2017-01-26T17:13:23.000Z","updated_at":"2017-11-29T01:10:45.000Z","dependencies_parsed_at":"2023-06-11T11:15:18.591Z","dependency_job_id":null,"html_url":"https://github.com/jonad/customer_segment","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jonad%2Fcustomer_segment","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jonad%2Fcustomer_segment/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jonad%2Fcustomer_segment/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jonad%2Fcustomer_segment/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jonad","download_url":"https://codeload.github.com/jonad/customer_segment/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239449588,"owners_count":19640535,"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":["clustering","clustering-algorithm","gaussian-mixture-models","jupyter-notebook","kmeans-clustering","matplotlib","numpy","pandas","python","scikit-learn"],"created_at":"2024-11-07T06:23:40.713Z","updated_at":"2025-11-05T05:30:35.167Z","avatar_url":"https://github.com/jonad.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Project: Creating Customer Segments\n\n### Install\n\nThis project requires **Python 2.7** and the following Python libraries installed:\n\n- [NumPy](http://www.numpy.org/)\n- [Pandas](http://pandas.pydata.org)\n- [matplotlib](http://matplotlib.org/)\n- [scikit-learn](http://scikit-learn.org/stable/)\n- [Jupyter Notebook](http://ipython.org/notebook.html)\n\nIf you do not have Python installed yet, it is highly recommended that you install the [Anaconda](http://continuum.io/downloads) distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer. \n\n### Run\n\nIn a terminal or command window, navigate to the top-level project directory `customer_segments/` (that contains this README) and run one of the following commands:\n\n```bash\nipython notebook customer_segments.ipynb\n```  \nor\n```bash\njupyter notebook customer_segments.ipynb\n```\n\nThis will open the Jupyter Notebook software and project file in your browser.\n\n## Data\n\nThe customer segments data is included as a selection of 440 data points collected on data found from clients of a wholesale distributor in Lisbon, Portugal. More information can be found on the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Wholesale+customers).\n\nNote (m.u.) is shorthand for *monetary units*.\n\n**Features**\n1) `Fresh`: annual spending (m.u.) on fresh products (Continuous); \n2) `Milk`: annual spending (m.u.) on milk products (Continuous); \n3) `Grocery`: annual spending (m.u.) on grocery products (Continuous); \n4) `Frozen`: annual spending (m.u.) on frozen products (Continuous);\n5) `Detergents_Paper`: annual spending (m.u.) on detergents and paper products (Continuous);\n6) `Delicatessen`: annual spending (m.u.) on and delicatessen products (Continuous); \n7) `Channel`: {Hotel/Restaurant/Cafe - 1, Retail - 2} (Nominal)\n8) `Region`: {Lisnon - 1, Oporto - 2, or Other - 3} (Nominal) \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjonad%2Fcustomer_segment","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjonad%2Fcustomer_segment","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjonad%2Fcustomer_segment/lists"}