{"id":28720225,"url":"https://github.com/tetsumichiumada/customer_segments","last_synced_at":"2026-05-02T05:33:27.981Z","repository":{"id":299062860,"uuid":"93001532","full_name":"TetsumichiUmada/customer_segments","owner":"TetsumichiUmada","description":"Identify customers by clustering them ","archived":false,"fork":false,"pushed_at":"2017-06-01T01:38:56.000Z","size":764,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-06-14T12:43:27.566Z","etag":null,"topics":["machine-learning","python","scikit-learn","unsupervised-learning"],"latest_commit_sha":null,"homepage":"","language":"HTML","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/TetsumichiUmada.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,"zenodo":null}},"created_at":"2017-06-01T00:49:41.000Z","updated_at":"2018-11-24T04:15:56.000Z","dependencies_parsed_at":"2025-06-14T12:43:28.943Z","dependency_job_id":"ba0cd063-41f5-41c9-9228-277873915833","html_url":"https://github.com/TetsumichiUmada/customer_segments","commit_stats":null,"previous_names":["tetsumichiumada/customer_segments"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/TetsumichiUmada/customer_segments","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TetsumichiUmada%2Fcustomer_segments","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TetsumichiUmada%2Fcustomer_segments/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TetsumichiUmada%2Fcustomer_segments/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TetsumichiUmada%2Fcustomer_segments/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/TetsumichiUmada","download_url":"https://codeload.github.com/TetsumichiUmada/customer_segments/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TetsumichiUmada%2Fcustomer_segments/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259934237,"owners_count":22934288,"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":["machine-learning","python","scikit-learn","unsupervised-learning"],"created_at":"2025-06-15T06:30:25.817Z","updated_at":"2026-05-02T05:33:27.939Z","avatar_url":"https://github.com/TetsumichiUmada.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Creating Customer Segments\n\nThe main objective of this project is to apply unsupervised learning techniques on product spending data collected for customers of a wholesale distributor to identify customer segments hidden in the data.\n\n## Software Requirements and Libraries\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\nYou will also need to have software installed to run and execute an [iPython Notebook](http://ipython.org/notebook.html)\n\nUdacity recommends our students install [Anaconda](https://www.continuum.io/downloads), a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.\n\n### Code\n\nTemplate code is provided in the notebook `customer_segments.ipynb` notebook file. Additional supporting code can be found in `renders.py`. While some code has already been implemented to get you started, you will need to implement additional functionality when requested to successfully complete the project.\n\n### Run\n\nIn a terminal or command window, navigate to the top-level project directory `creating_customer_segments/` (that contains this README) and run one of the following commands:\n\n```ipython notebook customer_segments.ipynb```\n```jupyter notebook customer_segments.ipynb```\n\nThis will open the iPython Notebook software and project file in your browser.\n\n## Data\n\nThe dataset used in this project is included as `customers.csv`. You can find more information on this dataset on the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Wholesale+customers) page.\n\nThis project is a part of the Machine Learning Engineer Nanodegree program at [Udacity](https://www.udacity.com/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftetsumichiumada%2Fcustomer_segments","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftetsumichiumada%2Fcustomer_segments","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftetsumichiumada%2Fcustomer_segments/lists"}