{"id":24102143,"url":"https://github.com/rubynixx/bpp_telecomm_churn","last_synced_at":"2026-05-16T07:33:52.398Z","repository":{"id":256187708,"uuid":"854548362","full_name":"RubyNixx/BPP_Telecomm_Churn","owner":"RubyNixx","description":"Programming assignment on predicting churn of a customer base using open sample Telecomm customer data.","archived":false,"fork":false,"pushed_at":"2024-12-27T10:36:05.000Z","size":3173,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-10T17:43:53.112Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/RubyNixx.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":"2024-09-09T11:25:14.000Z","updated_at":"2024-12-27T10:36:10.000Z","dependencies_parsed_at":"2024-12-27T17:31:21.378Z","dependency_job_id":null,"html_url":"https://github.com/RubyNixx/BPP_Telecomm_Churn","commit_stats":null,"previous_names":["rubynixx/bpp_telecomm_churn"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RubyNixx%2FBPP_Telecomm_Churn","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RubyNixx%2FBPP_Telecomm_Churn/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RubyNixx%2FBPP_Telecomm_Churn/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RubyNixx%2FBPP_Telecomm_Churn/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/RubyNixx","download_url":"https://codeload.github.com/RubyNixx/BPP_Telecomm_Churn/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241055816,"owners_count":19901661,"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":[],"created_at":"2025-01-10T17:36:15.648Z","updated_at":"2026-05-16T07:33:52.369Z","avatar_url":"https://github.com/RubyNixx.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cb\u003eTopic:\u003c/b\u003e Programming for Data Analysts\n\n\u003cb\u003eAssignment:\u003c/b\u003e 1 - Produce a python notebook to answer the business problem.\n\n\nThe python file can be found within this repository or alternatively opened in google colab below:\n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)]([https://colab.research.google.com/github/USERNAME/REPO/blob/BRANCH/PATH/TO/NOTEBOOK.ipynb](https://colab.research.google.com/drive/1VVJBRud9zGyRA-w-PMuqUZuURGZLU-9I#scrollTo=z6qkxuUH8NmF))\n\nProject: Analysing historic customer churn at BPP Telecom and prediciting future churn.\n\n\u003cb\u003eBackground:\u003c/b\u003e\n\nBPP Telecom, a leading telecommunications provider headquartered in the UK, has been on a progressive journey, expanding its offerings from traditional phone services to a broad spectrum encompassing high-speed internet and cutting-edge streaming services.\n\n\u003cb\u003eBusiness Problem:\u003c/b\u003e\n\nDespite the diversification and growth of its services, BPP has been encountering a rising tide of customer churn. This escalating issue has begun to erode its customer base and revenues, posing a clear constraint to the company's future growth trajectory.\n\n\u003cb\u003eData used:\u003c/b\u003e\n\nThe dataset this model is based on is sourced from Customer information. This dataset captures an array of attributes for each customer, ranging from demographics to service usage, churn and charges. The 2 data files needed are available as files within this repo.\n\n\u003cb\u003eAim of this notebook:\u003c/b\u003e\n\nExtract key insights from the customer data to construct a predictive model anticipating customer churn.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frubynixx%2Fbpp_telecomm_churn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frubynixx%2Fbpp_telecomm_churn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frubynixx%2Fbpp_telecomm_churn/lists"}