{"id":22848672,"url":"https://github.com/code-str8/customer-chun-prediction","last_synced_at":"2025-06-24T08:36:27.188Z","repository":{"id":213887684,"uuid":"724596060","full_name":"Code-str8/customer-chun-prediction","owner":"Code-str8","description":"This data science project is designed to categorize potential customers of a telecommunications company, determining whether they are likely to discontinue their services (churn) or remain with the company.","archived":false,"fork":false,"pushed_at":"2024-02-19T18:08:14.000Z","size":2631,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-06T10:29:32.937Z","etag":null,"topics":["machine-learning","pipelines","visualization"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Code-str8.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2023-11-28T12:05:21.000Z","updated_at":"2024-03-14T11:39:40.000Z","dependencies_parsed_at":null,"dependency_job_id":"7af0e34d-c7d1-471d-ab39-d854ae259772","html_url":"https://github.com/Code-str8/customer-chun-prediction","commit_stats":null,"previous_names":["code-str8/customer-chun-prediction"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Code-str8%2Fcustomer-chun-prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Code-str8%2Fcustomer-chun-prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Code-str8%2Fcustomer-chun-prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Code-str8%2Fcustomer-chun-prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Code-str8","download_url":"https://codeload.github.com/Code-str8/customer-chun-prediction/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246423728,"owners_count":20774820,"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","pipelines","visualization"],"created_at":"2024-12-13T04:13:48.533Z","updated_at":"2025-03-31T06:12:33.105Z","avatar_url":"https://github.com/Code-str8.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Customer-Chun-Prediction\nCustomer Churn Prediction\nObjective:\n\nThe primary objective of the Telco Customer Churn Prediction project is to develop a machine learning model that can predict whether a customer is likely to churn or not based on various attributes and behaviors. By identifying key drivers of churn, the project aims to provide actionable insights to reduce customer attrition and enhance customer retention strategies.\n\nBusiness Context:\n\nThe telecommunications industry is currently facing significant challenges, including high customer churn rates and intense competition. In order to address these issues, it is crucial for the business to understand the factors influencing customer churn. This project aligns with the business goal of improving customer retention by leveraging predictive analytics.\n\nData:\n\nThe dataset comprises various attributes related to customer demographics, usage patterns, and service-related information. Key features include gender, senior citizen status, partner and dependent status, tenure, type of services used (e.g., internet, streaming), contract details, billing preferences, payment methods, and financial details such as monthly and total charges.\n\nMethodology:\n\nThe project will involve exploratory data analysis (EDA) to gain insights into the dataset, identify patterns, and understand feature importance. Subsequently, a machine learning model will be developed using classification algorithms to predict customer churn. The model will be trained on historical data, and its performance will be evaluated using relevant metrics.\n\nExpected Outcomes:\n\nIdentification of key features influencing customer churn.\nDevelopment of a predictive model capable of classifying customers into churn and non-churn categories.\nProvision of actionable insights to improve customer retention strategies.\nEnhanced understanding of customer behavior and preferences.\n\nStakeholders:\nStakeholders involved in this project include telecommunications executives, marketing teams, and customer relationship management teams. The insights gained from the predictive model can inform targeted marketing efforts and customer engagement initiatives to reduce churn and foster long-term customer loyalty.\n\nArticle\nAn article was publised on this project on medium. Kinldy access it https://medium.com/@ndunda.alex/telco-customer-churn-prediction-1fcb08582e60 .\n\nThank you.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcode-str8%2Fcustomer-chun-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcode-str8%2Fcustomer-chun-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcode-str8%2Fcustomer-chun-prediction/lists"}