{"id":21405758,"url":"https://github.com/burcuyesilyurt/teco_customer_segmentation","last_synced_at":"2026-05-15T20:03:49.512Z","repository":{"id":253235959,"uuid":"842631040","full_name":"burcuyesilyurt/Teco_Customer_Segmentation","owner":"burcuyesilyurt","description":"Develop Machine Learning model to predict customer churn.","archived":false,"fork":false,"pushed_at":"2024-08-14T19:20:17.000Z","size":1442,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-08-04T20:20:01.004Z","etag":null,"topics":["customer-segmentation","machine-learning","prediction","python","supervised-machine-learning"],"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/burcuyesilyurt.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":"2024-08-14T18:36:26.000Z","updated_at":"2024-08-15T09:35:01.000Z","dependencies_parsed_at":"2024-08-15T12:12:06.515Z","dependency_job_id":"9464dc84-bd72-46e3-96ab-e60b74d2a1f3","html_url":"https://github.com/burcuyesilyurt/Teco_Customer_Segmentation","commit_stats":null,"previous_names":["burcuyesilyurt/teco_customer_segmentation"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/burcuyesilyurt/Teco_Customer_Segmentation","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/burcuyesilyurt%2FTeco_Customer_Segmentation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/burcuyesilyurt%2FTeco_Customer_Segmentation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/burcuyesilyurt%2FTeco_Customer_Segmentation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/burcuyesilyurt%2FTeco_Customer_Segmentation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/burcuyesilyurt","download_url":"https://codeload.github.com/burcuyesilyurt/Teco_Customer_Segmentation/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/burcuyesilyurt%2FTeco_Customer_Segmentation/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33077965,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-15T11:35:32.926Z","status":"ssl_error","status_checked_at":"2026-05-15T11:35:31.362Z","response_time":103,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["customer-segmentation","machine-learning","prediction","python","supervised-machine-learning"],"created_at":"2024-11-22T16:28:12.739Z","updated_at":"2026-05-15T20:03:49.495Z","avatar_url":"https://github.com/burcuyesilyurt.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Description:\n\nFor this task, I will develop a machine learning model to predict customer churn, a common business problem. Customer churn prediction is crucial for businesses as it helps in identifying customers who are likely to stop using a service or product, allowing the company to take proactive steps to retain them.\n\n## Dataset Selection\nI'll use the popular \"Telco Customer Churn\" dataset, which contains information about customers of a telecom company. The dataset includes various features such as customer demographics, account information, and services used, as well as a label indicating whether the customer churned or not.\n\n## Problem Definition\nThe problem is a binary classification task, where the goal is to predict whether a customer will churn (1) or not churn (0) based on the features provided in the dataset.\n\n## Challenges:\nHandling class imbalance if the dataset has more non-churned customers than churned ones.\nAvoiding overfitting.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fburcuyesilyurt%2Fteco_customer_segmentation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fburcuyesilyurt%2Fteco_customer_segmentation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fburcuyesilyurt%2Fteco_customer_segmentation/lists"}