{"id":23983544,"url":"https://github.com/anushkundu/churn-prediction","last_synced_at":"2026-04-21T13:02:52.454Z","repository":{"id":270519343,"uuid":"910627690","full_name":"anushkundu/Churn-Prediction","owner":"anushkundu","description":"Telecom Customer Churn Prediction Using Machine Learning!","archived":false,"fork":false,"pushed_at":"2025-02-10T21:36:10.000Z","size":617,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-10T22:30:23.531Z","etag":null,"topics":["accuracy-score","classification-algorithm","classification-report","data-analysis","data-science","deep-learning","gradient-boosting-classifier","keras-tensorflow","logistic-regression","machine-learning","random-forest-classifier","recall-precision","roc-auc-score","smote-sampling","svm-classifier"],"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/anushkundu.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":"2024-12-31T21:34:39.000Z","updated_at":"2025-02-10T21:36:13.000Z","dependencies_parsed_at":null,"dependency_job_id":"100d7e38-530e-44f7-8c08-1d684d461b0e","html_url":"https://github.com/anushkundu/Churn-Prediction","commit_stats":null,"previous_names":["anushkundu/churn-prediction"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anushkundu%2FChurn-Prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anushkundu%2FChurn-Prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anushkundu%2FChurn-Prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anushkundu%2FChurn-Prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/anushkundu","download_url":"https://codeload.github.com/anushkundu/Churn-Prediction/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240576485,"owners_count":19823293,"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":["accuracy-score","classification-algorithm","classification-report","data-analysis","data-science","deep-learning","gradient-boosting-classifier","keras-tensorflow","logistic-regression","machine-learning","random-forest-classifier","recall-precision","roc-auc-score","smote-sampling","svm-classifier"],"created_at":"2025-01-07T12:17:47.523Z","updated_at":"2026-04-21T13:02:47.409Z","avatar_url":"https://github.com/anushkundu.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Telecom Customer Churn Prediction Using Machine Learning!\n\nThe **Telco Customer Churn Dataset** is commonly used for predicting customer retention in the telecommunications industry. Here’s a breakdown of the dataset and its significance:\n\n\n**Dataset Overview:**\n\n*Rows:* Each row represents a unique customer.\n\n*Columns:* Contain information about customer demographics, account details, services subscribed to, and whether the customer has churned (left the service).\n\n**Key Features:**\n\n*Demographic Information:* gender, SeniorCitizen, Partner, Dependents.\n\n*Service Details:* PhoneService, MultipleLines, InternetService, \nOnlineSecurity, TechSupport, etc.\n\n*Account Information:* tenure (how long the customer has been with the company), Contract, MonthlyCharges, TotalCharges.\n\n*Target Variable:* Churn – whether the customer left the company or stayed (Yes/No).\n\n**Objective:**\n\nMain goal is to predict customer churn, which refers to whether a customer will leave the company based on their historical data.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanushkundu%2Fchurn-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanushkundu%2Fchurn-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanushkundu%2Fchurn-prediction/lists"}