{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/predicting-customer-churn-extreme-gradient","title":"Predicting Customer Churn: Extreme Gradient Boosting with Temporal Data","arxiv_id":"1802.03396","date":"2018-02-09","proceeding":null,"authors":["Bryan Gregory"],"abstract":"Accurately predicting customer churn using large scale time-series data is a\ncommon problem facing many business domains. The creation of model features\nacross various time windows for training and testing can be particularly\nchallenging due to temporal issues common to time-series data. In this paper,\nwe will explore the application of extreme gradient boosting (XGBoost) on a\ncustomer dataset with a wide-variety of temporal features in order to create a\nhighly-accurate customer churn model. In particular, we describe an effective\nmethod for handling temporally sensitive feature engineering. The proposed\nmodel was submitted in the WSDM Cup 2018 Churn Challenge and achieved\nfirst-place out of 575 teams.","url_abs":"http://arxiv.org/abs/1802.03396v1","url_pdf":"http://arxiv.org/pdf/1802.03396v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"predicting-customer-churn-extreme-gradient","repo_url":"https://github.com/Atharv17/Churn-Prediction-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"predicting-customer-churn-extreme-gradient","repo_url":"https://github.com/SAURAVBORAH22/Churn-Prediction-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}