{"payload":{"header_redesign_enabled":false,"results":[{"id":"328662321","archived":false,"color":"#DA5B0B","followers":3,"has_funding_file":false,"hl_name":"sailyshah/Telecom-churn-case-study","hl_trunc_description":"Analysing customer-level data of a leading telecom firm, building predictive models to identify customers at high risk of churn and ident…","language":"Jupyter Notebook","mirror":false,"owned_by_organization":false,"public":true,"repo":{"repository":{"id":328662321,"name":"Telecom-churn-case-study","owner_id":60376416,"owner_login":"sailyshah","updated_at":"2021-01-11T13:16:35.482Z","has_issues":true}},"sponsorable":false,"topics":["pca","logistic-regression","incremental-pca","telecom-churn-prediction","telecom-churn-analysis"],"type":"Public","help_wanted_issues_count":0,"good_first_issue_issues_count":0,"starred_by_current_user":false}],"type":"repositories","page":1,"page_count":1,"elapsed_millis":57,"errors":[],"result_count":1,"facets":[],"protected_org_logins":[],"topics":null,"query_id":"","logged_in":false,"sign_up_path":"/signup?source=code_search_results","sign_in_path":"/login?return_to=https%3A%2F%2Fgithub.com%2Fsearch%3Fq%3Drepo%253Asailyshah%252FTelecom-churn-case-study%2B%2Blanguage%253A%2522Jupyter%2BNotebook%2522","metadata":null,"csrf_tokens":{"/sailyshah/Telecom-churn-case-study/star":{"post":"mU2wMS4DnFCpBMryBZ0sLv1o-L0E91HykHtdFSaTQiNpOgxg9XdhM_lXD3ohPbZy51xYEsQRIROtAKgG-d7aHA"},"/sailyshah/Telecom-churn-case-study/unstar":{"post":"aps8ObirhnMQF7Cia_Kn6NwihiVpntd1MC6xUElnFoVrqC34qRmMKdYQ7WPec7nvOaD6UhdQdQWfekhJZxmLcw"},"/sponsors/batch_deferred_sponsor_buttons":{"post":"qdyDI_wBQNtQ9h4uSceMBDwTr9O9ANjhQ4yF7oI63UvGUrLefhHdPBIyrFg49aCyv51_oxxvsmwoKnaIMz8jNA"}}},"title":"Repository search results"}