[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119953-en":3,"doc-seo-119953-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119953,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Predicting HR Churn with Python and Machine Learning","Employee turnover creates major financial pressure and disrupts continuity of work, making retention a high-priority objective. The study applies HR analytics with a structured supervised machine learning classification workflow to estimate the likelihood that current employees will leave. Data from former employees is used to model churn probability, while variables such as recruitment cost, sign-on bonuses, and onboarding productivity loss are analyzed to explain when and why departures occur. The approach supports pre-emptive retention policies, enabling organizations to mitigate both financial and operational burdens by acting on root causes and timing revealed by the model.","Journal of Advanced Zoology  \nISSN: 0253-7214  \nVolume 44 IssueS-8 Year 2023 Page 164-172  \nPredicting HR Churn with Python and Machine Learning  \nJ. K. Patil1*, P. M. Jadhav2  \n1,2 Department of Computer Science, Changu Kana Thakur Arts, Commerce and Science College, New Panvel, [jadhavprati2013@gmail.com](jadhavprati2013@gmail.com)  \n*Corresponding Author: J. K. Patil  \n* Department of Computer Science, Changu Kana Thakur Arts, Commerce and Science College, New Panvel, [jadhavprati2013@gmail.com](jadhavprati2013@gmail.com)  \n\n| CC License\u003Cbr>CC-BY-NC-SA 4.0 | Abstract\u003Cbr>Employee turnover imposes a substantial financial burden, necessitating proactive retention strategies. The aim is to leverage HR analytics, specifically employing a systematic machine learning approach, to predict the likelihood of active employees leaving the company. Using a systematic approach for supervised classification, the study leverages data on former employees to predict the probability of current employees leaving. Factors such as recruitment costs, sign-on bonuses, and onboarding productivity loss are analysed to explain when and why employees are prone to leave. The project aims to empower companies to take pre-emptive measures for retention. Contributing to HR Analytics, it provides a methodological framework applicable to various machine learning problems, optimizing human resource management, and enhancing overall workforce stability. This research contributes not only to predicting turnover but also proposes policies and strategies derived from the model's results. By understanding the root causes and timing of employee departures, companies can proactively implement measures to mitigate turnover, thereby minimizing the associated financial and operational burdens.\u003Cbr>Keywords: Employee Turnover, HR Analytics, Churn, Retention. |\n| --- | --- |\n\nI. INTRODUCTION  \nIn today's rapidly evolving business landscape, employee retention has become a critical concern for organizations. Given how expensive it is to replace talented workers and the impact it has on ongoing work and productivity, companies are increasingly turning to data-driven approaches for HR churn analysis.  \nThe use of HR analytics has been pivotal in not only gathering data but also predicting how to improve processes to achieve the desired goal. This is particularly important in predicting employee churn and its impact on retention. Telecommunication companies have sought to be more proactive by participating in data mining and machine learning-based models for churn analysis. Additionally, the use of data analytics and artificial intelligence-based methods has proven to be effective in understanding customer churn and developing profitable customer retention programs. Notably, machine learning classification and assembling methods have been incorporated for customer churn analysis, and research has explored support vector machine  \nmethods for predicting employee turnover in the IT industry. This shows a growing trend towards leveraging advanced analytics and machine learning for churn prediction and management.  \nII. RELATED WORK  \nThis paper examines HR analytics and discovers that, despite proof of its benefits, adoption is minimal. Inadequate IT infrastructure and gap of skills are two issues that HR must deal with. The report highlighted how HR's strategic role is changing and calls for more research to address challenges and hiccups in integrating analytics into HR procedures [1] .  \nThis study addressed the significant impact of employee churn on organizations and aimed to identify the best prediction model among naïve Bayes, decision tree, and random forest. Analyzing data from a telecom company in Indonesia, the study concluded that the random forest was the most reliable model, achieving an impressive accuracy of 97.5%. This finding is critical for averting negative organizational consequences [2] . This paper highlighted the significant impact of employee income on ","cbCaikkFxsg4WPzt","https://ap.wps.com/l/cbCaikkFxsg4WPzt","pdf",1284229,1,9,"English","en",105,"# Introduction\n# Related Work","[{\"question\":\"What problem does the paper address in HR analytics?\",\"answer\":\"It addresses employee turnover (HR churn) and the need to predict which current employees are likely to leave so organizations can improve retention.\"},{\"question\":\"How does the study predict HR churn?\",\"answer\":\"It uses a supervised machine learning classification approach, leveraging data from former employees to estimate the probability that current employees will leave.\"},{\"question\":\"Which factors are analyzed to explain why employees leave?\",\"answer\":\"The study analyzes recruitment costs, sign-on bonuses, and onboarding productivity loss to understand both timing and underlying drivers of churn.\"}]","Predicting HR Churn with Python and Machine Learning | PDF",1785727164,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-hr-churn-with-python-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-hr-churn-with-python-and-machine-learning/119953/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in HR analytics?","Question",{"text":75,"@type":76},"It addresses employee turnover (HR churn) and the need to predict which current employees are likely to leave so organizations can improve retention.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study predict HR churn?",{"text":80,"@type":76},"It uses a supervised machine learning classification approach, leveraging data from former employees to estimate the probability that current employees will leave.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors are analyzed to explain why employees leave?",{"text":84,"@type":76},"The study analyzes recruitment costs, sign-on bonuses, and onboarding productivity loss to understand both timing and underlying drivers of churn.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]