[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122708-en":3,"doc-seo-122708-105":29,"detail-sidebar-cat-0-en-105":90},{"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},122708,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","A Novel Approach To Unveiling Employee Attrition Patterns using Machine Learning Algorithms","Employee turnover undermines workplace productivity and can slow long-term growth initiatives, making attrition a priority issue for organizations. To address this, the study builds a predictive model using HR analytics data from Kaggle. Multiple machine learning techniques are evaluated for forecasting employee attrition rates, including Random Forest, Logistic Regression, Gradient Boosting, CatBoost, Extreme Gradient Boosting, and LightGBM. Beyond forecasting, the research examines factors beyond the immediate work environment that contribute to turnover, supporting retention-focused decision-making. Future work is suggested to incorporate additional variables such as feedback, recognition, hiring practices, and organizational culture.","A Novel Approach To Unveiling Employee Attrition Patterns using Machine Learning Algorithms  \n1Dr. Raafiya Gulmeher, 2 Umama Aiman  \n1 Assistant professor, CSE Department, KBN University, Kalaburagi, India [profraafiya.cse@gmail.com](profraafiya.cse@gmail.com)  \n2M. Tech Student, CSE Department, KBN University, Kalaburagi, India  \n[umamaaiman7@gmail.com](umamaaiman7@gmail.com)  \n\n| ABSTRACT |\n| --- |\n| The negative effects of employee turnover upon productivity at work as well as long-term growth initiatives make ita top issue for businesses. To combat this issue, businesses are increasingly relying on machine learning tools for accurate turnover forecasting and management. In this study, we set out to create a model that can accurately forecast future rates of employee turnover. In this research, we use HR analytics data from the Kaggle platform to predict outcomes using a variety of different machine learning techniques, including the Random Forest, Logistic Regressor, Gradient Boosting Classifier, CatBoost Classifier, Extreme Gradient Boosting, and Light GBM. This research goes beyond simple forecasting to investigate the many elements outside of the workplace that contribute to employee turnover. Moreover, our findings aim to provide top management with an insightful perspective, empowering informed decisions concerning strategies for workforce retention. Looking ahead, future research could refine the analysis by encompassing additional factors. Factors such as feedback, recognition, hiring procedures, and organizational culture, which have been observed to positively influence employee attrition rates, hold promise in offering a more comprehensive understanding and effective mitigation strategies. |\n| Keywords-Employee Attrition, Gradient Boosting Classifier, Machine Learning, Random Forest Regressor. |\n\nI. INTRODUCTION  \nEmployee Attrition, also recognized as Employee Turnover, stands as a foundational concern prevailing within today's industries. This issue holds substantial gravity across most companies, warranting serious attention. Attrition denotes \" a decrease in available personnel as a result of attrition (people leaving their jobs, retiring, or dying) .\" The term attrition encompasses various definitions, with this study primarily focusing on two vital aspects: employee departures and retirements from an organization.The ongoing occurrence of employee attrition remains a persistent concern for Human Resources departments. Over time, the incidence of employee turnover has demonstrated an upward trajectory. Consequently, employers face the challenge of deciphering whether employee departures stem from dissatisfaction or alternative motivations. Prior to implementing radical measures, a judicious approach involves probing the underlying reasons for the issue. In the contemporary professional landscape, employees display an unprecedented willingness to transition between organizations in pursuit of more favorable prospects. As a result, employee turnover has evolved into a critical challenge for a majority of organizational structures.  \nEmployee attrition happens when employees leave a company due to reasons like personal issues, not enjoying their job, getting paid less, or experiencing a negative work environment. It's divided into two types: voluntary, where employees leave by their choice, and involuntary, where managers ask employees to leave, often because of poor performance or business needs.Even though the employer wants them to remain, even the best workers may quit on their own will. They might leave because they found better opportunities or want to retire early. Voluntary attrition can happen when employees retire early or get job offers from other companies. Companies that care about their employees usually invest in them by providing good training anda positive workplace. But even these companies can still have employees leave on their own or lose valuable workers. Replacing employees who leave is e","cbCaifyYJqZKv086","https://ap.wps.com/l/cbCaifyYJqZKv086","pdf",890179,1,"English","en",105,"# Introduction\n# Methodology\n## Machine Learning Classification Algorithms\n## Logistic Regression Model\n## Decision Tree Models","[{\"question\":\"What problem does the study address regarding employee attrition?\",\"answer\":\"The study targets the negative effects of employee turnover on productivity and long-term business growth. It emphasizes forecasting turnover and understanding underlying drivers behind attrition.\"},{\"question\":\"Which machine learning algorithms are used to predict employee attrition?\",\"answer\":\"The research trains and evaluates six algorithms: decision tree, extreme boosting, cat boosting, logistic regression, and LightGBM (along with related boosting approaches mentioned in the abstract).\"},{\"question\":\"How does the study go beyond simple turnover forecasting?\",\"answer\":\"It investigates additional elements outside the workplace that may influence employee turnover. The goal is to provide management with insight for workforce retention strategies.\"}]","A Novel Approach To Unveiling Employee Attrition Patterns using Machine Learning Algorithms | PDF",1785812435,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"a-novel-approach-to-unveiling-employee-attrition-patterns-using-machine-learning-algorithms","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/a-novel-approach-to-unveiling-employee-attrition-patterns-using-machine-learning-algorithms/122708/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the study address regarding employee attrition?","Question",{"text":74,"@type":75},"The study targets the negative effects of employee turnover on productivity and long-term business growth. It emphasizes forecasting turnover and understanding underlying drivers behind attrition.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning algorithms are used to predict employee attrition?",{"text":79,"@type":75},"The research trains and evaluates six algorithms: decision tree, extreme boosting, cat boosting, logistic regression, and LightGBM (along with related boosting approaches mentioned in the abstract).",{"name":81,"@type":72,"acceptedAnswer":82},"How does the study go beyond simple turnover forecasting?",{"text":83,"@type":75},"It investigates additional elements outside the workplace that may influence employee turnover. 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