[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126034-en":3,"doc-seo-126034-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126034,2336474466412,"Ezra","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Gender-Based Analysis of Employee Attrition Prediction Using Machine Learning","Employee turnover creates major productivity and cost burdens, and organizations increasingly use predictive analytics to anticipate who is likely to leave. This study applies machine learning models that incorporate gender-related effects to forecast employee attrition and assess which factors drive dropout. The workflow includes data cleaning, gender-wise dataset splitting, training separate Random Forest classifiers, and evaluating performance with accuracy, precision/recall, ROC, and AUC. Feature importance and SHAP explanations reveal different influences for male versus female employees, informing gender-sensitive HR and accommodation policies.","Gender-Based Analysis of Employee Attrition Prediction Using  \nMachine Learning  \nJamshaid Basit1, Farhan Nawaz Cheema2  \n1 Department of Computer Science and Software Engineering, National University of Sciences and Technology, Islamabad, Pakistan.  \n2 Department of Computer Science and Software Engineering, National University of Sciences and Technology, Islamabad, Pakistan.  \n*[Correspondence:](Correspondence:jbasit.msse23mcs@student.nust.edu.pk)[jbasit.msse23mcs@student.nust.edu.pk](Correspondence:jbasit.msse23mcs@student.nust.edu.pk), , [fnawaz.msse2023mcs@student.nust.edu.pk](fnawaz.msse2023mcs@student.nust.edu.pk)  \nCitation | Basit. J, Cheema. F. N,“Gender-Based Analysis of Employee Attrition Prediction Using Machine Learning”, IJIST, Vol. 6 Issue. 3 pp 1137-1150, Aug 2024  \nReceived| July 27, 2024 Revised| Aug 19, 2024 Accepted| Aug 20, 2024 Published| Aug 21, 2024.  \nEmployee turnover is a significant problem  \nproductivity and cost implications. This turnover using machine learning techniques  \nin organizations because it comes with paper focuses on predicting employee that incorporate gender aspects. We used  \nstrong Random Forest classifiers to predict attrition based on a wide cross-section of the employee’s activities and the feature importance assessment. The procedure involved data cleaning, splitting the dataset for males and females, creating models for them, and using assessment tests with different measures. When we separated the database by gender, our analysis identified unique factors that predisposed the two groups to drop out. The importance of features, the ROC curve, and the SHAP map showed how variables such as \"job role,\"\"monthly income,\" and \"work-life balance\" affected attrition differently between males and females. For female employees, job satisfaction and time directly influenced attrition, whereas for male employees, previous companies and distance from home had a greater impact. The results of the research therefore imply the need for gender-sensitive HR practices that can inform the development of gender-sensitive accommodation policies as a way of responding to the challenges facing each gender. This approach aids in the explanation of attrition tendencies and the provision of better organizational practices.  \nKeywords: Employee Attrition; Machine Learning; Gender Analysis; Random Forest; SHAP Values  \nAug 2024 |Vol 6 | Issue 3 Page | 1137  \nIntroduction:  \nJob satisfaction is a critical determinant of employee turnover, as it has a direct impact on organizational costs in terms of recruitment, training, and losing experienced employees [1]  \n[2] . It is critical to understand the reasons for voluntary turnover before adopting strategic solutions that will increase retention rates. However, gender differences in employee attrition have received limited attention. Analyzing gender differences to each type of turnover is crucial when analyzing the consequences of turnover predictors, which vary for male and female employees. Therefore, organizations can use this information to guide their decisionmaking process when implementing specific retention strategies. For example, satisfaction with working conditions, overtime, availability for work, customs, etc. may affect male and female personnel differently. Understanding the causes of these differences makes it easier to find a cure, thereby reducing turnover costs.  \nOther studies have identified antecedents such as job satisfaction, organizational commitment, work-life interface, pay, career mobility, and job content. However, the extension of statistical and machine learning methods to investigate the gender-specific effects is quite limited. Shortly, due to modern development in machine learning, especially with the use of Random Forests, it becomes possible to handle an increased number of variables within their interactions to provide a better understanding of the turnover factors. SHAP provides extra information on feature attribution","cbCaiaQU65VHo5GO","https://ap.wps.com/l/cbCaiaQU65VHo5GO","pdf",930653,10,1,14,"English","en",105,"# Introduction\n## Research Aim and Motivation\n# Literature Review\n## Key Factors Affecting Turnover\n# Methodology\n## Data Pre-processing and Gender Splitting\n## Gender-Specific Random Forest Modeling\n## Evaluation with ROC/AUC-ROC\n# Explainability and Comparison\n## Feature Importance and SHAP Maps\n## Differences in Male vs Female Predictors\n# Implications\n## Gender-Sensitive HR Practices","[{\"question\":\"What is the main goal of the study on employee attrition prediction?\",\"answer\":\"The study develops and evaluates machine learning algorithms tailored to analyze employee attrition with gender-specific factors, aiming to improve retention strategies.\"},{\"question\":\"How are the prediction models built and evaluated?\",\"answer\":\"The approach cleans the data, splits the dataset into male and female subsets, trains separate two-class Random Forest classifiers, and evaluates them using metrics including precision, recall, and AUC-ROC.\"},{\"question\":\"Which variables most affect attrition differently for males and females?\",\"answer\":\"Feature importance and SHAP results indicate that job role, monthly income, and work-life balance influence attrition differently by gender; job satisfaction and time are more influential for females, while previous companies and distance from home are more impactful for males.\"}]","Gender-Based Analysis of Employee Attrition Prediction Using Machine Learning | PDF",1785902653,35,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"gender-based-analysis-of-employee-attrition-prediction-using-machine-learning","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/gender-based-analysis-of-employee-attrition-prediction-using-machine-learning/126034/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-26","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What is the main goal of the study on employee attrition prediction?","Question",{"text":77,"@type":78},"The study develops and evaluates machine learning algorithms tailored to analyze employee attrition with gender-specific factors, aiming to improve retention strategies.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How are the prediction models built and evaluated?",{"text":82,"@type":78},"The approach cleans the data, splits the dataset into male and female subsets, trains separate two-class Random Forest classifiers, and evaluates them using metrics including precision, recall, and AUC-ROC.",{"name":84,"@type":75,"acceptedAnswer":85},"Which variables most affect attrition differently for males and females?",{"text":86,"@type":78},"Feature importance and SHAP results indicate that job role, monthly income, and work-life balance influence attrition differently by gender; job satisfaction and time are more influential for females, while previous companies and distance from home are more impactful for males.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,122,125,130,133,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":120,"slug":121},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":123,"slug":124},30,"research-report",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":127,"show_sort_weight":128,"slug":129},9,"Religion & Spirituality",20,"religion-spirituality",{"id":128,"doc_module":4,"doc_module_name":47,"category_name":131,"show_sort_weight":128,"slug":132},"World Cup","world-cup",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":20,"slug":135},"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]