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Anticipating whether employees are unhappy can enable timely interventions to reduce turnover for both organizations and employees. The research evaluates four machine-learning models—decision tree, random forest, SVM, and artificial neural network—using two experimental setups: organization-wide prediction and department-wise prediction. Random forest achieves the strongest results, reaching 64.39% accuracy and 63.96% F1-score.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/leveraging-machine-learning-to-predict-employee-attrition-msc-research-project/290518/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/leveraging-machine-learning-to-predict-employee-attrition-msc-research-project/290518.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is predicting employee attrition important in this project?","Question",{"text":113,"@type":114},"The project explains that attrition consumes time and resources due to hiring needs. Predicting unhappy employees helps organizations take actions to avert turnover.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which machine-learning models are studied for attrition prediction?",{"text":118,"@type":114},"The study evaluates decision tree, random forest, SVM, and artificial neural network.",{"name":120,"@type":111,"acceptedAnswer":121},"How are the experiments structured for model evaluation?",{"text":122,"@type":114},"Two experiments are used: one predicts attrition for the whole organization, and the other predicts attrition department-wise.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290518,1789641918,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","Levereging Machine Learning to Predict Employee Attrition: India  \nMSc Research Project  \nData Analytics  \nSangeeta Kumari  \nStudent ID: x21141088  \nSchool of Computing  \nNational College of Ireland  \nSupervisor: Dr. Catherine Mulwa  \nNational College of Ireland  \nMSc Project Submission Sheet  \nSchool of Computing  \nStudent Sangeeta Kumari    \nName:  \nStudent ID: x21141088    \nProgramme: Msc in Data Analytics   Year:   2022   . .  \nModule: Research Project   .    \nSupervisor: Dr.Catherine Mulwa   . .   .   Submission  \nDue Date: 15th Dec 2022    \nProject Title: Leveraging Machine Learning to Predict Employee Attrition…  \nWord Count: 8337   Page Count…29   .   . .  \nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author's written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \nSignature:   Sangeeta Kumari    \nDate:   31st Jan 2023    \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both for your own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator Office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nLeveraging Machine Learning to Predict Employee Attrition  \nSangeeta Kumari  \nX21141088  \nAbstract  \nEmployee attrition is an important issue that affects businesses and organizations across the world. This can be attributed to the fact that hiring new employees takes time and consumes alot of resources. Hence, predicting if an employee is unhappy at an organization can help the organization take necessary steps to avert attrition. Hence, developing a model that can predict employee attrition is very beneficial for the organization and for the employee as well.  \nThis research studied the performances of four machine-learning models viz. decision tree, random forest, SVM, and artificial neural network. The models were implemented through two experiments in which the first experiment involved predicting the attrition for the whole organization whereas the second experiment involved predicting the attrition department-wise.  \nThe random forest model of machine learning model implemented in the model achieved the best performance with accuracy of 64.39% and f1-score of 63.96% .  \n1 Introduction  \nIn the current business environment, it has been identified that companies are highly focused on implementing different technology in their operating and decision-making process to attain a higher level of business efficiency. Technologies, like machine learning, provided an opportunity for organisations to acquire relevant data, process the same and make appropriate predictions based on which the course of actions in an organisation could be determined in an appropriate and effective manner. Thus, machine learning processes also play a key contributing role in the process of identifying the potential events or events which could create an impact on the operating processes of the companies. In this regard, Zhou (2021) defined machine learning as a key part of artificial intelligence (AI) that shows the capability or potential of a machine to imitate the behaviour o","cbCaiisG9XsJHAI6","https://ap.wps.com/l/cbCaiisG9XsJHAI6","pdf",855059,29,"English","# Introduction\n## Motivation and Project Background","[{\"question\":\"Why is predicting employee attrition important in this project?\",\"answer\":\"The project explains that attrition consumes time and resources due to hiring needs. Predicting unhappy employees helps organizations take actions to avert turnover.\"},{\"question\":\"Which machine-learning models are studied for attrition prediction?\",\"answer\":\"The study evaluates decision tree, random forest, SVM, and artificial neural network.\"},{\"question\":\"How are the experiments structured for model evaluation?\",\"answer\":\"Two experiments are used: one predicts attrition for the whole organization, and the other predicts attrition department-wise.\"}]","Leveraging Machine Learning to Predict Employee Attrition - MSc Research Project | PDF",73]