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It covers data preprocessing steps such as missing value handling, categorical transformation, feature scaling, outlier removal, balancing, and feature selection, plus model development, testing, and evaluation. Reported results indicate a stacking ensemble achieving 99.59% accuracy with 100% precision, 98.30% recall, and 99.14% F1 score, and explains ensemble value for imbalanced HR data and noisy inputs.",{"@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/configuration-manual-enhancing-efficiency-of-employee-attrition-prediction-using-machine-learning-and-ensemble-techniques/291136/",{"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/configuration-manual-enhancing-efficiency-of-employee-attrition-prediction-using-machine-learning-and-ensemble-techniques/291136.png","ImageObject",300,407,{"name":92,"@type":93},"Theodora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","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},"What is the purpose of the configuration manual in this project?","Question",{"text":113,"@type":114},"It describes the hardware, software, and methodologies used to develop and evaluate the machine learning models for employee attrition prediction.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which preprocessing steps are included before model development?",{"text":118,"@type":114},"The manual covers missing value handling, categorical transformation, feature scaling, outlier removal, data balancing, and feature selection.",{"name":120,"@type":111,"acceptedAnswer":121},"What performance metrics does the stacking ensemble achieve?",{"text":122,"@type":114},"The stacking model reports 99.59% accuracy, 100% precision, 98.30% recall, and 99.14% F1 score.","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},291136,1789643540,{"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},687197207919,"https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552","Configuration Manual  \nMSc Research Project Master of Science in Data Analytics  \nLinu Anil Student ID: X23183852  \nSchool of Computing National College of Ireland  \nSupervisor: Anh Duong Trinh  \nNational College of Ireland MSc Project Submission Sheet  \nSchool of Computing  \nStudent Name:  \nStudent ID:  \nLinu Anil  \nX23183853  \nMaster of Science in Data Analytics Programme: ………………………………………………………………  \nYear: 2024-2025  \n.  \nModule: Research Project  \nSupervisor: Anh Doung Trinh  \nSubmission  \nDue Date: 29/12/1014  \nProject Title:  \nWord Count:   1339   Page Count   20   .  \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: Linu Anil  \nDate:  \n28/12/2024  \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) | □ |\n| --- | --- |\n\n\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| --- | --- |\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\nConfiguration Manual Enhancing Efficiency of Employee Attrition Prediction Using Machine Learning and Ensemble  \nTechniques  \nLinu Anil  \nX23183853  \n1. Introduction  \nIn the subsequent parts of this configuration manual the hardware, the software, and the methodologies applied to carry out the research project will be described. Framework provides description of the tools and environment that are required for the development of the machine learning models used in this experimentation as well as training and testing of these models. They encompasses data preprocessing involving missing value handling, categorical variable transformation, feature scaling, outlier removal, data balancing and feature selection, model development and testing and model evaluation . In the manual there are also included few lines of python code used during development and analyses of the research project. The evaluation section outlines the of the results of the findings from the work on: “Enhancing Efficiency of Employee Attrition Prediction Using Machine Learning and Ensemble Techniques”. The result of the experimented model shows that stacking model perform with highest accuracy of 99.59%, 100% precision, 98.30% recall and F1 score of 99.14% . The study also highlights how the case of the ensemble learning is useful when handling multiple dimensions of HR data, including handling issues of class imbalanced data and noisy input data. While the scope of generalization is limited by the work’s data set, the results show that machine learning can indeed improve workforce analysis .  \n2. System Configuration  \nThe hardware information and software information used in this research project are as follows:  \n2.1. Hardware Configuration  \n• Operating system: macOS Sequoia 15.1.1  \n• Processor: Apple M1 Chip  \n• System Type: 64-bit operating system, x64-based processor  \n• Hard Disk: 256GB SSD  \n• Installed physical memory RAM: 8GB  \n2.2. Software Configurations:  \nTable 1. Software tools specifications  \n\n| Software/Tools | Version | Details |\n| --- | --- | --- |\n| Anaconda","cbCaitUgbNag60xP","https://ap.wps.com/l/cbCaitUgbNag60xP","pdf",5242675,23,"English","# 1. Introduction\n# 2. System Configuration\n## 2.1. Hardware Configuration\n## 2.2. Software Configurations\n# 3. Download and Implementation","[{\"question\":\"What is the purpose of the configuration manual in this project?\",\"answer\":\"It describes the hardware, software, and methodologies used to develop and evaluate the machine learning models for employee attrition prediction.\"},{\"question\":\"Which preprocessing steps are included before model development?\",\"answer\":\"The manual covers missing value handling, categorical transformation, feature scaling, outlier removal, data balancing, and feature selection.\"},{\"question\":\"What performance metrics does the stacking ensemble achieve?\",\"answer\":\"The stacking model reports 99.59% accuracy, 100% precision, 98.30% recall, and 99.14% F1 score.\"}]","Configuration Manual - Enhancing Efficiency of Employee Attrition Prediction Using Machine Learning and Ensemble Techniques | PDF",58]