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It records the project environment and execution prerequisites, including Windows 11 Pro hardware specifications and the required tools such as Jupyter Notebook and Python 3.11.8 with supporting libraries. It details dataset origin from the AI4I 2020 predictive maintenance repository, subsequent data transformation, and the model-building workflow for Decision Tree, Random Forest, and XGBoost, including grid-search cross-validation hyperparameter tuning and feature transformation guidance.",{"@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/predictive-maintenance-of-equipment-using-machine-learning-algorithms-configuration-manual/290534/",{"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/predictive-maintenance-of-equipment-using-machine-learning-algorithms-configuration-manual/290534.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","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 system requirements are specified for running the project?","Question",{"text":113,"@type":114},"The manual lists hardware and software prerequisites, including Windows 11 Pro and tools such as Jupyter Notebook and Python 3.11.8 with Word and Anaconda.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Where does the equipment dataset come from?",{"text":118,"@type":114},"The dataset is obtained from the learning repository named AI41 2020 Predictive Maintenance dataset, linked to the UCI archive dataset page for AI4I 2020 predictive maintenance.",{"name":120,"@type":111,"acceptedAnswer":121},"Which machine learning models are implemented and tuned?",{"text":122,"@type":114},"The project builds Decision Tree, Random Forest, and XGBoost models, and performs hyperparameter tuning using grid search with cross-validation for these model types.","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},290534,1789641955,{"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":34,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"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","Configuration Manual  \nMSc Research Project Data Analytics  \nSakshi Sanjay Kale Student ID: x22219340  \nSchool of Computing National College of Ireland  \nSupervisor: Naushad Alam  \nNational College of Ireland MSc Project Submission Sheet School of Computing  \nStudent  \nName: Sakshi Sanjay Kale  \nStudent ID: …x22219340…………………………………………………………………………………………………  \nProgramme: Data Analytics Year: ……2023-2024  \nModule:   MSc Research Project  \nLecturer:   Naushad Alam  \nSubmission  \nDue Date:   12/08/2024  \nProject Predictive Maintenance of Equipment using Machine Learning Algorithms  \nTitle:  \nWord Count:……………………………………… Page Count: ………………………………….…….………  \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:    \nDate:    \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\nConfiguration Manual  \nSakshi Kale  \n[X22219340@student.ncirl.ie](X22219340@student.ncirl.ie)  \nData Analytics  \nNational College of Ireland  \n1 Overview  \nThis is the configuration manual for the “ Predictive Maintenance of Equipment” work. The environment, pre-requests, and code execution will all be covered in detail in this configuration manual.  \n2 Hardware / Software Requirements  \n2.1 Hardware Requirements  \nThe hardware configuration of the system on which this research project is built and executed is as follows:  \nOperating System Name: Microsoft Windows 11 Pro  \nOperating System Version: 10.0.22631 N/A Build 22631  \nCaption: Intel64 Family 6 Model 140 Stepping 1  \nDeviceID : CPU0  \nMaxClockSpeed : 2419  \nName: 11th Gen Intel(R) Core(TM) i5-1135G7 @ 2.40GHz  \nTotal Physical Memory: 16,210 MB  \n2.2 Software Requirements  \nSoftware required for build and execution:  \n• Development Environment: Jupyter Notebook  \n• Scripting Language: Python 3.11.8  \n• Other Tool: Word, Anaconda.  \n2 Data Selection  \nThe dataset about equipment is obtained from the learning repository which is named as AI41 2020 Predictive Maintenance dataset. It is obtained from the following link:  \n[https://archive.ics.uci.edu/dataset/601/ai4i](https://archive.ics.uci.edu/dataset/601/ai4i)+2020+predictive+maintenance+dataset\\  \nFigure 1: Dataset Website  \n3 Data transformation and Model Building  \n3.1 Dataset  \nThe dataset below contains the following attributes like Air temperature, Process temperature, Rotational Speed, Torque, Tool Wear, Machine failure, TWF  \nFigure 2: Dataset  \nPackage installations and library importing  \nThe required libraries are installed such as pandas as pd, ydata_profiling as pdpf, numpy as np, matplotlib.pyplot as plt, seaborn as sns  \nFigure 3 : Package Installation  \nThe below figure is for libraries for Decision Tree Models such as GridSearchCV, DecisionTreeClassifier, classification_report, etc.  \nFigure 4: Package Installation for Decision Tree Model  \nThe below figure is for libraries for Random","cbCaimMn0rtlLzAB","https://ap.wps.com/l/cbCaimMn0rtlLzAB","pdf",901356,"English","# 1 Overview\n## 2 Hardware / Software Requirements\n### 2.1 Hardware Requirements\n### 2.2 Software Requirements\n# 2 Data Selection\n# 3 Data transformation and Model Building\n## 3.1 Dataset\n# 4 Model Implementation\n## 4.1 Decision Tree Model\n## 4.2 Random Forest Model\n## 4.3 XGBoost Model\n## 4.3.1 Hyperparameter Tuning Using Grid Search\n## 4.4 Feature Transformation","[{\"question\":\"What system requirements are specified for running the project?\",\"answer\":\"The manual lists hardware and software prerequisites, including Windows 11 Pro and tools such as Jupyter Notebook and Python 3.11.8 with Word and Anaconda.\"},{\"question\":\"Where does the equipment dataset come from?\",\"answer\":\"The dataset is obtained from the learning repository named AI41 2020 Predictive Maintenance dataset, linked to the UCI archive dataset page for AI4I 2020 predictive maintenance.\"},{\"question\":\"Which machine learning models are implemented and tuned?\",\"answer\":\"The project builds Decision Tree, Random Forest, and XGBoost models, and performs hyperparameter tuning using grid search with cross-validation for these model types.\"}]","Predictive Maintenance of Equipment using Machine Learning Algorithms - Configuration Manual | PDF",18]