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Specifies system configuration requirements (OS, hardware, Python/Jupyter setup) and outlines dataset description for restaurant operations. Provides data preprocessing steps, feature engineering example code, scaling approach, and exploratory data analysis examples covering summary statistics and visualizations for sales trends and customer segmentation.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/msc-research-project-msc-data-analysis-configuration-manual-project-title-utilizing-machine-learning-for-predictive-analysis-and-optimizing-restautant-operations/291235/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/msc-research-project-msc-data-analysis-configuration-manual-project-title-utilizing-machine-learning-for-predictive-analysis-and-optimizing-restautant-operations/291235.png","ImageObject",300,407,{"name":42,"@type":43},"Ophelia","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",11,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What project this submission sheet is for?","Question",{"text":63,"@type":64},"The project title is “Utilizing machine learning for predictive analysis and optimizing restautant operations.” The submission is for an MSc Research Project under MSc Data Analysis.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which system and software requirements are specified?",{"text":68,"@type":64},"Recommended OS includes Ubuntu 20.04 LTS or Windows 10/11, and supported macOS 10.15+ is listed. The document requires Python 3.8+ with Jupyter Notebook (Anaconda or pip), and mentions Git for version control.",{"name":70,"@type":61,"acceptedAnswer":71},"What dataset types are described for the restaurant operations study?",{"text":72,"@type":64},"The dataset covers customer demographics, sales, inventory, and employee performance data, including example fields such as sales amounts and order IDs, customer age and spending patterns, inventory stock levels, and employee performance scores.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},291235,1789643826,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":102,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":80,"read_time":144,"preview_page_count":4},7971461741311,"https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826","Configuration Manual  \nMSc Research Project MSc Data Analysis  \nNishika Gala  \nStudent ID: XXX  \nSchool of Computing National College of Ireland  \nSupervisor: XXX  \nNational College of Ireland  \nMSc Project Submission Sheet  \nSchool of Computing  \nStudent Nishika Gala……………………………………………………………………  \nName:  \nStudent ID: X22214062…………………………………………………………………………………..……  \nProgramme: MSc Data Analysis………………………………… Year: 2023-24  \nModule: ………………………………………………………………………………………………………….………  \nLecturer: 12th August 2024…………………………………………………………………….………  \nSubmission  \nDue Date: ………………………………………………………………………………………………………….………  \nProject Title: Utilizing machine learning for predictive analysis and optimizing restautant operations   .    \nWord Count:   895   Page Count: 4   .   .    \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: Nishika Gala    \nDate: 12th August 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| 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 isnot 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  \nForename Surname  \nStudent ID:  \n1. System Configuration  \n1.1 Operating System  \n-Recommended: Ubuntu 20.04 LTS or Windows 10/11  \n-Supported: macOS 10.15 or later, Windows 7/8  \n1.2 Hardware Requirements  \n-Processor: Intel Core i5 or AMD Ryzen 5 equivalent or higher  \n-Memory: Minimum 8 GB RAM (16 GB or higher recommended)  \n-Storage: At least 100 GB free space on SSD  \n1.3 Software Configuration  \n-Python: Version 3.8 or higher  \n-Jupyter Notebook: Installed via Anaconda or pip  \n-IDE: (Optional) Visual Studio Code, PyCharm  \n2. System Requirements  \n2.1 Required Software  \n-Python: Python 3.8 or higher (Anaconda distribution recommended)  \n-Jupyter Notebook: Available via the Anaconda distribution or install separately using pip  \n-Git: Version control system for managing your project  \n2.2 Python Environment Setup  \nTo set up the Python environment, follow these steps:  \n1. Install Anaconda: Download and install the [Anaconda distribution]([https://www.anaconda.com/products/individual](https://www.anaconda.com/products/individual)).  \n2. Create a Virtual Environment:  \n3. Install Required Libraries: Once inside the environment, install the required libraries.  \n3. Python Libraries  \nEnsure the following Python libraries are installed in your environment:```  \n-pandas: For data manipulation and analysis  \n-numpy: For numerical computations  \n-matplotlib & seaborn: For data visualization  \n-scikit-learn: For machine learning algorithms  \n-tensorflow & keras: For deep learning models  \n-xgboost: For gradient boosting algorithms  \n-jupyterlab: For working with Jupyter Notebooks  \n4. Dataset  \n4.1 Dataset Description  \n-The dataset used for this thesis focuses on restaurant operations, including customer demographics, sales, inventory, and employee performance data.  \n-Example datasets:  \n-Sales Data: Date, Time, Order ID, Items Sold, Sales Amount,","cbCaifUHw7Ggw600","https://ap.wps.com/l/cbCaifUHw7Ggw600","pdf",196292,"English","# Project Submission Sheet\n# Configuration Manual\n## System Configuration\n## Python Environment Setup\n## Dataset and Loading\n## Data Preprocessing and Feature Engineering\n## Data Analysis and EDA Examples","[{\"question\":\"What project this submission sheet is for?\",\"answer\":\"The project title is “Utilizing machine learning for predictive analysis and optimizing restautant operations.” The submission is for an MSc Research Project under MSc Data Analysis.\"},{\"question\":\"Which system and software requirements are specified?\",\"answer\":\"Recommended OS includes Ubuntu 20.04 LTS or Windows 10/11, and supported macOS 10.15+ is listed. The document requires Python 3.8+ with Jupyter Notebook (Anaconda or pip), and mentions Git for version control.\"},{\"question\":\"What dataset types are described for the restaurant operations study?\",\"answer\":\"The dataset covers customer demographics, sales, inventory, and employee performance data, including example fields such as sales amounts and order IDs, customer age and spending patterns, inventory stock levels, and employee performance scores.\"}]","MSc Research Project - MSc Data Analysis - Configuration Manual - Project Title - Utilizing machine learning for predictive analysis and optimizing restautant operations | PDF",15]