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It further outlines dataset characteristics and proceeds into exploratory data analysis, including EDA aims and preliminary visual outputs.",{"@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/dynamic-pricing-using-machine-learning-for-emerging-ride-on-demand-service-configuration-manual/291132/",{"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/dynamic-pricing-using-machine-learning-for-emerging-ride-on-demand-service-configuration-manual/291132.png","ImageObject",300,407,{"name":92,"@type":93},"Theodora","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What hardware and OS setup is used for the research implementation?","Question",{"text":113,"@type":114},"The project is implemented on a personal laptop with a 13th Gen Intel i5-13500H CPU, 16 GB RAM, and a 64-bit x64-based operating system.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which software environment is configured before running the work?",{"text":118,"@type":114},"Windows 11 Pro (64-bit) is used with Visual Studio Code configured with the Jupyter Notebook extension, and Python 3.12.5 as the programming language.",{"name":120,"@type":111,"acceptedAnswer":121},"How is the dataset prepared for modeling?",{"text":122,"@type":114},"The dataset is downloaded from Kaggle (Uber fares dataset), extracted into a preferred folder, and loaded into a Pandas DataFrame; the main features include fare_amount, pickup/dropoff times and locations, and passenger_count.","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},291132,1789643530,{"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 Data Analytics (MSCDAD_JAN24A_O)  \nMuhammad Abdur Rabb  \nStudent ID: x23237511  \nSchool of Computing National College of Ireland  \nSupervisor: Dr. David Hamill  \nStudent Name:  \nNational College of Ireland MSc Project Submission Sheet School of Computing  \n  Muhammad Abdur Rabb    \n……………  \nStudent ID:   x23237511    \nProgramme:   M.Sc. Data Analytics   Year:   2024…..  \nModule:   Research Project   .    \nLecturer:   Dr. David Hamill   Submission  \nDue Date:   12/12/2024    \nProject   Dynamic Pricing using Machine Learning for Emerging  \nTitle: Ride-on-demand Service    \nWord  \nCount: …………1533……………………… Page Count: …………………21……………….…….………  \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:   Muhammad Abdur Rabb    \nDate:   11/12/2024    \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) | □ |\n| --- | --- |\n| 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  \nMuhammad Abdur Rabb  \nx23237511  \n1 Introduction  \nThis document provides comprehensive instructions about both hardware and software settingsand will explain the practical implementation of the research by describing dataset preparation, pre-processing, model building, and evaluation.  \n2 Hardware and Software Requirements  \n2.1 Hardware Configuration  \nThis research work was done on a personal laptop and system configuration settings are shown in Figure 1. The hardware configuration is as follows:  \n• Processor: 13th Gen Intel(R) Core(TM) i5-13500H CPU @ 2.60 GHz  \n• Installed RAM: 16 GB (15.6 GB usable)  \n• System Type: 64-bit operating system, x64-based processor  \nFigure 1: System Configuration  \n2.2 Software Configuration  \nThis section describes all the environments that were configured and used for the implementation, which should have been ready in advance. Following software or applications have been setup and need to be installed onto the system before going ahead with the process:  \n• Operating System: Windows 11 Pro (64-bit)  \n• Development Environment: Visual Studio Code (VSCode) configured with the Jupyter Notebook extension for interactive coding and analysis.  \n• Programming Language: Python (latest version at the time of development, Python  \n3.12.5) .  \nIt should be underlined at this point that this setting is not mandatory in terms of software. The project may also be executed for other environments, like Google Colab, Anaconda, or similar, including macOS-based ones. In such a case, however, users would be expected to have the environment properly prepared to work with the toolset and workflows concerned.  \n3 Packages & Libraries  \nData analysis and any kind of machine learning require the importing of certain packages and libraries. Figure 2 shows the compilation of libraries taken in use for this project. These should be installed before the actual running of code so that the availability of ","cbCaicmtcnKNGKIJ","https://ap.wps.com/l/cbCaicmtcnKNGKIJ","pdf",1885503,23,"English","# 1 Introduction\n# 2 Hardware and Software Requirements\n## 2.1 Hardware Configuration\n## 2.2 Software Configuration\n# 3 Packages & Libraries\n# 4 Dataset\n# 5 Exploratory Data Analysis\n## 5.1 Basic EDA","[{\"question\":\"What hardware and OS setup is used for the research implementation?\",\"answer\":\"The project is implemented on a personal laptop with a 13th Gen Intel i5-13500H CPU, 16 GB RAM, and a 64-bit x64-based operating system.\"},{\"question\":\"Which software environment is configured before running the work?\",\"answer\":\"Windows 11 Pro (64-bit) is used with Visual Studio Code configured with the Jupyter Notebook extension, and Python 3.12.5 as the programming language.\"},{\"question\":\"How is the dataset prepared for modeling?\",\"answer\":\"The dataset is downloaded from Kaggle (Uber fares dataset), extracted into a preferred folder, and loaded into a Pandas DataFrame; the main features include fare_amount, pickup/dropoff times and locations, and passenger_count.\"}]","Dynamic Pricing using Machine Learning for Emerging Ride-on-demand Service - Configuration Manual | PDF",58]