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The manual specifies system requirements, including an Intel Core i3 CPU and 8.0 GB RAM, and details the software environment with Jupyter Notebook, Python, Tableau, and MS Office. It lists installed libraries such as Pandas, Matplotlib, Seaborn, Warnings, and scikit-learn, plus key ML packages for model training and evaluation. It further outlines implementation steps for data extraction, missing value checks, preprocessing, feature selection via correlation heatmaps, dataset splitting, and initial model-building workflow.",{"@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/earthquake-magnitude-modelling-using-machine-learning-technology-configuration-manual/290699/",{"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/earthquake-magnitude-modelling-using-machine-learning-technology-configuration-manual/290699.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","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",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the goal of the configuration manual in this project?","Question",{"text":113,"@type":114},"It documents the hardware, software, libraries, and code snippets used during implementation and supports replication of the research project on earthquake magnitude modelling.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which hardware and software requirements are listed for the system?",{"text":118,"@type":114},"The manual lists an 8th Gen Intel Core i3-8130U processor with 8.0 GB RAM, and software including Jupyter Notebook (6.4.12), Python (3.9.9), Tableau (2023.3), and MS Office (2018).",{"name":120,"@type":111,"acceptedAnswer":121},"How does the project handle data before training models?",{"text":122,"@type":114},"It extracts the earthquake dataset from NCEDC, checks for missing values, preprocesses data by renaming columns and saving to an .xlsx file, then performs feature selection using a correlation heatmap and splits the data into train and test sets.","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},290699,1789642386,{"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},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Configuration Manual  \nMSc Research Project Data Analytics  \nBenjamin Kelani Student ID: 21226181  \nSchool of Computing National College of Ireland  \nSupervisor: Prof. Cristina Hava Muntean  \nNational College of Ireland   \nMSc Project Submission Sheet   \nSchool of Computing  \nStudent Benjamin Kelani    \nName:  \nStudent ID: 21226181    \nProgramme: Data Analytics   Year: 2023   . .  \nModule: M.Sc Research Project   .    \nLecturer:    \nSubmission  \nDue Date:    \nProject Title: Earthquake Magnitude Modelling using Machine Learning technology Word Count: 792   Page Count: 8   .   .    \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: Benjamin Kelani    \nDate: 14th December 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\u003Cbr>|  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n\nPenalty Applied (if applicable):   \nConfiguration Manual  \nBenjamin Kelani  \n21226181  \n1 Introduction  \nThis configuration manual includes the hardware and software configurations, libraries and important snippets of codes utilized during the implementation process. The aim of the documentation is to facilitate the reciprocation of this research project which is ‘Earthquake Magnitude Modelling Using Machine Learning Technology’.  \n2 System Requirements  \n2.1 Hardware Requirements  \nThe system hardware requirements is shown in the Table 1 below.  \nTable 1: Hardware requirements  \n\n| Processor | 8th Gen intel® Core™ i3-8130U @2.20GHz 2.21 GHz |\n| --- | --- |\n| RAM | 8.0 GB (7 . 87 GB usable) |\n\n2.2 Software Requirements  \nThe python programming language is used in the Jupyter notebook and Tableau is also utilized for data visualizations. Software and versions used are shown in Table 2 below.  \nTable 2: Software and Versions  \n\n| Software Type | Software Name | Version |\n| --- | --- | --- |\n| IDE | Jupyter notebook | 6.4.12 |\n| Programming Language | Python | 3.9.9 |\n| Data Visualization Software | Tableau | 2023.3 |\n| Productivty and task completion | MS Office | 2018 |\n\n2.2.1 Libraries and Packages  \nThe table 3 below shows all the libraries and versions that were installed for research study.  \nTable 3: Libraries and versions  \n\n| Library | Description | Version |\n| --- | --- | --- |\n| Pandas | Data manipulation and analysis | 2.1.4 |\n| Matplotlib | Data visualisation | 2.0 |\n| Seaborns | Data Visualization | 12.0b3 |\n| Warnings | Customizing error display |  |\n\n\n| Scikit-learn | Machine learning tasks | 1.3 |\n| --- | --- | --- |\n\nTable 4 contains the packages used for this research project.  \n\n| Packages | Use |\n| --- | --- |\n| Train_test_split | To split dataset into trainset and test set |\n| LinearRegression | To build a linear regression model |\n| R2_score | To utilizer R-Squared as an evaluation algorithm |\n| Mean_squared_error | To utilize mean squared error as an evaluation algorithm |\n| RandomForestRegressor | To build a random forest model |\n| SVR | To build a s","cbCainzQGdUcfsDP","https://ap.wps.com/l/cbCainzQGdUcfsDP","pdf",2093116,11,"English","# 1 Introduction\n## Configuration Manual purpose\n# 2 System Requirements\n## 2.1 Hardware Requirements\n## 2.2 Software Requirements\n### 2.2.1 Libraries and Packages\n# 3 Research Implementation\n## 3.1 Data Extraction\n## 3.1.2 Missing Value Check\n## 3.2 Data Transformation\n## 3.3 Feature Selection and Data Split\n## 3.4 Model Building","[{\"question\":\"What is the goal of the configuration manual in this project?\",\"answer\":\"It documents the hardware, software, libraries, and code snippets used during implementation and supports replication of the research project on earthquake magnitude modelling.\"},{\"question\":\"Which hardware and software requirements are listed for the system?\",\"answer\":\"The manual lists an 8th Gen Intel Core i3-8130U processor with 8.0 GB RAM, and software including Jupyter Notebook (6.4.12), Python (3.9.9), Tableau (2023.3), and MS Office (2018).\"},{\"question\":\"How does the project handle data before training models?\",\"answer\":\"It extracts the earthquake dataset from NCEDC, checks for missing values, preprocesses data by renaming columns and saving to an .xlsx file, then performs feature selection using a correlation heatmap and splits the data into train and test sets.\"}]","Earthquake Magnitude Modelling using Machine Learning technology - Configuration Manual | PDF",28]