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The document records student identity, programme year, internship/practicum part, lecturer and submission due date, and the project title focused on URL phishing detection using machine learning. It also outlines a step-by-step workflow covering dataset loading, library imports, data analysis, categorical handling, feature dropping, correlation and averaging, removing highly correlated features, optional recursive feature elimination, model training and evaluation, and result visualizations such as decision tree, confusion matrix, feature importance, and ROC curve.",{"@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/msc-project-submission-sheet-url-phishing-detection-using-machine-learning/290531/",{"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/msc-project-submission-sheet-url-phishing-detection-using-machine-learning/290531.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the project title stated in the submission sheet?","Question",{"text":113,"@type":114},"The project title is \"URL phishing detection Using machine learning\".","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What key workflow steps are included for the machine learning pipeline?",{"text":118,"@type":114},"The workflow covers adding the dataset, importing required libraries, loading the dataset, analyzing column values, identifying categorical columns, dropping irrelevant columns, correlation calculation, removing highly correlated features, optional recursive feature elimination, training and evaluation, and multiple visualizations.",{"name":120,"@type":111,"acceptedAnswer":121},"Which evaluation and visualization outputs are specified in the process?",{"text":122,"@type":114},"It includes decision tree visualization, confusion matrix plotting, feature importance plotting, ROC curve plotting, and implementation of models such as K-Neighbors Classifier, Naive Bayes, and Decision Tree Classifier.","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},290531,1789641947,{"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},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 MSc Cybersecurity  \nSconal Godfrey Gonsalves Student ID: X23235551  \nSchool of Computing  \nNational College of Ireland  \nSupervisor: Arghir Nicolae Moldovan  \nNational College of Ireland  \nMSc Project Submission Sheet  \nSchool of Computing  \nSconal Godfrey Gonsalves  \nStudent    \nName:  \nX23235551  \nStudent ID: ……………………………………………………………………………………………………………..……  \nMSc Cybersecurity 2024  \nProgramme: ……………………………………………………………… Year: …………………………..  \nInternship/practicum-part 2  \nModule:  \nLecturer: Submission Due Date:  \nProject Title:  \nWord Count:  \n\n| Arghir Nicolae Moldovan |\n| --- |\n| 12th December 2024 |\n| URL phishing detection Using machine learning |\n\n334 11 ……………………………………… 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.  \nSconal Godfrey Gonsalves  \nSignature: ………………………………………………………………………………………………………………  \n12th December 2024  \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  \nSconal Gonsalves  \nx23235551  \n1 Step 1-Adding the Dataset  \nClick on the folder icon which there on the left-hand side of your working environment so that the dataset could be added.  \n2 Step 2-importing all the required libraries  \nTo ensure that all the python libraries are been imported so that the tasks are been carried out.  \n3 Step 3-Loading the dataset  \nLoading the Whole Dataset  \n4 Step 4-Analysing the Column Data  \nAnalysing all the unique values present in the column  \n5 Step 5-To identify all the categorical columns  \nTo find out and list all the columns that are categorical  \n6 Step 6-Dropping the columns and enumerating it  \nDropping the irrelevant columns and mapping of unique values  \n7 Step 7-Correlation calculation  \nCalculating the correlation  \n8 Step-8 Calculation of Average Calculation  \nAverage Calculation of the correlation matrix  \n9 Step 9-Removing all the Highly correlated features  \nRemoval of all the features which are highly correlated  \n10 Recursive Feature Elimination (Optional Step)  \nOther Feature Selection method which was applied  \n11 Step 10-Training and Evaluation  \nTraining and Evaluation of ML models  \n12 Step 11-Decision Tree Visualization  \nVizualizing the decision tree  \n13 Step 12-Confusion matrix plotting  \nPlotting of confusion matrix  \n14 Step 13-Feature Importance Plotting  \nPlotting which features are important  \n15 Step 14-ROC Curve Plotting  \nPlotting of ROC curve  \n16 Step 15-Model Implementation  \nImplementation of all the models  \nK-Neighbors Classifier  \nNaive Bayes  \nDecision Tree Classifier","cbCailDlxYEhuKjZ","https://ap.wps.com/l/cbCailDlxYEhuKjZ","pdf",1099571,13,"English","# MSc Project Submission Sheet\n## Certification and submission instructions\n## Project workflow steps","[{\"question\":\"What is the project title stated in the submission sheet?\",\"answer\":\"The project title is \\\"URL phishing detection Using machine learning\\\".\"},{\"question\":\"What key workflow steps are included for the machine learning pipeline?\",\"answer\":\"The workflow covers adding the dataset, importing required libraries, loading the dataset, analyzing column values, identifying categorical columns, dropping irrelevant columns, correlation calculation, removing highly correlated features, optional recursive feature elimination, training and evaluation, and multiple visualizations.\"},{\"question\":\"Which evaluation and visualization outputs are specified in the process?\",\"answer\":\"It includes decision tree visualization, confusion matrix plotting, feature importance plotting, ROC curve plotting, and implementation of models such as K-Neighbors Classifier, Naive Bayes, and Decision Tree Classifier.\"}]","MSc Project Submission Sheet - URL phishing detection Using machine learning | PDF",33]