[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290851-105":59,"doc-detail-290851-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","crime-category-classification-in-san-francisco-using-machine-learning-techniques-abstract","Crime Category Classification in San Francisco using Machine Learning Techniques - Abstract","","This research addresses the challenge of accurately categorizing crime incidents based on textual descriptions, aiming to enhance crime analysis and law enforcement strategies. The study investigates the effectiveness of multiple machine learning models, including LSTM, GRU, Logistic Regression, SVM, and Random Forest. Evaluation shows Logistic Regression, SVM, and Random Forest achieving about 99.91% to 99.97% average accuracy across categories. The work demonstrates the value of simpler algorithms, supports reliability through results aligned with state-of-the-art, and highlights future study needs for distinguishing complex crime categories.",{"@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/crime-category-classification-in-san-francisco-using-machine-learning-techniques-abstract/290851/",{"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/crime-category-classification-in-san-francisco-using-machine-learning-techniques-abstract/290851.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","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 problem does the research focus on?","Question",{"text":113,"@type":114},"The research focuses on accurately categorizing crime incidents using textual descriptions to support crime analysis and law enforcement decision-making.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which machine learning models are evaluated in the study?",{"text":118,"@type":114},"The study evaluates LSTM, GRU, Logistic Regression, SVM, and Random Forest models for crime categorization.",{"name":120,"@type":111,"acceptedAnswer":121},"What accuracy do the best-performing models achieve?",{"text":122,"@type":114},"Logistic Regression, SVM, and Random Forest achieve an average accuracy of roughly 99.91% to 99.97% across categories.","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},290851,1789642804,{"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},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Crime Category Classification in San Francisco using Machine Learning  \nTechniques  \nMSc Research Project MSc Data Analytics  \nJerin Eldho  \nStudent ID: x21196737  \nSchool of Computing National College of Ireland  \nSupervisor: Dr. Catherine Mulwa  \nNational College of Ireland Project Submission Sheet School of Computing  \n| Student Name: | Jerin Eldho |\n| --- | --- |\n| Student ID: | x21196737 |\n| Programme: | MSc Data Analytics |\n| Year: | 2023 |\n| Module: | MSc Research Project |\n| Supervisor: | Dr. Catherine Mulwa |\n| Submission Due Date: | 18/09/2023 |\n| Project Title: | Crime Category Classification in San Francisco using Machine Learning Techniques |\n| Word Count: | 9,544 |\n| Page Count: | 25 |\n\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.  \n\n| Signature: | Jerin Eldho |\n| --- | --- |\n| Date: | 18th September 2023 |\n\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 foryour 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\nCrime Category Classification in San Francisco using Machine Learning Techniques  \nJerin Eldho  \nx21196737  \nAbstract  \nThis research addresses the challenge of accurately categorizing crime incidents based on textual descriptions, aiming to enhance crime analysis and law enforcement strategies. The study investigates the effectiveness of various machine learning models, including LSTM, GRU, Logistic Regression, SVM, and Random Forest. Through comprehensive evaluation, the Logistic Regression, SVM, and Random Forest models exhibited remarkable accuracy, achieving an average of around 99 .91% to 99 .97% across categories. This work contributes by showcasing the potential of simpler algorithms in crime categorization tasks, highlighting their reliability and effectiveness. The results are consistent with the state of theart, stressing the importance of customized algorithm selection and feature representation. In reality, these models provide precise crime classification information for better public safety and law enforcement decisions. However, issues in differentiating complicated criminal categories remain, suggesting future study possibilities.  \n1 Introduction  \nThe importance of utilizing machine learning algorithms to categorize crimes based on textual descriptions was addressed in this research. The study’s goal was to assess how well these algorithms categorize crimes with accuracy, which would advance the fields of crime analysis and law enforcement. It is essential for law enforcement organizations to comprehend and classify crimes based on their textual descriptions. But traditional review of a high volume of crime reports might take a while and be subject to human error. The project’s objective was to categorize the crimes according to the description of the crimes using various machine learning algorithms and methods for natural language processing that could swiftly and reliably detect","cbCaiiVyVmlD6bJ9","https://ap.wps.com/l/cbCaiiVyVmlD6bJ9","pdf",1598602,27,"English","# Abstract\n# 1 Introduction\n## 1.1 Motivation and background","[{\"question\":\"What problem does the research focus on?\",\"answer\":\"The research focuses on accurately categorizing crime incidents using textual descriptions to support crime analysis and law enforcement decision-making.\"},{\"question\":\"Which machine learning models are evaluated in the study?\",\"answer\":\"The study evaluates LSTM, GRU, Logistic Regression, SVM, and Random Forest models for crime categorization.\"},{\"question\":\"What accuracy do the best-performing models achieve?\",\"answer\":\"Logistic Regression, SVM, and Random Forest achieve an average accuracy of roughly 99.91% to 99.97% across categories.\"}]","Crime Category Classification in San Francisco using Machine Learning Techniques - Abstract | PDF",68]