[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124839-en":3,"doc-seo-124839-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124839,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","CRIME DATA PREDICTION BASED ON GEOGRAPHICAL LOCATION USING MACHINE LEARNING - Master of Science Project","This project uses machine learning models to monitor crime activity by geographical location and identify high-risk areas. Four algorithms—K Nearest Neighbors, Random Forest, Logistic Regression, and Decision Tree—are implemented and tuned to improve precision in predicting crime levels. The approach combines complementary strengths: KNN leverages coordinate-based proximity, Logistic Regression models the relationship between factors such as location and time and incident likelihood, and tree-based methods support structured decision paths. Performance is evaluated with accuracy, precision, and recall using a dataset containing crime occurrences, geographic coordinates, and time information.","California State University, San Bernardino  \nCSUSB ScholarWorks  \n\n| Electronic Theses, Projects, and Dissertations | Office of Graduate Studies |\n| --- | --- |\n| 8-2024\u003Cbr>CRIME DATA PREDICTION BASED ON GEOGRAPHICAL LOCATION USING MACHINE LEARNING\u003Cbr>Sai Bharath Yarlagadda\u003Cbr>Follow this and additional works at: [https://scholarworks.lib.csusb.edu/etd](https://scholarworks.lib.csusb.edu/etd)\u003Cbr> Part of the Computer and Systems Architecture Commons, Data Storage Systems Commons, and the Other Computer Engineering Commons |  |\n\nRecommended Citation  \nYarlagadda, Sai Bharath, \"CRIME DATA PREDICTION BASED ON GEOGRAPHICAL LOCATION USING MACHINE LEARNING\" (2024) . Electronic Theses, Projects, and Dissertations. 2016.  \n[https://scholarworks.lib.csusb.edu/etd/2016](https://scholarworks.lib.csusb.edu/etd/2016)  \nThis Project is brought to you for free and open access by the Office of Graduate Studies at CSUSB ScholarWorks. It has been accepted for inclusion in Electronic Theses, Projects, and Dissertations by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [scholarworks@csusb.edu](scholarworks@csusb.edu).  \nCRIME DATA PREDICTION BASED ON  \nGEOGRAPHICAL LOCATION USING MACHINE LEARNING  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nIn Partial Fulfillment of the Requirements for the Degree Master of Science in Computer Science  \nby Sai Bharath Yarlagadda  \nAugust 2024  \nCRIME DATA PREDICTION BASED ON  \nGEOGRAPHICAL LOCATION USING MACHINE LEARNING  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nby  \nSai Bharath Yarlagadda  \nAugust 2024  \nApproved by:  \nDr. Yan Zhang, Advisor, Computer Science and Engineering Dr. Jennifer Jin, Committee Member Dr. Qingquan Sun, Committee Member  \n© 2024 Y Sai Bharath  \nABSTRACT  \nThis project employs machine learning methods like K Nearest Neighbors (KNN) , Random Forest, Logistic Regression, and Decision Tree algorithms to monitor crime data based on location and pinpoint areas with risks. The project implements and tunes the four models to improve the precision of predicting crime levels. These models collaborate to offer a trustworthy evaluation of crime patterns. K Nearest Neighbors (KNN) categorizes locations by examining the proximity of data points considering coordinates and other factors to identify trends linked to increased crime data. Logistic Regression gauges the likelihood of crime incidents by studying the connection, between factors (like location and time ) and the crime activity, assisting in forecasting crimes in various regions. Decision Tree Classifier uses a tree structure to make decisions based on feature values dividing the data into branches representing decision paths. This approach is particularly useful for identifying high-risk areas using crime data. Random Forest Classifier constructs decision trees and combines their results for classification purposes, resulting in enhanced prediction accuracy and robustness by merging outcomes from multiple trees, thus reducing the risks of overfitting and improving generalization to unseen data.  \nThe system’s efficiency is assessed using a crime dataset that includes information, about crime occurrences, geographical locations , and time-related data. Metrics, like accuracy, precision , and recall are employed to assess the model’s ability to anticipate crimes and identify hotspots accurately.  \nACKNOWLEDGEMENTS  \nI sincerely extend my thanks to my project committee, Dr. Yan Zhang (Advisor), Dr. Jennifer Jin (Committee Member), and Dr. Qingquan Sun (Committee Member) . I want to thank my friends and family for their continuous support.  \nTABLE OF CONTENTS  \nABSTRACT........................................................................................................... iii  \nACKNOWLEDGEMENTS ....................................................................................... iv  \nLIST OF TABLES...............","cbCaiuj51h95hfnR","https://ap.wps.com/l/cbCaiuj51h95hfnR","pdf",1023887,1,60,"English","en",105,"# Abstract\n# Acknowledgements\n# Table of Contents\n## Chapter One: Introduction\n## Background\n## Motivation\n## Problem Statement\n## Challenges\n## Proposed System\n## Objectives of the Paper\n## Chapter Two: Literature Review\n## Chapter Three: Data Collection and Preprocessing\n## Data Collection\n## Preprocessing\n## Chapter Four: Methodologies\n## K-Nearest Neighbors\n## Logistic Regression\n## Decision Tree Classifier\n## Random Forest Classifier\n## Chapter Five: Experimental Result\n## Evaluation Metrics\n## Accuracy\n## Precision\n## Recall\n## F1 Score\n## Model Evaluation\n## Model Comparison\n## Chapter Six: System Design\n## Component Diagram\n## Class Diagram\n## Privacy Concern\n## Chapter Seven: Conclusion\n## Future Work","[{\"question\":\"Which machine learning algorithms are used for predicting crime levels based on location?\",\"answer\":\"The project implements K Nearest Neighbors (KNN), Random Forest, Logistic Regression, and Decision Tree to model and predict crime levels by geographical location.\"},{\"question\":\"How does KNN help identify patterns related to increased crime?\",\"answer\":\"KNN categorizes locations by examining the proximity of data points using coordinates and related factors, helping reveal trends associated with higher crime.\"},{\"question\":\"What metrics are used to evaluate the model’s prediction performance?\",\"answer\":\"Accuracy, precision, and recall are used to assess how well the models anticipate crimes and identify hotspots, with additional reporting including F1 score.\"}]","CRIME DATA PREDICTION BASED ON GEOGRAPHICAL LOCATION USING MACHINE LEARNING - Master of Science Project | PDF",1785894922,151,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"crime-data-prediction-based-on-geographical-location-using-machine-learning-master-of-science-project","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/crime-data-prediction-based-on-geographical-location-using-machine-learning-master-of-science-project/124839/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning algorithms are used for predicting crime levels based on location?","Question",{"text":75,"@type":76},"The project implements K Nearest Neighbors (KNN), Random Forest, Logistic Regression, and Decision Tree to model and predict crime levels by geographical location.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does KNN help identify patterns related to increased crime?",{"text":80,"@type":76},"KNN categorizes locations by examining the proximity of data points using coordinates and related factors, helping reveal trends associated with higher crime.",{"name":82,"@type":73,"acceptedAnswer":83},"What metrics are used to evaluate the model’s prediction performance?",{"text":84,"@type":76},"Accuracy, precision, and recall are used to assess how well the models anticipate crimes and identify hotspots, with additional reporting including F1 score.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":21,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]