[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119641-en":3,"doc-seo-119641-105":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},119641,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Developing Personal Crime Prediction Model Using Machine Learning Approach","Crime involves intentional or unintentional actions causing physical or mental harm and property loss. Personal crimes target individuals, where harm to victims can be linked to specific offender behavior, covering offenses such as murder, aggravated assault, rape, and robbery. This study develops a personal crime prediction model using machine learning on a Jimma Zone dataset collected from secondary sources, using 6,000 instances and 16 attributes from September 2010 to April 2015 E.C. Models are trained and evaluated with multiple train-test splits and show strong performance, leading to deployment with a random forest approach.","JIMMA UNIVERSITY  \nJIMMA INSTITUTE OF TECHNOLOGY  \nFaculty of Computing and Informatics  \nDepartment of Information Technology (MSc)  \nDeveloping Personal Crime Prediction Model Using Machine Learning  \nApproach  \nBy: -  \nAdugna Teferra  \nA Thesis Report submitted to the School of Graduate Studies of Jimma University Institute of technology in Partial fulfillment of the requirements for the Degree of Masters of Science in Information Technology  \nJuly 2023 Jimma, Ethiopia  \nJIMMA INIVERSITY  \nJIMMAA INSTITUTE OF TECHNOLOGY  \nFaculty of Computing and Informatics  \nDepartment of Information Technology (MSc)  \nDeveloping Personal Crime Prediction Model Using Machine Learning  \nApproach  \nBy:  \nAdugna Teferra  \nPrincipal Advisor: Amanuel Ayde (PhD)  \nCo-Advisor: Hailu Beshada (MSc)  \nA Thesis Proposal submitted to the School of Graduate Studies of Jimma University Institute of technology in Partial fulfillment of the requirements for the Degree of Masters of Science in Information Technology  \nJuly 2023 Jimma, Ethiopia  \nDeclaration  \nI, Adugna Teferra, the undersigned, declare that this thesis entitled: “Developing Personal Crime Prediction Model using Machine Learning Approach” is my original work. I have undertaken the research work independently with the guidance and support of the research advisor. This study has not been submitted for any degree or diploma program in this or any other institution and all sources of materials used for the thesis have been duly acknowledged.  \nDeclared by:  \nName: Adugna Teferra  Signature:   Date:    \nCertificate of Approval  \nThis is to certify that the thesis prepared by Adugna Teferra, entitled “Developing Personal Crime Predictive Model using Machine Learning Approach” and submitted in partial fulfillment of the requirements for the Degree of Masters of Science in Information Technology (IT Stream) complies with the regulations of the University and meets the accepted standards with respect to originality and quality.  \nName of Candidate: Adugna Teferra  Signature:   Date:   \nName of Principal Advisor: Amauel Ayde (PhD), Signature:  Date:  ` Name of Co-Advisor: Hailu Beshada (MSc), Signature:  Date:   \nBoard of Examiners  \nChairperson: Dawud Yimar (MSc)  Signature:   Date:    \nInternal examiner: Tefary Kababa (MSc)  Signature:   Date:   \nExternal examiner: Takilu Urgesa (PhD)  Signature:__    Date:    \nApproval Sheet  \nSubmitted by  \nAdugna Teferra      \nInvestigator Signature Date  \nApproved by  \nAmanuel Ayde (PhD)      \nPI Advisor Signature Date  \nHailu Beshada (MSc)    \nCo-Advisor Signature Date  \nChairman Signature Date  \nDean, Faculty of computing  \nSignature  \nDate  \nDirector, Post graduate Office Signature Date  \nAbstract  \nCrime is an intentional and unintentional act that causes physical harm, mental harm, property damage, or loss. Personal crimes are the type of crime against a person; it is a crime that is directed at an individual and harm or injury can be traced to the victim, including murder, aggravated assault, rape, robbery, and others. This study attempts to develop a personal crime prediction model by using a machine learning approach. The proposed predictive model is trained on a dataset that includes personal crime cases that occurred from September 2010 to April 2015 E. C in Jimma Zone. The dataset was collected from secondary sources of data from Jimma Zone high court, which includes 6000 instances, and 16 attributes of the cases were used. That data contains detailed information about criminals and types of personal crimes. The algorithms used in this study are machine-learning algorithms DT, RF, NB, and RF. The dataset was split into training and testing 80%:20% datasets respectively. The decision trees have the highest evaluation metric precision of 85 values, the random forest has a recall of 98, naive Bayesian has a recall of 88, and Extreme Gradient Boosting (XGB) has a recall of 79 values. And the researcher also took another training and testing dataset 85%:","cbCaiqFR4lN8gpjf","https://ap.wps.com/l/cbCaiqFR4lN8gpjf","pdf",1938110,1,88,"English","en",105,"# Abstract\n# Acknowledgement\n# List of figures","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study develops a personal crime prediction model to forecast personal crimes using a machine learning approach.\"},{\"question\":\"What data and features are used to train the model?\",\"answer\":\"The model is trained on 6,000 personal crime instances with 16 case attributes collected from Jimma Zone high court secondary sources.\"},{\"question\":\"Which machine learning algorithms were evaluated and how did they perform?\",\"answer\":\"DT, RF, NB, and XGB are evaluated using different train-test splits. Random forest and XGB show close evaluation metrics, while random forest achieves the highest overall evaluation metrics across the splits, leading to deployment.\"}]","Developing Personal Crime Prediction Model Using Machine Learning Approach | PDF",1785725432,222,{"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},"developing-personal-crime-prediction-model-using-machine-learning-approach","",{"@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/developing-personal-crime-prediction-model-using-machine-learning-approach/119641/",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","2026-08-03",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},"What problem does the study address?","Question",{"text":75,"@type":76},"The study develops a personal crime prediction model to forecast personal crimes using a machine learning approach.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and features are used to train the model?",{"text":80,"@type":76},"The model is trained on 6,000 personal crime instances with 16 case attributes collected from Jimma Zone high court secondary sources.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithms were evaluated and how did they perform?",{"text":84,"@type":76},"DT, RF, NB, and XGB are evaluated using different train-test splits. 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