Publication:
Crime Prediction Using Machine Learning

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dc.citedby0
dc.contributor.authorLing H.G.en_US
dc.contributor.authorJian T.W.en_US
dc.contributor.authorMohanan V.en_US
dc.contributor.authorYeo S.F.en_US
dc.contributor.authorJothi N.en_US
dc.contributor.authorid59208465400en_US
dc.contributor.authorid59208132500en_US
dc.contributor.authorid36069451500en_US
dc.contributor.authorid56489745300en_US
dc.contributor.authorid54928769700en_US
dc.date.accessioned2025-03-03T07:46:32Z
dc.date.available2025-03-03T07:46:32Z
dc.date.issued2024
dc.description.abstractThe widespread occurrence of criminal activities poses a substantial threat to public safety and property. Hence, the proactive prediction of crimes is vital as it empowers law enforcement agencies to make decisions on resource allocation and targeted interventions based on the data, ultimately leading to a more secure and protected community. Additionally, such initiatives raise public awareness, encouraging vigilance during periods of heightened criminal activity. In this project, machine learning techniques are leveraged to forecast the crime rate in the city of Chicago. This research introduces a more efficient data preparation method, optimizing data representation to enable machine learning models to capture patterns and learn from the information provided effectively. After training the models using LightGBM, XGBoost, CatBoost, and Gradient Boosting, the models achieved R2 scores of 0.8086, 0.8088, 0.8094, and 0.8084, respectively. An ensemble method combining these individual models was implemented to improve the prediction performance. Through the voting ensemble method, the final R2 score for crime rate prediction was enhanced to 0.8104. ? The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-031-62871-9_8
dc.identifier.epage103
dc.identifier.scopus2-s2.0-85197819372
dc.identifier.spage92
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85197819372&doi=10.1007%2f978-3-031-62871-9_8&partnerID=40&md5=c82815c7c76d707b3a0f76e143182aac
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37005
dc.identifier.volume1035 LNNS
dc.pagecount11
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Networks and Systems
dc.subjectForecasting
dc.subjectMachine learning
dc.subjectCrime prediction
dc.subjectCriminal activities
dc.subjectEnsemble methods
dc.subjectLaw-enforcement agencies
dc.subjectMachine-learning
dc.subjectProperty
dc.subjectPublic awareness
dc.subjectPublic safety
dc.subjectResources allocation
dc.subjectTime series forecasting
dc.subjectCrime
dc.titleCrime Prediction Using Machine Learningen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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