Crime Prediction Using Machine Learning
The 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 lead...
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Springer Science and Business Media Deutschland GmbH
2025
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| _version_ | 1833413770131537920 |
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| author | Ling H.G. Jian T.W. Mohanan V. Yeo S.F. Jothi N. |
| author2 | 59208465400 |
| author_facet | 59208465400 Ling H.G. Jian T.W. Mohanan V. Yeo S.F. Jothi N. |
| author_sort | Ling H.G. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The 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. |
| format | Conference paper |
| id | my.uniten.dspace-37005 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| spelling | my.uniten.dspace-370052025-03-03T15:46:32Z Crime Prediction Using Machine Learning Ling H.G. Jian T.W. Mohanan V. Yeo S.F. Jothi N. 59208465400 59208132500 36069451500 56489745300 54928769700 Forecasting Machine learning Crime prediction Criminal activities Ensemble methods Law-enforcement agencies Machine-learning Property Public awareness Public safety Resources allocation Time series forecasting Crime The 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. Final 2025-03-03T07:46:32Z 2025-03-03T07:46:32Z 2024 Conference paper 10.1007/978-3-031-62871-9_8 2-s2.0-85197819372 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197819372&doi=10.1007%2f978-3-031-62871-9_8&partnerID=40&md5=c82815c7c76d707b3a0f76e143182aac https://irepository.uniten.edu.my/handle/123456789/37005 1035 LNNS 92 103 Springer Science and Business Media Deutschland GmbH Scopus |
| spellingShingle | Forecasting Machine learning Crime prediction Criminal activities Ensemble methods Law-enforcement agencies Machine-learning Property Public awareness Public safety Resources allocation Time series forecasting Crime Ling H.G. Jian T.W. Mohanan V. Yeo S.F. Jothi N. Crime Prediction Using Machine Learning |
| title | Crime Prediction Using Machine Learning |
| title_full | Crime Prediction Using Machine Learning |
| title_fullStr | Crime Prediction Using Machine Learning |
| title_full_unstemmed | Crime Prediction Using Machine Learning |
| title_short | Crime Prediction Using Machine Learning |
| title_sort | crime prediction using machine learning |
| topic | Forecasting Machine learning Crime prediction Criminal activities Ensemble methods Law-enforcement agencies Machine-learning Property Public awareness Public safety Resources allocation Time series forecasting Crime |
| url_provider | http://dspace.uniten.edu.my/ |