Crime rate prediction using machine learning

As crime is a plague to society, every country has been actively trying to come up with solutions to reduce crimes. From things like campaigns to raise money for low-income household, crime watches, more frequent patrols, etc. However, even with these measures crime rates still remains at an all-tim...

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Main Author: Chee, Man Hang
Format: Final Year Project / Dissertation / Thesis
Published: 2022
Subjects:
Online Access:http://eprints.utar.edu.my/4689/
http://eprints.utar.edu.my/4689/1/fyp_CS_2022_CMH.pdf
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author Chee, Man Hang
author_facet Chee, Man Hang
author_sort Chee, Man Hang
building UTAR Institutional Repository
collection Online Access
description As crime is a plague to society, every country has been actively trying to come up with solutions to reduce crimes. From things like campaigns to raise money for low-income household, crime watches, more frequent patrols, etc. However, even with these measures crime rates still remains at an all-time high. Therefore, with the implementation of this crime rate prediction system, the police can employ predictive policing whereby they can patrol the areas with a higher chance of crimes. With this, they can make a more informed decision on the areas to patrol. To develop this system, I used the San Francisco crime dataset. With this I have employed Feature engineering to aid the system in getting higher accuracies. I have also employed various ensemble learning methods such as XGBoost classifier, Decision tree, and Random Forest Classifier. After which I performed hyperparameter tuning with RandomSearchCV to aid in increasing the accuracies of the prediction of the system. One additional model was also used which was the SARIMAX model which was used to forecast future crime statistics for each Police District.
first_indexed 2025-11-15T19:34:58Z
format Final Year Project / Dissertation / Thesis
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institution Universiti Tunku Abdul Rahman
institution_category Local University
last_indexed 2025-11-15T19:34:58Z
publishDate 2022
recordtype eprints
repository_type Digital Repository
spelling utar-46892023-01-15T13:48:57Z Crime rate prediction using machine learning Chee, Man Hang T Technology (General) As crime is a plague to society, every country has been actively trying to come up with solutions to reduce crimes. From things like campaigns to raise money for low-income household, crime watches, more frequent patrols, etc. However, even with these measures crime rates still remains at an all-time high. Therefore, with the implementation of this crime rate prediction system, the police can employ predictive policing whereby they can patrol the areas with a higher chance of crimes. With this, they can make a more informed decision on the areas to patrol. To develop this system, I used the San Francisco crime dataset. With this I have employed Feature engineering to aid the system in getting higher accuracies. I have also employed various ensemble learning methods such as XGBoost classifier, Decision tree, and Random Forest Classifier. After which I performed hyperparameter tuning with RandomSearchCV to aid in increasing the accuracies of the prediction of the system. One additional model was also used which was the SARIMAX model which was used to forecast future crime statistics for each Police District. 2022-09-07 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4689/1/fyp_CS_2022_CMH.pdf Chee, Man Hang (2022) Crime rate prediction using machine learning. Final Year Project, UTAR. http://eprints.utar.edu.my/4689/
spellingShingle T Technology (General)
Chee, Man Hang
Crime rate prediction using machine learning
title Crime rate prediction using machine learning
title_full Crime rate prediction using machine learning
title_fullStr Crime rate prediction using machine learning
title_full_unstemmed Crime rate prediction using machine learning
title_short Crime rate prediction using machine learning
title_sort crime rate prediction using machine learning
topic T Technology (General)
url http://eprints.utar.edu.my/4689/
http://eprints.utar.edu.my/4689/1/fyp_CS_2022_CMH.pdf