Predicting Crime Categories and Their Frequency using Machine Learning Techniques

TITLE	Predicting Crime Categories and Their Frequency using Machine Learning Techniques
ABSTRACT	This project focuses on predicting crime trends using publicly available datasets to enhance public safety and assist law enforcement agencies in proactive decision-making. By analyzing historical crime records through advanced machine learning techniques, particularly the Random Forest Classifier, the system uncovers complex patterns and relationships between factors such as time, location, crime type, and frequency. The model’s high prediction accuracy allows authorities to anticipate high-risk areas and times, optimize resource allocation, and implement targeted preventive strategies. Additionally, the system can be integrated into surveillance and monitoring platforms to provide real-time alerts, aiding rapid response and crime deterrence. This intelligent crime prediction framework demonstrates the transformative potential of AI in fostering safer communities through data-driven policing.
AUTHOR	Raksha T Student, Masters of Computer Applications, CMR Institute of Technology, Bengaluru, India
PUBLICATION DATE	2025-09-15
VOLUME	12
DOI	DOI:10.15680/IJARETY.2025.1205004
PDF	4_Predicting Crime Categories and Their Frequency using Machine Learning Techniques.pdf
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