Crime Prediction Using Machine Learning and Deep Learning

P. Karthik; P. Jayanth; K. Tharun Nayak; K. Anil Kumar

doi:10.32628/IJSRSET241134

Authors

P. Karthik UG Student, Department of Computer Science and Engineering (Internet of Things), Guru Nanak Institutions Technical Campus, Hyderabad, Telangana, India Author
P. Jayanth UG Student, Department of Computer Science and Engineering (Internet of Things), Guru Nanak Institutions Technical Campus, Hyderabad, Telangana, India Author
K. Tharun Nayak UG Student, Department of Computer Science and Engineering (Internet of Things), Guru Nanak Institutions Technical Campus, Hyderabad, Telangana, India Author
K. Anil Kumar Assistant Professor, Department of Computer Science and Engineering (Internet of Things), Guru Nanak Institutions Technical Campus, Hyderabad, Telangana, India Author

DOI:

https://doi.org/10.32628/IJSRSET241134

Keywords:

Machine Learning, Deep Learning, Research Review, Crime Prediction, Algorithm Application, Dataset Analysis, Trend Identification, Criminal Activity Factors, Predictive Accuracy, Future Directions, Law Enforcement Strategies

Abstract

The utilization of machine learning and deep learning methods for crime prediction has become a focal point for researchers, aiming to decipher the complex patterns and occurrences of crime. This review scrutinizes an extensive collection of over 150 scholarly articles to delve into the assortment of machine learning and deep learning techniques employed in forecasting criminal behaviour. It grants access to the datasets leveraged by researchers for crime forecasting and delves into the key methodologies utilized in these predictive algorithms. The study sheds light on the various trends and elements associated with criminal behaviour and underscores the existing deficiencies and prospective avenues for advancing crime prediction precision. This thorough examination of the current research on crime forecasting through machine learning and deep learning serves as an essential resource for scholars in the domain. A more profound comprehension of these predictive methods will empower law enforcement to devise more effective prevention and response strategies against crime.

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