International Journal on Science and Technology

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Call for Paper Volume 17 Issue 2 April-June 2026 Submit your research before last 3 days of June to publish your research paper in the issue of April-June.
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Volume 17 Issue 2
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Unsupervised Domain Adaptation for Crime Risk Prediction

Author(s) Ms. G. TEJASREE, H. ISWARYA, B.V. S VYSHNAVI, K M. ESWAR, S. KUSHALATHA
Country India
Abstract This paper relies on publicly available crime data on Kaggle to suggest a data-driven way of analyzing the pattern of crime. The main tasks are to find the latest, the most common, and the location-specific criminal activity and understand how these incidences evolve over time. To reveal the patterns of the significance of crime, cleaning, converting, and analyzing the data were considered significant in terms of bringing out the most common categories in addition to showing the most common time, day, or location of these categories. The insights are essential to improve the safety measures of the population and support law enforcement in defining the possible criminal hotspots.
Various machine learning models have been evaluated to do the predictive analysis, the best and most accurate results were obtained with the help of Random Forest Classifier. It has shown a strong capability to deal with complex datasets of numerous crime features, which were previously reported to be only handled with. The model gave practical aid to prediction of crimes and strategic policing because it was able to establish significant variables that influence prevalence of crimes. This plan shows how technologies based on deep learning and machine learning can assist law enforcement institutions to decide and allocate resources in a timely fashion. The former, we can expand our mission to exhibit some levels of advanced functionality to retain precision.
Keywords machine learning, random forest classifier, crime type and occurrence prediction.
Field Engineering
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-04-04
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