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CRIME PREDICTION AND ANALYSIS USING MACHINE LEARNING

AUTHORS:
Hemanathan D
Dinesh S
Mentor
A.S. Arunachalam
Affiliation
Department of Computer Science and Information Technology,Vels Institute of Science, Technology and Advanced Studies (VISTAS), Chennai, India
DOI: 10.55041/ijsmt.v2i5.040
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

Crime prediction has become an important application of artificial intelligence because public safety agencies need faster and more reliable ways to identify crime trends. This project presents CrimeCast, a web-based crime prediction and analysis system developed with Python and Flask, trained on crime data from Tamil Nadu, India. The system uses a Random Forest Classifier to predict the most likely crime type from inputs such as state, city, latitude, longitude, year, and domestic status. The model is designed to classify six major crime categories: Assault, Burglary, Cyber Crime, Domestic Violence, Robbery, and Theft. The application combines machine learning with a secure, responsive web interface built with Bootstrap and an SQLite database for storing user accounts and prediction history. It also includes analytics dashboards that show crime distribution, yearly trends, city-wise frequency, and domestic versus non-domestic comparisons. An interactive heatmap built with Leaflet.js provides a geographic visualization of crime density across Tamil Nadu. With its prediction engine, history tracking, and visual

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PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 05
Review Rounds:
2
Article Type:
Research Article
Publication Date:
May 05 2026
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D, H. & S, D. (2026). Crime Prediction and Analysis using Machine Learning. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.040

D, Hemanathan, and Dinesh S. "Crime Prediction and Analysis using Machine Learning." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.040.

D, Hemanathan, and Dinesh S. "Crime Prediction and Analysis using Machine Learning." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.040.

References
[1]. N. Shah, N. Bhagat, and M. Shah, “Crime forecasting: A machine learning and computer vision approach to crime prediction and prevention,” Visual Computing for Industry, Biomedicine, and Art, vol. 4, no. 1, pp. 1–14, 2021.

[2].  S. S. Kshatri, D. Singh, B. Narain, S. Bhatia, M. T. Quasim, and G. R. Sinha,

[3]. “An empirical analysis of machine learning algorithms for crime prediction using stacked generalization,” IEEE Access, vol. 9, pp. 67488–67500, 2021.

[4]. W. Safat, S. Asghar, and S. A. Gillani,“Empirical analysis for crime prediction and forecasting using machine learning and deep learning techniques,” IEEE Access, vol. 9,70080–70094, 2021.

[5]. N. Kanimozhi, N. V. Keerthana, G. S. Pavithra, G. Ranjitha, and S. Yuvarani,“Crime type and occurrence prediction using machine learning algorithm,”in Proc. Int. Conf. Artificial Intelligence and Smart Systems (ICAIS), 2021, pp. 266–273.

[6]. M. Sabarish and A. S. Arunachalam, "A Trust Secure Attacker Detection with Upgraded Deep Learning-Assistance for SDN Networks," 2023 12th International Conference on System Modeling & Advancement in Research Trends (SMART), Moradabad, India, 2023, pp. 121-125, doi: 10.1109/SMART59791.2023.10428532.

[7]. H. Al-Ghushami, D. Syed, J. Sessa, and A. Zainab, “Intelligent automation of crime prediction using data mining, “in Proc. IEEE 31st Int. Symp. Industrial Electronics (ISIE), 2022.
Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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