Deployment of Machine Learning Model for Predictive Analysis of Rising Crime Trends

Meghna Halder; Payal Sinha; Digonto Biswas; Tiansheng Yang; Rajkumar Singh Rathore

doi:10.1007/978-981-96-8104-4_27

Deployment of Machine Learning Model for Predictive Analysis of Rising Crime Trends

Meghna Halder
, Payal Sinha
, Digonto Biswas
, Tiansheng Yang^*
, Rajkumar Singh Rathore

^*Awdur cyfatebol y gwaith hwn

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad mewn cynhadledd › adolygiad gan gymheiriaid

Crynodeb

This research paper aims to improve public safety through the use of crime prediction and its analysis. This research predicts crime hotspots across the country by applying various classifiers such as, Gradient Boosting, Decision Tree, Random Forest, and Support Vector Machine. Data augmentation methods like scaling and SMOTE has been used to help balance class distribution and increase accuracy. Support Vector Machine obtains an accuracy value of 0.96 in our model when SMOTE and feature scaling are used together. This machine learning model provides the particular pattern to predict future crimes which ultimately helps the law enforcement to make the society safe and free of crimes.

Iaith wreiddiol	Saesneg
Teitl	Proceedings of 6th Doctoral Symposium on Computational Intelligence - DoSCI 2025
Golygyddion	Abhishek Swaroop, Vineet Kansal, Aboul Ella Hassanien
Cyhoeddwr	Springer Science and Business Media Deutschland GmbH
Tudalennau	419-433
Nifer y tudalennau	15
ISBN (Argraffiad)	9789819681037
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1007/978-981-96-8104-4_27
Statws	Cyhoeddwyd - 15 Ion 2026
Digwyddiad	6th Doctoral Symposium on Computational Intelligence, DoSCI 2025 - Lucknow, Hybrid, India Hyd: 28 Maw 2025 → 29 Maw 2025

Cyfres gyhoeddiadau

Enw	Lecture Notes in Networks and Systems
Cyfrol	1498 LNNS
ISSN (Argraffiad)	2367-3370
ISSN (Electronig)	2367-3389

Cynhadledd

Cynhadledd	6th Doctoral Symposium on Computational Intelligence, DoSCI 2025
Gwlad/Tiriogaeth	India
Dinas	Lucknow, Hybrid
Cyfnod	28/03/25 → 29/03/25

NDC y CU

Mae’r allbwn hwn yn cyfrannu at y Nod(au) Datblygu Cynaliadwy canlynol

NDC 16 Heddwch, Cyfiawnder a Sefydliadau Cadarn

Mynediad at Ddogfen

10.1007/978-981-96-8104-4_27

Ffeiliau eraill a chysylltiadau

Dolen i’r cyhoeddiad yn Scopus

Dyfynnu hyn

Halder, M., Sinha, P., Biswas, D., Yang, T., & Rathore, R. S. (2026). Deployment of Machine Learning Model for Predictive Analysis of Rising Crime Trends. Yn A. Swaroop, V. Kansal, & A. E. Hassanien (Gol.), Proceedings of 6th Doctoral Symposium on Computational Intelligence - DoSCI 2025 (tt. 419-433). (Lecture Notes in Networks and Systems; Cyfrol 1498 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-96-8104-4_27

Halder, Meghna ; Sinha, Payal ; Biswas, Digonto et al. / Deployment of Machine Learning Model for Predictive Analysis of Rising Crime Trends. Proceedings of 6th Doctoral Symposium on Computational Intelligence - DoSCI 2025. Gol. / Abhishek Swaroop ; Vineet Kansal ; Aboul Ella Hassanien. Springer Science and Business Media Deutschland GmbH, 2026. tt. 419-433 (Lecture Notes in Networks and Systems).

@inproceedings{3d6103c862f244559206d305a86cbc49,

title = "Deployment of Machine Learning Model for Predictive Analysis of Rising Crime Trends",

abstract = "This research paper aims to improve public safety through the use of crime prediction and its analysis. This research predicts crime hotspots across the country by applying various classifiers such as, Gradient Boosting, Decision Tree, Random Forest, and Support Vector Machine. Data augmentation methods like scaling and SMOTE has been used to help balance class distribution and increase accuracy. Support Vector Machine obtains an accuracy value of 0.96 in our model when SMOTE and feature scaling are used together. This machine learning model provides the particular pattern to predict future crimes which ultimately helps the law enforcement to make the society safe and free of crimes.",

keywords = "Crime prediction, Decision tree, Feature scaling, Gradient boosting, Public safety, Random forest, SMOTE, Support vector machine",

author = "Meghna Halder and Payal Sinha and Digonto Biswas and Tiansheng Yang and Rathore, \{Rajkumar Singh\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 6th Doctoral Symposium on Computational Intelligence, DoSCI 2025 ; Conference date: 28-03-2025 Through 29-03-2025",

year = "2026",

month = jan,

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doi = "10.1007/978-981-96-8104-4\_27",

language = "English",

isbn = "9789819681037",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

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Halder, M, Sinha, P, Biswas, D, Yang, T & Rathore, RS 2026, Deployment of Machine Learning Model for Predictive Analysis of Rising Crime Trends. yn A Swaroop, V Kansal & AE Hassanien (gol.), Proceedings of 6th Doctoral Symposium on Computational Intelligence - DoSCI 2025. Lecture Notes in Networks and Systems, cyfrol. 1498 LNNS, Springer Science and Business Media Deutschland GmbH, tt. 419-433, 6th Doctoral Symposium on Computational Intelligence, DoSCI 2025, Lucknow, Hybrid, India, 28/03/25. https://doi.org/10.1007/978-981-96-8104-4_27

Deployment of Machine Learning Model for Predictive Analysis of Rising Crime Trends. / Halder, Meghna; Sinha, Payal; Biswas, Digonto et al.
Proceedings of 6th Doctoral Symposium on Computational Intelligence - DoSCI 2025. gol. / Abhishek Swaroop; Vineet Kansal; Aboul Ella Hassanien. Springer Science and Business Media Deutschland GmbH, 2026. t. 419-433 (Lecture Notes in Networks and Systems; Cyfrol 1498 LNNS).

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Cyfraniad mewn cynhadledd › adolygiad gan gymheiriaid

TY - GEN

T1 - Deployment of Machine Learning Model for Predictive Analysis of Rising Crime Trends

AU - Halder, Meghna

AU - Sinha, Payal

AU - Biswas, Digonto

AU - Yang, Tiansheng

AU - Rathore, Rajkumar Singh

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

PY - 2026/1/15

Y1 - 2026/1/15

N2 - This research paper aims to improve public safety through the use of crime prediction and its analysis. This research predicts crime hotspots across the country by applying various classifiers such as, Gradient Boosting, Decision Tree, Random Forest, and Support Vector Machine. Data augmentation methods like scaling and SMOTE has been used to help balance class distribution and increase accuracy. Support Vector Machine obtains an accuracy value of 0.96 in our model when SMOTE and feature scaling are used together. This machine learning model provides the particular pattern to predict future crimes which ultimately helps the law enforcement to make the society safe and free of crimes.

AB - This research paper aims to improve public safety through the use of crime prediction and its analysis. This research predicts crime hotspots across the country by applying various classifiers such as, Gradient Boosting, Decision Tree, Random Forest, and Support Vector Machine. Data augmentation methods like scaling and SMOTE has been used to help balance class distribution and increase accuracy. Support Vector Machine obtains an accuracy value of 0.96 in our model when SMOTE and feature scaling are used together. This machine learning model provides the particular pattern to predict future crimes which ultimately helps the law enforcement to make the society safe and free of crimes.

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KW - Public safety

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Halder M, Sinha P, Biswas D, Yang T, Rathore RS. Deployment of Machine Learning Model for Predictive Analysis of Rising Crime Trends. Yn Swaroop A, Kansal V, Hassanien AE, golygyddion, Proceedings of 6th Doctoral Symposium on Computational Intelligence - DoSCI 2025. Springer Science and Business Media Deutschland GmbH. 2026. t. 419-433. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-96-8104-4_27