ItemConference Paper

Prediction of Crime Hotspots Using Machine-Learning Techniques

Title
Prediction of Crime Hotspots Using Machine-Learning Techniques
Creator
Pala, Allansius E.; Moses, Joseph; Ahamed, Shaik Sardar Zubair; Kokatnoor, Sujatha Arun; Kumar, Sandeep
Description
Crime prediction is critical in improving police strategies and implementing measures for crime prevention and control. In recent years, machine learning has emerged as a critical way to predictive analytics in this domain. However, few studies have thoroughly compared various machine-learning algorithms for crime prediction. This study investigates the predicting capacities of various machine learning and ensemble approaches using historical public property crime data from a large city in India. Five ensemble models, Random Forest, AdaBoost, CatBoost, Gradient Boosting Machine (GBM) and eXtreme Gradient Boosting (XGBoost) and Four machine learning models, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Nae Bayes and Decision Trees are used for crime predictive analysis in this study. The XGBoost model outperformed the other models tested, based primarily on historical crime data. XGBoost being an ensemble approachcombines multiple weak classifiers to create an effective classifier. Every weak learner concentrates on the faults made by the preceding ones, enabling the model to refine its predictions and fix errors repeatedly. When compared with other models used in the study, this resultedin higher accuracy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Lecture Notes in Networks and Systems;Volume;1263 LNNS;pp.549-560
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
AdaBoost; CatBoost; Crime prediction; Decision trees; Gradient boosting machine (GBM); K-nearest neighbor; Nae Bayes; Random forest; Support vector machine (SVM); XGBoost
Coverage
Pala A.E., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Moses J., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Ahamed S.S.Z., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India; Kumar S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981962723-3;
Format
online
Language
English
Type
Conference paper

Citation

Pala, Allansius E.; Moses, Joseph; Ahamed, Shaik Sardar Zubair; Kokatnoor, Sujatha Arun; Kumar, Sandeep, “Prediction of Crime Hotspots Using Machine-Learning Techniques,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25503.

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