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An Empirical Analysis of Machine Learning Algorithms for Crime Prediction using Stacked Ensemble Learning

by Sandeep Kamble, Ankit Temurnikar, Neha Madame
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 79
Year of Publication: 2026
Authors: Sandeep Kamble, Ankit Temurnikar, Neha Madame
10.5120/ijca2026926355

Sandeep Kamble, Ankit Temurnikar, Neha Madame . An Empirical Analysis of Machine Learning Algorithms for Crime Prediction using Stacked Ensemble Learning. International Journal of Computer Applications. 187, 79 ( Feb 2026), 31-38. DOI=10.5120/ijca2026926355

@article{ 10.5120/ijca2026926355,
author = { Sandeep Kamble, Ankit Temurnikar, Neha Madame },
title = { An Empirical Analysis of Machine Learning Algorithms for Crime Prediction using Stacked Ensemble Learning },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2026 },
volume = { 187 },
number = { 79 },
month = { Feb },
year = { 2026 },
issn = { 0975-8887 },
pages = { 31-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number79/an-empirical-analysis-of-machine-learning-algorithms-for-crime-prediction-using-stacked-ensemble-learning/ },
doi = { 10.5120/ijca2026926355 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-02-21T01:27:49.985220+05:30
%A Sandeep Kamble
%A Ankit Temurnikar
%A Neha Madame
%T An Empirical Analysis of Machine Learning Algorithms for Crime Prediction using Stacked Ensemble Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 79
%P 31-38
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The rapid growth of digital technologies has led to a significant increase in crime and cybercrime incidents, necessitating the development of accurate and reliable predictive models to support proactive law enforcement and policy planning. Traditional machine learning approaches often rely on single classifiers, which suffer from limited generalization capability and higher prediction error when dealing with complex and heterogeneous crime data. To address these limitations, this work proposes a stacked ensemble learning framework for zone-wise crime and cybercrime risk prediction, integrating multiple machine learning algorithms with a meta-learning strategy. The proposed methodology employs heterogeneous base classifiers, including Decision Tree, Naïve Bayes, Random Forest, and Support Vector Machine, whose individual predictions are combined using a Support Vector Machine-based meta-classifier through stacked generalization. A rigorous mathematical formulation is presented to model data normalization, base learner predictions, meta-feature construction, and ensemble optimization. Additionally, spatial risk modeling and clustering techniques are incorporated to identify high-risk zones and generate actionable crime vulnerability insights. Experimental evaluation demonstrates that the proposed stacked ensemble framework significantly outperforms individual classifiers in terms of accuracy, precision, recall, and error reduction metrics such as MAE and RMSE. The results confirm the effectiveness of ensemble stacking in capturing complex crime patterns and improving predictive reliability. The proposed model offers a scalable and robust solution for crime risk forecasting and can be effectively utilized by law enforcement agencies for early warning systems, targeted interventions, and data-driven urban safety planning.

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Index Terms

Computer Science
Information Sciences

Keywords

Stacked Ensemble Learning Crime Prediction Cybercrime Risk Analysis Meta-Classifier Zone-Wise Risk Modeling

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