Crime Prevention and Detection of Using Predictive Algorithms: A Case Study of Maiduguri Metropolis – Volume 12 Issue 5

Crime prevention and detection are critical challenges for law enforcement globally. With increasing crime data and advancements in machine learning, predictive algorithms offer a promising solution. Maiduguri has experienced rising crime rates, yet lacks intelligent systems to aid security efforts. This study aims to model crime prevention and detection using predictive algorithms, specifically comparing Decision Trees (DT), Random Forest (RF), and Support Vector Machines (SVM). A quantitative approach was adopted, with data collected from Maiduguri security agencies and online sources. The algorithms were analyzed using Python, evaluating performance via accuracy, precision, recall, and F1-score. Results showed DT and RF excelled: DT achieved perfect scores (1.000) across all metrics, while RF scored highly (accuracy: 0.9950, precision: 0.9955, recall: 0.9950, F1-score: 0.9951). In contrast, SVM performed poorly (accuracy: 0.5075, precision: 0.5372, recall: 0.5075, F1-score: 0.4910), highlighting the importance of algorithm selection. Visualizations of crime frequency by location, time, and victim age distribution further aid law enforcement in resource allocation and targeted interventions. The study demonstrated that DT and RF are highly effective for crime prediction in Maiduguri, offering actionable insights for improving public safety. SVM’s limitations suggest it is less suitable for this task, emphasizing the need for appropriate algorithm choices in crime analytics.

The study shows how well machine learning algorithms, in particular, decision trees and random forests predict and categorize events connected to crime. These models’ great accuracy and resilience make them appropriate for use in actual crime prevention and detection situations. Law enforcement organizations can more efficiently allocate resources and put targeted preventive measures into place with the aid of the visualizations’ insightful information about crime trends. Support vector machines’ (SVM) subpar performance raises the possibility that this approach is inappropriate for this kind of dataset or task, underscoring the significance of choosing the right models depending on the data’s characteristics and the issue at hand.

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