SVM vs Decision Tree Algorithm Cost Effective Comparison to Enhance Crime Detection and Prevention
Main Article Content
Abstract
Aim: The main objective of this research is to find the patterns of criminal activities in a particular area and knowing the efficiency of decision trees and support vector machine algorithms. Materials and Methods: The sample size for decision tree (N=10) and for support vector machines (N=10) with the G power value of 80% and datasets are collected from various web sources with recent study findings and threshold 0.05%, confidence interval 95% mean and standard deviation. was iterated 20 times to obtain data. Results: Decision tree algorithm has significantly better accuracy compared to support vector machine algorithm accuracy. The statistical significance difference 0.025 (p<0.05 Independent Sample T-Test) value states that the results are significant. Conclusion: The results depicted that a Decision tree provides good in detecting crimes over support vector machine algorithms.