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Murad, Masrah Azrifah Azmi
- An Experimental Study of Classification Algorithms for Crime Prediction
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PDF Views:279
Authors
Rizwan Iqbal
1,
Masrah Azrifah Azmi Murad
1,
Aida Mustapha
1,
Payam Hassany Shariat Panahy
1,
Nasim Khanahmadliravi
1
Affiliations
1 Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, MY
1 Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, MY
Source
Indian Journal of Science and Technology, Vol 6, No 3 (2013), Pagination: 4219-4225Abstract
Classification is a well-known supervised learning technique in data mining. It is used to extract meaningful information from large datasets and can be effectively used for predicting unknown classes. In this research, classification is applied to a crime dataset to predict 'Crime Category' for different states of the United States of America. The crime dataset used in this research is real in nature, it was collected from socio-economic data from 1990 US Census, law enforcement data from the 1990 US LEMAS survey, and crime data from the 1995 FBI UCR. This paper compares the two different classification algorithms namely, Naïve Bayesian and Decision Tree for predicting 'Crime Category' for different states in USA. The results from the experiment showed that, Decision Tree algorithm out performed Naïve Bayesian algorithm and achieved 83.9519% Accuracy in predicting 'Crime Category' for different states of USA.Keywords
Crime Prediction, Crime Category, AlgorithmFull Text
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