An experimental study of classification algorithms for crime prediction.
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 dif...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Indian Society for Education and Environment
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/30625/ http://psasir.upm.edu.my/id/eprint/30625/1/An%20experimental%20study%20of%20classification%20algorithms%20for%20crime%20prediction.pdf |
| _version_ | 1848846731177885696 |
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| author | Iqbal, Rizwan Azmi Murad, Masrah Azrifah Mustapha, Aida Panahy, Payam Hassany Shariat Khanahmadliravi, Nasim |
| author_facet | Iqbal, Rizwan Azmi Murad, Masrah Azrifah Mustapha, Aida Panahy, Payam Hassany Shariat Khanahmadliravi, Nasim |
| author_sort | Iqbal, Rizwan |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T09:07:22Z |
| format | Article |
| id | upm-30625 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T09:07:22Z |
| publishDate | 2013 |
| publisher | Indian Society for Education and Environment |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-306252015-09-21T02:20:19Z http://psasir.upm.edu.my/id/eprint/30625/ An experimental study of classification algorithms for crime prediction. Iqbal, Rizwan Azmi Murad, Masrah Azrifah Mustapha, Aida Panahy, Payam Hassany Shariat Khanahmadliravi, Nasim 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. Indian Society for Education and Environment 2013-03 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/30625/1/An%20experimental%20study%20of%20classification%20algorithms%20for%20crime%20prediction.pdf Iqbal, Rizwan and Azmi Murad, Masrah Azrifah and Mustapha, Aida and Panahy, Payam Hassany Shariat and Khanahmadliravi, Nasim (2013) An experimental study of classification algorithms for crime prediction. Indian Journal of Science and Technology, 6 (3). pp. 4219-4225. ISSN 0974-6846; ESSN: 0974-5645 http://www.indjst.org/index.php/indjst/issue/view/2922 English |
| spellingShingle | Iqbal, Rizwan Azmi Murad, Masrah Azrifah Mustapha, Aida Panahy, Payam Hassany Shariat Khanahmadliravi, Nasim An experimental study of classification algorithms for crime prediction. |
| title | An experimental study of classification algorithms for crime prediction. |
| title_full | An experimental study of classification algorithms for crime prediction. |
| title_fullStr | An experimental study of classification algorithms for crime prediction. |
| title_full_unstemmed | An experimental study of classification algorithms for crime prediction. |
| title_short | An experimental study of classification algorithms for crime prediction. |
| title_sort | experimental study of classification algorithms for crime prediction. |
| url | http://psasir.upm.edu.my/id/eprint/30625/ http://psasir.upm.edu.my/id/eprint/30625/ http://psasir.upm.edu.my/id/eprint/30625/1/An%20experimental%20study%20of%20classification%20algorithms%20for%20crime%20prediction.pdf |