Predicting and Preventing Crime: A Crime Prediction Model Using San Francisco Crime Data by Classification Techniques
The crime is difficult to predict; it is random and possibly can occur anywhere at any time, which is a challenging issue for any society. The study proposes a crime prediction model by analyzing and comparing three known prediction classification algorithms: Naive Bayes, Random Forest, and Gradient...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/4830411 |
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| author | Muzammil Khan Azmat Ali Yasser Alharbi |
| author_facet | Muzammil Khan Azmat Ali Yasser Alharbi |
| author_sort | Muzammil Khan |
| collection | DOAJ |
| description | The crime is difficult to predict; it is random and possibly can occur anywhere at any time, which is a challenging issue for any society. The study proposes a crime prediction model by analyzing and comparing three known prediction classification algorithms: Naive Bayes, Random Forest, and Gradient Boosting Decision Tree. The model analyzes the top ten crimes to make predictions about different categories, which account for 97% of the incidents. These two significant crime classes, that is, violent and nonviolent, are created by merging multiple smaller classes of crimes. Exploratory data analysis (EDA) is performed to identify the patterns and understand the trends of crimes using a crime dataset. The accuracies of Naive Bayes, Random Forest, and Gradient Boosting Decision Tree techniques are 65.82%, 63.43%, and 98.5%, respectively, and the proposed model is further evaluated for precision and recall matrices. The results show that the Gradient Boosting Decision Tree prediction model is better than the other two techniques for predicting crime, based on historical data from a city. The analysis and prediction model can help the security agencies utilize the resources efficiently, anticipate the crime at a specific time, and serve society well. |
| format | Article |
| id | doaj-art-fb2409b6110a4c29bfe3f1c54e2d4acc |
| institution | OA Journals |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-fb2409b6110a4c29bfe3f1c54e2d4acc2025-08-20T02:22:56ZengWileyComplexity1099-05262022-01-01202210.1155/2022/4830411Predicting and Preventing Crime: A Crime Prediction Model Using San Francisco Crime Data by Classification TechniquesMuzammil Khan0Azmat Ali1Yasser Alharbi2Department of Computer & Software TechnologySchool of Computer ScienceCollege of Computer ScienceThe crime is difficult to predict; it is random and possibly can occur anywhere at any time, which is a challenging issue for any society. The study proposes a crime prediction model by analyzing and comparing three known prediction classification algorithms: Naive Bayes, Random Forest, and Gradient Boosting Decision Tree. The model analyzes the top ten crimes to make predictions about different categories, which account for 97% of the incidents. These two significant crime classes, that is, violent and nonviolent, are created by merging multiple smaller classes of crimes. Exploratory data analysis (EDA) is performed to identify the patterns and understand the trends of crimes using a crime dataset. The accuracies of Naive Bayes, Random Forest, and Gradient Boosting Decision Tree techniques are 65.82%, 63.43%, and 98.5%, respectively, and the proposed model is further evaluated for precision and recall matrices. The results show that the Gradient Boosting Decision Tree prediction model is better than the other two techniques for predicting crime, based on historical data from a city. The analysis and prediction model can help the security agencies utilize the resources efficiently, anticipate the crime at a specific time, and serve society well.http://dx.doi.org/10.1155/2022/4830411 |
| spellingShingle | Muzammil Khan Azmat Ali Yasser Alharbi Predicting and Preventing Crime: A Crime Prediction Model Using San Francisco Crime Data by Classification Techniques Complexity |
| title | Predicting and Preventing Crime: A Crime Prediction Model Using San Francisco Crime Data by Classification Techniques |
| title_full | Predicting and Preventing Crime: A Crime Prediction Model Using San Francisco Crime Data by Classification Techniques |
| title_fullStr | Predicting and Preventing Crime: A Crime Prediction Model Using San Francisco Crime Data by Classification Techniques |
| title_full_unstemmed | Predicting and Preventing Crime: A Crime Prediction Model Using San Francisco Crime Data by Classification Techniques |
| title_short | Predicting and Preventing Crime: A Crime Prediction Model Using San Francisco Crime Data by Classification Techniques |
| title_sort | predicting and preventing crime a crime prediction model using san francisco crime data by classification techniques |
| url | http://dx.doi.org/10.1155/2022/4830411 |
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