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    Crime Prediction Using Decision Tree (J48) Classification Algorithm.

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    Date
    2017
    Author
    Ivan, Niyonzima
    Emmanuel Ahishakiye
    Elisha Opiyo Omulo
    Danison Taremwa
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    Abstract
    There had been an enormous increase in the crime in the recent past. Crimes are a common social problem affecting the quality of life and the economic growth of a society. With the increase of crimes, law enforcement agencies are continuing to demand advanced systems and new approaches to improve crime analytics and better protect their communities. Decision tree (J48) applied in the context of law enforcement and intelligence analysis holds the promise of alleviating such problem. Data mining is a way to extract knowledge out of usually large data sets; in other words it is an approach to discover hidden relationships among data by using artificial intelligence methods of which decision tree (J48) is inclusive. The wide range of machine learning applications has made it an important field of research. Criminology is one of the most important fields for applying data mining. Criminology is a process that aims to identify crime characteristics. This study considered the development of crime prediction prototype model using decision tree (J48) algorithm because it has been considered as the most efficient machine learning algorithm for prediction of crime data as described in the related literature. From the experimental results, J48 algorithm predicted the unknown category of crime data to the accuracy of 94.25287% which is fair enough for the system to be relied on for prediction of future crimes.
    URI
    http://hdl.handle.net/20.500.12493/113
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    • Faculty of Computing ,Library and Information Science (FCLIS). [23]

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