Crime Prediction Using Machine Learning and Testing With Classification Models
DOI:
https://doi.org/10.61841/vv53m548Keywords:
Crime Prediction, Social Problem, Real-World Crimes,, Data Mining,, Testing, AwarenessAbstract
Crimes are a common social problem affecting the quality of life and the economic growth of a society. It is considered an essential factor that determines whether or not people move to a new city and what places should be avoided when they travel. With the increase of crimes, law enforcement agencies are continuing to demand advanced geographic information systems and new data mining approaches to improve crime analytics and better protect their communities.Although crimes could occur everywhere, it is common that criminals work on crime opportunities they face in most familiar areas for them. By providing a data mining approach to determine the most criminal hotspots and find the type, location and time of committed crimes, It is hope to raise people’s awareness regarding the dangerous locations in certain time periods. Therefore, this proposed solution can potentially help people stay away from the locations at a certain time of the day along with saving lives. In addition, having this kind of knowledge would help people to improve their living place choices. On the other hand, police forces can use this solution to increase the level of crime prediction and prevention. Moreover, this would be useful for police resthisces allocation. It can help in the distribution of police at most likely crime places for any given time, to grant an efficient usage of police resthisces. By having all of this information available, It is hope to make this community safer for the people living there and also for others who will travel there.This project analyses two different real-world crimes datasets for Denver and Los Angeles and provides a comparison betItisen the two datasets through a statistical analysis supported by several graphs. Then, it clarifies how It is conducted Apriori algorithm to produce interesting frequent patterns for criminal hotspots. The results of this solution could be used to raise people’s awareness regarding the dangerous locations and to help agencies to predict future crimes in a specific location within a particular time.
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