Crime Prediction Using Data Mining and Machine Learning
In order to predict the crime in YD county, data mining and machine learning are used in this paper. The aim of the study is to show the pattern and rate of crime in YD county based on the data collected and to show the relationships that exist among the various crime types and crime Variable. Analyzing this data set can provide insight on crime activities within YD county. By introducing formula and methods of Bayesian network, random tree and neural network in machine learning and big data, to analyze the crime rules from the collected data. According to the statistics released by the YD county From 2012-09-01 to 2015-07-21, The crime of smuggling, selling, transporting and manufacturing drugs, Theft, Intentional injury, Illegal business crime, Illegal possession of drugs, Rape, Crime of fraud, Gang fighting, manslaughter, Robbery made the top ten list of crime types with high number of crimes. The crime rate of drugs was the highest, reaching 46.86%, farmers are the majority, accounting for 97.07%, people under the age of 35 are the subject of crime. Males accounted for 90.17% of crimes committed, while females accounted for 9.83%. For ethnic groups, the top five were han, yi, wa, dai and lang, accounting for 68.43%, 23.43%, 1.88%, 1.67% and 1.25% respectively. By adopting random forest, Bayesian networks, and neural network methods, we obtained the decision rules for criminal variables. By comparison, the classification effect of Random Trees is better than that of Neural Networks and Bayesian Networks. Through the data collection of the three algorithms, the validity and accuracy of the random tree algorithm in predicting crime data are observed. The performance of the Bayesian network algorithm is relatively poor, probably due to the existence of certain random factors in various crimes and related features (the correlation between the three algorithms is low).
KeywordsCrime prediction Data mining Machine learning
This study is supported by scientific research projects of National Social Science Foundation (13CFX038). The authors would like to express their gratitude to the Office of the national social science foundation.
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