Crime Analysis and Prediction Using Fuzzy C-Means Algorithm


Crime analysis is methodological approach for identify the crime areas. The crime areas are mainly based on the crime type these identified crime areas are helpful to reduce the crime rate. This can be very easy to identify the crime areas, based on this process the crime rate can be analyzed. With the increasing of computer systems the crime data analysts can help to the crime investigators to analyze the crime. Based on the clustering and preprocessing extract the crime areas from a structured data. The cause of occurrences of crimes like crime details of person and other factors we are focusing mainly on crime factors of previous years. This system is mainly focus on in which area the crime will occur, does not focus on the identify the criminal. In the existing system naive bayes classification was used In the present system, the fuzzy C-Means algorithm will be use to cluster the crime data for total cognizable crimes such as Kidnapping, murder, Theft, Burglary, cheating, crime against women, robbery and other such crimes.



Machine learning algorithms

Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL