Introduction: (Crime Rate Prediction System using Python)

Criminals are nuisance for the society in all corners of world for a long time now and measures are required to eradicate crimes from our world. Our mission is to offer crime prevention application to keep public safe. Current policing strategies work towards finding the criminals, basically after the crime has occurred. But, with the help of technological advancement, we can use historic crime data to recognize crime patterns and use these patterns to predict crimes beforehand.

We are using clustering algorithms to predict crime prone areas. There are many clustering algorithms to group the relevant data into desired clusters. The large volumes of crime data-sets as well as the complexity of relationships between these kinds of data have made criminology an appropriate field for applying data mining techniques. Criminology is an area that focuses the scientific study of crime and criminal behavior and law enforcement and is a process that aims to identify crime characteristics. It is one of the most important fields where the application of data mining techniques can produce important results. Identifying crime characteristics is the first step for developing further analysis. The knowledge gained from data mining approaches is a very useful tool which can help and support police forces. Clustering techniques converts data-sets to clusters which are further examined for determining crime prone areas. These clusters visually represent group of crimes overlaid on map of police jurisdiction. Clusters store location of crimes along with other credentials of crime like type and time. These clusters are classified on the basis of their members. Densely populated clusters become crime prone areas whereas clusters with fewer members are ignored. Preventive measures are implemented according to crime type in crime prone areas.

K-means is the simplest and most commonly used clustering algorithm in scientific and industrial software. Due to less computational complexity, it is suitable for clustering large data sets. As such, it has been successfully used in various topics, including market segmentation, computer vision, geostatistics, astronomy and agriculture. It often is used as a pre-processing step for other algorithms, for example to find a starting configuration.

We chose clustering technique over any other supervised technique such as classification, since crimes vary in nature widely and crime database are often filled with unsolved crimes. Therefore, classification technique that will rely on the existing and known solved crimes, will not give good predictive quality for future crimes.

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