Chicago Crime Analysis using R Programming

Abstract

In recent years law enforcement have improved by taking better strategies, computer aided technology, efficient use of resource, etc. As a result of these over the couple of years there has been a steep decline in crime rate in the US (United States). Law enforcement have turned to data science for insights (ranging from reports, corrective analysis and behavior modelling). There has been an overall drop in crime rates in Chicago in recent years. In fact, these rates are at the lowest when compared to the previous decades. This paper uses the criminal dataset found at “data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2” to describe historical trends, insights, etc. in Chicago from 1965 to 2018 and not to assign any casual interpretation of the vanguards of crime rates during this period. Here K-Nearest Neighbor (KNN) classification is used for training and crime predication. Discussions on future investigation can also be found. The proposed model has an accuracy of 83.2%.