Predictive Modelling of Crime Dataset Using Data Mining

With a substantial increase in crime across the globe, there is a need for analyzing the crime data to lower the crime rate. This helps the police and citizens to take necessary actions and solve the crimes faster. In this paper, data mining techniques are applied to crime data for predicting features that affect the high crime rate. Supervised learning uses data sets to train, test and get desired results on them whereas Unsupervised learning divides an inconsistent, unstructured data into classes or clusters. Decision trees, Naive Bayes and Regression are some of the supervised learning methods in data mining and machine learning on previously collected data and thus used for predicting the features responsible for causing crime in a region or locality. Based on the rankings of the features, the Crimes Record Bureau and Police Department can take necessary actions to decrease the probability of occurrence of the crime.