Preventing crimes ahead of time by predicting crime propensity in released prisoners using data mining techniques

Criminologists and psychologists around the world are finding new initiatives to identify criminals and understand crime scenes. This work focuses on predicting the occurrence of crimes for a released prisoner, based on crime propensity prediction, using a supervised machine learning technique. This original research is intended to design and develop a new dataset of 30 attributes that exists nowhere and is exclusively created to define prisoners so as to differentiate them by their propensity to crime using psychological and behavioural factors obtained from jails and assorted sources. The research incorporates an analysis of seven search methods, in tandem with seven subset evaluation techniques, to undertake feature selection, and nine classification algorithms for the classification of prisoners. It is found that the wolf search algorithm, used with the correlation-based feature subset evaluation technique and radial basis function classifier, performs best providing 97.8% precision, 97.5% recall and low error values.