Decision tree Model: Predicting Sexual Offenders on the Basis of Minor and Major Victims

Sexual offences spoil the whole culture of any society, city, state or country. So, the identification and the prediction of the sexual offenders are very important. This research paper proposed a model to predict the sexual offenders on the basis of major or minor victims which could help to take any kind of decision by police departments, sexual harassment cells, and law enforcement agencies to differentiate the sexual offenders of major or minor victims to enhance the implementation of security accordingly for crime prevention. The model first classifies the sexual offenders then does their prediction through the predictor variables age, race, and weight. To deploy the decision model overall dataset has been divided into 70:30 (training data: test data) ratio. The proposed decision tree model has been resulted with 79.8% accuracy rate with the 70% of test data and model validated through 30% of remaining data as given 79.1% accuracy rate. Model can predict 82.7% sexual offenders on the basis of minor victims and 75.5% on the basis of major victims.