Crime Analysis for Multistate Network using Naive Bayes Classifier

The process which is used to extract all necessary and useful information for data analysis is called data mining. The K-nearest neighbor classifier is utilized to compute good performance optimal values. Bayesian Network is a graphical model. This model is utilized to establish associations which are beneficial for a set of variables. These networks represent Statistical learning algorithms. The associations that are structural in behavior occur for old information. These classifiers cannot be applied for data sets having large number of features and this is the main drawback of this classifier. The performance of Naïve Bayes classifier is compared with the KNN classifier. The proposed approach is applied in Anaconda. The simulations results depicts that Naïve Bayes algorithm has high accuracy rate and less execution time.