Naive Bayes algorithms are a class of supervised gadget mastering models which are typically used for a variety of obligations, together with statistics mining, textual content category, and regression. This studies explores the ability of applying these algorithms to the evaluation of community time series statistics. Thru empirical experimentation, the effectiveness of diverse Naive Bayes algorithms is as compared the usage of empirical performance metrics. The results show that Naive Bayes algorithms can successfully be used to identify patterns in community time series, carry out regression responsibilities, and offer feature representations. The results spotlight the capability of Naive Bayes algorithms as a tool for the evaluation of network time collection statistics.