  Anticipating the political behavior of people will be considerable help for
election candidates to assess the possibility of their success and to be
acknowledged about the public motivations to select them. In this paper, we
provide a general schematic of the architecture of participation anticipating
system in presidential election by using KNN, Classification Tree and Na\"ive
Bayes and tools orange based on crisp which had hopeful output. To test and
assess the proposed model, we begin to use the case study by selecting 100
qualified persons who attend in 11th presidential election of Islamic republic
of Iran and anticipate their participation in Kohkiloye & Boyerahmad. We
indicate that KNN can perform anticipation and classification processes with
high accuracy in compared with two other algorithms to anticipate
participation.
