Participation anticipating in elections using data mining methods

TL;DR

This paper presents a data mining-based system to predict voter participation in elections, utilizing algorithms like KNN, Classification Tree, and Naive Bayes, tested on Iranian presidential election data.

Contribution

It introduces a participation anticipation system using data mining techniques and compares the effectiveness of different algorithms for election prediction.

Findings

KNN outperforms other algorithms in prediction accuracy

The system successfully predicts voter participation with high reliability

Data mining methods can effectively forecast election engagement

Abstract

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.