The Comparison of Data Classification Efficiency to Predict the Decision-Making in Future Elections Among Thai Businesspersons Using Data Mining Techniques
Keywords:
Decision tree, naïve bayes, logistic regression, random forestAbstract
This research aims to compare the efficiency of models used for predicting decision-making in future elections among Thai businesspersons. Data were collected regarding the election decision-making from businesspersons across 7 regions in Thailand. The dataset comprises 6 attributes and 2,917 records. RapidMiner Studio program version 10.1 was used to identify the number of relational attributes and models, and 10-fold cross-validation was employed to evaluate the following models: Decision Tree, Naïve Bayes, Logistic Regression, Deep Learning, and Random Forest. The results indicate that the accuracies of Random Forest, Decision Tree, Naïve Bayes, and Logistic Regression are 88.85%, 88.51%, 88.46%, and 88.11% respectively. This research could be useful for predicting and analyzing election campaigns of political parties and developing an information system to support policymaking in developing countries.
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