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http://dx.doi.org/10.13067/JKIECS.2019.14.4.775

A Comparative Study on the Accuracy of Important Statistical Prediction Techniques for Marketing Data  

Cho, Min-Ho (Dept. Computer System Engineering, JungWon University)
Publication Information
The Journal of the Korea institute of electronic communication sciences / v.14, no.4, 2019 , pp. 775-780 More about this Journal
Abstract
Techniques for predicting the future can be categorized into statistics-based and deep-run-based techniques. Among them, statistic-based techniques are widely used because simple and highly accurate. However, working-level officials have difficulty using many analytical techniques correctly. In this study, we compared the accuracy of prediction by applying multinomial logistic regression, decision tree, random forest, support vector machine, and Bayesian inference to marketing related data. The same marketing data was used, and analysis was conducted by using R. The prediction results of various techniques reflecting the data characteristics of the marketing field will be a good reference for practitioners.
Keywords
Statistical Forecasting; R; Regression; Random Forest; Decision Tree; Support Vector Machine;
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