콘텐츠 본문
논문 해외 국제전문학술지(SCI급) Predicting Agricultural and Livestock Products Purchases Using the Internet Search Index and Data Mining Techniques
- 학술지 구분 국제전문학술지(SCI급)
- 게재년월 2021
- 저자명 Hoyoung Rho, Keunho Choi, Donghee Yoo
- 학술지명 DATA TECHNOLOGIES AND APPLICATIONS
- 발행처명 EMERALD GROUP PUBLISHING LTD
- 발행국가 해외
- 논문언어 외국어
- 전체저자수 3
논문 초록 (Abstract)
Purpose This study identifies whether the Internet search index can be used as effective enough data to identify agricultural and livestock product demand and compare the accuracy of the prediction of major agricultural and livestock products purchases between these prediction models using artificial neural network, linear regression and a decision tree. Design/methodology/approach Artificial neural network, linear regression and decision tree algorithms were used in this study to compare the accuracy of the prediction of major agricultural and livestock products purchases. The analysis data were studied using 10-fold cross validation. Findings First, the importance of the Internet search index among the 20 explanatory variables was found to be high for most items, so the Internet search index can be used as a variable to explain agricultural and livestock products purchases. Second, as a result of comparing the accuracy of the prediction of six agricultural and livestock purchases using three models, beef was the most predictable, followed by radishes, chicken, Chinese cabbage, garlic and dried peppers, and by model, a decision tree shows the highest accuracy of prediction, followed by linear regression and an artificial neural network. Originality/value This study is meaningful in that it analyzes the purchase of agricultural and livestock products using data from actual consumers' purchases of agricultural and livestock products. In addition, the use of data mining techniques and Internet search index in the analysis of agricultural and livestock purchases contributes to improving the accuracy and efficiency of agricultural and livestock purchase predictions.