데이터마이닝 기법을 이용한 소비자의 농축산물 구매 분석

• 영문명: Predicting Consumers Purchase of Agricultural and Livestock Products Using Data Mining Techniques
• 발행기관: 한국농식품정책학회
• 저자명: 노호영(Rho, Ho Young) 김성용(Kim, Sung-Yong) 유동희(Yoo, Dong Hee)
• 간행물 정보: 『농업경영.정책연구』농업경영·정책연구 48권 3호, 420~440쪽, 전체 21쪽
• 주제분류: 농수해양 > 식품과학
• 파일형태: PDF
• 발행일자: 2021.09.30

영문 초록

The purpose of this study is to compare the prediction power of agricultural product purchase analysis using the decision tree model and neural network model with the existing econometrics model. The research subjects are beef, Chinese cabbage, radish, red pepper, garlic and onion, which are very vulnerable in terms of supply and demand at the Korean agricultural products markets. Using the three models, we predicted the 1,314 households purchase of agricultural products with the 2016~2017 consumers panel data provided by the Korea Rural Development Administration and the Internet search index obtained from the Naver Data Lab. The main results of this study are as follows. First, based on the MAPE, the decision tree model had the highest predictive power, while the panel Tobit model had the lowest predictive power. Second, with the exception of some products, the predictive rates of peak season were higher than those of off-season. Therefore, the data mining technique is considered to be a complementary method to the existing econometric models in agri-food consumption analysis in terms of predictive power.

목차

Ⅰ. 서 론

Ⅱ. 분석모형

Ⅲ. 분석 자료

Ⅳ. 분석 결과

Ⅴ. 요약 및 결론

참고문헌

키워드

Data Mining Decision Tree Analysis Artificial Neural Network Model Agri-food Products Purchase