Yeong-Hwil Ahn, Koo-Rack Park, Dong-Hyun Kim, Do-yeon Kim
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引用次数: 0
Abstract
This study was conducted to propose a product planning prediction model using logistic regression algorithm to predict seasonal factors and rapidly changing product trends. First, we collected unstructured data of consumers in portal sites and online markets using web crawling, and analyzed meaningful information about products through preprocessing for transformation of standardized data. The datasets of 11,200 were analyzed by Logistic Regression to analyze consumer satisfaction, frequency analysis, and advantages and disadvantages of products. The result of analysis showed that the satisfaction of consumers was 92% and the defective issues of products were confirmed through frequency analysis. The results of analysis on the use satisfaction, system efficiency, and system effectiveness items of the developed product planning prediction program showed that the satisfaction was high. Defective issues are very meaningful data in that they provide information necessary for quickly recognizing the current problem of products and establishing improvement strategies.
期刊介绍:
The Journal of Korean Chemical Society has been published since 1949 as the official research journal of the Korean Chemical Society. It is now published bimonthly. The Journal of Korean Chemical Society accepts creative research papers in all fields of pure and applied chemistry including chemical education written by in Korean and English. - Physical Chemistry - Inorganic Chemistry - Analytical Chemistry - Organic Chemistry - Biochemistry - Macromolecular Chemistry - Industrial Chemistry - Materials Chemistry - Chemical Education