A Study on the Development of Product Planning Prediction Model Using Logistic Regression Algorithm

IF 0.3 Q4 CHEMISTRY, MULTIDISCIPLINARY
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.
基于Logistic回归算法的产品计划预测模型开发研究
本研究旨在提出一种使用逻辑回归算法预测季节因素和快速变化的产品趋势的产品计划预测模型。首先,我们使用网络爬行收集了门户网站和在线市场中消费者的非结构化数据,并通过标准化数据转换的预处理分析了有关产品的有意义的信息。采用Logistic回归分析11200个数据集,分析消费者满意度、频率分析以及产品的优缺点。分析结果表明,消费者的满意度为92%,通过频率分析确认了产品的缺陷问题。对所开发的产品计划预测程序的使用满意度、系统效率和系统有效性项目进行分析,结果表明满意度较高。缺陷问题是非常有意义的数据,因为它们提供了快速识别产品当前问题和制定改进策略所需的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.60
自引率
40.00%
发文量
0
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
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