Predictive Analysis in Business Analytics: Application of Decision Tree in Business Decision Making

Q2 Decision Sciences Advances in Decision Sciences Pub Date : 2021-12-28 DOI:10.47654/v26y2022i1p1-29
Chee Sun Lee, Peck Yeng Sharon Cheang
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引用次数: 15

Abstract

Business Analytics was defined as one of the most important aspects of combinations of skills, technologies and practices which scrutinize a corporation’s data and performance to transpire a data driven decision making analysis for a corporation’s future direction and investment plans. In this paper, much of the focus will be given to the predictive analysis which is a branch of business analytics which scrutinize the application of input data, statistical combinations and intelligence machine learning (ML) statistics on predicting the plausibility of a particular event happening, forecast future trends or outcomes utilizing on hand data with the final objective of improving performance of the corporation. Predictive analysis has been gaining much attention in the late 20th century and it has been around for decades, but as technology advances, so does this technique and the techniques include data mining, big data analytics, and prescriptive analytics. Last but not least, the decision tree methodology (DT) which is a supervised simple classification tool for predictive analysis which be fully scrutinized below for applying predictive business analytics and DT in business applications
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商业分析中的预测分析:决策树在商业决策中的应用
商业分析被定义为技能、技术和实践结合的最重要方面之一,它审查公司的数据和绩效,为公司的未来方向和投资计划提供数据驱动的决策分析。在本文中,大部分重点将放在预测分析上,这是业务分析的一个分支,它审查输入数据、统计组合和智能机器学习(ML)统计在预测特定事件发生的合理性方面的应用,利用手头数据预测未来趋势或结果,最终目标是提高公司的绩效。预测分析在20世纪后期获得了很多关注,它已经存在了几十年,但随着技术的进步,这种技术也在发展,其中包括数据挖掘、大数据分析和规范分析。最后但并非最不重要的是决策树方法(DT),它是一种用于预测分析的监督简单分类工具,下面将对其进行全面审查,以便在业务应用中应用预测业务分析和DT
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来源期刊
Advances in Decision Sciences
Advances in Decision Sciences Mathematics-Applied Mathematics
CiteScore
4.70
自引率
0.00%
发文量
18
审稿时长
29 weeks
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