Machine Learning based Sales Prediction and Characterization using Consumer Perceptions

J. Sreemathy, N. Prasath
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Abstract

In today’s age of automated scenarios and digital lifestyle, online shopping has really made its way to everyone’s household, with one touch anyone can order the required products. The use of digital marketing over conventional marketing is often favored. It is beneficial to both social media marketing professionals and technicians. When conducting research, one may gain preliminary insights into consumers’ perceptions of social media advertisements and online buying habits. Online knowledge exchange allows researchers, academics, and business people to swiftly and easily connect with individuals while conducting searchable mobile brand website research. This research provides a methodical description of a study that only aids consumers in making the optimal smartphone decision for their own parametric needs. A given dataset will be examined utilising machine learning methods, such as brand name predictions with regression and precise results. Groups of people are frequently paid by brands to create internet evaluations, which may be favourable to them or unfavourable to their competitors.
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使用消费者感知的基于机器学习的销售预测和表征
在当今自动化场景和数字化生活方式的时代,网上购物已经真正进入了每个家庭,只要轻轻一触,任何人都可以订购所需的产品。数字营销比传统营销更受青睐。这对社会媒体营销专业人员和技术人员都是有益的。在进行研究时,可以初步了解消费者对社交媒体广告的看法和在线购买习惯。在线知识交换允许研究人员、学者和商业人士在进行可搜索的移动品牌网站研究时快速、轻松地与个人建立联系。这项研究提供了一项研究的系统描述,该研究仅帮助消费者根据自己的参数需求做出最佳的智能手机决策。给定的数据集将使用机器学习方法进行检查,例如带有回归和精确结果的品牌名称预测。品牌经常雇佣一些人在网上进行评估,这些评估可能对自己有利,也可能对竞争对手不利。
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