Exloratory Data Analysis Untuk Data Belanja Pelanggan dan Pendapatan Bisnis

Widi Hastomo, Adhitio Satyo Bayangkari Karno, Sudjiran, Dodi Arif, Eka Sally Moreta
{"title":"Exloratory Data Analysis Untuk Data Belanja Pelanggan dan Pendapatan Bisnis","authors":"Widi Hastomo, Adhitio Satyo Bayangkari Karno, Sudjiran, Dodi Arif, Eka Sally Moreta","doi":"10.35970/infotekmesin.v13i2.1547","DOIUrl":null,"url":null,"abstract":"A more quantifiable perspective is assuming the role of mechanistic management in an effort to enhance business based on its capacity to transform data into knowledge and insight. The industry has not completely supported its business strategy also with driven data. Using a transaction dataset taken from one of the Kaggle.com challenges, this experiment attempts to determine consumer spending patterns and Retail Fashion business revenues (H&M Personalized Fashion Recommendations). The results of the experiment are the number of transactions based on customer age, the most sales product and one-time purchased item, and the type of product that generates the highest and smallest income. The approach employed is EDA using the Python language. In order for businesses to generate analytical findings that provide future perspectives and to help identify the gap by delivering analytical results in the form of suggestions that can be perpetuated, the findings of this experiment are intended to support the capabilities of simulation. The challenge in this experiment is the abundance of datasets, which necessitates a suitable operating environment.","PeriodicalId":33598,"journal":{"name":"Infotekmesin Media Komunikasi Ilmiah Politeknik Cilacap","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Infotekmesin Media Komunikasi Ilmiah Politeknik Cilacap","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35970/infotekmesin.v13i2.1547","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

A more quantifiable perspective is assuming the role of mechanistic management in an effort to enhance business based on its capacity to transform data into knowledge and insight. The industry has not completely supported its business strategy also with driven data. Using a transaction dataset taken from one of the Kaggle.com challenges, this experiment attempts to determine consumer spending patterns and Retail Fashion business revenues (H&M Personalized Fashion Recommendations). The results of the experiment are the number of transactions based on customer age, the most sales product and one-time purchased item, and the type of product that generates the highest and smallest income. The approach employed is EDA using the Python language. In order for businesses to generate analytical findings that provide future perspectives and to help identify the gap by delivering analytical results in the form of suggestions that can be perpetuated, the findings of this experiment are intended to support the capabilities of simulation. The challenge in this experiment is the abundance of datasets, which necessitates a suitable operating environment.
客户和企业采购数据的丰富数据分析
一个更可量化的视角是承担机械化管理的角色,努力基于将数据转化为知识和洞察力的能力来增强业务。该行业还没有完全支持其商业战略,也没有用数据驱动。本实验使用Kaggle.com挑战中的一个交易数据集,试图确定消费者支出模式和零售时尚业务收入(H&M个性化时尚推荐)。实验的结果是基于客户年龄的交易次数、销售最多的产品和一次性购买的物品,以及产生最高和最小收入的产品类型。所采用的方法是使用Python语言的EDA。为了让企业产生分析结果,提供未来的视角,并通过以建议的形式提供分析结果来帮助识别差距,本实验的结果旨在支持模拟的能力。这个实验的挑战是数据集的丰富性,这需要一个合适的操作环境。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
30
审稿时长
12 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信