{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T13:08:32Z","timestamp":1763644112743},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684444","type":"print"},{"value":"9781643684451","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,30]]},"abstract":"<jats:p>linear regression is a common technique in machine learning, in the process of our use of machine learning, we will inevitably use linear regression for statistics, this time we used the correlation model of linear regression to study part of the public data set of Olist store Brazilian e-commerce in 2016-2018. The OLS method is mainly adopted, and the relevant sales volume and total sales price are predicted according to the existing data, and the relevant data in the future are predicted. In solving this problem, we have encountered many problems, including the classification of data, and which specific model can be selected to make the fitting effect better is a problem that we need to repeatedly consider.<\/jats:p>","DOI":"10.3233\/faia230892","type":"book-chapter","created":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:57:21Z","timestamp":1701446241000},"source":"Crossref","is-referenced-by-count":1,"title":["Linear Regression Predicts Future Sales and Selling Prices"],"prefix":"10.3233","author":[{"given":"Jiheng","family":"Xin","sequence":"first","affiliation":[{"name":"Major Internet of Things, Academy Hebei University of Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Advances in Artificial Intelligence, Big Data and Algorithms"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230892","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T15:57:23Z","timestamp":1701446243000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230892"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,30]]},"ISBN":["9781643684444","9781643684451"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230892","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,30]]}}}