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This research suggests a model based on machine learning to analyse customer preferences and forecast the demand for new products. To understand customer preferences, the fitting room data are analysed, and customer profiles are created. <jats:italic>K<\/jats:italic>\u2010means clustering, an unsupervised machine learning algorithm, is applied to form clusters by grouping similar profiles. The clusters were assigned weights related to the percentage of product in each cluster. Following the clustering process, a decision tree classification model is used to classify the new product into one of the predefined clusters to predict demand for the new product. This demand forecasting approach will enable retailers to stock products that align with customer preferences, thereby minimising excess inventory.<\/jats:p>","DOI":"10.1155\/2024\/8425058","type":"journal-article","created":{"date-parts":[[2024,7,11]],"date-time":"2024-07-11T13:11:52Z","timestamp":1720703512000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Demand Forecasting Model Leveraging Machine Learning to Decode Customer Preferences for New Fashion Products"],"prefix":"10.1155","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0114-8538","authenticated-orcid":false,"given":"S.","family":"Anitha","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9737-322X","authenticated-orcid":false,"given":"R.","family":"Neelakandan","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2024,7,11]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"crossref","DOI":"10.1155\/2019\/9067367","article-title":"An improved demand forecasting model using deep learning approach and proposed decision integration strategy for supply chain","volume":"2019","author":"Kilimci Z. 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