Authors : Pankaj Kumar Gupt; Dr. Anita Pal

Volume/Issue : Volume 10 - 2025, Issue 2 - February

Google Scholar : https://tinyurl.com/549r5f6j

Scribd : https://tinyurl.com/24pckbub

DOI : https://doi.org/10.5281/zenodo.14964552

Abstract : Predictive analytics in fashion retail leverages data mining techniques to forecast trends, customer preferences, and inventory demands. This paper explores the application of data mining algorithms to predict sales, optimize stock levels, and enhance customer experiences. By analyzing historical sales data, customer behaviors, and external factors, the study aims to provide insights that empower retailers to make informed business decisions. The research adopts a structured methodology, employing machine learning models and data preprocessing techniques to ensure accurate predictions. Results indicate significant improvements in inventory management and customer satisfaction, validating the potential of predictive analytics in fashion retail.

Keywords : Predictive Analytics, Fashion Retail, Data Mining, Machine Learning, Sales Forecasting, Inventory Optimization, Customer Behavior.

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