Predictive Analytics for Fashion Retail A Data Mining Approach

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.