User Based Sale Optimization for Fashion Retail

Abstract

The fashion industry is a vibrant and large commercial sector that is known for its ongoing evolution and expansion. Fashion retailing plays a crucial role in this area by acting as a link between producers and customers. The industry's inherent volatility has made maximizing sales performance a crucial requirement. The main objective of this study is to better understand consumer behavior and product dynamics to increase sales in the fashion retail industry. This study employs big data and machine learning, leveraging its revolutionary potential, to create a web application specifically designed for women’s clothing to overcome the struggles regarding gaining sales confronted by the industry. The recommendation algorithm is made by using customer segmentation, and predictive customer demand analytics, which are all easily integrated into the platform. Each component of the research pipeline uses a different set of inputs to launch the prediction and visualization processes. The K-means algorithm and the Naive Bayes algorithm serve as the foundation for the built-in models. The research's conclusion is the creation of anticipated consequences for particular inputs. These findings include predictions of product-specific sales, consumer categorization insights, and predictions of the characteristics of the most in-demand fashion goods. The current study carefully explains the sequential steps of data preparation, model creation, and the results that each component of this multidimensional research project produced.