Abstract
The Fashion industry is one of the extensive, changeable, and growing businesses to exist. It encompasses fashion retailing which functions as a mediator between the manufacturers and clients. On account of the inconsistency of this industry, maximizing sales has been a crucial task. The objective of this research study is to analyze and explore product and consumer behavior and thereby maximize sales in the fashion retail industry for women’s clothing to overcome the struggles regarding gaining sales confronted by the industry. The emergence of big data and machine learning has a positive influence on fashion retailing. ML has been utilized in this research to implement a web application that aids in optimizing sales. It comprehends sales forecasting, customer segmentation, and customer demand analytics. Each research component obtains diverse inputs to initialize the prediction and visualization procedure. The models are built employing the Extra Trees Regressor algorithm, K-means algorithm, and Naïve Bayes algorithm. Finally, for specified inputs, results will be predicted that comprise sales forecasts for products, segmentation of consumers, and forecasts about most demanded fashion item’s characteristics. This paper portrays the proceedings of data preparation, model development, and results of each research component.
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