Chapter - Exploring the Usage of Data Science Techniques for Assessment and Prediction of Fashion Retail - A Case Study Approach

Abstract

In this article, the insights of a garment retail store have been studied with respect to the attributes of the dresses and sales information. Mention that each dress in fashion retail has several attributes or features. These features play a critical role in the selection of consumers or customers. This study tries to establish the relationship among these features by which the importance of the attributes is evaluated concerning sales. Furthermore, this paper tries to automate the process of the recommendation of the dresses by using these attributes. It is merely a binary classification but useful for retail sales. Moreover, the demand for sales is estimated over a period. All these objectives are achieved through using one or more data science techniques. The case study shows that the algorithms of data science are helpful in the decision-making of fashion retail.

Keywords: Categorical Features, Data Wrangling, Feature Engineering, Market Basket Analysis, Time Series Forecasting.

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