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.