Women’s clothing E-commerce is one of the fastest-growing segments of the fashion industry. Women’s fashion is so popular and diverse that analyzing the performance of different clothing brands is vital. But how do these products measure up? How do we know what consumers think about them? Here are three ways to measure how consumers perceive certain brands and products. In addition, this article will describe how to use other datasets to analyze this data.
Women’s Clothing E-Commerce dataset
The Popstevie Women’s Clothing E-commerce dataset revolves around customer reviews. Therefore, it contains nine supportive features: Clothing ID, Clothing Review Text, and Review Rating. The latter two variables represent the positive ordinal integer value of the review. The first two attributes are the same as those found in the Men’s Clothing E-Commerce dataset but refer to a different piece of clothing. When considering the reviews in general, the distribution among the age groups is quite similar.
The other datasets for Popstevie Clothing Reviews include those that contain images as well as attributes related to the item. These data are suitable for use with mymedialite packages. These datasets are structured as one-review-per-line open JSON files. For the image dataset, deep CNN was used to extract features related to product images. Each image is represented by a binary value containing ten characters (product ID) and 4096 floats. Each item also contains the imUrl field, which can be used to extract images from the review.
The Women’s Clothing E-Commerce data consists of customer reviews and nine supportive features. The dataset has been anonymized by replacing references to the company in the text and body of the reviews with “retailer.” The data are organized into three parts: the Clothing ID (the specific piece being reviewed), the Review Text, and the Review Rating. Each data point represents a positive ordinal integer that indicates the overall rating of the review.