Looking for Fashion Datasets For Your Data Science Projects?
Best publicly available fashion datasets for your data science projects.
I know the search for fashion datasets could be daunting, especially when you need quantitative datasets as a beginner or ideas on possible data science projects to do.
That’s why I have put together some open-source fashion datasets that would be useful for your data science projects giving you a feel of fashion data analytics.
The datasets are categorized into product listings, product reviews, image data, and text data. Each dataset would have a brief description and a link to its source.
NB:
This resource will continually be updated and if you have more fashion dataset sources, please share in the comment session, or send to info@data4fashion.org.
Find the datasets below:
Product Listings
Adidas Fashion Retail Products Dataset: This is a comma-separated file with information on over 1500 Adidas fashion products. It has 21 columns with variables such as name, selling price, original price, currency, availability, color, brand, country, average rating, and review count. Suitable for fashion retail analysis.
Adidas Vs Nike: The dataset consists of 3268 products from Nike and Adidas with 10 columns and fields such as ratings, discount, sales price, listed price, product description, and the number of reviews. Suitable for clustering and competitor analysis.
Amazon Men’s Shoes: It consists of 17 columns and 10,000 rows with fields such as unique_id, price, review, manufacturer, etc.
Clothes-Size-Prediction: A relatively small dataset with 4 columns and fields such as weight, age, height, and size. It is suitable for predicting clothing sizes for online purchases.
Users of a C2C Fashion Store: This data was scraped from an online C2C fashion store with over 9 million registered users. It consists of 98,913 rows, and 27 columns with table names such as country, language, products sold,gender, etc.
Fashion Clothing Products Dataset: This dataset consists of 8 columns and 12,000 products from Myntra, a major Indian fashion e-commerce site.
Fashion Product Data: It consists of both an image and a CSV file with table headers like gender, season, year, colour, etc. Suitable for image classification and product analysis.
Gymboree fashion products dataset: This dataset has 395 rows of product listings from Gymboree.
H&M Product Dataset: Product information and images on the purchase history of customers across time.
Myntra Fashion Product Dataset: Myntra’sproduct listings with 11 columns with headers like price, name, colour, brand, rating, etc
Myntra Mens Product Dataset: Myntra's product listings on men's fashion items.
Myntra Men’s T-shirt: This dataset includes all information about all the products available in the men’s t-shirt section on myntra.com. Table headings include price, brand, price, discount, etc.
Myntra_Fashion_Products: This consists of a CSV file with 10 columns and column headings such as SKU, price, product name, product in stock or not, etc.
Nike fashion products dataset: Nike US fashion products dataset with 17 fields such as name, subtitle, brand, model, color, price, currency, availability, description, rawdescription, avgrating, reviewcount, etc.
Wish E-commerce summer clothes sales data: It consists of summer-related products that were available for sale as of July 2020. 43 columns and column names such as units sold, currency, ratings, rating counts, etc.
Women’s Shoe Prices: A list of 10,000 women’s shoes and various product information, including their various prices.
Product Reviews
Amazon reviews on Women's Dresses: Customer feedback on the items purchased.
Women’s E-Commerce Clothing Reviews: This dataset consists of product reviews written by customers.
Image Datasets
The following datasets consist of fashion images suitable for computer vision, product recommendation, and other deep-learning projects.
Fashion Product Images Dataset
Nike & Adidas Shoes for Image Dataset
Text Datasets
Here are a few text datasets for Natural Language Processing (NLP) projects.
Fashion conversation data on Instagram
And it’s a wrap!
As mentioned earlier, this resource will be continuously updated and open to contributions. Please let me know your experience with using any of these datasets in the comments section.
Hope you enjoyed reading this article as much as I enjoyed writing it.
Don’t hesitate to drop your questions and contributions in the comment session.
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CHEERS!
