Fashion Dataset – Clothing, Accessories, Dresses, Bags & Apparel

Fashion dataset from target

The Fashion Dataset offers a comprehensive collection of product-level data covering multiple fashion categories including clothing, accessories, dresses, bags, and apparel. With structured fields and metadata, this dataset provides a robust foundation for e-commerce, AI, analytics, and research use cases.

Fashion is one of the most dynamic industries, constantly evolving with new trends, consumer preferences, and product innovations. This dataset captures fashion-related product information across a wide spectrum, ranging from everyday clothing to high-demand accessories. Each record contains essential details along with category and breadcrumb hierarchies, making it easy to organize, analyze, and integrate into various applications.

Researchers, developers, and businesses can leverage this dataset for multiple purposes. It can serve as training data for AI and NLP models, enhancing their understanding of fashion-specific terminology and context. It also supports trend and market analysis, helping retailers and brands track consumer interest across categories like clothing, bags, and dresses. For recommendation systems, this dataset enables personalized product suggestions, improving user experience in online shopping platforms. Additionally, regulatory and compliance teams can use the structured data to verify labeling, product classification, and category accuracy.

The Fashion Dataset is designed for flexibility, with records organized into major categories:

• Clothing: A wide range of garments covering multiple styles and types.

• Accessories: Fashion items that complement clothing, including jewelry and more.

• Dresses: Formal and casual dress listings with structured data.

• Bags: Handbags, purses, and other fashion bags.

• Apparel: Broader category items classified under general apparel.

By combining structured product data with category hierarchies, this dataset empowers users to conduct retail intelligence, consumer behavior analysis, product classification, and AI-driven insights. It is a valuable resource for businesses seeking to innovate in the digital fashion economy.

Note: Each record includes both a url (main product page) and a buy_url (purchase page). Records are based on the buy_url to ensure unique, product-level data rather than generic landing pages.

🌟 Highlights

• Comprehensive coverage of fashion products across multiple categories.

• Clean, structured data with category and breadcrumb hierarchy.

• Useful for AI, analytics, market research, and recommendation systems.

• Includes url and buy_url fields for accurate product-level references.

 

Last crawled:

Jun 2025

Data points:

link, title, category, breadcrumbs, brand, department, avg_rating, reviews_count, reviews, tcin, buy_url, meta_data, current_price, regular_price, savings_in_percent, formatted_current_price_type, currency, upc, primary_image, alternate_images, description, specification, highlights, ingredients, nutrients, serving_size, serving_description, warning, at_a_glance, uniq_id, scraped_at