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Fashion Product & Seller Analytics Dataset
Fashion & Apparel Trends
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Free
About
Product listings from Vestiaire Collective, an online marketplace for pre-owned luxury fashion items, offer a detailed look into the second-hand luxury market. This information was gathered using Python and the Hrequests Library, providing a basis for analysing trends in brands, pricing, and product types. It is particularly useful for understanding current market dynamics, geographical activity of buyers and sellers, and for building predictive models for item pricing.
Columns
- product_id: The unique identifier for the product.
- product_type: The type of product (e.g., Jacket, Sunglasses).
- product_name: The name of the product.
- product_description: A description of the product.
- product_keywords: Keywords associated with the product listing.
- product_gender_target: The intended gender for the product (e.g., Women, Men).
- product_category: The category the product belongs to (e.g., Women Clothing, Men Clothing).
- product_season: The suitable season for the product (e.g., All seasons, Autumn / Winter).
- product_condition: The condition of the item (e.g., Very good condition, Never worn).
- product_like_count: The number of likes the product has received.
- sold: A boolean value indicating if the product has been sold.
- reserved: A boolean value indicating if the product is reserved.
- available: A boolean value indicating if the product is available.
- in_stock: A boolean value indicating if the product is in stock.
- should_be_gone: A boolean value indicating if the product should be gone.
- brand_id: The unique identifier for the brand.
- brand_name: The name of the brand (e.g., Gucci, Burberry).
- brand_url: The URL for the brand's page on Vestiaire Collective.
- product_material: The primary material of the product (e.g., Leather, Cotton).
- product_color: The colour of the product (e.g., Black, Blue).
- price_usd: The price of the product in US Dollars.
- seller_price: The price set by the seller.
- seller_earning: The amount the seller earns from the sale.
- seller_badge: The seller's badge or status (e.g., Common, Expert).
- has_cross_border_fees: A boolean value indicating if cross-border fees apply.
- buyers_fees: The fees paid by the buyer.
- warehouse_name: The location of the warehouse (e.g., Tourcoing, Brooklyn).
- seller_id: The unique identifier for the seller.
- seller_username: The username of the seller.
- usually_ships_within: The typical timeframe for shipping (e.g., 1-2 days).
- seller_country: The country of the seller.
- seller_products_sold: The total number of products sold by the seller.
- seller_num_products_listed: The total number of products listed by the seller.
- seller_community_rank: The seller's rank within the community.
- seller_num_followers: The number of followers the seller has.
- seller_pass_rate: The seller's pass rate metric.
Distribution
The data is provided in a single CSV file named
vestiaire.csv with a size of 493.09 MB. It contains approximately 900,000 rows and 36 columns.Usage
- Trend Analysis: Investigate current trends in the second-hand luxury fashion market, including popular brands, product types, and pricing strategies to understand market dynamics.
- Geographical Analysis: Analyse the most active countries for buyers and sellers on the platform and identify demographic trends in different regions.
- Item Price Prediction: Use machine learning algorithms to build models that can predict the price of listed items based on features like brand, condition, and category.
Coverage
The dataset covers product listings from Vestiaire Collective, an online marketplace. It includes geographical data such as seller country (88 unique countries identified, with Italy being the most common) and warehouse locations (e.g., Tourcoing, Brooklyn). Demographic information is limited to the gender target for products, which is predominantly 'Women' (54%) and 'Men' (46%). A specific time range for the listings is not provided.
License
CC0: Public Domain
Who Can Use It
- Data Scientists and Analysts: Can perform trend analysis, market research, and build predictive pricing models.
- Business Strategists: Can analyse market trends and geographical user activity to inform business decisions in the retail and e-commerce sectors.
- Machine Learning Engineers: Can use the data to develop and train regression models for price prediction.
- Market Researchers: Can study consumer behaviour and preferences within the luxury resale market.
Dataset Name Suggestions
- Second-Hand Luxury Fashion Listings
- Vestiaire Collective Product Data
- Pre-Owned Luxury Fashion Market Analysis
- Luxury Resale E-commerce Listings
- Fashion Product & Seller Analytics Dataset
Attributes
Original Data Source: Fashion Product & Seller Analytics Dataset
QUALITY