Mastering Beauty Product Datasets: From Ingre...
The beauty industry is evolving fast—and data is at t...
Do you want to build smarter beauty AI?
Data is the first step. Raw models need real product examples. Beauty Feeds offers ready-made beauty product datasets you can use today. These samples speed up experiments and help test models on real-world product data.
What are sample beauty datasets?
Sample datasets are trimmed exports of real product listings.
They include names, descriptions, brands, URLs, and sometimes ingredients.
They let you test pipelines without building a crawler. Beauty Feeds provides several such samples and documentation on how to use them.
Which datasets does Beauty Feeds offer?
Beauty Feeds currently lists six sample datasets. They contain thousands of records and are free to download, provided attribution is given.
Amazon (US) — Eye Makeup with Ingredients
This dataset 3,280 eye makeup listings with ingredient fields. Use it for ingredient NER, allergy filters, and “clean beauty” classifiers. Great for prototyping product-safety features.
Amazon (UK) — Face Product Listings
A UK-facing export of 3,482 face-care and makeup SKUs. Use it to test regional taxonomy, search ranking, and catalog mapping. Helpful for models that must handle locale-specific titles and sizes.
BeautyBay — Product Listings
A focused set of 394 BeautyBay records. Use this small sample for UI prototyping, label testing, and quick feature checks without heavy compute.
Skincare & Hair Care — Ingredients (multiple retailers)
1,539 records with ingredient-level detail from several sellers. Ideal for formulation analysis, ingredient clustering, and developing ingredient-based recommendation filters.
Priceline — Skincare Product Listings
A 1,980-record export that reflects catalog placement and naming conventions used by Priceline (AU). Use it to test category mapping and price/availability features.
Ulta — Lipstick Product Listings
2,090 lipstick SKUs from Ulta. Use this to build product-type classifiers, analyze naming patterns, or run A/B tests on product-tagging rules.
How can these datasets power AI and ML projects?
Short answer: they let you build and validate faster. Long answer below.
- Train named-entity recognition (NER) for ingredients and brand extraction.
- Build product-taxonomy models and category mappers.
- Prototype recommendation and search relevance features.
- Test data pipelines, schema mapping, and export flows before scale.
Good data practices matter. Poor labels or messy ingredient fields will break your model. Research shows data quality has a measurable effect on ML performance and reliability. Use curated samples to spot these issues early.
Where else should you look for datasets and benchmarks?
Public repositories help you compare approaches. UCI hosts classic ML datasets used for benchmarking. Kaggle is another place to find real-world datasets and community notebooks that show how others preprocess data. Use these to shape baselines or augment your beauty data.
How to download and test BeautyFeeds samples
Each sample page provides a free Excel download. You’ll typically enter an email and get a link. The files include clear field lists and are updated (July 2025 on several samples). When sharing, include the attribution noted on the site.
If you want quick guides on how to use product datasets, read Beauty Feeds’ blog posts about data-as-a-service and dataset best practices. They show practical use cases and implementation tips.
Quick checklist for AI teams (use these before training)
- Inspect ingredient fields for consistent separators.
- Confirm SKU-level uniqueness.
- Normalize brand names.
- Map categories to your taxonomy.
- Hold out a retailer-based test set (e.g., train on Amazon, test on Ulta).
These steps improve model transfer across stores and reduce label noise. Research supports profiling and cleaning data before model training.
Conclusion — so, are beauty product datasets essential?
Yes. They cut months of scraping work. They give you real product signals for model training. They reveal data quality gaps fast. Beauty Feeds’ sample datasets are a low-cost way to run experiments on real cosmetics data. Start with a small sample. Test your pipeline. Then scale. Explore the sample datasets and read the practical guides on the BeautyFeeds blog to move faster.
Want next steps?
Download a sample to test an ingredient NER. Or read the price-tracking guide to see how product feeds support competitive analytics.
