For Data Scientists & ML Engineers · Personal Care Products
Personal Care Products Data for Data Scientists
Personal Care Products data for data scientists: 7 datasets on one shelf. Every one delivered as API, files, or warehouse rows.
financial time series api for backtesting · alternative data for quantitative research · where to get training data for personal care products models
API, files, or your warehouse. Daily, weekly, or hourly.
Which personal care products datasets should data scientists pull first?
The full 11-record slice - the seven primaries ranked here plus four pooled secondaries such as OpenFDA and USDA FoodData Central - lives on the personal-care-products data hub.
How do you join these sources into one modeling table?
Normalize first, join second. Treat EU CosIng as the ingredient dictionary: parse each product's INCI string, resolve tokens against CosIng's INCI, CAS/EC and function fields, and keep annex references and SCCS opinions as compliance features for Regulation 1223/2009 screening. Open Beauty Facts supplies the product spine - GTIN barcodes plus brand and category fields - so exploded product-to-ingredient rows accept COSDNA's acne and irritant scores and INCIDecoder's ratings as a clean left join on resolved ingredient names. Retail signals resist keying: Sephora's JSON-LD pages carry price, rating and shade data but share no identifier with OBF barcodes, so brand-plus-name fuzzy matching is the realistic bridge.
Straight answers
Is there a financial-style time series API here for backtesting?
Not directly - nothing here prices securities or tracks a market clock. The closest analogs are Sephora and Ulta, whose storefronts mirror prices, star ratings and assortment in real time for cross-sectional pricing panels, plus Open Beauty Facts' 14-day delta exports for point-in-time snapshots. COSDNA's dated community comments reach back to 2014.
Can personal care products data serve as alternative data for quantitative research?
Yes, at the consumer-demand margin. Review velocity in the ~700,000-row commercial delivery terms Amazon corpus gives a sentiment signal you can align to launch dates.
Where do I get training data for personal care products models?
Match the modality. Product categorisation and INCI parsing: Open Beauty Facts' 73,522-product dumps. Compliance classifiers: EU CosIng's ~24,526 annotated ingredient records.
Rows before rollout
Sample rows from any shelf entry — the field dictionary and coverage notes ride along. If the shelf misses what you need, say so; sourcing requests are half our job.
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