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Product & Retail Datasets
8 listingsRetail data feeds recommendation engines, catalog enrichment, and e-commerce market analysis. This category covers product catalogs, SKU-level attributes, customer reviews, and inventory snapshots from real storefronts. Field coverage varies by seller, so check the declared schema and sample rows to confirm the attributes your pipeline needs are present.
Multilingual Amazon Product Reviews — 2.5M Labeled
2,520,000 Amazon product reviews in English, Japanese, German, French, Chinese, and Spanish (collected 2015–2019), each labeled with a star rating. Built for multilingual sentiment analysis and text classification.
CPU Activity (cpu_act)
Classic regression benchmark predicting CPU user-mode utilization from 21 system activity measures collected on a Sun Sparcstation. 8,192 rows, widely used in ML evaluation.
Adult (Census Income) — UCI/OpenML Benchmark
Classic UCI 'Adult' census income dataset (~48K rows, 14 features) for predicting whether income exceeds $50K/yr. Widely used for tabular ML benchmarking, fairness research, and AutoML evaluation.
Telco Customer Churn Prediction (IBM Sample)
Classic IBM telco customer churn dataset (~7K rows) with demographics, service subscriptions, account info, and churn label. Tabular CSV, ideal for ML classification tutorials, benchmarks, and agent-driven feature engineering.
Cirrus SR22 USA For-Sale Listings + 10,816 Photos — May 2026 Snapshot
The complete pre-owned Cirrus SR22 market in the United States as of May 19, 2026 — 314 aircraft listed across Controller, Trade-A-Plane, GlobalAir, and Barnstormers, N-number-deduplicated and joined to the FAA Aircraft Registry and NTSB event history. Includes 47 structured fields per aircraft (price, hours, avionics, damage history, location) plus 10,816 bundled listing photos (~1.25 GB).
Open Food Facts Product Database
1.7M+ food products with ingredients, allergens, nutrition facts, and label data from 150 countries, contributed by 25k+ volunteers. Multilingual tabular dataset under ODbL/AGPL.
Digital Commerce Readiness — 261 Countries (1980–2023)
Expansive dataset of 25 digital commerce, trade, and economic readiness indicators for 261 countries spanning 1980-2023. Sourced from World Bank Open Data API, covering internet penetration, mobile subscriptions, broadband access, ICT trade flows, logistics performance, high-tech exports, consumer spending patterns, GDP metrics, labor force statistics, and urbanization. Ideal for e-commerce market analysis, cross-country digital divide research, retail expansion planning, and economic development studies. Data is normalized with ISO3 country codes and cleaned for quality (minimum 3 non-null indicators per row).
US FDA Safety Recalls & Enforcement Actions — 45,000 Records (Food, Drug, Device)
Wide-coverage dataset of 45,000 US FDA enforcement actions spanning food, drug, and medical device recalls. Sourced from three openFDA enforcement APIs and unified into a single normalized schema with 22 columns. **Sources:** - openFDA Food Enforcement API (15,000 records) - openFDA Drug Enforcement API (15,000 records) - openFDA Device Enforcement API (15,000 records) **Coverage:** Recalls from across the United States and international markets, spanning multiple years of FDA enforcement activity. **Schema (22 columns):** - `recall_number` — unique FDA recall identifier - `product_type` — food, drug, or device - `event_id` — FDA event identifier - `status` — Ongoing, Terminated, Completed - `classification` — Class I (dangerous/defective), Class II (may cause health problems), Class III (unlikely to cause harm) - `recalling_firm` — company issuing the recall - `city`, `state`, `country` — firm location - `voluntary_mandated` — whether the recall was voluntary or FDA-mandated - `initial_firm_notification` — how the public was notified - `product_description` — detailed product description - `reason_for_recall` — why the recall was initiated - `distribution_pattern` — geographic distribution of the product - `product_quantity` — amount of product recalled - `code_info` — lot numbers, UPC codes, expiration dates - `recall_initiation_date` — when the recall started (ISO 8601) - `center_classification_date` — when FDA classified the recall - `report_date` — when the recall was reported - `termination_date` — when the recall ended (if applicable) - `recall_year` — extracted year for easy filtering - `recall_class_num` — numeric class (1, 2, or 3) for sorting/analysis **Use cases:** Product safety analytics, regulatory compliance research, supply chain risk assessment, consumer protection analysis, ML classification models, public health surveillance.
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