Product and Category

• A product category is a group of similar products that share related characteristics. Product category marketing focuses on promoting certain categories to meet consumer expectations. Your distinct offerings and customer personas should guide the organization and grouping of your product categories.

According to the Statista Digital Market Insights, retail e-commerce revenues from physical goods in Latin America and the Caribbean were estimated at more than *** billion U.S. dollars in 2026. By 2030, the electronics category is expected to surpass the ***** billion dollar mark in the region, while forecasts suggest that fashion items would reach revenues of around ***** billion dollars.

This Amazon Clothing and Accessories Dataset is a rich resource for building Product Recommendation Systems, performing Sentiment Analysis, and analyzing E-Commerce Trends.

It contains detailed product metadata, customer reviews, and sentiment scores, making it perfect for AI-driven insights, recommendation engines, and market research.

🔹 Key Features

• Product Details 🏷️ – Includes brand_name, model_number, title, price_value, and availability.
• Customer Reviews 💬 – Review text, average rating (rating_stars), total count (rating_count), and distribution of ratings (1–5 stars).
• Sentiment Analysis 📊 – Cleaned review text with computed sentiment scores.
• E-Commerce Metrics 🚀 – Includes best_sellers_rank, recent_purchases, seller_name, and scrape_time.
• Image Links 🖼️ – Direct URLs for product images.
• Preprocessed & Ready to Use ✅ – Ideal for recommendation systems, NLP models, and business insights.

📌 Inspiration

This dataset was designed to enhance hybrid recommendation systems by leveraging both:
- Content-based filtering (using product metadata)
- Collaborative filtering (based on reviews & ratings)

Additionally, sentiment analysis offers deeper insights into customer satisfaction and product perception, making this dataset useful for data scientists, researchers, and e-commerce strategists.

Product Data Columns

• s.no – Serial number of the product entry.
• about_item – Short description of the product.
• asin – Unique Amazon Standard Identification Number.
• availability – Availability status of the product.
• best_sellers_rank – Product ranking in Amazon’s best sellers.
• brand_name – Name of the brand.
• brand_page_url – URL to the brand’s page.
• breadcrumbs – Category navigation trail.
• customer_review_summary – Summary of customer reviews.
• default_variant/0, 1, 2 – Different product variations.
• delivery_date – Expected delivery date.
• fastest_delivery_date – Earliest available delivery date.
• list_price – Original price before discounts.
• manufacturer – Name of the manufacturer.
• model_number – Product model number.
• price_value – Current selling price.
• product_description – Detailed product description.
• product_url – Amazon product page link.
• rating_count – Total number of ratings.
• rating_distribution/1star - 5star – Breakdown of ratings by stars.
• rating_stars – Average product rating.
• recent_purchases – Recent purchase count.
• scrape_time – Timestamp when the data was collected.
• seller_name – Name of the seller.
• seller_page_url – URL to the seller’s page.
• title – Product title.
• all_images – Links to product images.
• rank_1 – Product’s rank in its category.

Review Data Columns

• s.no – Serial number of the record.
• helpfulVote – Number of users who found the review helpful.
• images/0 … images/7 – Links or placeholders for up to 8 review images.
• productASIN – Amazon Standard Identification Number of the product.
• productVariant – Variant details such as color, size, etc.
• rating – Rating given by the user (1–5 stars).
• reviewID – Unique identifier for the review.
• reviewMetadata – Metadata such as country and date of review.
• reviewPosition – Position/order of the review (if available).
• reviewText – Full textual content of the review.
• reviewTitle – Title/summary of the review.
• reviewURL – Direct URL link to the review on Amazon.
• verifiedPurchase – Whether the purchase was verified (TRUE/FALSE).
• videos/0 – Links or placeholders for video reviews (if any).
• cleaned_review_text – Preprocessed/cleaned version of the review text (lowercased, stopwords removed, etc.).
• sentiment_score – Sentiment score derived from the cleaned review text (range 0–1).

This database contains medical device names and associated information developed by the Center. It includes a three letter device product code and a Device Class that refers to the level of CDRH regulation of a given device.

A sourced compilation of published price elasticity of demand coefficients across ecommerce-relevant product categories, drawn from peer-reviewed academic research and government economic data.

This statistic shows the premium product share of selected product category sales in the United States as of 2016. As of 2016, premium products had a ** percent share of the personal care category in the United States.

📄 Overview This dataset contains a curated catalog of retail products commonly found in online and offline fashion stores. It includes detailed attributes of products such as their unique ID, name, brand, category, price, description, and color. The dataset is ideal for applications such as:

E-commerce product analysis

Price comparison engines

Recommendation systems

Brand and category analysis

Retail inventory management simulations

The Public Goods dataset contains detailed ecommerce product information including URLs, item IDs, variant IDs, titles, descriptions, product categories, category trees, and brand data. Ideal for competitive analysis, market research, price tracking, and product intelligence gathering.

The Super Arbor dataset provides product catalog information with URLs, item IDs, variant IDs, titles, descriptions, product categories, category hierarchies, and brands. Use for competitive intelligence, market research, pricing strategies, and assortment planning.

Transformed E-commerce Product Data: Ready for Analysis!

Sharpen your data analysis skills with this cleaned and enhanced e-commerce product dataset. This dataset was originally spread across 20+ CSV files with over 80,000 products, this dataset now offers a streamlined single CSV for you to focus on analysis.

What's Inside:

Clean and Consistent Data: Missing values, formatting inconsistencies, and errors have been addressed, ensuring a foundation for reliable analysis.

Organized Product Information: Explore a variety of product categories, including:

• Apparel & Accessories
• Electronics
• Home & Kitchen
• Men's Clothing
• Women's Clothing
• Swimsuits and Sleepwear
• And more!

Detailed Product Records:

• Product Title
• Category
• Price
• Discount Information
• Product subcategory
• Product rank
• Quantity sold
• Color count
• Image source URL

Dive right into creating new features from existing data, such as:

• Extracting keywords from titles and descriptions
• Deriving price categories
• Calculating average discounts

The Brother USA dataset provides detailed ecommerce product information including URLs, item IDs, variant IDs, titles, descriptions, product categories, category trees, and brand data. Ideal for competitive analysis, price tracking, market research, and product intelligence gathering.

Between 2022 and 2027, fashion and apparel is forecast to be Amazon's fastest-growing product category, with sales expected to grow at a compound annual growth rate of **** percent. The health and beauty segment is close, expected to increase at a CAGR of **** percent, followed by office products and electricals, with **** percent.

Dataset

This dataset was created by Shair Khan

Released under CC0: Public Domain

Contents

According to a survey conducted in Southeast Asia from June to August 2024, around ** percent of respondents reported having purchased beauty items due to recommendations from an influencer or celebrity. In comparison, around ** percent of respondents said they had purchased products in the travel category based on an influencer or celebrity's recommendation.

Amongst respondents who had previously participated in online video shopping events in the United States, over half (** percent) said that their favorite products to purchase in such events were items of clothing, while 17 percent answered electronics. Amongst non-watchers, clothing was also the most popular product category, with 29 percent. Notably, household goods were favored significantly more by those who didn't watch these events (** percent) than those who did (**** percent).

Access The Tile Shop dataset with product URLs, item IDs, variant IDs, titles, descriptions, product categories, category trees, and brand information. Ideal for competitive analysis, market research, price monitoring, and ecommerce intelligence across the tile retail sector.

The Online Sales Dataset provides a detailed overview of global online sales transactions across various product categories. It includes transaction details such as order ID, date, product category, product name, quantity, unit price, total price, region, and payment method.

The KaTom dataset provides detailed restaurant supply product information including URLs, item IDs, variant IDs, titles, descriptions, product categories, category trees, and brand data. Ideal for competitive analysis, market research, price tracking, and ecommerce intelligence.

The Global E-Commerce Market worth USD 6,419 billion in 2025 is growing at a CAGR of 7.20% to reach USD 9,743 billion by 2031. Amazon, Alibaba Group, JD.com, PDD Holdings and Mercado Libre are the major companies operating in this market.