About the Dataset:

Amazon is one of the biggest online retailers in the USA that sells over 12 million products. With this dataset, you can get an in-depth idea of what products sell best, which SEO titles generate the most sales, the best price range for a product in a given category, and much more.

If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

Similar datasets:

Amazon UK Products

Amazon Canada Products

Amazon India Products

Interesting Task Ideas:

1. Uncover trending product categories and their sales performance.
2. Analyze customer ratings to find top-rated products.
3. Train a product title generator that can generate sales-worthy product titles based on products with the most sales.
4. Gain insight into the best price for any given product based on sales data and competition.
5. Identify which niches are the easiest to make sales in.
6. Gain insights into the general spending habits of online shoppers.
7. Use as a seed dataset for practicing database management and performance optimization.
8. Train (or learn to train) an AI-based search model to recommend Amazon products.

Checkout my other datasets

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List of products with the attributes

• S.No
• BrandName
• Product ID
• Product Name
• Brand Desc
• Product Size
• Currency
• MRP
• SellPrice
• Discount

• Category

  Kari, Venkatram (2023), “Product Dataset”, Mendeley Data, V1, doi: 10.17632/v8yt3r8th2.1

Popular e-commerce product categories with buying guides, pricing data, and SEO fields. Perfect for building product category landing pages, affiliate comparison sites, and e-commerce stores.

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.

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.

This dataset provides a structured collection of 100 e-commerce products, including key attributes such as pricing, stock availability, ratings, discounts, and sales performance. It is designed for data analysis, business intelligence, and machine learning applications, enabling users to derive insights into e-commerce trends, pricing strategies, and customer preferences.

The dataset includes a variety of product categories, ranging from electronics and clothing to home essentials, making it useful for diverse analytical tasks, including predictive modeling, recommendation systems, and sales trend analysis.

Dataset Features: Each record in the dataset represents a unique product with the following attributes:

Product ID: A unique identifier assigned to each product. Product Name: The name or title of the product as listed on the e-commerce platform. Category: The product category (e.g., Electronics, Clothing, Home & Kitchen). Price (USD): The price of the product in US dollars. Stock Quantity: The number of units available in stock. Rating: The average customer rating (out of 5), reflecting user satisfaction. Number of Reviews: The total number of customer reviews received for the product. Seller Name: The name of the vendor or seller offering the product. Discount Percentage: The discount applied to the product price (if any). Sales Count: The total number of units sold. Potential Use Cases: This dataset can be leveraged for multiple real-world applications, including:

✅ Sales Trend Analysis – Understanding product demand and seasonality trends. ✅ Price Optimization Studies – Identifying the impact of discounts and pricing on sales performance. ✅ Predictive Modeling for Sales – Developing machine learning models to forecast product sales. ✅ Business Intelligence & Insights – Extracting key business metrics to improve marketing and inventory strategies. ✅ Recommendation Systems – Using ratings and reviews to build product recommendation engines.

In 2023, the prevailing product category purchased on social media in the United States was apparel. As indicated by a survey, 25.6 percent of users reported this category as their primary choice for making purchases on social networks. Following closely were beauty products and home goods, with 19.4 percent and 13.5 percent of respondents favoring these respective categories.

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).

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.

1) Data Introduction • The Grocery Sales Database is a retail dataset of relational tables of grocery store sales transactions, customer information, product details, employee records, geographic information, and more across cities and countries.

2) Data Utilization (1) Grocery Sales Database has characteristics that: • The data consists of seven tables, including product categories, city/country information, customer/employee/product details, and sales details, each of which is interconnected by a unique ID. • Sales data are linked to products, customers, employees, and regions, enabling a variety of business analyses, including monthly sales, popular products, customer behavior, and regional performance. (2) Grocery Sales Database can be used to: • Analysis of sales trends and popular products: It can be used to identify trends and derive best-selling products by analyzing sales by monthly and category and sales by product. • Customer Segmentation and Marketing Strategy: Define customer groups based on customer frequency of purchases, total expenditure, and regional information and apply them to developing customized marketing and promotion strategies.

In the United States, ** percent of millennials bought clothing online in 2025. This was the most popular product category among millennials in all the countries analyzed.

This dataset was initially created for a small e-commerce website, with size reduction and content filtering to optimize computation and training. Later, it was adapted for synthesizing user interaction data to facilitate further analysis and modeling.

This dataset consists of individual CSV files, each representing a specific category of products typically found in an e-commerce platform. Categories include clothing (e.g., Men's Shirts, Women's Fashion, Kids Clothing), footwear (e.g., Women's Sandals, Men's Sports Shoes), accessories (e.g., Watches, Handbags, Motorbike Accessories), electronics (e.g., Televisions, Cameras, Phones, Speakers), and more. Additionally, niche categories like Musical Instruments, Strength Training equipment, and Baby Products are also included. Each file contains relevant, filtered product data tailored for optimized computation and training tasks

name: The name or title of the product, typically containing keywords to describe its features, brand, or category. Example: "Samsung Galaxy S23 Smartphone" or "Nike Air Zoom Pegasus 39 Running Shoes".

main_category: The primary category to which the product belongs, such as "Men's Innerwear," "Watches," or "Televisions."

image: A URL link to the product's image, hosted on the e-commerce platform or an external image storage service. Example: "https://example.com/images/product123.jpg".

ratings: The average user rating for the product on a scale (e.g., 1 to 5), providing insight into customer satisfaction. Example: 4.3 (out of 5).

no_of_ratings: The total number of user ratings the product has received, indicating its popularity and reliability. Example: 1,254 ratings.

actual_price: The listed price of the product. Example: $199.99 or ₹1,499.

In March 2025, consumers in the United Kingdom (UK) gave insight into what products they most often buy through social commerce. The product category that was most purchased, by almost **** of the respondents, was clothing and accessories. This was followed by beauty and personal care items, with ** percent of respondents, while digital products were purchased the least.

A survey conducted in the United States in 2024 shows the product categories in which different age groups of consumers have used augmented reality (AR) while online shopping. The age groups 18–29 and 55-64 used AR the most when buying clothing and accessories online. The groups 30-29, 40-54, and **+ used the technology the most when they bought furniture. However, many of the survey respondents had never used AR while purchasing products over the internet. For those aged 65 and up, around ** percent of them had never engaged in AR online shopping, nor had roughly ** percent of those aged 55-64.

Asos E-Commerce Dataset - 30,845 products, text classification dataset

Using web scraping, we collected information on over 30,845 clothing items from the Asos website. The dataset can be applied in E-commerce analytics in the fashion industry. The dataset is similar to SheIn E-Commerce Dataset.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on our website to buy the dataset

Dataset Info

For each item, we extracted:

• url - link to the item on the website
• name - item's name
• size - sizes available on the website
• category - product's category
• price - item's price
• color - item's color
• SKU - unique identifier of the item
• date - date of web scraping; for all items - March 11, 2023
• description - additional description, including product's brand, composition, and care instructions, in JSON format
• images - photographs from the item description

🧩 This is just an example of the data. Leave a request here to learn more

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keywords: web scraping dataset, dataset marketplace, web scraping data, e-commerce dataset, e-commerce marketplace, e-commerce marketplace scraping dataset, e-commerce sales dataset, ecommerce clothing site, e-commerce user behavior dataset, e-commerce text dataset, e-commerce product dataset, text dataset, ratings, product recommendation, text classification, text mining dataset, text data

Between January 1 and June 14, 2023, fashion and accessories were featured more than any other product on Instagram posts among consumers in the United States. Almost ************* posts were related to fashion and accessory products in the examined period. Lifestyle products racked up *** million posts, whilst food and beverages were posted about on Instagram *** million times in the U.S. in the examined period.

Across all product categories, Amazon was the place where online shoppers in the United States most often began searching for specific products in 2022. For household products, ** percent of shoppers reported beginning their searching on the e-commerce giant's platform. Additionally, ** percent started their household item searches on Walmart's online platform. Fashion e-commerce in the U.S. The internet, social media, and the proliferation of inexpensive clothing have opened doors to U.S. fashion e-commerce like never before. The U.S. apparel, footwear, and accessories retail e-commerce market is worth a remarkable *** billion U.S. dollars, according to 2021 estimates, and it is set to surpass the *** billion dollar mark by 2025. Millennials shaping the future of U.S. e-commerce In general, Millennials are hyper-connected and better educated than previous generations. Over the past decade, they have become the largest generation group in the U.S. Also known as Generation Y, Millennials are more tech-savvy consumers than their antecessors. In 2019, people born between 1983 and 1998 were found to be more influenced by bloggers when buying apparel than previous generations. Millennials also outrank Gen X-ers and baby boomers in digital buyer penetration in the United States, with over ** percent as of ********.

The Complete Myntra Products Dataset is a powerful tool for e-commerce professionals, data analysts, and AI developers seeking actionable insights into the online retail industry. This dataset offers a detailed breakdown of products available on Myntra, one of India’s leading fashion and lifestyle e-commerce platforms.

With information on product names, descriptions, pricing, categories, brand details, ratings, and reviews, the dataset provides a comprehensive view of Myntra’s product inventory. It's a perfect resource for businesses looking to perform market analysis, price comparison, trend forecasting, or competitive research in the e-commerce domain.

For AI and machine learning enthusiasts, this dataset is invaluable for training models in recommendation systems, sentiment analysis, and product classification. Its structured format ensures easy integration into popular programming tools like Python, R, or SQL, enabling efficient data manipulation and visualization.

Fashion startups and retailers can leverage this dataset to understand popular categories, identify top-selling products, and improve customer targeting strategies. Additionally, researchers can explore trends in customer reviews and ratings to develop insights into consumer behavior.

Key features of the Myntra Products Dataset include:

• Extensive Product Coverage: Over thousands of listings across diverse categories like clothing, footwear, accessories, and more.
• Comprehensive Details: Includes product descriptions, discounts, and availability.
• E-commerce Insights: Perfect for building pricing algorithms and market competition analysis.

By accessing the Complete Myntra Products Dataset, users gain a competitive edge in the dynamic fashion and e-commerce industries. This dataset is a must-have for professionals seeking reliable, well-organized, and up-to-date retail data.

Amazon Sales Dataset Description This dataset contains 250 records of Amazon sales transactions, including details about the products sold, customers, payment methods, and order statuses.

Columns Description: Order ID - Unique identifier for each order (e.g., ORD0001).

Date - Date of the order.

Product - Name of the product purchased.

Category - Product category (Electronics, Clothing, Home Appliances, etc.).

Price - Price of a single unit of the product.

Quantity - Number of units purchased in the order.

Total Sales - Total revenue from the order (Price × Quantity).

Customer Name - Name of the customer.

Customer Location - City where the customer is based.

Payment Method - Mode of payment (Credit Card, Debit Card, PayPal, etc.).

Status - Order status (Completed, Pending, or Cancelled).

This dataset can be used for sales analysis, customer behavior insights, and revenue trends visualization. 🚀

This dataset contains monthly retail price data for a wide range of consumer products sold in various Canadian provinces over several years. It has been enriched with tax, category, and classification metadata for deeper insights.

Usefulness of the Dataset

This dataset can be used for:

Purpose and Use Cases

This dataset is ideal for:

🏛️ Policy Analysis

Understand how federal and provincial taxes shape price access — especially for essentials like milk, bread, or medications.

🧍‍♀️ Consumer Insights

See how costs for personal care, food, and baby goods evolve month-over-month in each region.

💸 Inflation & Seasonality

Analyze how monthly or yearly trends (e.g., holiday spikes or inflation events) affect product pricing.

🌍 Social Impact Studies

Measure product accessibility gaps between provinces for low-income consumers or high-tax regions.

🛍️ Retail & Budget Planning

Guide families, retailers, or policymakers on where and when to buy or subsidize certain products.