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

USA Unemployment Rates by Demographics & Race

USA Hispanic-White Wage Gap Dataset

Median and Avg Hourly Wages in the USA

Health Insurance Coverage in the USA

Black-White Wage Gap in the USA Dataset

Product title classification is an important task in e-commerce, as it helps to categorize and organize millions of products available online.

This dataset provides a large-scale collection of product titles from Amazon USA, Canada, and UK, along with their corresponding categories.

With over 5 million samples and 700+ categories, this dataset is ideal for training models to suggest the best category for a given product title.

Please upvote if you find this dataset useful! 😊💙

Interesting Task Ideas:

1. Train a text classification model to automatically categorize products based on their titles.
2. Explore the distribution of categories and identify the most frequent and rare ones.
3. Evaluate and compare different machine learning algorithms and deep learning architectures for product title classification.
4. Implement transfer learning techniques to improve the classification performance with limited labeled data.
5. Pretrain language models on this dataset for downstream tasks like product recommendation, search ranking, and sentiment analysis.
6. Apply clustering techniques to identify the relationships between different categories based on the similarity of product titles.

Photo by Tim Mossholder on Unsplash

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.

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.

According to a 2023 survey, clothing and accessories were the most purchased direct-to-consumer (D2C) product categories. Approximately ** percent of respondents had already purchased these items directly from a brand's website, while ** percent were considering doing so. Another top D2C product category was electronics, which ********** of buyers reported buying. Consumers showed the least interest in purchasing food and beverages directly from a brand's e-commerce site.

📦 Ecommerce Dataset (Products & Sizes Included)

🛍️ Essential Data for Building an Ecommerce Website & Analyzing Online Shopping Trends 📌 Overview This dataset contains 1,000+ ecommerce products, including detailed information on pricing, ratings, product specifications, seller details, and more. It is designed to help data scientists, developers, and analysts build product recommendation systems, price prediction models, and sentiment analysis tools.

🔹 Dataset Features

Column Name Description product_id Unique identifier for the product title Product name/title product_description Detailed product description rating Average customer rating (0-5) ratings_count Number of ratings received initial_price Original product price discount Discount percentage (%) final_price Discounted price currency Currency of the price (e.g., USD, INR) images URL(s) of product images delivery_options Available delivery methods (e.g., standard, express) product_details Additional product attributes breadcrumbs Category path (e.g., Electronics > Smartphones) product_specifications Technical specifications of the product amount_of_stars Distribution of star ratings (1-5 stars) what_customers_said Customer reviews (sentiments) seller_name Name of the product seller sizes Available sizes (for clothing, shoes, etc.) videos Product video links (if available) seller_information Seller details, such as location and rating variations Different variants of the product (e.g., color, size) best_offer Best available deal for the product more_offers Other available deals/offers category Product category

📊 Potential Use Cases

📌 Build an Ecommerce Website: Use this dataset to design a functional online store with product listings, filtering, and sorting. 🔍 Price Prediction Models: Predict product prices based on features like ratings, category, and discount. 🎯 Recommendation Systems: Suggest products based on user preferences, rating trends, and customer feedback. 🗣 Sentiment Analysis: Analyze what_customers_said to understand customer satisfaction and product popularity. 📈 Market & Competitor Analysis: Track pricing trends, popular categories, and seller performance. 🔍 Why Use This Dataset? ✅ Rich Feature Set: Includes all necessary ecommerce attributes. ✅ Realistic Pricing & Rating Data: Useful for price analysis and recommendations. ✅ Multi-Purpose: Suitable for machine learning, web development, and data visualization. ✅ Structured Format: Easy-to-use CSV format for quick integration.

📂 Dataset Format CSV file (ecommerce_dataset.csv) 1000+ samples Multi-category coverage 🔗 How to Use? Download the dataset from Kaggle. Load it in Python using Pandas: python Copy Edit import pandas as pd
df = pd.read_csv("ecommerce_dataset.csv")
df.head() Explore trends & patterns using visualization tools (Seaborn, Matplotlib). Build models & applications based on the dataset!

Project Overview

The E-Commerce Product Intelligence Dataset is a synthetically generated, multi-table relational dataset simulating 3.5 years of customer activity for a mid-size online retailer. It is designed to support the full spectrum of modern ML and data science workloads — from classic recommendation algorithms through graph neural networks to agentic AI evaluation.

Dataset Statistics

Table Overview

Key Metrics

Distribution Highlights

Income Level: low 24.9% | medium 39.9% | high 25.0% | very_high 10.1%

Loyalty Tier: bronze 79.3% | silver 14.3% | gold 5.0% | platinum 1.4%

Device Mix: mobile 57.3% | desktop 32.3% | tablet 10.4%

Referrer Source: organic_search 36.0% | direct 24.6% | social_media 14.7% | email 9.8% | paid_search 7.8% | referral 5.2% | display_ad 2.0%

Supported Tasks

This dataset was designed to support a wide range of machine learning, data science, and analytics workloads.

Recommendation Systems

• Collaborative Filtering
• Matrix Factorization
• Session-Based Recommendation
• Sequential Recommendation
• Graph Neural Network Recommendation
• Product Similarity Modeling

Customer Analytics

• Customer Segmentation
• Cohort Analysis
• Behavioral Funnel Analysis
• Conversion Optimization
• Customer Lifetime Value (CLV)
• Churn Prediction

NLP & LLM Applications

• Sentiment Analysis
• Review Classification
• Product Retrieval
• Retrieval-Augmented Generation (RAG)
• Agentic AI Evaluation

Forecasting

• Demand Forecasting
• Product Popularity Prediction
• Revenue Forecasting

Included Files

Key Features

• 6 relational tables connected through validated foreign keys
• 100,000 interaction events across 19,315 browsing sessions
• 10 product categories and 53 subcategories
• Session-based user behavior generated through a Markov chain model
• Verified and unverified product reviews
• Product descriptions and review text suitable for NLP tasks
• Cold-start users and products for recommendation system evaluation
• Deterministic generation and reproducible results
• Complete documentation including ER diagram and data dictionary

Data Quality

• 10/10 foreign key chains validated
• Zero orphan records
• 87/100 overall quality score
• 100% automated validation pass rate
• 115+ validation and test checks across all tables

For full schema details, relationships, and generation methodology, see the included documentation files.

Blush product image classification dataset, containing 5000 high-quality product images, assisting e-commerce platform product classification and recommendation.

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 2020 survey, ** percent of respondents in Finland had purchased clothing and footwear products online within the past year. The second most popular category for online purchases was home electronics, followed by books and audiobooks.

This dataset contains 100,000 synthetic Amazon-style e-commerce sales transactions, designed to closely resemble real-world online retail behavior. With 20 clean and well-structured columns, it captures detailed information about customers, products, pricing, payments, logistics, and order outcomes.

Although the data is artificially generated, it reflects realistic patterns such as:

Dynamic product pricing

Varying discounts and taxes

Multiple product categories & brands

Seasonal order trends

Payment method diversity

Realistic customer names & locations

Order statuses like Delivered, Cancelled, Shipped, Returned

This makes the dataset highly suitable for analytics, machine learning, data visualization, dashboards, and business case studies.

📊 Column Overview

The dataset includes:

🧾 Order Details

OrderID

OrderDate

OrderStatus

SellerID

👤 Customer Information

CustomerID

CustomerName

City, State, Country

📦 Product Information

ProductID

ProductName

Category

Brand

Quantity

💰 Pricing & Revenue Metrics

UnitPrice

Discount

Tax

ShippingCost

TotalAmount

💳 Payment Details

PaymentMethod

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.

According to a survey on e-commerce and online shopping in Kazakhstan as of November 2023, roughly ********** of the respondents preferred to shop household products online. This was followed by electronics and household appliances products with ** percent of the survey participants.

The highest CAGR for the period of 2017 and 2028 was estimated to be in the food segment, amounting to almost 25 percent growth. The segment of beauty, health, personal & household care is estimated to have over 14 percent compound annual growth rate during this period.

A 2024 survey revealed that global consumers research the product category of electronics the most through user-generated content (UGC). Approximately ** percent of those surveyed used UGC to research electronics products. Another popular product category to research through UGC was apparel, which was done by roughly ** percent of consumers. Next, with about ** percent of respondents, was the health and beauty category. Global shoppers prioritize the value for money, the product's suitability for their intended purpose, and the delivery services offered when evaluating UGC.

This is the classification based E-commerce text dataset for 4 categories - "Electronics", "Household", "Books" and "Clothing & Accessories", which almost cover 80% of any E-commerce website.

The dataset is in ".csv" format with two columns - the first column is the class name and the second one is the datapoint of that class. The data point is the product and description from the e-commerce website.

The dataset has the following features :

Data Set Characteristics: Multivariate

Number of Instances: 50425

Number of classes: 4

Area: Computer science

Attribute Characteristics: Real

Number of Attributes: 1

Associated Tasks: Classification

Missing Values? No

Gautam. (2019). E commerce text dataset (version - 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3355823

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.

Usage data for the Product Categories Designs for WooCommerce WordPress plugin: 495 live installs across the web, with a 0% share of its plugin category. Adoption broken down by country and top-level domain. Compiled by WPoptic.

Dataset Information

The idea of the dataset is precisely to have categories of products that based on the business are only sold in certain periods of time, for example apple picking, is only carried out in certain periods of the year, and i have generated random dataset with numpy and random library for python for simulate / generate this scenario for study. 😁

For example: The Candy assortment is selled only in four months in two last year months and the two start months of next year. (Nov, Dec, Jan, Feb). And another sales of another assortments is selled in other months!

This dataset contains sales of three different product categories (assortment column), each of these categories are sold in different periods of the year (different months), so the challenge is to make a forecast for the next year based on the sales of past years.

In the image below, is possible to see first 10 sales of drink assortment.

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Columns Information

1. Index: Row indicator of dataset.
2. Date: Year, Month, day of dataset.
3. Sales: Total Sales (or product quantity) given a date and assortment.
4. Assortment: Product Sales Category.

The Amazon Pets Category Images Dataset is a curated collection of high-resolution images sourced from the pet products category on Amazon. This dataset contains images across various subcategories, such as pet food, toys, grooming tools, bedding, and accessories. With a wide range of products for pets like dogs, cats, birds, and more, this dataset is perfect for researchers, developers, and businesses interested in studying product visuals, conducting market analysis, or training AI models focused on pet-related imagery.

The dataset consists solely of product images, without accompanying metadata or descriptions, offering a straightforward resource for visual analysis, product comparison, or training image-based machine learning models.

Key Features:

• Product Categories: Includes pet food, toys, grooming products, bedding, and accessories.
• Image Quality: High-resolution images suitable for detailed visual analysis and machine learning.
• Pet Types: Covers a variety of pet-related products for dogs, cats, birds, fish, and more.
• Source: Extracted from Amazon’s pet products section.
• Image Count: Hundreds to thousands of images based on different product categories.

Use Cases:

• Image Classification and AI Training: Use the dataset to train machine learning models for product identification and categorization.
• Product Comparison: Compare different product designs and packaging in the pet industry using visual data.
• Visual Marketing Research: Analyze how pet products are visually represented on Amazon for trends and branding strategies.
• Content Creation: Leverage these images for creating pet-related content, including marketing, advertising, and social media.

Also check: High-Quality Ecommerce Product Images