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

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

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.

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

🚀 You can learn more about our high-quality unique datasets here

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

😍Upvote and share this would help me alot Thank You!

Description: The E-commerce Sales Data dataset provides a comprehensive collection of information related to user profiles, product details, and user-product interactions. It is a valuable resource for understanding customer behavior, preferences, and purchasing trends on an e-commerce platform.

Dataset Structure:

User Sheet: This sheet contains user profiles, including details such as user ID, name, age, location, and other relevant information. It helps in understanding the demographics and characteristics of the platform's users.

Product Sheet: The product sheet offers insights into the various products available on the e-commerce platform. It includes product IDs, names, categories, prices, descriptions, and other product-specific attributes.

Interactions Sheet: The interactions sheet is a crucial component of the dataset, capturing the interactions between users and products. It records details of user actions, such as product views, purchases, reviews, and ratings. This data is essential for building recommendation systems and understanding user preferences.

Potential Use Cases:

Recommendation Systems: With the user-product interaction data, this dataset is ideal for building recommendation systems. It allows the development of personalized product recommendations to enhance the user experience.

Market Basket Analysis: The dataset can be used for market basket analysis to understand which products are frequently purchased together, aiding in inventory management and targeted marketing.

User Behavior Analysis: By analyzing user interactions, you can gain insights into user behavior, such as popular product categories, browsing patterns, and the impact of user reviews and ratings on purchasing decisions.

Targeted Marketing: The dataset can inform marketing strategies, enabling businesses to tailor promotions and advertisements to specific user segments and product categories.

This E-commerce Sales Data dataset is a valuable resource for e-commerce platforms and data scientists seeking to optimize the shopping experience, enhance customer satisfaction, and drive business growth through data-driven insights.

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.

In 2022, apparel and accessories was the category with the largest percentage of discounted products advertised by online retailers around the world at nearly ** percent. Second on the list was health and beauty products with more than ** percent, followed closely by furniture.

Product Sales and Marketing Analytics Dataset This dataset provides a comprehensive view of product performance across various categories, focusing on sales metrics, marketing efforts, and consumer feedback. With 500 rows and 15 columns, it is an ideal resource for analyzing trends, optimizing marketing strategies, and predicting product success.

Key Features:

Product Details: Product_Name: Name of the product. Category: General category (e.g., Home & Kitchen, Sports & Outdoors). Sub_category: Specific sub-category (e.g., Cookware, Outdoor Gear). Pricing and Discounts: Price: Product price in local currency. Discount: Discount percentage offered on the product. Customer Feedback: Rating: Average customer rating (scale of 1 to 5). No_rating: Total number of customer reviews. Sales Metrics: Sales_y: Total yearly sales. Sales_m: Monthly sales, providing a more granular sales trend. Marketing and Operational Data: M_Spend: Marketing expenditure for the product. Supply_Chain_E: Efficiency rating of the supply chain. Market and Seasonal Trends: Market_T: Market trend index (indicates current market conditions). Seasonality_T: Seasonality trend index (impact of seasonal factors). Performance Metric: Success_Percentage: Success rate of the product, combining multiple performance indicators. Potential Use Cases:

Sales Forecasting: Use historical sales data and trends to predict future sales. Marketing Optimization: Identify products that yield the highest returns for marketing investment. Customer Insights: Analyze ratings and reviews to understand customer preferences. Trend Analysis: Study the impact of market and seasonality trends on sales. Product Success Prediction: Assess key factors contributing to a product’s success.

Target Audience: This dataset is designed for data analysts, business strategists, and machine learning enthusiasts looking to explore:

1. Sales forecasting models.
2. Marketing spend optimization
3. Consumer behavior analysis.

Additional Notes: Data is pre-cleaned and ready for analysis. Suitable for regression, classification, and clustering tasks.

In 2023, direct sellers in the Asia Pacific Region generated nearly ** percent of their retail sales from cosmetics, personal care, and wellness products alone.

Gain extensive insights with our Amazon datasets, encompassing detailed product information including pricing, reviews, ratings, brand names, product categories, sellers, ASINs, images, and much more. Ideal for market researchers, data analysts, and eCommerce professionals looking to excel in the competitive online marketplace. Over 425M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Title Asin Main Image Brand Name Description Availability Subcategory Categories Parent Asin Type Product Type Name Model Number Manufacturer Color Size Date First Available Released Model Year Item Model Number Part Number Price Total Reviews Total Ratings Average Rating Features Best Sellers Rank Subcategory Buybox Buybox Seller Id Buybox Is Amazon Images Product URL And more

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.

The provided dataset appears to be a sales dataset from a company called "**T-Mart.**" The dataset contains various columns with information about the sales transactions, including the date of the transaction, product details, quantity, sales type, location, payment mode, product category, unit of measurement (UOM), purchase price, and some additional labels and counts.

Based on the given information, here's a brief description of the dataset:

The "T-Mart" sales dataset captures sales transactions with details such as the transaction date, unique product identifier (PRODUCT ID), quantity sold, sales type (Direct Sales, Online, etc.), sales location (e.g., California, Alabama), payment mode (Cash, Online), product details (PRODUCT, CATEGORY, UOM), purchase price, and some additional label-based information.

This dataset provides insights into various aspects of the company's sales operations, including the distribution of sales across different categories, products, and locations, as well as information about the payment modes used for transactions.

Analyzing this dataset can help identify trends, popular products, sales performance by location, and preferred payment methods. It's essential for understanding the company's sales dynamics and making informed business decisions.

This dataset appears to be rich in information, and with the right data visualization techniques, we can uncover valuable insights that can be used for strategic planning and optimizing sales strategies.

This dataset contains sales data of various SKUs across the state of Maharashtra. It has overall inventory data with SKU codes of products, Sales of products and sales in various cities across the state of Maharashtra.

The data is distributed into 4 different .csv files

Dataset Description

This dataset contains 436,689 records of commercial transactions from a retail store/marketplace, recorded between December 2014 and December 2015. Each row represents the purchase of a single product within a specific invoice.

Main Columns:

1. InvoiceID: Unique identifier for the invoice (categorical).
2. CustomerID: Unique identifier for the customer (categorical).
3. date: Date of the transaction (temporal), useful for time series and seasonality analysis.
4. item: Product description (text/categorical).
5. quantity: Quantity purchased for the individual product (numeric, discrete).
6. price: Unit price of the product (numeric, continuous).
7. type: Sales channel (e.g., online, supermarket), categorical.
8. category: Product category, useful for grouping and pattern mining (categorical).
9. total_quantity: Total quantity of products purchased in the same invoice (numeric, discrete).
10. customer_type: Type of customer (e.g., private, wholesaler), categorical.
11. product_id: Unique identifier for the product (categorical).

Potential Data Mining Applications: * Market Basket Analysis / Association Rules: Discover frequent combinations of products purchased together. * Customer Segmentation: Cluster customers based on purchase quantity, customer type, and product categories. * Forecasting: Predict future sales for products or categories. * Anomaly Detection: Detect unusual transactions in terms of quantity or price. * Sales Analysis: Identify trends, top-selling products, and seasonal patterns.

General Characteristics: * Large dataset with a mix of online and in-store sales. * Combination of numeric and categorical variables, suitable for classification, clustering, and association rule mining. * Includes a temporal variable for sequential and predictive analysis.

The Orders database contains information on the following variables. • Continuous variables: Row ID, Order ID, Order Date, Ship Date, Customer ID, Product ID, Sales, Quantity, Discount, Profit, Shipping Cost
• Categorical variables: Ship Mode, Customer Name, Segment, Postal Code, City, State, Country, Region, Market, Category, Subcategory, Product Name, Order Priority

The purpose of this project: 1. To use descriptive statistics methods to assess the sales performance across various segments, markets, product categories and subcategories; 2. To use diagnostic analytics methods to understand the statistical significance of the factors that influence sales; 3. Use predictive analytics (regression) to understand the strengths of the relationship between sales and sales drivers and generate a regression formula to predict sales 4. develop a sales forecasting model based on the insights.

Descriptive analytics Descriptive statistics for sales https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F848f47b38b7f2360163bb2221703c658%2FPicture2.png?generation=1715109635788424&alt=media" alt="">

Frequency distribution for sales Around 44,500 transactions of value >=USD 500. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F39cfd8ffd8fdf296300bb9f1fa5243e2%2FPicture3.png?generation=1715109667755923&alt=media" alt="">

Sales values across markets https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F3385959d11b6daafae24c848b4b00f13%2FPicture4.png?generation=1715109744629587&alt=media" alt="">

We see an increase in sales across all markets and throughout 2012-2015. We have high sales volumes in the USCA and LATAM markets:
• USCA: USD 757,108 in 2015; • LATAM: USD 706,632 in 2015.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F4aa59b5a5b980aad6873c8a4af4cd223%2FPicture1.png?generation=1715109770510368&alt=media" alt="">

Sales across product categories https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F867cbe622bf94d25a25a1c4b9281656d%2FPicture5.png?generation=1715109794950614&alt=media" alt="">

Office supplies were the largely sold product category in 2012-2015. Technology was the least sold product category by quantity. However, the Technology category yields high sales. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F5c74664f77cce2bc2f7c77c7b01e9890%2FPicture6.png?generation=1715109834309500&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2Fd3bb766183e9f58fbf009a998c01adf6%2FPicture7.png?generation=1715109872961254&alt=media" alt="">

Further analysis of profitable products reveals that phones and copiers demonstrate high sales. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F109c4c3eab81fa581c19a5c09beff839%2FPicture9.png?generation=1715109914590660&alt=media" alt="">

Sales across segments The data reveals that there are high sales in the Consumer segment across all product categories. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F65075cc20028a37a1aff6932fa89d3d5%2FPicture10.png?generation=1715109992655572&alt=media" alt="">

Diagnostic analytics

Two sample T-test Using a t-test, we can evaluate how sales differ across different segments, regions, and product types. T-test allows us to evaluate the statistical significance of sales samples. The two-sample t-test of sales numbers across markets resulted in the statistical significance of sales in USCA and LATAM markets with p-values >0.05. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F7b7264d5f44a9a79b352028b28d1c618%2FPicture11.png?generation=1715110082746375&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F4061ef38ea83d7e3bbd252a802863e8f%2FPicture12.png?generation=1715110097203251&alt=media" alt="">

The two-sample t-test of sales numbers across product categories resulted in the statistical significance of sales in Office supplies and Technology categories with p-values >0.05. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2Fd9994377d605222d77ef67af3e273771%2FPicture13.png?generation=1715110126112322&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20744393%2F669779e9aad19d51a28fb44e7c484bc7%2FPicture14.png?generation=1715110140543290&alt=media" alt="">

Pearson correlation The correlation of continuous values in the dataset allows us to see the relationship between sales, quantity sold, shipping costs and profit. ![](https://www.googleapis.com/download/sto...

With over 1.5 million unique products, this dataset offers a comprehensive view of the products available on Amazon.in, one of India's leading online retailers. Collected through a web scraping process in 2023, this dataset provides valuable insights into product titles, pricing, ratings, and more.

If you find this dataset helpful for your project or analysis, consider upvoting it! ⭐️

Similar datasets:

Amazon UK Products

Amazon Canada Products

Amazon USA Products

Interesting Task Ideas:

1. Analyze pricing trends across different product categories.
2. Explore the correlation between customer ratings and the number of reviews.
3. Identify the best-selling products on Amazon.in.
4. Study the relationship between pricing and customer reviews.
5. Create a recommendation system based on customer ratings and product categories.
6. Analyze the availability of products in different price ranges.

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).

Product Sku Classification

## Overview

Product Sku Classification is a dataset for object detection tasks - it contains Product Skus annotations for 314 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Description: This synthetic yet realistic dataset contains 2,000 records representing product performance metrics in an e-commerce environment. Designed for intermediate-level data science and machine learning tasks, the dataset includes natural randomness, missing values (~5% per column), and varying distributions, mimicking real-world conditions.

Columns Explanation:

Product_Price: The listed price of the product in USD (range: 5 to 1000). Discount_Rate: Discount rate applied to the product (0.0 to 0.8). Product_Rating: Customer rating on a scale from 1 to 5. Number_of_Reviews: Total number of user reviews (0 to 5000, highly skewed). Stock_Availability: Product availability in stock (1 = available, 0 = out of stock). Days_to_Deliver: Number of days it takes to deliver the product (1 to 30). Return_Rate: Proportion of items returned after purchase (0.0 to 0.9). Category_ID: ID of the product category (integer from 1 to 10).

The E-commerce Product Dataset is a comprehensive collection tailored for the e-commerce sector, featuring a wide range of products from 16 main categories including shoes, hats, bags, furniture, digital products, jewelry, and more. With over 200k SKUs, this dataset is equipped with bounding boxes and category tags, making it a pivotal resource for product classification and inventory management.