About

This data is from E-Commerce. I used postgreSQL for data cleaning. I transformed NULL values to 'Not defined' and orginal data have only category name column(which was 'category_code') and that was 'DOT' seperated value which show us the products class from wide to specific. So I split them with delimeter('.').

The orignal data have record with 5 months but I only used December of 2019. If you want more data you can visit the link above and use.

File structure

Acknowledgements

Many thanks Thanks to REES46 Marketing Platform for this dataset and Michael Kechinov

Using datasets in your works, books, education materials

You can use this dataset for free. Just mention the source of it: link to this page and link to REES46 Marketing Platform and Origin data provider

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

Unlock the power of Flipkart's extensive product catalog with our meticulously curated e-commerce dataset. This dataset provides detailed information on a wide range of products available on Flipkart, including product names, descriptions, prices, customer reviews, ratings, and images. Whether you're working on data analysis, machine learning models, or conducting in-depth market research, this dataset is an invaluable resource.

With our Flipkart e-commerce dataset, you can easily analyze trends, compare products, and gain insights into consumer behavior. The dataset is structured and high-quality, ensuring that you have the best foundation for your projects.

Flipkart is largest E-commerce website based out india. Pre crawled dataset having more than 5.7 million records.

Where to use dataset

• Train machine learning algorithms
• Check discounts of various categories fields
• Find out which brand and categories having best discounts

📦 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!

Introducing E-Commerce Product Datasets!

Unlock the full potential of your product strategy with E-Commerce Product Datasets. Gain invaluable insights to optimize your product offerings and pricing, analyze top-selling strategies, and assess customer sentiment.

Our E-Commerce Datasets Source:

1. Amazon: Access accurate product data from Amazon, including categories, pricing, reviews, and more.

2. Walmart: Receive comprehensive product information from Walmart, covering pricing, sellers, ratings, availability, and more.

E-Commerce Product Datasets provide structured and actionable data, empowering you to understand customer needs and enhance product strategies. We deliver fresh and precise public e-commerce data, including product names, brands, prices, number of sellers, review counts, ratings, and availability.

You have the flexibility to tailor data delivery to your specific needs:

• Receive datasets in various formats, including JSON and CSV.
• Choose delivery via SFTP or directly to your cloud storage (e.g., AWS S3, Google Cloud Storage).
• Select from one-time, monthly, quarterly, or bi-annual data delivery frequencies.

Why Choose Oxylabs E-Commerce Datasets:

1. Fresh and accurate data: Access clean and structured public e-commerce data collected by our leading web scraping professionals.

2. Time and resource savings: Let our experts handle data extraction at an affordable cost, allowing you to focus on your core business objectives.

3. Customizable solutions: Share your unique business needs, and our team will craft customized dataset solutions tailored to your requirements.

4. Legal compliance: Partner with a trusted leader in ethical data collection, endorsed by Fortune 500 companies and fully compliant with GDPR and CCPA regulations.

Pricing Options:

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Unlock the potential of your e-commerce strategy with E-Commerce Product Datasets!

1) Data Introduction • The E-Commerce Data Dataset contains actual transaction records from an online retail company based in the UK. It includes various transaction-related attributes such as customer ID, product information, transaction date, quantity, and country.

2) Data Utilization (1) Characteristics of the E-Commerce Data Dataset: • This dataset is structured as time-series consumer behavior data at the transaction level. It includes attributes such as product category, quantity, unit price, and country, making it suitable for analyzing country-specific consumption patterns and developing region-based classification models.

(2) Applications of the E-Commerce Data Dataset: • Developing country-specific marketing strategies: By analyzing purchasing trends, frequently bought product categories, and transaction frequency by country, the dataset can be used to design regionally tailored marketing strategies.

International e-Commerce Benchmarking

Source agency: Office for National Statistics

Designation: Supporting material

Language: English

Alternative title: e-Commerce

Explore the booming Big Data in E-commerce market, driven by personalized experiences and AI. Discover market size, CAGR, key drivers, trends, and regional growth forecasts for 2025-2033.

Unlock the potential of data with our extensive Furniture Images and Product Schema dataset from Target.

This meticulously curated dataset provides high-quality images and detailed product information, making it an invaluable resource for e-commerce platforms, market analysis, and data-driven insights.

What’s Included:

• High-resolution furniture images
• Detailed product schema with descriptions, prices, categories, and more
• Comprehensive metadata for each product
• Structured data ready for immediate use

Benefits:

• E-commerce Integration: Enhance your online store with rich product images and accurate information.
• Market Analysis: Gain valuable insights into furniture trends and consumer preferences.
• Data-driven Decisions: Leverage structured data to inform your business strategies.

Use Cases:

• Populate your e-commerce platform with high-quality furniture images and detailed product descriptions.
• Conduct market research to identify trends and optimize your product offerings.
• Develop machine learning models with structured product data for better recommendations and insights.

Total records count 40K+ and total images 212K+.

Local image file path mentioned in JSON file.

E-commerce Product Dataset - Clean and Enhance Your Data Analysis Skills or Check Out The Cleaned File Below!

This dataset offers a comprehensive collection of product information from an e-commerce store, spread across 20+ CSV files and encompassing over 80,000+ products. It presents a valuable opportunity to test and refine your data cleaning and wrangling skills.

What's Included:

A variety of product categories, including:

• Apparel & Accessories
• Electronics
• Home & Kitchen
• Beauty & Health
• Toys & Games
• Men's Clothes
• Women's Clothes
• Pet Supplies
• Sports & Outdoor
• (and more!)

Each product record contains details such as:

• Product Title
• Category
• Price
• Discount information
• (and other attributes)

Challenges and Opportunities:

Data Cleaning: The dataset is "dirty," containing missing values, inconsistencies in formatting, and potential errors. This provides a chance to practice your data-cleaning techniques such as:

• Identifying and handling missing values
• Standardizing data formats
• Correcting inconsistencies
• Dealing with duplicate entries

Feature Engineering: After cleaning, you can explore opportunities to create new features from the existing data, such as: - Extracting keywords from product titles and descriptions - Deriving price categories - Calculating average discounts

Who can benefit from this dataset?

• Data analysts and scientists looking to practice data cleaning and wrangling skills on a real-world e-commerce dataset
• Machine learning enthusiasts interested in building models for product recommendation, price prediction, or other e-commerce tasks
• Anyone interested in exploring and understanding the structure and organization of product data in an e-commerce setting
• By contributing to this dataset and sharing your cleaning and feature engineering approaches, you can help create a valuable resource for the Kaggle community!

OpenWeb Ninja's Amazon Data API provides fast and reliable access to real-time Amazon data across all 22 Amazon domains. With over 600 million product listings and more than 40 data points per product, the API makes it simple to search products, query by category, and extract structured ecommerce product data at scale.

Key capabilities: - Product Search & Categories: search Amazon by keyword or retrieve products directly from categories. - Product Data: titles, descriptions, images, pricing, availability, attributes. - Amazon Reviews Data: full review content, ratings, timestamps, helpful counts. - Offers & Sellers Data: all current offers, with sellers data, and more. - Amazon Sellers Data: Amazon sellers profile, sold products, and seller reviews. - Best Sellers & Deals: Amazon Best Sellers by category, Today’s Deals, and promotions. - ASIN to GTIN: convert ASIN to GTIN/EAN/ISBN for external integrations.

Coverage & Scale: - 600M+ products across all major categories and industries. - 22 Amazon countries/domains supported. - 40+ structured data points per product. - Real-time updates, delivered via a fast and reliable REST API.

Use cases: - Pricing and product comparison tools. - Ecommerce and market research. - Seller and competitor monitoring. - Product discovery and trend analysis. - Sentiment analysis with customer product reviews data.

With OpenWeb Ninja's Amazon Data API, you get the most complete Amazon data - from product details and reviews to best sellers and deals - always delivered in real time through a fast and reliable REST API.

Dataset Description

The E-commerce Order Dataset provides comprehensive information related to orders, items within orders, customers, payments, and products for an e-commerce platform. This dataset is structured with multiple tables, each containing specific information about various aspects of the e-commerce operations.

Dataset Features

Orders Table:

• order_id: Unique identifier for an order, acting as the primary key.
• customer_id: Unique identifier for a customer. This table may not be unique at this level.
• order_status: Indicates the status of an order (e.g., delivered, cancelled, processing, etc.).
• order_purchase_timestamp: Timestamp when the order was made by the customer.
• order_approved_at: Timestamp when the order was approved from the seller's side.
• order_delivered_timestamp: Timestamp when the order was delivered at the customer's location.
• order_estimated_delivery_date: Estimated date of delivery shared with the customer while placing the order.

Order Items Table

• order_id: Unique identifier for an order.
• order_item_id: Item number in each order, acting as part of the primary key along with the order_id.
• product_id: Unique identifier for a product.
• seller_id: Unique identifier for the seller.
• price: Selling price of the product.
• shipping_charges: Charges associated with the shipping of the product.

Customers Table

• customer_id: Unique identifier for a customer, acting as the primary key.
• customer_zip_code_prefix: Customer's Zip code.
• customer_city: Customer's city.
• customer_state: Customer's state.

Payments Table

• order_id: Unique identifier for an order.
• payment_sequential: Provides information about the sequence of payments for the given order.
• payment_type: Type of payment (e.g., credit_card, debit_card, etc.).
• payment_installments: Payment installment number in case of credit cards.
• payment_value: Transaction value.

Products Table

• product_id: Unique identifier for each product, acting as the primary key.
• product_category_name: Name of the category the product belongs to.
• product_weight_g: Product weight in grams.
• product_length_cm: Product length in centimeters.
• product_height_cm: Product height in centimeters.
• product_width_cm: Product width in centimeters.

📦 DataSignals E-Commerce Signals (v1)

High-frequency web signals dataset generated automatically from E-commerce pages. This dataset contains structured signals extracted from HTML snapshots of E-commerce websites.The goal is to provide high-quality, machine-readable signals useful for:

🧠 AI training
📈 Market intelligence
🔎 Change detection
🛒 E-commerce research
🔮 Price prediction models
⚡ Real-time data pipelines

  📊 Dataset Contents

Each row… See the full description on the dataset page: https://huggingface.co/datasets/datasignals-lab/datasignals-ecommerce-1.

Data Lakes Market size was valued at USD 17.21 Billion in 2024 and is projected to reach USD 79.09 Billion by 2031, growing at a CAGR of 21.00% during the forecasted period 2024 to 2031.

The data lakes market is driven by the growing need for organizations to manage and analyze vast amounts of unstructured and structured data for better decision-making and insights. As businesses increasingly rely on big data analytics, machine learning, and artificial intelligence to gain competitive advantages, data lakes provide a scalable and cost-effective solution to store raw data from diverse sources. The rising adoption of cloud-based solutions further fuels the market, as cloud data lakes offer flexibility, agility, and seamless integration with analytics tools. Additionally, the growing emphasis on digital transformation, real-time data processing, and enhanced data governance are key factors pushing the demand for data lakes across industries such as finance, healthcare, retail, and manufacturing.

The size of the Slovakia E-Commerce Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 14.22% during the forecast period. Recent developments include: December 2021 - The Slovak Republic's Ministry of Finance announced the implementation of a central national electronic invoicing system which allows businesses to send e-invoices in a structured data format to the tax authority. Such developments are expected to aid the growth of the studied market in the country.. Key drivers for this market are: High Emphasis on Fashion. Potential restraints include: , Lack of Comprehensiveness of Chinese Digital Design Tools. Notable trends are: Significant Growth in E-Commerce is Expected due to Digital Transformation.

Retail Data Monetization Platform Market Outlook

According to our latest research, the global retail data monetization platform market size stood at USD 4.8 billion in 2024, with an observed compound annual growth rate (CAGR) of 19.2% from 2025 to 2033. The market is projected to reach USD 20.3 billion by 2033, driven primarily by the increasing adoption of advanced analytics, the proliferation of e-commerce, and the growing demand for actionable insights to enhance customer experiences in the retail sector. As per our latest research, the robust growth trajectory is underpinned by retailers’ urgent need to leverage data assets for competitive advantage, optimize operations, and unlock new revenue streams through data-driven strategies.

The primary growth factor for the retail data monetization platform market is the exponential increase in data generation across retail touchpoints, including point-of-sale systems, online transactions, customer loyalty programs, and social media interactions. Retailers are increasingly realizing the untapped value of their data assets, which can be transformed into actionable intelligence for internal optimization and external monetization. The integration of artificial intelligence (AI) and machine learning (ML) technologies within these platforms further enhances their predictive capabilities, enabling retailers to anticipate consumer trends, personalize marketing efforts, and drive operational efficiency. This trend is particularly pronounced among large enterprises, which possess the infrastructure and resources to invest in sophisticated data monetization solutions, thereby fueling overall market growth.

Another significant driver is the rapid digital transformation of the global retail landscape. The shift to omnichannel retailing and the rise of e-commerce have created vast reservoirs of structured and unstructured data. Retailers and e-commerce companies are increasingly seeking platforms that can aggregate, cleanse, analyze, and monetize this data, both internally and through partnerships with third parties such as consumer goods manufacturers, financial institutions, and advertising agencies. The increasing regulatory emphasis on data privacy and security, especially in regions like North America and Europe, has also prompted the adoption of platforms that offer robust compliance features, further boosting market demand. As retailers strive to deliver hyper-personalized experiences and optimize their supply chains, the adoption of retail data monetization platforms is set to accelerate.

The proliferation of cloud computing and the availability of scalable Software-as-a-Service (SaaS) solutions have democratized access to advanced data monetization tools, making them accessible to small and medium enterprises (SMEs) as well. Cloud-based deployment models offer cost-effectiveness, flexibility, and ease of integration with existing retail IT ecosystems, enabling even resource-constrained businesses to participate in the data economy. This democratization is expected to broaden the addressable market and drive higher adoption rates across diverse retail segments. Furthermore, the emergence of data marketplaces and collaborative analytics ecosystems is enabling retailers to monetize their data assets externally, creating new revenue streams and fostering innovation.

From a regional perspective, North America currently dominates the retail data monetization platform market, accounting for the largest market share in 2024, followed closely by Europe and Asia Pacific. The high concentration of technologically advanced retailers, mature e-commerce ecosystems, and a strong focus on data-driven decision-making are key factors supporting market leadership in these regions. Asia Pacific is anticipated to exhibit the fastest growth over the forecast period, fueled by rapid digitalization, expanding middle-class consumer base, and increasing investments in retail technology infrastructure. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, driven by growing retail modernization initiatives and rising awareness of the benefits of data monetization.

Looking for a free Walmart product dataset? The Walmart Products Free Dataset delivers a ready-to-use ecommerce product data CSV containing ~2,100 verified product records from Walmart.com. It includes vital details like product titles, prices, categories, brand info, availability, and descriptions — perfect for data analysis, price comparison, market research, or building machine-learning models.

Key Features

Complete Product Metadata: Each entry includes URL, title, brand, SKU, price, currency, description, availability, delivery method, average rating, total ratings, image links, unique ID, and timestamp.

CSV Format, Ready to Use: Download instantly - no need for scraping, cleaning or formatting.

Good for E-commerce Research & ML: Ideal for product cataloging, price tracking, demand forecasting, recommendation systems, or data-driven projects.

Free & Easy Access: Priced at USD $0.0, making it a great starting point for developers, data analysts or students.

Who Benefits?

• Data analysts & researchers exploring e-commerce trends or product catalog data.
• Developers & data scientists building price-comparison tools, recommendation engines or ML models.
• E-commerce strategists/marketers need product metadata for competitive analysis or market research.
• Students/hobbyists needing a free dataset for learning or demo projects.

Why Use This Dataset Instead of Manual Scraping?

• Time-saving: No need to write scrapers or deal with rate limits.
• Clean, structured data: All records are verified and already formatted in CSV, saving hours of cleaning.
• Risk-free: Avoid Terms-of-Service issues or IP blocks that come with manual scraping.
  Instant access: Free and immediately downloadable.

This dataset provides a detailed, structured catalog of e-commerce products, including SKUs, descriptions, pricing, categories, images, inventory status, and store associations. It is ideal for powering online storefronts, supporting inventory management, and enabling advanced analytics for merchandising and sales optimization.

This dataset contains detailed information on 100 e-commerce products, including product titles, descriptions, pricing, categories, and ratings. It is ideal for learning data cleaning, text processing (NLP), or building recommendation systems.

The data has been sourced via the DummyJSON API and structured to support tasks like:

Exploratory Data Analysis (EDA)

Building product recommendation models

Sentiment or review-based analysis

Data visualization and dashboarding

Columns include fields like title, description, brand, category, price, rating, and more.

Dataset Description

This dataset contains historical sales data for an e-commerce platform, including customer behavior, product preferences, and transaction details. The data is structured to support analyses aimed at understanding customer behavior, predicting product preferences, and improving overall revenue through strategic marketing and sales efforts. Full data: Brazilian E-commerce Public Dataset

Key Components:

• Customer Data: Includes demographic information, purchase history, and browsing behavior.
• Product Data: Details about the products, such as category, price, and sales volume.
• Transaction Data: Records of individual transactions, including timestamps, purchased items, and total amounts.
• Reviews Data: Customer reviews and ratings for products, useful for sentiment analysis and product feedback.

Usage:

The dataset is intended for: - Analyzing customer behavior to improve marketing strategies. - Predicting product preferences to enhance cross-selling and up-selling. - Automating reporting and creating real-time dashboards. - Implementing and testing machine learning models for sales prediction and customer retention strategies.

Format:

The dataset is provided in CSV format, with each file corresponding to a different aspect of the e-commerce data (e.g., customers, products, transactions, reviews). Each file includes relevant columns for the type of data it contains.