AbhayBhan/SalesData dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset simulates sales transactions for mobile phones and laptops, including product specifications, customer details, and sales information. It contains 50,000 rows of randomly generated data to help analyze product sales trends, customer purchasing behavior, and regional distribution of sales.

Dataset Overview

• Dataset Type: Structured tabular data
• Number of Rows: 50,000
• Number of Columns: 16

Purpose of the Dataset
This dataset can be used for:
✅ Sales Analysis – Understanding product demand and pricing trends.
✅ Customer Behavior Analysis– Identifying buying patterns across locations.
✅ Inventory Management – Monitoring inward and dispatched product movements.
✅ Machine Learning & AI – Predicting sales trends, customer preferences, and stock management.

Key Features in the Dataset

1. Product Information

   • Product: Type of product (Mobile Phone / Laptop).
   • Brand: Various brands like Apple, Samsung, Dell, Lenovo, OnePlus, etc.
   • Product Code: Unique identifier for each product.
   • Product Specification: Brief description of the product features.

2. Sales & Pricing Details

   • Price: Cost of the product (randomly generated).
   • Inward Date: Date when the product was received in stock.
   • Dispatch Date: Date when the product was sold/dispatched.
   • Quantity Sold: Number of units sold per transaction.

3. Customer & Location Details

   • Customer Name: Randomly generated customer names.
   • Customer Location: City of the customer.
   • Region: Sales region (North, South, East, West, Central).

4. Technical Specifications -Core Specification (For Laptops): Includes processor models like i3, i5, i7, i9, Ryzen 3-9.
   -Processor Specification (For Mobiles): Includes processors like Snapdragon, Exynos, Apple A-Series, and MediaTek Dimensity.
   -RAM: Randomly assigned memory sizes (4GB to 32GB).
   -ROM: Storage capacity (64GB to 1TB).
   -SSD (For Laptops): Additional storage (256GB to 2TB), "N/A" for mobile phones.

Potential Use Cases: Business Intelligence Dashboards Market Trend Analysis Supply Chain Optimization
Customer Segmentation
Machine Learning Model Training (Sales Prediction, Price Optimization, etc.)

Dataset

This dataset was created by Victor Nguyen

Contents

A aggregate collection of Commercial Platforms sales across all platforms

📊 Supplement Sales Data (2020–2025) Overview This dataset contains weekly sales data for a variety of health and wellness supplements from January 2020 to April 2025. The data includes products in categories like Protein, Vitamins, Omega, and Amino Acids, among others, and covers multiple e-commerce platforms such as Amazon, Walmart, and iHerb. The dataset also tracks sales in several locations including the USA, UK, and Canada.

Dataset Details Time Range: January 2020 to April 2025

Frequency: Weekly (Every Monday)

Number of Rows: 4,384

Columns:

Date: The week of the sale.

Product Name: The name of the supplement (e.g., Whey Protein, Vitamin C, etc.).

Category: The category of the supplement (e.g., Protein, Vitamin, Omega).

Units Sold: The number of units sold in that week.

Price: The selling price of the product.

Revenue: The total revenue generated (Units Sold * Price).

Discount: The discount applied on the product (as a percentage of original price).

Units Returned: The number of units returned in that week.

Location: The location of the sale (USA, UK, or Canada).

Platform: The e-commerce platform (Amazon, Walmart, iHerb).

Use Cases This dataset is ideal for:

Time-series forecasting and sales trend analysis 📈

Price vs. demand analysis and revenue prediction 📊

Sentiment analysis and impact of promotions (Discounts) on sales 🛍️

Product performance tracking across different platforms and locations 🛒

Business optimization in the health and wellness e-commerce sector 💼

Potential Applications Build predictive models to forecast future sales 📅

Analyze the effectiveness of discounts and promotions 💸

Create recommendation systems for supplement products 🧠

Perform exploratory data analysis (EDA) and uncover trends 🔍

Model return rates and their effect on overall revenue 📉

Why This Dataset? This dataset provides an excellent starting point for those interested in building business intelligence tools, e-commerce forecasting models, or exploring health & wellness trends. It also serves as a perfect dataset for data science learners looking to apply regression, time-series analysis, and predictive modeling techniques.

This table contains property sales information including sale date, price, and amounts for properties within Fairfax County. There is a one to many relationship to the parcel data. Refer to this document for descriptions of the data in the table.

tonyassi/clothing-sales-data dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset contains sales data, including order dates, order IDs, item details, costs, and revenues, primarily featuring USB novelty items and mugs.

This is the data set of residential sales used by the Cook County Assessor in their Computer Assisted Mass Appraisal system used to assess residential property values. This data set contains property information for every arm's-length residential sale in Cook County where class is within a specified list of classes. For disclaimers, see data set narrative at https://datacatalog.cookcountyil.gov/stories/s/p2kt-hk36 This data set was replaced by the more comprehensive data set Cook County Assessor's Sales.

The FCA has published the latest edition of its Product Sales Data (PSD) statistics. The FCA publishes the aggregated PSD received from firms operating in the mortgages, retail investments 1 January 2018 to 31 December 2023.

The FCA uses this data to assist it in regulating firms and to spot trends in the products sold in the UK market. It publishes this data so that consumers and market participants can see what firms are selling and understand the trends.

China EP Sales: Data Transmission Machine data was reported at 7.462 Unit th in 2016. This records an increase from the previous number of 1.936 Unit th for 2015. China EP Sales: Data Transmission Machine data is updated yearly, averaging 2.480 Unit th from Dec 1995 (Median) to 2016, with 19 observations. The data reached an all-time high of 67,911.106 Unit th in 2011 and a record low of 0.004 Unit th in 1999. China EP Sales: Data Transmission Machine data remains active status in CEIC and is reported by Ministry of Industry and Information Technology. The data is categorized under Global Database’s China – Table CN.RFA: Electronic Product Sales.

Envestnet®| Yodlee®'s Ecommerce Sales Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Dataset

This dataset was created by Kashuf Naeem

Released under Apache 2.0

Contents

Total Vehicle Sales in the United States decreased to 15.65 Million in May from 17.27 Million in April of 2025. This dataset provides the latest reported value for - United States Total Vehicle Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset includes sales data for POLICE INTERCEPTOR UTILITY VEHICLES that were sold in the current and previous three years. This dataset does not include sales data for Seattle City Light (SCL) fleet equipment.

Update Frequency: Yearly

Access to Residential, Condominium, Commercial, Apartment properties and vacant land sales history data.

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

This dataset contains every wholesale purchase of liquor in the State of Iowa by retailers for sale to individuals since January 1, 2012. The State of Iowa controls the wholesale distribution of liquor intended for retail sale, which means this dataset offers a complete view of retail liquor sales in the entire state. The dataset contains every wholesale order of liquor by all grocery stores, liquor stores, convenience stores, etc., with details about the store and location, the exact liquor brand and size, and the number of bottles ordered. In addition to being an excellent dataset for analyzing liquor sales, this is a large and clean public dataset of retail sales data. It can be used to explore problems like stockout prediction, retail demand forecasting, and other retail supply chain problems. The data dictionary is available from the State of Iowa's Alcoholic Beverages Division , within the Iowa Department of Commerce . There are some minor discrepancies in the data, discussed in the web view of the data . This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery.

The global Sales Data Fusion market is experiencing robust growth, driven by the increasing need for businesses to gain actionable insights from disparate data sources. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated value of $45 billion by 2033. This expansion is fueled by several key factors. The rise of big data and the proliferation of data sources across various departments (sales, marketing, customer service) necessitates sophisticated data fusion techniques to generate a unified, accurate view of customers and sales performance. Furthermore, the adoption of advanced analytics and AI-powered solutions enhances the ability of organizations to extract valuable insights for improved sales strategies, targeted marketing campaigns, and enhanced customer relationship management. The increasing demand for real-time data analysis further boosts market growth, empowering businesses to make faster, more informed decisions. Large enterprises are currently the primary adopters of Sales Data Fusion solutions due to their higher budgets and greater complexity of data management needs; however, growing adoption among SMEs is expected to significantly contribute to market expansion in the coming years. Market segmentation by service type (managed and professional services) further reveals a preference for managed services due to the specialized expertise and reduced operational overhead they provide. Geographical analysis reveals that North America currently dominates the market, primarily due to the early adoption of advanced technologies and the presence of major market players. However, substantial growth opportunities exist in the Asia-Pacific region, driven by rapid digital transformation and economic expansion in countries like China and India. While challenges such as data security concerns and integration complexities persist, the overall market outlook remains positive, driven by continuous technological advancements and the undeniable need for businesses to harness the power of unified sales data for competitive advantage. The market is expected to see continued consolidation, with larger players potentially acquiring smaller specialized firms.

Paraguay: New passenger car sales, numbers per year: The latest value from 2019 is 20599 passenger cars, a decline from 25204 passenger cars in 2018. In comparison, the world average is 459159 passenger cars, based on data from 140 countries. Historically, the average for Paraguay from 2005 to 2019 is 14983 passenger cars. The minimum value, 3000 passenger cars, was reached in 2005 while the maximum of 25204 passenger cars was recorded in 2018.

A data set that collects sales and distribution data of seventeen German gaming magazines 1980-2000, based on the print run lists ("Auflagelisten") published by the Informationsgemeinschaft zur Feststellung der Verbreitung von Werbeträgern (Information Community for the Assessment of the Circulation of Media – IVW). More information on the dataset and its structure can be found here: https://chludens.hypotheses.org/1228