****Attribute information:****

Row ID: A unique identifier for each row in the table Order ID: The identifier for each sales order Order Date: The date the order was placed Ship Date: The date the order was shipped Delivery Duration: The amount of time it took to deliver the order Ship Mode: The shipping method used for the order Customer ID: The identifier for the customer who placed the order Customer Name: The name of the customer who placed the order Country: The customer's country City: The customer's city State: The customer's state Postal Code: The customer's postal code Region: The customer's region Product ID: The identifier for the product that was ordered Category: The category of the product that was ordered (e.g., furniture, office supplies, technology) Sub-Category - This attribute likely refers to a subcategory within a larger product category (e.g., Tables within Furniture). (Bookcases - Chairs - Labels - Tables - Storage - Furnishings - Art - Phones - Binders - Appliances - Paper - Others). Product Name - This attribute specifies the name of the product sold. (Bush Somerset Collection Bookcase - Hon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back - Self-Adhesive Address Labels for Typewriters by Universal - Bretford CP4500 Series Slim Rectangular Table - Others).

Sales - This attribute shows the total sales amount for each product. Values are listed in currency format Quantity - This attribute specifies the number of units sold for each product. Integer values. Discount - This attribute indicates the discount offered on the product. Discount Value - This attribute shows the total discount amount applied to the product. Profit - This attribute shows the profit earned on the sale of each product. COGS - This attribute likely refers to each product's Cost of Goods Sold. COGS = Sales - Profit

Explore the comprehensive history and modern trends of electric vehicles with our Global EV Sales Data. Discover key innovations, market growth.

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.

Dataset

This dataset was created by Mark Medhat

Released under Apache 2.0

Contents

Distribution of Home Sales reports the number of single family home sales by Price Range (2001-2016). Domain

Retail Sales - Table 620-67001 : Total Retail Sales

Total Vehicle Sales in the United States decreased to 15.30 Million in June from 15.70 Million in May 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.

In 2022, U.S. auto shoppers bought approximately 2.86 million autos. Meanwhile, light trucks accounted for more than 79 percent of light vehicles sold to individual customers and corporate fleets in the United States.

Graph and download economic data for Retail Sales (DISCONTINUED) (RETAIL) from Jan 1947 to Apr 2001 about retail trade, sales, retail, and USA.

This table contains 3 series, with data for years 2016 - 2017 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Sales (3 items: Retail trade; Electronic shopping and mail-order houses; Retail E-commerce sales).

E-commerce sales of enterprises by NACE Rev. 2 activity

This synthetic dataset simulates daily-level FMCG sales transactions for three consecutive years (2022, 2023, 2024), designed for practicing time series forecasting, demand planning, and machine learning in realistic business conditions.

Inspired by real-world scenarios (e.g. Nestlé, Unilever, P&G), it includes: - Product hierarchy: SKU → Brand → Segment → Category - Sales channels: Retail / Discount / E-commerce - Regions: Central, North, and South (Poland) - Daily sales quantities, prices, promotions, stock, delivery lag (lead time) - Pack types: Single / Multipack / Carton - Seasonality and product introductions: - New SKUs are introduced in 2024 only - Prices gradually increase over the years

Possible Use Cases - Weekly sales forecasting - Promotion effect analysis - Seasonality and trend modeling - New product forecasting (cold start) - Feature engineering for ML models

Created by: Beata Faron
LinkedIn profile
Data Scientist working on demand forecasting, NLP, and business-oriented ML.

Image by iuriimotov on Freepik

This statistic illustrates the sales volume of rice in Italy between ************* and *************, by sale channel. According to data, discounts were the largest channel for rice sale, selling within a year ** tons. Supermarkets ranked second with ** tons, followed by hypermarkets with ** tons rice.

This is a realistic and structured pizza sales dataset covering the time span from **2024 to 2025. ** Whether you're a beginner in data science, a student working on a machine learning project, or an experienced analyst looking to test out time series forecasting and dashboard building, this dataset is for you.

📁 What’s Inside? The dataset contains rich details from a pizza business including:

✅ Order Dates & Times ✅ Pizza Names & Categories (Veg, Non-Veg, Classic, Gourmet, etc.) ✅ Sizes (Small, Medium, Large, XL) ✅ Prices ✅ Order Quantities ✅ Customer Preferences & Trends

It is neatly organized in Excel format and easy to use with tools like Python (Pandas), Power BI, Excel, or Tableau.

💡** Why Use This Dataset?** This dataset is ideal for:

📈 Sales Analysis & Reporting 🧠 Machine Learning Models (demand forecasting, recommendations) 📅 Time Series Forecasting 📊 Data Visualization Projects 🍽️ Customer Behavior Analysis 🛒 Market Basket Analysis 📦 Inventory Management Simulations

🧠 Perfect For: Data Science Beginners & Learners BI Developers & Dashboard Designers MBA Students (Marketing, Retail, Operations) Hackathons & Case Study Competitions

pizza, sales data, excel dataset, retail analysis, data visualization, business intelligence, forecasting, time series, customer insights, machine learning, pandas, beginner friendly

Graph and download economic data for Advance Retail Sales: Nonstore Retailers (MARTSMPCSM454USS) from Feb 1992 to May 2025 about retail trade, percent, sales, retail, and USA.

Data scraped from National Retail Federation webpage for 2020.

Graph and download economic data for Retail Sales: Nonstore Retailers (MRTSSM454USS) from Jan 1992 to May 2025 about retail trade, sales, retail, and USA.

United States Retail Sales Nowcast: sa: YoY data was reported at 4.089 % in 12 May 2025. This records an increase from the previous number of 3.963 % for 05 May 2025. United States Retail Sales Nowcast: sa: YoY data is updated weekly, averaging 3.924 % from Feb 2020 (Median) to 12 May 2025, with 274 observations. The data reached an all-time high of 44.471 % in 17 May 2021 and a record low of -13.873 % in 25 May 2020. United States Retail Sales Nowcast: sa: YoY data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s United States – Table US.CEIC.NC: CEIC Nowcast: Retail Sales.

JuanjoJ55/video-games-sales dataset hosted on Hugging Face and contributed by the HF Datasets community

The dataset is a comprehensive sales pipeline report that records individual deals managed by various sales representatives. It includes details such as the reporting date, customer name, country, deal size, and sales stage. Each deal is also associated with a probability of closure, which is used to calculate a weighted forecast. The dataset captures the sales channel used, expected close date, and next steps for each deal. This data can be useful for tracking sales performance, forecasting revenue, and identifying opportunities in different regions and sales stages.