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Daily Electronics Sales Forecast Data
Retail & Consumer Behavior
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About
Offers statistical data on electronic store product sales over a two-year period, designed to help predict future sales and analyse product performance. This dataset includes key metrics such as sales figures, online views, and pricing information, reflecting real-world scenarios that may include data noise. It provides a foundation for developing sales forecasting models and understanding market dynamics.
Columns
- date: The date of the recorded event.
- id: A unique identifier for the product.
- category_id: A unique identifier for the product's category.
- sales: The total sales amount for the product on the given date.
- views: The total number of times the product was viewed on the website.
- price_cost: The cost price of the product.
- price_retail: The retail price of the product, which may be lower than the cost if sold at a loss.
Distribution
- Format: The data is provided in a CSV file named "sales.csv".
- Size: The file size is approximately 78.98 MB.
- Structure: Contains 2.55 million rows and 7 columns.
Usage
Ideal applications for this dataset include:
- Developing and training machine learning models for sales forecasting.
- Conducting market basket analysis to identify product associations.
- Performing pricing strategy analysis to optimise retail prices.
- Analysing the relationship between online product views and actual sales.
Coverage
- Geographic Scope: The geographic location of the electronic store is not specified.
- Time Range: The data covers a two-year period from 24 February 2022 to 25 February 2024.
- Demographic Scope: No demographic information is included in this dataset.
License
CC0: Public Domain.
Who Can Use It
- Data Scientists: For building predictive sales models and performing time-series analysis.
- Business Analysts: To analyse sales trends, product performance, and pricing effectiveness.
- Retail Managers: To gain insights into inventory management and promotional strategies.
- Academic Researchers: For studying retail analytics and consumer behaviour patterns.
Dataset Name Suggestions
- Electronic Store Sales Analytics
- Retail Product Sales & Pricing Data
- Daily Electronics Sales Forecast Data
- E-commerce Product Performance Metrics
Attributes
Original Data Source: Daily Electronics Sales Forecast Data
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