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Pricing Regression Dataset for Laptops
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About
This collection offers detailed insights into 1303 distinct laptop configurations, encompassing technical attributes such as CPU type, RAM capacity, storage configuration, screen size, and operating system. Its main purpose is to allow users to investigate the relationship between physical hardware components and their corresponding final market price within the Indian economy, thereby supporting price prediction and trend identification.
Columns
The dataset includes twelve attributes:
- id: A unique identifying number for each laptop record.
- Company: The name of the manufacturer or brand.
- TypeName: The classification or category of the laptop (e.g., Notebook, Gaming).
- Inches: The measurement of the laptop screen diagonal size.
- ScreenResolution: Specific details regarding the display's pixel setup (e.g., Full HD 1920x1080).
- Cpu: The specifications of the Central Processing Unit.
- Ram: The installed size of Random Access Memory, typically in GB.
- Memory: Details concerning the primary storage configurations and types (e.g., 256GB SSD, 1TB HDD).
- Gpu: The name and model of the Graphics Processing Unit.
- OpSys: The installed operating system (e.g., Windows 10, No OS).
- Weight: The approximate mass of the laptop, usually listed in kilograms.
- Price: The monetary cost of the laptop, quoted in INR.
Distribution
The data is delivered in a flat file structure, specifically a CSV format named
laptop_data.csv, which is approximately 182.05 kB in size. It contains 1303 distinct records, each featuring 12 attributes. The dataset is static, with an expected update frequency stated as 'Never'.Usage
Ideal applications involve the development and evaluation of machine learning models for regression analysis, specifically tailored to predict the price of a laptop configuration. It is also highly effective for market research analysts studying hardware trends, assessing pricing bands, and comparing specifications across various manufacturers. It is recommended training material for data science students learning fundamental regression methodologies.
Coverage
The data is centred on laptop pricing expressed solely in INR, focusing coverage on the Indian electronics market. The time frame represents a snapshot of the market, though the exact acquisition date is not specified. Coverage includes a wide array of manufacturers, with Dell and Lenovo being the most frequently represented brands (each accounting for 23% of records). The most common product type observed is the Notebook category (56%).
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For building and testing predictive regression models on pricing.
- Students and Beginners: For foundational practice utilising a structured, real-world dataset tagged for categorical analysis.
- Electronics Market Researchers: For establishing benchmarks for current hardware pricing and evaluating the value contribution of different features.
Dataset Name Suggestions
- Laptop Hardware Price Forecasting (INR)
- Indian Consumer Electronics Specification Data
- Pricing Regression Dataset for Laptops
- Laptop Configuration and Price Catalogue
Attributes
Original Data Source: Pricing Regression Dataset for Laptops
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