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Retail Laptop Regression Dataset
Retail & Consumer Behavior
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Free
About
Presents detailed information on various laptop models intended specifically for training and evaluating price prediction models using regression techniques. The data was gathered through a web scraping process focused on the Flipkart e-commerce platform. It provides crucial consumer electronics specifications alongside corresponding retail prices, making it a valuable resource for educational data science projects.
Columns
The dataset contains 10 columns detailing essential product characteristics:
- Brand: The manufacturer of the laptop (e.g., HP, ASUS).
- Model Name: The specific product identifier for the laptop.
- Processor: Details the processor type and brand (e.g., Core i5, Core i3).
- Operating System (OS): The installed operating system (e.g., Windows 11 Home).
- Storage: The capacity and type of storage installed, typically in GB (e.g., 512 GB).
- RAM: The Random Access Memory capacity in GB (e.g., 8 GB, 16 GB).
- Screen Size: The display dimensions, typically in centimeters and inches.
- Touch_Screen: A Boolean indicator showing if the display supports touch input.
- Price: The laptop's selling price, denominated in Rupees.
Distribution
The data is provided in a single CSV file named Laptops.csv, with a size of approximately 80.69 kB. The structure includes 10 attributes across 837 valid records, ensuring high data integrity with minimal missing or mismatched entries.
Usage
This dataset is ideally suited for academic study and projects in machine learning, particularly:
- Developing and testing regression algorithms for predicting product pricing.
- Performing exploratory data analysis to understand how features like RAM, Storage, and Processor type correlate with market price.
- Benchmarking different predictive modelling strategies.
- Analysing market trends and brand prevalence within the consumer electronics sector.
Coverage
The data reflects the product availability and pricing structure observed on the Flipkart platform, focusing on the Indian retail market (prices are in Rupees). The specifications provided indicate coverage of modern laptop configurations, including common features like Windows 11 Home and widely used Core processor variants. The dataset represents a snapshot collected via web scraping and is not designed for future updates.
License
CC0: Public Domain
Who Can Use It
- University Students: Utilising the data for coursework or final year projects focusing on predictive statistics.
- Data Science Beginners: Practising fundamental data cleaning, feature engineering, and linear regression techniques.
- E-commerce Analysts: Gaining insights into product specifications driving price points within the online retail space.
Dataset Name Suggestions
- Flipkart Laptop Price Prediction Data
- E-commerce Laptop Pricing Model Inputs
- Indian Market Laptop Specification Dataset
- Retail Laptop Regression Dataset
Attributes
Original Data Source: Retail Laptop Regression Dataset
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