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Kindle Pricing and Review Data
Software and Technology
Tags and Keywords
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Free
About
This dataset provides details on Amazon Kindle products, designed primarily to support the prediction of Kindle prices from their product names. It serves as a valuable resource for applications in price forecasting and the use of natural language processing techniques on consumer product data. The underlying data was gathered directly from Amazon.com.
Columns
Name: This column contains the full name or title of the Amazon Kindle product.Review: This column lists the customer review rating for each product, typically expressed on a scale from 0 to 5 stars.Price($): This column specifies the price of the product in US dollars, which is the primary target variable for predictive analysis.
Distribution
The dataset is typically structured as a data file, commonly provided in CSV format. While exact row counts are not specified, it consists of a collection of structured records, each detailing various attributes of Kindle products. The data is organised clearly with separate fields for product name, review score, and price.
Usage
- Price Prediction: Ideal for developing and training machine learning models to forecast Amazon Kindle prices based on their product names.
- Natural Language Processing (NLP): Useful for conducting text analysis on product names, enabling insights into product characteristics and naming conventions.
- Regression Analysis: Applicable for various regression tasks, especially in the context of predicting continuous values like product prices.
- Market Research: Can be utilised by analysts to understand pricing trends, competitive positioning, and product strategies within the e-reader market.
Coverage
- Geographic Scope: The dataset is designated as Global, indicating its applicability and relevance across different international regions.
- Time Range: The dataset was listed on 26 June 2025.
- Data Attributes: It includes product names, associated customer review scores, and pricing information.
License
CC0
Who Can Use It
- Data Scientists: Those focused on building and refining predictive models for e-commerce pricing.
- Machine Learning Engineers: Individuals working on natural language processing tasks or developing algorithms for price forecasting.
- Researchers: Academics or professionals investigating consumer product data, market dynamics, or the application of artificial intelligence in e-commerce.
- Business Analysts: Professionals seeking to gain insights into Amazon Kindle pricing strategies and market behaviour.
Dataset Name Suggestions
- Amazon Kindle Price Prediction Data
- Kindle Product Pricing Analytics
- Amazon E-reader Price Dataset
- Kindle Pricing and Review Data
Attributes
Original Data Source: Amazon Kindle prices
QUALITY