Use our Best Buy products to collect ratings, prices, and descriptions about products from an e-commerce online web. You can purchase either the entire dataset or a customized subset, depending on your requirements. The Best Buy Products Dataset stands as a comprehensive resource for businesses, researchers, and analysts aiming to navigate the vast array of products offered by Best Buy, a leading retailer in consumer electronics and technology. Tailored to provide a deep understanding of Best Buy's e-commerce ecosystem, this dataset facilitates market analysis, pricing optimization, customer behavior comprehension, and competitor assessment. At its core, the dataset encompasses essential attributes such as product ID, title, descriptions, ratings, reviews, pricing details, and seller information. These fundamental data elements empower users to glean insights into product performance, customer sentiment, and seller credibility, thereby facilitating informed decision-making processes. Whether you're a retailer looking to enhance your product portfolio, a researcher investigating trends in consumer electronics, or an analyst seeking to refine e-commerce strategies, the Best Buy Products Dataset offers a valuable resource for uncovering opportunities and driving success in the ever-evolving landscape of retail.

About This Data

This is a list of over 7,000 online reviews for 50 electronic products from websites like Amazon and Best Buy provided by Datafiniti's Product Database. The dataset includes the review date, source, rating, title, reviewer metadata, and more.

Note that this is a sample of a large dataset. The full dataset is available through Datafiniti.

What You Can Do With This Data

You can use this data to identify how consumer feedback impacts the product buying process. E.g.:

• What are the different trends for electronic products?
• What is the correlation between star ratings and positive reviews?
• What is the online reputation of different brands?

Data Schema

A full schema for the data is available in our support documentation.

About Datafiniti

Datafiniti provides instant access to web data. We compile data from thousands of websites to create standardized databases of business, product, and property information. Learn more.

Interested in the Full Dataset?

Get this data and more by creating a free Datafiniti account or requesting a demo.

This dataset consists of over 64,000 product reviews for a wide range of Windows Laptops, mobiles and 25+ more catagorizes, collected from Best Buy. Each product entry includes detailed information such as the product name, SKU, customer rating, and a direct link to the product page. The dataset contains reviews spanning multiple years, written by verified purchasers.

Key Features: Product Information: Each review is associated with a product, including fields like CategoryName (e.g., "Windows Laptops"), ProductName, sku, and a URL linking to the product page. Customer Reviews: Each product contains multiple customer reviews with: Review Title: A brief headline summarizing the customer's opinion. Review Body: The full text of the review, offering insights into user experience. Rating: The star rating given by the reviewer (out of 5). Submission Date: The date and time when the review was posted. Reviewer Information: Includes the reviewer's name and a flag indicating whether the review is from a verified purchase. Applications: Sentiment Analysis: The text reviews provide a rich source for performing sentiment analysis, identifying positive, neutral, and negative feedback about various laptops. Product Comparison: With reviews for multiple laptop models, this dataset can be used to compare user satisfaction and ratings across different products. Time-Series Analysis: By analyzing the SubmissionDate, trends in customer satisfaction and product performance can be tracked over time. Recommendation Systems: The dataset can be used to build recommendation engines that suggest products based on user preferences and ratings. This comprehensive dataset provides an in-depth look at user feedback for Windows Laptops, making it ideal for machine learning projects, customer satisfaction analysis, and product performance monitoring.

Overview:

The "Best Buy 2024 💻 Windows Laptops" dataset is a comprehensive collection of Windows laptop listings from Best Buy, tailored for the year 2024. With 489 entries, the dataset spans a wide array of brands and models, providing a deep dive into the specifications, consumer ratings, and pricing details of the latest laptop offerings.

Data Science Applications:

This dataset is ripe for various data science applications, including but not limited to: - Exploratory Data Analysis (EDA): Uncover trends and patterns in laptop specifications and consumer preferences. - Predictive Modeling: Forecast laptop ratings based on specifications or predict price points using features data. - Sentiment Analysis: Leverage rating and review counts to gauge consumer satisfaction across different brands or models. - Feature Extraction: Utilize descriptions to extract and analyze key laptop features.

Column Descriptors:

• brand (489 non-null, object): The manufacturer of the laptop, covering a diverse range of well-known companies such as Dell, Lenovo, Microsoft, and more, indicating the dataset's breadth in representing different manufacturers.
• model (488 non-null, object): The specific model identifier for each laptop, providing a unique distinction between different laptop offerings.
• aggregateRating/ratingValue (357 non-null, float64): The average consumer rating for each laptop on a scale, reflecting overall consumer satisfaction.
• aggregateRating/reviewCount (357 non-null, float64): The total number of reviews submitted for each laptop, offering insight into the level of consumer engagement and feedback.
• offers/price (262 non-null, float64): The listed selling price for each laptop on Best Buy, crucial for price trend analysis and market positioning.
• offers/priceCurrency (489 non-null, object): The currency of the listed price, uniformly denoted as 'USD', ensuring consistency in price data.
• depth (431 non-null, float64): The front-to-back measurement of each laptop, contributing to the understanding of laptop size and portability.
• width (431 non-null, float64): The side-to-side measurement, further detailing the physical dimensions of the laptops.
• features/0/description (486 non-null, object): A description of a primary feature or selling point for the laptop, highlighting unique or standout specifications.
• features/1/description (483 non-null, object): Descriptions of a secondary feature, providing additional insights into the laptops' capabilities and attractions.

Ethically Mined Data:

This dataset has been compiled with strict adherence to ethical data collection practices, ensuring that all information was gathered from publicly accessible sources without infringing on personal privacy or data ownership rights.

Acknowledgments:

We extend our heartfelt thanks to Best Buy for the comprehensive and accessible data that forms the backbone of this dataset.

Image Acknowledgment:

Special acknowledgment to Freepik for providing the compelling visual that accompanies this dataset: Image by Freepik.

Context

There is a lack of a connection between how reviews affect revenue. This dataset will allow you to make a connection between the two.

Content

In this dataset you can find the details about over 7,000 Amazon and Best Buys Reviews on over 50 electronics products.

Acknowledgements

The data was compiled from https://data.world/datafiniti/amazon-and-best-buy-electronics.

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This dataset(uncleaned) provides detailed information about mirrorless cameras available at Best Buy. It includes the following attributes:

Camera: The product name and specifications. Model: The unique model identifier for each camera. Rating: Customer ratings (e.g., "Rating 4.9 out of 5 stars"). Number of Reviews: The total number of customer reviews. Price: The price of the product as listed (e.g., "$799.99"). The dataset contains 188 rows and 5 columns, with some missing values in the "Model," "Number of Reviews," and "Price" columns. This data can be used for analytics, consumer insights, or product comparisons.

Weekly statistics showing how many Best Buy coupon codes were verified by the CouponBirds team. This dataset reflects real-time coupon validation activity to ensure coupon accuracy and reliability.

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Online Retail Transaction Data

UK Online Retail Sales and Customer Transaction Data

By UCI [source]

About this dataset

Comprehensive Dataset on Online Retail Sales and Customer Data

Welcome to this comprehensive dataset offering a wide array of information related to online retail sales. This data set provides an in-depth look at transactions, product details, and customer information documented by an online retail company based in the UK. The scope of the data spans vastly, from granular details about each product sold to extensive customer data sets from different countries.

This transnational data set is a treasure trove of vital business insights as it meticulously catalogues all the transactions that happened during its span. It houses rich transactional records curated by a renowned non-store online retail company based in the UK known for selling unique all-occasion gifts. A considerable portion of its clientele includes wholesalers; ergo, this dataset can prove instrumental for companies looking for patterns or studying purchasing trends among such businesses.

The available attributes within this dataset offer valuable pieces of information:

• InvoiceNo: This attribute refers to invoice numbers that are six-digit integral numbers uniquely assigned to every transaction logged in this system. Transactions marked with 'c' at the beginning signify cancellations - adding yet another dimension for purchase pattern analysis.

• StockCode: Stock Code corresponds with specific items as they're represented within the inventory system via 5-digit integral numbers; these allow easy identification and distinction between products.

• Description: This refers to product names, giving users qualitative knowledge about what kind of items are being bought and sold frequently.

• Quantity: These figures ascertain the volume of each product per transaction – important figures that can help understand buying trends better.

• InvoiceDate: Invoice Dates detail when each transaction was generated down to precise timestamps – invaluable when conducting time-based trend analysis or segmentation studies.

• UnitPrice: Unit prices represent how much each unit retails at — crucial for revenue calculations or cost-related analyses.

Finally,

• Country: This locational attribute shows where each customer hails from, adding geographical segmentation to your data investigation toolkit.

This dataset was originally collated by Dr Daqing Chen, Director of the Public Analytics group based at the School of Engineering, London South Bank University. His research studies and business cases with this dataset have been published in various papers contributing to establishing a solid theoretical basis for direct, data and digital marketing strategies.

Access to such records can ensure enriching explorations or formulating insightful hypotheses about consumer behavior patterns among wholesalers. Whether it's managing inventory or studying transactional trends over time or spotting cancellation patterns - this dataset is apt for multiple forms of retail analysis

How to use the dataset

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance. You can use the Quantity and UnitPrice fields to calculate metrics like revenue, and further combine it with InvoiceNo information to understand sales over individual transactions.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description). You could analyse which products are most popular based on Quantity sold or look at popularity per transaction by considering both Quantity and InvoiceNo.

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better. Concatenating invoice numbers (which stand for separate transactions) per client will give insights about your clients as well.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

Practical applications

Understand what products sell best where - It can help drive tailored marketing strategies. Anomalies detection – Identify unusual behaviors that might lead frau...

Extract detailed property data points — address, URL, prices, floor space, overview, parking, agents, and more — from any real estate listings. The Rankings data contains the ranking of properties as they come in the SERPs of different property listing sites. Furthermore, with our real estate agents' data, you can directly get in touch with the real estate agents/brokers via email or phone numbers.

A. Usecase/Applications possible with the data:

1. Property pricing - accurate property data for real estate valuation. Gather information about properties and their valuations from Federal, State, or County level websites. Monitor the real estate market across the country and decide the best time to buy or sell based on data

2. Secure your real estate investment - Monitor foreclosures and auctions to identify investment opportunities. Identify areas within special economic and opportunity zones such as QOZs - cross-map that with commercial or residential listings to identify leads. Ensure the safety of your investments, property, and personnel by analyzing crime data prior to investing.

3. Identify hot, emerging markets - Gather data about rent, demographic, and population data to expand retail and e-commerce businesses. Helps you drive better investment decisions.

4. Profile a building’s retrofit history - a building permit is required before the start of any construction activity of a building, such as changing the building structure, remodeling, or installing new equipment. Moreover, many large cities provide public datasets of building permits in history. Use building permits to profile a city’s building retrofit history.

5. Study market changes - New construction data helps measure and evaluate the size, composition, and changes occurring within the housing and construction sectors.

6. Finding leads - Property records can reveal a wealth of information, such as how long an owner has currently lived in a home. US Census Bureau data and City-Data.com provide profiles of towns and city neighborhoods as well as demographic statistics. This data is available for free and can help agents increase their expertise in their communities and get a feel for the local market.

7. Searching for Targeted Leads - Focusing on small, niche areas of the real estate market can sometimes be the most efficient method of finding leads. For example, targeting high-end home sellers may take longer to develop a lead, but the payoff could be greater. Or, you may have a special interest or background in a certain type of home that would improve your chances of connecting with potential sellers. In these cases, focused data searches may help you find the best leads and develop relationships with future sellers.

How does it work?

• Analyze sample data
• Customize parameters to suit your needs
• Add to your projects
• Contact support for further customization

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Overview

The Market_Basket_Optimisation dataset is a classic transactional dataset often used in association rule mining and market basket analysis.
It consists of multiple transactions where each transaction represents the collection of items purchased together by a customer in a single shopping trip.

• File Name: Market_Basket_Optimisation.csv
• Format: CSV (Comma-Separated Values)
• Structure: Each row corresponds to one shopping basket. Each column in that row contains an item purchased in that basket.
• Nature of Data: Transactional, categorical, sparse.
• Primary Use Case: Discovering frequent itemsets and association rules to understand shopping patterns, product affinities, and to build recommender systems.

Detailed Information

📊 Dataset Composition

• Transactions: 7,501 (each row = one basket).
• Items (unique): Around 120 distinct products (e.g., bread, mineral water, chocolate, etc.).
• Columns per row: Up to 20 possible items (not fixed; some rows have fewer, some more).
• Data Type: Purely categorical (no numerical or continuous features).
• Missing Values: Present in the form of empty cells (since not every basket has all 20 columns).
• Duplicates: Some baskets may appear more than once — this is acceptable in transactional data as multiple customers can buy the same set of items.

🛒 Nature of Transactions

• Basket Definition: Each row captures items bought together during a single visit to the store.
• Variability: Basket size varies from 1 to 20 items. Some customers buy only one product, while others purchase a full set of groceries.
• Sparsity: Since there are ~120 unique items but only a handful appear in each basket, the dataset is sparse. Most entries in the one-hot encoded representation are zeros.

🔎 Examples of Data

Example transaction rows (simplified):

Here, empty cells mean no item was purchased in that slot.

📈 Applications of This Dataset

This dataset is frequently used in data mining, analytics, and recommendation systems. Common applications include:

1. Association Rule Mining (Apriori, FP-Growth):

   • Discover rules like {Bread, Butter} ⇒ {Jam} with high support and confidence.
   • Identify cross-selling opportunities.

2. Product Affinity Analysis:

   • Understand which items tend to be purchased together.
   • Helps with store layout decisions (placing related items near each other).

3. Recommendation Engines:

   • Build systems that suggest "You may also like" products.
   • Example: If a customer buys pasta and tomato sauce, recommend cheese.

4. Marketing Campaigns:

   • Bundle promotions and discounts on frequently co-purchased products.
   • Personalized offers based on buying history.

5. Inventory Management:

   • Anticipate demand for certain product combinations.
   • Prevent stockouts of items that drive the purchase of others.

📌 Key Insights Potentially Hidden in the Dataset

• Popular Items: Some items (like mineral water, eggs, spaghetti) occur far more frequently than others.
• Product Pairs: Frequent pairs and triplets (e.g., pasta + sauce + cheese) reflect natural meal-prep combinations.
• Basket Size Distribution: Most customers buy fewer than 5 items, but a small fraction buy 10+ items, showing long-tail behavior.
• Seasonality (if extended with timestamps): Certain items might show peaks in demand during weekends or holidays (though timestamps are not included in this dataset).

📂 Dataset Limitations

1. No Customer Identifiers:

   • We cannot track repeated purchases by the same customer.
   • Analysis is limited to basket-level insights.

2. No Timestamps:

   • No temporal analysis (trends over time, seasonality) is possible.

3. No Quantities or Prices:

   • We only know whether an item was purchased, not how many units or its cost.

4. Sparse & Noisy:

   • Many baskets are small (1–2 items), which may produce weak or trivial rules.

🔮 Potential Extensions

• Synthetic Timestamps: Assign simulated timestamps to study temporal buying patterns.
• Add Customer IDs: If merged with external data, one can perform personalized recommendations.
• Price Data: Adding cost allows for profit-driven association rules (not just frequency-based).
• Deep Learning Models: Sequence models (RNNs, Transformers) could be applied if temporal ordering of items is introduced.

...

About Dataset

This dataset contains detailed information about phones listed on Amazon, including product specifications, user reviews, ratings, and pricing. The dataset can be useful for analyzing product trends, consumer preferences, pricing strategies, and technical features of smartphones sold on the platform. It includes both new and Amazon-renewed phones.

Description

The dataset includes the following key features:

• Color: The available color of the phone.
• Image Links: URLs to the images of the products.
• Descriptions: Detailed descriptions of the phone, including specifications.
• Kind Product: The type or category of the product (smartphones, accessories, etc.).
• Ratings: User ratings (out of 5 stars).
• Number of Ratings: Total count of ratings the product has received.
• Status: Availability status (e.g., In Stock, Out of Stock).
• Number of Buyers Last Month More Than: Approximate number of buyers in the previous month.
• Typical Price: The regular price with usd of the phone without any discounts.
• Price: The current price with usd of the phone.
• You Save: The amount saved if the phone is on discount.
• Discount: The percentage discount offered on the product.
• Brand: The brand name of the phone (e.g., Apple, Samsung).
• OS: The operating system of the phone (e.g., Android, iOS).
• CPU Model: The model of the processor used in the phone.
• Resolution: The screen resolution of the phone.
• Name: The product name as listed on Amazon.
• Wireless Carrier: The supported wireless carrier (e.g., Verizon, AT&T).
• Cellular Technology: The cellular network technology (e.g., 4G, 5G).
• Dimensions: Physical dimensions of the phone.
• ASIN: Amazon Standard Identification Number, a unique product identifier.
• Model: The model number of the phone.
• Amazon Renewed: Indicates whether the product is part of the Amazon Renewed program (refurbished).
• Renewed Smartphones: Additional flag indicating if the phone is renewed.
• Battery Capacity: The capacity of the phone’s battery (in mAh).
• Battery Power: The power rating of the battery.
• Charging Time: Time taken to charge the phone fully.
• RAM: The amount of RAM in the phone.
• Storage: Internal storage capacity of the phone.
• Screen Size: Size of the display (in inches).
• Connectivity Technologies: Wireless technologies supported by the phone (e.g., Bluetooth, Wi-Fi).
• Wireless Network: Type of wireless networks supported (e.g., Wi-Fi 6).
• CPU Speed: The speed of the phone’s CPU (in GHz).
• Reviews USA: User reviews originating from the USA.
• Reviews Other: User reviews from countries other than the USA.

Detail

This dataset includes a comprehensive range of variables, offering insight into both the technical aspects and customer perceptions of various smartphones sold on Amazon. The dataset allows for:

• Product Comparisons: Comparison of specifications like RAM, CPU, storage, battery life, screen size, etc.
• Pricing Analysis: Understanding pricing trends, discounts, and price fluctuations across different brands and models.
• Consumer Insights: Analysis of consumer behavior through ratings, reviews, and the number of buyers over time.
• Product Availability: Insights into stock availability and how often certain products are sold or renewed.

Usage

The dataset can be used for several purposes, including but not limited to:

1. Market Research: Analyze product popularity and trends in smartphone sales on Amazon.
2. Sentiment Analysis: Perform sentiment analysis on reviews (USA and other countries) to understand customer satisfaction.
3. Price Forecasting: Build models to forecast price changes or identify the best time to buy based on historical data.
4. Product Recommendations: Develop recommendation systems based on user reviews, ratings, and product features.
5. Competitive Analysis: Compare different brands and models to identify strengths and weaknesses of various smartphones.
6. Feature Engineering for ML Models: Use product specifications like RAM, CPU speed, and battery power to create features for predictive machine learning models.

Summary

This Amazon product phones dataset provides an in-depth look at smartphones sold on Amazon, covering everything from technical specifications to user reviews and pricing. It is ideal for anyone looking to analyze trends in the smartphone market, consumer preferences, or technical specifications. The data can be leveraged for a wide array of projects such as market analysis, machine learning, and competitive intelligence.

Context

The Customer Shopping Preferences Dataset offers valuable insights into consumer behavior and purchasing patterns. Understanding customer preferences and trends is critical for businesses to tailor their products, marketing strategies, and overall customer experience. This dataset captures a wide range of customer attributes including age, gender, purchase history, preferred payment methods, frequency of purchases, and more. Analyzing this data can help businesses make informed decisions, optimize product offerings, and enhance customer satisfaction. The dataset stands as a valuable resource for businesses aiming to align their strategies with customer needs and preferences. It's important to note that this dataset is a Synthetic Dataset Created for Beginners to learn more about Data Analysis and Machine Learning.

Content

This dataset encompasses various features related to customer shopping preferences, gathering essential information for businesses seeking to enhance their understanding of their customer base. The features include customer age, gender, purchase amount, preferred payment methods, frequency of purchases, and feedback ratings. Additionally, data on the type of items purchased, shopping frequency, preferred shopping seasons, and interactions with promotional offers is included. With a collection of 3900 records, this dataset serves as a foundation for businesses looking to apply data-driven insights for better decision-making and customer-centric strategies.

Dataset Glossary (Column-wise)

• Customer ID - Unique identifier for each customer
• Age - Age of the customer
• Gender - Gender of the customer (Male/Female)
• Item Purchased - The item purchased by the customer
• Category - Category of the item purchased
• Purchase Amount (USD) - The amount of the purchase in USD
• Location - Location where the purchase was made
• Size - Size of the purchased item
• Color - Color of the purchased item
• Season - Season during which the purchase was made
• Review Rating - Rating given by the customer for the purchased item
• Subscription Status - Indicates if the customer has a subscription (Yes/No)
• Shipping Type - Type of shipping chosen by the customer
• Discount Applied - Indicates if a discount was applied to the purchase (Yes/No)
• Promo Code Used - Indicates if a promo code was used for the purchase (Yes/No)
• Previous Purchases - The total count of transactions concluded by the customer at the store, excluding the ongoing transaction
• Payment Method - Customer's most preferred payment method
• Frequency of Purchases - Frequency at which the customer makes purchases (e.g., Weekly, Fortnightly, Monthly)

Structure of the Dataset

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Acknowledgement

This dataset is a synthetic creation generated using ChatGPT to simulate a realistic customer shopping experience. Its purpose is to provide a platform for beginners and data enthusiasts, allowing them to create, enjoy, practice, and learn from a dataset that mirrors real-world customer shopping behavior. The aim is to foster learning and experimentation in a simulated environment, encouraging a deeper understanding of data analysis and interpretation in the context of consumer preferences and retail scenarios.

Cover Photo by: Freepik

Thumbnail by: Clothing icons created by Flat Icons - Flaticon

Context:

This dataset, which was gathered via an online retail platform, offers comprehensive information about consumer purchasing habits. It records different facets of consumer behavior, such as transactional patterns, buying preferences, and demographic information. The dataset is intended to assist in the analysis of purchasing trends, the assessment of the impact of variables like seasons, payment methods, and discounts on decisions to buy, and the comprehension of the relationship between consumer qualities and product choices and spending patterns.

Content:

Each customer's personal information (age, gender, location), purchase details (item purchased, category, price, color, and size), and transactional aspects (discounts, shipping type, payment method, and use of promo codes) are among the many attributes included in the dataset. Behavioral data like subscription status, past purchases, and frequency of transactions are also included, as well feedback measures like review scores. All things considered, the dataset offers a thorough understanding of consumer interactions and preferences in an online shopping setting.

This dataset contains three files:

The cellphone data.csv contains data on the most popular cell phones in the US in 2022. The data for each cell phone consists of the most notable features such as performance rating (AnTuTu), memory size, camera's resolution, battery size, screen size, release date, etc. The price of each cell phone collected from Amazon and Best-Buy (in Aug 22). Overall, in our dataset there are 34 cell phones with 13 features.

The user's data and their ratings are in cellphones users.csv and cellphones ratings.csv. To elicit the ratings, we conducted a survey on Mechanical Turk. Each participant was presented with 10 random cell phones, and she was asked to indicate how likely she is to purchase each of the cell phones at the given price, on a scale from 1 (very unlikely) to 10 (very likely). We also asked each participant to add personal information: age, gender, and occupation.

This dataset can be used for building a recommendation system model that relies mainly on the features of the items.

Personal Location Data Market

Data From: "There’s a Multibillion-Dollar Market for Your Phone’s Location Data"

Original source. Author: The Markup

About this dataset

There’s a multibillion-dollar market for your phone’s location data. We surveyed 100 companies to find out who they are, what they do with your data, and whether they follow best practices.

Your phone’s location is constantly being tracked and collected by hundreds of companies, many of which are unknown to you. This data is valuable—and it’s being bought and sold in a thriving industry with little regulation.

The Markup surveyed 100 companies that collect or sell location data to get a better understanding of this industry and what it means for your privacy. We asked these companies about their policies and practices around collecting, using, and selling location data. We also reviewed their public statements and website disclosures related to privacy.

What we found was an industry that lacks transparency and accountability, with few companies following best practices around protecting the privacy of their users’ data. In many cases, these companies are collecting more data than they need, retaining it for longer than necessary, sharing it with third parties without user consent, or failing to secure it properly—putting users at risk of identity theft, fraud, or other harms.

If you care about your privacy, you should know who has access to your location data—and what they’re doing with it. This dataset contains information on the 100 companies we surveyed so that you can make informed choices about which ones to trust with your personal data

How to use the dataset

This dataset contains information on companies that collect and sell location data. The data includes the company name, website, logo, narrative, company response, privacy email, privacy policy, and whether or not the company is a California-licensed data broker

Research Ideas

• To study how location data is collected and sold
• To understand the business model of location data companies
• To learn about the privacy policies of these companies

Acknowledgements

This dataset was compiled and analyzed by The Markup. The Markup is a nonprofit newsroom that investigates how powerful institutions impact our lives

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: location-data-companies.csv | Column name | Description | |:-------------------|:--------------------------------------------------------------------| | name | The name of the company. (String) | | website | The company's website. (String) | | logo | The company's logo. (String) | | narrative | A description of the company. (String) | | privacy_email | The company's privacy email address. (String) | | privacy_policy | The company's privacy policy. (String) | | CA_broker | Whether the company is a California-licensed data broker. (Boolean) |