business

Showing 1-7 of 7 results

Goodreads-books

Creators: Zając, Zygmunt
Publication Date: 2019
Creators: Zając, Zygmunt

The primary reason for creating this dataset is the requirement of a good clean dataset of books. It contains book names, authors, ratings and review counts. The data set is 1.56 MB large and was scraped via the Goodreads API

US Funds dataset from Yahoo Finance

Creators: (Leone, Stefano)
Publication Date: 2018
Creators: (Leone, Stefano)

The file contains 24,821 Mutual Funds and 1,680 ETFs with general aspects (as Total Net Assets, management company and size), portfolio indicators (as cash, stocks, bonds, and sectors), returns (as yeartodate, 2020-11) and financial ratios (as price/earning, Treynor and Sharpe ratios, alpha, and beta). The data set is 1.7 GB large.

Amazon product co-purchasing network metadata

Creators: Leskovec, Jure
Publication Date: 2006
Creators: Leskovec, Jure

The data was collected by crawling Amazon website and contains product metadata and review information about 548,552 different products (Books, music CDs, DVDs and VHS video tapes). For each product the following information is available:

Title
Salesrank
List of similar products (that get co-purchased with the current product)
Detailed product categorization
Product reviews: time, customer, rating, number of votes, number of people that found the review helpful

The data was collected in summer 2006.

Yelp Open Dataset

Creators: Yelp, Inc.
Publication Date: 2015
Creators: Yelp, Inc.
The Yelp dataset is a subset of businesses, reviews, and user data for use in personal, educational, and academic purposes. It contains 6.9M online reviews for 150k businesses. It also includes more than 200,000 images related to the reviews.The data consists of multiple sub datasets:

  1. Yelp Business data: Contains business data including location data, attributes, and categories.
  2. Yelp Review data: Contains full review text data including the user_id that wrote the review and the business_id the review is written for.
  3. Yelp User data: User data including the user’s friend mapping and all the metadata associated with the user.
  4. Yelp Checkin data: Checkins on a business.
  5. Yelp Tip data: Tips written by a user on a business. Tips are shorter than reviews and tend to convey quick suggestions.
  6. Yelp Photo data: Contains photo data including the caption and classification (one of “food”, “drink”, “menu”, “inside” or “outside”).

Available as JSON files, use can use it to teach students about databases, to learn NLP, or for sample production data while you learn how to make mobile apps.

 

Consumer Complaint Database

Creators: Consumer Financial Protection Bureau (CFPB)
Publication Date: 2011
Creators: Consumer Financial Protection Bureau (CFPB)
Each week we send thousands of consumers’ complaints about financial products and services to companies for response. Those complaints are published here after the company responds, confirming a commercial relationship with the consumer, or after 15 days, whichever comes first. Complaint narratives are consumers’ descriptions of their experiences in their own words. By adding their voice, consumers help improve the financial marketplace. The database generally updates daily. 

Future of Business - Survey Results

Creators: Facebook; OECD; World Bank
Publication Date: 2018
Creators: Facebook; OECD; World Bank

The Future of Business survey is a collaboration between Facebook, the OECD and the World Bank to provide timely insights on the perceptions, challenges, and outlook of online Small and Medium Enterprises (SMEs). The Future of Business survey was first launched as a monthly survey in 17 countries in February 2016 and expanded to 42 countries in 2018. In 2019, the Future of Business survey increased coverage to 97 countries and moved to a bi-annual cadence.

The target population consists of SMEs that have an active Facebook business Page and include both newer and longer-standing businesses, spanning across a variety of sectors. To date, more than 90 million SMEs have created a Facebook Page, and more than 700,000 of these Facebook Page owners have taken the survey. With more businesses leveraging online tools each day, the survey provides a lens into a new mobilized, digital economy and, in particular, insights on the actors: a relatively unmeasured community worthy of deeper consideration and considerable policy interest.

The survey includes questions about perceptions of current and future economic activity, challenges, business characteristics and strategy. Custom modules include questions related to regulation, access to finance, digital payments, and digital skills.

Advertisement CTR Prediction Data

Creators: Huawei
Publication Date: 2020
Creators: Huawei

Advertisement CTR prediction is the key problem in the area of computing advertising. Increasing the accuracy of Advertisement CTR prediction is critical to improve the effectiveness of precision marketing. In this competition, we release big advertising datasets that are anonymized. Based on the datasets, contestants are required to build Advertisement CTR prediction models. The aim of the event is to find talented individuals to promote the development of Advertisement CTR prediction algorithms. The datasets contain the advertising behavior data collected from seven consecutive days, including a training dataset and a testing dataset.

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