1. Absenteeism at work: The database was created with records of absenteeism at work from July 2007 to July 2010 at a courier company in Brazil.
2. Bank Marketing: The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y).
3. Blood Transfusion Service Center: Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan -- this is a classification problem.
4. Cargo 2000 Freight Tracking and Tracing: Sanitized and anonymized Cargo 2000 (C2K) airfreight tracking and tracing events, covering five months of business execution (3,942 process instances, 7,932 transport legs, 56,082 activities).
5. CNAE-9: This is a data set containing 1080 documents of free text business descriptions of Brazilian companies categorized into a
subset of 9 categories
6. Credit Approval: This data concerns credit card applications; good mix of attributes
7. default of credit card clients: This research aimed at the case of customers’ default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods.
8. Dow Jones Index: This dataset contains weekly data for the Dow Jones Industrial Index. It has been used in computational investing research.
9. Farm Ads: This data was collected from text ads found on twelve websites that deal with various farm animal related topics. The binary labels are based on whether or not the content owner approves of the ad.
10. ISTANBUL STOCK EXCHANGE: Data sets includes returns of Istanbul Stock Exchange with seven other international index; SP, DAX, FTSE, NIKKEI, BOVESPA, MSCE_EU, MSCI_EM from Jun 5, 2009 to Feb 22, 2011.
11. Japanese Credit Screening: Includes domain theory (generated by talking to Japanese domain experts); data in Lisp
12. Las Vegas Strip: This dataset includes quantitative and categorical features from online reviews from 21 hotels located in Las Vegas Strip, extracted from TripAdvisor (http://www.tripadvisor.com).
13. Machine Learning based ZZAlpha Ltd. Stock Recommendations 2012-2014: The data here are the ZZAlpha® machine learning recommendations made for various US traded stock portfolios the morning of each day during the 3 year period Jan 1, 2012 - Dec 31, 2014.
14. Online News Popularity: This dataset summarizes a heterogeneous set of features about articles published by Mashable in a period of two years. The goal is to predict the number of shares in social networks (popularity).
15. Online Retail: This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.
16. Online Shoppers’ Purchasing Intention Dataset: Of the 12,330 sessions in the dataset,
84.5% (10,422) were negative class samples that did not
end with shopping, and the rest (1908) were positive class
samples ending with shopping.
17. Polish companies bankruptcy data: The dataset is about bankruptcy prediction of Polish companies.The bankrupt companies were analyzed in the period 2000-2012, while the still operating companies were evaluated from 2007 to 2013.
18. Reuters Transcribed Subset: This dataset is created by reading out 200 files from the 10 largest Reuters
classes and using an Automatic Speech Recognition system to create
19. Statlog (Australian Credit Approval): This file concerns credit card applications. This database exists elsewhere in the repository (Credit Screening Database) in a slightly different form
20. Statlog (German Credit Data): This dataset classifies people described by a set of attributes as good or bad credit risks. Comes in two formats (one all numeric). Also comes with a cost matrix
21. Wholesale customers: The data set refers to clients of a wholesale distributor. It includes the annual spending in monetary units (m.u.) on diverse product categories
22. Wine Quality: Two datasets are included, related to red and white vinho verde wine samples, from the north of Portugal. The goal is to model wine quality based on physicochemical tests (see [Cortez et al., 2009], http://www3.dsi.uminho.pt/pcortez/wine/).