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. default of credit card clients: This research aimed at the case of customersâ€™ default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods.
7. Dow Jones Index: This dataset contains weekly data for the Dow Jones Industrial Index. It has been used in computational investing research.
8. 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.
9. 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).
10. Machine Learning based ZZAlpha Ltd. Stock Recommendations 2012-2014: The data here are the ZZAlphaÂ® 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.
11. 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).
12. 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.
13. 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.
14. 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.
15. 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
16. 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/).