1. 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.
2. Stock portfolio performance: The data set of performances of weighted scoring stock portfolios are obtained with mixture design from the US stock market historical database.
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. 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/).
5. 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).
6. 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.
7. South German Credit: 700 good and 300 bad credits with 20 predictor variables. Data from 1973 to 1975. Stratified sample from actual credits with bad credits heavily oversampled. A cost matrix can be used.
8. Stock keeping units: The dataset is provided by the “Trialto Latvia LTD”, the third-party logistics operator. Each observation stands for a distinct type of item for sale.
9. Vehicle routing and scheduling problems: Data collection was conducted through notes taken during the distribution of orders in a courier company that operates in the region and in the city of São Paulo (Brazil).
10. clickstream data for online shopping: The dataset contains information on clickstream from online store offering clothing for pregnant women.
11. South German Credit (UPDATE): 700 good and 300 bad credits with 20 predictor variables. Data from 1973 to 1975. Stratified sample from actual credits with bad credits heavily oversampled. A cost matrix can be used.
12. Stock keeping units: The dataset is provided by the “Trialto Latvia LTD”, the third-party logistics operator. Each observation stands for a distinct type of item for sale.
13. 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).
14. Productivity Prediction of Garment Employees: This dataset includes important attributes of the garment manufacturing process and the productivity of the employees which had been collected manually and also been validated by the industry experts.
15. 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.
16. 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.
17. Non verbal tourists data: This dataset contains the information about non-verbal preferences of tourists
18. 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.
19. 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.
20. Real estate valuation data set: The “real estate valuation” is a regression problem. The market historical data set of real estate valuation are collected from Sindian Dist., New Taipei City, Taiwan.
21. Online Retail II: A real online retail transaction data set of two years.
22. 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
23. Iranian Churn Dataset: This dataset is randomly collected from an Iranian telecom company’s database over a period of 12 months.
24. Taiwanese Bankruptcy Prediction: The data were collected from the Taiwan Economic Journal for the years 1999 to 2009. Company bankruptcy was defined based on the business regulations of the Taiwan Stock Exchange.
25. Iranian Churn Dataset: This dataset is randomly collected from an Iranian telecom company’s database over a period of 12 months.
26. Facebook metrics: Facebook performance metrics of a renowned cosmetic's brand Facebook page.
27. 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).
28. Facebook Live Sellers in Thailand: Facebook pages of 10 Thai fashion and cosmetics retail sellers. Posts of a different nature (video, photos, statuses, and links). Engagement metrics consist of comments, shares, and reactions.
29. 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
30. Incident management process enriched event log: This event log was extracted from data gathered from the audit system of an instance of the ServiceNow platform used by an IT company and enriched with data loaded from a relational database.
31. Japanese Credit Screening: Includes domain theory (generated by talking to Japanese domain experts); data in Lisp
32. Credit Approval: This data concerns credit card applications; good mix of attributes
33. 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
34. 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
35. Apartment for rent classified: This is a dataset of classified for apartments for rent in USA.
36. in-vehicle coupon recommendation: This data studies whether a person will accept the coupon recommended to him in different driving scenarios