1. Iranian Churn Dataset: This dataset is randomly collected from an Iranian telecom company’s database over a period of 12 months.
2. Facebook metrics: Facebook performance metrics of a renowned cosmetic's brand Facebook page.
3. 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).
4. 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).
5. Daily Demand Forecasting Orders: The dataset was collected during 60 days, this is a real database of a brazilian logistics company.
6. 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.
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. clickstream data for online shopping: The dataset contains information on clickstream from online store offering clothing for pregnant women.
9. 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.
10. 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).
11. 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.
12. 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/).