1. Breast Cancer Wisconsin (Prognostic): Prognostic Wisconsin Breast Cancer Database
2. Forest Fires: This is a difficult regression task, where the aim is to predict the burned area of forest fires, in the northeast region of Portugal, by using meteorological and other data (see details at: http://www.dsi.uminho.pt/~pcortez/forestfires).
3. Concrete Slump Test: Concrete is a highly complex material. The slump flow of concrete is not only determined by the water content, but that is also influenced by other concrete ingredients.
4. Tennis Major Tournament Match Statistics: This is a collection of 8 files containing the match statistics for both women and men at the four major tennis tournaments of the year 2013. Each file has 42 columns and a minimum of 76 rows.
5. Student Performance: Predict student performance in secondary education (high school).
6. 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.
7. Facebook metrics: Facebook performance metrics of a renowned cosmetic's brand Facebook page.
8. Early biomarkers of Parkinson’s disease based on natural connected speech: Predict a pattern of neurodegeneration in the dataset of speech features obtained from patients with early untreated Parkinsonâ€™s disease and patients at high risk developing Parkinsonâ€™s disease.
9. Optical Interconnection Network : This dataset contains 640 performance measurements from a simulation of 2-Dimensional Multiprocessor Optical Interconnection Network.
10. Algerian Forest Fires Dataset : The dataset includes 244 instances that regroup a data of two regions of Algeria.
11. 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.
12. Heart failure clinical records: This dataset contains the medical records of 299 patients who had heart failure, collected during their follow-up period, where each patient profile has 13 clinical features.
13. Bone marrow transplant: children: The data set describes pediatric patients with several hematologic diseases, who were subject to the unmanipulated allogeneic unrelated donor hematopoietic stem cell transplantation.
14. 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.