1. 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/). 2. 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). 3. 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. 4. Facebook metrics: Facebook performance metrics of a renowned cosmetic's brand Facebook page. 5. 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. 6. clickstream data for online shopping: The dataset contains information on clickstream from online store offering clothing for pregnant women. 7. Iranian Churn Dataset: This dataset is randomly collected from an Iranian telecom company’s database over a period of 12 months. 8. Iranian Churn Dataset: This dataset is randomly collected from an Iranian telecom company’s database over a period of 12 months. 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. 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. | 
