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1. 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.

2. Facebook metrics: Facebook performance metrics of a renowned cosmetic's brand Facebook page.

3. 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.

4. 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.

5. 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.

6. Iranian Churn Dataset: This dataset is randomly collected from an Iranian telecom company’s database over a period of 12 months.

7. Iranian Churn Dataset: This dataset is randomly collected from an Iranian telecom company’s database over a period of 12 months.

8. 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/).

9. 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).

10. clickstream data for online shopping: The dataset contains information on clickstream from online store offering clothing for pregnant women.


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