1. Airfoil Self-Noise: NASA data set, obtained from a series of aerodynamic and acoustic tests of two and three-dimensional airfoil blade sections conducted in an anechoic wind tunnel.
2. Beijing PM2.5 Data: This hourly data set contains the PM2.5 data of US Embassy in Beijing. Meanwhile, meteorological data from Beijing Capital International Airport are also included.
3. Challenger USA Space Shuttle O-Ring: Task: predict the number of O-rings that experience thermal distress on a flight at 31 degrees F given data on the previous 23 shuttle flights
4. Concrete Compressive Strength: Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients.
5. 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).
6. Greenhouse Gas Observing Network: Design an observing network to monitor emissions of a greenhouse gas (GHG) in California given time series of synthetic observations and tracers from weather model simulations.
7. Individual household electric power consumption: Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available.
8. PM2.5 Data of Five Chinese Cities: This hourly data set contains the PM2.5 data in Beijing, Shanghai, Guangzhou, Chengdu and Shenyang. Meanwhile, meteorological data for each city are also included.
9. Solar Flare: Each class attribute counts the number of solar flares of a certain class that occur in a 24 hour period
10. Yacht Hydrodynamics: Delft data set, used to predict the hydodynamic performance of sailing yachts from dimensions and velocity.