Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact

Repository Web            Google
View ALL Data Sets

Browse Through:

Default Task

Classification (3)
Regression (5)
Clustering (2)
Other (0)

Attribute Type

Categorical (1)
Numerical (7)
Mixed (0)

Data Type - Undo

Multivariate (8)
Univariate (0)
Sequential (0)
Time-Series (2)
Text (0)
Domain-Theory (0)
Other (0)

Area - Undo

Life Sciences (20)
Physical Sciences (8)
CS / Engineering (30)
Social Sciences (5)
Business (5)
Game (2)
Other (12)

# Attributes - Undo

Less than 10 (8)
10 to 100 (26)
Greater than 100 (7)

# Instances

Less than 100 (2)
100 to 1000 (1)
Greater than 1000 (5)

Format Type

Matrix (7)
Non-Matrix (1)

8 Data Sets

Table View  List View

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. 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

3. 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.

4. HTRU2: Pulsar candidates collected during the HTRU survey. Pulsars are a type of star, of considerable scientific interest. Candidates must be classified in to pulsar and non-pulsar classes to aid discovery.

5. 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.

6. Shuttle Landing Control: Tiny database; all nominal values

7. Statlog (Shuttle): The shuttle dataset contains 9 attributes all of which are numerical. Approximately 80% of the data belongs to class 1

8. Yacht Hydrodynamics: Delft data set, used to predict the hydodynamic performance of sailing yachts from dimensions and velocity.

Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML