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


Repository Web            Google
View ALL Data Sets

× Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns. Click here to try out the new site.

Browse Through:

Default Task - Undo

Classification (18)
Regression (10)
Clustering (2)
Other (2)

Attribute Type - Undo

Categorical (0)
Numerical (18)
Mixed (0)

Data Type

Multivariate (14)
Univariate (0)
Sequential (1)
Time-Series (3)
Text (1)
Domain-Theory (1)
Other (2)

Area - Undo

Life Sciences (36)
Physical Sciences (18)
CS / Engineering (97)
Social Sciences (3)
Business (16)
Game (1)
Other (14)

# Attributes

Less than 10 (3)
10 to 100 (12)
Greater than 100 (3)

# Instances - Undo

Less than 100 (1)
100 to 1000 (9)
Greater than 1000 (18)

Format Type

Matrix (16)
Non-Matrix (2)

18 Data Sets

Table View  List View


1. Weight Lifting Exercises monitored with Inertial Measurement Units: Six young health subjects were asked to perform 5 variations of the biceps curl weight lifting exercise. One of the variations is the one predicted by the health professional.

2. SUSY: This is a classification problem to distinguish between a signal process which produces supersymmetric particles and a background process which does not.

3. HIGGS: This is a classification problem to distinguish between a signal process which produces Higgs bosons and a background process which does not.

4. Musk (Version 2): The goal is to learn to predict whether new molecules will be musks or non-musks

5. HEPMASS: The search for exotic particles requires sorting through a large number of collisions to find the events of interest. This data set challenges one to detect a new particle of unknown mass.

6. Waveform Database Generator (Version 1): CART book's waveform domains

7. Waveform Database Generator (Version 2): CART book's waveform domains

8. Statlog (Landsat Satellite): Multi-spectral values of pixels in 3x3 neighbourhoods in a satellite image, and the classification associated with the central pixel in each neighbourhood

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

10. MAGIC Gamma Telescope: Data are MC generated to simulate registration of high energy gamma particles in an atmospheric Cherenkov telescope

11. Ozone Level Detection: Two ground ozone level data sets are included in this collection. One is the eight hour peak set (eighthr.data), the other is the one hour peak set (onehr.data). Those data were collected from 1998 to 2004 at the Houston, Galveston and Brazoria area.

12. Steel Plates Faults: A dataset of steel plates’ faults, classified into 7 different types. The goal was to train machine learning for automatic pattern recognition.

13. MiniBooNE particle identification: This dataset is taken from the MiniBooNE experiment and is used to distinguish electron neutrinos (signal) from muon neutrinos (background).

14. EMG Physical Action Data Set: The Physical Action Data Set includes 10 normal and 10 aggressive physical actions that measure the human activity. The data have been collected by 4 subjects using the Delsys EMG wireless apparatus.

15. Vicon Physical Action Data Set: The Physical Action Data Set includes 10 normal and 10 aggressive physical actions that measure the human activity. The data have been collected by 10 subjects using the Vicon 3D tracker.

16. Amazon Commerce reviews set: The dataset is used for authorship identification in online Writeprint which is a new research field of pattern recognition.

17. 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.

18. Electrical Grid Stability Simulated Data : The local stability analysis of the 4-node star system (electricity producer is in the center) implementing Decentral Smart Grid Control concept.


Supported By:

 In Collaboration With:

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