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 (5)
Regression (1)
Clustering (2)
Other (0)

Attribute Type

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

Data Type - Undo

Multivariate (12)
Univariate (2)
Sequential (3)
Time-Series (6)
Text (5)
Domain-Theory (0)
Other (0)

Area - Undo

Life Sciences (0)
Physical Sciences (0)
CS / Engineering (5)
Social Sciences (2)
Business (0)
Game (0)
Other (3)

# Attributes - Undo

Less than 10 (5)
10 to 100 (4)
Greater than 100 (4)

# Instances

Less than 100 (0)
100 to 1000 (2)
Greater than 1000 (3)

Format Type - Undo

Matrix (1)
Non-Matrix (5)

5 Data Sets

Table View  List View

1. Youtube cookery channels viewers comments in Hinglish: The datasets are taken from top 2 Indian cooking channel named Nisha Madhulika channel and Kabita’s Kitchen channel. The data set is in Hinglish Language.

2. Syskill and Webert Web Page Ratings: This database contains HTML source of web pages plus the ratings of a single user on these web pages. Web pages are on four seperate subjects (Bands- recording artists; Goats; Sheep; and BioMedical)

3. Labeled Text Forum Threads Dataset: The dataset is a collection of text forum threads with class labels reflects the reply quality to the Initial-Post, 3 for complete relevant, 2 for partially relevant, and 1 for irrelevant

4. YouTube Spam Collection: It is a public set of comments collected for spam research. It has five datasets composed by 1,956 real messages extracted from five videos that were among the 10 most viewed on the collection period.

5. YouTube Comedy Slam Preference Data: This dataset provides user vote data on which video from a pair of videos is funnier collected on YouTube Comedy Slam. The task is to automatically predict this preference based on video metadata.

Supported By:

 In Collaboration With:

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