1. Audit Data: Exhaustive one year non-confidential data in the year 2015 to 2016 of firms is collected from the Auditor Office of India to build a predictor for classifying suspicious firms.
2. BuddyMove Data Set: User interest information extracted from user reviews published in holidayiq.com about various types of point of interests in South India
3. Chronic_Kidney_Disease: This dataset can be used to predict the chronic kidney disease and it can be collected from the hospital nearly 2 months of period.
4. CMU Face Images: This data consists of 640 black and white face images of people taken with varying pose (straight, left, right, up), expression (neutral, happy, sad, angry), eyes (wearing sunglasses or not), and size
5. Connectionist Bench (Vowel Recognition - Deterding Data): Speaker independent recognition of the eleven steady state vowels of British English using a specified training set of lpc derived log area ratios.
6. Hill-Valley: Each record represents 100 points on a two-dimensional graph. When plotted in order (from 1 through 100) as the Y co-ordinate, the points will create either a Hill (a “bump” in the terrain) or a Valley (a “dip” in the terrain).
7. Japanese Vowels: This dataset records 640 time series of 12 LPC cepstrum coefficients taken from nine male speakers.
8. Libras Movement: The data set contains 15 classes of 24 instances each. Each class references to a hand movement type in LIBRAS (Portuguese
name 'LÍngua BRAsileira de Sinais', oficial brazilian signal language).
9. Statlog (Vehicle Silhouettes): 3D objects within a 2D image by application of an ensemble of shape feature extractors to the 2D silhouettes of the objects.
10. Synthetic Control Chart Time Series: This data consists of synthetically generated control charts.
11. Tennis Major Tournament Match Statistics: This is a collection of 8 files containing the match statistics for both women and men at the four major tennis tournaments of the year 2013. Each file has 42 columns and a minimum of 76 rows.
12. Travel Reviews: Reviews on destinations in 10 categories mentioned across East Asia. Each traveler rating is mapped as Excellent(4), Very Good(3), Average(2), Poor(1), and Terrible(0) and average rating is used.
13. USPTO Algorithm Challenge, run by NASA-Harvard Tournament Lab and TopCoder Problem: Pat: Data used for USPTO Algorithm Competition. Contains drawing pages from US patents with manually labeled figure and part labels.