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. Automobile: From 1985 Ward's Automotive Yearbook
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. Flags: From Collins Gem Guide to Flags, 1986
5. Folio: 20 photos of leaves for each of 32 different species.
6. Japanese Vowels: This dataset records 640 time series of 12 LPC cepstrum coefficients taken from nine male speakers.
7. 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).
8. Meta-data: Meta-Data was used in order to give advice about which classification method is appropriate for a particular dataset (taken from results of Statlog project).
9. Pittsburgh Bridges: Bridges database that has original and numeric-discretized datasets
10. 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.
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. University: Data in original (LISP-readable) form