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. Auto MPG: Revised from CMU StatLib library, data concerns city-cycle fuel consumption
3. Automobile: From 1985 Ward's Automotive Yearbook
4. BuddyMove Data Set: User interest information extracted from user reviews published in holidayiq.com about various types of point of interests in South India
5. Flags: From Collins Gem Guide to Flags, 1986
6. ICMLA 2014 Accepted Papers Data Set: This data set compromises the metadata for the 2014 ICMLA conference's accepted papers, including ID, paper titles, author's keywords, abstracts and sessions in which they were exposed.
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. 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).
10. MONK's Problems: A set of three artificial domains over the same attribute space; Used to test a wide range of induction algorithms
11. Pittsburgh Bridges: Bridges database that has original and numeric-discretized datasets
12. 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.
13. Teaching Assistant Evaluation: The data consist of evaluations of teaching performance; scores are "low", "medium", or "high"
14. 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.
15. 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.