1. Abalone: Predict the age of abalone from physical measurements 2. Adult: Predict whether income exceeds $50K/yr based on census data. Also known as "Census Income" dataset. 3. Car Evaluation: Derived from simple hierarchical decision model, this database may be useful for testing constructive induction and structure discovery methods. 4. Census Income: Predict whether income exceeds $50K/yr based on census data. Also known as "Adult" dataset. 5. Chess (King-Rook vs. King-Pawn): King+Rook versus King+Pawn on a7 (usually abbreviated KRKPA7). 6. Chess (King-Rook vs. King): Chess Endgame Database for White King and Rook against Black King (KRK). 7. Connect-4: Contains connect-4 positions 8. Contraceptive Method Choice: Dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. 9. Covertype: Forest CoverType dataset 10. Image Segmentation: Image data described by high-level numeric-valued attributes, 7 classes 11. Internet Advertisements: This dataset represents a set of possible advertisements on Internet pages. 12. ISOLET: Goal: Predict which letter-name was spoken--a simple classification task. 13. Letter Recognition: Database of character image features; try to identify the letter 14. Multiple Features: This dataset consists of features of handwritten numerals (`0'--`9') extracted from a collection of Dutch utility maps 15. Mushroom: From Audobon Society Field Guide; mushrooms described in terms of physical characteristics; classification: poisonous or edible 16. Musk (Version 2): The goal is to learn to predict whether new molecules will be musks or non-musks 17. Nursery: Nursery Database was derived from a hierarchical decision model originally developed to rank applications for nursery schools. 18. Page Blocks Classification: The problem consists of classifying all the blocks of the page layout of a document that has been detected by a segmentation process. 19. Optical Recognition of Handwritten Digits: Two versions of this database available; see folder 20. Pen-Based Recognition of Handwritten Digits: Digit database of 250 samples from 44 writers 21. Spambase: Classifying Email as Spam or Non-Spam 22. Waveform Database Generator (Version 1): CART book's waveform domains 23. Waveform Database Generator (Version 2): CART book's waveform domains 24. Yeast: Predicting the Cellular Localization Sites of Proteins 25. Australian Sign Language signs: This data consists of sample of Auslan (Australian Sign Language) signs. Examples of 95 signs were collected from five signers with a total of 6650 sign samples. 26. Australian Sign Language signs (High Quality): This data consists of sample of Auslan (Australian Sign Language) signs. 27 examples of each of 95 Auslan signs were captured from a native signer using high-quality position trackers 27. Census-Income (KDD): This data set contains weighted census data extracted from the 1994 and 1995 current population surveys conducted by the U.S. Census Bureau. 28. KDD Cup 1999 Data: This is the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-99 29. 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 30. Statlog (Image Segmentation): This dataset is an image segmentation database similar to a database already present in the repository (Image segmentation database) but in a slightly different form. 31. Statlog (Shuttle): The shuttle dataset contains 9 attributes all of which are numerical. Approximately 80% of the data belongs to class 1 32. Poker Hand: Purpose is to predict poker hands 33. MAGIC Gamma Telescope: Data are MC generated to simulate registration of high energy gamma particles in an atmospheric Cherenkov telescope 34. Dexter: DEXTER is a text classification problem in a bag-of-word representation. This is a two-class classification problem with sparse continuous input variables. This dataset is one of five datasets of the NIPS 2003 feature selection challenge.
35. Dorothea: DOROTHEA is a drug discovery dataset. Chemical compounds represented by structural molecular features must be classified as active (binding to thrombin) or inactive. This is one of 5 datasets of the NIPS 2003 feature selection challenge. 36. Gisette: GISETTE is a handwritten digit recognition problem. The problem is to separate the highly confusible digits '4' and '9'. This dataset is one of five datasets of the NIPS 2003 feature selection challenge.
37. Madelon: MADELON is an artificial dataset, which was part of the NIPS 2003 feature selection challenge. This is a two-class classification problem with continuous input variables. The difficulty is that the problem is multivariate and highly non-linear. 38. 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. 39. Semeion Handwritten Digit: 1593 handwritten digits from around 80 persons were scanned, stretched in a rectangular box 16x16 in a gray scale of 256 values. 40. URL Reputation: Anonymized 120-day subset of the ICML-09 URL data containing 2.4 million examples and 3.2 million features. 41. p53 Mutants: The goal is to model mutant p53 transcriptional activity (active vs inactive) based on data extracted from biophysical simulations.
42. Cardiotocography: The dataset consists of measurements of fetal heart rate (FHR) and uterine contraction (UC) features on cardiotocograms classified by expert obstetricians. 43. Wall-Following Robot Navigation Data: The data were collected as the SCITOS G5 robot navigates through the room following the wall in a clockwise direction, for 4 rounds, using 24 ultrasound sensors arranged circularly around its 'waist'. 44. MiniBooNE particle identification: This dataset is taken from the MiniBooNE experiment and is used to distinguish electron neutrinos (signal) from muon neutrinos (background). 45. Record Linkage Comparison Patterns: Element-wise comparison of records with personal data from a record linkage setting. The task is to decide from a comparison pattern whether the underlying records belong to one person. 46. Bank Marketing: The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y). 47. Gas Sensor Array Drift Dataset: This archive contains 13910 measurements from 16 chemical sensors utilized in simulations for drift compensation in a discrimination task of 6 gases at various levels of concentrations. 48. OPPORTUNITY Activity Recognition: The OPPORTUNITY Dataset for Human Activity Recognition from Wearable, Object, and Ambient Sensors is a dataset devised to benchmark human activity recognition algorithms (classification, automatic data segmentation, sensor fusion, feature extraction, etc). 49. PAMAP2 Physical Activity Monitoring: The PAMAP2 Physical Activity Monitoring dataset contains data of 18 different physical activities, performed by 9 subjects wearing 3 inertial measurement units and a heart rate monitor. 50. CNAE-9: This is a data set containing 1080 documents of free text business descriptions of Brazilian companies categorized into a
subset of 9 categories 51. SECOM: Data from a semi-conductor manufacturing process 52. Reuter_50_50: The dataset is used for authorship identification in online Writeprint which is a new research field of pattern recognition. 53. Human Activity Recognition Using Smartphones: Human Activity Recognition database built from the recordings of 30 subjects performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors. 54. Wine Quality: Two datasets are included, related to red and white vinho verde wine samples, from the north of Portugal. The goal is to model wine quality based on physicochemical tests (see [Cortez et al., 2009], http://www3.dsi.uminho.pt/pcortez/wine/). 55. KEGG Metabolic Relation Network (Directed): KEGG Metabolic pathways modeled as directed relation network. Variety of graphical features presented. 56. KEGG Metabolic Reaction Network (Undirected): KEGG Metabolic pathways modeled as un-directed reaction network. Variety of graphical features presented. | 
