1. Synthetic Control Chart Time Series: This data consists of synthetically generated control charts. 2. Badges: Badges labeled with a "+" or "-" as a function of a person's name 3. Haberman's Survival: Dataset contains cases from study conducted on the survival of patients who had undergone surgery for breast cancer 4. Balance Scale: Balance scale weight & distance database 5. Iris: Famous database; from Fisher, 1936 6. Servo: Data was from a simulation of a servo system 7. EEG Database: This data arises from a large study to examine EEG correlates of genetic predisposition to alcoholism. It contains measurements from 64 electrodes placed on the scalp sampled at 256 Hz 8. Hayes-Roth: Topic: human subjects study 9. Teaching Assistant Evaluation: The data consist of evaluations of teaching performance; scores are "low", "medium", or "high" 10. Blood Transfusion Service Center: Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan -- this is a classification problem. 11. Mammographic Mass: Discrimination of benign and malignant mammographic masses based on BI-RADS attributes and the patient's age. 12. Acute Inflammations: The data was created by a medical expert as a data set to test the expert system,
which will perform the presumptive diagnosis of two diseases of the urinary system.
13. Vertebral Column: Data set containing values for six biomechanical features used to classify orthopaedic patients into 3 classes (normal, disk hernia or spondilolysthesis) or 2 classes (normal or abnormal). 14. Liver Disorders: BUPA Medical Research Ltd. database donated by Richard S. Forsyth 15. MONK's Problems: A set of three artificial domains over the same attribute space; Used to test a wide range of induction algorithms 16. seeds: Measurements of geometrical properties of kernels belonging to three different varieties of wheat. A soft X-ray technique and GRAINS package were used to construct all seven, real-valued attributes. 17. Yacht Hydrodynamics: Delft data set, used to predict the hydodynamic performance of sailing yachts from dimensions and velocity. 18. Auto MPG: Revised from CMU StatLib library, data concerns city-cycle fuel consumption 19. Ecoli: This data contains protein localization sites 20. Pima Indians Diabetes: From National Institute of Diabetes and Digestive and Kidney Diseases; Includes cost data (donated by Peter Turney) 21. Energy efficiency: This study looked into assessing the heating load and cooling load requirements of buildings (that is, energy efficiency) as a function of building parameters. 22. Breast Cancer: Breast Cancer Data (Restricted Access) 23. Computer Hardware: Relative CPU Performance Data, described in terms of its cycle time, memory size, etc. 24. Tic-Tac-Toe Endgame: Binary classification task on possible configurations of tic-tac-toe game 25. Daphnet Freezing of Gait: This dataset contains the annotated readings of 3 acceleration sensors at the hip and leg of Parkinson's disease patients that experience freezing of gait (FoG) during walking tasks.
26. Breast Cancer Wisconsin (Original): Original Wisconsin Breast Cancer Database 27. Glass Identification: From USA Forensic Science Service; 6 types of glass; defined in terms of their oxide content (i.e. Na, Fe, K, etc) 28. Concrete Slump Test: Concrete is a highly complex material. The slump flow of concrete is not only determined by the water content, but that is also influenced by other concrete ingredients. 29. Breast Tissue: Dataset with electrical impedance measurements of freshly excised tissue samples from the breast. 30. Echocardiogram: Data for classifying if patients will survive for at least one year after a heart attack 31. Japanese Vowels: This dataset records 640 time series of 12 LPC cepstrum coefficients taken from nine male speakers. 32. Pittsburgh Bridges: Bridges database that has original and numeric-discretized datasets 33. Wine: Using chemical analysis determine the origin of wines 34. Statlog (Heart): This dataset is a heart disease database similar to a database already present in the repository (Heart Disease databases) but in a slightly different form 35. Forest Fires: This is a difficult regression task, where the aim is to predict the burned area of forest fires, in the northeast region of Portugal, by using meteorological and other data (see details at: http://www.dsi.uminho.pt/~pcortez/forestfires). 36. Planning Relax: The dataset concerns with the classification of two mental stages from recorded EEG signals: Planning (during imagination of motor act) and Relax state. 37. Housing: Taken from StatLib library 38. Statlog (Australian Credit Approval): This file concerns credit card applications. This database exists elsewhere in the repository (Credit Screening Database) in a slightly different form 39. Credit Approval: This data concerns credit card applications; good mix of attributes 40. Congressional Voting Records: 1984 United Stated Congressional Voting Records; Classify as Republican or Democrat 41. Primary Tumor: From Ljubljana Oncology Institute 42. Zoo: Artificial, 7 classes of animals 43. Coil 1999 Competition Data: This data set is from the 1999 Computational Intelligence and Learning (COIL) competition. The data contains measurements of river chemical concentrations and algae densities. 44. Lymphography: This lymphography domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. (Restricted access) 45. 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. 46. Hepatitis: From G.Gong: CMU; Mostly Boolean or numeric-valued attribute types; Includes cost data (donated by Peter Turney) 47. Statlog (German Credit Data): This dataset classifies people described by a set of attributes as good or bad credit risks. Comes in two formats (one all numeric). Also comes with a cost matrix 48. 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). 49. SPECT Heart: Data on cardiac Single Proton Emission Computed Tomography (SPECT) images. Each patient classified into two categories: normal and abnormal. 50. Parkinsons: Oxford Parkinson's Disease Detection Dataset 51. Automobile: From 1985 Ward's Automotive Yearbook 52. Horse Colic: Well documented attributes; 368 instances with 28 attributes (continuous, discrete, and nominal); 30% missing values 53. Flags: From Collins Gem Guide to Flags, 1986 54. Breast Cancer Wisconsin (Diagnostic): Diagnostic Wisconsin Breast Cancer Database 55. Dermatology: Aim for this dataset is to determine the type of Eryhemato-Squamous Disease. 56. Breast Cancer Wisconsin (Prognostic): Prognostic Wisconsin Breast Cancer Database 57. Ionosphere: Classification of radar returns from the ionosphere 58. Soybean (Large): Michalski's famous soybean disease database 59. Annealing: Steel annealing data 60. Water Treatment Plant: Multiple classes predict plant state 61. Cylinder Bands: Used in decision tree induction for mitigating process delays known as "cylinder bands" in rotogravure printing 62. SPECTF Heart: Data on cardiac Single Proton Emission Computed Tomography (SPECT) images. Each patient classified into two categories: normal and abnormal. 63. Restaurant & consumer data: The dataset was obtained from a recommender system prototype. The task was to generate a top-n list of restaurants according to the consumer preferences. 64. Molecular Biology (Promoter Gene Sequences): E. Coli promoter gene sequences (DNA) with partial domain theory 65. Connectionist Bench (Sonar, Mines vs. Rocks): The task is to train a network to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock. 66. Audiology (Standardized): Standardized version of the original audiology database 67. 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). 68. Musk (Version 1): The goal is to learn to predict whether new molecules will be musks or non-musks 69. Arrhythmia: Distinguish between the presence and absence of cardiac arrhythmia and classify it in one of the 16 groups. 70. Arcene: ARCENE's task is to distinguish cancer versus normal patterns from mass-spectrometric data. This is a two-class classification problem with continuous input variables. This dataset is one of 5 datasets of the NIPS 2003 feature selection challenge. 71. PEMS-SF: 15 months worth of daily data (440 daily records) that describes the occupancy rate, between 0 and 1, of different car lanes of the San Francisco bay area freeways across time. | 
