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The databases available here were in used in the European StatLog project, which involves comparing the performances of machine learning, statistical, and neural network algorithms on data sets from real-world industrial areas including medicine, finance, image analysis, and engineering design. Not all of the databases used in the project are available in this repository. Databases: (a) Vehicle Silhouettes: The original purpose was to find a method of distinguishing 3D objects within a 2D image by application of an ensemble of shape feature extractors to the 2D silhouettes of the objects. (b) Landsat Satellite: The database consists of the multi-spectral values of pixels in 3x3 neighbourhoods in a satellite image, and the classification associated with the central pixel in each neighbourhood. The aim is to predict this classification, given the multi-spectral values. In the sample database, the class of a pixel is coded as a number. (c) Shuttle: The shuttle dataset contains 9 attributes all of which are numerical. Approximately 80% of the data belongs to class 1. (d) Australian Credit Approval: This file concerns credit card applications. All attribute names and values have been changed to meaningless symbols to protect confidentiality of the data. This database exists elsewhere in the repository (Credit Screening Database) in a slightly different form. (e) Heart Disease: 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. This database contains 13 attributes (which have been extracted from a larger set of 75). (f) 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. The instances were drawn randomly from a database of 7 outdoor images. The images were handsegmented to create a classification for every pixel. Each instance is a 3x3 region. (g) German Credit: 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.

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