Tuesday, May 19, 2020

A Report On The Data - 2636 Words

Based on the objectives of the experiment, it is important to describe the credit datasets, the classi ers, the combination techniques of the classi ers, and lastly the software used in carrying out the experiments.. 3.1 Datasets To access the prediction accuracy of the four classi ers and their combinations in the two class classi cation problem analysis, two real life datasets taken from the University of California, Irvine(UCI) repository were used. These datasets are described below [28]. 3.1.1 German Credit Datasets This is a nancial dataset which is made up of 1,000 instances. This dataset records 700 cases of creditworthy applicants and 300 cases of applicants who are not credit-worthy. It contains categorical and symbolic attributes. The German credit dataset is of two forms [26] [29].  The original dataset german.data consists of 20 attributes of which,7 are numerical and 13 categorical).  The german.data-numeric an edited copy of the original dataset which consist of 24 numeric attributes. The german.data-numeric which has 24 input variable basically represents 19 attributes with 4 of these attributes changed to dummy variables. The 20 attributes in the German.data or the 24 attributes in the german.data-numeric are some basic information about applicants needed in creating a score card which will be used to predict if an applicant will default or not. This information is seen in the table below. Attribute Information Attribute 1 Status of existingShow MoreRelatedMapinfo Data Report758 Words   |  4 Pagesanalysing the spatial data of the new location site. Moreover, this software helped me to provide 3D dimensional view of the terrain land and also rotating as well as magnifying the projected maps. I correlated the Google earth which produce earth based satellites maps and photographs for getting more information about neighbouring BTS towers nearby the new location. 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