German Credit Data Set Arff
- German Credit Data Set Arff System
- German Credit Data Set Arff Service
- German Credit Data Set Arff Code
Fraud Detection using Data Mining Project. Will be under a Classification task type as well. 7 DATA PREPARATION The process used to was very simple for the German Credit fraud data. Decided to use an ARFF file format due to the source of the data from UC Irvine, Machine Learning Repository to organize. Examining the first data set of the. I have a fraud detection algorithm, and I want to check to see if it works against a real world data set. My algorithm says that a claim is usual or not. Are there any data sets available?
German Credit Data Set Arff System
UCI Machine Learning Repository: Statlog (German Credit Data) Data Set Repository Web Statlog (German Credit Data) Data Set Download:, Abstract: 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 Data Set Characteristics: Multivariate Number of Instances: 1000 Area: Financial Attribute Characteristics: Categorical, Integer Number of Attributes: 20 Date Donated 1994-11-17 Associated Tasks: Classification Missing Values? N/A Number of Web Hits: 345527 Source: Professor Dr.
German Credit Data Set Arff Service
Hans Hofmann Institut f'ur Statistik und 'Okonometrie Universit'at Hamburg FB Wirtschaftswissenschaften Von-Melle-Park 5 2000 Hamburg 13 Data Set Information: Two datasets are provided. The original dataset, in the form provided by Prof. Hofmann, contains categorical/symbolic attributes and is in the file 'german.data'. For algorithms that need numerical attributes, Strathclyde University produced the file 'german.data-numeric'. This file has been edited and several indicator variables added to make it suitable for algorithms which cannot cope with categorical variables.
German Credit Data Set Arff Code
Several attributes that are ordered categorical (such as attribute 17) have been coded as integer. This was the form used by StatLog. This dataset requires use of a cost matrix (see below). 1 2 - 1 0 1 - 2 5 0 (1 = Good, 2 = Bad) The rows represent the actual classification and the columns the predicted classification.
It is worse to class a customer as good when they are bad (5), than it is to class a customer as bad when they are good (1). Attribute Information: Attribute 1: (qualitative) Status of existing checking account A11.