German credit dataset arff
WebThe German Credit Data contains data on 20 variables and the classification whether an applicant is considered a Good or a Bad credit risk for 1000 loan applicants. When a bank receives a loan application, based on the applicant’s profile the bank has to make a decision regarding whether to go ahead with the loan approval or not. Web1. Title: German Credit data 2. Source Information Professor Dr. Hans Hofmann Institut f"ur Statistik und "Okonometrie Universit"at Hamburg FB Wirtschaftswissenschaften Von-Melle-Park 5 2000 Hamburg 13 3. Number of Instances: 1000 Two datasets are provided. the original dataset, in the form provided by Prof. Hofmann, contains categorical ...
German credit dataset arff
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WebMar 31, 2016 · View Full Report Card. Fawn Creek Township is located in Kansas with a population of 1,618. Fawn Creek Township is in Montgomery County. Living in Fawn … WebAbout the dataset The dataset used is a german credit dataset, and is available from (Hofman). The data has been compiled by Professor Dr. Hans Hofmann, Institut f"ur Statistik und "Okonometrie, University of Hamburg, Germany. ... Open the file named credit-g.arff, available from the source above. The file is pre-processed, we can use the data ...
WebThe research conducted by Bindhia [15], this study applied Gain Ratio as selection feature to German Credit Datasets. The method used is Decision Tree with several algorithms … WebAbout this dataset. This file concerns credit card applications. All attribute names and values have been changed to meaningless symbols to protect confidentiality of the data. This dataset is interesting because there is a good mix of attributes -- continuous, nominal with small numbers of values, and nominal with larger numbers of values.
WebOct 18, 2015 · 1. Datasets like this will typically be "academic", meaning scrubbed and anonymized and used for demo or publishing purposes. One example is the "German …
WebNov 20, 2024 · German credit dataset was used in order to develop a decision tree with J.48 algorithm. We present characteristics of the dataset and the main results with the focus to the interpretation of Weka output. Paper could be useful for the users of Weka that aim to use it for credit scoring analysis.
WebMay 19, 2024 · The risk prediction is a standard supervised classification task: Supervised: The labels are included in the training data and the goal is to train a model to learn to predict the labels from the ... dave haskell actorWebNumber of Instances: 1000 % % Two datasets are provided. the original dataset, in the form provided % by Prof. Hofmann, contains categorical/symbolic attributes and % is in … dave harlow usgsWebDescription of the German credit dataset. 1. Title: German Credit data 2. Source Information Professor Dr. Hans Hofmann Institut f"ur Statistik und "Okonometrie Universit"at Hamburg FB Wirtschaftswissenschaften Von-Melle-Park 5 2000 Hamburg 13 3. Number of Instances: 1000 Two datasets are provided. the original dataset, in the form provided by … dave hatfield obituaryWebJan 4, 2013 · Below are some datasets I found that might be related. Credit fraud. German credit fraud dataset: in weka's arff format; Email fraud. Enron dataset; Credit Approval. … dave hathaway legendsWebExplore and run machine learning code with Kaggle Notebooks Using data from German Credit Risk. code. New Notebook. table_chart. New Dataset. emoji_events. New Competition. No Active Events. Create notebooks and keep track of their status here. add New Notebook. auto_awesome_motion. 0. 0 Active Events. expand_more. call_split. … dave harvey wineWebThe data contains 1000 observations (700 good loans, 300 bad loans) and the following variables: Account_status: a factor with four levels representing the amount of money in the account or "no chcking account" . Duration: a continuous variable, the duration in months. Credit_history: a factor with five levels representing possible credit ... dave harkey construction chelanWebProject 2 – German Credit Dataset. Let’s read in the data and rename the columns and values to something more readable data (note: you didn’t have to rename the values.) Now, let’s set things up so that Good Credit is 1, and Bad Credit is 0. dave harrigan wcco radio