UCICreditCard

<p>This research aimed at the case of customers's default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods.
 This research employed a binary variable, default payment (Yes = 1, No = 0), as the response variable. This study reviewed the literature and used the following 24 variables as explanatory variables</p>

datasets


Provides a highly efficient R tool suite for Credit Modeling, Analysis and Visualization.Contains infrastructure functionalities such as data exploration and preparation, missing values treatment, outliers treatment, variable derivation, variable selection, dimensionality reduction, grid search for hyper parameters, data mining and visualization, model evaluation, strategy analysis etc. This package is designed to make the development of binary classification models (machine learning based models as well as credit scorecard) simpler and faster. The references including: 1 Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS; 2 Bezdek, James C.FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences (0098-3004),<DOI:10.1016/0098-3004(84)90020-7>.

Dongping Fan

creditmodel

Toolkit for Credit Modeling, Analysis and Visualization

UCICreditCard function

<p>A data frame with 30000 rows and 26 variables.</p>

UCICreditCard: UCI Credit Card data

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