Walkthrough - Credit risk prediction

February 16, 2017
This is the experiment created using an example credit risk prediction walkthrough.
This is the initial experiment that's created when you follow the first four steps in the article, [Walkthrough: Develop a predictive analytics solution for credit risk assessment in Azure Machine Learning][1]. Using available credit data, the experiment sets up two models to predict credit risk from credit application information, and then compares the results. The final two steps in the walkthrough show you how to deploy the model as a web service and generate predictions from new credit data. This experiment is a simplified version of [Binary Classfication: Credit risk prediction][2] in the Gallery. The training data in this experiment was derived from the "UCI Statlog (German Credit Data) Data Set" from the UCI Machine Learning repository: [http://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data)][3]. [1]: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-walkthrough-develop-predictive-solution [2]: https://gallery.cortanaintelligence.com/Experiment/Binary-Classfication-Credit-risk-prediction-5 [3]: http://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29