Credit Scoring

#1

This KNIME workflow focuses on creating a credit scoring model based on historical data. As with all data mining modeling activities, it is unclear in advance which analytic method is most suitable. This workflow therefore uses three different methods simultaneously – Decision Trees, Neural Networking and SVM – then automatically determines which model is most accurate and writes that model out for further use. This workflow manipulates the data so it is suitable for a variety of modeling techniques by converting nominals to numerics. The data was enhanced so that understandable labels are used. It uses metanodes to “package” each technique suitable for reuse. Each Model uses a Test / Learn and cross validated process to ensure accuracy. The workflow writes out the model in the official PMML format, so that other applications can use the model.


This is a companion discussion topic for the original entry at https://hub.knime.com/knime/workflows/Examples/50_Applications/02_Credit_Scoring/01_CreditScoring*CB0u_eLmzlghiZI2
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#2

The approach looks impressive. Is there a video explaining this workflow in detail like the one for churn prediction?

There is another credit scoring flow in the example server, which uses a python script. Can anyone tell me what the script does? I can’t seem to find the actual py script as well.

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#3

Hi,

Thanks for you question.

Unfortunately we don’t have a video script describing exactly this workflow.
Maybe these videos are helpful for you:
Model Selection with KNIME
KNIME Analytics: a review

Could you give the name/link to the workflow with Python script that you are referring to? I could take a look at it, but coundln’t find it at first glance.

Best,
Maarit

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