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Credit Scoring

Credit Scoring Credit Rating Customer Risk Decision Tree Neural Network
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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.

External resources

  • Credit Scoring / Credit Rating / Customer Risk

Used extensions & nodes

Created with KNIME Analytics Platform version 4.2.0
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

    knime
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    KNIME Ensemble Learning Wrappers Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

    knime
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    KNIME JavaScript Views Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

    knime
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    KNIME Javasnippet Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.2.0

    knime
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