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Bank Loan Modeling with Auto Categorical Features Embedding

Bank Loan default Feature transformation Logistic Regression

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This workflow demonstrates use of Auto Categorical Features Embedding node. It is a Bank Loan data where likelihood of default is to be predicted. The dataset has 18 categorical features. These features get transformed to numeric features using the three transformation methods available in the node. Results are comparable with and without feature transformation--feature transformed data having a minor edge.

External resources

  • Bank Loan Default Modeling--Kaggle

Used extensions & nodes

Created with KNIME Analytics Platform version 4.4.1
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    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.1

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    KNIME Python Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.1

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    KNIME Quick Forms Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.4.1

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