Credit Scoring Model(diksha kumari)
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.
Nodes
- Constant Value Column6 ×
- Row Filter4 ×
Column Filter3 ×- Cross Joiner3 ×
- PMML To Cell3 ×
- RowID3 ×
- Rule Engine3 ×
- Scorer3 ×
- X-Aggregator3 ×
- X-Partitioner3 ×
- Decision Tree Learner2 ×
Normalizer2 ×- RProp MLP Learner2 ×
- SVM Learner2 ×
- Bar Chart1 ×
- Category To Number1 ×
- Cell To PMML1 ×
- Concatenate (Optional in)1 ×
- Decision Tree Predictor1 ×
- File Reader1 ×
- MultiLayerPerceptron Predictor1 ×
- PMML Writer1 ×
- SVM Predictor1 ×
- Sorter1 ×
Extensions
- FeatureKNIME Base nodes
- FeatureKNIME Ensemble Learning Wrappers
- FeatureKNIME JavaScript Views
- FeatureKNIME Javasnippet