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shan

Data preparation and Feature Selection

Model Training & Testing

- Duplicate check- Encoding ofcategorical features
Encoding
XGBoost Predictor
Feature Selection Filter
Handledin targetcolumn
Class Imbalance
Optimizing accuracy
Feature Selection
Rule Engine
Accuracy:90.5% approx
Scorer
Top: train (70%)Bottom: test (30%)
Table Partitioner
Loan approvaldataset 1 = approved; 0 = rejected
CSV Reader
Inspect ROC Curve
ROC Curve
XGBoost Tree Ensemble Learner
Z score
Normalizer
Feature Selection Filter
Normalizer (Apply)

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: HAhadyDolatsara
Created at: 2025-03-20
On NodePit since: 2025-10-30
Last update: 2025-10-30
Created with KNIME version: v5.8.0

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