Predictive Modeling
Histograms - distribution and dispersion of each numerical variable
Box Plots - variability and potential outliers within the dayaset
Scatter Plots - potential relationships between customer behaviour variables and monetary value
Column Filter - feature selection
Duplicate Row Filter - data cleaning
Missing value - data cleaning
Normalizer - data transformation
FINAL CLUSTERING SOLUTION
Missing values - data cleaning
Outliers - anomalies handling
Feature engineering - creating value
Normalizer - feature scaling
Feature selection - reducing noise
To use this workflow in KNIME, download it from the below URL and open it in KNIME:
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