Reads the file
Gives us stats on missing values etc
Treats the missing values (income) by extrapolating an average.
Converts churn_flag to a string
Partitions the data into testing and training data, 80:20
Determines possible range of each column
Learns based on the data using decision trees
Predicts churn based on the data learned previously
Scores how well the model did.
To use this workflow in KNIME, download it from the below URL and open it in KNIME:
Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.
