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Churn_​Predictor

Training a Churn Predictor

This workflow is an example of how to train a basic machine learning model for a churn prediction task. In this case we use a Decision Tree algorithm. However, the Learner-Predictor construct is common to all supervised algorthms.

Read Data Contract Data Calls Data Conversions Churn and Area Code to String Color Churn 0 -> Blue 1 -> Red Partitioning 80% training 20% testing Pre-processing / Data Preparation Train Model Decision tree Save to PMML Evaluation Apply and score Model Churn Prediction This workflow is an example of how to train a basic machine learning model for a churn prediction task, using a Decision Tree algorithm. 80%vs. 20%colorby churnclass = churnarea codeand churn ->StringCalls datacontract dataperformancescoringapply decision treePartitioning Color Manager DecisionTree Learner Number To String Excel Reader PMML Writer CSV Reader Scorer Decision TreePredictor Joiner Read Data Contract Data Calls Data Conversions Churn and Area Code to String Color Churn 0 -> Blue 1 -> Red Partitioning 80% training 20% testing Pre-processing / Data Preparation Train Model Decision tree Save to PMML Evaluation Apply and score Model Churn Prediction This workflow is an example of how to train a basic machine learning model for a churn prediction task, using a Decision Tree algorithm. 80%vs. 20%colorby churnclass = churnarea codeand churn ->StringCalls datacontract dataperformancescoringapply decision treePartitioning Color Manager DecisionTree Learner Number To String Excel Reader PMML Writer CSV Reader Scorer Decision TreePredictor Joiner

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

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Created by: rosy
Created at: 2015-04-23
On NodePit since: 2023-02-06
Last update: 2024-12-17
Created with KNIME version: v4.7.0
Tags: Customer IntelligenceCIchurndecision treePracticing Data Science

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