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Deploying a Churn Predictor

<p><strong>Deploying a churn predictor</strong><br><br>This workflow is an example of how to deploy a basic machine learning model (built in workflow "01_Training_a_Churn_Predictor") for churn prediction. <br><br>Notice the component "Churn Visualization" at the end of the workflow, which performs the visualization of (1) the input customers in a tile view and (2) the churning probabilities in a bar chart.</p>

URL: Churn Prediction https://www.knime.org/knime-applications/churn-prediction

2. Apply trained Random Forest model over the newly read data.

3. Produce ScoreCards and perform visualizations.

1. Read datasets.

Below we read new data (contract data and calls data) and the model created in workflow "Building Churn Predictor".

Deploying a Churn Prediction

This workflow is an example of how to deploy a basic machine learning model (built in workflow "Building Churn Predictor") for churn prediction.

preparing for visualization
Data Prep
Read new data
CSV Reader
Apply model over new data
Random Forest Predictor
tile view & bar chart
Churn Visualization
From Random Forest Predictor
Model Reader

Nodes

Extensions

Links

Download

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

Download Workflow
On NodePit since: 2025-12-18
Last update: 2026-01-07
Created with KNIME version: v5.9.0
Tags: Customer IntelligenceCIchurndeploymentdata visualizationdatavizMarketing Analytics

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