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Customer Churn Prediction

1. Data Understanding

Statistics and table specification

2. Data Cleaning

Duplicate removal, column filtering, missing value handling

3. Data Preparation

One-hot encoding and target preparation

4. Model Training

Partitioning and Random Forest training

5. Model Evaluation

Prediction and scoring

4. Model Training

Partitioning and Random Forest training

5. Model Evaluation

Prediction and scoring

4. Model Training

Partitioning and Random Forest training

5. Model Evaluation

Prediction and scoring

4. Model Training

Partitioning and Random Forest training

5. Model Evaluation

Prediction and scoring

Generate descriptive statistics
Statistics
Display column names and data types
Extract Table Spec
Logistic Regression Learner
Remove duplicate records
Duplicate Row Filter
Remove irrelevant identifier columns
Column Filter
Handle missing values
Missing Value
One-hot encode categorical predictors
One to Many
Convert numeric Churn target to string if needed
Number to String
Logistic Regression Predictor
Predict churn on test data
Random Forest Predictor
Evaluate classification performance
Scorer
Split data into training and testing sets
Table Partitioner
Evaluate classification performance
Scorer
Train Random Forest classifier
Random Forest Learner
Statistics view overview
Statistics View
Evaluate classification performance
Scorer
Naive Bayes Learner
Bar chart for Churndistribution
Bar Chart
Naive Bayes Predictor
Decision Tree Predictor
Evaluate classification performance
Scorer
CSV Reader
Decision Tree Learner

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: Gubernatr
Created at: 2026-06-05
On NodePit since: 2026-06-14
Last update: 2026-06-21
Created with KNIME version: v5.11.0

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