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01_​Training_​a_​Churn_​Predictor_​LogReg

Four basic steps in Data Preparation before Training a Churn Predictor

Here you can see an example for four basic data preparation steps: conversion to number and to category, missing value imputation, normalization, SMOTE. Notice also the node (Apply) in the testing part of the workflow to avoid data leakage.
The workflow trains a logistic regression for the binary classification problem of churn prediction using the telco dataset. Instead of the logistic regression any other classification algorithm could be used. However, the Learner-Predictor construct is common to all supervised algorthms.

Read Data Contract Data Calls Data Training SMOTE Logistic Regression Learner Save all Models Churn Prediction - Training - Logistic Regression This workflow is an example of how to train a basic machine learning model for a churn prediction task, using a Logistic Regression algorithm. Notice the four basic data prep steps: missing value imputation, normalization, type conversion, and SMOTE. Also notice the usage of (Apply) nodes to prevent data leakage. Testing all transformations (Apply) Logistic regression Predictor Scorer Training Data Preparation Partitioning 80%-20% Data Explorer for no of missing values Number to String (Churn) Data Preparation Category To Number (State) Missing Value (Median, Median, Unknown) Normalizer (z-score) Testing 80%vs. 20%churn -> StringCalls datacontract dataperformancescoringpredict churnremove rowswith misisng valuesz-scoreoversamplingminority classStatechurn -> StringPartitioning Joiner Number To String Excel Reader CSV Reader Scorer LogisticRegression Learner Logistic RegressionPredictor Missing Value Missing Value(Apply) Normalizer Normalizer (Apply) Model Writing SMOTE Category To Number Category ToNumber (Apply) Number To String Data Explorer Read Data Contract Data Calls Data Training SMOTE Logistic Regression Learner Save all Models Churn Prediction - Training - Logistic Regression This workflow is an example of how to train a basic machine learning model for a churn prediction task, using a Logistic Regression algorithm. Notice the four basic data prep steps: missing value imputation, normalization, type conversion, and SMOTE. Also notice the usage of (Apply) nodes to prevent data leakage. Testing all transformations (Apply) Logistic regression Predictor Scorer Training Data Preparation Partitioning 80%-20% Data Explorer for no of missing values Number to String (Churn) Data Preparation Category To Number (State) Missing Value (Median, Median, Unknown) Normalizer (z-score) Testing 80%vs. 20%churn -> StringCalls datacontract dataperformancescoringpredict churnremove rowswith misisng valuesz-scoreoversamplingminority classStatechurn -> StringPartitioning Joiner Number To String Excel Reader CSV Reader Scorer LogisticRegression Learner Logistic RegressionPredictor Missing Value Missing Value(Apply) Normalizer Normalizer (Apply) Model Writing SMOTE Category To Number Category ToNumber (Apply) Number To String Data Explorer

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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: 2025-03-08
Last update: 2026-08-14
Created with KNIME version: v4.3.2
Tags: Customer IntelligenceCIchurndata preparationnormalizationSMOTEmissing value imputationtype conversion

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