Hub
  • Software
  • Blog
  • Forum
  • Events
  • Documentation
  • About KNIME
  • KNIME Hub
  • rs1
  • Spaces
  • Public
  • 01_Training_a_Churn_Predictor_LogReg
WorkflowWorkflow

Four basic steps in Data Preparation before Training a Churn Predictor

Customer Intelligence CI Churn Data preparation Normalization
+3

Last edited: 

Drag & drop
Like
Download workflow
Copy short link
Workflow preview
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.

Used extensions & nodes

Created with KNIME Analytics Platform version 4.3.2
  • Go to item
    KNIME Base nodes Trusted extension

    KNIME AG, Zurich, Switzerland

    Versions 4.3.1, 4.3.2

  • Go to item
    KNIME Excel Support Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.1

  • Go to item
    KNIME JavaScript Views (Labs) Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.3.0

  1. Go to item
  2. Go to item
  3. Go to item
  4. Go to item
  5. Go to item
  6. Go to item

Legal

By using or downloading the workflow, you agree to our terms and conditions.

Discussion
Discussions are currently not available, please try again later.

KNIME
Open for Innovation

KNIME AG
Hardturmstrasse 66
8005 Zurich, Switzerland
  • Software
  • Getting started
  • Documentation
  • E-Learning course
  • Solutions
  • KNIME Hub
  • KNIME Forum
  • Blog
  • Events
  • Partner
  • Developers
  • KNIME Home
  • KNIME Open Source Story
  • Careers
  • Contact us
Download KNIME Analytics Platform Read more on KNIME Server
© 2022 KNIME AG. All rights reserved.
  • Trademarks
  • Imprint
  • Privacy
  • Terms & Conditions
  • Credits