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Data Science with KNIME - An Introduction to Data Science using Churn Prediction

This workflow illustrates how to do churn prediction with KNIME Analytics Platform.

Task 2: Train and apply a data mining model1. Train a Decision Tree model to predict churn (Decision TreeLearner node)2. Apply trained model to testing data set (Decision TreePredictor node) Data Preparation Task 3: Evaluate the quality of your model1. Use a Scorer node to compare actual churn andpredicted churn Task 1: Split Data intoTraining and Test Sets1. Use Partitioning nodewith an 80/20 split andstratified sampling Task 4: Using Parameter Optimization in KNIME1. Drag and drop the Parameter Optimization component from KNIME Community Hub (https://hub.knime.com/knime/spaces/Examples/latest/00_Components/Automation/Parameter%20Optimization~A_91QC387NtvJ6g8)2. Convert the optimized parameters to flow variables for your model (Table Row to Variable node)3. Train and apply a Random Forest model with the optimized parameters4. Compare actual churn and predicted churn (Scorer node) Read and Combine Data Sources Partioning Data80/20Area Codeand Churn ->StringFilter Non-RelevantColumnsSimple BusinessRulesContract DataCall Data ParameterOptimization Decision TreePredictor Random ForestLearner Random ForestPredictor DecisionTree Learner Partitioning Number To String Column Filter Rule Engine Math Formula CSV Reader Excel Reader Joiner Scorer Scorer Table Rowto Variable Task 2: Train and apply a data mining model1. Train a Decision Tree model to predict churn (Decision TreeLearner node)2. Apply trained model to testing data set (Decision TreePredictor node) Data Preparation Task 3: Evaluate the quality of your model1. Use a Scorer node to compare actual churn andpredicted churn Task 1: Split Data intoTraining and Test Sets1. Use Partitioning nodewith an 80/20 split andstratified sampling Task 4: Using Parameter Optimization in KNIME1. Drag and drop the Parameter Optimization component from KNIME Community Hub (https://hub.knime.com/knime/spaces/Examples/latest/00_Components/Automation/Parameter%20Optimization~A_91QC387NtvJ6g8)2. Convert the optimized parameters to flow variables for your model (Table Row to Variable node)3. Train and apply a Random Forest model with the optimized parameters4. Compare actual churn and predicted churn (Scorer node) Read and Combine Data Sources Partioning Data80/20Area Codeand Churn ->StringFilter Non-RelevantColumnsSimple BusinessRulesContract DataCall DataParameterOptimization Decision TreePredictor Random ForestLearner Random ForestPredictor DecisionTree Learner Partitioning Number To String Column Filter Rule Engine Math Formula CSV Reader Excel Reader Joiner Scorer Scorer Table Rowto Variable

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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: lars.schweikardt
Created at: 2019-11-21
On NodePit since: 2023-01-27
Last update: 2024-09-16
Created with KNIME version: v4.7.0
Tags: learning KNIMEbeginnerswebinarOnboardingData Science

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