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Data_​Science_​with_​KNIME

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 Area Codeand Churn ->StringFilter Non-RelevantColumnsSimple BusinessRulesContract DataCall Data Number To String Column Filter Rule Engine Math Formula CSV Reader Excel Reader Joiner 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 Area Codeand Churn ->StringFilter Non-RelevantColumnsSimple BusinessRulesContract DataCall Data Number To String Column Filter Rule Engine Math Formula CSV Reader Excel Reader Joiner

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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: 2024-12-17
Created with KNIME version: v4.7.0
Tags: learning KNIMEbeginnerswebinarOnboardingData Science

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