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justknimeit-24

justknimeit-24
Preparing the dataSet Area Code & Churn to String via TransformationTab2 Nodes Used Model Training2 Nodes Used Model Evaluation1 Node UsedAccuracy = 95% Challenge 23: Modeling Churn Predictions - Part 2Description: Just like in last week’s challenge, a telecom company wants you to predict which customers are going to churn (that is, going to canceltheir contracts) based on attributes of their accounts. One of your colleagues said that she was able to achieve a bit over 95% accuracy for the testdata without modifying the training data at all, and using all given attributes exactly as they are. Again, the target class to be predicted is Churn(value 0 corresponds to customers that do not churn, and 1 corresponds to those who do). What model should you train over the training dataset toobtain this accuracy over the test dataset? Can this decision be automated? DONENote 1: A simple, automated solution to this challenge consists of 5 nodes. DONENote 2: In this challenge, do not change the statistical distribution of any attribute or class in the datasets, and use all available attributes. DONE Read test dataRead training dataMatch original vs. predicted Churn valuesApply the trained modelto predict ChurnTarget Class = Churn CSV Reader CSV Reader Scorer Tree EnsemblePredictor Tree EnsembleLearner Preparing the dataSet Area Code & Churn to String via TransformationTab2 Nodes Used Model Training2 Nodes Used Model Evaluation1 Node UsedAccuracy = 95% Challenge 23: Modeling Churn Predictions - Part 2Description: Just like in last week’s challenge, a telecom company wants you to predict which customers are going to churn (that is, going to canceltheir contracts) based on attributes of their accounts. One of your colleagues said that she was able to achieve a bit over 95% accuracy for the testdata without modifying the training data at all, and using all given attributes exactly as they are. Again, the target class to be predicted is Churn(value 0 corresponds to customers that do not churn, and 1 corresponds to those who do). What model should you train over the training dataset toobtain this accuracy over the test dataset? Can this decision be automated? DONENote 1: A simple, automated solution to this challenge consists of 5 nodes. DONENote 2: In this challenge, do not change the statistical distribution of any attribute or class in the datasets, and use all available attributes. DONE Read test dataRead training dataMatch original vs. predicted Churn valuesApply the trained modelto predict ChurnTarget Class = ChurnCSV Reader CSV Reader Scorer Tree EnsemblePredictor Tree EnsembleLearner

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Created by: UserID
Created at: 2022-06-29
On NodePit since: 2022-07-08
Last update: 2024-12-21
Created with KNIME version: v4.6.0
Tags: justknimeit-24

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