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justKnimeit-23

justKnimeit-23
Challenge 23: Modeling Churn Predictions - Part 1Description: A telecom company wants you to predict which customers are going to churn (that is, are going to cancel their contracts)based on attributes of their accounts. To this end, you are expected to use a decision tree classifier. The company gives you twodatasets (training and test), both with many attributes and the class ‘Churn’ to be predicted (value 0 corresponds to customers that donot churn, and 1 corresponds to those who do). You should train the decision tree classifier with the training data, and assess its qualityover the test data (calculate the accuracy, precision, recall, and confusion matrix for example). Training dataTest dataPruninig enabledChurn -> stringChurn -> string CSV Reader CSV Reader DecisionTree Learner Decision TreePredictor Scorer Number To String Number To String ROC Curve Challenge 23: Modeling Churn Predictions - Part 1Description: A telecom company wants you to predict which customers are going to churn (that is, are going to cancel their contracts)based on attributes of their accounts. To this end, you are expected to use a decision tree classifier. The company gives you twodatasets (training and test), both with many attributes and the class ‘Churn’ to be predicted (value 0 corresponds to customers that donot churn, and 1 corresponds to those who do). You should train the decision tree classifier with the training data, and assess its qualityover the test data (calculate the accuracy, precision, recall, and confusion matrix for example). Training dataTest dataPruninig enabledChurn -> stringChurn -> string CSV Reader CSV Reader DecisionTree Learner Decision TreePredictor Scorer Number To String Number To String ROC Curve

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Created by: eyilmaz35
Created at: 2022-06-30
On NodePit since: 2022-07-01
Last update: 2024-04-28
Created with KNIME version: v4.5.2
Tags: justKnimeit-23

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