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justknimeit-25

Challenge 25: Modeling Churn Predictions - Part 3 Description: In this challenge series, the goal is to predict which customers of a certain telecom company are going to churn (that is, going to cancel their contracts) based on attributes of theiraccounts. Here, the target class to be predicted is Churn (value 0 corresponds to customers that do not churn, and 1 corresponds to those who do).After automatically picking a classification model for the task, you achieved an accuracy of about 95% for the test data, but the model does not perform uniformly for both classes. In fact, it is betterat predicting when a customer will not churn (Churn = 0) than when they will (Churn = 1). This imbalance can be verified by looking at how precision and recall differ for these two classes, or bychecking how metric Cohen’s kappa is a bit lower than 80% despite a very high accuracy. How can you preprocess and re-sample the training data in order to make the classification a bit morepowerful for class Churn = 1? Note 1: Need more help to understand the problem? https://www.knime.com/blog/predict-customer-churn-low-code-ml-exampleNote 2: This problem is hard: do not expect to see a major performance increase for class Churn = 1. Also, verifying if the performance increase is statistically significant will not be trivial. Still...give this challenge your best try! Preparing the data Model Training Model EvaluationAccuracy = 95.8%Cohen's kappa = 82.5% Change static seed AccuracyRead training dataRead test dataNode 1195execute up-streambefore configurationNode 1197static seed inputNode 1203Node 1204 Scorer CSV Reader CSV Reader Workflow Executor AutoML SMOTE Table Row ToVariable Loop Start Table Creator Loop End Column Filter Row Filter Challenge 25: Modeling Churn Predictions - Part 3 Description: In this challenge series, the goal is to predict which customers of a certain telecom company are going to churn (that is, going to cancel their contracts) based on attributes of theiraccounts. Here, the target class to be predicted is Churn (value 0 corresponds to customers that do not churn, and 1 corresponds to those who do).After automatically picking a classification model for the task, you achieved an accuracy of about 95% for the test data, but the model does not perform uniformly for both classes. In fact, it is betterat predicting when a customer will not churn (Churn = 0) than when they will (Churn = 1). This imbalance can be verified by looking at how precision and recall differ for these two classes, or bychecking how metric Cohen’s kappa is a bit lower than 80% despite a very high accuracy. How can you preprocess and re-sample the training data in order to make the classification a bit morepowerful for class Churn = 1? Note 1: Need more help to understand the problem? https://www.knime.com/blog/predict-customer-churn-low-code-ml-exampleNote 2: This problem is hard: do not expect to see a major performance increase for class Churn = 1. Also, verifying if the performance increase is statistically significant will not be trivial. Still...give this challenge your best try! Preparing the data Model Training Model EvaluationAccuracy = 95.8%Cohen's kappa = 82.5% Change static seed AccuracyRead training dataRead test dataNode 1195execute up-streambefore configurationNode 1197static seed inputNode 1203Node 1204 Scorer CSV Reader CSV Reader Workflow Executor AutoML SMOTE Table Row ToVariable Loop Start Table Creator Loop End Column Filter Row Filter

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Created by: ardda
Created at: 2022-06-30
On NodePit since: 2022-07-19
Last update: 2024-04-27
Created with KNIME version: v4.6.0

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