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justknimeit-24

Challenge 24: Modeling Churn Predictions - Part 2 Description: Just like in last week’s challenge, a telecom company wants you to predict which customers are going to churn (that is, going tocancel their contracts) based on attributes of their accounts. One of your colleagues said that she was able to achieve a bit over 95% accuracy forthe test data without modifying the training data at all, and using all given attributes exactly as they are. Again, the target class to be predicted isChurn (value 0 corresponds to customers that do not churn, and 1 corresponds to those who do). What model should you train over the trainingdataset to obtain this accuracy over the test dataset? Can this decision be automated? Note 1: A simple, automated solution to this challenge consists of 5 nodes. Note 2: In this challenge, do not change the statistical distribution of any attribute or class in the datasets, and use allavailable attributes. Preparing the data Model Training Model EvaluationAccuracy = 95.052% AccuracyRead training dataRead test dataNode 1195execute up-streambefore configuration Scorer CSV Reader CSV Reader Workflow Executor AutoML Challenge 24: Modeling Churn Predictions - Part 2 Description: Just like in last week’s challenge, a telecom company wants you to predict which customers are going to churn (that is, going tocancel their contracts) based on attributes of their accounts. One of your colleagues said that she was able to achieve a bit over 95% accuracy forthe test data without modifying the training data at all, and using all given attributes exactly as they are. Again, the target class to be predicted isChurn (value 0 corresponds to customers that do not churn, and 1 corresponds to those who do). What model should you train over the trainingdataset to obtain this accuracy over the test dataset? Can this decision be automated? Note 1: A simple, automated solution to this challenge consists of 5 nodes. Note 2: In this challenge, do not change the statistical distribution of any attribute or class in the datasets, and use allavailable attributes. Preparing the data Model Training Model EvaluationAccuracy = 95.052% AccuracyRead training dataRead test dataNode 1195execute up-streambefore configuration Scorer CSV Reader CSV Reader Workflow Executor AutoML

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Created by: ardda
Created at: 2022-06-30
On NodePit since: 2022-07-12
Last update: 2024-04-27
Created with KNIME version: v4.6.0

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