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jKi-23

jKi-23
Challenge 23: Modeling Churn Predictions - Part 1Level: EasyDescription: A telecom company wants you to predict whichcustomers are going to churn (that is, are going to canceltheir contracts) based on attributes of their accounts. Tothis end, you are expected to use a decision tree classifier.The company gives you two datasets (training and test),both with many attributes and the class ‘Churn’ to bepredicted (value 0 corresponds to customers that do notchurn, and 1 corresponds to those who do). You shouldtrain the decision tree classifier with the training data, andassess its quality over the test data (calculate the accuracy,precision, recall, and confusion matrix for example). Note1: This challenge is a simple introduction to predictiveproblems, focusing on classification. You are expected tojust apply a decision tree classifier (and get an accuracy ofabout 92%). A simple solution should consist of 5 nodes.Note 2: In this challenge, do not change the statisticaldistribution of any attribute or class in the datasets, anduse all available attributes. Note 3: Need more help tounderstand the problem? Check this blog post out. Test dataTraining dataNode 3Node 6Node 9Node 10Node 11Node 12 CSV Reader CSV Reader Data Explorer Number To String DecisionTree Learner Decision TreePredictor Scorer Number To String Challenge 23: Modeling Churn Predictions - Part 1Level: EasyDescription: A telecom company wants you to predict whichcustomers are going to churn (that is, are going to canceltheir contracts) based on attributes of their accounts. Tothis end, you are expected to use a decision tree classifier.The company gives you two datasets (training and test),both with many attributes and the class ‘Churn’ to bepredicted (value 0 corresponds to customers that do notchurn, and 1 corresponds to those who do). You shouldtrain the decision tree classifier with the training data, andassess its quality over the test data (calculate the accuracy,precision, recall, and confusion matrix for example). Note1: This challenge is a simple introduction to predictiveproblems, focusing on classification. You are expected tojust apply a decision tree classifier (and get an accuracy ofabout 92%). A simple solution should consist of 5 nodes.Note 2: In this challenge, do not change the statisticaldistribution of any attribute or class in the datasets, anduse all available attributes. Note 3: Need more help tounderstand the problem? Check this blog post out. Test dataTraining dataNode 3Node 6Node 9Node 10Node 11Node 12CSV Reader CSV Reader Data Explorer Number To String DecisionTree Learner Decision TreePredictor Scorer Number To String

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Created by: franzc
Created at: 2022-06-30
On NodePit since: 2022-07-04
Last update: 2024-05-02
Created with KNIME version: v4.6.0
Tags: justknimeit-23

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