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Challange 23

Challange 23
KNIME IT Challange 23Description: A telecom company wants you to predict which customers are going to churn (that is, are going to cancel their contracts) basedon attributes of their accounts. To this end, you are expected to use a decision tree classifier. The company gives you two datasets (trainingand test), both with many attributes and the class ‘Churn’ to be predicted (value 0 corresponds to customers that do not churn, and 1corresponds to those who do). You should train the decision tree classifier with the training data, and assess its quality over the test data(calculate the accuracy, precision, recall, and confusion matrix for example). Note 1: This challenge is a simple introduction to predictiveproblems, focusing on classification. You are expected to just apply a decision tree classifier (and get an accuracy of about 92%). A simplesolution should consist of 5 nodes. Note 2: In this challenge, do not change the statistical distribution of any attribute or class in thedatasets, and use all available attributes. Note 3: Need more help to understand the problem? Check this blog post out. Node 1Node 2Node 3Node 4Node 5 CSV Reader CSV Reader DecisionTree Learner Decision TreePredictor Scorer KNIME IT Challange 23Description: A telecom company wants you to predict which customers are going to churn (that is, are going to cancel their contracts) basedon attributes of their accounts. To this end, you are expected to use a decision tree classifier. The company gives you two datasets (trainingand test), both with many attributes and the class ‘Churn’ to be predicted (value 0 corresponds to customers that do not churn, and 1corresponds to those who do). You should train the decision tree classifier with the training data, and assess its quality over the test data(calculate the accuracy, precision, recall, and confusion matrix for example). Note 1: This challenge is a simple introduction to predictiveproblems, focusing on classification. You are expected to just apply a decision tree classifier (and get an accuracy of about 92%). A simplesolution should consist of 5 nodes. Note 2: In this challenge, do not change the statistical distribution of any attribute or class in thedatasets, and use all available attributes. Note 3: Need more help to understand the problem? Check this blog post out. Node 1Node 2Node 3Node 4Node 5 CSV Reader CSV Reader DecisionTree Learner Decision TreePredictor Scorer

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Created by: ruben_gelder
Created at: 2022-06-29
On NodePit since: 2022-06-30
Last update: 2024-05-03
Created with KNIME version: v4.5.2
Tags: justknimeit-23Transfer-Solutions

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