Icon

justknimeit-23

Challenge 23: Modeling Churn Predictions - Part 1 Description: A telecom company wants you to predict which customers are going to churn (that is, are going to cancel their contracts) based onattributes of their accounts. To this 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 be predicted (value 0 corresponds to customers that do not churn, and 1 corresponds to thosewho 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 predictive problems, focusing onclassification. You are expected to just apply a decision tree classifier (and get an accuracy of about 92%). A simple solution should consist of 5nodes. Note 2: In this challenge, do not change the statistical distribution of any attribute or class in the datasets, and use all available attributes.Note 3: Need more help to understand the problem? Check this blog post out. Preparing the data Model Training Model EvaluationAccuracy = 94.453% AccuracyRead training dataRead test data Scorer CSV Reader CSV Reader DecisionTree Learner Decision TreePredictor Challenge 23: Modeling Churn Predictions - Part 1 Description: A telecom company wants you to predict which customers are going to churn (that is, are going to cancel their contracts) based onattributes of their accounts. To this 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 be predicted (value 0 corresponds to customers that do not churn, and 1 corresponds to thosewho 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 predictive problems, focusing onclassification. You are expected to just apply a decision tree classifier (and get an accuracy of about 92%). A simple solution should consist of 5nodes. Note 2: In this challenge, do not change the statistical distribution of any attribute or class in the datasets, and use all available attributes.Note 3: Need more help to understand the problem? Check this blog post out. Preparing the data Model Training Model EvaluationAccuracy = 94.453% AccuracyRead training dataRead test data Scorer CSV Reader CSV Reader DecisionTree Learner Decision TreePredictor

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: ardda
Created at: 2022-06-30
On NodePit since: 2022-07-01
Last update: 2024-05-03
Created with KNIME version: v4.6.0

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!