Icon

justknimeit-23

justknimeit-23
Preparing the dataSet Area Code & Churn to String via TransformationTab2 Nodes Used Model Training2 Nodes Used Model Evaluation1 Node UsedAccuracy = 93% Challenge 23: Modeling Churn Predictions - Part 1Description: 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 on classification. You are expected to just apply a decision treeclassifier (and get an accuracy of about 92%). A simple solution should consist of 5 nodes. DONENote 2: In this challenge, do not change the statistical distribution of any attribute or class in the datasets, and use all available attributes. DONE Read test dataRead training dataApply the trained modelto predict ChurnTarget Class = ChurnBuild ROC for predictions(Optional)Match original vs. predicted Churn values CSV Reader CSV Reader Decision TreePredictor DecisionTree Learner ROC Curve Scorer Preparing the dataSet Area Code & Churn to String via TransformationTab2 Nodes Used Model Training2 Nodes Used Model Evaluation1 Node UsedAccuracy = 93% Challenge 23: Modeling Churn Predictions - Part 1Description: 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 on classification. You are expected to just apply a decision treeclassifier (and get an accuracy of about 92%). A simple solution should consist of 5 nodes. DONENote 2: In this challenge, do not change the statistical distribution of any attribute or class in the datasets, and use all available attributes. DONE Read test dataRead training dataApply the trained modelto predict ChurnTarget Class = ChurnBuild ROC for predictions(Optional)Match original vs. predicted Churn valuesCSV Reader CSV Reader Decision TreePredictor DecisionTree Learner ROC Curve Scorer

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: UserID
Created at: 2022-06-29
On NodePit since: 2022-06-30
Last update: 2024-11-21
Created with KNIME version: v4.6.0
Tags: justknimeit-23

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

Try NodePit Runner!