Icon

jefleisc-knime_​challenge-23

knime_challenge-23-churn_prediction

My solution to Challenge #23.

Challenge 23: Modeling Churn Predictions - Part 101JUL2022Description:A telecom company wants you to predict which customers are going to churn (that is, are going to cancel theircontracts) based on attributes of their accounts. To this end, you are expected to use a decision tree classifier. Thecompany 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 those who do). You should train thedecision 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 tojust apply a decision tree classifier (and get an accuracy of about 92%). A simple solution should consist 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.Note 3: Need more help to understand the problem?https://www.knime.com/blog/predict-customer-churn-low-code-ml-example Load train datachurn_problem_training_data.csvLoad test dataNode 6DefaultparametersDefaultparameters CSV Reader CSV Reader Scorer DecisionTree Learner Decision TreePredictor Challenge 23: Modeling Churn Predictions - Part 101JUL2022Description:A telecom company wants you to predict which customers are going to churn (that is, are going to cancel theircontracts) based on attributes of their accounts. To this end, you are expected to use a decision tree classifier. Thecompany 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 those who do). You should train thedecision 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 tojust apply a decision tree classifier (and get an accuracy of about 92%). A simple solution should consist 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.Note 3: Need more help to understand the problem?https://www.knime.com/blog/predict-customer-churn-low-code-ml-example Load train datachurn_problem_training_data.csvLoad test dataNode 6DefaultparametersDefaultparameters CSV Reader CSV Reader Scorer 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: jefleisc
Created at: 2022-07-01
On NodePit since: 2022-07-04
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!