Challenge 23 - Churning Problem - Solution

Challenge 23 - Modeling Churn Predictions - Solution

Challenge 23 - Modeling Churn Predictions

A telecom company wants you to predict which customers are going to churn (that is, going to cancel their contracts) based on attributes 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 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 predictive problems, focusing on classification. You are expected to just 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 all available attributes. Note 3: Need more help to understand the problem? Check this blog post out: https://www.knime.com/blog/predict-customer-churn-low-code-ml-example.

External resources

• Churn Prediction