Churn Prediction with Decision Tree

Build and train a decision tree classifier to predict customer churn.

Decision Tree

A decision tree is a tree-based algorithm that can be used for classification and regression problems. In a decision tree, the dataset is transformed into a tree-like structure of conditional control statements, and predictions are made by following the conditional steps of the tree to the leaf node.

Let’s consider a hypothetical scenario where we try to estimate customer churn based on three features: gender, marital status, and internet service. We can form a decision tree based on those features, where the features are the nodes of our tree and the possible values are the edges. The node at the top is called the root node. For each customer, the algorithm starts evaluating from the root node and follows the edges depending on the feature values. At the leaf node, there is the target (churn or no churn).

Traversing through the decision tree, we can see that marital status is a stronger predictor of churn than gender is, as unmarried individuals are more likely to churn regardless of their gender, whereas married persons with internet service are likely to stay (not churn).

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