Churn Prediction

Customer Churn prediction means knowing which customers are likely to leave or unsubscribe from your service. For many companies, this is an important prediction. This is because acquiring new customers often costs more than retaining existing ones. Once you’ve identified customers at risk of churn, you need to know exactly what marketing efforts you should make with each customer to maximize their likelihood of staying.

Customers have different behaviors and preferences, and reasons for cancelling their subscriptions. Therefore, it is important to actively communicate with each of them to keep them on your customer list. You need to know which marketing activities are most effective for individual customers and when they are most effective.

Impact of customer churn on businesses

A company with a high churn rate loses many subscribers, resulting in lower growth rates and a greater impact on sales and profits. Companies with low churn rates can retain customers.

Brief Review on Decision Tree : Decision trees are a nonparametric supervised learning method used for classification and regression. The goal is to build a model that predicts the value of a target variable by learning simple decision rules derived from the properties of the data. A tree can be viewed as a piecewise constant approximation.

For example, in the following example, a decision tree learns from data to approximate a sine wave using a series of if-then-else decision rules. The deeper the tree, the more complex the decision rules and the better the model.