Build a Customer Churn Prediction Model using Decision Trees

It can effectively forecast customer churn, which will enable the companies to recognize the likely-to-churn customers.

Organizations are in a position to address churn risks if they are identified making it easy to employ suitable retention tools.

Offers important information about the customers and their behaviors and tendencies that lead to churn.

It assists companies to allocate resources and concentrate on the customers who pose more risks.

Combines commonly used SMOTE in order to achieve better model accuracy due to class imbalance.

Allows business organizations to define marketing strategies to be implemented on customers who constantly churn.

Builts up comprehensive model evaluation based on accuracy, precision, and ROC-AUC for a better understanding of the performances.

Find out which customers' attributes have a higher propensity to churn.

The model is not limited to use on small data sets and can easily accommodate large sets of data which makes it an ideal business especially those that are growing rapidly.

Enables businesses to find new ways of enhancing products or services in a bid to combat the churn rate.