Machine Learning based Customer Churn Prediction with Random Forest Algorithm

The basic random forests are combined with a sampling method, cost sensitive learning, and improved balanced random forests (IBRF), which utilized to predict client churn. We may repeatedly learn the best features of IBRF by changing the distribution of the classes and toughening the punishment for misclassifying the minority class. They are demonstrated to considerably increase prediction accuracy when compared to other methods such as artificial neural networks, decision trees, and class-weighted core support vector machines when applied to a database of credit debt clients from an anonymous commercial bank in China (CWCSVM). To evaluate these algorithms' qualities, they are compared and graded. The sampling plan and data processing are explained in great detail