Customer Churn Prediction using Machine Learning

The basic random forests are combined with a sampling method, cost sensitive learning, and improved balanced random forests (IBRF), which we utilize 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 (CWC-SVM). To evaluate these algorithms' qualities, they are compared and graded. The sampling plan and data processing are explained in great detail. This article demonstrates how machine learning handles such Random Forest can effectively forecast which customers are mostly will leave the company or organization in the future by using data such as personal information, bank information, etc. The end goal of churn prediction is to understand when exactly a customer is expected to churn so from there, organization can prioritize at-risk customers which are valuable for growth and execute personalized strategies that to reengage those customers again