Artificial Intelligence Application for Customer Behavior and Churn Prediction

Customer churn is one of the most common problems in a company's business. In this paper, a machine learning algorithm is used to predict customer behavior and the occurrence of this problem. The study compared and analyzed the performance of different algorithms, including decision tree, random forest, and gradient boosting, in predicting customer churn based on historical data on customer behavior. The results showed that random forest and gradient boosting had good values of evaluation metrics, while the decision tree algorithm performed worse. The study also provided details on the software implementation and data analysis tools, as well as the necessary libraries and versions for running the program. The findings of this study suggest that the use of machine learning algorithms effectively predicts customer behavior and churn, which is crucial for companies to retain existing customers and improve their marketing campaigns.