Efficacy of Customer Churn Prediction System

Customer churn, defined as customer defection is a big problem for businesses in today’s competition. This paper takes an in-depth look at the application of machine learning (ML) in customer churn prediction to identify the best models for various industries. quantitative performance metrics of seven commonly used machine learning algorithms: Logistic Regression, Decision Trees, Random Forests, Naive Bayes, K-Nearest Neighbors (KNN), AdaBoost, and XGBoost. The theoretical background, application context, and performance evaluation of each model are examined, focusing on precision, accuracy, recall, and F1 score. Evaluation of the performance of the model on different business data is also done to ensure that the application is real in the world. This comparison shows the advantages and disadvantages of each model and guides providers in their selection based on the specific needs of the business and profile features. Additionally, this paper explores future changes in predictions and highlights the changing role of machine learning technologies, especially deep learning, in improving the accuracy of predictions and personalization. This review is very useful for professionals and experts; It provides a better understanding of the current machine learning model for customer churn prediction and provides insight into future research and practical applications.