Machine Learning and Ensemble Prediction for Customer Churn Prediction Analysis

Customer churn prediction is super important for businesses. It helps them keep customers around make money over time. In this study, we look at ways to predict when customers might leave using machine learning. We use different methods called ensemble techniques like Random Forest Gradient Boosting Machines (GBM). These tools help build strong models that can spot customers who may churn. Our goals are simple: we want better prediction accuracy, strong stable models, and useful tips to improve retention strategies. After testing our ensemble model, we found it works better in accuracy compared to single models or older methods. This means that businesses can understand their customers better. They can also create smart strategies to keep customers happy, which helps lower churn rates and boosts long-term success.