The objective of this project is to design a predictive analytics system that identifies customers likely to discontinue services. By analyzing historical customer behavior and usage patterns, the system assists businesses in reducing churn through proactive retention strategies.
Gather customer data such as usage history, complaints, and subscription duration Perform exploratory data analysis to identify churn patterns Preprocess data including encoding, normalization, and class balancing Implement classification algorithms like Logistic Regression, KNN, and XGBoost Evaluate models using confusion matrix, ROC curve, and F1-score Optimize model performance through hyperparameter tuning Build a prediction module for real-time churn detection Design a dashboard to display churn probability insights Integrate visualization tools for managerial decision-making Implement role-based access control Test system accuracy with unseen datasets Document results, limitations, and future enhancements
