Customer Churn Prediction System Based on Flask

The article designs and implements a customer churn prediction system based on Flask, aimed at helping businesses identify potential customer churn risks through machine learning techniques. The system first loads and processes the input customer data, then utilizes various classifiers (such as Random Forest, Support Vector Machine, and XGBoost) to predict churn probabilities and provide corresponding customer retention suggestions. Methodologically, this study employs grid search for hyperparameter optimization to help the models achieve optimal performance. Additionally, a stacking approach is used to integrate the top-performing models from earlier stages, further enhancing predictive accuracy. Experimental results show that the ensemble method yields the best overall performance, achieving a prediction accuracy of 87%. In the future, the system can be expanded to include multi-source data fusion, real-time predictions, and the integration of feedback mechanisms, thereby realizing intelligent and personalized customer relationship management, providing new insights and technical support for customer retention in businesses.