Summary

The project aims to predict customer churn rate based on multiple data sources, using a set of classification models to select the one that offers the highest accuracy. The models are evaluated using metrics such as F1 Score and AUC-ROC to ensure their performance.

Conclusion: The best model is selected to predict customer churn rate, and recommendations are provided for its implementation in the system.

Github repository