As the banking industry faces increasing competition and higher customer expectations, accurately predicting customer churn has become a critical factor for banks in their efforts to retain customers and enhance overall customer satisfaction. Understanding the underlying factors that contribute to customer churn can provide valuable insights that help banks develop targeted retention strategies, optimize the customer experience, and improve overall customer relationship management practices. By leveraging advanced analytics and data-driven approaches, banks can gain a deeper understanding of customer behavior and preferences, enabling them to address customer churn more effectively.
The primary objective of this project is to develop a robust churn prediction model that can accurately identify potential churners among bank customers. Ultimately, the development of an accurate churn prediction model can significantly contribute to the reduction of customer churn rates and improve overall customer retention for banks.
- Anaconda 3
- Python 3
