This project aims to develop a robust Credit Card Users Churn prediction model to proactively identify customers at risk of canceling their service.
Project Purpose
Develop a predictive model to identify credit card users at risk of churning (canceling their service) and predict the reasons behind it.
Goals
- Build a highly accurate model to predict potential credit card users churn.
- Analyze the data to understand key factors influencing credit card users churn behavior.
- Generate actionable insights for targeted interventions to retain valuable customers.
Challenges
- Imbalanced datasets: A common challenge in credit card users churn prediction is the disproportionate number of customers who stay versus churn.
- Feature engineering: Selecting and transforming raw data into features that significantly impact the model’s ability to predict credit card users churn requires careful consideration.
- Model interpretability: Understanding how the model arrives at its credit card Users churn predictions is crucial for building trust and implementing effective interventions.
Achievements
- Achieved over 90% accuracy in predicting credit card user churn.
- Conducted feature importance analysis to identify critical factors influencing churn, such as spending habits, credit utilization, and customer service interactions.
- Developed a robust model using techniques like cross-validation and ensemble learning to improve generalization and avoid overfitting.
