Predicting Credit Card Customers Segmentation Project

• Performed Attrition Analysis for 25K Credit Card Users using SQL to identify factors contributing to churn rate across different countries.

• Developed Customer Churning Analysis Visualizations, identified critical patterns of root causes behind attrition using Tableau.

• Deployed Supervised ML Models to improve retention rate achieving accuracy of 75.28% on test data for Random Forest Classifier(RFC).

Excel for Data Cleaning

Tableau for creating dashboard

SQL for analysis

Python for Model Building

By implementing these recommendations, the business can enhance customer repayment behavior, cater to specific age-related credit needs, provide valuable financial education, and develop occupation-specific credit solutions. These initiatives will contribute to improved credit scores, customer satisfaction, and long-term financial stability for both customers and the business.

Checkout this link for complete project.