Credit-card-defaulters-prediction

This project aims to develop a predictive model for identifying potential credit card defaulters using machine learning techniques. Utilizing IBM Watsonx Studio and AutoAI, the model leverages historical data on customer demographics, transaction behavior, and payment history. The XGBoost algorithm was selected for its robust performance in classification tasks. The model's accuracy and reliability were validated using metrics such as accuracy, precision, and recall. The final deployment on IBM Cloud ensures scalability and real-time prediction capabilities. This solution not only aids in proactive risk management but also opens up future possibilities for enhanced customer insights and more sophisticated predictive analytics.