This study investigates customer attrition prediction in the credit card segment of the banking industry, with a focus on enhancing classification performance through advanced feature engineering techniques. The preprocessing pipeline includes encoding of categorical variables, normalization of numerical features, and dimensionality reduction via mixed-type Principal Component Analysis (PCA and K-means clustering to identify latent customer segments. Feature importance visualizations are leveraged to guide model refinement and interpretability. Among the evaluated models, Extreme Gradient Boosting (XGBoost) demonstrates superior predictive performance, achieving an overall accuracy of 95.82%, sensitivity of 84.17%, and a balanced accuracy of 91.10%. Notably, XGBoost exhibits robust specificity (98.03%) and recall (97.04%) in identifying attrited customers, outperforming other classifiers such as Random Forest and Support Vector Machines (SVM), which show marginal declines in performance post-feature selection. Overall, the findings highlight the critical role of feature selection and engineering in optimizing churn prediction models.