In machine learning, dealing with binary imbalanced data classification is challenging due to unequal class sizes, leading to model bias. We propose a unique method that uses filtering, ADASYN oversampling, and ENN cleaning to balance data, improve minority class accuracy, and boost overall model performance, showing significant improvements in AUC, F1, and G-mean metrics.