A firm needs to classify user behavior as fraudulent or normal using two features: account age (x) and transaction month (y). The plotted class distribution shows a pattern where the classes are not linearly separable. Which model will achieve the HIGHEST classification accuracy?

An SVM with an RBF kernel is ideal for non-linearly separable data. The RBF kernel transforms the data into a higher-dimensional space, allowing a linear decision boundary to separate the classes effectively. Since the problem states the classes are not linearly separable, this approach directly addresses that challenge. A Linear SVM would perform poorly as it can only find linear decision boundaries. A Decision Tree could work but might overfit or create overly complex boundaries compared to a well-tuned RBF SVM. A single-layer perceptron with a Tanh activation is also limited to finding linear or nearly linear decision boundaries, making it unsuitable for non-linearly separable data.