#AIImpactOnForex Support Vector Machines (SVM) in Currency Prediction Support Vector Machines (SVM) are powerful supervised learning models used for classification and regression tasks. In currency prediction, SVMs help forecast future exchange rates by identifying patterns in historical financial data. They work by finding the optimal hyperplane that separates data points of different classes (e.g., currency uptrend vs. downtrend) with maximum margin. SVMs are effective in handling non-linear relationships by using kernel functions, such as the radial basis function (RBF). Their strength lies in their ability to generalize well even with limited or noisy data, making them popular for financial time series analysis. However, SVM performance heavily depends on proper feature selection, parameter tuning, and data preprocessing.