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Support Vector Machine (SVM) is a supervised machine learning algorithm widely used for classification problems. It finds an optimal hyperplane that separates data points belonging to different classes. The hyperplane is selected in such a way that the margin between the nearest data points of different classes is maximized. These nearest points are called support vectors. SVM is particularly effective for datasets having a clear separation between classes and can also handle non-linear classification using kernel functions. For this implementation, the Iris dataset is selected. It is a commonly used dataset containing measurements of iris _________ _______ ____ ______ ______ ________ ______ ___ _________.
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