The goal of the SVM algorithm is to find a hyperplane in an N-dimensional space that categorises data points clearly. The hyperplane's size is determined by the number of features. If there are only two input characteristics, the hyperplane is merely a line.
SVM is a supervised machine learning technique that can be used to solve problems like classification and regression. It transforms your data using a technique known as the kernel trick, and then calculates an ideal boundary between the available outputs based on these alterations.
where are datasets of all this available
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