The Support Vector Machine, or SVM, is a linear model that can be used to solve classification and regression issues. It can solve both linear and nonlinear problems and is useful for a wide range of applications. SVM is a basic concept: The method divides the data into classes by drawing a line or hyperplane.
The name SVM, or Support Vector Machine, is well-known among those who work in Machine Learning or Data Science. SVR, on the other hand, is not the same as SVM. As the name implies, SVR is a regression algorithm, which means we can use it instead of SVM for working with continuous values.
The support vector machine method's purpose is to find a hyperplane in an n-dimensional space, where n is the number of features or independent variables. Let me give you an example; we're using classification as an example since it's how data is classified in the vast majority of cases.
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