SVM — Support Vector Machine 🐱‍🚀

SVM (Support Vector Machine) algorithm is used to find the best fit hyperplane in n dimensional space.

Here n — no. of features or independent variable (xi), SVM can be used for both regression and classification (SVR and SVC).

Why SVM?

SVM is a linear model for classification and regression problem, SVM can solve linear and non-linear data in effective way. Let’s us take non-linear data set like this 👇

Here it’s not practical to draw a line to classify or to perform regression, no other algorithm can solve it too (Linear , Logistic , ..), we can use SVM to achieve the desired results.

Let’s see HOW?

In SVM there is a concept called kernel, it helps plotting non-linear data to higher dimensional to fit the hyperplane.

In higher dimension we can fit the hyperplane, so we can able to classify the data point. After the fitting the hyperplane we can convert to original dimension.

SVM can be used for Classification and Regression

Let’s how to implement SVR in python