Support Vector Machine is a supervised Machine Learning algorithm. Support Vector Machine algorithm can be used for both Regression and Classification

The main task of the SVM algorithm is to find the Right hyperplane between groups. For the given groups there will be many possible hyperplanes in between them. But which is right among all? Let’s find it.

Suppose Take 2 groups as stars and circles.

Let’s take Another Example.

Now have a look at this graph. What do you think is the best hyperplane?? Here all 3 hyperplanes segregate them well. In this case, we have to see a margin. Margin is the distance between the hyperplane to the nearest data point. B has maximum margin when compared to A and C. Hyperplane with the highest margin is the best hyperplane. Because the chances of getting the wrong classification will be less if the margin is more.

Let’s have another example.

Now, which hyperplane will you decide on??? Hyperplane A has the highest margin and Hyperplane B segregates them well. So here Classification is our main motto. Hyperplane A has a classification error. whereas, B classifies well. So Hyperplane B is correct.

In this way, Hyperplane is decided.

Let’s have look at the code.

We can see in the Image that 1st step is creating a model. Here SVC model is created using the SVM library and then the model is trained by using the fit method. Prediction is done by using predict method. We can find the accuracy of the model by using the accuracy_score method. Here we got 72% accuracy