Data Science, Machine Learning, Algorithms, Support Vector Machine, Supervised Learning.
let's see if a more simplistic approach could have solved our problem. To fit an SVM with a linear kernel we just need to update the kernel parameter. model = svm.SVC(kernel='linear') model.fit(x_train, y_train) And plot the decision boundary the same way we did back there. Dataset after