x_min, x_max = x_train[:, 0].min() - .5, x_train[:, 0].max() + .5
    y_min, y_max = x_train[:, 1].min() - .5, x_train[:, 1].max() + .5
    h = (x_max / x_min)/100
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
    np.arange(y_min, y_max, h))
    plt.subplot(1, 1, 1)
    Z = svc.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)
    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
    plt.xlabel('Sepal length')
    plt.ylabel('Sepal width')
    plt.xlim(xx.min(), xx.max())
    plt.title(title)
    plt.show()

[93.86879233 84.77565909 14.79950721 30.08036637 28.32257801 13.65629103
 -1.4152549  -1.06058228  1.08335583]

prediction = svm.predict(caracteristicas.reshape(-1,1))[0]

#Crear clasificador SVM
svm = SVC(kernel='rbf')
#Entrenar SVM:
svm.fit(x_train.reshape(-1, 1), y_train)