Speed for different kernels in scikit-learn's SVM
One option is to speed up your code is to standardize features, removing the mean and scaling to unit variance.
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X = scaler.fit_transform(X.reshape(-1, 1))
y = scaler.fit_transform(y.reshape(-1, 1))

After I standardized the data, each model fit in less than 500ms.
Support vector machines (SVMs) need to calcuate the distance between each data. Standardize numbers make the calculations faster.