Use Sklearn.svm$.$SVC with linear kernel to classify the dataset below. Change parameter $C$ in the range $0\mathrm{.}01,0\mathrm{.}1,1,10,100$ and see its effect on hyperplane and support vectors.

from sklearn. datasets import make$\_$blobs

$x,y=$ make$\_$blobs $($n$\_$samples $=100,n_f$ eatures $=2,$ centers $=2,$ cluster$\_$std $=0\mathrm{.}7,$ random$\_$state $=0)$

import matplotlib. pyplot as plt

#p$1$t$.$ scatter $)=(y$

#scale data

from sklearn. preprocessing import StandardScaler

scale $=$ StandardScaler $()$

scale. fit $(x)$

$x=$ scale. transform $(x)$

plt$.)=(y$

plt$.$ Xlabel $(\text{'}$feature $1\text{'})$

p$1$t$.$ ylabel $(\text{'}$feature $2\text{'})$

plt$.$ title$(\text{'}$ scaled data'$)$