Linear SVC grid search in Python

linearSVCgridsearch.py

from sklearn.pipeline import Pipeline
from sklearn.svm import LinearSVC
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler

SVCpipe = Pipeline([('scale', StandardScaler()),
                   ('SVC',LinearSVC())])

# Gridsearch to determine the value of C
param_grid = {'SVC__C':np.arange(0.01,100,10)}
linearSVC = GridSearchCV(SVCpipe,param_grid,cv=5,return_train_score=True)
linearSVC.fit(X_train,y_train)
print(linearSVC.best_params_)
#linearSVC.coef_
#linearSVC.intercept_

bestlinearSVC = linearSVC.best_estimator_
bestlinearSVC.fit(X_train,y_train)
bestlinearSVC.coef_ = bestlinearSVC.named_steps['SVC'].coef_
bestlinearSVC.score(X_train,y_train)