ROC Curve and Saving the Model

# Required imports from scikit-learn 
from sklearn.metrics import roc_curve, roc_auc_score
# Area Under the ROC Curve
grid_auc = roc_auc_score(y_test, grid.predict_proba(X_test)[:,1]) 
# setting the figure size 
plt.figure(figsize = (18, 6)) 
# Computing Receiver operating characteristic (ROC)
fpr_grid, tpr_grid, thresholds_grid = roc_curve(y_test, grid.predict_proba(X_test)[:,1])
# plot no skill - A line for random guess
plt.plot([0, 1], [0, 1], linestyle='--', label = 'Random guess' )
    
# plotting ROC Curve for skilled svm model 
plt.plot(fpr_grid, tpr_grid, marker='.', label = 'ROC - AUC - Grid-Search SVC: %.3f' % grid_auc)
# good to put title and labels
plt.title('SVM results after Grid-Search on scaled features')
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
    
# putting the legends  
plt.legend();