text: import numpy as np 
  from sklearn import datasets
  from sklearn.pipeline import Pipeline 
  from sklearn.preprocessing import StandardScaler
  from sklearn.svm import LinearSVC
  
  iris = datasets.load_iris()
  x = iris["data"][:, (2,3)]
  y = (iris["target"] == 2).astype(np.float64)
  
  svm_clf = Pipeline([
      ("scaler", StandardScaler()),
      ("linear_svc", LinearSVC(C=1, loss="hinge"))
  ])
  
  svm_clf.fit(x, y)
  
  svm_clf.predict([[5.5, 1.7]])
  
  #trying SVC Polynominal SVM
  from sklearn.datasets import make_moons
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import PolynomialFeatures
  
  polynominal_svm_clf = Pipeline([
      ("poly_features", PolynomialFeatures(degree=3)),
      ("scaler", StandardScaler()),
      ("svm_clf", LinearSVC(C=10, loss="hinge"))
  ])
  
  polynominal_svm_clf.fit(x, y)
  
  polynominal_svm_clf.predict([[5.5, 1.7]])
  
  #adding new parameters
  from sklearn.svm import SVC
  
  poly_kernel_svc_clf = Pipeline([
      ("scaler", StandardScaler()),
      ("svc_clf", SVC(kernel='poly', degree=3, coef0=1, C=5))
  ])
  
  polynominal_svm_clf.fit(x, y)
  
  polynominal_svm_clf.predict([[5.5, 1.7]])
  
  #linear kernel
  from sklearn.svm import SVC
  
  poly_kernel_svc_clf = Pipeline([
      ("scaler", StandardScaler()),
      ("svc_clf", SVC(kernel='linear', degree=10, coef0=1, C=5))
  ])
  
  polynominal_svm_clf.fit(x, y)
  
  polynominal_svm_clf.predict([[5.5, 1.7]])