continuing with svm

SVM/svm_model.py

@@ -19,4 +19,30 @@
 #%%
 X_scaled  = StandardScaler(X)
 svm_clf.fit(X,Y)
-svm_clf.predict([[5.5,1.7]])
+svm_clf.predict([[5.5,1.7]])
+
+#%%
+from sklearn.datasets import make_moons
+from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import PolynomialFeatures
+
+X,Y = make_moons()
+print(X)
+print(Y)
+
+#%%
+polynomial_svm_clf = Pipeline((
+    ("poly_features",PolynomialFeatures()),
+    ('scaler',StandardScaler()),
+    ('linear_svc',LinearSVC(C=10,loss="hinge")),
+))
+
+polynomial_svm_clf.fit(X,Y)
+
+#%%
+from sklearn.svm import SVC
+poly_kernel_svm_clf = Pipeline((
+("scaler", StandardScaler()),
+("svm_clf", SVC(kernel="poly", degree=3, coef0=1, C=5))
+))
+poly_kernel_svm_clf.fit(X, Y)