Can someone explain this code
Support Vector Machines (S array np.array(df1) X array[:,1].reshape(-1, 1) Y array:,0)...
Support Vector Machines (S array np.array(df1) X array[:,1].reshape(-1, 1) Y array:,0) train J, test X, train,y, test,y train, test split, Y, test,sizese.28) elf : SVR(kernels' rbf', С-10, degree:3, gama:, scale") pred,y clf.fit(train, X, train y).predict(test. X) r r2 score(test.y, pred y) se mean squared,error(test y, pred.y) print("r2 for SVR: ", r) # Plot the graph x-grid : np.arange(mǐn(test_A), max(testX), θ. 1) X.grid a X.grid.reshape (Len(X.grid), 1)) plt.title('SVN for AND') plt.scatter (test X, test.y, colore'black', label "Scatter plots') plt.plot(X.grid, nodel.predict(x_grid), color'red', Linewidth:3, label-'Regression Line') plt. legend() plt.showl) aker notes @nctu