from itertools import product

X_Reduced, X_Test_Reduced, Y_Reduced, Y_Test_Reduced = train_test_split(X_pca, Y,
                                                                        test_size = 0.30,
                                                                        random_state = 101)
trainedforest = RandomForestClassifier(n_estimators=700).fit(X_Reduced,Y_Reduced)

x_min, x_max = X_Reduced[:, 0].min() - 1, X_Reduced[:, 0].max() + 1
y_min, y_max = X_Reduced[:, 1].min() - 1, X_Reduced[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1), np.arange(y_min, y_max, 0.1))
Z = trainedforest.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z,cmap=plt.cm.coolwarm, alpha=0.4)
plt.scatter(X_Reduced[:, 0], X_Reduced[:, 1], c=Y_Reduced, s=20, edgecolor='k')
plt.xlabel('Principal Component 1', fontsize = 12)
plt.ylabel('Principal Component 2', fontsize = 12)
plt.title('Random Forest', fontsize = 15)
plt.show()