Hi I need help with this Polynomial Regression assignment .

a. Perform a polynomial transformation on your features.

b. From the sklearn.linear_model library, import the LinearRegression class. Instantiate an object of this class called model, and fit it to the data. x and y will be your training data and z will be your response. Print the optimal model parameters to the screen by completing the following print() statements.

c. Use the following x_fit and y_fit data to compute z_fit by invoking the model's predict() method. This will allow you to plot the line of best fit that is predicted by the model.

# Plot Curve Fit x_fit = np.linspace(-21,21,1000) y_fit = x_fit

This is the code I need to use to be able to plot the line of best fit.

from mpl_toolkits.mplot3d import Axes3D

fig, axs = plt.subplots(2, 2, figsize=(15, 10), subplot_kw={'projection': '3d'}) axs = axs.ravel()

#Image for scatterplot 1 axs[0].scatter3D(x,y,z,c=z, cmap='jet') axs[0].set_xlabel('x', c='r', size =12) axs[0].set_ylabel('y', c='r', size =12) axs[0].set_zlabel('z', c='r', size =12) axs[0].view_init(1,86)

#Image for scatterplot 2 axs[1].scatter3D(x,y,z,c=z, cmap='jet') axs[1].set_xlabel('x', c='r', size =12) axs[1].set_ylabel('y', c='r', size =12) axs[1].set_zlabel('z', c='r', size =12) axs[1].view_init(90,0)

#Image for scatterplot 3 axs[2].scatter3D(x,y,z,c=z, cmap='jet') axs[2].set_xlabel('x', c='r', size =12) axs[2].set_ylabel('y', c='r', size =12) axs[2].set_zlabel('z', c='r', size =12) axs[2].view_init(37,46)

#Image for scatterplot 4 axs[3].scatter3D(x,y,z,c=z, cmap='jet') axs[3].set_xlabel('x', c='r', size =12) axs[3].set_ylabel('y', c='r', size =12) axs[3].set_zlabel('z', c='r', size =12) axs[3].view_init(17,81)

plt.show()