Create Second Image 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 from mpl_toolkits import mplot3d from matplotlib.ticker import LinearLocator, FormatStrFormatter %matplotlib inline %matplotlib notebook x_fit = np.linspace(-21, 21,1000)| y_fit = x_fit x_fit = x_fit.reshape(-1,1) y_fit = y_fit.reshape(-1,1) PolyReg LinearRegression() PolyReg.fit(x_fit, y_fit) mixData = pd.DataFrame({'x_fit': [x_fit], 'y_fit': [y_fit]}) z_fit = PolyReg. predict (mixData["x_fit"][0]) plt.scatter(x_fit, y_fit) plt.plot(x fit 7 fit C='vellow')

Create Second Image 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 from mpl_toolkits import mplot3d from matplotlib.ticker import LinearLocator, FormatStrFormatter %matplotlib inline %matplotlib notebook x_fit = np.linspace(-21, 21,1000)| y_fit = x_fit x_fit = x_fit.reshape(-1,1) y_fit = y_fit.reshape(-1,1) PolyReg LinearRegression() PolyReg.fit(x_fit, y_fit) mixData = pd.DataFrame({'x_fit': [x_fit], 'y_fit': [y_fit]}) z_fit = PolyReg. predict (mixData["x_fit"][0]) plt.scatter(x_fit, y_fit) plt.plot(x fit 7 fit C='vellow')

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Question

Create Second Image
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.
7]: # Plot Curve Fit
from mpl_toolkits import mplot3d
from matplotlib.ticker import LinearLocator, FormatStrFormatter
%matplotlib inline
%matplotlib notebook
x_fit = np.linspace(-21, 21, 1000)|
y_fit = x_fit
x_fit = x_fit.reshape(-1,1)
y_fit = y_fit.reshape(-1,1)
PolyReg = LinearRegression()
PolyReg.fit(x_fit, y_fit)
mixData = pd.DataFrame({'x_fit': [x_fit], 'y_fit': [y_fit]})
z_fit = PolyReg.predict (mixData["x_fit"][0])
plt.scatter(x_fit, y_fit)
plt.plot(x_fit, z_fit, c='yellow')

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