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Database System Concepts
7th Edition
ISBN: 9780078022159
Author: Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher: McGraw-Hill Education
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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')](https://content.bartleby.com/qna-images/question/9a439e6e-0de6-4cc8-b17b-db5a75b83157/cdd8bca2-d47e-45e7-bb0f-4814e36832f2/vl3johm_thumbnail.png)
Transcribed Image Text: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')
 alt="Z
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