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TODO 1 We divided our columns into features and labels using the clean_df sfunction. Slice and index our label "area," then put the result in the variable y. # TODO 1 y = display(y) todo_check([ (y.shape == (517,),'y does not have the correct shape of (517,)'), (np.all(np.isclose(y.values[-5:], np.array([2.00687085,4.01259206,2.49815188,0. ,0. ]),rtol=.01)),'y has the incorrect values'), ])   Expected output:

TODO 1 We divided our columns into features and labels using the clean_df sfunction. Slice and index our label "area," then put the result in the variable y. # TODO 1 y = display(y) todo_check([ (y.shape == (517,),'y does not have the correct shape of (517,)'), (np.all(np.isclose(y.values[-5:], np.array([2.00687085,4.01259206,2.49815188,0. ,0. ]),rtol=.01)),'y has the incorrect values'), ])   Expected output:

Database System Concepts
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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TODO 1


We divided our columns into features and labels using the clean_df sfunction.

Slice and index our label "area," then put the result in the variable y.

# TODO 1
y =
display(y)

todo_check([
(y.shape == (517,),'y does not have the correct shape of (517,)'),
(np.all(np.isclose(y.values[-5:], np.array([2.00687085,4.01259206,2.49815188,0. ,0. ]),rtol=.01)),'y has the incorrect values'),
])
 
Expected output: 
 

 

OHN 34 0 1 2 0.000000 0.000000 0.000000 0.000000 0.000000 512 513 514 515 516 Name: area, Length: 517, dtype: float64 Your code PASSED the code check! 2.006871 4.012592 2.498152 0.000000 0.000000
Transcribed Image Text:OHN 34 0 1 2 0.000000 0.000000 0.000000 0.000000 0.000000 512 513 514 515 516 Name: area, Length: 517, dtype: float64 Your code PASSED the code check! 2.006871 4.012592 2.498152 0.000000 0.000000
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