Answered: TODO 11 Using the clean_df split our…

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

See similar textbooks

TODO 11

Using the clean_df split our columns into features and labels.

Index/slice our label 'area' and store the output into the variable y.
Index/slice all other features EXCEPT 'area' into the variable X. To do so you can use the Pandas DataFrame drop() method or slicing with iloc, loc or [ ].

# TODO 11.1
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'),
])

# TODO 11.2
X =
display(X)

todo_check([
(X.shape == (517, 29), 'X does not have the correct shape of (517, 29)! Make sure the `area` column is not included!'),
(np.all(np.isclose(X.values[-5:, -4], np.array([27.8, 21.9, 21.2, 25.6, 11.8]),rtol=.01)),'X has the incorrect values'),
])

Expert Solution

Step 1

In this task, the goal is to split the clean_df DataFrame into two separate variables y and X:

y is the label data, which is the "area" column of the clean_df DataFrame.
X is the feature data, which is all the columns of clean_df except the "area" column.

Trending now

This is a popular solution!

Step by step

Solved in 4 steps with 1 images

SEE SOLUTION Check out a sample Q&A here

Knowledge Booster

Learn more about

Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.

Similar questions