Correlation analysis in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the Pearson correlation coefficient between two lists.

ML Python

import numpy as np
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
correlation = np.[1](x, y)
print(correlation)

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Acorrcoef

Bmean

Csum

Dstd

Attempts:

3 left

2fill in blank

medium

Complete the code to compute the correlation matrix of a pandas DataFrame.

ML Python

import pandas as pd
data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
df = pd.DataFrame(data)
correlation_matrix = df.[1]()
print(correlation_matrix)

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Acorr

Bsum

Cmean

Dstd

Attempts:

3 left

3fill in blank

hard

Fix the error in the code to correctly calculate the correlation between two numpy arrays.

ML Python

import numpy as np
x = np.array([1, 2, 3, 4])
y = np.array([4, 3, 2, 1])
correlation = np.corrcoef(x, [1])
print(correlation[0, 1])

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Anp.mean

Bnp.array

Cx

Dy

Attempts:

3 left

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

ML Python

words = ['cat', 'house', 'tree', 'a', 'dog']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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Alen(word)

B>

C<

Dword

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their lengths for words longer than 2 characters.

ML Python

words = ['apple', 'be', 'cat', 'dog']
result = { [1]: [2] for word in words if len(word) [3] 2 }
print(result)

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Aword.upper()

Blen(word)

C>

Dword

Attempts:

3 left