Data distributions and outliers in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the mean of the data list.

ML Python

mean_value = sum(data) / [1]

Drag options to blanks, or click blank then click option'

Amax(data)

Bsum(data)

Clen(data)

Dmin(data)

Attempts:

3 left

2fill in blank

medium

Complete the code to calculate the median of a sorted list named sorted_data.

ML Python

mid_index = len(sorted_data) // 2
median = sorted_data[[1]] if len(sorted_data) % 2 != 0 else (sorted_data[mid_index - 1] + sorted_data[mid_index]) / 2

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Amid_index

B0

Clen(sorted_data)

Dmid_index + 1

Attempts:

3 left

3fill in blank

hard

Fix the error in the code to detect outliers using the interquartile range (IQR).

ML Python

iqr = q3 - q1
lower_bound = q1 - 1.5 * [1]
upper_bound = q3 + 1.5 * iqr
outliers = [x for x in data if x < lower_bound or x > upper_bound]

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Aiqr

Bq1

Cq3

Dmean

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

lengths = {word: [1] for word in words if len(word) [2] 3}

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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 4 characters.

ML Python

result = { [1]: [2] for w in words if len(w) [3] 4}

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Aw.upper()

Blen(w)

C>

Dw

Attempts:

3 left