Creating interaction features in ML Python - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a new feature by multiplying two existing features.

ML Python

df['interaction'] = df['feature1'] [1] df['feature2']

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

A*

B+

C-

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of multiplication.

Using division which may cause errors if denominator is zero.

Multiplying two features creates an interaction feature that captures combined effects.

2fill in blank

medium

Complete the code to create interaction features for all pairs of columns in the DataFrame.

ML Python

from itertools import combinations

for col1, col2 in combinations(df.columns, 2):
    df[f'{col1}_x_{col2}'] = df[col1] [1] df[col2]

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

A+

B*

C/

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition which does not capture interaction.

Using division which can cause errors.

Multiplying pairs of features creates interaction terms that capture combined effects.

3fill in blank

hard

Fix the error in the code to correctly create an interaction feature between 'age' and 'income'.

ML Python

df['age_income_interaction'] = df['age'] [1] df['income']

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

A+

B/

C*

D-

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of multiplication.

Using division which may cause errors.

The interaction feature should multiply 'age' and 'income' to capture their combined effect.

4fill in blank

hard

Fill both blanks to create a dictionary of interaction features for words longer than 3 characters.

ML Python

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

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

A**

B%

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using modulus '%' instead of power '**'.

Using '<' instead of '>' for filtering.

The code squares the length of words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary of interaction features where keys are uppercase words and values are their lengths if length is greater than 4.

ML Python

interaction_features = [1]: [2] for word in words if len(word) [3] 4}

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

Aword.upper()

Blen(word)

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for filtering.

Using word instead of word.upper() for keys.

The dictionary maps uppercase words to their lengths for words longer than 4 characters.