Practice Handling Missing Values (2.2) - Data Wrangling and Feature Engineering

Practise this section

9 questions

Flashcard drill
5 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
Sign up to start the exercise
Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
Sign up to start the quiz

6 sample questions

Question 1.
What does MCAR stand for?
Show hintHide hint
Think about how randomness can affect missing data.
Question 2.
Name one technique to handle missing data.
Show hintHide hint
This method involves removing data.
3Question 3.
What does MAR stand for?
- Missing At Random
- Missing All Random
- Missing And Random
Show hintHide hint
This relates to the characteristics of the recorded data.
4Question 4.
True or False: Deletion is the only method to handle missing values.
- True
- False
Show hintHide hint
Consider the diversity in techniques.
Question 5.
You have a dataset on customer spending habits with missing values labeled as MNAR due to high spending customers dropping out of the survey. How would you handle such missing values?
Show hintHide hint
Consider approaches that focus on understanding customer behavior.
Question 6.
Describe a situation in which using mean imputation might be acceptable and when it would be inappropriate.
Show hintHide hint
Think about how distributions affect statistical measures.

5 more questions in this section. Enroll free to practise them all.