Practice Data Transformation Techniques (2.3) - Data Wrangling and Feature Engineering

Practice: Data Transformation Techniques

Lesson 2.3Data Wrangling and Feature Engineering

Practise this section

10 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
4 questions · about 3 min · pass mark 70%. Hints available.
Sign up to start the quiz

6 sample questions

Question 1.
What is normalization?
Show hintHide hint
Think about how you adjust numbers to make them fit within a range.
Question 2.
Define one-hot encoding.
Show hintHide hint
Consider how categories can be represented as yes/no for machine learning input.
3Question 3.
What does normalization achieve in data transformation?
- It adjusts data to have a mean of 0.
- It resizes data to fit within a range, typically [0, 1].
- It converts categorical data into numeric codes.
Show hintHide hint
Think about scaling the data values.
4Question 4.
True or False: One-hot encoding creates multiple binary columns for each category.
- True
- False
Show hintHide hint
Consider how categories can be restructured.
Question 5.
You have a dataset with both categorical and continuous variables. How would you prepare this data for a machine learning model? Include which transformation techniques you would use for each variable type and why.
Show hintHide hint
Think through the nature of the data.
Question 6.
Explain how log transformation might impact a regression analysis model based on skewed data. What are the potential benefits and drawbacks?
Show hintHide hint
Consider both analysis outcomes and interpretation challenges.

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