Practice Data Transformation Techniques - 2.3 | 2. Data Wrangling and Feature Engineering | Data Science Advance
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2.3 - Data Transformation Techniques

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is normalization?

💡 Hint: Think about how you adjust numbers to make them fit within a range.

Question 2

Easy

Define one-hot encoding.

💡 Hint: Consider how categories can be represented as yes/no for machine learning input.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

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.

💡 Hint: Think about scaling the data values.

Question 2

True or False: One-hot encoding creates multiple binary columns for each category.

  • True
  • False

💡 Hint: Consider how categories can be restructured.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

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.

💡 Hint: Think through the nature of the data.

Question 2

Explain how log transformation might impact a regression analysis model based on skewed data. What are the potential benefits and drawbacks?

💡 Hint: Consider both analysis outcomes and interpretation challenges.

Challenge and get performance evaluation