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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does data preprocessing involve?
💡 Hint: Think about what happens to data before it's inputted into a model.
Question 2
Easy
Why is handling missing data important?
💡 Hint: Remember the impact of 'Garbage in, garbage out.'
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
Which of the following is NOT a reason for data preprocessing?
- Remove noise
- Encode categorical data
- Increase model complexity
💡 Hint: Focus on the purpose of preprocessing.
Question 2
True or False: Missing values can be left unhandled in a dataset for a machine learning algorithm.
- True
- False
💡 Hint: Think about the implications of having missing values.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a dataset with numerous NaNs, propose a comprehensive strategy to handle the missing values, detailing your steps and rationale.
💡 Hint: Consider both the volume and the significance of the missing data when deciding how to handle it.
Question 2
You have a dataset where 'Country' has high cardinality. Discuss the trade-offs of using OneHotEncoder versus Label Encoding for preprocessing.
💡 Hint: Think about how the algorithm perceives numerical relationships.
Challenge and get performance evaluation