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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is data wrangling?
💡 Hint: Think about the two primary processes involved in initial data handling.
Question 2
Easy
Define feature engineering in your words.
💡 Hint: Consider how this relates to making data more useful for models.
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 is the main goal of data wrangling?
- To analyze data
- To clean and transform data
- To visualize data
💡 Hint: Think about the processes involved before data can be analyzed.
Question 2
True or False: Feature engineering can help reduce overfitting.
- True
- False
💡 Hint: Consider how features impact model complexity.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Assume you have a dataset with a notable number of missing entries for a critical variable. Discuss a comprehensive plan for handling these missing values while ensuring minimal loss of data integrity.
💡 Hint: Reflect on how the type of missing data might guide your approach.
Question 2
Reflect on how overfitting can occur due to irrelevant features in a dataset. From your understanding of feature engineering, propose strategies to avoid this issue.
💡 Hint: Consider the methods available for feature selection that can help with this challenge.
Challenge and get performance evaluation