2 - Data Wrangling and Feature Engineering
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Practice Questions
Test your understanding with targeted questions
What is data wrangling?
💡 Hint: Think about the two primary processes involved in initial data handling.
Define feature engineering in your words.
💡 Hint: Consider how this relates to making data more useful for models.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What is the main goal of data wrangling?
💡 Hint: Think about the processes involved before data can be analyzed.
True or False: Feature engineering can help reduce overfitting.
💡 Hint: Consider how features impact model complexity.
2 more questions available
Challenge Problems
Push your limits with advanced challenges
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.
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.
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Reference links
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