Practice Data Wrangling And Feature Engineering (2) - Data Wrangling and Feature Engineering

Practice: Data Wrangling and Feature Engineering

Lesson 2Data Wrangling and Feature Engineering

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10 questions

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6 sample questions

Question 1.
What is data wrangling?
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Think about the two primary processes involved in initial data handling.
Question 2.
Define feature engineering in your words.
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Consider how this relates to making data more useful for models.
3Question 3.
What is the main goal of data wrangling?
- To analyze data
- To clean and transform data
- To visualize data
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Think about the processes involved before data can be analyzed.
4Question 4.
True or False: Feature engineering can help reduce overfitting.
- True
- False
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Consider how features impact model complexity.
Question 5.
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.
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Reflect on how the type of missing data might guide your approach.
Question 6.
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.
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Consider the methods available for feature selection that can help with this challenge.

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