Practice What Is Feature Engineering? (2.4.1) - Data Wrangling and Feature Engineering

Practice: What is Feature Engineering?

Lesson 2.4.1Data Wrangling and Feature Engineering

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9 questions

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6 sample questions

Question 1.
What is feature engineering?
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Think about how transforming data could impact model predictions.
Question 2.
Name one technique used in feature extraction.
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Consider how we turn text into numbers.
3Question 3.
What does feature engineering primarily focus on?
- Cleaning data
- Creating and modifying features
- Visualizing data
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Consider the aspect of data handling that enhances model insights.
4Question 4.
True or False: Feature selection is unimportant because all the features in a dataset should be used in a model.
- True
- False
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Reflect on why simplifying a model can lead to better performance.
Question 5.
You're tasked with improving the accuracy of a model predicting house prices. Your dataset includes features such as square footage, number of bedrooms, and location. What new feature could you construct, and why would it benefit the model?
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Think about ratios that might offer better insights.
Question 6.
Analyze a dataset containing customer transactions and brainstorm three new features that could be valuable for predicting customer lifetime value.
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What metrics can give insight into long-term customer relationships?

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