Practice Missing Values (6.4.1) - Data Exploration - CBSE 10 AI (Artificial Intelleigence)

Practice: Missing Values

Lesson 6.4.1Data Exploration

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9 questions

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3 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What are missing values?
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Think about situations where data might be incomplete.
Question 2.
Name one common cause of missing data.
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Consider how people might accidentally skip information.
3Question 3.
What is a common cause of missing data?
- Human error
- Data redundancy
- Data modeling
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Think about processes that involve human interaction.
4Question 4.
True or False: Ignoring missing values can lead to biased data analysis.
- True
- False
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Reflect on the importance of complete datasets.
Question 5.
How would you write a report on customer satisfaction if your data had 20% missing entries? What strategies would you use to fill in the missing pieces while maintaining the integrity of the analysis?
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Consider various data filling techniques and discuss their impact.
Question 6.
You are working with a dataset that has undergone cleaning, and 10% of the rows have missing values for a significant variable (e.g., income). Discuss how this absence may influence your predictive analytics model.
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Think about how different datasets can affect analytical outcomes.

5 more questions in this section. Enroll free to practise them all.