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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does NaN stand for in a dataset?
💡 Hint: Think about what missing data implies.
Question 2
Easy
What is one method to handle missing data?
💡 Hint: Consider ways to eliminate incompleteness.
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 does NaN stand for?
💡 Hint: Consider what 'missing' means.
Question 2
What is one method to handle missing data?
- Remove rows with NaN
- Ignore rows
- Data augmentation
💡 Hint: Think about how completeness affects data quality.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a dataset with missing values in multiple columns, provide a Python code to impute using both mean and median.
💡 Hint: Remember to import the necessary libraries first.
Question 2
Discuss a scenario where imputing data could introduce bias and provide a suggestion to mitigate this.
💡 Hint: Think about distribution shapes and imputation differences.
Challenge and get performance evaluation