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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is data preprocessing?
💡 Hint: Think about the steps needed before analyzing data.
Question 2
Easy
Why is handling missing data important?
💡 Hint: Recall the effect of missing information on predictions.
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 data preprocessing involve?
- Cleaning data
- Visualization of data
- Storing data
- All of the above
💡 Hint: Focus on the faults related to data.
Question 2
True or False: All machine learning algorithms can handle missing values without preprocessing.
- True
- False
💡 Hint: Reflect on processing methods needed.
Solve and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Suppose you have a dataset with over 25% missing values in a crucial feature. How would you address this before modeling?
💡 Hint: Analyze the impact of missing data percentages.
Question 2
If a feature comprises numbers in differing ranges (e.g., 1-10 and 1000-10000), how would you prepare this data for a machine learning model?
💡 Hint: Reflect on why feature dominance can alter predictions.
Challenge and get performance evaluation