Practice What Is Data Preprocessing? (5.1) - Chapter 5: Data Preprocessing for Machine Learning

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6 sample questions

Question 1.
What is data preprocessing?
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Think about the steps needed before analyzing data.
Question 2.
Why is handling missing data important?
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Recall the effect of missing information on predictions.
3Question 3.
What does data preprocessing involve?
- Cleaning data
- Visualization of data
- Storing data
- All of the above
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Focus on the faults related to data.
4Question 4.
True or False: All machine learning algorithms can handle missing values without preprocessing.
- True
- False
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Reflect on processing methods needed.
Question 5.
Suppose you have a dataset with over 25% missing values in a crucial feature. How would you address this before modeling?
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Analyze the impact of missing data percentages.
Question 6.
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?
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Reflect on why feature dominance can alter predictions.

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