Practice Techniques To Handle Missing Data (2.2.2) - Data Wrangling and Feature Engineering

Practice: Techniques to Handle Missing Data

Lesson 2.2.2Data Wrangling and Feature Engineering

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9 questions

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4 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 is the purpose of deletion in handling missing values?
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Think about what happens when you remove data.
Question 2.
Describe mean imputation.
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What do we calculate to find the 'mean'?
3Question 3.
What is the primary method for filling in small amounts of missing data?
- Deletion
- KNN
- MICE
- Mean Imputation
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Think about removing minimal versus large amounts of missing information.
4Question 4.
True or False: Mean imputation can introduce bias if the data distribution is skewed.
- True
- False
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Consider how averages behave in skewed distributions.
Question 5.
You have a dataset with 20% missing values in a feature. When considering deletion and imputation, what factors would influence your decision?
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Reflect on the balance between data quantity and quality.
Question 6.
Design an experiment where you use KNN for imputation, outlining your dataset, chosen neighbors, and the method to evaluate accuracy.
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Remember to tune the number of neighbors for optimal performance.

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