Practice Normalization (min-max Scaling) (5.8.1) - Data Cleaning and Preprocessing

Practice: Normalization (Min-Max Scaling)

Lesson 5.8.1Data Cleaning and Preprocessing

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9 questions

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6 sample questions

Question 1.
What is normalization in data analysis?
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Think about why we might want to adjust values.
Question 2.
What does Min-Max Scaling do?
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Consider the limits of the new scale.
3Question 3.
What does Min-Max Scaling do to the data?
- Brings data to a range of [0
- 1]
- Removes outliers
- Standardizes data
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Think about how we order the data.
4Question 4.
True or False: Normalization is unnecessary for all machine learning algorithms.
- True
- False
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Consider models that distance features in their calculations.
Question 5.
Imagine you have a dataset consisting of people's ages and incomes. The ages range from 10 to 80 years, and incomes from $20,000 to $120,000. If you were to apply Min-Max Scaling, show how the transformed values for age 40 and an income of $50,000 would appear.
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Apply the Min-Max Scaling formula for each value.
Question 6.
Provide a scenario where applying Min-Max Scaling can lead to misleading results and explain why.
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Reflect on the effects of extreme values in normalization.

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