Practice Step 2: Data Preprocessing (9.3) - Chapter 9: End-to-End Machine Learning Project – Predicting Student Exam Performance

Practice: Step 2: Data Preprocessing

Lesson 9.3End-to-End Machine Learning Project – Predicting Student Exam Performance

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9 questions

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Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is the purpose of mapping categorical variables?
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Think of why numbers are easier for algorithms than words.
Question 2.
Name the pandas function used for mapping.
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What do you use in Python to transform data?
3Question 3.
What is the mapping for 'no' in the context of 'preparation_course'?
- 1
- 0
- 2
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Remember the mapping assigned in the data preprocessing step.
4Question 4.
Is it true that without preprocessing, machine learning models cannot process categorical data?
- True
- False
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Think about what type of data is required for algorithms to work.
Question 5.
Given a dataset with multiple categorical features, design a mapping strategy that efficiently converts these into numerics. Consider how you would handle unseen categories when applying your mapping.
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Think about how categories can change over time and how you will maintain integrity in your model.
Question 6.
Reflect on a dataset you encountered. Identify a categorical feature and explain how you would map it. Discuss the implications of not mapping it correctly.
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Consider the impact of each category on your data analysis.

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