Practice Step 1: Data Exploration (9.2) - Chapter 9: End-to-End Machine Learning Project – Predicting Student Exam Performance

Practice: Step 1: Data Exploration

Lesson 9.2End-to-End Machine Learning Project – Predicting Student Exam Performance

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9 questions

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2 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 does the command df.describe() do?
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Think about the summary you can get about numbers.
Question 2.
What is the purpose of examining categorical variables?
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Consider why we investigate different groups in data.
3Question 3.
What is the purpose of df.info() in data analysis?
- To display summary statistics
- To show information about DataFrame structure
- To count unique values in a column
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Think about what foundational insights you get from a dataset.
4Question 4.
True or False: Understanding descriptive statistics is important before starting machine learning.
- True
- False
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Consider what helps guide model development.
Question 5.
Suppose your attendance column has missing values. How would you approach imputation? Select a strategy and justify your choice.
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Think about which measure reflects your data's distribution best.
Question 6.
If you find that most students in preparation_course passed, while those who didn’t often failed, how could this information guide your machine learning model?
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Consider how features impact model decisions.

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