Practice Data Cleaning And Preprocessing (5) - Data Cleaning and Preprocessing

Practice: Data Cleaning and Preprocessing

Lesson 5Data Cleaning and Preprocessing

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10 questions

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3 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
4 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What function would you use to check for missing values in a DataFrame?
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Think of which method allows you to see null values.
Question 2.
How would you remove duplicates from a DataFrame?
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Look for a method that deals with duplication.
3Question 3.
What is the primary purpose of data cleaning?
- To enhance data visualization
- To ensure data accuracy
- To increase data size
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Think about why we start any analysis.
4Question 4.
True or False: Dropping rows with missing data is always the best solution.
- True
- False
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Consider the balance between data loss and integrity.
Question 5.
You are given a dataset with missing values, duplicates, and outliers. Describe a stepwise approach you would take to preprocess the data for analysis.
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Think through each preprocessing step logically and sequentially.
Question 6.
A dataset shows high variance in income values that negatively impact a predictive model's performance. Propose a solution for this issue.
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Consider techniques that adjust data distributions.

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