Practice Data Cleaning and Preprocessing - 5 | Data Cleaning and Preprocessing | Data Science Basic
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What function would you use to check for missing values in a DataFrame?

πŸ’‘ Hint: Think of which method allows you to see null values.

Question 2

Easy

How would you remove duplicates from a DataFrame?

πŸ’‘ Hint: Look for a method that deals with duplication.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the primary purpose of data cleaning?

  • To enhance data visualization
  • To ensure data accuracy
  • To increase data size

πŸ’‘ Hint: Think about why we start any analysis.

Question 2

True or False: Dropping rows with missing data is always the best solution.

  • True
  • False

πŸ’‘ Hint: Consider the balance between data loss and integrity.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You are given a dataset with missing values, duplicates, and outliers. Describe a stepwise approach you would take to preprocess the data for analysis.

πŸ’‘ Hint: Think through each preprocessing step logically and sequentially.

Question 2

A dataset shows high variance in income values that negatively impact a predictive model's performance. Propose a solution for this issue.

πŸ’‘ Hint: Consider techniques that adjust data distributions.

Challenge and get performance evaluation