Practice Data Wrangling and Feature Engineering - 2 | 2. Data Wrangling and Feature Engineering | Data Science Advance
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skills—perfect for learners of all ages.

Typing
Memory
Math
English Adventures
Knowledge

2 - Data Wrangling and Feature Engineering

Enroll to start learning

You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is data wrangling?

💡 Hint: Think about the two primary processes involved in initial data handling.

Question 2

Easy

Define feature engineering in your words.

💡 Hint: Consider how this relates to making data more useful for models.

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 main goal of data wrangling?

  • To analyze data
  • To clean and transform data
  • To visualize data

💡 Hint: Think about the processes involved before data can be analyzed.

Question 2

True or False: Feature engineering can help reduce overfitting.

  • True
  • False

💡 Hint: Consider how features impact model complexity.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Assume you have a dataset with a notable number of missing entries for a critical variable. Discuss a comprehensive plan for handling these missing values while ensuring minimal loss of data integrity.

💡 Hint: Reflect on how the type of missing data might guide your approach.

Question 2

Reflect on how overfitting can occur due to irrelevant features in a dataset. From your understanding of feature engineering, propose strategies to avoid this issue.

💡 Hint: Consider the methods available for feature selection that can help with this challenge.

Challenge and get performance evaluation