Practice Handling Missing Values (1.4.3) - ML Fundamentals & Data Preparation
Students

Academic Programs

AI-powered learning for grades 8-12, aligned with major curricula

Professional

Professional Courses

Industry-relevant training in Business, Technology, and Design

Games

Interactive Games

Fun games to boost memory, math, typing, and English skills

Handling Missing Values

Practice - Handling Missing Values

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is a missing value?

💡 Hint: Think about what happens when data is not recorded.

Question 2 Easy

What is row-wise deletion?

💡 Hint: Consider how it's different from deleting columns.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What are the two main strategies for handling missing values?

Deletion and Imputation
Filtering and Aggregation
Regression and Classification

💡 Hint: Think about how we manage incomplete data.

Question 2

True or False: Mean imputation can distort relationships in data.

True
False

💡 Hint: Consider the impact of using the average on diverse datasets.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

A dataset has 25% missing values in a crucial numeric column. Discuss whether you would choose deletion or imputation and justify your answer.

💡 Hint: Think about how critical the data is for your analysis.

Challenge 2 Hard

After conducting K-NN imputation, you observe that the variance of the column has significantly decreased. What might be the reason, and how could this affect your analysis?

💡 Hint: Consider how K-NN works in relation to input data points.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.