You've learned the fundamentals of tabular data: features, labels, and data types. Now, let's put that knowledge into practice by identifying these components in different scenarios.
Engagement Message
Ready to test your skills?
Type
Multiple Choice
Practice Question
Imagine you're building a model to predict whether a customer will renew their subscription. Which of the following would be the label?
A. Customer Age B. Monthly Bill Amount C. Subscription Status (Renewed/Canceled) D. Last Login Date
Suggested Answers
- A
- B
- C - Correct
- D
Type
Sort Into Boxes
Practice Question
Sort these attributes into the correct feature type.
Labels
- First Box Label: Numerical Feature
- Second Box Label: Categorical Feature
First Box Items
- Age
- Account Balance
- Years as Customer
Second Box Items
- City
- Product Category
- Job Title
Type
Fill In The Blanks
Markdown With Blanks
Let's spot some data quality issues. Fill in the blanks to identify the problems in this dataset description.
In the 'Age' column, one entry is blank, which is a [[blank:missing value]]. In the 'Country' column, we see "USA", "U.S.A.", and "United States", which is an [[blank:inconsistent format]] issue.
Suggested Answers
- missing value
- inconsistent format
- error
- label
Type
Swipe Left or Right
Practice Question
When working with real estate data, some attributes work well as features while others could serve as labels for different prediction models. Sort these attributes based on their typical role.
Labels
- Left Label: Good Feature
- Right Label: Potential Label
Left Label Items
- Square Footage
- Number of Bedrooms
- Neighborhood
- Year Built
Right Label Items
- Sale Price
- Rental Income
- Time on Market
- Assessed Tax Value
Type
Multiple Choice
Practice Question
Look at this small dataset. What is the most obvious data quality problem?
A. Missing values B. Inconsistent formatting C. An outlier or error in the 'Age' column D. The 'Name' column should be numerical
Suggested Answers
- A
- B
- C - Correct
- D
