Data Cleaning Techniques Quiz

Quiz covering Feature Engineering

Questions in this quiz

A preview of the 5 questions covered. Start the quiz above to answer them, check your score, and read the explanations.

1. 1

   When handling missing numerical data, which of the following is the most common 'easy' approach to fill gaps without significantly altering the dataset's distribution?

2. 2

   You are preparing a dataset where a feature represents 'Annual Income' and contains several extreme outliers. Which scaling technique would be most appropriate to ensure these outliers do not disproportionately influence your model?

3. 3

   When performing One-Hot Encoding on a categorical feature with high cardinality (e.g., 'Zip Code'), what is the primary risk?

4. 4

   Why is it important to perform data splitting (train/test split) BEFORE applying data normalization or imputation techniques?

5. 5

   You are dealing with a dataset where a feature has a highly skewed distribution (long tail). You decide to apply a log transformation. What is the mathematical requirement for this operation to be valid?