DA0-002 Data Analysis Practice Question
A dataset contains a column 'Income' with values in different scales (some in thousands, some in hundreds). What is the best way to standardize this column for use in a machine learning model?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply standard scaling (Z-score normalization)
Standard scaling (Z-score normalization) is the best approach because it transforms the data to have a mean of 0 and a standard deviation of 1, making values on different scales (thousands vs hundreds) directly comparable. Min-max scaling also rescales to [0,1], but it is sensitive to outliers and does not handle different scales as effectively when the distribution is not uniform. Log transformation is for reducing skewness, not for standardizing scales. Removing the column discards useful information.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply min-max scaling to range [0,1]
Why it's wrong here
Min-max scaling is sensitive to outliers and doesn't center data.
- ✓
Apply standard scaling (Z-score normalization)
Why this is correct
Standard scaling centers and scales data, suitable for inconsistent scales.
- ✗
Apply log transformation
Why it's wrong here
Log transformation reduces skew but does not standardize scale.
- ✗
Remove the column
Why it's wrong here
Removing data is not best practice if transformation is possible.
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