MLS-C01 Exploratory Data Analysis Practice Question
A team is building a model to predict house prices. They have a dataset with features like 'SquareFootage', 'Bedrooms', 'YearBuilt', and 'Neighborhood'. They notice that 'SquareFootage' has a few extreme values (e.g., 50,000 sq ft) that are likely data entry errors. They want to handle these outliers without losing all the data. Which of the following approaches is most robust?
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
✓
Cap 'SquareFootage' at the 99th percentile value.
Capping 'SquareFootage' at the 99th percentile limits extreme values while retaining most data points, making it robust against data entry errors. Option B is incorrect because replacing extreme values with the mean distorts the distribution and can bias the model. Option C is incorrect because a log transformation does not fix data entry errors; it only changes the scale. Option D is incorrect because removing rows with values above 3 standard deviations from the mean may discard valid data points and is not robust when the data contains errors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Cap 'SquareFootage' at the 99th percentile value.
Why this is correct
Capping limits extremes while retaining the records.
- ✗
Replace extreme values with the mean of 'SquareFootage'.
Why it's wrong here
Mean imputation can distort the distribution and is not robust.
- ✗
Apply log transformation to 'SquareFootage'.
Why it's wrong here
Log transformation reduces skew but does not fix erroneous extreme values.
- ✗
Remove rows where 'SquareFootage' is above 3 standard deviations from the mean.
Why it's wrong here
This removes data points that could be legitimate large houses.
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