MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with missing values. Which THREE methods are appropriate for handling missing data during EDA and preprocessing?
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
✓
Remove rows with any missing values
(remove rows with any missing values) is appropriate if missing data is random and limited. Option B (impute with mean) is commonly used for numeric features without outliers. Option E (impute with median) is robust to outliers. Option C (replace missing values with 0) is generally not recommended as it can introduce bias unless 0 is a valid value. Option D (ignore missing values and proceed with modeling) is problematic because most algorithms cannot handle missing values and will raise 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.
- ✓
Remove rows with any missing values
Why this is correct
Listwise deletion is acceptable if missing is MCAR and few rows.
- ✓
Impute missing values with the mean of the column
Why this is correct
Mean imputation is simple and common.
- ✗
Replace missing values with 0
Why it's wrong here
Replacing with 0 can distort the distribution.
- ✗
Ignore missing values and proceed with modeling
Why it's wrong here
Most models cannot handle missing values directly.
- ✓
Impute missing values with the median of the column
Why this is correct
Median imputation is robust to outliers.
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