MLS-C01 Exploratory Data Analysis Practice Question
Which TWO of the following are appropriate techniques for handling missing data during exploratory data analysis? (Select TWO.)
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
✓
Impute missing values using mean or median for numerical features
Options C and D are correct. Imputing missing values using mean or median for numerical features (C) is a common technique during EDA to preserve data size. Visualizing the missing data pattern with heatmaps or bar charts (D) helps understand the distribution and mechanism of missingness. Option A is incorrect because ignoring missing values can introduce bias and lead to inaccurate models. Option B is incorrect because replacing with -1 may distort the data distribution and is not a standard practice. Option E is incorrect because deleting all rows with missing values can cause significant data loss, especially if missingness is not random.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Ignore missing values and proceed with modeling
Why it's wrong here
Ignoring missing values can cause errors in modeling.
- ✗
Replace missing values with -1 to indicate missing
Why it's wrong here
Replacing with -1 may introduce artificial patterns.
- ✓
Impute missing values using mean or median for numerical features
Why this is correct
Mean/median imputation is a common EDA technique.
- ✓
Visualize the missing data pattern using heatmaps or bar charts
Why this is correct
Visualizing missing data helps understand the pattern.
- ✗
Delete all rows with any missing values
Why it's wrong here
Deleting all rows may discard valuable data.
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