MLS-C01 Exploratory Data Analysis Practice Question
A data engineer is exploring a dataset with 1 million rows and 50 features. They notice that some features have missing values. The 'Age' column has 5% missingness, and 'Income' has 20% missingness. The target variable is 'LoanDefault' (binary). The engineer wants to impute missing values. Which of the following strategies is most appropriate?
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 'Age' with median and 'Income' with median.
Median imputation is robust to outliers and appropriate for numerical features like Age and Income. Using median preserves the central tendency without being affected by extreme values. Option B is incorrect because mode is suitable for categorical features, not continuous numerical ones. Option C is incorrect because k-NN imputation, while possible, is more complex and typically used after simpler methods in EDA. Option D is incorrect because dropping rows with missing values would discard a significant portion of the dataset (up to 25% if missingness is independent), which is not ideal for initial analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Impute missing 'Age' with median and 'Income' with median.
Why this is correct
Median is robust to outliers and suitable for skewed distributions.
- ✗
Impute missing 'Age' with mode and 'Income' with mode.
Why it's wrong here
Mode is appropriate for categorical, not continuous, features.
- ✗
Use a k-NN model to predict missing values.
Why it's wrong here
k-NN imputation is computationally expensive for large datasets and not necessary for initial EDA.
- ✗
Drop all rows with missing values.
Why it's wrong here
This would discard a significant portion of data and may introduce bias.
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