MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is working with a dataset that has missing values in 30% of rows for a categorical feature 'city'. Which EDA step should be performed before deciding on imputation?
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
✓
Check if missingness is related to other features or random
Before deciding on imputation for the 'city' feature, the first exploratory data analysis (EDA) step is to investigate the pattern of missingness. Option A is correct because you must determine whether the missing data are Missing Completely at Random (MCAR), Missing at Random (MAR), or Missing Not at Random (MNAR). This involves checking if missingness in 'city' is related to other features or is random. Understanding the missing mechanism informs the appropriate imputation strategy. Option B (impute with mode) is an imputation method, not a diagnostic step; applying it without prior analysis risks introducing bias. Option C (drop rows) may be valid only if missingness is MCAR and the amount of data loss is acceptable, but it should not be the first step. Option D (label encoding) transforms categorical data and does not address missing values.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Check if missingness is related to other features or random
Why this is correct
Before deciding on imputation, you must investigate the pattern of missingness to determine if it is MCAR, MAR, or MNAR. This involves checking if missingness in 'city' is related to other features or random. Understanding the missing mechanism informs the appropriate imputation strategy.
- ✗
Impute missing values with the mode of the column
Why it's wrong here
Imputing missing values with the mode is an imputation method, not a diagnostic step. Applying it without prior analysis of the missingness pattern risks introducing bias and may lead to incorrect conclusions.
- ✗
Drop all rows with missing values
Why it's wrong here
This is wrong because dropping rows with missing values is a data cleaning action, not an initial EDA step. It may be considered after analyzing the missingness pattern, but is not the first step.
- ✗
Encode the city feature using label encoding
Why it's wrong here
This is wrong because label encoding is a categorical data transformation, not related to handling missing values. It does not address the missing data issue.
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