While performing exploratory data analysis on a dataset, you find that an important categorical feature has 5% null values. You want to minimize the bias that could result from the missing values…

The correct answer is C. Replace the missing values with a placeholder category indicating a missing value. This approach is often referred to as "imputing" missing values, and it is a common technique for dealing with missing data in categorical features. By using a placeholder category, you explicitly indicate that the value is missing, rather than assuming that the missing value…

Submitted by rania.sa· Apr 18, 2026Data processing and feature engineering

Question

While performing exploratory data analysis on a dataset, you find that an important categorical feature has 5% null values. You want to minimize the bias that could result from the missing values. How should you handle the missing values?

Options

ARemove the rows with missing values, and upsample your dataset by 5%.
BReplace the missing values with the feature's mean.
CReplace the missing values with a placeholder category indicating a missing value.
DMove the rows with missing values to your validation dataset.

How the community answered

(26 responses)

A
8% (2)
B
12% (3)
C
81% (21)

Explanation

This approach is often referred to as "imputing" missing values, and it is a common technique for dealing with missing data in categorical features. By using a placeholder category, you explicitly indicate that the value is missing, rather than assuming that the missing value is a particular category. This can help to minimize bias in downstream analyses, as it does not introduce any assumptions about the missing data that could bias your results.

Topics

#Missing Data Imputation#Categorical Data#Bias Reduction#Feature Engineering

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