How do you handle missing values in a dataset?

Missing values should be handled based on why the data is missing, how much is missing, and how important the affected feature is.

There is no single best method for every dataset.

Start by understanding the missingness

Before filling or removing values, check:

• which columns contain missing values
• how many values are missing
• whether missingness is concentrated in certain groups
• whether the missing value itself carries useful information

Missing data can happen because a value was not collected, was not applicable, was lost during processing, or was unavailable at the time.

Common ways to handle missing values

1. Remove rows

Rows with missing values may be removed when:

• only a very small number of rows are affected
• enough data remains after removal
• the missing rows are not systematically different from the rest

Removing too many rows can waste information or introduce bias.

2. Remove columns

A feature may be dropped when most of its values are missing and it provides little useful information.

However, an important feature should not be removed only because it contains some missing values.

3. Impute missing values

Imputation replaces missing values with estimated or representative values.

Common simple approaches include:

• mean imputation for numerical data
• median imputation for numerical data with skew or outliers
• mode imputation for categorical data
• a separate category such as Unknown for some categorical features

More advanced methods can estimate missing values using other features.

Missingness can itself be informative

Sometimes whether a value is missing contains useful predictive information.

For example, the fact that a customer did not provide a certain field may be meaningful.

In such cases, a separate missing-value indicator can be added.

Prevent leakage during imputation

Any imputation value learned from the data should be calculated using the training set only.

For example, if the median is used to fill missing values, calculate the median from the training data and then apply that same value to validation and test data.

Consider models that handle missing values

Some machine learning algorithms can handle missing values directly, depending on the implementation.

Even then, the meaning and pattern of missingness should still be investigated.

In short, handle missing values by first understanding their cause and extent, then choose an approach such as removal, imputation, missing indicators, or native model handling while avoiding data leakage.