I have a massive dataset where nearly 30% of the "annual income" column is missing. In my Data Science workflow, should I just drop those rows, or is "Mean Imputation" still considered acceptable? I’m worried that filling in the blanks might introduce too much noise and lead to a model that doesn't generalize well.
3 answers
Mean imputation is generally discouraged because it artificially reduces the variance of your data and ignores relationships between variables. If the data is "Missing At Random" (MAR), a much better approach is Multiple Imputation by Chained Equations (MICE). MICE models each variable with missing values as a function of the other variables, providing a much more realistic estimation. Alternatively, if you're using tree-based models like XGBoost, they can often handle missing values natively without any imputation. Always visualize the "missingness" first to see if there is a pattern—missing income might be a signal in itself (e.g., very high earners often hide their income).
Visualizing "missingness" is a great tip. Have you tried using a "shadow matrix" or a heatmap to see if the missing income values correlate with other features like "employment status"?
For 30% missing data, dropping the rows is usually a bad idea as you lose too much information. K-Nearest Neighbors (KNN) imputation is often a solid middle ground.
Good point, Tag. KNN is much more sophisticated than mean imputation because it looks at "similar" people to guess the missing value, though it is computationally expensive for huge datasets.
We used a Nullity Correlation heatmap and found that the missing income was highly correlated with "self-employed" status. This told us the data wasn't missing at random; people were likely choosing not to report it. Instead of just imputing a number, we decided to create a new binary feature called "Income_Reported" (Yes/No). This actually improved our model's predictive power because the "fact" that someone didn't report their income was a very strong predictor for the specific financial behavior we were trying to model in the first place.