Engineer and Impute ZIP Features

Q: Engineer and Impute ZIP Features

This question evaluates feature engineering, missing-data imputation, high-cardinality encoding, and considerations of fairness, privacy, and data leakage in predictive modeling.

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Question

You are building a predictive model for a product team. For some users, you have address fields such as street, city, state, and ZIP code.

What features would you derive directly from the address, and what external public datasets could you join on ZIP code or geography to create additional model features?
If ZIP code is missing for some users, how would you handle those cases?

In your answer, discuss:

useful geographic and socioeconomic features
high-cardinality encoding choices
when to drop vs. impute missing ZIP codes
missingness as a potentially informative signal
fairness, privacy, and leakage risks
how you would evaluate whether these features improve model performance

Engineer and Impute ZIP Features

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