Optimize Feature Selection and Handling in Machine Learning Models
Company: Amazon
Role: Data Scientist
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
##### Scenario
Feature engineering for a customer-propensity machine-learning model.
##### Question
When do we need to standardize or normalize variables? How would you handle numeric predictors that contain many null or zero values? If several features are highly correlated, how would you decide which one(s) to keep?
##### Hints
Discuss scaling impact, imputation vs flagging, and multicollinearity remedies.
Quick Answer: This question evaluates feature engineering competencies—scaling and normalization effects on different algorithms, strategies for handling missing or zero-inflated numeric predictors, and approaches to detect and remediate multicollinearity for feature selection in predictive models.
