Handle Missing Values and Choose ML Algorithms Wisely
Company: Amazon
Role: Data Scientist
Category: Machine Learning
Difficulty: medium
Interview Round: Technical Screen
##### Scenario
General ML theory and practice questions during a technical interview.
##### Question
a) How do you handle missing values before model training and why?
b) Given a business scenario, how would you choose an appropriate ML algorithm and justify it?
c) Explain Random Forests in lay terms and contrast them with linear regression.
d) Define overfitting vs. underfitting and methods to detect/mitigate each.
##### Hints
Cover imputation, algorithm bias-variance trade-off, ensemble intuition, cross-validation and regularization.
Quick Answer: This question evaluates a data scientist's competencies in data preprocessing (handling missing values), algorithm selection and justification, model interpretation (contrasting Random Forests with linear regression), and understanding of model generalization (overfitting and underfitting).
