You are asked to choose and tune models for forecasting marketplace demand and detecting fraud in a highly imbalanced dataset.

Explain how ordinary least squares linear regression works and state its key assumptions. Compare gradient-boosted trees, random forests, and bagging; when would you prefer each? Your positive class is 0.2 % of the data.  How would you handle this imbalance during model training and evaluation? Describe a full workflow for building a time-series forecasting model when seasonality and trend are present.

Cover data preprocessing, feature engineering, resampling/weighting, proper metrics, and cross-validation for temporal data.

Amazon Data Scientist interview question: Choose Models for Imbalanced Data and Time-Series Forecasting. {"blocks": [{"key": "9afc1a9b", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {...