A retail company needs to create a machine learning model to predict product sales using historical data from BigQuery, which includes features like date, location, product category, and promotions. The goal is to choose the most accurate model and feature engineering techniques.
The suggested answer is B, using a boosted tree model with label encoding and numerical date transformation.
You work at a retail company, and are tasked with developing an ML model to predict product sales. Your company’s historical sales data is stored in BigQuery and includes features such as date, store location, product category, and promotion details. You need to choose the most effective combination of a BigQuery ML model and feature engineering to maximize prediction accuracy. What should you do?
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