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

Question

Options

• AUse a linear regression model. Perform one-hot encoding on categorical features, and create
• BUse a boosted tree model. Perform label encoding on categorical features, and transform the date
• CUse an autoencoder model. Perform label encoding on categorical features, and normalize the
• DUse a matrix factorization model. Perform one-hot encoding on categorical features, and create

• A
  76% (16)
• B
  5% (1)
• C
  14% (3)
• D
  5% (1)

Explanation

Linear regression model: This is a suitable choice for predicting continuous variables like product sales. BigQuery ML supports linear regression, making it a practical option for this use One-hot encoding on categorical features: This encoding method is effective for handling categorical variables (e.g., store location, product category) without imposing an ordinal relationship that doesn't exist. Feature engineering for date-related features: Creating features like "day of the week" or "month" helps the model capture temporal patterns in the sales data, which are often significant predictors in retail.

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