You work on the data science team for a multinational beverage company. You need to develop an ML model to predict the company's profitability for a new line of naturally flavored bottled waters in di

Question

Options

• AUse latitude, longitude, and product type as features. Use profit as model output.
• BUse latitude, longitude, and product type as features. Use revenue and expenses as model
• CUse product type and the feature cross of latitude with longitude, followed by binning, as features.
• DUse product type and the feature cross of latitude with longitude, followed by binning, as features.

• A
  4% (1)
• B
  4% (1)
• C
  76% (19)
• D
  16% (4)

Explanation

A feature cross of latitude × longitude encodes geographic location as a single composite feature, and applying binning afterward discretizes the continuous geographic space into regional buckets - capturing regional market effects far more effectively than raw floating-point coordinates that a linear model would treat independently. The model output should be profit directly, since profit is available in the historical data and is the business metric of interest. Predicting revenue and expenses separately (as in choices B/D) adds unnecessary complexity and requires a post-processing step to compute profit. Option A uses raw lat/lon without a feature cross, missing the interaction between the two dimensions that defines a geographic region.

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