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…

Option A is incorrect because using latitude, longitude, and product type as features, and using profit

as model output is not the best way to develop an ML model to predict the company’s profitability

for a new line of naturally flavored bottled waters in different locations. This option does not capture

the interaction between latitude and longitude, which may affect the profitability of the product. For

example, the same product may have different profitability in different regions, depending on the

climate, culture, or p

of the customers. Moreover, this option does not account for the

granularity of the location data, which may be too fine or too coarse for the model. For example,

using the exact coordinates of a city may not be meaningful, as the profitability may vary within the

city, or using the country name may not be informative, as the profitability may vary across the

country.

Option B is incorrect because using latitude, longitude, and product type as features, and using

revenue and expenses as model outputs is not a suitable way to develop an ML model to predict the

company’s profitability for a new line of naturally flavored bottled waters in different locations. This

option has the same drawbacks as option A, as it does not capture the interaction between latitude

and longitude, or account for the granularity of the location data. Moreover, this option does not

directly predict the profitability of the product, which is the target variable of interest. Instead, it

predicts the revenue and expenses of the product, which are intermediate variables that depend on

other factors, such as the price, the cost, or the demand of the product. To obtain the profitability, we

would need to subtract the expenses from the revenue, which may introduce errors or uncertainties

in the prediction.

Option C is correct because using product type and the feature cross of latitude with longitude,

followed by binning, as features, and using profit as model output is a good way to develop an ML

model to predict the company’s profitability for a new line of naturally flavored bottled waters in

different locations. This option captures the interaction between latitude and longitude, which may

affect the profitability of the product, by creating a feature cross of these two features. A feature

cross is a synthetic feature that combines the values of two or more features into a single feature1.

This option also accounts for the granularity of the location data, by binning the feature cross into

discrete buckets. Binning is a technique that groups continuous values into intervals, which can

reduce the noise and complexity of the data2. Moreover, this option directly predicts the profitability

of the product, which is the target variable of interest, by using it as the model output.

Option D is incorrect because using product type and the feature cross of latitude with longitude,

followed by binning, as features, and using revenue and expenses as model outputs is not a valid way

to develop an ML model to predict the company’s profitability for a new line of naturally flavored

bottled waters in different locations. This option has the same advantages as option C, as it captures

the interaction between latitude and longitude, and accounts for the granularity of the location data,

by creating a feature cross and binning it. However, this option does not directly predict the

profitability of the product, which is the target variable of interest, but rather predicts the revenue

and expenses of the product, which are intermediate variables that depend on other factors, as

explained in option B.

Reference:

Feature cross

Binning

[Profitability]

[Revenue and expenses]

[Latitude and longitude]

[Product type]