An analytics consultant at an insurance company has gathered data to train a model to predict the best communications channel to contact a potential customer with an offer of a new insurance product. The following table contains an extract of the data.

The variables in this dataset are defined as follows:

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AGE: The customer$$s age

GENDER: The customer$$s gender $($male or female$)$

LOC: The customer$$s location $($rural or urban$)$

OCC: The customer$$s occupation

MOTORINS: Whether the customer holds a motor insurance policy with the company $($yes or no$)$

MOTORVALUE: The value of the car on the motor policy

HEALTHINS: Whether the customer holds a health insurance policy with the company $($yes or no$)$

HEALTHTYPE: The type of the health insurance policy $($PlanA$,$ PlanB, or PlanC$)$

HEALTHDEPSADULTS: How many dependent adults are included on the health insurance policy

HEALTHDEPSKIDS: How many dependent children are included on the health insurance policy

PREFCHANNEL: Preferred channel

The graphs below illustrate the relationship between a feature variable $($GENDER$)$ and the target variable $($PREFCHANNEL$).$ There are four plots: one plot of the distribution of values of the feature variable in the entire dataset, and three plots illustrating the distribution of the feature variable for each category of the target variable.

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Based on the strength of relationship between the feature variable $($GENDER$)$ and target variable $($PREFCHANNEL$),$ would you include GENDER in a predictive model to predict PREFCHANNEL? In up to $3$ sentences, describe your reasoning, which should be based on the observations in the plots.