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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: Der ter ORSA in 1 1 Mehr V wy RU ye ye 2121 PR TA he 1 ST 21 1 1 . Sh . 1 w 19 31 15 M 16 11 18 Ash HN 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 . . . . 2. tv AM W MacBook Air 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 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. ProfChannel SMS SO 3 : PrutChannel - Phone PretChannel Email Discuss the strength of the relationships between the feature variable and target variable. Would you include the feature variable in a predictive model? Your reasoning should be based on the observations in the plots. Question 14 pts 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: PRE QUANNEL Oce Student ID 1 2 3 4 5 GENDERAGE LOC female female 57 al made 21 nal fumate make 55 HEALTH HEALTH MOTOR MOTO HEALTH HEALTH DEP Ders VALUE INS TYPE ADULTS Kios yes 42612 yes Plac 1 22. Plan 1 2 yes yes 21. MO Plan 1 3 yes 13.976 no Doctor Sheril Paine nul 19 51 . IN yes 14 15 Manager male 16 Farmer make 17 female 15 Analy mate 12.799 phone phone you 1 1-4767 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 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 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. PretChannel - SMS $ 01 od Gander PrefChannel Phone Gende PreChannel Email Gender PrefChannel - SMS 50 0.6 Co Donely 02 01 Od Gender PretChannel Phone PretChannel Email Discuss the strength of the relationships between the feature variable and target variable. Would you include the feature variable in a predictive model? Your reasoning should be based on the observations in the plots. Your