Background In the modern world, customer details are very important to suggest any product for buying. Gender, age and education have impact on level of consumption of different products. So, it is essential for businesses to analyse their customer details to better understand consumer behaviour and their impact on various products.

Dataset filename: Customer relationship marketing (CRM).csv - (https://github.com/sovikc/SIT720_A4_Dataset/blob/main/Customer%20relationship%20marketing%20(CRM).csv)

Dataset description: This dataset includes data on customer details and their response to buy any products. The data contains 20 attributes and 9134 records. Features and labels:

The attribute names are listed below.

I. State

II. Customer Lifetime Value

III. Response

IV. Coverage

V. Education

VI. Effective To Date

VII. EmploymentStatus

VIII. Gender

IX. Income

X. Location Code

XI. Marital Status

XII. Monthly Premium Auto

XIII. Months Since Last Claim

XIV. Number of Open Complaints

XV. Number of Policies * Policy

XVI. Renew Offer Type

XVII. Sales Channel

XVIII. Total Claim Amount

XIX. Vehicle Class

QUESTION

Create three supervised machine learning (ML) models except any ensemble approach for predicting customer response.

• Report performance score using a suitable metric. Is it possible that the presented result is an overfitted one? Justify.
• Justify different design decisions for each ML model used to answer this question.
• Have you optimised any hyper-parameters for each ML model? What are they? Why have you done that? Explain.
• Finally, make a recommendation based on the reported results and justify it