Please describe in detail what type of modelling technique$($s$)$ you would use for the following problems.

Class of Model: Supervised vs$.$ Unsupervised,

Type of Model: Classification, Prediction, Segmentation, etc.,

Modeling Technique: Regression, Decision Tree, Time$-$Series, etc.

Please suggest at least two modeling techniques in each case and explain why you selected these techniques. $(25$ Points$)$

a$.$ A data set that contains financial information for a Non$-$Profit company. The management wants to forecast Donor Revenue for the next $5$ years. Historical Monthly Donor Revenue data is available for the last $15$ years.

b$.$ You are given a data set of macroeconomic indicators for US by state for the year $2022.$ You are asked to build a model that will help explain and at the same time estimate future consumption volumes by state utilizing other macroeconomic indicators $($Interest Rates, Disposable Income, Money Supply and US Dollar Exchange Rates$)$

c$.$ A data set that contains fraud cases $($Yes$/$No$)$ with multiple attributes on type of transactions for a commercial bank. The bank wants to be able to have a model that will help identify future fraudulent transactions.

d$.$ A data set of loan applications for a regional bank. The data set includes the list of customers applied, each customers$$ loan status $($Accepted$/$Denied$)$ and a series of demographic and behavioral attributes on all the customers. The company wants to run a campaign to attract new applicants and determine who they should be targeting.

e$.$ A data set for Furniture Manufacturing Companies with detailed information on their sales, costs, profitability, number of customers, number of furniture categories, and shipping methods. You are working for a company that produces and sells plywood to these companies and you were asked to develop a marketing strategy to target these Furniture Manufacturing Companies.