$2.$ Based on the pre$-$processed training data from question $1,$ create three supervised

machine learning $($ML$)$ models for predicting $$Status$.$

a$.$ Use an appropriate validation method, report performance score using a

suitable metric. Is it possible that the presented result is an under$-$fitted or

over$-$fitted one? Justify.

b$.$ Justify different design decisions for each ML model used to answer this

question.

c$.$ Have you optimized any hyper$-$parameters for each ML model? What are

they? Why have you done that? Explain.

d$.$ Use a method to deal with the label imbalance issue and indicate whether

there is a model improvement after you balance the dataset.

e$.$ Finally, make a model recommendation based on the reported results and

justify it