Consider the following data containing four features. Each example represents one of two classes $($Setosa $(0),$ Verginica $(1)).$

a$.$ Apply principal component analysis and retain $95\%$ of variance.

b$.$ Appl t$-$test and select the most significant feature.

c$.$ Reconstruct the hypothesis of logistic regression after two iterations of the gradient descent algorithm to reduce the cost function. You may initialize the parameter

with any values. Also, take any learning rate.

d$.$ Repeat part c and include the regularization term in the cost function.

DONT DO IT BY CODING, USE FORMULAS