n the lectures, we discussed simple linear regression, which considers only one

independent variable. Suppose that you want to use a gradient decent algorithm to

train a linear regression model to predict house price based on house size and age of

home, i$.$e$.,$ there are two independent variables. You have training data as follows:

Size$($feets$^2)$ Age of home

$($years$)$

Price $($$$1000)$

$210445460$

$141640232$

$153430315$

$.........$

$85236178$

a$)(8\%)$ What data preprocessing would you carry out before training the linear

regression model? Please explain why this is important.

b$)(12\%)$ Please write down your hypothesis function. How many parameters

will you have? Please explain.

c$)(10\%)$ Please write down the objective $($cost$)$ function.