Please Help and explain,

Task $6-$ To improve convergence rate, we can scale $($standardize$)$ the inputs

Below is the scatter plot of $x$ before scaling. Create X$\_$scaled, for every column $($except the first as it is already scaled$)$ such that $=\frac{-\text{m}\text{e}\text{a}\text{n}()}{s}td()$

In $[]$: $ Create a figure instance

fig $=$ pit.figure $,$ figsize $=(9,6)$

$4$ Create an axes instance

$ax=$ fig.add$\_$subplot$(111)$

$$ Create the boxplot

$bp=ax*boxp1ot(x)$

p$1$t$.$xlabel$("$xe$,$ x$2,$ x$2\text{'})$

plt$.$title$("$Before feature scaling"$)$

In $[47]=$

$x_{{?}_r\text{i}\text{e}\text{a}\text{n}}=$ np$.$mean $,$ axis $=0$ wimean of columns

mu$1=x_{n_{e}an[1]}$

mu$2=x_{-}$nean $[2]$

$x_{-}$var$=$ np$.$var $,$ axis $=0$

s$1=$ np$.$sqrt $($X$\_$var$[1])$

$s2=$ np$.$sqrt $($X$\_$var$[2])$

x$\_$scaled $=x\%$ intialize

In $[48]$: & your code here to calculate X$\_$scaled using mu$1,$ wu$2,$ s$1,$ s$2.$ Avoid using for loop and use vector operations

outputs: updated $x_{-}$scaled

In $[]$: $ Create a figure instance

fig $=$ pit.figure $,$ figsize $=(9,6)$

Create an axes instance

$ax=$ fig.add$\_$subplot$(111)$

A Create the boxplot

$bp=ax.$boxplot $($X$\_$scaled$)$

p$1$t$.$xlabel$(\text{'}$xe$,\frac{x}{b}ar(2),x{2}^{\text{'}})$

plt$.$title$(\text{'}$After feature scaling'$)$

Show that your output looks like this:Task $6-$ To improve convergence rate, we can scale $($standardize$)$ the inputs

Below is the scatter plot of $x$ before scaling. Create X$\_$scaled, for every column $($except the first as it is already scaled$)$ such that $=\frac{-\text{m}\text{e}\text{a}\text{n}()}{s}td()$

In $[]$: $ Create a figure instance

fig $=$ pit.figure $,$ figsize $=(9,6)$

$4$ Create an axes instance

$ax=$ fig.add$\_$subplot$(111)$

$$ Create the boxplot

$bp=ax*boxp1ot(x)$

p$1$t$.$xlabel$("$xe$,$ x$2,$ x$2\text{'})$

plt$.$title$("$Before feature scaling"$)$

In $[47]=$

$x_{{?}_r\text{i}\text{e}\text{a}\text{n}}=$ np$.$mean $,$ axis $=0$ wimean of columns

mu$1=x_{n_{e}an[1]}$

mu$2=x_{-}$nean $[2]$

$x_{-}$var$=$ np$.$var $,$ axis $=0$

s$1=$ np$.$sqrt $($X$\_$var$[1])$

$s2=$ np$.$sqrt $($X$\_$var$[2])$

x$\_$scaled $=x\%$ intialize

In $[48]$: & your code here to calculate X$\_$scaled using mu$1,$ wu$2,$ s$1,$ s$2.$ Avoid using for loop and use vector operations

outputs: updated $x_{-}$scaled

In $[]$: $ Create a figure instance

fig $=$ pit.figure $,$ figsize $=(9,6)$

Create an axes instance

$ax=$ fig.add$\_$subplot$(111)$

A Create the boxplot

$bp=ax.$boxplot $($X$\_$scaled$)$

p$1$t$.$xlabel$(\text{'}$xe$,\frac{x}{b}ar(2),x{2}^{\text{'}})$

plt$.$title$(\text{'}$After feature scaling'$)$

Show that your output looks like this: