Use Python to implement functions for above descriptions of Standardization, Error $($cost

function$),$ Gradient Partial Derivatives and Updating m and b using a learning rate

$$ initialize m$=0\mathrm{.}5,$b$=0$ and $\backslash$alpha $=0\mathrm{.}0001$

$$ Show the datapoints from the student marks data set in a figure where the x$-$axis is midterm

mark and the y$-$axis is the final mark

$$ Show the initial regression line on the same figure $($m$=0\mathrm{.}5,$b$=0)$

$$ Update b and m $100$ times, and create another figure showing the regression line and datapoints

in the same figure

$$ Create a new graph showing Error at each iteration $($from the initial point to iteration $100).$

the x$-$axis is the iteration number and the y$-$axis is the Error

$$ Update b and m for $2000$ iterations $($each update is one iteration$),$ and create another graph

$$ create a new graph showing Error at each iteration $($from the initial point to iteration $2000).$

x$-$axis is the iteration number and the y$-$axis is the Error

$$ verify your results using Python API

$$ perform the above steps once with standardized features and once without Standardization

$3$