$"$ Implement polynomial features $[10$p$]$

$"$

$"*$ Implement minmax normalisation $[10$p$]$

$"$

$"*$ Implement logistic regression loss function and gradient descent. algorithm $[10$p$]1n"$

$"*$ Plot learning and test curve $[10$p$]\frac{?}{?}$

$"$

$"*$ Generate test prediction $($test dataset$)[10$p$]n"$

$"*$ Fill the confusion matrix $($test dataset$)[10$p$]\frac{?}{?}$

$"$

$"*$ Compute F$1$ score, precision, recall and accuracy using confusion matrix $($test dataset$)[10$p$]n"$

$"*$ Try various learning rate and compute F$1$ score of each learning rate $($test dataset$)[10$p$]\frac{\frac{?}{?}}{?}"$

$"*$ Learning rates: $(0\mathrm{.}0001,0\mathrm{.}0005,0\mathrm{.}0007,0\mathrm{.}001,0\mathrm{.}005,0\mathrm{.}001,0\mathrm{.}01,0\mathrm{.}05,0\mathrm{.}09,0\mathrm{.}1,0\mathrm{.}4,0\mathrm{.}7)[10p]n$

$"*$ Generate the lists for each learning rate and its associated F$1$ score and plot $($test dataset$)[10$p$]"$