Hi I need help with this assignment on machine learning using python programming.

Question: Write a single python file to perform the following tasks:

$($a$)$ Get dataset "from sklearn. datasets import load$\_$iris". This dataset has $4$ features.

Split the dataset into two sets: $30\%$ of samples for training, and $70\%$ of samples for testing.

NOTE $1$: Please use "from sklearn.model$\_$selection $1$mport train$\_$test$\_$split"

with "random$\_$state$=$N$"$ and "test$\_$size$=0\mathrm{.}7".$

NOTE $2$: The offset$/$bias column is not needed here for augmenting the input features.

$($b$)$ Generate the target output using one$-$hot encoding for both the training set and the test set.

$($c$)$ Using the same training and test sets generated above, perform a polynomial regression $($utilizing $"fx$@$m$

sklearn.preprocessing $1$mport PolynomialFeatures"$)$ from orders $1$ to $8($adopting

the weight$-$decay L$\mathrm{.}2$ regularization with regularization factor $=0\mathrm{.}0001)$ for classification $($based on the

one$-$hot encoding$)$ and compute the number of training and test samples that are classified correctly.

NOTE $1$: The offsetbias atigmentation will be automatically generated by Polynomial Features.

NOTE $2$: If the number of rows in the training polynomial matrix is less than of equal to the number of

columns, then use the dual form of ridge regression $($Lecture $6).$ If not, use the primal form $($Lecture $6).$

Instructions: please submit a single python file with filename $"$A$2\_$StudentMatriculationNumber.py$".$ It should

contain a function A$.\_$MatricNumber that takes in an integer $"N^{\text{'}\text{'}}$

random$\_$state as input and returns the following outputs in the following order:

X$\_$train : training numpy feature matrix with dimensions $($number$\_$of$\_$training$\_$samples $4).(1\%)$

$y_{-}^{-}$train: training target numpy array $($containing values $0,1$ and $2)$ of length

number$\_$of$\_$training$\_$samples. $(1\%)$

X$\_$test : test numpy feature matrix with dimensions $($number$\_$of$\_$test$\_$samples $4.(1\%)$

$y_{-}^{-}$test : test target numpy array $($containing values $0,1$ and $2)$ of length number$\_$of$\_$test$\_$samples. $(1\%)$

Ytx: one$-$hot encoded training target numpy matrix $($containing only values $0$ and $1)$ with dimension

$($number$\_$of$\_$training$\_$samples $3).)$

Yts : one$-$hot encoded test target numpy matrix $($containing only values $0$ and $1)$ with dimension

$($number$\_$of$\_$test$\_$samples $3).(1\%)$

Ptrain$\_1$ist: list of training polynomial matrices for orders $1$ to $8.$ Ptrain$\_$list$[0]$ should be polynomial

matrices for order $1($size number$\_$of$\_$training$\_$samples x $5).$ Ptrain$\_$list$[1]$ should be polynomial matrices for

order $2($size number$\_$of$\_$training$\_$samples $x15),$ etc. $)$

Ptest$\_11$st : list of test polynomial matrices for orders $1$ to $8.$ Ptest$\_$list$[0]$ should be polynomial matrices

for order $1,$ Ptest$\_$list$[1]$ should be polynomial matrices for order $2,$ etc. $(1\mathrm{.}5\%)$

$,"\_11$st : list of estimated regression coefficients for orders $1$ to $8.$ w$\_$list$[0]$ should be estimated regression

coefficients for order $1,$ w$\_$list$[1]$ should be estimated regression coefficients for order $2,$ etc. $(2\%)$

exxox$\_$txain$\_$axxay: numpy array of training error counts $($error count $=$ number of samples classified

incorrectly$)$ for orders $1$ to $8.$ error$\_$train$\_$array$[0]$ is error count for polynomial order $1,$ error$\_$train$\_$array$[1]$ is

error count for polynomial order $2,$ etc. $(2\%)$

error$\_$test$\_$array: numpy array of test error counts $($error count $=$ number of samples classified

incorrectly$)$ for orders $1$ to $8.$ error$\_$test$\_$array$[0]$ is error count for polynomial order $1,$ error$\_$test$\_$array$[1]$ is

error count for polynomial order $2,$ etc. $(2\%)$

The way the code will be run is like this:$>$ N$-5$w$\_1$ist, error$\_$train$\_$array, error$\_$test$\_$array $=$ grading.A$2\_$A$1234567$R $($N$)$

The sample python template provided to work on is below, not allowed to comment out any lines.

Please replace "Matrickuber" with your actual natric nuber here and in the filenane

A$2.$ MatricNuber $($N$)$ :

Inpat type

in typei int

Return type

:X$\_$train type: nupy,ndarray of size $($number$\_$ot$\_$training$\_$saup$1$es$,4)$

:Y$\_$trate type: nupy$-$ndarray of size $($number$\_$ot$\_$training$\_$sauples,$)$

iX$\_$test type: numpy, sdarray of size $($number$\_$of$\_$teat$\_$sacples, $4)$

:y$\_$test type: nuopy.sdarray of size $($nuber$\_$of$\_$test$\_$asiples,$)$

sYte typet numpy.ndarray of size $($number of eraining$\_$samples, $3)$

sYts type: numpy,ndarray of size $($nuaber$\_$of$\_$test$\_$samples, $3)$

ifrain list type: List $($nuapy$-$ndarray$]$

ifteat Iist type: hist $[$numpy$,$ ndarkay$]$

sw$\_$list type: Liot $($numpy$,$ ndarray$)$

serror train a