Hi I need help with my Machine Learning assignment that requires the use of python language. The variables and labels stated in the question must be strictly followed. The template python file was also provided and all the test conditions and variable and function names are stated there for reference. Please help to provide a description or explanation on what each line of the solution code is doing for my understanding. Thank you.

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 import 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 "from

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

the weight$-$decay L$2$ 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 offset$/$bias augmentation will be automatically generated by PolynomialFeatures.

NOTE $2$: If the number of rows in the training polynomial matrix is less than or 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$2$ MatricNumber that takes in an integer $"N"$

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 $4$

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. $(1\%)$

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

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

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

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

Ptrain$\_$list: 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 x $15),$ etc. $(1\mathrm{.}5\%)$

Ptest$\_$list: 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. $(1\mathrm{.}5\%)$

w$\_$list: 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. $(2\%)$

error$\_$train$\_$array: 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. $(2\%)$

Please use the python template provided to you. Do not comment out any lines. Remember to rename both

$"$A$2\_$StudentMatriculationNumber.py$"$ and $"$A$2\_$MatricNumber" using your student matriculation number. For

example, if your matriculation ID is A$1234567$R$,$ then you should submit $"$A$2\_$A$1234567$R$.$py$"$ that contains the

function $"$A$2\_$A$1234567$R$".$ Please do NOT zip$/$compress your file. Please test your code at least once.

Because of the large class size, points will be deducted if instructions are not followed. The way we would run

your code might be something like this:$>$ N$=5$w$\_$list, error$\_$train$\_$array, error$\_$test$\_$array $=$ grading.A$2\_$A$1234567$ bar$($R$)($N$)$

limport numpy as np

# Please replace "Mat def A$2\_$A$0255256$B$($N$)$:

Input