Hello! Help me with python ML$,$ please!

For numerical features, impelement a class that normalizes each feature values by subtracting the mean and dividing by the standard deviation.

Implement a fit method that takes as input feature matrix X and computes and saves mean and std values of each feature $($assuming that all the features are numerical$).$ The feature matrix can be either pd$.$DataFrame, or np$.$ndarray.

Implement a transform method that takes as input feature matrix X and normalizes each column using precomputed mean and std values for this feature.

from sklearn.base import TransformerMixin, BaseEstimator

class Scaler$($BaseEstimator$,$ TransformerMixin$)$:

def fit$($self$,$ X$,$ y$=$None$)$:

$"""$

Compute and save mean and standard deviation values

of each feature in the feature matrix.

Parameters

$----------$

X : pd$.$DataFrame or numpy.ndarray of shape $($n$\_$samples, n$\_$features$)$

Feature matrix.

y : pd$.$Series or numpy.ndarray of shape $($n$\_$samples,$)($default : None$)$

Target values. Optional.

Returns

$-------$

self : returns an instance of self.

$"""$

self.column$\_$mean $=[]$

self.column$\_$std $=[]$

for i in range$($X$.$shape$[1])$:

# extract column values

if type$($X$)$ is not np$.$ndarray:

X$\_$i $=$ X$.$values$[$:$,$ i$]$

else:

X$\_$i $=$ X$[$:$,$ i$]$

# compute mean and std

# YOUR CODE HERE

raise NotImplementedError$()$

return self

def transform$($self$,$ X$)$:

$"""$

Normalizes values of each feature

by subtructing the corresponding precomputed feature mean

and dividing by the corresponding standard deviation.

Parameters

$----------$

X : pd$.$DataFrame or numpy.ndarray of shape $($n$\_$samples, n$\_$features$)$

Feature matrix.

Returns

$-------$

X$\_$transformed : array$-$like of shape $($n$\_$samples, n$\_$features$)$

Transformed feature matrix.

$"""$

X$\_$transformed $=[]$

for i in range$($len$($self$.$column$\_$mean$))$:

# extract column values

if type$($X$)$ is not np$.$ndarray:

X$\_$i $=$ X$.$values$[$:$,$ i$]$

else:

X$\_$i $=$ X$[$:$,$ i$]$

# YOUR CODE HERE

raise NotImplementedError$()$

X$\_$transformed $=$ np$.$column$\_$stack$($X$\_$transformed$)$

return X$\_$transformed

# TEST Scaler class

A $=$ np$.$arange$(9).$reshape$(3,3)$

scaler $=$ Scaler$()$

scaler.fit$($A$)$

assert np$.$allclose$($scaler$.$column$\_$mean, $[3\mathrm{.}0,4\mathrm{.}0,5\mathrm{.}0]),$ "Computed mean values are incorrect."

assert np$.$allclose$($scaler$.$column$\_$std$,[2\mathrm{.}44949,2\mathrm{.}44949,2\mathrm{.}44949]),$ "Computed std values are incorrect."

scaler.fit$($pd$.$DataFrame$($A$))$

A$\_$transformed $=$ scaler.transform$($pd$.$DataFrame$($A$))$

assert np$.$allclose$($A$\_$transformed,

$[[-1\mathrm{.}22474,-1\mathrm{.}22474,-1\mathrm{.}22474],$

$[0.,0.,0.],$

$[1\mathrm{.}22474,1\mathrm{.}22474,1\mathrm{.}22474]]),$ "Scaled values are incorrect."