Implement Logistic regression using Newton method. You should implement your code in the

provided logistic$\_$regression$\_$newton.py file. This python file takes input features X and target labels y$,$ and returns the predictions. For example, in the following usage scenario, $$newton$.$csv$$

is the csv file.

$python$3$ logistic$\_$regression$\_$newton.py$-$data newton.csv

Output the prediction

provided logistic$\_$regression$\_$newton.py file,is as follow:

import numpy as np

import argparse

def sigmoid$($z$)$:

return $\text{'}\text{'}\text{'}$complete code here'$\text{'}\text{'}$

def logistic$\_$regression$\_$newton$($X$,$ y$,$ learning$\_$rate$=0\mathrm{.}001,$ n$\_$iterations$=1000,$ tol$=1$e$-6)$:

n$\_$samples, n$\_$features $=$ X$.$shape

weights $=$ np$.$zeros$($n$\_$features$)$

bias $=0$

for $\_$ in range$($n$\_$iterations$)$:

linear$\_$model $=$ np$.$dot$($X$,$ weights$)+$ bias

y$\_$predicted $=$ sigmoid$($linear$\_$model$)$

# Compute gradient

gradient $=\text{'}\text{'}\text{'}$complete code here'$\text{'}\text{'}$

# Compute Hessian matrix

hessian $=\text{'}\text{'}\text{'}$complete code here'$\text{'}\text{'}$

# Update parameters using Newton's method

$\text{'}\text{'}\text{'}$complete code here'$\text{'}\text{'}$

linear$\_$model $=$ np$.$dot$($X$,$ weights$)+$ bias

y$\_$predicted $=$ sigmoid$($linear$\_$model$)$

y$\_$predicted$\_$cls $=[1$ if i $>0\mathrm{.}5$ else $0$ for i in y$\_$predicted$]$

return np$.$array$($y$\_$predicted$\_$cls$)$

if $\_\_$name$\_\_=="\_\_$main$\_\_"$:

parser $=$ argparse.ArgumentParser$($description$=$"Logistic Regression with Newton's Method"$)$

parser.add$\_$argument$("--$data", type$=$str$,$ help$=$"Path to data file $($CSV format$)")$

args $=$ parser.parse$\_$args$()$

if args.data:

data $=$ np$.$genfromtxt$($args$.$data, delimiter$=\text{'},\text{'})$

X $=$ data$[$:$,$ :$-1]$

y $=$ data$[$:$,-1]$

print$($y$)$

predictions $=$ logistic$\_$regression$\_$newton$($X$,$ y$)$

print$("$Predictions:$",$ predictions$)$

else:

print$("$Please provide the path to the data file using the $\text{'}--$data' argument."$)$

i have attached the image of csv file