Can you help me with my finished code? It is not working as of right now. Please only edit steps $2,3,4,6,7,8$ and $9$ with the marking ### YOUR CODE. Thank you.

Step $1$

Import the libraries.

import numpy as np

from csv import reader

from random import seed

from random import randrange

Step $2$

Load the csv file.

def load$\_$csv$($filename$,$ skip $=$ False$)$:

dataset $=$ list$()$

with open$($filename$,\text{'}$r$\text{'})$ as file:

csv$\_$reader $=$ reader$($file$)$

if skip:

next$($csv$\_$reader$)$ # Skip header

for row in csv$\_$reader:

if not row:

continue

dataset.append$($row$)$

return dataset

Step $3$

Split the dataset into X$\_$train, Y$\_$train, X$\_$test, Y$\_$test sets.

def train$\_$test$\_$split$($dataset$,$ split$)$:

$$

X$\_$train, y$\_$train, X$\_$test, y$\_$test $=$ functions.train$\_$test$\_$split$($dataset$,$ split$)$

$$

return X$\_$train, y$\_$train, X$\_$test, y$\_$test

Step $4$

Defining the Perceptron class that contains the weights, bias, learning rate and epochs.

def $\_\_$init$\_\_($self$,$ input$\_$size, bias, learning$\_$rate, epochs$)$:

self.weights $=$ np$.$zeros$($input$\_$size$)$

self.bias $=$ bias

self.learning$\_$rate $=$ learning$\_$rate

self.epochs $=$ epochs

class Perceptron:

def $\_\_$init$\_\_($self$,$ input$\_$size, bias, learning$\_$rate, epochs$)$:

self.weights $=$ np$.$zeros$($input$\_$size$)$

self.bias $=$ bias

self.learning$\_$rate $=$ learning$\_$rate

self.epochs $=$ epochs

Step $5$

Define the activation function.

def activation$\_$function$($x$)$:

return functions.activation$\_$function$($x$)$

Step $6$

Defining the predict function with the inputs, weights and bias values.

def predict$($inputs$,$ weights, bias$)$:

###

### YOUR CODE HERE

###

return activation$\_$function$($weighted$\_$sum$)$

Step $7$

Define the train function.

def train$($X$\_$train, y$\_$train, learning$\_$rate, epochs, weights, bias$)$:

prediction $=$ None

error $=$ None

$$

for $\_$ in range$($epochs$)$:

###

### YOUR CODE HERE

weights, bias $=$ functions.train$($X$\_$train, y$\_$train, learning$\_$rate, weights, bias, prediction, error$)$

###

$$

return weights, bias

Step $8$

Define the accuracy for the perceptron.

def perceptron$\_$accuracy$($y$,$ y$\_$hat$)$:

# overwrite the accuracy value with your own code

accuracy $=0$

###

### YOUR CODE HERE

accuracy $=$ functions.perceptron$\_$accuracy$($y$,$ y$\_$hat$)$

###

return accuracy

Step $9$

Implemented the Perceptron Nerual Network.

append$($prediction$)$

print$($f$"$Input: $\{$X$\_$test$[$i$]\},$ Predicted: $\{$prediction$\},$ Actual: $\{$y$\_$test$[$i$]\}")$

# Test for Accuracy

perceptron$\_$accuracy$($y$\_$test, y$\_$hat$)$

# Set the seed

seed$(1)$

$$

# Load the csv file

$$

filename $=$ 'moons.csv$\text{'}$

dataset $=$ load$\_$csv$($filename$,$ skip$=$True$)$

$$

# Configure the perception with the bias, learning rate and epochs

$$

# Note the initial values are dummy and must changed for an accurate network

$$

# The split value for the training and test sets

custom$\_$split $=0$

$$

# The bias term is a constant value added to the weighted sum of inputs

custom$\_$bias $=-1$

$$

# The learning rate controls how much the weights are adjusted during training

custom$\_$learning$\_$rate $=-1$

$$

# The number of epochs defines how many times the perceptron will iterate over the training data

custom$\_$epochs $=-1$

$$

# Set your values here

$$

###

### YOUR CODE HERE

custom$\_$split, custom$\_$bias, custom$\_$learning$\_$rate, custom$\_$epochs $=$ functions.config$\_$hyperparameters$()$

###

$$

# Split the dataset for both training and testing

$$

X$\_$train, y$\_$train, X$\_$test, y$\_$test $=$ train$\_$test$\_$split$($dataset$,$ split$=$custom$\_$split$)$

$$

perceptron $=$ Perceptron$($input$\_$size$=2,$ bias$=$custom$\_$bias, learning$\_$rate$=$custom$\_$learning$\_$rate, epochs$=$custom$\_$epochs$)$

$$

# Training

weights, bias $=$ train$($X$\_$train, y$\_$train, perceptron.learning$\_$rate, perceptron.epochs, perceptron.weights, perceptron.bias$)$

$$

# Predictions

y$\_$hat $=[]$

$$

# Testing

for i in range$($len$($X$\_$test$))$:

prediction $=$ predict$($X$\_$test$[$i$],$ weights, bias$)$

y$\_$hat.append$($prediction$)$

print$($f$"$Input: $\{$X$\_$test$[$i$]\},$ Predicted: $\{$prediction$\},$ Actual: $\{$y$\_$test$[$i$]\}")$

$$

# Test for Accuracy

perceptron$\_$accuracy$($y$\_$test, y$\_$hat$)$