Write a Matlab script for the function below, that runs the script 5 times and plots a bar graph and error bar for the average accuracy. Here is the function: function perceptron_classifier() % load data data = load('bill_authentication.txt'); % split data into features and labels X = data(:,1:4); y = data(:,5); % normalize features X = normalize(X); % split data into training and testing sets [X_train, y_train, X_test, y_test] = split_data(X, y, 0.8); % train the perceptron classifier mdl = fitcsvm(X_train, y_train, 'KernelFunction', 'linear', 'Standardize', true); % predict on the test set y_pred = predict(mdl, X_test); % evaluate the model accuracy = sum(y_pred == y_test)/length(y_test); fprintf('Accuracy: %.2f%%\n', accuracy*100); end function [X_train, y_train, X_test, y_test] = split_data(X, y, train_ratio) % randomly shuffle data idx = randperm(size(X,1)); X = X(idx,:); y = y(idx); % split data into training and testing sets split_idx = floor(size(X,1)*train_ratio); X_train = X(1:split_idx,:); y_train = y(1:split_idx); X_test = X(split_idx+1:end,:); y_test = y(split_idx+1:end); end.