% Load dataset
data = readtable('Lymphoma.csv');  % Replace with your dataset file

% Separate features (X) and target (y)
X = data(:, setdiff(data.Properties.VariableNames, {'Target'}));  % Feature matrix
y = data.Target;                                                  % Target vector

% Convert table to matrix if needed
X_mat = table2array(X);

% Split the data into training and testing sets
cv = cvpartition(y, 'HoldOut', 0.3);
X_train = X_mat(training(cv), :);
y_train = y(training(cv));
X_test = X_mat(test(cv), :);
y_test = y(test(cv));

% Create an SVM model
tic;
svmModel = svm(X_train, y_train, 'KernelFunction', 'rbf', 'BoxConstraint', 1, 'KernelScale', 'auto'); % Train the SVM model according to our SVM solver
train_time = toc;
fprintf('Training Time: %.4f seconds\n', train_time);

% Measure testing time
tic;
y_pred = predict(svmModel, X_test);
test_time = toc;
fprintf('Testing Time: %.4f seconds\n', test_time);

% Evaluate model performance
accuracy = mean(y_pred == y_test);
fprintf('Accuracy: %.4f\n', accuracy);

% Classification report
confMat = confusionmat(y_test, y_pred);
disp('Confusion Matrix:');
disp(confMat);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Load dataset
data = readtable('Leukemia.csv');  % Replace with your dataset file

% Separate features (X) and target (y)
X = data(:, setdiff(data.Properties.VariableNames, {'Target'}));  % Feature matrix
y = data.Target;                                                  % Target vector

% Convert table to matrix if needed
X_mat = table2array(X);

% Split the data into training and testing sets
cv = cvpartition(y, 'HoldOut', 0.3);
X_train = X_mat(training(cv), :);
y_train = y(training(cv));
X_test = X_mat(test(cv), :);
y_test = y(test(cv));

% Create an SVM model
tic;
svmModel = svm(X_train, y_train, 'KernelFunction', 'rbf', 'BoxConstraint', 1, 'KernelScale', 'auto');  % Train the SVM model according to our SVM solver

train_time = toc;
fprintf('Training Time: %.4f seconds\n', train_time);

% Measure testing time
tic;
y_pred = predict(svmModel, X_test);
test_time = toc;
fprintf('Testing Time: %.4f seconds\n', test_time);

% Evaluate model performance
accuracy = mean(y_pred == y_test);
fprintf('Accuracy: %.4f\n', accuracy);

% Classification report (confusion matrix)
confMat = confusionmat(y_test, y_pred);
disp('Confusion Matrix:');
disp(confMat);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Load dataset
data = readtable('ecoli.csv');  % Replace with your dataset file

% Separate features (X) and target (y)
X = data(:, setdiff(data.Properties.VariableNames, {'Target'}));  % Feature matrix
y = data.Target;                                                  % Target vector

% Convert categorical/string columns to numeric using Label Encoding
varNames = X.Properties.VariableNames;
for i = 1:numel(varNames)
    if iscellstr(X.(varNames{i})) || isstring(X.(varNames{i}))
        X.(varNames{i}) = grp2idx(categorical(X.(varNames{i})));  % Label encoding
    end
end

% Convert table to matrix if needed
X_mat = table2array(X);

% Split the data into training and testing sets
cv = cvpartition(y, 'HoldOut', 0.3);
X_train = X_mat(training(cv), :);
y_train = y(training(cv));
X_test = X_mat(test(cv), :);
y_test = y(test(cv));

% Create an SVM model
tic;
svmModel = svm(X_train, y_train, 'KernelFunction', 'rbf', 'BoxConstraint', 1, 'KernelScale', 'auto');  % Train the SVM model according to our SVM solver

train_time = toc;
fprintf('Training Time: %.4f seconds\n', train_time);

% Measure testing time
tic;
y_pred = predict(svmModel, X_test);
test_time = toc;
fprintf('Testing Time: %.4f seconds\n', test_time);

% Evaluate model performance
accuracy = mean(y_pred == y_test);
fprintf('Accuracy: %.4f\n', accuracy);

% Classification report (confusion matrix)
confMat = confusionmat(y_test, y_pred);
disp('Confusion Matrix:');
disp(confMat);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Load dataset
data = readtable('balance-scale.csv');  % Replace with your dataset file

% Separate features (X) and target (y)
X = data(:, setdiff(data.Properties.VariableNames, {'Target'}));  % Feature matrix
y = data.Target;                                                  % Target vector

% Convert table to matrix if needed
X_mat = table2array(X);

% Split the data into training and testing sets
cv = cvpartition(y, 'HoldOut', 0.3);
X_train = X_mat(training(cv), :);
y_train = y(training(cv));
X_test = X_mat(test(cv), :);
y_test = y(test(cv));

% Create an SVM model
tic;
svmModel = svm(X_train, y_train, 'KernelFunction', 'rbf', 'BoxConstraint', 1, 'KernelScale', 'auto'); % Train the SVM model according to our SVM solver
train_time = toc;
fprintf('Training Time: %.4f seconds\n', train_time);

% Measure testing time
tic;
y_pred = predict(svmModel, X_test);
test_time = toc;
fprintf('Testing Time: %.4f seconds\n', test_time);

% Evaluate model performance
accuracy = mean(y_pred == y_test);
fprintf('Accuracy: %.4f\n', accuracy);

% Classification report (confusion matrix)
confMat = confusionmat(y_test, y_pred);
disp('Confusion Matrix:');
disp(confMat);
<pre>An Error occurred while handling another error:
yii\web\HeadersAlreadySentException: Headers already sent in  on line 0. in /var/www/html/prof-homepages/vendor/yiisoft/yii2/web/Response.php:366
Stack trace:
#0 /var/www/html/prof-homepages/vendor/yiisoft/yii2/web/Response.php(339): yii\web\Response-&gt;sendHeaders()
#1 /var/www/html/prof-homepages/vendor/yiisoft/yii2/web/ErrorHandler.php(136): yii\web\Response-&gt;send()
#2 /var/www/html/prof-homepages/vendor/yiisoft/yii2/base/ErrorHandler.php(135): yii\web\ErrorHandler-&gt;renderException()
#3 [internal function]: yii\base\ErrorHandler-&gt;handleException()
#4 {main}
Previous exception:
yii\web\HeadersAlreadySentException: Headers already sent in  on line 0. in /var/www/html/prof-homepages/vendor/yiisoft/yii2/web/Response.php:366
Stack trace:
#0 /var/www/html/prof-homepages/vendor/yiisoft/yii2/web/Response.php(339): yii\web\Response-&gt;sendHeaders()
#1 /var/www/html/prof-homepages/vendor/yiisoft/yii2/base/Application.php(656): yii\web\Response-&gt;send()
#2 /var/www/html/prof-homepages/vendor/faravaghi/yii2-filemanager/models/Files.php(696): yii\base\Application-&gt;end()
#3 /var/www/html/prof-homepages/vendor/faravaghi/yii2-filemanager/controllers/FilesController.php(484): faravaghi\filemanager\models\Files-&gt;getFile()
#4 [internal function]: faravaghi\filemanager\controllers\FilesController-&gt;actionGetFile()
#5 /var/www/html/prof-homepages/vendor/yiisoft/yii2/base/InlineAction.php(57): call_user_func_array()
#6 /var/www/html/prof-homepages/vendor/yiisoft/yii2/base/Controller.php(180): yii\base\InlineAction-&gt;runWithParams()
#7 /var/www/html/prof-homepages/vendor/yiisoft/yii2/base/Module.php(528): yii\base\Controller-&gt;runAction()
#8 /var/www/html/prof-homepages/vendor/yiisoft/yii2/web/Application.php(103): yii\base\Module-&gt;runAction()
#9 /var/www/html/prof-homepages/vendor/yiisoft/yii2/base/Application.php(386): yii\web\Application-&gt;handleRequest()
#10 /var/www/html/prof-homepages/backend/web/index.php(16): yii\base\Application-&gt;run()
#11 {main}</pre>