diff --git a/test/mlpp_tests.cpp b/test/mlpp_tests.cpp
index fed2ba3..888d892 100644
--- a/test/mlpp_tests.cpp
+++ b/test/mlpp_tests.cpp
@@ -184,6 +184,119 @@ void MLPPTests::test_univariate_linear_regression() {
 	is_approx_equals_dvec(dstd_vec_to_vec(model.modelSetTest(ds->input)), dstd_vec_to_vec(slr_res), "stat.mode(x)");
 }
 
+void MLPPTests::test_multivariate_linear_regression_gradient_descent(bool ui) {
+	MLPPData data;
+	MLPPLinAlg alg;
+
+	Ref<MLPPDataSimple> ds = data.load_california_housing(_load_california_housing_data_path);
+
+	MLPPLinReg model(ds->input, ds->output); // Can use Lasso, Ridge, ElasticNet Reg
+
+	model.gradientDescent(0.001, 30, ui);
+	alg.printVector(model.modelSetTest(ds->input));
+}
+
+void MLPPTests::test_multivariate_linear_regression_sgd(bool ui) {
+	MLPPData data;
+	MLPPLinAlg alg;
+
+	Ref<MLPPDataSimple> ds = data.load_california_housing(_load_california_housing_data_path);
+
+	MLPPLinReg model(ds->input, ds->output); // Can use Lasso, Ridge, ElasticNet Reg
+
+	model.SGD(0.00000001, 300000, ui);
+	alg.printVector(model.modelSetTest(ds->input));
+}
+
+void MLPPTests::test_multivariate_linear_regression_mbgd(bool ui) {
+	MLPPData data;
+	MLPPLinAlg alg;
+
+	Ref<MLPPDataSimple> ds = data.load_california_housing(_load_california_housing_data_path);
+
+	MLPPLinReg model(ds->input, ds->output); // Can use Lasso, Ridge, ElasticNet Reg
+
+	model.MBGD(0.001, 10000, 2, ui);
+	alg.printVector(model.modelSetTest(ds->input));
+}
+
+void MLPPTests::test_multivariate_linear_regression_normal_equation(bool ui) {
+	MLPPData data;
+	MLPPLinAlg alg;
+
+	Ref<MLPPDataSimple> ds = data.load_california_housing(_load_california_housing_data_path);
+
+	MLPPLinReg model(ds->input, ds->output); // Can use Lasso, Ridge, ElasticNet Reg
+
+	model.normalEquation();
+	alg.printVector(model.modelSetTest(ds->input));
+}
+
+void MLPPTests::test_multivariate_linear_regression_adam() {
+	MLPPData data;
+	MLPPLinAlg alg;
+
+	Ref<MLPPDataSimple> ds = data.load_california_housing(_load_california_housing_data_path);
+
+	MLPPLinReg adamModel(alg.transpose(ds->input), ds->output);
+	alg.printVector(adamModel.modelSetTest(ds->input));
+	std::cout << "ACCURACY: " << 100 * adamModel.score() << "%" << std::endl;
+}
+
+void MLPPTests::test_multivariate_linear_regression_score_sgd_adam(bool ui) {
+	MLPPData data;
+	MLPPLinAlg alg;
+
+	Ref<MLPPDataSimple> ds = data.load_california_housing(_load_california_housing_data_path);
+
+	const int TRIAL_NUM = 1000;
+
+	double scoreSGD = 0;
+	double scoreADAM = 0;
+	for (int i = 0; i < TRIAL_NUM; i++) {
+		MLPPLinReg modelf(alg.transpose(ds->input), ds->output);
+		modelf.MBGD(0.001, 5, 1, ui);
+		scoreSGD += modelf.score();
+
+		MLPPLinReg adamModelf(alg.transpose(ds->input), ds->output);
+		adamModelf.Adam(0.1, 5, 1, 0.9, 0.999, 1e-8, ui); // Change batch size = sgd, bgd
+		scoreADAM += adamModelf.score();
+	}
+
+	std::cout << "ACCURACY, AVG, SGD: " << 100 * scoreSGD / TRIAL_NUM << "%" << std::endl;
+	std::cout << std::endl;
+	std::cout << "ACCURACY, AVG, ADAM: " << 100 * scoreADAM / TRIAL_NUM << "%" << std::endl;
+}
+
+void MLPPTests::test_multivariate_linear_regression_epochs_gradient_descent(bool ui) {
+	MLPPData data;
+	MLPPLinAlg alg;
+
+	Ref<MLPPDataSimple> ds = data.load_california_housing(_load_california_housing_data_path);
+
+	std::cout << "Total epoch num: 300" << std::endl;
+	std::cout << "Method: 1st Order w/ Jacobians" << std::endl;
+
+	MLPPLinReg model3(alg.transpose(ds->input), ds->output); // Can use Lasso, Ridge, ElasticNet Reg
+	model3.gradientDescent(0.001, 300, ui);
+	alg.printVector(model3.modelSetTest(ds->input));
+}
+
+void MLPPTests::test_multivariate_linear_regression_newton_raphson(bool ui) {
+	MLPPData data;
+	MLPPLinAlg alg;
+
+	Ref<MLPPDataSimple> ds = data.load_california_housing(_load_california_housing_data_path);
+
+	std::cout << "--------------------------------------------" << std::endl;
+	std::cout << "Total epoch num: 300" << std::endl;
+	std::cout << "Method: Newtonian 2nd Order w/ Hessians" << std::endl;
+	MLPPLinReg model2(alg.transpose(ds->input), ds->output);
+
+	model2.NewtonRaphson(1.5, 300, ui);
+	alg.printVector(model2.modelSetTest(ds->input));
+}
+
 void MLPPTests::is_approx_equalsd(double a, double b, const String &str) {
 	if (!Math::is_equal_approx(a, b)) {
 		ERR_PRINT("TEST FAILED: " + str + " Got: " + String::num(a) + " Should be: " + String::num(b));
@@ -285,6 +398,7 @@ IAEDMAT_FAILED:
 
 MLPPTests::MLPPTests() {
 	_load_fires_and_crime_data_path = "res://datasets/FiresAndCrime.csv";
+	_load_california_housing_data_path = "res://datasets/CaliforniaHousing.csv";
 }
 
 MLPPTests::~MLPPTests() {
@@ -294,4 +408,13 @@ void MLPPTests::_bind_methods() {
 	ClassDB::bind_method(D_METHOD("test_statistics"), &MLPPTests::test_statistics);
 	ClassDB::bind_method(D_METHOD("test_linear_algebra"), &MLPPTests::test_linear_algebra);
 	ClassDB::bind_method(D_METHOD("test_univariate_linear_regression"), &MLPPTests::test_univariate_linear_regression);
+
+	ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_gradient_descent", "ui"), &MLPPTests::test_multivariate_linear_regression_gradient_descent, false);
+	ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_sgd", "ui"), &MLPPTests::test_multivariate_linear_regression_sgd, false);
+	ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_mbgd", "ui"), &MLPPTests::test_multivariate_linear_regression_mbgd, false);
+	ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_normal_equation", "ui"), &MLPPTests::test_multivariate_linear_regression_normal_equation, false);
+	ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_adam"), &MLPPTests::test_multivariate_linear_regression_adam);
+	ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_score_sgd_adam", "ui"), &MLPPTests::test_multivariate_linear_regression_score_sgd_adam, false);
+	ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_epochs_gradient_descent", "ui"), &MLPPTests::test_multivariate_linear_regression_epochs_gradient_descent, false);
+	ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_newton_raphson", "ui"), &MLPPTests::test_multivariate_linear_regression_newton_raphson, false);
 }
diff --git a/test/mlpp_tests.h b/test/mlpp_tests.h
index a999013..ba9f44a 100644
--- a/test/mlpp_tests.h
+++ b/test/mlpp_tests.h
@@ -18,6 +18,15 @@ public:
 	void test_linear_algebra();
 	void test_univariate_linear_regression();
 
+	void test_multivariate_linear_regression_gradient_descent(bool ui = false);
+	void test_multivariate_linear_regression_sgd(bool ui = false);
+	void test_multivariate_linear_regression_mbgd(bool ui = false);
+	void test_multivariate_linear_regression_normal_equation(bool ui = false);
+	void test_multivariate_linear_regression_adam();
+	void test_multivariate_linear_regression_score_sgd_adam(bool ui = false);
+	void test_multivariate_linear_regression_epochs_gradient_descent(bool ui = false);
+	void test_multivariate_linear_regression_newton_raphson(bool ui = false);
+
 	void is_approx_equalsd(double a, double b, const String &str);
 	void is_approx_equals_dvec(const Vector<double> &a, const Vector<double> &b, const String &str);
 	void is_approx_equals_dmat(const Vector<Vector<double>> &a, const Vector<Vector<double>> &b, const String &str);
@@ -29,6 +38,7 @@ protected:
 	static void _bind_methods();
 
 	String _load_fires_and_crime_data_path;
+	String _load_california_housing_data_path;
 };
 
 #endif