diff --git a/matrix_calculus/matrix_processor.py b/matrix_calculus/matrix_processor.py
new file mode 100644
index 0000000..b3f9652
--- /dev/null
+++ b/matrix_calculus/matrix_processor.py
@@ -0,0 +1,70 @@
+from __future__ import annotations
+
+
+class MatrixProcessor:
+    @staticmethod
+    def gaussian_elimination(matrix: list[list[float]]) -> list[list[float]]:
+        M = [row[:] for row in matrix]
+        n = len(M)
+        eps = 1e-12
+
+        def swap_rows(row1, row2):
+            for col in range(n):
+                M[row1][col], M[row2][col] = M[row2][col], M[row1][col]
+
+        for diagonal_element in range(n):
+
+            diagonal_element_value = M[diagonal_element][diagonal_element]
+
+            if abs(diagonal_element_value) < eps:
+                for row in range(diagonal_element + 1, n):
+                    if abs(M[row][diagonal_element]) > eps:
+                        diagonal_element_value = M[row][diagonal_element]
+
+                        swap_rows(diagonal_element, row)
+
+            if abs(diagonal_element_value) < eps:
+                raise ValueError("Matrix is singular")
+
+            for col in range(diagonal_element, n):
+                M[diagonal_element][col] /= diagonal_element_value
+
+            for row in range(diagonal_element + 1, n):
+                factor = M[row][diagonal_element]
+                for col in range(diagonal_element, n):
+                    M[row][col] -= factor * M[diagonal_element][col]
+
+        return M
+
+    @staticmethod
+    def gaussian_elimination_pivoting(matrix: list[list[float]]) -> list[list[float]]:
+        M = [row[:] for row in matrix]
+        n = len(M)
+        eps = 1e-12
+
+        def swap_rows(row1, row2):
+            for col in range(n):
+                M[row1][col], M[row2][col] = M[row2][col], M[row1][col]
+
+        for diagonal_element in range(n):
+
+            max_pivot_value = abs(M[diagonal_element][diagonal_element])
+            highest_pivot_row = diagonal_element
+            for row in range(diagonal_element + 1, n):
+                if abs(M[row][diagonal_element]) > max_pivot_value:
+                    max_pivot_value = abs(M[row][diagonal_element])
+                    highest_pivot_row = row
+
+            if highest_pivot_row != diagonal_element:
+                swap_rows(diagonal_element, highest_pivot_row)
+
+            pivot_value = M[diagonal_element][diagonal_element]
+            if abs(pivot_value) < eps:
+                raise ValueError("Matrix is singular")
+
+            for row in range(diagonal_element + 1, n):
+                factor = M[row][diagonal_element] / pivot_value
+                for col in range(diagonal_element, n):
+                    M[row][col] -= factor * M[diagonal_element][col]
+
+        return M
diff --git a/tests/gaussian_elimination_test.py b/tests/gaussian_elimination_test.py
new file mode 100644
index 0000000..c78e090
--- /dev/null
+++ b/tests/gaussian_elimination_test.py
@@ -0,0 +1,49 @@
+from __future__ import annotations
+
+import pytest  # noqa: F401
+
+from matrix_calculus.matrix_processor import MatrixProcessor
+
+
+def test_gaussian_elimination():
+    # Test 1x1 matrix
+    matrix = [[2]]
+    res = MatrixProcessor.gaussian_elimination(matrix)
+    assert res == [[1.0]]
+
+    # Test 2x2 matrix
+    matrix = [[2, 4], [1, 3]]
+    res = MatrixProcessor.gaussian_elimination(matrix)
+    assert res == [[1.0, 2.0], [0.0, 1.0]]
+
+    # Test 3x3 singular matrix
+    with pytest.raises(ValueError):
+        matrix = [[2, 4, 6], [1, 3, 5], [0, 2, 4]]
+        MatrixProcessor.gaussian_elimination(matrix)
+
+    # Test 3x3 matrix
+    matrix = [[2, 4, 6], [1, 2, 5], [0, 2, 4]]
+    res = MatrixProcessor.gaussian_elimination(matrix)
+    assert res == [[1.0, 2.0, 3.0], [0.0, 1.0, 2.0], [0.0, 0.0, 1.0]]
+
+
+def test_gaussian_elimination_pivoting():
+    # Test 1x1 matrix
+    matrix = [[2]]
+    res = MatrixProcessor.gaussian_elimination_pivoting(matrix)
+    assert res == [[2.0]]
+
+    # Test 2x2 matrix
+    matrix = [[2, 4], [1, 3]]
+    res = MatrixProcessor.gaussian_elimination_pivoting(matrix)
+    assert res == [[2.0, 4.0], [0.0, 1.0]]
+
+    # Test 3x3 singular matrix
+    with pytest.raises(ValueError):
+        matrix = [[2, 4, 6], [1, 3, 5], [0, 2, 4]]
+        MatrixProcessor.gaussian_elimination_pivoting(matrix)
+
+    # Test 3x3 matrix
+    matrix = [[2, 4, 6], [1, 2, 5], [0, 2, 4]]
+    res = MatrixProcessor.gaussian_elimination_pivoting(matrix)
+    assert res == [[2, 4, 6], [0, 2, 4], [0, 0, 2]]