Add examples to demonstrate usage of mean_absolute_error function

src/matrics_calculator/MAE.py

@@ -1,6 +1,6 @@
 # Mean Absolute Error (MAE) calculation
 def mean_absolute_error(y_true, y_pred):
- """
+    """
     Calculate the Mean Absolute Error (MAE) metric for regression.
 
     This function computes the average absolute difference between the predicted values (`y_pred`) 
@@ -17,4 +17,18 @@ def mean_absolute_error(y_true, y_pred):
     Returns:
     -------
     float
-        The Mean Absolute Error.
+        The Mean Absolute Error.
+
+    Notes:
+    ------
+    MAE is defined as:
+        MAE = (1 / n) * sum(|y_true - y_pred|)
+    where n is the number of observations.
+
+    Examples:
+    ---------
+    >>> y_true = [100, 200, 300]
+    >>> y_pred = [110, 190, 290]
+    >>> mean_absolute_error(y_true, y_pred)
+    10.0
+    """