diff --git a/machine_learning/scoring_functions.py b/machine_learning/scoring_functions.py
index 2b24287b3..5c84f7026 100755
--- a/machine_learning/scoring_functions.py
+++ b/machine_learning/scoring_functions.py
@@ -16,6 +16,16 @@ import numpy as np
 
 # Mean Absolute Error
 def mae(predict, actual):
+    """
+    Examples(rounded for precision):
+    >>> actual = [1,2,3];predict = [1,4,3]
+    >>> np.around(mae(predict,actual),decimals = 2)
+    0.67
+
+    >>> actual = [1,1,1];predict = [1,1,1]
+    >>> mae(predict,actual)
+    0.0
+    """
     predict = np.array(predict)
     actual = np.array(actual)
 
@@ -27,6 +37,16 @@ def mae(predict, actual):
 
 # Mean Squared Error
 def mse(predict, actual):
+    """
+    Examples(rounded for precision):
+    >>> actual = [1,2,3];predict = [1,4,3]
+    >>> np.around(mse(predict,actual),decimals = 2)
+    1.33
+
+    >>> actual = [1,1,1];predict = [1,1,1]
+    >>> mse(predict,actual)
+    0.0
+    """
     predict = np.array(predict)
     actual = np.array(actual)
 
@@ -39,6 +59,16 @@ def mse(predict, actual):
 
 # Root Mean Squared Error
 def rmse(predict, actual):
+    """
+    Examples(rounded for precision):
+    >>> actual = [1,2,3];predict = [1,4,3]
+    >>> np.around(rmse(predict,actual),decimals = 2)
+    1.15
+
+    >>> actual = [1,1,1];predict = [1,1,1]
+    >>> rmse(predict,actual)
+    0.0
+    """
     predict = np.array(predict)
     actual = np.array(actual)
 
@@ -51,6 +81,16 @@ def rmse(predict, actual):
 
 # Root Mean Square Logarithmic Error
 def rmsle(predict, actual):
+    """
+    Examples(rounded for precision):
+    >>> actual = [10,10,30];predict = [10,2,30]
+    >>> np.around(rmsle(predict,actual),decimals = 2)
+    0.75
+
+    >>> actual = [1,1,1];predict = [1,1,1]
+    >>> rmsle(predict,actual)
+    0.0
+    """
     predict = np.array(predict)
     actual = np.array(actual)
 
@@ -68,15 +108,29 @@ def rmsle(predict, actual):
 
 # Mean Bias Deviation
 def mbd(predict, actual):
+    """
+    This value is Negative, if the model underpredicts,
+    positive, if it overpredicts.
+
+    Example(rounded for precision):
+
+    Here the model overpredicts
+    >>> actual = [1,2,3];predict = [2,3,4]
+    >>> np.around(mbd(predict,actual),decimals = 2)
+    50.0
+
+    Here the model underpredicts
+    >>> actual = [1,2,3];predict = [0,1,1]
+    >>> np.around(mbd(predict,actual),decimals = 2)
+    -66.67
+    """
     predict = np.array(predict)
     actual = np.array(actual)
 
     difference = predict - actual
     numerator = np.sum(difference) / len(predict)
     denumerator = np.sum(actual) / len(predict)
-    print(numerator)
-    print(denumerator)
-
+    # print(numerator, denumerator)
     score = float(numerator) / denumerator * 100
 
     return score