diff --git a/machine_learning/scoring_functions.py b/machine_learning/scoring_functions.py
new file mode 100644
index 000000000..4204716cd
--- /dev/null
+++ b/machine_learning/scoring_functions.py
@@ -0,0 +1,63 @@
+import numpy
+
+""" Here I implemented the scoring functions.
+    MAE, MSE, RMSE, RMSLE are included.
+
+    Those are used for calculating differences between
+    predicted values and actual values.
+
+    Metrics are slightly differentiated. Sometimes squared, rooted,
+    even log is used.
+
+    Using log and roots can be perceived as tools for penalizing big
+    erors. However, using appropriate metrics depends on the situations,
+    and types of data
+"""
+
+#Mean Absolute Error
+def mae(predict, actual):
+    predict = np.array(predict)
+    actual = np.array(actual)
+
+    difference = abs(predict - actual)
+    score = difference.mean()
+
+    return score
+
+#Mean Squared Error
+def mse(predict, actual):
+    predict = np.array(predict)
+    actual = np.array(actual)
+
+    difference = predict - actual
+    square_diff = np.square(difference)
+
+    score = square_diff.mean()
+    return score
+
+#Root Mean Squared Error
+def rmse(predict, actual):
+    predict = np.array(predict)
+    actual = np.array(actual)
+
+    difference = predict - actual
+    square_diff = np.square(dfference)
+    mean_square_diff = square_diff.mean()
+    score = np.sqrt(mean_square_diff)
+    return score
+
+#Root Mean Square Logarithmic Error
+def rmsle(predict, actual):
+    predict = np.array(predict)
+    actual = np.array(actual)
+
+    log_predict = np.log(predict+1)
+    log_actual = np.log(actual+1)
+
+    difference = log_predict - log_actual
+    square_diff = np.square(difference)
+    mean_square_diff = square_diff.mean()
+
+    score = np.sqrt(mean_square_diff)
+
+    return score