diff --git a/other/scoring_algorithm.py b/other/scoring_algorithm.py
index 77e614e2622c19b795d2a0fb9e3f0abc77eba1b3..cc17440126715bb459fdc600867cfbff66db16db 100644
--- a/other/scoring_algorithm.py
+++ b/other/scoring_algorithm.py
@@ -20,39 +20,38 @@ We want the vehicle with the lowest price,
 lowest mileage but newest registration year.
 Thus the weights for each column are as follows:
 [0, 0, 1]
-
->>> procentual_proximity([[20, 60, 2012],[23, 90, 2015],[22, 50, 2011]], [0, 0, 1])
-[[20, 60, 2012, 2.0], [23, 90, 2015, 1.0], [22, 50, 2011, 1.3333333333333335]]
 """
 
 
-def procentual_proximity(source_data: list, weights: list) -> list:
+def procentual_proximity(
+    source_data: list[list[float]], weights: list[int]
+) -> list[list[float]]:
 
     """
     weights - int list
     possible values - 0 / 1
     0 if lower values have higher weight in the data set
     1 if higher values have higher weight in the data set
+
+    >>> procentual_proximity([[20, 60, 2012],[23, 90, 2015],[22, 50, 2011]], [0, 0, 1])
+    [[20, 60, 2012, 2.0], [23, 90, 2015, 1.0], [22, 50, 2011, 1.3333333333333335]]
     """
 
     # getting data
-    data_lists = []
-    for item in source_data:
-        for i in range(len(item)):
-            try:
-                data_lists[i].append(float(item[i]))
-            except IndexError:
-                # generate corresponding number of lists
+    data_lists: list[list[float]] = []
+    for data in source_data:
+        for i, el in enumerate(data):
+            if len(data_lists) < i + 1:
                 data_lists.append([])
-                data_lists[i].append(float(item[i]))
+            data_lists[i].append(float(el))
 
-    score_lists = []
+    score_lists: list[list[float]] = []
     # calculating each score
     for dlist, weight in zip(data_lists, weights):
         mind = min(dlist)
         maxd = max(dlist)
 
-        score = []
+        score: list[float] = []
         # for weight 0 score is 1 - actual score
         if weight == 0:
             for item in dlist:
@@ -75,7 +74,7 @@ def procentual_proximity(source_data: list, weights: list) -> list:
         score_lists.append(score)
 
     # initialize final scores
-    final_scores = [0 for i in range(len(score_lists[0]))]
+    final_scores: list[float] = [0 for i in range(len(score_lists[0]))]
 
     # generate final scores
     for i, slist in enumerate(score_lists):