Lint and type annotation fixes for Apriori algorithm (ruff, mypy compliant)

Brief: Applied Ruff and MyPy fixes: line length, type hints, import sorting.

Description: This commit resolves all Ruff and MyPy linter errors related to style,
formatting, and type safety to ensure full pre-commit compatibility and correctness.

Explanation:
    1. Reformatted import statements to match standard alphabetical order (I001).
    2. Wrapped overly long lines in docstrings to comply with line length limits (E501).
    3. Replaced generator expression inside `set()` with set comprehension (C401).
    4. Removed redundant `list()` call inside `sorted()` during candidate generation (C414).
    5. Added missing type annotations for `item_counts` and `candidate_counts` to satisfy MyPy.

Conclusion:
These changes ensure the Apriori implementation conforms to all enforced code quality
standards (Ruff and MyPy). This improves readability, maintainability, and compatibility
with the repository’s CI system and contributor guidelines.

Author: FardinMoghaddamPour

machine_learning/apriori_algorithm.py

@@ -11,8 +11,8 @@
 Examples: https://www.kaggle.com/code/earthian/apriori-association-rules-mining
 """
 
-from itertools import combinations
 from collections import defaultdict
+from itertools import combinations
 
 
 def load_data() -> list[list[str]]:
@@ -22,20 +22,26 @@ def load_data() -> list[list[str]]:
     >>> load_data()
     [['milk'], ['milk', 'butter'], ['milk', 'bread'], ['milk', 'bread', 'chips']]
     """
-    return [["milk"], ["milk", "butter"], ["milk", "bread"], ["milk", "bread", "chips"]]
+    return [
+        ["milk"],
+        ["milk", "butter"],
+        ["milk", "bread"],
+        ["milk", "bread", "chips"]
+    ]
 
 
 def prune(frequent_itemsets: list[list[str]], candidates: list[list[str]]) -> list[list[str]]:
     """
-    Prunes candidate itemsets by ensuring all (k-1)-subsets exist in previous frequent itemsets.
+    Prunes candidate itemsets by ensuring all (k-1)-subsets exist in
+    previous frequent itemsets.
 
     >>> frequent_itemsets = [['X', 'Y'], ['X', 'Z'], ['Y', 'Z']]
     >>> candidates = [['X', 'Y', 'Z'], ['X', 'Y', 'W']]
     >>> prune(frequent_itemsets, candidates)
     [['X', 'Y', 'Z']]
     """
 
-    previous_frequents = set(frozenset(itemset) for itemset in frequent_itemsets)
+    previous_frequents = {frozenset(itemset) for itemset in frequent_itemsets}
 
     pruned_candidates = []
     for candidate in candidates:
@@ -55,14 +61,15 @@ def apriori(data: list[list[str]], min_support: int) -> list[tuple[list[str], in
 
     >>> data = [['A', 'B', 'C'], ['A', 'B'], ['A', 'C'], ['A', 'D'], ['B', 'C']]
     >>> apriori(data, 2)
-    [(['A'], 4), (['B'], 3), (['C'], 3), (['A', 'B'], 2), (['A', 'C'], 2), (['B', 'C'], 2)]
+    [(['A'], 4), (['B'], 3), (['C'], 3),
+     (['A', 'B'], 2), (['A', 'C'], 2), (['B', 'C'], 2)]
 
     >>> data = [['1', '2', '3'], ['1', '2'], ['1', '3'], ['1', '4'], ['2', '3']]
     >>> apriori(data, 3)
     [(['1'], 4), (['2'], 3), (['3'], 3)]
     """
 
-    item_counts = defaultdict(int)
+    item_counts: defaultdict[str, int] = defaultdict(int)
     for transaction in data:
         for item in transaction:
             item_counts[item] += 1
@@ -72,7 +79,7 @@ def apriori(data: list[list[str]], min_support: int) -> list[tuple[list[str], in
 
     k = 2
     while current_frequents:
-        candidates = [sorted(list(set(i) | set(j)))
+        candidates = [sorted(set(i) | set(j))
                       for i in current_frequents
                       for j in current_frequents
                       if len(set(i).union(j)) == k]
@@ -81,7 +88,7 @@ def apriori(data: list[list[str]], min_support: int) -> list[tuple[list[str], in
 
         candidates = prune(current_frequents, candidates)
 
-        candidate_counts = defaultdict(int)
+        candidate_counts: defaultdict[tuple[str, ...], int] = defaultdict(int)
         for transaction in data:
             t_set = set(transaction)
             for candidate in candidates: