[pre-commit.ci] auto fixes from pre-commit.com hooks

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machine_learning/apriori_algorithm.py

@@ -1,14 +1,14 @@
 """
-Apriori Algorithm is a Association rule mining technique, 
-also known as market basket analysis, 
-aims to discover interesting relationships or associations 
-among a set of items in a transactional or relational database. 
-
-For example, Apriori Algorithm state: 
-"If a customer buys item A and item B, 
-then they are likely to buy item C." 
-This rule suggests a relationship between items A, B, and C, 
-indicating that customers who purchased A and B are more 
+Apriori Algorithm is a Association rule mining technique,
+also known as market basket analysis,
+aims to discover interesting relationships or associations
+among a set of items in a transactional or relational database.
+
+For example, Apriori Algorithm state:
+"If a customer buys item A and item B,
+then they are likely to buy item C."
+This rule suggests a relationship between items A, B, and C,
+indicating that customers who purchased A and B are more
 likely to purchase item C as well.
 
 WIKI: https://en.wikipedia.org/wiki/Apriori_algorithm
@@ -21,8 +21,8 @@ def load_data() -> list[list[str]]:
     Returns a sample transaction dataset.
 
     >>> load_data()
-    [['milk', 'bread'], ['milk', 'butter'], ['milk', 'bread', 'nuts'], 
-    ['milk', 'bread', 'chips'], ['milk', 'butter', 'chips'], 
+    [['milk', 'bread'], ['milk', 'butter'], ['milk', 'bread', 'nuts'],
+    ['milk', 'bread', 'chips'], ['milk', 'butter', 'chips'],
     ['milk', 'bread', 'butter', 'cola'], ['nuts', 'bread', 'butter', 'cola'],
     ['bread', 'butter', 'cola', 'ice'], ['bread', 'butter', 'cola', 'ice', 'bun']]
     """
@@ -68,8 +68,8 @@ def prune(
     # Prune candidate itemsets
     """
     The goal of pruning is to filter out candidate itemsets that are not frequent.
-    This is done by checking if all the (k-1) subsets of a candidate itemset 
-    are present in the frequent itemsets of the previous iteration 
+    This is done by checking if all the (k-1) subsets of a candidate itemset
+    are present in the frequent itemsets of the previous iteration
     (valid subsequences of the frequent itemsets from the previous iteration).
 
     Prunes candidate itemsets that are not frequent.