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

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

machine_learning/best_random_state_in_random_forest.py

@@ -5,10 +5,12 @@
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.preprocessing import StandardScaler
 
-warnings.filterwarnings('ignore')
+warnings.filterwarnings("ignore")
 
 
-def find_best_random_state(data: pd.DataFrame, target_column: str, iterations: int = 200) -> int:
+def find_best_random_state(
+    data: pd.DataFrame, target_column: str, iterations: int = 200
+) -> int:
     """
     Find the best random state for the Random Forest Classifier that maximizes accuracy.
 
@@ -25,7 +27,9 @@ def find_best_random_state(data: pd.DataFrame, target_column: str, iterations: i
     target = data[target_column]
 
     # Split dataset into train and test sets
-    X_train, X_test, y_train, y_test = train_test_split(predictors, target, test_size=0.20, random_state=0)
+    X_train, X_test, y_train, y_test = train_test_split(
+        predictors, target, test_size=0.20, random_state=0
+    )
 
     # Scale features
     scaler = StandardScaler()
@@ -40,19 +44,21 @@ def find_best_random_state(data: pd.DataFrame, target_column: str, iterations: i
         rf = RandomForestClassifier(random_state=random_state)
         rf.fit(X_train_scaled, y_train)
         y_pred_rf = rf.predict(X_test_scaled)
-        
+
         current_accuracy = round(accuracy_score(y_test, y_pred_rf) * 100, 2)
         if current_accuracy > max_accuracy_rf:
             max_accuracy_rf = current_accuracy
             best_random_state = random_state
 
-    print(f"The best random state is: {best_random_state} with an accuracy score of: {max_accuracy_rf} %")
+    print(
+        f"The best random state is: {best_random_state} with an accuracy score of: {max_accuracy_rf} %"
+    )
     return best_random_state
 
 
 if __name__ == "__main__":
     # Load dataset
     dataset = pd.read_csv("heart.csv")
-    
+
     # Find the best random state
     best_state = find_best_random_state(dataset, target_column="target")