diff --git a/data_science/data_preprocessing.py b/data_science/data_preprocessing.py
index 181699f..bbd562b 100644
--- a/data_science/data_preprocessing.py
+++ b/data_science/data_preprocessing.py
@@ -1,5 +1,6 @@
-import pandas as pd
 import numpy as np
+import pandas as pd
+
 
 def preprocess_data(data):
     # Preprocess data using Pandas or NumPy
@@ -9,14 +10,18 @@ def preprocess_data(data):
     data = data.fillna(data.mean())
 
     # Normalize numerical data
-    numerical_data = data.select_dtypes(include=['int64', 'float64'])
-    numerical_data = (numerical_data - numerical_data.min()) / (numerical_data.max() - numerical_data.min())
+    numerical_data = data.select_dtypes(include=["int64", "float64"])
+    numerical_data = (numerical_data - numerical_data.min()) / (
+        numerical_data.max() - numerical_data.min()
+    )
 
     # Encode categorical data
-    categorical_data = data.select_dtypes(include=['object'])
+    categorical_data = data.select_dtypes(include=["object"])
     encoded_data = pd.get_dummies(categorical_data)
 
     # Concatenate the preprocessed data
-    preprocessed_data = np.concatenate([numerical_data.values, encoded_data.values], axis=1)
+    preprocessed_data = np.concatenate(
+        [numerical_data.values, encoded_data.values], axis=1
+    )
 
     return preprocessed_data