diff --git a/blockchain_integration/pi_network/utils/data_processing_helpers.py b/blockchain_integration/pi_network/utils/data_processing_helpers.py
index e9d292376..30c2d467b 100644
--- a/blockchain_integration/pi_network/utils/data_processing_helpers.py
+++ b/blockchain_integration/pi_network/utils/data_processing_helpers.py
@@ -1,44 +1,59 @@
-import pandas as pd
 import numpy as np
-from sklearn.preprocessing import StandardScaler
+import pandas as pd
 from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+
 
 def load_data(file_path):
     return pd.read_csv(file_path)
 
+
 def preprocess_data(data):
     scaler = StandardScaler()
-    data[['feature1', 'feature2', 'feature3']] = scaler.fit_transform(data[['feature1', 'feature2', 'feature3']])
+    data[["feature1", "feature2", "feature3"]] = scaler.fit_transform(
+        data[["feature1", "feature2", "feature3"]]
+    )
     return data
 
+
 def split_data(data, target_column):
     X = data.drop(columns=[target_column])
     y = data[target_column]
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.2, random_state=42
+    )
     return X_train, X_test, y_train, y_test
 
+
 def handle_missing_values(data):
     data.fillna(data.mean(), inplace=True)
     return data
 
+
 def convert_data_type(data, column, data_type):
     data[column] = data[column].astype(data_type)
     return data
 
+
 def aggregate_data(data, column, aggregation_function):
     return data.groupby(column).agg(aggregation_function)
 
+
 def merge_data(data1, data2, on):
     return pd.merge(data1, data2, on=on)
 
+
 def filter_data(data, column, value):
     return data[data[column] == value]
 
+
 def sort_data(data, column, ascending):
     return data.sort_values(by=column, ascending=ascending)
 
+
 def group_data(data, column):
     return data.groupby(column)
 
+
 def pivot_data(data, index, columns, values):
     return data.pivot(index=index, columns=columns, values=values)