Create data_utils.py

src/cosmic_pi_network/utils/data_utils.py

@@ -0,0 +1,40 @@
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+
+class DataUtils:
+    def __init__(self):
+        pass
+
+    def load_data(self, file_path):
+        return pd.read_csv(file_path)
+
+    def preprocess_data(self, data):
+        scaler = StandardScaler()
+        data[['column1', 'column2', 'column3']] = scaler.fit_transform(data[['column1', 'column2', 'column3']])
+        return data
+
+    def handle_missing_values(self, data):
+        data.fillna(data.mean(), inplace=True)
+        return data
+
+    def split_data(self, data, test_size=0.2):
+        from sklearn.model_selection import train_test_split
+        X = data.drop('target', axis=1)
+        y = data['target']
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)
+        return X_train, X_test, y_train, y_test
+
+    def save_data(self, data, file_path):
+        data.to_csv(file_path, index=False)
+
+# Example usage
+data_utils = DataUtils()
+data = data_utils.load_data("data.csv")
+data = data_utils.preprocess_data(data)
+data = data_utils.handle_missing_values(data)
+X_train, X_test, y_train, y_test = data_utils.split_data(data)
+data_utils.save_data(X_train, "X_train.csv")
+data_utils.save_data(X_test, "X_test.csv")
+data_utils.save_data(y_train, "y_train.csv")
+data_utils.save_data(y_test, "y_test.csv")