--- a
+++ b/data/data_loader.py
@@ -0,0 +1,61 @@
+# data/data_loader.py
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+import logging
+from app.config import DATA_PATH, FEATURES, TARGET, TEST_SIZE, RANDOM_STATE
+
+def load_data(file_path='data/patient_data.csv'):
+    """
+    Load data from CSV file
+    """
+    try:
+        df = pd.read_csv(file_path)
+        print(f"Data loaded successfully with {len(df)} rows")  # Debug print
+        return df
+    except Exception as e:
+        print(f"Error loading data: {e}")
+        return None
+
+def preprocess_data(df):
+    """
+    Preprocess the data and split into features and target
+    Returns:
+        X_train, X_test, y_train, y_test, feature_names, scaler
+    """
+    print("Starting preprocessing...")  # Debug print
+    
+    if df is None:
+        print("DataFrame is None")  # Debug print
+        return None, None, None, None, None, None
+    
+    try:
+        # Separate features and target
+        X = df.drop('readmitted', axis=1)
+        y = df['readmitted']
+        
+        # Get feature names
+        feature_names = X.columns.tolist()
+        print(f"Features: {feature_names}")  # Debug print
+        
+        # Scale the features
+        scaler = StandardScaler()
+        X_scaled = scaler.fit_transform(X)
+        X_scaled = pd.DataFrame(X_scaled, columns=feature_names)
+        
+        # Split the data
+        X_train, X_test, y_train, y_test = train_test_split(
+            X_scaled, y, test_size=0.2, random_state=42, stratify=y
+        )
+        
+        print("Preprocessing completed successfully")  # Debug print
+        
+        # Explicitly create the return tuple
+        result = (X_train, X_test, y_train, y_test, feature_names, scaler)
+        print(f"Number of return values: {len(result)}")  # Debug print
+        return result
+    
+    except Exception as e:
+        print(f"Error in preprocessing: {e}")
+        return None, None, None, None, None, None
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