diff --git a/model_training/tools/scaler.py b/model_training/tools/scaler.py
new file mode 100644
index 0000000..7449c9e
--- /dev/null
+++ b/model_training/tools/scaler.py
@@ -0,0 +1,98 @@
+from sklearn.preprocessing import MinMaxScaler, StandardScaler
+import pandas as pd
+
+def fit_normalizer(train_data, au_columns, method='standard', scope='global'):
+    """
+    Fit normalization scalers on training data.
+    
+    Parameters:
+    -----------
+    train_data : pd.DataFrame
+        Training dataframe with AU columns and subjectID
+    au_columns : list
+        List of AU column names to normalize
+    method : str, default='standard'
+        Normalization method: 'standard' for StandardScaler or 'minmax' for MinMaxScaler
+    scope : str, default='global'
+        Normalization scope: 'subject' for per-subject or 'global' for across all subjects
+    
+    Returns:
+    --------
+    dict
+        Dictionary containing fitted scalers
+    """
+    # Select scaler based on method
+    if method == 'standard':
+        Scaler = StandardScaler
+    elif method == 'minmax':
+        Scaler = MinMaxScaler
+    else:
+        raise ValueError("method must be 'standard' or 'minmax'")
+    
+    scalers = {}
+    
+    if scope == 'subject':
+        # Fit one scaler per subject
+        for subject in train_data['subjectID'].unique():
+            subject_mask = train_data['subjectID'] == subject
+            scaler = Scaler()
+            scaler.fit(train_data.loc[subject_mask, au_columns])
+            scalers[subject] = scaler
+    
+    elif scope == 'global':
+        # Fit one scaler for all subjects
+        scaler = Scaler()
+        scaler.fit(train_data[au_columns])
+        scalers['global'] = scaler
+    
+    else:
+        raise ValueError("scope must be 'subject' or 'global'")
+    
+    return {'scalers': scalers, 'method': method, 'scope': scope}
+
+def apply_normalizer(data, au_columns, normalizer_dict):
+    """
+    Apply fitted normalization scalers to data.
+    
+    Parameters:
+    -----------
+    data : pd.DataFrame
+        Dataframe with AU columns and subjectID
+    au_columns : list
+        List of AU column names to normalize
+    normalizer_dict : dict
+        Dictionary containing fitted scalers from fit_normalizer()
+    
+    Returns:
+    --------
+    pd.DataFrame
+        DataFrame with normalized AU columns
+    """
+    normalized_data = data.copy()
+    scalers = normalizer_dict['scalers']
+    scope = normalizer_dict['scope']
+    
+    if scope == 'subject':
+        # Apply per-subject normalization
+        for subject in data['subjectID'].unique():
+            subject_mask = data['subjectID'] == subject
+            
+            # Use the subject's scaler if available, otherwise use a fitted scaler from training
+            if subject in scalers:
+                scaler = scalers[subject]
+            else:
+                # For new subjects not seen in training, use the first available scaler
+                # (This is a fallback - ideally all test subjects should be in training for subject-level normalization)
+                print(f"Warning: Subject {subject} not found in training data. Using fallback scaler.")
+                scaler = list(scalers.values())[0]
+            
+            normalized_data.loc[subject_mask, au_columns] = scaler.transform(
+                data.loc[subject_mask, au_columns]
+            )
+    
+    elif scope == 'global':
+        # Apply global normalization
+        scaler = scalers['global']
+        normalized_data[au_columns] = scaler.transform(data[au_columns])
+    
+    return normalized_data
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