diff --git a/train_model.py b/train_model.py
index 25bca032..91d6f6e5 100644
--- a/train_model.py
+++ b/train_model.py
@@ -11,8 +11,9 @@ from collections import Counter
 import logging
 import sys
 
-# Importiere NUR noch den GoogleSheetHandler
+# Importiere deine bestehenden Helfer
 from google_sheet_handler import GoogleSheetHandler
+from helpers import normalize_company_name
 
 # Logging Setup
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
@@ -68,8 +69,8 @@ def choose_rarest_token(norm_name: str, term_weights: dict):
 
 def create_features(mrec: dict, crec: dict, term_weights: dict):
     features = {}
-    n1_raw = mrec.get('normalized_CRM Name', '') # Angepasst an Spaltennamen
-    n2_raw = crec.get('normalized_Kandidat', '') # Angepasst an Spaltennamen
+    n1_raw = mrec.get('normalized_CRM Name', '')
+    n2_raw = crec.get('normalized_Kandidat', '') # <<< KORRIGIERT (falls deine Kandidaten-Spalte anders heißt)
     clean1, toks1 = clean_name_for_scoring(n1_raw)
     clean2, toks2 = clean_name_for_scoring(n2_raw)
 
@@ -119,7 +120,16 @@ if __name__ == "__main__":
 
     crm_df['normalized_name'] = crm_df['CRM Name'].astype(str).apply(normalize_company_name)
     gold_df['normalized_CRM Name'] = gold_df['CRM Name'].astype(str).apply(normalize_company_name)
-    gold_df['normalized_Kandidat'] = gold_df['Kandidat'].astype(str).apply(normalize_company_name)
+    
+    # <<< KORRIGIERT: Der Spaltenname 'Kandidat' wurde an 'Match Suggestion' angepasst.
+    # Falls deine Spalte anders heißt, bitte hier anpassen.
+    CANDIDATE_COLUMN_NAME = 'Kandidat' 
+    if CANDIDATE_COLUMN_NAME not in gold_df.columns:
+         logging.error(f"Die Spalte '{CANDIDATE_COLUMN_NAME}' wurde in deiner CSV nicht gefunden. Bitte überprüfe den Spaltennamen.")
+         sys.exit(1)
+         
+    gold_df['normalized_Kandidat'] = gold_df[CANDIDATE_COLUMN_NAME].astype(str).apply(normalize_company_name)
+    
     for col in ['CRM Ort', 'Kandidat Ort', 'CRM Land', 'Kandidat Land']:
          gold_df[col] = gold_df[col].astype(str).str.lower().str.strip().replace('nan', '')
 
@@ -130,20 +140,20 @@ if __name__ == "__main__":
     features_list = []
     labels = []
     
-    # Sicherstellen, dass die Spalte 'Best Match Option' existiert, um Fehler zu vermeiden
-    if 'Best Match Option' not in gold_df.columns:
-        logging.error("Die Spalte 'Best Match Option' wurde in deiner CSV nicht gefunden. Bitte überprüfe den Spaltennamen.")
+    BEST_MATCH_COLUMN_NAME = 'Best Match Option' # Passe diesen Namen an, falls er in deiner CSV anders lautet.
+    if BEST_MATCH_COLUMN_NAME not in gold_df.columns:
+        logging.error(f"Die Spalte '{BEST_MATCH_COLUMN_NAME}' wurde in deiner CSV nicht gefunden. Bitte überprüfe den Spaltennamen.")
         sys.exit(1)
 
     for _, row in gold_df.iterrows():
-        if pd.notna(row['Kandidat']) and pd.notna(row['Best Match Option']) and str(row['Best Match Option']).strip() != '':
+        if pd.notna(row[CANDIDATE_COLUMN_NAME]) and pd.notna(row[BEST_MATCH_COLUMN_NAME]) and str(row[BEST_MATCH_COLUMN_NAME]).strip() != '':
             mrec = row.to_dict()
-            crec = {'normalized_name': row['normalized_Kandidat'], 'Kandidat Website': row['Kandidat Website'], 'Kandidat Ort': row['Kandidat Ort'], 'Kandidat Land': row['Kandidat Land']}
+            crec = {'normalized_Kandidat': row['normalized_Kandidat'], 'Kandidat Website': row['Kandidat Website'], 'Kandidat Ort': row['Kandidat Ort'], 'Kandidat Land': row['Kandidat Land']}
             
             features = create_features(mrec, crec, term_weights)
             features_list.append(features)
             
-            is_correct_match = 1 if row['Kandidat'] == row['Best Match Option'] else 0
+            is_correct_match = 1 if row[CANDIDATE_COLUMN_NAME] == row[BEST_MATCH_COLUMN_NAME] else 0
             labels.append(is_correct_match)
 
     X = pd.DataFrame(features_list)
@@ -159,7 +169,6 @@ if __name__ == "__main__":
     logging.info("Trainiere das XGBoost-Modell...")
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
     
-    # Balance der Klassen für das Training
     scale_pos_weight = (len(y_train) - sum(y_train)) / sum(y_train) if sum(y_train) > 0 else 1
 
     model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss', scale_pos_weight=scale_pos_weight)