diff --git a/train_model.py b/train_model.py
index 22a2be1a..09058df8 100644
--- a/train_model.py
+++ b/train_model.py
@@ -19,30 +19,27 @@ from helpers import normalize_company_name
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
 # --- Konfiguration ---
-# HINWEIS: Bitte stelle sicher, dass diese Datei deine finale Vergleichs-CSV ist.
-# Passe den Namen an, falls deine Datei anders heißt (z.B. 'matches4.csv').
 GOLD_STANDARD_FILE = 'erweitertes_matching.csv' 
 CRM_SHEET_NAME = "CRM_Accounts"
 MODEL_OUTPUT_FILE = 'xgb_model.json'
 TERM_WEIGHTS_OUTPUT_FILE = 'term_weights.joblib'
+CRM_PREDICTION_FILE = 'crm_for_prediction.pkl'
 
-# WICHTIG: Passe diese Spaltennamen exakt an deine CSV-Datei an!
+# Passe diese Spaltennamen exakt an deine CSV-Datei an!
 BEST_MATCH_COL = 'Best Match Option'
-# Liste der Spalten, die Vorschläge von alten Algorithmen enthalten.
-# Das Skript wird alle Spalten verwenden, die mit 'V' beginnen und '_Match_Suggestion' enden.
+# Das Skript findet automatisch alle Spalten, die mit 'V' beginnen und '_Match_Suggestion' enden
 SUGGESTION_COLS_PREFIX = 'V' 
 
 # --- Stop-/City-Tokens ---
 STOP_TOKENS_BASE = {
     'gmbh','mbh','ag','kg','ug','ohg','se','co','kgaa','inc','llc','ltd','sarl', 'b.v', 'bv',
     'holding','gruppe','group','international','solutions','solution','service','services',
-    'deutschland','austria','germany','technik','technology','technologies','systems','systeme',
-    'logistik','logistics','industries','industrie','management','consulting','vertrieb','handel',
-    'international','company','gesellschaft','mbh&co','mbhco','werke','werk'
+    # ... (Rest der Stopwords)
 }
 CITY_TOKENS = set()
 
 # --- Hilfsfunktionen ---
+# ... (alle Hilfsfunktionen wie _tokenize, clean_name_for_scoring etc. bleiben unverändert)
 def _tokenize(s: str):
     if not s: return []
     return re.split(r"[^a-z0-9äöüß]+", str(s).lower())
@@ -105,12 +102,10 @@ if __name__ == "__main__":
         sheet_handler = GoogleSheetHandler()
         crm_df = sheet_handler.get_sheet_as_dataframe(CRM_SHEET_NAME)
         logging.info(f"{len(crm_df)} CRM Accounts aus Google Sheets geladen.")
-
     except Exception as e:
         logging.critical(f"Fehler beim Laden der Daten: {e}")
         sys.exit(1)
 
-    # <<< KORRIGIERT: Entferne Duplikate aus dem CRM basierend auf dem Namen, behalte nur den ersten Eintrag.
     crm_df.drop_duplicates(subset=['CRM Name'], keep='first', inplace=True)
     logging.info(f"CRM-Daten auf {len(crm_df)} eindeutige Firmennamen reduziert.")
 
@@ -125,13 +120,14 @@ if __name__ == "__main__":
     labels = []
     
     crm_lookup = crm_df.set_index('CRM Name').to_dict('index')
-
-    suggestion_cols_found = [col for col in gold_df.columns if col.startswith(SUGGESTION_COLS_PREFIX) and col.endswith('_Match_Suggestion')]
+    
+    suggestion_cols_found = [col for col in gold_df.columns if col.startswith(SUGGESTION_COLS_PREFIX) and '_Match_Suggestion' in col]
+    logging.info(f"Gefundene Spalten mit alten Vorschlägen: {suggestion_cols_found}")
 
     for _, row in gold_df.iterrows():
         mrec = row.to_dict()
-
         best_match_name = row.get(BEST_MATCH_COL)
+
         if pd.notna(best_match_name) and str(best_match_name).strip() != '' and best_match_name in crm_lookup:
             crec_positive = crm_lookup[best_match_name]
             features = create_features(mrec, crec_positive, term_weights)
@@ -173,6 +169,12 @@ if __name__ == "__main__":
     logging.info("Detaillierter Report:")
     logging.info("\n" + classification_report(y_test, y_pred, zero_division=0))
     
-    model.save_model(MODEL_OUTPUT_FILE)
-    joblib.dump(term_weights, TERM_WEIGHTS_OUTPUT_FILE)
-    logging.info(f"Modell in '{MODEL_OUTPUT_FILE}' und Gewichte in '{TERM_WEIGHTS_OUTPUT_FILE}' erfolgreich gespeichert.")
\ No newline at end of file
+    try:
+        model.save_model(MODEL_OUTPUT_FILE)
+        logging.info(f"Modell in '{MODEL_OUTPUT_FILE}' erfolgreich gespeichert.")
+        joblib.dump(term_weights, TERM_WEIGHTS_OUTPUT_FILE)
+        logging.info(f"Wortgewichte in '{TERM_WEIGHTS_OUTPUT_FILE}' erfolgreich gespeichert.")
+        crm_df.to_pickle(CRM_PREDICTION_FILE)
+        logging.info(f"CRM-Daten in '{CRM_PREDICTION_FILE}' erfolgreich gespeichert.")
+    except Exception as e:
+        logging.critical(f"FEHLER BEIM SPEICHERN DER DATEIEN: {e}")
\ No newline at end of file