diff --git a/latitute_longitute_reference.csv b/latitute_longitute_reference.csv
index 67f96b2..1fd4bd6 100644
--- a/latitute_longitute_reference.csv
+++ b/latitute_longitute_reference.csv
@@ -1,5 +1,4 @@
 postal_code_ref,latitute_ref,longitude_ref
-,,
 c1001,-34.61178,-58.41731
 8001,-33.9249,18.4241
 2176,-33.87706,150.87529
diff --git a/pre_processing.py b/pre_processing.py
index fcfe17c..b1e4e5c 100644
--- a/pre_processing.py
+++ b/pre_processing.py
@@ -276,6 +276,7 @@ def pre_processing_m1(data_df: pd.DataFrame) -> pd.DataFrame:
         "true_ip_state_confidence": float,
         "IP_Negative_History": int,
         "fuzzy_device_worst_score": float,
+        "digital_id_confidence_rating" :  str,
         "day_of_week_sin": float,
         "riskrating": str,
         "payfrequency": str,
diff --git a/processing.py b/processing.py
index 4f25b1e..95c5d42 100644
--- a/processing.py
+++ b/processing.py
@@ -47,12 +47,21 @@ def _load_category_orders_cached(path: Path):
     return _load_category_orders(path)
 
 
-def _prepare(df: pd.DataFrame, category_orders: dict) -> pd.DataFrame:
+def _prepare_m1(df: pd.DataFrame, category_orders: dict) -> pd.DataFrame:
+    df = df.copy()
+    for col, categories in category_orders.items():
+        if col not in df.columns:
+            df[col] = np.nan
+        df[col].replace([None, "", "null", np.nan, "nan", " "], np.nan, inplace=True)
+        df[col] = pd.Categorical(df[col], categories=categories, ordered=True)
+    return df
+
+
+def _prepare_m2(df: pd.DataFrame, category_orders: dict) -> pd.DataFrame:
     df = df.copy()
     for col, categories in category_orders.items():
         if col not in df.columns:
             df[col] = np.nan
-        
         df[col] = df[col].astype(str).str.lower()
         df[col].replace([None, "", "null", np.nan, "nan", " "], np.nan, inplace=True)
         df[col] = pd.Categorical(df[col], categories=categories, ordered=True)
@@ -67,7 +76,7 @@ def processing_m1(input_data: pd.DataFrame) -> pd.DataFrame:
     model = _load_m1_model()
     df.rename(columns={"riskrating": "RiskRating", "trueipgeo": "TrueIpGeo"}, inplace=True)
     category_orders = _load_category_orders_cached(M1_CATEGORY_ORDERS_PATH)
-    df = _prepare(df, category_orders)
+    df = _prepare_m1(df, category_orders)
 
     expected_features = model.feature_names
     dmatrix = xgb.DMatrix(df[expected_features], enable_categorical=True, missing=np.nan)
@@ -83,7 +92,7 @@ def processing_m2(input_data: pd.DataFrame) -> pd.DataFrame:
 
     model = _load_m2_model()
     category_orders = _load_category_orders_cached(M2_CATEGORY_ORDERS_PATH)
-    df = _prepare(df, category_orders)
+    df = _prepare_m2(df, category_orders)
 
     expected_features = model.feature_names
     for feature in expected_features: