diff --git a/AMLNotebooks/01_Create_CreditRisk_AML_Pipeline.ipynb b/AMLNotebooks/01_Create_CreditRisk_AML_Pipeline.ipynb
index 185fbaa..f8ca363 100644
--- a/AMLNotebooks/01_Create_CreditRisk_AML_Pipeline.ipynb
+++ b/AMLNotebooks/01_Create_CreditRisk_AML_Pipeline.ipynb
@@ -297,7 +297,7 @@
     "    \n",
     "    df_data = df_data.fillna(df_data.mean())\n",
     "    OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
-    "    OH_cols= pd.DataFrame(OH_encoder.fit_transform(df_data[catColumns]),columns = list(OH_encoder.get_feature_names(catColumns)))\n",
+    "    OH_cols= pd.DataFrame(OH_encoder.fit_transform(df_data[catColumns]),columns = list(OH_encoder.get_feature_names_out(catColumns)))\n",
     "    \n",
     "    # Remove categorical columns (will replace with one-hot encoding)\n",
     "    numeric_cols = df_data.drop(catColumns, axis=1)\n",
@@ -413,7 +413,7 @@
     "    \n",
     "    df_data = df_data.fillna(df_data.mean())\n",
     "    OH_encoder = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
-    "    OH_cols= pd.DataFrame(OH_encoder.fit_transform(df_data[catColumns]),columns = list(OH_encoder.get_feature_names(catColumns)))\n",
+    "    OH_cols= pd.DataFrame(OH_encoder.fit_transform(df_data[catColumns]),columns = list(OH_encoder.get_feature_names_out(catColumns)))\n",
     "    \n",
     "    # Remove categorical columns (will replace with one-hot encoding)\n",
     "    numeric_cols = df_data.drop(catColumns, axis=1)\n",