diff --git a/src/autogluon_assistant/transformer/feature_transformers/base.py b/src/autogluon_assistant/transformer/feature_transformers/base.py
index b8f16f2..df7b728 100644
--- a/src/autogluon_assistant/transformer/feature_transformers/base.py
+++ b/src/autogluon_assistant/transformer/feature_transformers/base.py
@@ -21,7 +21,7 @@ def _fit_dataframes(self, train_X: pd.DataFrame, train_y: pd.Series, **kwargs) -
     def fit(self, task: TabularPredictionTask) -> "BaseFeatureTransformer":
         try:
             train_x = task.train_data.drop(
-                columns=task.columns_in_train_but_not_test + [task.test_id_column],
+                columns=task.columns_in_train_but_not_test + [task.train_id_column],
                 errors="ignore",
             )
             train_y = task.train_data[task.label_column]
@@ -47,26 +47,52 @@ def _transform_dataframes(self, train_X: pd.DataFrame, test_X: pd.DataFrame) ->
     def transform(self, task: TabularPredictionTask) -> TabularPredictionTask:
         try:
             train_x = task.train_data.drop(
-                columns=task.columns_in_train_but_not_test + [task.test_id_column],
+                columns=task.columns_in_train_but_not_test + [task.train_id_column],
                 errors="ignore",
             )
+            label_column_available_in_test_set = False
+            if task.label_column in train_x.columns:
+                # Label Column also present in test data
+                # requires explicit dropping of the column
+                # from train set before feature transformation
+                train_x = train_x.drop(columns=[task.label_column])
             train_y = task.train_data[task.label_column]
-            test_x = task.test_data.drop(columns=[task.test_id_column])
+
+            if task.test_id_column in task.test_data.columns:
+                # Skip if test_id_column is not found
+                test_x = task.test_data.drop(columns=[task.test_id_column])
+            else:
+                test_x = task.test_data
+            if task.label_column in test_x.columns:
+                # Label Column also present in test data
+                # requires explicit dropping of the column
+                # from test set before feature transformation
+                label_column_available_in_test_set = True
+                test_x = test_x.drop(columns=[task.label_column])
+                test_y = task.test_data[task.label_column]
 
             train_x, test_x = self._transform_dataframes(train_X=train_x, test_X=test_x)
 
-            # add back id and label columns
+            # add back label columns
             transformed_train_data = pd.concat([train_x, train_y.rename(task.label_column)], axis=1)
-            if task.test_id_column in task.train_data.columns:
+            if label_column_available_in_test_set:
+                # Add back label column to test set as it was available before
+                transformed_test_data = pd.concat([test_x, test_y.rename(task.label_column)], axis=1)
+            else:
+                transformed_test_data = test_x
+
+            # add back id columns
+            if task.train_id_column in task.train_data.columns:
                 transformed_train_data = pd.concat(
-                    [transformed_train_data, task.train_data[task.test_id_column]], axis=1
+                    [transformed_train_data, task.train_data[task.train_id_column]], axis=1
                 )
-            transformed_test_data = pd.concat([test_x, task.test_data[task.test_id_column]], axis=1)
+            if task.test_id_column in task.test_data.columns:
+                transformed_test_data = pd.concat([transformed_test_data, task.test_data[task.test_id_column]], axis=1)
 
             task = copy.deepcopy(task)
             task.train_data = transformed_train_data
             task.test_data = transformed_test_data
-        except:
-            logger.warning(f"FeatureTransformer {self.__class__.__name__} failed to transform.")
+        except Exception as e:
+            logger.warning(f"FeatureTransformer {self.__class__.__name__} failed to transform. Error: {str(e)}")
         finally:
             return task