diff --git a/freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py b/freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py
index 389aa6155..a44214367 100644
--- a/freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py
+++ b/freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py
@@ -26,7 +26,7 @@ class PyTorchMLPClassifier(BasePyTorchClassifier):
                 "trainer_kwargs": {
                     "max_iters": 5000,
                     "batch_size": 64,
-                    "max_n_eval_batches": None,
+                    "max_n_eval_batches": null,
                 },
                 "model_kwargs": {
                     "hidden_dim": 512,
@@ -49,7 +49,7 @@ class PyTorchMLPClassifier(BasePyTorchClassifier):
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary constructed by DataHandler to hold
-                                all the training and test data/labels.
+        all the training and test data/labels.
         :raises ValueError: If self.class_names is not defined in the parent class.
         """
 
diff --git a/freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py b/freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py
index ca6a13f6e..6fc2be1a5 100644
--- a/freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py
+++ b/freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py
@@ -27,7 +27,7 @@ class PyTorchMLPRegressor(BasePyTorchRegressor):
                 "trainer_kwargs": {
                     "max_iters": 5000,
                     "batch_size": 64,
-                    "max_n_eval_batches": None,
+                    "max_n_eval_batches": null,
                 },
                 "model_kwargs": {
                     "hidden_dim": 512,
@@ -50,7 +50,7 @@ class PyTorchMLPRegressor(BasePyTorchRegressor):
         """
         User sets up the training and test data to fit their desired model here
         :param data_dictionary: the dictionary constructed by DataHandler to hold
-                                all the training and test data/labels.
+        all the training and test data/labels.
         """
 
         n_features = data_dictionary["train_features"].shape[-1]