diff --git a/gamestonk_terminal/prediction_techniques/neural_networks_view.py b/gamestonk_terminal/prediction_techniques/neural_networks_view.py
index faafe3c0d51..5dcdbc0cb49 100644
--- a/gamestonk_terminal/prediction_techniques/neural_networks_view.py
+++ b/gamestonk_terminal/prediction_techniques/neural_networks_view.py
@@ -1023,7 +1023,6 @@ def lstm(other_args: List[str], s_ticker: str, df_stock: pd.DataFrame):
         )
         if not ns_parser:
             return
-
         # Setup backtesting
         # df_stock, df_future = _setup_backtesting(df_stock, ns_parser)
 
@@ -1046,7 +1045,7 @@ def lstm(other_args: List[str], s_ticker: str, df_stock: pd.DataFrame):
             test_data,
             dates_test,
             scaler,
-        ) = prepare_scale_train_valid_test(df_stock, ns_parser)
+        ) = prepare_scale_train_valid_test(df_stock["5. adjusted close"], ns_parser)
         # Build Neural Network model
         model = build_neural_network_model(
             cfg_nn_models.Long_Short_Term_Memory, ns_parser.n_inputs, ns_parser.n_days
@@ -1061,6 +1060,13 @@ def lstm(other_args: List[str], s_ticker: str, df_stock: pd.DataFrame):
             validation_data=(X_test, y_test),
         )
 
+        preds = model.predict(X_test.reshape(X_test.shape[0], X_test.shape[1], 1))
+
+        plt.scatter(df_stock.index, df_stock["5. adjusted close"].values, s= 3)
+        for i in range(len(y_test)):
+            plt.plot(y_dates_test[i], scaler.inverse_transform(preds[i].reshape(-1,1)), "r", lw=3)
+            plt.fill_between(y_dates_test[i])
+        plt.show()
         # for idx_loop in range(ns_parser.n_loops):
         # Train our model
         #    model.fit(
diff --git a/gamestonk_terminal/prediction_techniques/pred_helper.py b/gamestonk_terminal/prediction_techniques/pred_helper.py
index 72f7071d970..5f5757d2b1d 100644
--- a/gamestonk_terminal/prediction_techniques/pred_helper.py
+++ b/gamestonk_terminal/prediction_techniques/pred_helper.py
@@ -102,10 +102,10 @@ def prepare_scale_train_valid_test(
             dates[idx + n_input_days : idx + n_input_days + n_predict_days]
         )
 
-    input_dates = np.array(input_dates)
+    input_dates = np.asarray(input_dates)
     input_prices = np.array(input_prices)
     next_n_day_prices = np.array(next_n_day_prices)
-    next_n_day_dates = np.array(next_n_day_dates)
+    next_n_day_dates = np.asarray(next_n_day_dates)
 
     (
         X_train,
@@ -123,7 +123,6 @@ def prepare_scale_train_valid_test(
         next_n_day_dates,
         test_size=test_size,
     )
-
     return (
         X_train,
         X_test,