diff --git a/src/Machine-Learning/LSTM-python/src/.python-version b/src/Machine-Learning/LSTM-python/src/.python-version
index 0c7d5f5..bd28b9c 100644
--- a/src/Machine-Learning/LSTM-python/src/.python-version
+++ b/src/Machine-Learning/LSTM-python/src/.python-version
@@ -1 +1 @@
-3.11.4
+3.9
diff --git a/src/Machine-Learning/LSTM-python/src/LSTMDQN.py b/src/Machine-Learning/LSTM-python/src/LSTMDQN.py
index 257d84a..6357e14 100644
--- a/src/Machine-Learning/LSTM-python/src/LSTMDQN.py
+++ b/src/Machine-Learning/LSTM-python/src/LSTMDQN.py
@@ -235,6 +235,8 @@ def parse_arguments():
                         help='Number of worker processes for data preprocessing. Defaults to (logical cores - 2).')
     parser.add_argument('--monitor_resources', action='store_true',
                         help='Enable real-time resource monitoring.')
+    parser.add_argument('--output_dir', type=str, default='output',
+                        help='Directory where all output files will be saved.')
     return parser.parse_args()
 
 # ============================
@@ -517,6 +519,8 @@ def train_and_evaluate_dqn(hyperparams, env_params, total_timesteps, eval_episod
 def main():
     args = parse_arguments()
     csv_path = args.csv_path
+    output_dir = args.output_dir
+    os.makedirs(output_dir, exist_ok=True)
     lstm_window_size = args.lstm_window_size
     dqn_total_timesteps = args.dqn_total_timesteps
     dqn_eval_episodes   = args.dqn_eval_episodes
@@ -529,8 +533,10 @@ def main():
     # -----------------------------
     logging.basicConfig(level=logging.INFO,
                         format='%(asctime)s - %(levelname)s - %(message)s',
-                        handlers=[logging.FileHandler("LSTMDQN.log"), logging.StreamHandler(sys.stdout)])
-
+                        handlers=[
+                            logging.FileHandler(os.path.join(output_dir, "LSTMDQN.log")),
+                            logging.StreamHandler(sys.stdout)
+                        ])
     # -----------------------------
     # Resource Detection & Logging
     # -----------------------------
@@ -703,7 +709,7 @@ def main():
         plt.title('LSTM: Actual vs Predicted Closing Prices')
         plt.legend()
         plt.grid(True)
-        plt.savefig('lstm_actual_vs_pred.png')
+        plt.savefig(os.path.join(output_dir, 'lstm_actual_vs_pred.png')
         plt.close()
 
         table = []
@@ -718,9 +724,9 @@ def main():
     _r2, _diracc = evaluate_final_lstm(final_lstm, X_test, y_test)
 
     # 9) Save final LSTM model and scalers
-    final_lstm.save('best_lstm_model.h5')
-    joblib.dump(scaler_features, 'scaler_features.pkl')
-    joblib.dump(scaler_target, 'scaler_target.pkl')
+    final_lstm.save(os.path.join(output_dir, 'best_lstm_model.h5')
+    joblib.dump(scaler_features, os.path.join(output_dir, 'scaler_features.pkl')
+    joblib.dump(scaler_target, os.path.join(output_dir, 'scaler_target.pkl')
     logging.info("Saved best LSTM model and scaler objects (best_lstm_model.h5, scaler_features.pkl, scaler_target.pkl).")
 
     ############################################################
@@ -766,7 +772,7 @@ def main():
         if net_worth >= PERFORMANCE_THRESHOLD:
             logging.info("Agent meets performance criteria!")
             best_agent = agent
-            best_agent.save("best_dqn_model_lstm.zip")
+            best_agent.save(os.path.join(output_dir, "best_dqn_model_lstm.zip")
             break
         else:
             logging.info("Performance below threshold. Adjusting hyperparameters and retrying...")