diff --git a/app/algorithms/auto_gluon.py b/app/algorithms/auto_gluon.py
index 0a99a1b..c66a9d6 100644
--- a/app/algorithms/auto_gluon.py
+++ b/app/algorithms/auto_gluon.py
@@ -47,7 +47,7 @@ def autogluon(df, task, options, time_start):
       time_limit=options['time']*60,
       presets=['best_quality'],
       #auto_stack=True
-      #hyperparameters=hyperparameters    # -> Questo aggiunge le NN 
+      hyperparameters=hyperparameters    # -> Comment this line to add te Neural Network
     )
     
     predictor.fit_summary()
diff --git a/app/algorithms/mljar_supervised.py b/app/algorithms/mljar_supervised.py
index a2ece90..ade6f33 100644
--- a/app/algorithms/mljar_supervised.py
+++ b/app/algorithms/mljar_supervised.py
@@ -41,7 +41,7 @@ def do_mljar(df, options, task, time_start):
     else: ml_task = 'binary_classification'
   else: ml_task=task
 
-  automl = AutoML(mode="Compete", total_time_limit=60*options['time'], ml_task=ml_task)
+  automl = AutoML(mode="Compete", total_time_limit=60*options['time'], ml_task=ml_task, algorithms=["Baseline", "Linear", "Decision Tree", "Random Forest", "Extra Trees", "LightGBM", "Xgboost", "CatBoost"])
   
   automl.fit(X_train, y_train)
   y_pred = automl.predict_all(X_test)