diff --git a/api/run.py b/api/run.py
index 7efe0bb047323a60f41a75647e1553d721beeae7..eaad0e64f787d8ece20b7ebb3da49908fb1899bc 100644
--- a/api/run.py
+++ b/api/run.py
@@ -57,9 +57,13 @@ model = Sequential()
 feature_length = 100
 testing_records=50
 batch_size = 5
-epochs = 100
+epochs = 10
 accuracy=0
 execute_time=0
+layer_1_units=100
+layer_2_units=50
+layer_3_units=50
+dense_units=1
 
 # Scaler 
 scaler =  MinMaxScaler()
@@ -254,18 +258,18 @@ def setup():
 
   #Add First LSTM Layer
 
-  model.add(LSTM(units = 100, activation = 'relu', input_shape = (time_steps, features), return_sequences=True))
+  model.add(LSTM(units = layer_1_units, activation = 'relu', input_shape = (time_steps, features), return_sequences=True))
 
   # Adding the  Second hidden layer and the LSTM layer
 
-  model.add(LSTM(units = 50, activation = 'relu', input_shape = (time_steps, features), return_sequences=True))
+  model.add(LSTM(units = layer_2_units, activation = 'relu', input_shape = (time_steps, features), return_sequences=True))
 
   # Adding the  Third hidden layer and the LSTM layer
-  model.add(LSTM(units = 50, activation = 'relu', return_sequences=False ))
+  model.add(LSTM(units = layer_3_units, activation = 'relu', return_sequences=False ))
 
 
   # Adding the output layer
-  model.add(Dense(units = 1))
+  model.add(Dense(units = dense_units))
 
   # Compiling model
   model.compile(optimizer = 'adam', loss = 'mean_squared_error')
@@ -302,29 +306,13 @@ def setup():
   # Generating predictions on full data
   TrainPredictions=scaler.inverse_transform(model.predict(x_train))
   TestPredictions=scaler.inverse_transform(model.predict(x_test))
-
   FullDataPredictions=np.append(TrainPredictions, TestPredictions)
-  FullDataOrig=data[time_steps:]
-
-  # plotting the full data
-  plt.plot(FullDataPredictions, color = 'blue', label = 'Predicted Price')
-  plt.plot(FullDataOrig , color = 'lightblue', label = 'Original Price')
-
-  plt.title('Stock Price Predictions')
-  plt.xlabel('Trading Date')
-  plt.ylabel('Stock Price')
-  plt.legend()
-  fig=plt.gcf()
-  fig.set_figwidth(20)
-  fig.set_figheight(8)
-  plt.show()
 
+  # Save data to result data set 
 
   result["Date"]=data_set.iloc[time_steps:]["Date"]
   result["Predictions"]=FullDataPredictions
   result["OrderDemand"]=data[time_steps:]
-
-  print(data_set.tail(10))
     
 # ====================================================================
 # main function call