I am working on a neural network regression code. The dataset includes 14 features in the range value between -1 and 1. while the target variable is changing among (0.000759) to (1100). The target values are scaled by three methods. method 1 : logarithmic scale method 2 : MinMaxScalar method 3 : divided by 1100 But these methods could not succeed to get the better result. The code is not able to learn and predict well. Specially in small values of the target and got minus values as the prediction.some useful information as follow; model = Sequential( ) model.add(InputLayer(input_shape= (14, ))) model.add(Dense (14,'tanh' )) model.add(Dense (5,'tanh' )) model.add(Dense (3,'tanh' )) model.add(Dense (1,'linear' )) Loss : MSE, optimizer : adam thank you in advance for your attention

I am working on a neural network regression code. The dataset includes 14 features in the range value between -1 and 1. while the target variable is changing among (0.000759) to (1100). The target values are scaled by three methods.

• method 1 : logarithmic scale
• method 2 : MinMaxScalar
• method 3 : divided by 1100

But these methods could not succeed to get the better result. The code is not able to learn and predict well. Specially in small values of the target and got minus values as the prediction.some useful information as follow;

model = Sequential( )
model.add(InputLayer(input_shape= (14, )))
model.add(Dense (14,'tanh' ))
model.add(Dense (5,'tanh' ))
model.add(Dense (3,'tanh' ))
model.add(Dense (1,'linear' ))

Loss : MSE, optimizer : adam 
thank you in advance for your attention