Here's some sample Python code for creating an MLP neural network with three inputs and one output using the Keras library:

```
from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(units=10, activation='relu', input_dim=3))
model.add(Dense(units=1, activation='sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
```

In the above code, we first import the necessary packages from Keras. Then we create a sequential model and add two dense layers to it. The first layer has 10 units with a ReLU activation function and an input dimension of 3, which corresponds to the three input variables. The second layer has one unit with a sigmoid activation function, which is the output of the network.

Finally, we compile the model using binary cross-entropy as the loss function, the Adam optimizer, and accuracy as the evaluation metric. Note that you would typically also need to provide some input data and train the model before it can make predictions.
Crea una rete neurale Multilayer Perceptron (MLP) con tre input e un output in grado di prevedere un output in base alle variabili di input.