import keras

from keras.models import Sequential

from keras.layers import Dense

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# Get number of predictor columns

n_cols = df.shape[1]

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# define the neural network model

model = Sequential()

model.add(Dense(5, activation='relu', input_shape=(n_cols,)))

model.add(Dense(5,  activation='relu'))

model.add(Dense(1, activation='linear'))

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# compile the model

model.compile(loss='mean_squared_error', optimizer='adam')

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# train the model

model.fit(X_Train, y_train, epochs=50, batch_size=10, verbose=0)

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# evaluate the model

loss = model.evaluate(X_Train, y_train)

print('Mean Squared Error:', loss)

​

# predict

predictions = model.predict(X_Test)