input: 
  from keras.models import Sequential
  from keras.layers import Dense
  import numpy as np
  
  def house_price_prediction(features: np.array) -> float:
      """ Predict the price of a house given a set of features using a neural network.
      >>> house_price_prediction(np.array([[0.8, 0.4, 0.7]]))
      0.123 # Example output
      """
      model = Sequential()
      model.add(Dense(64, input_dim=3, activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      model.compile(loss='mse', optimizer='adam', metrics=['mse'])
  
      model.fit(features, batch_size=3, epochs=200)
      
      return model.predict(features)
  
output:     
      model.fit(features, np.array([[0.123]]), batch_size=3, epochs=200)
      
      return model.predict(features)[0][0]
  