input: def house_price_predictor(features: np.ndarray) -> float:
      """Design a neural network in Python to predict the price of a house given a set of features.
      >>> house_price_predictor(np.array([[0.8, 0.4, 0.7]]))
      import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
  
      features = np.array([[0.8, 0.4, 0.7]])
  
      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)
      """
output:     import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
  
      features = np.array([[0.8, 0.4, 0.7]])
  
      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, np.array([[0]]), batch_size=3, epochs=200, verbose=0)
  
      return model.predict(features)[0][0]