input: def house_price_predictor(features: np.ndarray) -> np.ndarray:
      """Design and implement a neural network in Python to predict the price of a house given a set of features.
      The function will return the predicted price for the given features.
      >>> house_price_predictor(np.array([[0.8, 0.4, 0.7]]))
      array([[estimated_price]], dtype=float32)
      """
      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:     import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
  
      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.zeros((features.shape[0], 1)), batch_size=3, epochs=200, verbose=0)
  
      return model.predict(features)