input: Write a function to design and implement a neural network in Python to predict the price of a house given a set of features.
  
  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: def predict_house_price(features):
      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)