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. The function should define and compile a neural network using Keras, fit the model with provided features, and return the model.
  
  def predict_house_price(features):
      import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
      
      features = np.array([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
  
  assert predict_house_price([[0.8, 0.4, 0.7]]).compile(loss='mse', optimizer='adam', metrics=['mse'])==None
  assert predict_house_price([[0.5, 0.3, 0.2]]).compile(loss='mse', optimizer='adam', metrics=['mse'])==None
  assert predict_house_price([[0.1, 0.9, 0.6]]).compile(loss='mse', optimizer='adam', metrics=['mse'])==None
  assert predict_house_price([[0.7, 0.1, 0.4]]).compile(loss='mse', optimizer='adam', metrics=['mse'])==None
output: 
  def predict_house_price(features):
      import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
      
      features = np.array([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
  