input: Write a function to design and implement a neural network in Python using Keras to predict the price of a house given a set of features. The neural network should be a Sequential model with at least two Dense layers, using ReLU activation for the hidden layers and linear activation for the output layer. The model should be compiled with mean squared error (MSE) as the loss function and Adam as the optimizer. Ensure that the function fits the model with given features for a specified number of epochs and batch size.
  
  
  assert predict_house_price([0.8, 0.4, 0.7])==0
  
output: 
  def predict_house_price(features):
      import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
  
      model = Sequential()
      model.add(Dense(64, input_dim=len(features), activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      model.compile(loss='mean_squared_error', optimizer='adam')
  
      X = np.array([features])
      y = np.array([0])
  
      model.fit(X, y, epochs=10, batch_size=1, verbose=0)
  
      return model.predict(X)[0][0]
  