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 model should be trained using the given features.
  assert predict_house_price(np.array([[0.8, 0.4, 0.7]]))==your_expected_output
  assert predict_house_price(np.array([[0.9, 0.5, 0.8]]))==your_expected_output
  assert predict_house_price(np.array([[0.7, 0.3, 0.6]]))==your_expected_output
  assert predict_house_price(np.array([[0.6, 0.2, 0.5]]))==your_expected_output
  assert predict_house_price(np.array([[0.4, 0.1, 0.3]]))==your_expected_output
output: def predict_house_price(features):
      from keras.models import Sequential
      from keras.layers import Dense
      import numpy as np
      
      model = Sequential()
      model.add(Dense(10, input_dim=3, activation='relu'))
      model.add(Dense(1, activation='linear'))
      model.compile(optimizer='adam', loss='mean_squared_error')
      
      # Dummy training data
      X_train = np.array([[0.8, 0.4, 0.7], [0.9, 0.5, 0.8], [0.7, 0.3, 0.6], [0.6, 0.2, 0.5], [0.4, 0.1, 0.3]])
      y_train = np.array([100000, 120000, 90000, 80000, 60000])
      
      model.fit(X_train, y_train, epochs=100, verbose=0)
      
      predictions = model.predict(features)
      return predictions[0][0]