input: Write a function to create a Linear Regression model in Python that can predict the housing prices given the living area and number of rooms. The function should fit the model using the provided training data and return the predicted price for a given input. Import necessary libraries at the beginning of the function.
  
  def predict_housing_price(data, target_prices, input_features):
      import numpy as np
      from sklearn import linear_model
      
      X = np.array(data)
      y = np.array(target_prices)
      
      model = linear_model.LinearRegression()
      model.fit(X, y)
      
      prediction = model.predict(np.array([input_features]))
      return prediction[0]
  
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2200, 3])==69000.0
  assert predict_housing_price([[1800, 3], [1500, 2.5], [2200, 3.5], [1300, 2]], [70000, 38000, 60000, 29000], [1900, 2.8])==66000.0
  assert predict_housing_price([], [], [2200, 3])==0.0
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [1500, 2])==36000.0
output: def predict_housing_price(data, target_prices, input_features):
      import numpy as np
      from sklearn import linear_model
      
      if len(data) == 0 or len(target_prices) == 0:
          return 0.0
      
      X = np.array(data)
      y = np.array(target_prices)
      
      model = linear_model.LinearRegression()
      model.fit(X, y)
      
      prediction = model.predict(np.array([input_features]))
      return prediction[0]