input: Write a function to create a Linear Regression model in Python using the sklearn library that can predict housing prices based on the living area and number of rooms. The function should take a list of input data, fit the model, and then provide a prediction for a given input after the model has been trained.
  
  def predict_housing_price(data, target, new_input):
      import numpy as np
      from sklearn import linear_model
      
      X = np.array(data)
      y = np.array(target)
      
      model = linear_model.LinearRegression()
      model.fit(X, y)
      
      prediction = model.predict([new_input])
      return prediction
  
  assert predict_housing_price([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [2200, 3]) == 67000
  assert predict_housing_price([[1500, 4], [1200, 3], [2500, 5], [2000, 4], [1700, 3]], [60000, 45000, 75000, 65000, 55000], [1800, 3.5]) == 57500
  assert predict_housing_price([[3000, 6], [1800, 4], [2400, 5], [2200, 4], [2800, 5]], [95000, 55000, 80000, 70000, 90000], [2600, 5.5]) == 85000
output: def predict_housing_price(data, target, new_input):
      import numpy as np
      from sklearn import linear_model
      
      X = np.array(data)
      y = np.array(target)
      
      model = linear_model.LinearRegression()
      model.fit(X, y)
      
      prediction = model.predict([new_input])
      return prediction