input: Write a python function to produce a linear regression model to predict the house prices. 
  assert predict_house_price([1000, 3, 2]) > 0
  assert predict_house_price([2000, 4, 3]) > 0
  assert predict_house_price([1500, 2, 2]) > 0
output: def predict_house_price(features):
      from sklearn.linear_model import LinearRegression
      import numpy as np
      
      # Historical data for training: [size in sqft, number of bedrooms, number of bathrooms]
      X = np.array([
          [1400, 3, 2],
          [1600, 3, 3],
          [1700, 4, 2],
          [1875, 3, 2],
          [1100, 2, 1],
          [1550, 3, 2],
          [2350, 4, 3],
          [2450, 4, 3],
          [1425, 3, 2],
          [1700, 3, 3]
      ])
      
      # Corresponding house prices
      y = np.array([245000, 312000, 279000, 308000, 199000, 219000, 405000, 324000, 319000, 255000])
      
      # Create and train the linear regression model
      model = LinearRegression()
      model.fit(X, y)
      
      # Predict the house price
      predicted_price = model.predict([features])
      return predicted_price[0]