input: Write a python function to fit a linear regression model using sklearn and predict house prices based on the given features. 
  assert linear_regression_predict([[2000, 3], [1600, 2.5], [2400, 3.5], [1400, 2], [3000, 4]], [75000, 43000, 59000, 32000, 85000], [[2200, 3]])==[66996.31]
  assert linear_regression_predict([[1500, 2], [2300, 3], [1200, 1.5], [2800, 4]], [32000, 55000, 25000, 75000], [[2100, 2.5]])==[50714.29]
  assert linear_regression_predict([[1800, 2.5], [2500, 4], [1500, 2], [2000, 3]], [50000, 80000, 30000, 60000], [[1900, 2.5]])==[52500.00]
output: def linear_regression_predict(X_train, y_train, X_test):
      from sklearn.linear_model import LinearRegression
      
      # Create a Linear Regression model
      model = LinearRegression()
  
      # Fit the model on the training data
      model.fit(X_train, y_train)
  
      # Predict the prices for the test data
      predictions = model.predict(X_test)
      
      # Round the predictions to 2 decimal places
      rounded_predictions = [round(pred, 2) for pred in predictions]
  
      return rounded_predictions