input: Write a python function to train a linear regression model using sklearn on a given DataFrame and make a prediction based on input features.
  ```python
  import pandas as pd
  from sklearn.linear_model import LinearRegression
  
  def predict_price(df, size, rooms):
      X = df[['Size', 'Rooms']]
      Y = df['Price']
      reg = LinearRegression().fit(X, Y)
      return reg.predict([[size, rooms]])[0]
  
  # Test cases
  df_test = pd.DataFrame({'Size': [500, 700], 'Rooms': [4, 5], 'Price': [1715.667543, 2000.345]})
  
  assert round(predict_price(df_test, 500, 4), 5) == 1715.66754
  assert round(predict_price(df_test, 700, 5), 5) == 2000.345
output: def predict_price(df, size, rooms):
      X = df[['Size', 'Rooms']]
      Y = df['Price']
      reg = LinearRegression().fit(X, Y)
      return reg.predict([[size, rooms]])[0]