input: Write a python function to predict stock prices using linear regression with sklearn.
  assert len(predict_stock_price(pd.DataFrame({'feature1': [1, 2, 3, 4, 5], 'price': [2, 4, 6, 8, 10]}))) == 1
  assert len(predict_stock_price(pd.DataFrame({'feature1': [10, 20, 30, 40, 50], 'price': [20, 40, 60, 80, 100]}))) == 1
  assert len(predict_stock_price(pd.DataFrame({'feature1': [5, 10, 15, 20], 'feature2': [2, 4, 6, 8], 'price': [10, 20, 30, 40]}))) == 1
output: def predict_stock_price(df):
      from sklearn.linear_model import LinearRegression
      
      X = df.drop(columns=['price'])
      y = df['price']
      
      model = LinearRegression()
      model.fit(X, y)
      
      return model.predict(X[:1])