input: 
  def linear_regression_predictor(X: List[float], Y: List[float]) -> List[float]:
      """
      Create a Linear Regression model using the given dataset and predict the future values.
      X is the list of independent variables and Y is the list of dependent variables.
  
      >>> linear_regression_predictor([1,2,3,4,5], [3,5,7,9,13])
      array([15.])
      """
      import numpy as np
      from sklearn.linear_model import LinearRegression
  
      X = np.array(X).reshape(-1, 1)
      Y = np.array(Y)
  
      model = LinearRegression()
      model.fit(X, Y)
  
      # Predicts the value of y for a given x
      return model.predict([[7]])
  
output:     import numpy as np
      from sklearn.linear_model import LinearRegression
  
      X = np.array(X).reshape(-1, 1)
      Y = np.array(Y)
  
      model = LinearRegression()
      model.fit(X, Y)
  
      # Predicts the value of y for a given x
      return model.predict([[7]])