input: 
  import numpy as np
  from sklearn.linear_model import LinearRegression
  
  def linear_regression_prediction(X: List[float], Y: List[float]) -> np.ndarray:
      """
      Perform linear regression on given dataset X and Y to predict new values.
      X and Y are lists of floats representing independent and dependent variables respectively.
      Returns the predicted value for a given new input.
      
      >>> X = [1, 2, 3, 4, 5]
      >>> Y = [3, 5, 7, 9, 13]
      >>> linear_regression_prediction(X, Y)
      array([15.])
      """
      
      X = np.array(X).reshape(-1, 1)
      Y = np.array(Y)
      
      model = LinearRegression()
      model.fit(X, Y)
      
      # Predicts the value of y
      return model.predict([[7]])
  
output:     X = np.array(X).reshape(-1, 1)
      Y = np.array(Y)
      
      model = LinearRegression()
      model.fit(X, Y)
      
      # Predicts the value of y
      return model.predict([[7]])