input: Write a function to perform linear regression for predicting values in a given dataset. The function should take two lists as inputs, which represent the independent variable (X) and the dependent variable (Y). It should then fit a linear regression model to these data points and make a prediction for a new value of X. The prediction should be printed as the output. The function should not return any value.
  
  
  def linear_regression_prediction(X, Y):
      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 X = 7
      print(model.predict([[7]]))
  
  X = [1,2,3,4,5]
  Y = [3,5,7,9,13]
  linear_regression_prediction(X, Y)
  # Expected output: [[17.]]
  
output: def linear_regression_prediction(X, Y):
      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 X = 7
      print(model.predict([[7]]))