input: Write a function to perform linear regression on a given dataset and predict the value of a new data point. The function should take in two lists, X and Y, representing the independent and dependent variables respectively. The function should return the predicted value for a new input.
  
  import numpy as np
  from sklearn.linear_model import LinearRegression
  
  def linear_regression_prediction(X, Y, new_x):
      X = np.array(X).reshape(-1,1)
      Y = np.array(Y)
  
      model = LinearRegression()
      model.fit(X, Y)
  
      return model.predict([[new_x]])[0]
  
  
  assert linear_regression_prediction([1,2,3,4,5], [3,5,7,9,13], 7) == 15.0
output: def linear_regression_prediction(X, Y, new_x):
      X = np.array(X).reshape(-1,1)
      Y = np.array(Y)
  
      model = LinearRegression()
      model.fit(X, Y)
  
      return model.predict([[new_x]])[0]