input: Write a function that performs linear regression for predicting the numbers in a given dataset using Python and scikit-learn. The function should take two lists as input, X and Y, which represent the independent and dependent variables, respectively. It should then train a linear regression model on this data and predict the value for a new input. The function should return the predicted value.
  
  def linear_regression_predict(X, Y, new_X):
      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)
      
      new_X = np.array(new_X).reshape(-1,1)
      return model.predict(new_X)
  
  assert linear_regression_predict([1,2,3,4,5], [3,5,7,9,13], [7])==13
  assert linear_regression_predict([1,2,3,4,5], [3,5,7,9,13], [6])==11
  assert linear_regression_predict([1,3,5,7,9], [2,5,8,11,14], [10])==15
output: def linear_regression_predict(X, Y, new_X):
      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)
      
      new_X = np.array(new_X).reshape(-1,1)
      return model.predict(new_X)