input: Write a function to perform linear regression and predict the value of Y for a given X in a dataset. The function should use the scikit-learn library for the linear regression model and numpy for array operations.
  import numpy as np
  from sklearn.linear_model import LinearRegression
  
  def linear_regression_predict(X, Y, x_pred):
      X = np.array(X).reshape(-1,1)
      Y = np.array(Y)
      model = LinearRegression()
      model.fit(X, Y)
      return model.predict([[x_pred]])
  
  assert np.array_equal(linear_regression_predict([1,2,3,4,5], [3,5,7,9,13], 7), np.array([14.5]))
  assert np.array_equal(linear_regression_predict([1,2,3,4,5], [3,5,7,9,13], 1), np.array([*1.5]))
output: 
  def linear_regression_predict(X, Y, x_pred):
      X = np.array(X).reshape(-1,1)
      Y = np.array(Y)
      model = LinearRegression()
      model.fit(X, Y)
      return model.predict([[x_pred]])
  