input: Write a python function to optimize model hyperparameters using RandomizedSearchCV.
  assert model_optimizer(model, param_grid, data, target) is not None
  assert isinstance(model_optimizer(model, param_grid, data, target), dict)
  assert 'Best Parameters' in model_optimizer(model, param_grid, data, target).keys()
output: def model_optimizer(model, param_grid, data, target):
      from sklearn.model_selection import RandomizedSearchCV
  
      randomized_search = RandomizedSearchCV(estimator=model, param_distributions=param_grid, n_iter=100, cv=5, random_state=42, n_jobs=-1)
      randomized_search.fit(data, target)
  
      return {'Best Parameters': randomized_search.best_params_, 'Best Score': randomized_search.best_score_}