input: Write a python function to evaluate the accuracy of a logistic regression model.
  assert evaluate_model_accuracy([1.0472, 2.1930, 1.6025, 0.8886, 2.5850, 1.4692, 1.2548, 0.8496], [0, 0, 1, 0, 0, 0, 0, 1])==0.75
  assert evaluate_model_accuracy([1, 2, 3, 4, 5, 6, 7, 8], [0, 0, 0, 0, 1, 1, 1, 1])==1.0
output: def evaluate_model_accuracy(predictions, actuals):
      from math import exp
  
      # Convert predictions to binary outcomes using sigmoid function
      binary_predictions = [1 if 1 / (1 + exp(-p)) >= 0.5 else 0 for p in predictions]
  
      # Calculate accuracy
      correct_predictions = sum([1 for i in range(len(actuals)) if binary_predictions[i] == actuals[i]])
      accuracy = correct_predictions / len(actuals)
  
      return accuracy