text: import numpy
  import pandas
  import statsmodels.api as sm
  
  def simple_heuristic(file_path):
      '''
      In this exercise, we will perform some rudimentary practices similar to those of
      an actual data scientist.
      
      Part of a data scientist's job is to use her or his intuition and insight to
      write algorithms and heuristics. A data scientist also creates mathematical models 
      to make predictions based on some attributes from the data that they are examining.
  
      We would like for you to take your knowledge and intuition about the Titanic
      and its passengers' attributes to predict whether or not the passengers survived
      or perished. You can read more about the Titanic and specifics about this dataset at:
      http://en.wikipedia.org/wiki/RMS_Titanic
      http://www.kaggle.com/c/titanic-gettingStarted
          
      In this exercise and the following ones, you are given a list of Titantic passengers
      and their associated information. More information about the data can be seen at the 
      link below:
      http://www.kaggle.com/c/titanic-gettingStarted/data. 
  
      For this exercise, you need to write a simple heuristic that will use
      the passengers' gender to predict if that person survived the Titanic disaster.
      
      You prediction should be 78% accurate or higher.
          
      Here's a simple heuristic to start off:
         1) If the passenger is female, your heuristic should assume that the
         passenger survived.
         2) If the passenger is male, you heuristic should
         assume that the passenger did not survive.
      
      You can access the gender of a passenger via passenger['Sex'].
      If the passenger is male, passenger['Sex'] will return a string "male".
      If the passenger is female, passenger['Sex'] will return a string "female".
  
      Write your prediction back into the "predictions" dictionary. The
      key of the dictionary should be the passenger's id (which can be accessed
      via passenger["PassengerId"]) and the associated value should be 1 if the
      passenger survied or 0 otherwise.
  
      For example, if a passenger is predicted to have survived:
      passenger_id = passenger['PassengerId']
      predictions[passenger_id] = 1
  
      And if a passenger is predicted to have perished in the disaster:
      passenger_id = passenger['PassengerId']
      predictions[passenger_id] = 0
      
      You can also look at the Titantic data that you will be working with
      at the link below:
      https://s3.amazonaws.com/content.udacity-data.com/courses/ud359/titanic_data.csv
      '''
  
      predictions = {}
      df = pandas.read_csv(file_path)
      for passenger_index, passenger in df.iterrows():
          passenger_id = passenger['PassengerId']
        
          # Your code here:
          # For example, let's assume that if the passenger
          # is a male, then the passenger survived.
          if passenger['Pclass'] == 2 and passenger['Age'] < 10:
              predictions[passenger_id] = 1
          elif passenger['Sex'] == 'male' and passenger['Pclass'] > 1:
              predictions[passenger_id] = 0
          elif passenger['Sex'] == 'female' and passenger['Pclass'] < 3:
              predictions[passenger_id] = 1
          elif passenger['Age'] < 10 and passenger['Pclass'] == 2:
              predictions[passenger_id] = 1
          else:
              predictions[passenger_id] = 0
  
      
      return predictions
  
  if __name__ == "__main__":
      print(simple_heuristic('titanic_data.csv'))
  