old_contents: import pandas
  
  
  def main():
      train_all = pandas.DataFrame.from_csv('train.csv')
      train = train_all[['Survived', 'Sex', 'Fare']]
      print(train)
  
  
  if __name__ == '__main__':
      main()
  
new_contents: import pandas
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.cross_validation import train_test_split
  from sklearn.preprocessing import LabelEncoder
  
  
  def main():
      train_all = pandas.DataFrame.from_csv('train.csv')
      train = train_all[['Survived', 'Sex', 'Fare']][:20]
  
      gender_label = LabelEncoder()
      train.Sex = gender_label.fit_transform(train.Sex)
  
      X = train[['Sex', 'Fare']]
      y = train['Survived']
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.33, random_state=42)
  
      clf = MultinomialNB()
      clf.fit(X_train, y_train)
  
      print(clf.predict(X_test))
  
  
  if __name__ == '__main__':
      main()
  
subject: Make predictions with gender and ticket price