text: # Credit Udacity
  
  import numpy as np
  import pandas as pd
  
  admissions = pd.read_csv('binary.csv')
  
  # Make dummy variables for rank
  data = pd.concat([admissions, pd.get_dummies(
      admissions['rank'], prefix='rank')], axis=1)
  data = data.drop('rank', axis=1)
  
  # Standarize features
  for field in ['gre', 'gpa']:
      mean, std = data[field].mean(), data[field].std()
      data.loc[:, field] = (data[field] - mean) / std
  
  # Split off random 10% of the data for testing
  np.random.seed(21)
  sample = np.random.choice(data.index, size=int(len(data) * 0.9), replace=False)
  data, test_data = data.ix[sample], data.drop(sample)
  
  # Split into features and targets
  features, targets = data.drop('admit', axis=1), data['admit']
  features_test, targets_test = test_data.drop(
      'admit', axis=1), test_data['admit']
  