completed part 2 implementing gradient descent

python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/data_prep.py

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+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(42)
+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']