Matthew Mayo mmmayo13

## portilla-ann-1.py
from sklearn.datasets import load_breast_cancer
cancer = load_breast_cancer()

## portilla-ann-2.py
cancer.keys()

## portilla-ann-3.py
# Print full description by running:
# print(cancer['DESCR'])
# 569 data points with 30 features
cancer['data'].shape

## portilla-ann-4.py
X = cancer['data']
y = cancer['target']

## portilla-ann-5.py
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y)

## portilla-ann-6.py
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
# Fit only to the training data
scaler.fit(X_train)

## portilla-ann-6.py
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
# Fit only to the training data
scaler.fit(X_train)

## portilla-ann-7.py
# Now apply the transformations to the data:
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

## portilla-ann-7.py
from sklearn.neural_network import MLPClassifier

## portilla-ann-8.py
mlp = MLPClassifier(hidden_layer_sizes=(30,30,30))
	from sklearn.datasets import load_breast_cancer
	cancer = load_breast_cancer()
	# Print full description by running:
	# print(cancer['DESCR'])
	# 569 data points with 30 features
	cancer['data'].shape
	from sklearn.model_selection import train_test_split
	X_train, X_test, y_train, y_test = train_test_split(X, y)
	from sklearn.preprocessing import StandardScaler
	scaler = StandardScaler()
	# Fit only to the training data
	scaler.fit(X_train)
	# Now apply the transformations to the data:
	X_train = scaler.transform(X_train)
	X_test = scaler.transform(X_test)