Neural Network Tutorial

this tutorial about classifiation in machine learning -using neural network- about patient medical record data for Pima Indians and whether they had an onset of diabetes within five years

	# the tutorial about patient medical record data for Pima Indians and whether they had an onset of diabetes within five years
	# so we have a classification problem
	from keras.models import Sequential
	from keras.layers import Dense
	import numpy
	# fix random seed for reproducibility
	numpy.random.seed(7)
	# load pima indians dataset
	dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
	# split into input (X) and output (Y) variables
	X = dataset[:,0:8]
	Y = dataset[:,8]
	# create model
	model = Sequential()
	model.add(Dense(12, input_dim=8, activation='relu'))
	model.add(Dense(8, activation='relu'))
	model.add(Dense(1, activation='sigmoid'))
	# Compile model
	model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
	# Fit the model
	model.fit(X, Y, epochs=150, batch_size=10)
	# evaluate the model
	scores = model.evaluate(X, Y)
	print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

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